RESEARCH CENTRE

Inria Paris Centre

IN PARTNERSHIP WITH:

Valeo

2023
ACTIVITY REPORT

Project-Team

ASTRA

Automated and Safe TRAnsportation
systems

DOMAIN

Perception, Cognition and Interaction

THEME

Robotics and Smart environments

Contents

Project-Team ASTRA

1 Team members, visitors, external collaborators

2 Overall objectives

1

3

4

3 Research program

3.1 Research Axis 1: Vision and 3D Perception for Scene Understanding . . . . . . . . . . . . . .
3.1.1 Learning with less supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Vision in complex conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.3 3D scene understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Research Axis 2: Localization & Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Localization and Map Integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Online Alignment of Multiple Map Layers
. . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Georeferencing of maps without RTK GNSS and IMU . . . . . . . . . . . . . . . . . . .
3.3 Research Axis 3: Decision making, motion Planning & vehicle Control . . . . . . . . . . . . .

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3.3.1 Maneuver and trajectory prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3.2 Ego-vehicle actions and maneuvers decision making . . . . . . . . . . . . . . . . . . . 11
3.3.3 Trajectory planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3.4 Robust control of automated vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
13
3.4.1 Traffic prediction in urban settings: detecting extreme events . . . . . . . . . . . . . . 14
3.4.2 Taming highway traffic using cooperative automated vehicles
. . . . . . . . . . . . . 14
3.4.3 Crowdsourced mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.4.4 Cooperative automated driving involving V2X communications . . . . . . . . . . . . 15

3.4 Research Axis 4: Large scale modeling and deployment of mobility systems in Smart Cities

4 Application domains

15

5 Highlights of the year

17
5.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6 New software, platforms, open data

17
6.1 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

7 New results

17
7.1 3D scene reconstruction and completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.2 Cross-task learning for vision algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
7.3 Physics-guided learning vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
7.4 Language-driven vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.5 Misbehavior Detection for Collective Perception Services: A Systematic Trust-Based Evid-

ence Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

7.6 Hierarchical Attention and Graph Neural Networks for Drift-Free Pose Estimation of a

ground vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.7 Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios . 21
7.8 Interpretable Long Term Waypoint-Based Trajectory Prediction Model
. . . . . . . . . . . . 21
7.9 Motion planning and prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.10 Control and Human Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
7.11 Ethical decision-making for automated vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . 22
. . . . . . . . . . . . . . . . . . . . . . . . . 22
7.12 Landmark localization for Autonomous Vehicles
7.13 Shock Wave Estimation in Intelligent Driver Models
. . . . . . . . . . . . . . . . . . . . . . . 23
7.14 Reflected brownian motion in a non convex cone . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.15 A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with Buffering and

back-off stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.16 Blockchain adapted to IoT via green mining and variable Proof of Work . . . . . . . . . . . . 24
7.17 Random walks in orthants and lattice path combinatorics . . . . . . . . . . . . . . . . . . . . 24

8 Bilateral contracts and grants with industry

24
8.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

9 Partnerships and cooperations

25
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.2 International research visitors
9.2.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
9.3.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
9.3.2 ADEME – Bpifrance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
9.3.3 AMI – EquipEx+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
9.3.4 Competitivity Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

10 Dissemination

27
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
10.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
10.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

11 Scientific production

32
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
11.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
11.3 Cited publications

Project ASTRA

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Project-Team ASTRA

Creation of the Project-Team: 2022 July 01

Keywords

Computer sciences and digital sciences

A1.5. – Complex systems

A1.5.1. – Systems of systems

A1.5.2. – Communicating systems

A2.3. – Embedded and cyber-physical systems

A3.4. – Machine learning and statistics

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.3. – Reinforcement learning

A3.4.5. – Bayesian methods

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A5.3. – Image processing and analysis

A5.3.3. – Pattern recognition

A5.3.4. – Registration

A5.4. – Computer vision

A5.4.1. – Object recognition

A5.4.2. – Activity recognition

A5.4.4. – 3D and spatio-temporal reconstruction

A5.4.5. – Object tracking and motion analysis

A5.4.6. – Object localization

A5.5.1. – Geometrical modeling

A5.9. – Signal processing

A5.10. – Robotics

A5.10.2. – Perception

A5.10.3. – Planning

A5.10.4. – Robot control

A5.10.5. – Robot interaction (with the environment, humans, other robots)

A5.10.6. – Swarm robotics

A5.10.7. – Learning

A6. – Modeling, simulation and control

A6.1. – Methods in mathematical modeling

A6.2.3. – Probabilistic methods

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Inria Annual Report 2023

A6.2.6. – Optimization

A6.4.1. – Deterministic control

A6.4.3. – Observability and Controlability

A6.4.4. – Stability and Stabilization

A6.4.5. – Control of distributed parameter systems

A8.6. – Information theory

A8.9. – Performance evaluation

A9.2. – Machine learning

A9.3. – Signal analysis

A9.5. – Robotics

A9.6. – Decision support

A9.7. – AI algorithmics

Other research topics and application domains

B5.2.1. – Road vehicles

B5.6. – Robotic systems

B6.6. – Embedded systems

B7.1.2. – Road traffic

B7.2. – Smart travel

B7.2.1. – Smart vehicles

B7.2.2. – Smart road

B9.5.6. – Data science

Project ASTRA

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1 Team members, visitors, external collaborators

Research Scientists

• Fawzi Nashashibi [Team leader, INRIA, Senior Researcher, HDR]

• Zayed Alsayed [VALEO, Industrial member]

• Alexandre Boulc’H [VALEO, Industrial member]

• Andrei Bursuc [VALEO, Industrial member]

• Guy Fayolle [INRIA, Emeritus]

• Fernando Garrido [VALEO, Industrial member]

• Axel Jeanne [VALEO, Industrial member]

• Jean-Marc Lasgouttes [INRIA, Researcher]

• Gerard Le Lann [INRIA, Emeritus]

• Renaud Marlet [VALEO, Industrial member, HDR]

• Gilles Puy [VALEO, Industrial member]

• Patrick Pérez [VALEO, Industrial member, HDR]

• Paulo Resende [VALEO, Industrial member]

• Tuan Hung Vu [VALEO, Industrial member]

• Raoul de Charette [INRIA, Researcher, HDR]

Post-Doctoral Fellows

• Nelson De Moura Martins Gomes [INRIA]

• Tiago Rocha Goncalves [INRIA, until Apr 2023]

PhD Students

• Yacine Ben Ameur [INRIA, until Jun 2023]

• Anh Quan Cao [INRIA]

• Karim Essalmi [VALEO, CIFRE, from Mar 2023]

• Mohammad Fahes [INRIA]

• Amina Ghoul [INRIA]

• Ivan Lopes [INRIA]

• Tetiana Martyniuk [VALEO, CIFRE, from Dec 2023]

• Noel Nadal [INRIA]

• Jiahao Zhang [IRT System X, CIFRE]

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Inria Annual Report 2023

Interns and Apprentices

• Souhaiel Ben Salem [INRIA, Intern, from Apr 2023 until Oct 2023]

• Augustin Gervreau Mercier [INRIA, Intern, from May 2023 until Aug 2023]

• Tan Khiem Huynh [Inria, from Mar 2023 until Oct 2023]

Administrative Assistants

• Anne Mathurin [INRIA]

• Clotilde Monnet [INRIA , from Feb 2023, ASTRA Project Manager]

Visiting Scientist

• Weihao Xia [UCL, from Apr 2023, PhD visitor from UCL]

External Collaborator

• Itheri Yahiaoui [Université de Reims]

2 Overall objectives

Context

SAE International1 recently unveiled a new visual chart [91] that is designed to define the six levels of
driving automation, from SAE Level 0 (no automation) to SAE Level 5 (full vehicle autonomy). It serves as
the industry’s most-cited reference for automated-vehicle (AV) capabilities.

Fully autonomous cars (Level 5 of automation according to SAE J3016), which can work everywhere
in all conditions, are not yet on the roads. Nevertheless, major advances are making vehicle automation
a reality. Systems exist on serial vehicles with Level 2/2+ (assisted driving) and even Level 3 (high
automation, driving only upon system request) since 2021 on privately owned vehicles as well as on
public transport driverless vehicles are offered to passengers and goods around the world. Recent
demonstrators (automated shuttles and robotaxis) have the merit of proving the feasibility of automated
driving as a solution for improving mobility, comfort, safety and energy efficiency.

Current regulation (UN 157 – adopted in June 2020 and voted by 60 countries) allows today vehicles to
drive in L3 up to 60 km/h on carriageway roads. Original Equipment Manufacturers (OEMs) are pushing
for the extension of this regulation up to 130 km/h including automated lane changes. To allow that
(L3/L4 on the highway), many challenges are still to be taken up; technical challenges of course, but
also non-technical challenges which are not the easiest to deal with (legal, liability, ethical, monopoly,
acceptance, economical. . . ) and that are not in the scope of this document even though some intersect
with some technical considerations [80, 99, 123].

In this context, the official ambition of France was previously recalled by the President of France,
who reaffirmed his willingness to deploy these solutions, to extend transport services based on the
autonomous vehicle by 2021 whenever this is possible.

For public transportation, on-road experiments are conducted around the world in specific Opera-
tional Design Domains (ODDs) and first commercial services are being deployed. For example, in Russia,
Yandex has launched the first commercial service in Europe in 2019 in the city of Innopolis and Waymo.
One ride-hailing service using highly automated vehicles in the Phoenix metropolitan area (US). These
systems are operating in geofenced controlled environments due to the lack of technology maturity that
are able to deal with all road types (missing lines, construction areas, reckless road users behaviour like
scooters, etc.).

Therefore, the development of alternative solutions at a large scale needs other scientific foundations
and technological breakthroughs. Car makers, suppliers, infrastructure operators and academics across

1The Society of Automotive Engineers (SAE) : www.sae.org

Project ASTRA

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the world are working today on ways to make driving safer, more comfortable, more efficient and more
inclusive through automation, and the race is on to bring the technology to the mass market.

In this context Inria and Valeo are internationally distinguished players especially thanks to their
R&D activities on automated unmanned vehicles, Cybercars and more generally on the development of
advanced intelligent sensors-based decision systems.

Motivation

Partners in numerous collaborative research projects and bilateral projects, Inria and Valeo have also
collaborated in the supervision of doctoral and post-doctoral students. Many Inria researchers have
also joined Valeo’s R&D teams for several years. Finally, numerous technology transfer actions and joint
patent applications have taken place. Motivated by this very strong collaboration for over 15 years, Inria
and Valeo wanted to formalize this synergy by strengthening their links, both in the fields of research and
technology transfer.
What could be better than to create a joint research team to share the same visions on mobility and
transport automation? And what could be better than working together upstream on breakthrough
research topics? This naturally resulted in the creation of a joint research team: the ASTRA team. This
team brings together talents from three entities: the former RITS team at Inria (Paris), members of the
DAR team at Valeo (Créteil) and members at Valeo.ai (Paris). Beyond the strategic vision assumed by the
management of these three entities, the France Relance national plan was an important incentive for the
creation of this unusual joint entity.

3 Research program

Today, there are still many challenges facing the development and deployment of autonomous vehicles to
reach an exploitable and commercially viable solution. This is due equally to technical and non-technical
challenges. In particular, the challenges include aspects related to the performance of the systems, their
efficiency, their integrability and their costs, not to mention the legal, social and ethical aspects.

A classic robust autonomous navigation architecture should take into account additional aspects
related to real-time implementation, functional redundancy, durability, certification and purely technical
aspects related to the design and development of functional bricks as well.

As part of this project-team we focus mainly on developments related to automated sensor-based
navigation. The other aspects are be dealt with in the framework of collaborations and exchanges with
other academic, industrial and institutional partners. Therefore, we focus on four research topics that are
central to autonomous navigation and a major focus point for the scientific and technical communities.
These components are: perception and understanding of the scene, decision systems and vehicle control,
cooperative driving and system modeling. These components are linked one another through a complex
but straightforward architecture depicted in Fig. 1.

Obviously, the ability to perceive and understand the scene is the starting point of any navigation archi-
tecture since it represents the first step of processing sensory data, capturing the world state, and creating
the internal digital representations of the decision system. The latter relies on these representations, on
the ego vehicle localization and the positions of other road users and on contextual data to build decision
schemes which include maneuvers planning and trajectory generation. The control-command loop is
then responsible of the execution of the trajectories by the generation of control laws that control the
vehicle’s actuators.

All these modules interact as shown in Fig. 1 and ensure an autonomous but individual navigation
of a vehicle. However, it is important to study the behavior of these vehicles and their performance
when the penetration rate (i.e., their ratio to total traffic) of these vehicles becomes critical. It is also
very interesting to study the interactions between these vehicles and their potential cooperation. This is
called cooperative driving; it can only take place in the presence of connectivity. The latter also ensures
interaction and cooperation between autonomous vehicles and infrastructure. The benefits of this type
of cooperation are significant, both in terms of the individual performance of each vehicle but also of the
overall performance of the vehicle fleet and traffic in general.

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Inria Annual Report 2023

Figure 1: Automated Driving Functional Architecture

3.1 Research Axis 1: Vision and 3D Perception for Scene Understanding

Navigation for mobile robotics requires a robust understanding of the environment from 2D or 3D sensors.
Recent learning-based vision algorithms are now able to operate in highly cluttered environments, and
tasks which were considered challenging — such as semantic segmentation or object detection — are soon
to be solved to a certain extent. Still, the classical supervision paradigm, which relies on large annotated
datasets, cannot encompass in practice all outdoor conditions and scenarios. There is therefore a need
both to relax the requirement of massive annotations and to extend the perception capability to situations
unseen or rarely seen in the training data.

To that aim, in this research axis, we investigate several broad topics. First, we transversely investigate
learning with less supervision with applications to various perception tasks. Focusing on outdoor
vision, we conduct research relying on data-driven or physics-guided paradigms to hallucinate complex
lighting/weather conditions and compensate for missing data in the training sets. Because mobile
robots evolve in the physical world we also investigate how vision algorithms can provide in-depth 3D
understanding of the scene from images and/or LiDAR scans.

To evaluate our research as well as to foster reproducibility, we rely on relevant recent public datasets
(nuScenes [49], Waymo Open [142], Woodscapes [153], SemanticKITTI [40], CADCD [130], etc.) and
intend to openly share our research results.

3.1.1 Learning with less supervision

It is now widely accepted that supervised learning is a long-term dead end for computer vision. It relies
on costly human- biased annotations, which will soon be unbearable with regard to the ever-increasing
size of datasets, trying to cover data diversity. To circumvent the need for labels, strategies have been
developed where a trained model is either (almost) directly applicable to unseen conditions (i.e., zero-
/few-shot learning) or finetuned on a target domain (i.e., domain adaptation). On the need of data, we
investigate automatic generation of data with Generative Adversarial Networks (GANs). Following recent
work from the group members [94],[8],[145, 146, 132, 131, 151, 122], we contribute to these research
directions, investigating the remaining scientific locks that are detailed below.

Regarding zero-shot learning, we observe that current methods are limited by the low amount of
geometric information featured in the embeddings that are used as auxiliary information; we therefore
boost this geometric information in the embeddings, for example by jointly using text and images. As for
few-shot learning, we use high-contrast dictionary-based approaches where generalization is controlled
by the level of sparsity. We are also interested in category-agnostic models that can operate on (e.g.,
detect, segment) arbitrary objects, or that can adapt online to information retrieved from databases of

Project ASTRA

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rare objects. We build upon recent progress in representation learning to enforce separable features
representations [96] while enforcing orthogonality of features [144]. Besides, we investigate both zero-
and few-shot learning in the context of a complete perception pipeline, instead of focusing on individual
vision tasks as commonly done. In both cases, we will also investigate the use of multiple views and
multiple modalities (using both images and LiDAR scans).

Concerning domain adaptation, common unsupervised strategy exploits resemblance between a
source and a target domain using a self-supervised signal (e.g., pseudo labels [106]) to discover statistics in
the target domain. However, when the domain gap is too big, the model adaptation leads to sub-optimal
minima [154, 51]. To accommodate bigger domain gaps, we investigate the discovery of new statistics
with the support of several modalities (e.g., both 2D and 3D), for a variety of tasks (e.g., semantics, depth
and normal estimation). Regarding representation learning, we focus on disentangling latent space
representations, working towards domain-invariant features by enforcing orthogonality of the domain
features while enabling the discovery of exclusive task/domain features. We study bridging zero-/few-shot
to the domain adaptation paradigm, investigating the open domain adaptation setting that accounts for
novel unseen domains such as [114, 46].

Finally, to relax the need of training data we investigate automatic data generation with image-to-
image (i2i) translations and style-transfer techniques, which both can help training in self-supervision
settings [41, 131, 105]. We observe that GANs commonly lack diversity and controllability in the generated
data. To that aim, we study multi-domain setups [54] and automatic discovery of domain attributes [87]
to foster controllable latent representations. We fight the lack of diversity in the generated datasets [41]
with continuous [148] and multi-modal [131] strategies. Besides standard metrics, we also evaluate the
quality of our generated data by training proxy vision tasks.

3.1.2 Vision in complex conditions

The wide variety and continual physical nature of physics prevent any dataset to encompass all lighting
and weather conditions. Most outdoor datasets account exclusively for data recorded in clear weather
daytime while only a handful of them include adverse conditions. In fact, regardless of the recording
complexity some conditions are unlikely to be included in any dataset due to their inherent rarity (e.g.,
snow storm at sunset). Because they lead to drastically varying appearances we focus here on changing
weathers, seasons and lighting conditions; with the complimentary goals to improve robustness of vision
algorithms and to automatically assess failures cases.

Rather than agnostic data-driven models, we study training with a priori knowledge, with the ultimate
goal to get representations invariant to these conditions. To compensate for the scarcity of data as well as
to generalize training to unseen conditions, we rely on physics-guided learning to ease and accommodate
the discovery of statistics. We rely here on physical guidance to discover the continuous underlying
manifold where data lives [13]. Using physical models to guide the training helps vision algorithms
to accommodate better to partial or imbalanced distribution in the training set, as well as to better
extrapolate to unseen conditions. We are focusing on invariant representations that can improve both
the image translation setup and proxy vision tasks (segmentation, objects, etc.); relying on prior works
from group members [13], [134], [16, 14].

Sometimes, weather conditions go even beyond the sensing capabilities of sensors, e.g., sun glare or
very dark scenes can reduce dramatically the perception of standard cameras. In such cases, robustness is
difficult to attain and the system should rather trigger an alert or fail gracefully. Unseen weather conditions
encountered at runtime can be regarded as a dataset/distribution shift and can be addressed with
predictive uncertainty estimation methods [127]. Through a Bayesian lens we study and devise strategies
for automatic assessment and detection of dataset drifts by leveraging approximate ensembles [116, 35,
70], observer networks [60, 88], and complementary information from other sensors [42]. We rely on prior
findings and works from group members [60, 70, 69],[16], [134].

On application, we evaluate robustness of the proposed methods on core vision tasks of recent adverse

weather datasets [138, 155, 142, 49, 42].

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Inria Annual Report 2023

3.1.3 3D scene understanding

Robots still commonly lack the natural ability of humans to estimate the fine-grained geometry of a scene
while understanding object interactions and reasoning beyond their field of view. To provide accurate
geometry, 3D active sensors such as LiDARs are commonly used in autonomous driving [92], but they only
provide a sparse sensing of the scene. In this third topic, we seek a fine-grained geometrical/semantics
3D understanding of the scene with or without 3D sensing, while also relying on frugal supervision. This
topic benefits from prior work of group members [133, 44, 43, 94],[15],[93, 152, 100, 50].

Building up on recent methods [44, 43, 143, 112, 82] that efficiently convolve point clouds, we look
forward at improving 3D tasks (detection, segmentation, etc.) relying on contextual priors. Furthermore,
we address 3D generative tasks like point cloud up-sampling, completion and generation, as well as
surface reconstruction, which provides important navigation cues for robotics, and can also assist the
human driver in augmented reality scenarios, particularly in adverse conditions. Temporally consecutive
point clouds will also be leveraged to disambiguate occlusions and provide denser scene sensing [133, 50].
Regarding richer scene representations, we study the intertwined relation of geometry and semantics [140]
through the semantic scene completion task [15],[136, 135], which gained growing interest lately [40].

Another line of study is the interaction between modalities of different nature like for scene under-
standing, in particular the complementarity of 2D images and 3D scans. We study how multi-modal
features can jointly improve performance of core tasks, but also how it can lead to improving the per-
formance of single modalities by exploiting cross-modal features as self-supervision [94],[8].

Besides the use of 3D devices, we also investigate 3D understanding from 2D images. As they originate
from passive sensors, images carry less obvious geometrical cues but humans are still able to estimate
depth and understand 3D from a photograph, heavily reasoning on learned priors. We study here challen-
ging tasks like scene reconstruction or 6-DOF localization, which can be conveniently self-supervised
from either 3D sensing or sequential data.

3.2 Research Axis 2: Localization & Mapping

Vehicle localization and environmental mapping are pillars of the perception task for an autonomous
vehicle. While vehicle localization ensures the global positioning of the vehicle in its environment and
local positioning with regard to the road and to the close road features, environment mapping contributes
in building a useful internal representation that is exploited by the decision system.

Inria and Valeo teams have been working - separately and jointly - on the localization and mapping
solutions for over the past 15 years. Many algorithms have been developed and showed their effectiveness
in terms of accuracy, precision and safety expectations for autonomous driving. However, the integrity,
safety, data size and costs are still challenging points that ASTRA wants to address while pursuing research
on localization and pose registration using single/multisensor approaches.

3.2.1 Localization and Map Integrity

Many localization methods were developed mainly based on Particle Filter and GraphSLAM together
with a point cloud representation of the environment. These solutions mainly focus on the accuracy and
precision requirements of the pose estimations. Yet, the integrity of localization and integrity of maps
used for localization are critical to ensure a safe use of the localization system for autonomous driving.
State-of-the-art methods on localization integrity usually proceed by: 1. employing Fault Detection and
Isolation algorithms (FDI) to remove outliers from input data. 2. computing Protection Levels (PL) to
qualify the integrity zone [103] [86] [104] or by calculating the Protection Levels (without FDI) such as in
[109] [37]. Maps integrity is highly related to the feasibility to find a distinctive matching when using the
map for localization. Indeed the map can be explored by an algorithm that aims to identify the zones or
sections that represent a potential ambiguity for matching algorithms such as in [89].

3.2.2 Online Alignment of Multiple Map Layers

A wide diversity of maps that are dedicated to vehicle’s localization are nowadays available. These maps
are different from each other regarding different key localization features. The most important aspects
may be: the structure of the representation (e.g., grid, graph etc.), the underlying theory to represent the

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information of the environment (e.g., occupancy probabilities, landmarks, etc), and the sensor used to
collect information (LiDAR, camera, etc). Map providers, such as Here and TomTom, usually provide
maps with different layers to encode different information that are relevant to ADS features (Road model,
lanes, and road features). Valeo, having the advantage of being the leader of automotive LiDAR sensor,
wants to enhance his ADS solutions arsenal as a map provider by providing a map service based on the
laser point clouds and potentially other information layers that are relevant to ADS. For this purpose it is
important to find correspondences and align different map layers with other maps from maps providers.
This subject is addressed by considering semantic information that can be extracted from heterogeneous
sensors and maps data such as in [9] and [10].

3.2.3 Georeferencing of maps without RTK GNSS and IMU

Highly accurate maps that are used for AD localization are usually built using a very expensive Fusion box
that includes a very precise RTK_GPS receiver and a first grade IMU. These solutions for map building are
very expensive and require deployment of RTK bases in the environment to receive the corrections which
imply extra cost. The idea of this subject is to be able to use available sensors (such as standard GNSS,
IMU, CAN, LiDAR, Camera) and possibly maps from other providers to build a highly accurate (in the
global reference) map using point clouds. Different inputs from sensors and maps can be considered
together with an asynchronous fusion method to build an accurate estimation [11]. The method to
achieve this goal constitutes the subject of this study.

3.3 Research Axis 3: Decision making, motion Planning & vehicle Control

Decision-making, maneuver and motion planning, and vehicle control are extremely vital components
of the intelligent vehicle. These modules act as a bridge, connecting the perception subsystem of the
environment and the bottom-level control subsystem in charge of the execution of the motion. We
address these issues covering various strategies of designing the decision-making, trajectory planning,
and tracking control, as well as shared driving of the human-automation to adapt to different levels of the
automated driving system accounting with the driver profile.

The challenges related to decision making and path planning are mainly related to four distinct

elements:

1. Errors and uncertainties introduced by the perception subsystems

2. Environment static and dynamic occlusions

3. Lack of understanding and prediction of other road users behaviors

4. Simultaneous consideration of several constraints related to: vehicles dynamics, energy consump-

tion, passengers comfort, offense to driving rule. . .

Different approaches are investigated in the state of the art addressing one or several issues but, to
our knowledge, none are capable of addressing all of them simultaneously. More specifically in most
approaches decision and planning are dealt separately or in a way that favors one of them. Approaches
based on Markov decision process (MPD, POMDP,. . . ), path-speed profiles, ontologies, artificial potential
fields coupled to MPC controllers are able to show interesting results in dedicated environments or in
specific situations, however most of them do not tackle properly specific issues such as intention and
behavior predictions, interactions or multi-criteria real time optimal maneuver decision.

While continuing the investigation of end-to-end driving approaches based (inverse-)reinforcement
learning decision-making approaches, we keep on improving current path-planning methods already
developed by both teams at RITS and DAR: Reachable Interaction Sets [39], Artificial Potentials Fields
(coupled to MPC control) which are designed for obstacle avoidance, as well as traditional path planning
methods. Optimal methods based on the convex optimization and cubic splines are investigated at DAR
to design optimized and robust trajectories. More specifically, we are mainly focusing on the following
three scientific topics (detailed in the next sections):

• Maneuvers and trajectories prediction of surrounding road users

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Inria Annual Report 2023

• Schemes for ego-vehicle actions and maneuvers decision making and motion planning

• Motion planning and trajectories generation

3.3.1 Maneuver and trajectory prediction

To achieve a safe and comfortable driving, an autonomous driving system must have an accurate know-
ledge of the future motions of all other traffic agents surrounding the autonomous vehicle, such as cars,
pedestrians, cyclists, etc. Motion prediction is thus a key task in autonomous vehicles. Several methods
of motion prediction have been studied in the literature. Lefèvre et al [107] propose their classification in
three levels with an increasing degree of abstraction: Physics-based models, Maneuver-based models
and interaction-based models.

• Physics-based motion models. They consider that the motion of vehicles only depends on the laws
of physics. The future motion is predicted using dynamic and kinematic models linking some
control inputs car properties and external conditions. These models are limited to short term
prediction and are unable to anticipate any change in the motion of the car caused by the execution
of a particular maneuver.

• Maneuver-based motion models. They consider that the future motion of a vehicle also depends
on the maneuver that the driver intends to perform. The future motion of a vehicle on the road
network corresponds to a series of maneuvers executed independently from the other vehicles.
These models are Unadaptable to different road layouts.

• Interaction-aware motion models. They take into account the inter-dependencies between vehicles’
maneuvers. These models require computing all the potential trajectories of the vehicles which is
computationally expensive and no compatible with real-time risk assessment. Valeo has filed a
patent to overcome this issue [149]. This patented method is being developed in order to be tested
in the automated driving prototypes.

Fig. 2 shows a comparison of the different models including their challenges and the used al-
gorithms.

Figure 2: Motion prediction models comparison

Valeo has considered these categories in its development of the automated driving prototypes
Cruise4U and Drive4U. The physical-based model is used in situations when their is no knowledge
about the route geometry (for example in a big roundabout without lanes), the maneuver-based in
highway and urban environment when the road topology is available from HD Map or valeo Drive4U
Locate map.

In the few last years, machine learning based algorithms and particularly deep learning are used in
order to solve the limits of the current prediction methods. Human motion trajectory prediction has

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been addressed in the literature [45, 137]. A large amount of naturalistic road user trajectories in different
contexts (highways [58, 59, 97] or urban [49, 52]) needed to train and evaluate deep learning methods are
now available. Our first works [12],[121],[11],[119], taking as input the track history of a target vehicle
and of its surrounding moving road users, obtained accurate prediction results of the target vehicle
motion on highways and an extension [120], including the static scene structure, has been proposed for
an urban context. Valeo is involved in this research area with activities in prediction of other road users
and ego-vehicle trajectory. Different approaches have been implemented and tested in simulation and
on test cars [48, 47].

However, work has still to be done in this domain in terms of performance, robustness and gener-
alization before being used in real autonomous driving applications. In fact, the behavior of a human
driver depends also on the contextual knowledge of the environment (speed limits, traffic density, day
of the week, visibility, road equipment, driver’s country, etc.) and on its goal [157]. We plan to include
these contextual cues in a prediction method, which should also compute multiple plausible trajectories
representing the driver’s diverse possible behaviors, give uncertainties estimations on the predictions,
carry out multi-agents trajectory forecasts and should be usable in any environment. It will necessitate
the use of a more complete dataset [156] composed of various driving scenarios collected from different
countries, which may be completed by our own dataset collected with the help of Valeo if necessary. This
work will be done in collaboration with Itheri Yahiaoui from Reims University and within the starting
PhD thesis of Amina Ghoul funded by the SAMBA project.

3.3.2 Ego-vehicle actions and maneuvers decision making

The most important component of an autonomous vehicle navigation system is the decision system that
elaborates the coming tactical actions and maneuvers to be executed. The selection of the optimal man-
euver should be the result of relevant and simultaneous consideration of several factors. These factors are
mainly: safety and risk assessment, respect of the dynamic constraints of the vehicle and its controllability,
uncertainties related to the perception outputs, nearby uncertain interactions with/between close road
users, and finally the criterion related to the navigation objectives such as journey duration minimization,
driver/passenger comfort, fuel/energy consumption minimization, respect of driving rules, etc. The
latter being expressed in terms of kinematics constraints.

In the literature, there are very few approaches describing unified decision architectures capable of
taking into account all of the considerations mentioned above. Most approaches are developing planning
schemes which separate motion generation and decision making. In these approaches, motion planning
(including reactive planning) usually exploits geometry, configuration spaces and other optimization
techniques. Decision making schemes rely on AI logic based approaches such as rule based [126],
decision trees [56, 110], Finite Set Machines [158], Bayesian Networks and Markov Decision Processes
like approaches (MDP, POMDP. . . ), AI heuristics algorithms (SVM’s and evolutionary methods) or AI
approximate reasoning methods (fuzzy logic) and Artificial Neural Networks (CNN’s, Reinforcement
Learning. . . ) [111, 147, 53]. In [57] propose an architecture that provides an optimization of the motion
generation using the decision making function as the evaluation function, the aggregation of fuzzy logic
and belief theory allowing decision making on heterogeneous criteria and uncertain data.

In the coming period we will work on unified architectures, that tackles simultaneously decision
making and motion planning. Very likely, we will focus on deep learning techniques based on reinforce-
ment learning and inverse reinforcement learning where we deem a (dense) reward function that is
suitable for a large class of behavioural planning tasks. More generally, we will investigate model-free and
model-based approaches where some interesting approaches have already been initiated and showed
interesting results such as in [108]. In particular, in order to better evaluate safety costs, we will take as
input the output of the maneuvers and trajectories prediction system described in the previous section,
which has the advantage to better estimate the road users trajectories thanks to attention mechanisms
that encode interactions and behaviors. This work is done within the PhD of Yacine Ben Ameur funded
by the SAMBA project.

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Inria Annual Report 2023

3.3.3 Trajectory planning

State of the art on motion planning techniques have been mainly focusing on methods generating the
geometric path first, and then applying a speed profile to the generated path. To mention just a few, this
approach has been tackled by the following methods (or combinations): interpolating curve-based [79,
78], graph-search based [113], sampling-based [98] and optimization-based [83].

From the motion planning point of view, the inclusion of human factors is a key element for increasing
the acceptance of the automated vehicle behavior and for providing a more human-like response. For
that purpose, the use of data from real drivers should be envisaged to better define the motion constraints
in dynamic environments, allowing to adapt the trajectories to any specific road scenario (intersections,
roundabouts, merging, overtaking, lane driving, etc). For instance, motion constraints such as longitud-
inal and lateral accelerations as well as jerks should be properly taken into account in the generation of a
human-like speed-profile, as introduced in [36].

Furthermore, the inclusion of driving factors such as energy consumption or the traffic occupancy
should be considered in the multi-criteria optimization for better adapting to any driving situation.
This would help to reduce the driving time (such as the commute time) or even reduce the energetic
consumption and the stress of both driver and car passengers by reducing the traffic jams and the
corresponding repetitive acceleration and braking maneuvers.

Finally, this planning module must fit to the time constraints for its execution in real-time to ensure
safety. Thus, a complete and rapid motion planning approach is needed; it should consider the functional
safety to generate real-time collision-free trajectories considering the different interactions with the
surrounding vehicles to be tracked by the control. For that purpose, works presented at [38] will be
extended in order to consider the interaction among the several surrounding road users as one and not
as individual interactions, investigating the risk assessment metric that is the most appropriate for each
specific scenario.

3.3.4 Robust control of automated vehicles

In order to execute safely a planned trajectory or a reactive maneuver, it is essential that the vehicle
executes these trajectories taking into account the vehicle dynamics while ensuring safe, stable and
comfortable maneuvers. A tremendous effort was deployed the last 10 years by the team partners in
the area of motion planning and intelligent control. Seven PhD thesis were dedicated to the important
problem of path and motion planning as well as on corresponding control-command. All are addressing
the navigation of autonomous vehicles in structured but complex environments. Harsh configurations
such as intersections and roundabouts need specific planning approaches taking into account the
geometry and the topology of the places, but also the dynamic and kinematic constraints of each ego-
vehicle and as the safety and comfort constraints.

Previously, RITS team (Inria) also implemented specific control algorithms dedicated to specific road
maneuvers such as overtaking [128] and parking maneuvers [129]. Control laws were designed with the
theoretical proof of stability and optimality. Very interesting results were obtained in two major domains,
mainly related to the controllability and stability of dynamic complex systems which are key aspects
when it comes to design intelligent control algorithms for vehicles:

• Plug&Play control for highly non-linear systems: Stability analysis of autonomous vehicles. The
developed Plug&Play control is able to provide stability responses for autonomous vehicles under
uncontrolled circumstances, including modifications on the input/output sensors. Former RITS
team was among the very first to investigate these theories for automotive applications. They were
Investigated in the PhD thesis of Mr. F. Navas [125] and I. Mahtout [115]. The approach deals with:
the reconfiguration of existing controllers whenever changes are introduced in the system (gain
scheduling), online closed loop identification of the vehicle and its components, and Automatic
control reconfiguration to achieve optimal performance [124][10].

• Fractional Calculus for Cooperative Car Following Control A Car-Following gap regulation controller
using fractional order calculus, has been developed and has been proven to yield a more accurate
description of real processes and ensure string stability of the platoons or the vehicles involved
in a Cooperative Autonomous Cruise Control [66]. In an effort to combine fractional calculus

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robust control with plug&play control, a multi-model adaptive control (MMAC) algorithm based on
Youla-Kucera (YK) theory to deal with heterogeneity in cooperative adaptive cruise control (CACC)
systems was proposed[67].

ASTRA will evolve by introducing intelligent cooperation between vehicles and, at the same time,
autonomously driving the vehicle in a human driver way (increasing driver acceptability) but with
the safety and accuracy of optimized control algorithms. To achieve this, we will rely on the existing
approaches developed so far but no further research will be conducted in the lifetime of the joint
team. This is mainly due to the absence of a senior researcher at ASTRA capable of carrying this topic
independently at a high level. This also motivates the team to seek to recruit a new confirmed researcher
in the field of the control of dynamic systems, a crucial domain for a team willing to develop and deploy
advanced control architectures on real mobile platforms. In the meanwhile it would be very interesting
to envisage collaborations with other Inria teams working on similar topics. A perfect example is DISCO
team (Inria Saclay Research Center, head: Mrs. Catherine Bonnet). Among others, the research interests
of DISCO cover: the realization and reduction of infinite-dimensional systems, Robust H∞ and BIBO
parametrization and stabilization of infinite-dimensional systems, stabilization by finite-dimensional
controllers (PID control), delay systems and fractional systems.

This research direction comprises a big interaction with the research axis: Large scale modeling and
deployment of mobility systems in Smart Cities. The former will be essential when developing control
algorithms that rely on a very small communication delay for getting a stable latency, designing stable
systems. The latter will serve to analyze the effect over the traffic flow from a developed algorithm,
moving from the validation of a proposed controller in a limited number of vehicles to a its study from a
macroscopic perspective.

3.4 Research Axis 4: Large scale modeling and deployment of mobility systems in

Smart Cities

While axes 1 to 3 deal with subjects related to the on-board intelligence of an “individual” intelligent
vehicle and its autonomous navigation, axis 4 intervenes when it comes to many communicating,
autonomous or automated vehicles but also when it comes to the cooperation with the static environment
(infrastructure). The latter may contain and integrate: roadside and monitoring sensors (Cooperative Per-
ception Services), signaling, communication infrastructures, cloud... The research concerns in particular
the deployment of equipped vehicles on a large scale in a road or urban environment.

The research objectives are twofold.

• First, the focus is on the modeling of systems comprising a large number of vehicles, often seen as

random entities.
The methodology is mainly to explore the links between large random systems and statistical
physics. This approach proves very powerful, both for macroscopic (fleet management [64]) and
microscopic (car-level description of traffic, formation of jams [72, 139, 77, 76]) analysis. The general
setting is mathematical modelling of large systems (typically in the so-called thermodynamical
limit), without any a priori restriction: networks, random graphs, etc. One often aims at establishing
a classification based on criteria of a twofold nature: quantitative (performance, throughput, etc)
and qualitative (stability, asymptotic behavior, phase transition, complexity).

• The second objective concerns the cooperation of these communicating entities in order to address
the efficiency and safety of mobility. This cooperation takes several forms. Direct or indirect com-
munications (V2X) are dedicated to maneuver coordination, taming and improving traffic efficiency
(cf. section 4.4.2), platooning, safety critical distributed coordination (cf. 4.4.3)... Crowdsourcing
is another aspect that could be used for traffic modeling and prediction (cf. 4.4.1), environment
augmented mapping, or global vehicles localization. A Phd student will be hired this year to work
on this precise subject (cf. 4.5).

Beside this core methodology, other past activities of interest include discrete event simulation [55,

102] and resource allocation for ITS [101, 84, 85].

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Inria Annual Report 2023

Finally, axis 4 does not represent a structural unit like the other axes. Its objective is to deal with
the problem of scaling, deployment and multi-vehicle cooperation in a global and systemic way. On
the substance, methods and theories of modeling will be investigated and the design of secure telecom-
munication systems will be elaborated. These models and systems are intended to be implemented in
more global systems and architectures. They will interact with the other axes through these architectures
and will respond in a targeted way to needs; for example, whenever a need for probabilistic modeling is
expressed (e.g. section 4.5).

3.4.1 Traffic prediction in urban settings: detecting extreme events

A probabilistic forecasting method that can provide predictions of urban traffic at city level, accurate at
short term and meaningful for a horizon of up to several hours, has been devised in the team [75, 71, 74,
117, 118, 73][5], in collaboration with C. Furtlehner (TAU, Inria Saclay). It is designed to leverage spatial
and temporal dependency and can deal with missing data, both for training and running the model. The
method consists in learning a sparse Gaussian copula of traffic variables, compatible with the Gaussian
belief propagation algorithm. Results of tests performed on three urban datasets show a very good ability
to predict flow variables and reasonably good performances on speed variables.

When investigating the output of the model, some rare but large errors are noticeable. It turns out
that this corresponds to detectors which, for a long period, send values completely at odds with the ones
observed during training. These badly behaving detectors may either correspond to corrupted ones,
or to drastic changes of the traffic conditions on the corresponding segment, because of road work or
accidents for instance.

One way of examining these events has been proposed in [90], and we plan to investigate whether it
can be used to improve models. Separating sensor failure from extremal events is even more important,
and this is what we plan to investigate in a PhD thesis, by careful analysis of the correlation structure of
the model.

3.4.2 Taming highway traffic using cooperative automated vehicles

Several authors [68, 61, 150, 81] have suggested that it is possible to use a small proportion of automated
vehicles to regulate highway traffic. These studies are set in a traffic regime which exhibits string instability,
which means in terms of transfer function that any excitation of a frequency below a certain limit is
amplified. We are interested here in a slightly different setting, where reaction time is taken into account
for human drivers. We have shown [34] that the introduction of this delay involves a non rational transfer
function, implying in particular that the system is not always stable. We have proposed a complete
self-contained proof of stability conditions, based on classical complex analysis. Moreover, we bring to
light a phase transition with a new propagation regime, named partial string stability, situated between
string stability and string instability.

With these foundations established, the next steps are to devise a traffic stabilization scheme by
means of a fleet of cooperative automated vehicles. However, contrary to the work in [68], our approach
is based on a car-following model with reaction-time delay, rather than on a first order fluid model. The
continuation of these studies will concern shock wave analysis and adequate traffic-stabilizing control
strategies.

3.4.3 Crowdsourced mapping

The deployment of intelligent and connected vehicles, equipped with increasingly sophisticated equip-
ment, and capable of sharing accurate positions and trajectories, is expected to lead to a substantial
improvement of road safety and traffic efficiency. Nevertheless, in order to guarantee accurate positioning
in all conditions, including in dense zones where GNSS signals can get degraded by multi-path effects, it
is expected that sensory equipped vehicles will need to use precise maps of the environment to support
their localization algorithms. Crowdsourced mapping represents a cost-effective solution to this problem,
consisting in making use of measurements retrieved by multiple production vehicles equipped with
standard sensors in order to build an accurate map of landmarks and maintain it up-to-date in realistic,
long-term scenarios. Existing SoA crowdsourced mapping solutions rely on triangulation optimization
or graph-based optimization where trade-offs between the map quality and computational scalability

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Figure 3: Valeo Automated Driving roadmap

are still to be investigated. We propose to extend the work of [141] to improve scalability. One possible
approach is to rely on a Gaussian Belief algorithm to estimate and update the position of landmarks and
of the the vehicles, along with their corresponding uncertainties.

3.4.4 Cooperative automated driving involving V2X communications

Automated driving in a complex shared road requires cooperation among road entities in terms of
cooperative control, cooperative perception, and cooperative path planning. This poses new research
challenges that did not exist in the domain of vehicular communications e.g., communications for
cooperative automated driving and intention-aware communications. Based on our experiences and
know-how on mobile telecommunications, networking, and robotics domains, the ASTRA team will
conduct research activities within the following domains:

• Safety critical V2V communications.

• Safety critical distributed coordination.

• Safety and performance guided V2X communication and data processing

• Vehicles’ behaviors and intention-aware communications

4 Application domains

The aim of the project team is to tackle the challenges and provide breakthrough solutions for the
autonomous and connected mobility. It covers the improvement of the safety, the availability and the
performances of ADAS “Advanced Driver Assistance Systems” and the L3 automated systems (Traffic Jam
Pilot and Highway Pilot) for privately owned vehicles as well as L4 automated systems including Robotaxi
and automated transportation systems like autonomous shuttles. Enabled by 5G and the V2X connectivity
in general, the extension to cooperative Automated driving and the Smart city will also be considered.
There are more and more cities and highways equipping their infrastructures with sensors that can
enable extended and shared perception. During the project, the developed solutions are tested for these
applications. Valeo Automated Driving roadmap is addressing them through 3 programs. Cruise4U
Program for multiple carriageway/highways, Drive4U for Urban environment including autonomous
shuttles and eDeliver4U for last mile delivery as shown in Fig. 3.

The Cruise4U and Drive4U programs allowed to Valeo to perform open roads experiments around

the world with more than 200,000 km accumulated in real conditions with plenty of use cases.

Fig. 4 shows a part of the Cruise4U experiments, while Fig. 5 shows world premieres: Drive4U open

road experiments with only Valeo serial production sensors operating in Paris, Las Vegas and Tokyo.

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Inria Annual Report 2023

Figure 4: Cruise4U Program field testings

Figure 5: Drive4U Urban Pilot Program

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A dedicated Automated Driving platform for the project team is under discussion in order to allow

quick and easy integration, tests and validations of the Joint team developments.

5 Highlights of the year

• Noël Nadal obtained his Agrégation externe d’informatique.

• Raoul de Charette integrated the ELLIS scientific network.

5.1 Awards

• Kaouther Messaoud, Itheri Yahiaoui, Anne Verroust-Blondet and Fawzi Nashashibi received the
Georges N. Saridis Best Transactions Paper Award for their paper Attention Based Vehicle Trajectory
Prediction, IEEE Transactions on Intelligent Vehicles, 2021 (the awarded paper is selected among
all papers published during the three calendar years preceding the year of the award).

6 New software, platforms, open data

6.1 New platforms

Participants:

Paulo Resende, Benazouz BRADAI, Gaëtan Le Gall, Fawzi Nashashibi.

The creation of the team has resulted in the strengthening ofthe experimental side of the team which
has always had among its objectives the validation of work on real instrumented platforms. The creation
of the team has resulted in the strengthening of the experimental side of the team which has always had
among its objectives the validation of work on real instrumented platforms. Thus, the team is equipped
with at least four road vehicles (Cruise4U and Drive4U from Valeo, a C1 and a Zoé from Inria), 3 shuttles
(2 Cybus from Inria and a NAVYA from Valeo) and 2 Cybercars (Inria) (figure 6.1).

7 New results

7.1 3D scene reconstruction and completion

Participants:

Anh-Quan Cao, Raoul de Charette.

In this research axis, within the context of Anh Quan Cao PhD thesis, we have studied the ability to
estimate the 3D scene geometry and semantics from images or 3D data. In SceneRF [20], we introduced a
self-supervised method for monocular 3D scene reconstruction using Neural Radiance Fields (NeRF),
trained solely from multiple posed image sequences. To enhance geometry prediction, we present new
geometrical constraints and a novel probabilistic sampling strategy for effective radiance fields training.
The performance surpasses the state-of-the-art on multiple benchmarks and metrics. In a collaborative
work with the Technical University of Munich (TUM), we proposed a method named PaSCo [29]. The latter
investigates the popular Semantic Scene Completion (SSC) task, also addressed in multiple works of the
team, to incorporate uncertainty estimation. Here, we extended SSC to the Panoptic Scene Completion
(PSC) task, to include instance-level information, resulting in richer 3D scene understanding. Crucially,
we noticed that the literature overlooks uncertainty estimation despite it’s crucial importance for safety-
critical application like autonomous driving. To allow uncertainty-awareness, our approach follows utilize
a multi-input multi-output architecture to predict variations of the PSC output and therefore estimate

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Inria Annual Report 2023

Figure 6: ASTRA automated prototypes: Drive4U (left), Inria electric vehicles (right)

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the voxel-wise and instance-wise uncertainty. Experiments show our method outperforms all baselines
in both Panoptic Scene Completion and uncertainty estimation on three large-scale benchmarks.

SceneRF was presented at ICCV 2023 and PaSCo is accepted at CVPR 2024 [29]. Both works are shared

opensource.

7.2 Cross-task learning for vision algorithms

Participants:

Ivan Lopes, Tuan-Hung Vu, Raoul de Charette.

In [25] we explore the ability to learn multiple dense prediction tasks to train a more robust network.
Specifically, we jointly address semantic and geometry-related tasks which are known to be comple-
mentary, by leveraging attention mechanisms to enforce cross-task exchange. Extensive experiments are
conducted on three diverse multi-task setups, we show their benefit across all tasks on both indoor and
outdoor datasets. This work, named DenseMTL, is opensource and published in WACV 2023.

7.3 Physics-guided learning vision

Participants:

Ivan Lopes, Tan Khiem Huynh, Raoul de Charette.

A major axis of the vision group is the study of machine learning under physics guidance. We explore
two major direction: 1) the use of physics prior to solve vision, 2) the use of vision algorithms to discover
physics. In the first one, the team had major works in the past.

1) The use of physics knowledge for vision was extensively studied in the team, in particular in past

works [14, 13, 16, 7].

Recently, we addressed a novel problem of extraction of materials from an image in the context of
Ivan Lopes PhD thesis in a joint work with a collaborator from Oxford University. Following the common
practices in computer graphics, we formulate materials as Physically Based Rendering (PBR) to model
the interaction between materials and light sources. Because such PBR materials are hard to acquire, in
Material Palette [32], we propose the first method to extract PBR materials from a single real-world image.
To do so, we leveraged a large-scale diffusion model and training a multi-task network to decompose
textures. In addition to opensource libraries, we created a custom synthetically generated high-quality
dataset of 9,000 RGB textures, TexSD, openly shared to the public. The complete method sets a new
state-of-the-art. We also showcase the capabilities to edit 3D scenes with materials estimated from real
photographs.
In another direction, with Weihao Xia (PhD visit student) in collaboration with University College London
(UCL) and Cambridge, we introduced DREAM [27], a method for visual decoding using a biologically-
inspired architecture. In particular, we studied the ability to decode the brain activities of a patient
when looking at an images — thus reconstructing the image from fMRI data. This task, refered as visual
decoding, is usually addressed using the support of language. In DREAM, we reason on biological
principle, designing an architecture that reverses the human pathways to decipher geometry, color and
semantics. The resulting method outperformed the literature.

2) Another prospective direction is the use of vision algorithms to discover physics models. In partic-
ular we seek here to estimate the symbolic models ruling light propagation. This challenging research
direction, was initiated with an intern, focusing first on the discovery of the simple light attenuation
models in fog. There exist physicals model for atmospheric scattering, one commonly used estimates fog
−βd ); where d , β, and L∞ are the depth map, scattering
from a clear image I0 as I (d ) = I0e
coefficient, and atmospheric light, respectively. The method employed used a sequence of images under
foggy conditions to unsupervisedly discover the light attenuation models, by tracking the luminance of
salient points in the video. Although the project was not finalized, the groundwork accomplished during
the internship provides the foundation for a potential future publication.

−βd + L∞(1 − e

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Inria Annual Report 2023

In this axis, DREAM [27] is published at WACV 2024 and Material Palette [32] is accepted at CVPR
2024. Another work not detailed here is the journal [19],which extends a prior work and was recently
accepted in T-PAMI. All works are opensource.

7.4 Language-driven vision

Participants: Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez,

Raoul de Charette.

In this axis, in the context of Mohammad Fahes PhD thesis, we address a major problem of deep-
learning based computer vision algorithms which is their lack of generalization to out-of-distribution
samples. Here, we build on recent advances in Vision-Language Model (VLM) to adapt or generalize the
performance of a model.

In PODA [21] we propose a setting of “prompt-driven zero-shot domain adaptation”, where the only
target information is reduced to a description of the target domain in natural language (e.g. driving at
night). Our research builds on multimodal vision-language space (here, CLIP), to optimize the features
of a network, by steering them towards a target text embedding while preserving the features content
and semantics. Such augmented features can serve any proxy tasks, and our work demonstrates strong
performances in semantic segmentation, object detection and classification problems.

Rather than adaptation to a specific domain, in FAMix [30] we tackle the question of generalizing a
model trained on a source domain to any potential unseen domain. For instance, we were interested
in training a segmentation model on synthetic driving scenes images from a game engine (GTA5) to
real-world driving scenes — significantly more complex to acquire. With FAMix, we introduced a simple
framework for generalizing semantic segmentation networks by employing language as the source of
randomization. Our recipe comprises three key ingredients: i) the preservation of the intrinsic CLIP
robustness through minimal fine-tuning, ii) language-driven local style augmentation, and iii) randomiz-
ation by locally mixing the source and augmented styles during training. Extensive experiments report
state-of-the-art results on various generalization benchmarks.

The work PODA [21] is published at ICCV 2023 and FaMix [30] is at CVPR 2024. An extension of the

former work is next to ready for a submission in a journal. All works are shared opensource.

7.5 Misbehavior Detection for Collective Perception Services: A Systematic Trust-

Based Evidence Approach

Participants:

Jiahao Zhang, Fawzi Nashashibi.

This work has been conducted in collaboration with external partners from IRT-SystemX Institute:
Ines Ben-Jemaa and Francesca Bassi.

Collective perception services (CPS) allow vehicles to extend their perception of the environment
beyond their Field of View through V2X communication. To build an extended perception system,
received objects are fused with the local perceived objects. The association process is a crucial step in
the perceptual data fusion. We leverage the perception data association results to detect misbehaving
nodes when contradictory perceived information are received. Especially, we design and implement a
misbehavior detection architecture for CPS relying on an evidential object association approach [63]. The
result of the association process is used to assess the node trust worthiness through the subjective logic
mechanism [95].

7.6 Hierarchical Attention and Graph Neural Networks for Drift-Free Pose Estima-

tion of a ground vehicle

Project ASTRA

21

Participants:

Fawzi Nashashibi.

This work has been conducted in collaboration with Kathia Melbouci after her departure from ASTRA
at the end of 2022.

We explored an architecture that replaces traditional geometric registration and pose graph optimiza-
tion of a ground veicle with a learned model utilizing attention mechanisms and graph neural networks.
In a paper submitted to IEEE ICRA 2024, We propose a strategy to condense the data flow, preserving
essential information required for the precise estimation of rigid poses. Our results, derived from tests on
the KITTI Odometry dataset, demonstrate a significant improvement in pose estimation accuracy. This
improvement is especially notable in determining rotational components when compared with results
obtained through conventional multi-way registration via pose graph optimization. The corresponding
code will be made available as soon as the paper status is determined.

7.7 Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive

Scenarios

Participants:

Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi
.

The abilities to understand the social interaction behaviors between a vehicle and its surroundings
while predicting its trajectory in an urban environment are critical for road safety in autonomous driving.
Social interactions are hard to explain because of their uncertainty. In recent years, neural network-
based methods have been widely used for trajectory prediction and have been shown to outperform
hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to
overcome this limitation, we combine in [23] the interpretability of a discrete choice model with the high
accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive
environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate
the effectiveness of our proposed architecture to explain its predictions without compromising the
accuracy.

7.8 Interpretable Long Term Waypoint-Based Trajectory Prediction Model

Participants:

Amina Ghoul, Itheri Yahiaoui, Fawzi Nashashibi.

Predicting the future trajectories of dynamic agents in complex environments is crucial for a variety
of applications, including autonomous driving, robotics, and human-computer interaction. It is a
challenging task as the behavior of the agent is unknown and intrinsically multimodal. Our key insight
is that the agents behaviors are influenced not only by their past trajectories and their interaction with
their immediate environment but also largely with their long term waypoint (LTW). In [22], we study the
impact of adding a long-term goal on the performance of a trajectory prediction framework. We present
an interpretable long term waypoint-driven prediction framework (WayDCM). WayDCM first predict an
agent’s intermediate goal (IG) by encoding his interactions with the environment as well as his LTW using
a combination of a Discrete choice Model (DCM) and a Neural Network model (NN). Then, our model
predicts the corresponding trajectories. This is in contrast to previous work which does not consider
the ultimate intent of the agent to predict his trajectory. We evaluate and show the effectiveness of our
approach on the Waymo Open dataset.

7.9 Motion planning and prediction

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Inria Annual Report 2023

Participants: Nelson De Moura, Augustin Gervreau.

The study of different longitudinal behaviors for vehicles was completely restructured during 2023,
resulting in a two-step method based on the DTW dissimilarity matrix clustering with k-means, which
was published in [26]. However, for the longitudinal clustering, so as to extract the different behaviors,
another method had to be created, that considers the dynamic characteristics of each vehicle. This work
will be publish in 2024.

For pedestrians and cyclists the main difficulty is to filter the outlier trajectories and the ones that are
not representative of a overall behavior. This has been done with hierarchical clustering considering the
shape and the initial and final points for clustering. This work will also be published in 2024. And from
these results the first simulation agent models started to been trained.

7.10 Control and Human Factors

Participants: Nelson De Moura, Tiago Rocha.

Following prior member works [123, 62], we studied a risk prediction system to evaluate the trade-
off between ethical aspects and fuel-efficiency improvements in platooning systems. Leveraging the
harm concept from [123], an MDP approach is used to adjust dynamically controllers during platooning
operation. Results published in [24].

7.11 Ethical decision-making for automated vehicles

Participants: Nelson De Moura.

The ethics of automated vehicles (AV) has received a great amount of attention in recent years,
specifically in regard to their decisional policies in accident situations in which human harm is a likely
consequence. After a discussion about the pertinence and cogency of the term “artificial moral agent”
to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that
human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only
pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical
Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible
decision rules to determine the most suitable action given the specific environment and decision context.
The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to
provide a computational approach that is flexible enough to accommodate a number of human “moral
positions” concerning what morality demands and what road users may expect, offering an evaluation
tool for the social acceptability of an automated vehicle’s decision making. Published in [28]

7.12 Landmark localization for Autonomous Vehicles

Participants: Noël Nadal, Fawzi Nashashibi, Jean-Marc Lasgouttes.

We are interested in the case where we have a map in an urban environment, with landmarks whose
position is known, up to some Gaussian error. A vehicle, equipped with sensors to detect such landmarks,
must determine its position based on its knowledge of the map and its observations. The performance of
the proposed algorithm enables it to be used in real time at a speed consistent with the hypothesis of an
urban environment (30 to 50 km/h), and the accuracy of the localization performed (error of less than 10
cm) is good enough to enable it to be used by an autonomous vehicle.

Project ASTRA

23

7.13 Shock Wave Estimation in Intelligent Driver Models

Participants: Guy Fayolle, Jean-Marc Lasgouttes.

We investigate the transfer function Q(z) emanating from the linearization of a car-following model
for human drivers, when taking into account their reaction time, which is known to be a cause of the
stop-and-go traffic phenomenon. This leads to a non rational transfer function, implying nontrivial
stability conditions which are explicitly given. They are in particular satisfied whenever string stability
holds. It is also shown how this reaction time can introduce a regime of partial string stability, where the
transfer function modulus remains smaller than 1, up to some critical frequency. Conditions are explored
in the parameter space discriminating between 4 different regimes (instability, string stability, partial
string stability, string instability).

Starting from the study [34], we aim first to describe the schockwaves that may form in the string
instability regime. To this end, we are currently analyzing the transfer function Q(z)N , for of a string of N
vehicles, N sufficiently large. This preliminary step requires a sharp analysis, for which the usual saddle
point methods seem not to work.

7.14 Reflected brownian motion in a non convex cone

Participants: Guy Fayolle.

G. Fayolle, in collaboration with S. Franceschi (LMO, Paris-Saclay University) and K Raschel (CNRS, Tours
University), study the stationary reflected Brownian motion in a non-convex wedge, which, compared to
its convex analogue model, has been much rarely analyzed in the probabilistic literature. Two approaches
are proposed for the three-quarter plane. In [33], it was proved that the stationary distribution could
be found by solving a two dimensional vector boundary value problem (BVP) on a single curve (an
hyperbola) for the associated Laplace transforms. The reduction to this kind of vector BVP seems to be
quite new in the literature. As a matter of comparison, one single boundary condition is sufficient in
the convex case. When the parameters of the model (drift, reflection angles and covariance matrix) are
symmetric with respect to the bisector line of the cone, the model is reducible to a standard reflected
Brownian motion in a convex cone. Finally, we construct a one-parameter family of distributions, which
surprisingly provides, for any wedge (convex or not), one particular example of stationary distribution of
a reflected Brownian motion.

In [17], the main result is to show that the stationary distribution can in fact be obtained by solving a
boundary value problem of the same kind as the one encountered in the quarter plane, up to various
dualities and symmetries. The idea is to start from Fourier (and not Laplace) transforms, allowing to get a
functional equation for a single function of two complex variables.

7.15 A Markovian Analysis of IEEE 802.11 Broadcast Transmission Networks with

Buffering and back-off stages

Participants: Guy Fayolle.

Following their previous analysis [65] G. Fayolle and P. Mühlethaler analyze the so-called back-off
technique of the IEEE 802.11 protocol in broadcast mode with waiting queues. In contrast to existing
models, packets arriving when a station (or node) is in back-off state are not discarded, but are stored
in a buffer of infinite capacity. As in previous studies, the key point of our analysis hinges on the
assumption that the time on the channel is viewed as a random succession of transmission slots (whose
duration corresponds to the length of a packet) and mini-slots during which the back-off of the station

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Inria Annual Report 2023

is decremented. These events occur independently, with given probabilities. The state of a node is
represented by a three-dimensional Markov chain in discrete-time, formed by the back-off counter, the
number of packets at the station, and the back-off stage. The stationary behaviour can be explicitly
solved. In particular, stability (ergodicity) conditions are obtained and interpreted in terms of maximum
throughput [31].

7.16 Blockchain adapted to IoT via green mining and variable Proof of Work

Participants: Guy Fayolle.

Blockchain applications continue to grow in popularity, but their energy costs are clearly becoming
unsustainable. In most cases, the primary cost comes from the amount of energy required for proof-
of-work (PoW). G. Fayolle and Ph. Jacquet study the application of blockchains to the IoT, where most
devices are underpowered and would not support the energy cost of proof of work. PoW was originally
intended for two main uses: block moderation and protecting the blockchain from tampering. For IoT we
suggest to replace the expensive moderation of PoW with the proposed energy-efficient green mining.
The blockchain will be protected by a variable difficulty PoW. One of the results is the proof that the
average mining time in variable PoW actually depends only on the average difficulty. This crucial property
will allow low-difficulty PoWs to be reserved for devices with low computational capacity, while higher
difficulties will be reserved for devices with the highest computational power. The consequence is to give
equal opportunity to objects with low computational power compared to objects with high computational
power.

7.17 Random walks in orthants and lattice path combinatorics

Participants: Guy Fayolle.

In the revised version of the second edition of the book [4] (see also the corrections), original methods
were proposed to determine the invariant measure of random walks in the quarter plane with small
jumps (size 1), the general solution being obtained via reduction to boundary value problems. Among
other things, an important quantity, the so-called group of the walk, allows to deduce theoretical features
about the nature of the solutions. In particular, when the order of the group is finite and the underlying
algebraic curve is of genus 0 or 1, necessary and sufficient conditions have been given for the solution to
be rational, algebraic or D-finite (i.e. satisfying a linear differential equation). In this framework, number
of difficult open problems related to lattice path combinatorics are currently being explored by Guy
fayolle, in collaboration with A. Bostan and F. Chyzak, both from theoretical and computer algebra points
of view: concrete computation of the criteria, utilization of differential Galois theory, genus greater than 1
(i.e. when some jumps are of size ≥ 2), etc. A recent topic (mentioned in 2019) deals with the connections
between simple product-form stochastic networks (so-called Jackson networks) and explicit solutions of
functional equations for counting lattice walks. Some partial extensions of this earlier work are still under
development.

8 Bilateral contracts and grants with industry

8.1 Bilateral contracts with industry

Participants:

Jean-Marc Lasgouttes,
Fawzi Nashashibi, Raoul de Charette,
Benazouz Bradai, Paulo Resende, Zayed Alsayed, Fernando Garrido,
Axel Jeanne, Nelson de Moura Martins Gomes , Noel Nadal, Mo-
hammad Fahes, Karim Essalmi , Tetiana Martyniuk.

Project ASTRA

25

Valeo Group: As a result of a long-standing collaboration, the strategic partnership between INRA and
VALEO led to the establishment of a joint project team in 2022. Since that date, several bilateral contracts
were signed to conduct joint some of which are funded by Valeo.

• Several CIFRE theses have been developed throughout the year 2023 between Valeo and Inria :
Mr. Karim ESSALMI joined ASTRA in February 2023 as a new PhD student working on Maneuver
decision and Motion planning. Mrs Tetiana MARTYNIUK joined the team in June 2023 on a pre-
thesis contract with a CIFRE that will start in 2024 and is working on conditioned generation of
egocentric 3D driving scenes within the astra vision team.

• Other PhD students and post-docs are jointly funded by Valeo and Inria while Mr. Nelson de Moura

is hired as a 2-years post-doc thanks to the national Plan de relance Programme.

• Valeo is currently a major financing partner of the “GAT” international Chaire/JointLab in which
Inria is a partner. The other partners are: UC Berkeley, Shanghai Jiao-Tong University, EPFL,
IFSTTAR, Stellantis and SAFRAN.

• Technology transfer is also a major collaboration topic between ASTRA and Valeo as well as the

development of a road automated prototype.

• Finally, Inria and Valeo are partners of the French project SAMBA (Sécurité Active et MoBilités
Autonomes) including SAFRAN Group, Inria Paris, TwinswHeel, Soben, Stanley Robotics and
EXPLEO.

The work with Valeo Group is articulated around the collaboration of two Valeo teams:
Valeo DAR works on research and development for Advanced Driving Assistant Systems (ADAS).
Starting from July 2022, Zayed Alsayed, Axel Jeanne, Fernando Garrido, and Paulo Resende, employees
seconded by Valeo, joined the joint project team to work on the following scientific areas: localization
and mapping (Sec. 3.2), decision making, motion planning & vehicle control (Sec. 3.3), and large-scale
modeling and deployment of mobility systems in smart cities (Sec. 3.4).

Valeo.AI is the research laboratory of Valeo Group, and follows an academic research line. Valeo.AI
collaborates with the vision group (Sec. 3.1). Starting from July 2022, Alexandre Boulch, Andrei Bursuc,
Gilles Puy, Patrick Pérez, Renaud Marlet, Tuan-Hung Vu joined as part-time researchers in Astra, with
frequent joint group readings, workshops and seminars. Subsequently to his departure from Valeo, in Dec.
2023 Patrick Pérez also left Astra. In 2023 the collaboration led to 3 open source realizations, 3 top-tier
publications and the co-supervision of 1 internship, 2 PhDs.

9 Partnerships and cooperations

9.1 International initiatives

Palestine Polytechnic University: This a new-born collaboration between the Intelligent Systems Re-
search Centre of PPU and ASTRA team. The academic collaborations include:

• Bilateral academic visits

• Hosting and recruiting doctoral and post-doctoral PPU students

• Participation in joint National/Horizon collaborative projects

• Involvement in teaching & education (lectures, trainings mentorships, keynotes. . . )

9.2 International research visitors

9.2.1 Visits of international scientists

Participants:

Fawzi Nashashibi.

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Inria Annual Report 2023

International visits to the team

Professor Karim Tahboub

Palestine Polytechnic University (PPU) Intelligent Systems Research Center (ISRC)

Hebron, Palestine

Dates: July-August 2023

Context: He was a recipient of a Short-Term Research Mobility Grant. His two months visit was the
opportunity to collaborate on the research topic of Cognitive Human-Robot Interaction based
on Intention Recognition, focusing on human-autonomous vehicle interaction in crowded urban
environments.

9.3 National initiatives

9.3.1 ANR

SIGHT

• Title: viSIon throuGH weaTher

• Instrument: ANR JCJC

• Duration: January 2021- December 2024

• Coordinator: Raoul de Charette

• Partners: Inria Paris, Université Laval, Mines ParisTech

• Inria contact: Raoul de Charette

• Abstract: SIGHT investigates invariant algorithms for complex weather conditions (rain, snow, hail).
The project leverages un-/self-supervised algorithms with physic-guidance to model physically
realistic weather, and learn weather-invariant features to improve vision algorithms.

9.3.2 ADEME – Bpifrance

SAMBA

• Title: Sécurité Active et MoBilités Autonomes (SAMBA2022)

• Instrument: Plan de soutien R&D automobile France

• Duration: September 2020 – January 2023

• Grant: 902 302 €

• Partners: Valeo Group, SAFRAN Group, Inria Paris, TwinwHeel, Soben, Stanley Robotics, EXPLEO.

• Inria contact: Fawzi Nashashibi

• Abstract: The project aims to design active safety and autonomous mobility solutions that are
affordable and can be deployed quickly, particularly on private vehicles. Technological solutions
for new mobility services are proposed.

Project ASTRA

9.3.3 AMI – EquipEx+

TIRREX

27

• Inria is a major partner and beneficiary of the new EquipEx+ national initiative TIRREX (Infrastruc-
ture technologique pour la recherche d’excellence en robotique). ASTRA is an active participant of
the “Autonomous Land Robotics” axis.

• Project start: Dec. 18, 2021

• Kick-off: Jan. 14, 2022

9.3.4 Competitivity Clusters

NextMove
SMI (Systèmes de Mobilité Intelligents), for example.

(prev. MOV’EO): we are particularly involved in several technical committees like the DAS

Vedecom (IEED): main Inria contributor and active participant to the CD2 domain dedicated to
automated driving.

SystemX Institute : close partnership, with the jointly supervised PhD thesis of Jiahao Zhang.

10 Dissemination

Participants:

Patrick Pérez, Raoul de Charette, Guy Fayolle, Jean-Marc Lasgouttes,
Gérard Le Lann, Fawzi Nashashibi, Fabio Pizzati, Patrick Pérez, Ti-
ago Rocha Gonçalves, Tuan-Hung Vu, Itheri Yahiaoui.

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

• Raoul de Charette, Tuan Hung Vu, Andrei Bursuc, Patrick Pérez organized a Deep Learning Indaba
2023 workshop on “Weakly Supervised Computer Vision”, Accra, Ghana. (≈150 participants) [link]

• Tuan-Hung Vu, Andrei Bursuc, Patrick Pérez organized the ICCV’23 BRAVO workshop on “Robust-
ness and Reliability of Autonomous Vehicles in the Open-world”, Paris, France. (≈150 participants)
[link]

• Andrei Bursuc, Patrick Pérez organized the ICCV’23 tutorial “The Many Faces of Reliability of Deep

Learning for Real-World Deployment”, Paris, France. [link]

• Andrei Bursuc organized the ICCV’23 workshop on “Uncertainty Quantification for Computer

Vision”, Paris, France. [link]

• Fawzi Nashashibi is a member of the steering committee of VENITS’23, 6th international workshop

on Vehicular Networking and Intelligent Transportation Systems”, Hong Kong, China. [link]

10.1.2 Scientific events: selection

Member of the conference program committees Fawzi Nashashibi was a member of program commit-
tee of:

• (IARIA) SMART 2023 (June 26-30, Nice, Saint-Laurent-du-Var, France)

• ICCP 2023 (October 26-28, Cluj-Napoca, Romania)

• MT-ITS 2023 (June 14-16, Nice, France)

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Inria Annual Report 2023

• PSIVT 2023 (November 22-24, Auckland, New Zealand)

• VEHITS 2023 (April 26-28, Lisbon, Portugal)

Reviewer

• Anh-Quan Cao : CVPR 2023, ICCV 2023, CVPR 2024

• Raoul de Charette: CVPR 2023, ICCV 2023, ICCV-W Bravo 2023, WACV 2023, WSCV 2023.

• Mohammad Fahes: CVPR 2024

• Jean-Marc Lasgouttes: IV 2023.

• Fawzi Nashashibi: IV 2023, ITSC 2023, IROS 2023, VEHITS 2023, MT-ITS 2023, SMART 2023

10.1.3 Journal

Member of the editorial boards

• Guy Fayolle is associate editor of the journal Markov Processes and Related Fields (MPRF).

• Fawzi Nashashibi: Associate editor of the journals IEEE Transactions on Intelligent Vehicles (T-IV),
IEEE Transactions on Intelligent Transportation Systems (T-ITS); guest Editor of the IEEE Sensors
journal. Special Issue on “The Application of Sensors in Autonomous Vehicles”

Reviewer - reviewing activities

• Raoul de Charette: IJCV, T-PAMI, T-IV.

• Anh-Quan Cao: Pattern Recognition.

• Guy Fayolle: reviewed several papers and books submitted for publication in some majors journals,
e.g. Transactions of the American Mathematical Society, Markov Processes and Related Fields, Journal
of Statistical Physics, Physica A, etc.

• Amina Ghoul: IEEE Trans. Intelligent Vehicles

• Fawzi Nashashibi: IEEE Transactions on ITS, IEEE Transactions on IV, Journal of Traffic and Trans-

portation Engineering, Sensors, Engineering Applications of Artificial Intelligence

10.1.4 Invited talks

• Guy Fayolle: invited to the conference 40 years of RMB and related topics, held in Roscoff (April

24-28 2023)

• Raoul de Charette: Keynote SAUTOS summer school, “Towards unsupervised scene understanding”,

France. Oct. 25, 2023

• Raoul de Charette: Keynote ICCP, “Scene understanding : Do we even need labels and data ?”,

Romania. Oct. 26, 2023

• Raoul de Charette: Invited talk Makerere AI Lab “Multimodal scene understanding”, Ouganda

(virtual). Oct. 19, 2023

• Raoul de Charette: Invited talk Japan Embassy, “Visual driving scenes: beyond training data”, France.

Feb. 22, 2023

• Raoul de Charette: Keynote ICCV Workshop BRAVO, “Real-world understanding with intuitive

priors”, France. Oct. 3, 2023

• Raoul de Charette: Invited talk ENPC, “Video, Language, Physics”, France. Mar. 15, 2023

• Fawzi Nashashibi: Keynote ICCP, “Future Trends In Automated Driving Technology Development”,

Romania. Oct. 26, 2023.

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29

10.1.5 Scientific expertise

• Guy Fayolle: scientific advisor and associate researcher at the Centre for Robotics of Mines ParisTech.

• Guy Fayolle: member of the working group IFIP WG 7.3.

• Jean-Marc Lasgouttes: member of the Commission d’Évaluation of Inria (until August)

• Jean-Marc Lasgouttes: member of the CRCN recruiting commissions of Inria research centers of

Bordeaux and Lyon.

• Raoul de Charette: member of the Comité d’Evaluation Scientifique of Inria Paris.

• Raoul de Charette: Principal Investigator of ANR JCJC SIGHT on Vision Through Weather.

• Raoul de Charette: scientific evaluator of ANR scientific projects

• Fawzi Nashashibi: scientific reviewer of FNR projects (Luxembourg) under the CORE Programme

• Fawzi Nashashibi: scientific is member and evaluator of NextMove projects (SMIS Working Groups)

• Fawzi Nashashibi: scientific evaluator of ANR (French National Research Agency) scientific projects

• Fawzi Nashashibi: member of the Global Partnership on AI’s (GPAI) working group on Innovation

and Commercialization.

• Fawzi Nashashibi: member and representative of the french academics at Vedecom’s Working

Group on Vehicle Automation.

10.1.6 Research administration

• Jean-Marc Lasgouttes: member of the Comité Social d’Administration of Inria.

• Jean-Marc Lasgouttes: member of the Formation spécialisée en matière de santé, sécurité et condi-

tions de travail of Inria.

• Jean-Marc Lasgouttes: member of the Conseil d’administration of Inria.

• Jean-Marc Lasgouttes: member of the Formation spécialisée de site en matière de santé, sécurité et

conditions de travail of Inria Paris.

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

• Mastère : Raoul de Charette, “Scene Understanding with Computer Vision”, 20h, post master, Mines

ParisTech, France.

• Seminar: Fernando Garrido, Paulo Resende, “decision-making and planning for automated driving”,

16 hours, Valeo Créteil, France.

• Engineering: Fernando Garrido, Paulo Resende, “decision-making and planning for automated

driving”, 24 hours, École d’ingénieurs ESME Sudria, France.

• Engineering: Fernando Garrido, Paulo Resende, “decision-making and planning for autoamted
driving”, 24 hours, Institut Supérieur de l’Automobile et des Transports (ISAT) à Nevers, France.

• Mastère: Jean-Marc Lasgouttes, “Introduction au Boosting”, 10.5h, Mastère Spécialisé Expert en

sciences des données, INSA Rouen Normandie, France.

• Engineering, 1st year: Jean-Marc Lasgouttes, “Analyse de données”, 48h, L3, INSA Rouen Normandie,

France.

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Inria Annual Report 2023

• Engineering, 2nd year: Fawzi Nashashibi, “Image synthesis and 3D Infographics”, 12h, M2, INT

Télécom SudParis, IMA4503 “Virtual and augmented reality for autonomy”.

• Master: Fawzi Nashashibi, “Perception and Image processing for Mobile Autonomous Systems”,

12h, M2, University of Evry.

• Licence, 2nd year: Noël Nadal, “C avancé”, 10.5h, Sorbonne Université, France.

• Licence, 2nd year: Noël Nadal, “Programmation fonctionnelle”, 10.5h, Sorbonne Université, France.

• Licence, 2nd year: Noël Nadal, “Mathématiques discrètes”, 10.5h, Sorbonne Université, France.

• Engineering, 2nd year: Tiago Rocha Gonçalves, “Véhicule intelligent et communicant,”, 6h (TP),

CentraleSupélec, France.

10.2.2 Supervision

• PhD in progress: Tetiana Martyniuk, PSL Research University, “Conditioned generation of ego-
centric 3D driving scenes”, December 2023, supervisor: Raoul de Charette, co-supervisors: Renaud
Marlet.

• PhD in progress: Ahn Quan Cao, PSL Research University, “Unsupervised 3D scene understanding

from image(s)”, March 2021, supervisor: Raoul de Charette.

• PhD in progress: Karim Essalmi, “Maneuver Planner based on the Conservation of Resources
Theory and Quantum Game Theory”, march 2023, supervisor Fawzi Nashashibi, co-supervisor:
Fernando Garrido Carpio

• PhD in progress: Mohammad Fahes, Mines-ParisTech, “Crowdsourced Unsupervised Learning in
Adverse Conditions”, October 2022, supervisor: Raoul de Charette, co-supervisors: Tuan-Hung Vu,
Andrei Bursuc, Patrick Pérez.

• PhD in progress: Amina Ghoul, UPMC Sorbonne University, “Trajectory prediction in an urban
environment”, May 2021, supervisor Fawzi Nashashibi, co-supervisors: Anne Verroust-Blondet,
Itheri Yahiaoui.

• PhD in progress: Ivan Lopes, PSL Research University, “Physic-guided learning for vision in adverse

weather conditions”, November 2021, supervisor: Raoul de Charette.

• PhD in progress: Noël Nadal, “Cartographie et localisation crowdsourcées pour la conduite autonome

en environnement urbain”, October 2022, co-supervisors: Fawzi Nashashibi and Jean-Marc Lasgouttes.

• PhD in progress: Jiahao Zhang, "Misbehavior detection for collective perception in Intelligent
Transportation System", October 2021, UPMC Sorbonne University, supervisor Fawzi Nashashibi,
co-supervisor: Ines Ben Jemaa.

• M1 Internship: Augustin Gevreau, “Extension of vehicle behavior clustering to pedestrians and

cyclists”, June to August 2023, supervisor Nelson de Moura.

10.2.3 Juries

• Fawzi Nashashibi was the President of the PhD committee of Mr. Antoine Lima Cooperative
Perception Integrity for Intelligent Vehicles, defended at Université de Technologie de Compiègne,
May 23, 2023.

• Fawzi Nashashibi was President of the PhD committee of Mr. Haodi ZHANG Deep learning multi-
modal fusion based 3D object detection, defended at INSA de Rouen (Normandie Université), June
1st, 2023.

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• Fawzi Nashashibi was Reviewer of the PhD Committee of Mr. Saeed Saadatjejad Deep Generative
Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis, defended at
EPFL (Lausanne, Switzerland), June 30, 2023.

• Fawzi Nashashibi was Reviewer of the PhD Committee of Mrs. Lina ACHAJI Behavior prediction of
Road Users for Urban Autonomous Vehicles, defended at Inria de Nancy (Université de Lorraine),
July 05, 2023.

• Fawzi Nashashibi was Reviewer of the PHD Committee of Mr. Thomas Gilles Smart vehicle trajectory
prediction in various autonomous driving scenarios, defended at Mines ParisTech (PSL University),
April 21st, 2023.

• Fawzi Nashashibi was Reviewer of the PHD Committee of Mr. Abdelhak Bougouffa Fusion Mul-
tiphysique pour Système de Localisation Indoor Multiphysic Fusion for Indoor Localization System,
defended at the University of Paris-Saclay, September 20, 2023.

• Fawzi Nashashibi was the President and Reviewer of the PhD Committee of Mr. Karl Montalban
Perception LIDAR dans des environnements visuels dégradés : une approche probabiliste pour
l’analyse de la dégradation et l’inférence de la visibilité, defended at the University of Toulouse,
September 18, 2023.

• Fawzi Nashashibi was the Reviewer of the PhD Committee of Mr. Minh-Quan Dao Montlban Toward
Solving Occlusion and Sparsity in Deep Learning-Based 3D Object Detection through Collaborative
Perception, defended at Ecole Centrale de Nantes, November 24, 2023.

• Fawzi Nashashibi was Examiner of the PhD Committee of Mrs. Zaynab El-Mawas Gilles Localisation
coopérative tolérante aux fautes : apport de l’apprentissage pour le diagnostic, defended at the
University of Lille, December 18, 2023.

• Fawzi Nashashibi was part of the CSI (Comité de Suivi Individuel) of: Adrien LAFAGE (ENSTA Paris
/ Renault), Manuel DIAZ ZAPATA (INRIA Grenoble Rhône Alpes / INSA Lyon), Félix MARCOCCIA
(Inria / THALES), Maria RUCHIGA (Université Gustave Eiffel), Fabian GRAF (Inria / SIEMENS)

• Guy Fayolle was external examiner in the PhD of Vasilii Goriachkin (Critical Scaling in Particle

Systems and Random Graphs), defended at Lund University (Sweden).

• Raoul de Charette was examiner in the PhD of Arthur Lecert (Restauration d’images à faible lumin-

osité à l’aide de méthodes d’apprentissage profond), defended at Inria Rennes (France).

• Raoul de Charette was part of 3 Comité de Suivi Individuels (CSI).

10.3 Popularization

10.3.1 Articles and contents

• Fawzi Nashashibi: “L’IA examine tout les pixels pour y reconnaitre des formes, comme celles d’un

piéton”. GEO ADO - Hors Série. Journalist: Arthur Mestre, Edition : March 2024 P.44.

10.3.2 Interventions

• Raoul de Charette: intervention CHICHE at Lycée Jean-Zay, Aulnay-sous-Bois. May 17th 2023

• Raoul de Charette: talk on “Intelligence Artificielle” at Centre de Détention de Melun. May 9th 2023

• Raoul de Charette: intervention CHICHE at Lycée Charlemagne, Paris. April 4th 2023

• Raoul de Charette: intervention on “Batman and his Batmobile”. Musée des Arts et Métiers, Paris.

Mar. 31, 2023

• Fawzi Nashashibi: Conference at UTL University (Université du Temps Libre), “Les nouvelles

mobilités du 21ème siècle et leurs enjeux”, Epinay-sur-Orge, Oct. 10, 2023.

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11 Scientific production

11.1 Major publications

[1] Z. Alsayed, G. Bresson, A. Verroust-Blondet and F. Nashashibi. ‘2D SLAM Correction Prediction
in Large Scale Urban Environments’. In: ICRA 2018 - International Conference on Robotics and
Automation 2018. Brisbane, Australia, 21st May 2018. URL: https://hal.inria.fr/hal-01829
091.

[2] A.-Q. Cao and R. de Charette. ‘MonoScene: Monocular 3D Semantic Scene Completion’. In:
Conference on Computer Vision and Pattern Recognition (CVPR). New orleans, USA, United
States, 19th June 2022. URL: https://hal.science/hal-03498508.

[3] M. Fahes, T.-H. Vu, A. Bursuc, P. Pérez and R. de Charette. ‘PØDA: Prompt-driven Zero-shot
Domain Adaptation’. In: International Conference on Computer Vision (ICCV). Paris, France,
2nd Oct. 2023. URL: https://inria.hal.science/hal-03945337.

[4] G. Fayolle, R. Iasnogorodski and V. A. Malyshev. Random Walks in the Quarter Plane: Algebraic
Methods, Boundary Value Problems, Applications to Queueing Systems and Analytic Combinatorics.
Vol. 40. Probability Theory and Stochastic Modelling. Springer International Publishing, 8th Feb.
2017, p. 255. DOI: 10.1007/978-3-319-50930-3. URL: https://hal.inria.fr/hal-016519
19.

[5] C. Furtlehner, J.-M. Lasgouttes, A. Attanasi, M. Pezzulla and G. Gentile. ‘Short-term Forecast-
ing of Urban Traffic using Spatio-Temporal Markov Field’. In: IEEE Transactions on Intelligent
Transportation Systems 23.8 (2022), pp. 10858–10867. DOI: 10.1109/TITS.2021.3096798. URL:
https://hal.inria.fr/hal-03285664.

[6] D. González Bautista, J. Pérez, V. Milanés and F. Nashashibi. ‘A Review of Motion Planning Tech-
niques for Automated Vehicles’. In: IEEE Transactions on Intelligent Transportation Systems
(1st Apr. 2016). DOI: 10 . 1109 / TITS . 2015 . 2498841. URL: https : / / hal . inria . fr / hal -
01397924.

[7]

S. S. Halder, J.-F. Lalonde and R. de Charette. ‘Physics-Based Rendering for Improving Robustness
to Rain’. In: ICCV 2019 - International Conference on Computer Vision. Seoul, South Korea,
27th Oct. 2019. URL: https://inria.hal.science/hal-02385436.

[8] M. Jaritz, T.-H. Vu, R. de Charette, E. Wirbel and P. Pérez. ‘Cross-Modal Learning for Domain
Adaptation in 3D Semantic Segmentation’. In: IEEE Transactions on Pattern Analysis and Machine
Intelligence 45.2 (17th Mar. 2022), pp. 1533–1544. DOI: 10.1109/TPAMI.2022.3159589. URL:
https://hal.inria.fr/hal-03945378.

[9] G. Le Lann. Cyberphysical Constructs and Concepts for Fully Automated Networked Vehicles. RR-
9297. INRIA Paris-Rocquencourt, 16th Oct. 2019. URL: https://hal.inria.fr/hal-02318242.

[10]

I. Mahtout, F. Navas, V. Milanés and F. Nashashibi. ‘Advances in Youla-Kucera parametrization: A
Review’. In: Annual Reviews in Control (3rd June 2020). DOI: 10.1016/j.arcontrol.2020.04.0
15. URL: https://hal.inria.fr/hal-02748393.

[11] K. Messaoud, I. Yahiaoui, A. Verroust-Blondet and F. Nashashibi. ‘Attention Based Vehicle Tra-
jectory Prediction’. In: IEEE Transactions on Intelligent Vehicles 6.1 (2021), pp. 175–185. DOI:
10.1109/TIV.2020.2991952. URL: https://hal.inria.fr/hal-02543967.

[12] K. Messaoud, I. Yahiaoui, A. Verroust-Blondet and F. Nashashibi. ‘Non-local Social Pooling for
Vehicle Trajectory Prediction’. In: Intelligent Vehicles Symposium (IV). Paris, France, 10th June
2019. DOI: 10.1109/IVS.2019.8813829. URL: https://hal.inria.fr/hal-02160409.

[13] F. Pizzati, P. Cerri and R. de Charette. ‘CoMoGAN: continuous model-guided image-to-image
translation’. In: CVPR 2021 - IEEE Conference on Computer Vision and Pattern Recognition. IEEE
Conference on Computer Vision and Pattern Recognition. Online, France, 19th June 2021. URL:
https://hal.archives-ouvertes.fr/hal-03359098.

[14] F. Pizzati, P. Cerri and R. de Charette. ‘Model-based occlusion disentanglement for image-to-image
translation’. In: ECCV 2020 - European Conference on Computer Vision. ECCV 2020. Glasgow /
Virtual, United Kingdom, 23rd Aug. 2020. URL: https://hal.science/hal-02947036.

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[15] L. Roldão, R. de Charette and A. Verroust-Blondet. ‘3D Semantic Scene Completion: a Survey’.
In: International Journal of Computer Vision (2021). DOI: 10.1007/s11263-021-01504-5. URL:
https://hal.science/hal-03324932.

[16] M. Tremblay, S. S. Halder, R. de Charette and J.-F. Lalonde. ‘Rain Rendering for Evaluating and
Improving Robustness to Bad Weather’. In: International Journal of Computer Vision (6th Sept.
2020). DOI: 10.1007/s11263-020-01366-3. URL: https://hal.inria.fr/hal-03133284.

11.2 Publications of the year

International journals

[17] G. Fayolle, S. Franceschi and K. Raschel. ‘Stationary brownian motion in a 3/4-plane: Reduction
to a riemann-hilbert problem via fourier transforms’. In: Indagationes Mathematicae 34.5 (Sept.
2023), 17 (874–890). DOI: 10.1016/j.indag.2022.10.008. URL: https://hal.science/hal-
03832431.

[18]

J. Petit and G. Le Lann. ‘Next Generation Vehicles, Safety, and Cybersecurity—The CMX Frame-
work’. In: IEEE Transactions on Intelligent Transportation Systems 25.2 (Feb. 2024), pp. 1333–1345.
DOI: 10.1109/TITS.2023.3318376. URL: https://inria.hal.science/hal-04453750.

[19] F. Pizzati, P. Cerri and R. de Charette. ‘Physics-informed Guided Disentanglement in Generative
Networks’. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (Aug. 2023). DOI:
10.1109/tpami.2023.3257486. URL: https://inria.hal.science/hal-03498130.

International peer-reviewed conferences

[20] A.-Q. Cao and R. de Charette. ‘SceneRF: Self-Supervised Monocular 3D Scene Reconstruction
with Radiance Fields’. In: International Conference on Computer Vision (ICCV). Paris, France,
2nd Oct. 2023. URL: https://inria.hal.science/hal-03945327.

[21] M. Fahes, T.-H. Vu, A. Bursuc, P. Pérez and R. de Charette. ‘PØDA: Prompt-driven Zero-shot
Domain Adaptation’. In: International Conference on Computer Vision (ICCV). Paris, France,
2nd Oct. 2023. URL: https://inria.hal.science/hal-03945337.

[22] A. Ghoul, I. Yahiaoui and F. Nashashibi. ‘Interpretable Long Term Waypoint-Based Trajectory
Prediction Model’. In: IEEE International Conference on Intelligent Transportation Systems (ITSC).
Bilbao, Spain, 2023. URL: https://hal.science/hal-04329601.

[23] A. Ghoul, I. Yahiaoui, A. Verroust-Blondet and F. Nashashibi. ‘Interpretable Goal-Based model for
Vehicle Trajectory Prediction in Interactive Scenarios’. In: IEEE Intelligent Vehicles Symposium
(IV). Anchorage, United States, 4th June 2023, pp. 1–6. DOI: 10.1109/IV55152.2023.10186734.
URL: https://hal.science/hal-04108657.

[24] T. R. Gonçalves, N. de Moura and F. Nashashibi. ‘A fuel-efficient approach for platooning systems
with ethical decision-making implications’. In: 2023 IEEE International Conference on Advanced
Robotics and Its Social Impacts (ARSO). 2023 IEEE International Conference on Advanced Robotics
and Its Social Impacts (ARSO). Berlin, Germany: IEEE, 5th June 2023, pp. 95–102. DOI: 10.1109
/ARSO56563.2023.10187497. URL: https://inria.hal.science/hal-04138149.

[25]

I. Lopes, T.-H. Vu and R. de Charette. ‘Cross-task Attention Mechanism for Dense Multi-task
Learning’. In: WACV 2023 - Winter Conference on Applications of Computer Vision. Waikoloa,
Hawaii, United States, 2nd Jan. 2023. URL: https://inria.hal.science/hal-03805874.

[26] N. de Moura and F. Nashashibi. ‘Extraction of vehicle behaviors at intersections’. In: IEEE 2023
Intelligent Transportation Systems Conference. Bilbao, Spain: IEEE, 24th Sept. 2023, pp. 1779–
1786. DOI: 10.1109/ITSC57777.2023.10422152. URL: https://inria.hal.science/hal-0
4314028.

[27] W. Xia, R. de Charette, C. Öztireli and J.-H. Xue. ‘DREAM: Visual Decoding from Reversing Human
Visual System’. In: Winter Conference on Applications of Computer Vision (WACV). Waikola,
Hawaii, United States, 3rd Oct. 2023. URL: https://hal.science/hal-04366806.

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Scientific book chapters

[28] K. Evans, N. de Moura, S. Chauvier and R. Chatila. ‘Automated Driving Without Ethics: Meaning,
Design and Real-World Implementation’. In: Connected and Automated Vehicles: Integrating En-
gineering and Ethics. Studies in Applied Philosophy, Epistemology and Rational Ethics. 23rd Sept.
2023, pp. 123–143. DOI: 10.1007/978-3-031-39991-6_7. URL: https://hal.science/hal-0
4178428.

Reports & preprints

[29] A.-Q. Cao, A. Dai and R. de Charette. PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty

Awareness. 4th Dec. 2023. URL: https://hal.science/hal-04324930.

[30] M. Fahes, T.-H. Vu, A. Bursuc, P. Pérez and R. de Charette. A Simple Recipe for Language-guided
Domain Generalized Segmentation. 29th Nov. 2023. URL: https://hal.science/hal-0436679
8.

[31] G. Fayolle and P. Muhlethaler. A Markovian analysis of an IEEE-802.11 station with buffering.

22nd Dec. 2023. URL: https://inria.hal.science/hal-04362437.

[32]

I. Lopes, F. Pizzati and R. de Charette. Material Palette: Extraction of Materials from a Single Image.
28th Nov. 2023. URL: https://hal.science/hal-04366802.

11.3 Cited publications

[33] G. Fayolle, S. Franceschi and K. Raschel. ‘On the stationary distribution of reflected Brownian
motion in a non-convex wedge’. In: Markov Processes And Related Fields. Markov Processes ans
Related Fields 28.5 (Dec. 2022), p. 32. URL: https://hal.archives-ouvertes.fr/hal-03150
317.

[34] G. Fayolle, J.-M. Lasgouttes and C. Flores. ‘Stability and string stability of car-following models
with reaction-time delay’. In: SIAM Journal on Applied Mathematics 82.5 (2022), pp. 1661–1679.
DOI: 10.1137/21M1443650. URL: https://hal.inria.fr/hal-03697661.

[35] A. Amini, W. Schwarting, A. Soleimany and D. Rus. ‘Deep evidential regression’. In: Advances in

Neural Information Processing Systems (NeurIPS). 2020.

[36]

I. Bae, J. Moon, J. Jhung, H. Suk, T. Kim, H. Park, J. Cha, J. Kim, D. Kim and S. Kim. Self-Driving like
a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous
vehicles. 2020. eprint: 2001.03908.

[37] A. Balakrishnan, S. R. Florez and R. Reynaud. ‘Integrity Monitoring of Multimodal Perception

System for Vehicle Localization’. In: Sensors (2020).

[38] P. de Beaucorps. ‘Planification de trajectoire dans un environnement peu contraint et fortement

dynamique’. Theses. Sorbonne Université, 2019.

[39] P. de Beaucorps, A. Verroust-Blondet, R. Poncelet and F. Nashashibi. ‘RIS: A Framework for Motion
Planning Among Highly Dynamic Obstacles’. In: International Conference on Control, Automation,
Robotics and Vision (ICARCV). 2018.

[40]

J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss and J. Gall. ‘SemanticKITTI: A
Dataset for Semantic Scene Understanding of LiDAR Sequences’. In: International Conference~on
Computer Vision (ICCV). 2019.

[41] V. Besnier, H. Jain, A. Bursuc, M. Cord and P. Pérez. ‘This dataset does not exist: training models
from generated images’. In: International Conference on Acoustics, Speech and Signal Processing
(ICASSP). 2020.

[42] M. Bijelic, T. Gruber, F. Mannan, F. Kraus, W. Ritter, K. Dietmayer and F. Heide. ‘Seeing through fog
without seeing fog: Deep multimodal sensor fusion in unseen adverse weather’. In: Conference on
Computer Vision and Pattern Recognition (CVPR). 2020.

[43] A. Boulch. ‘ConvPoint: Continuous convolutions for point cloud processing’. In: Computers &

Graphics (2020).

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[44] A. Boulch, G. Puy and R. Marlet. ‘FKAConv: Feature-Kernel Alignment for Point Cloud Convolution’.

In: Asian Conference on Computer Vision (ACCV). 2020.

[45] K. Brown, K. R. Driggs-Campbell and M. J. Kochenderfer. ‘A Taxonomy and Review of Algorithms

for Modeling and Predicting Human Driver Behavior’. In: CoRR (2020).

[46] M. Bucher, T.-H. Vu, M. Cord and P. Pérez. ‘BUDA: Boundless Unsupervised Domain Adaptation

in Semantic Segmentation’. In: arXiv preprint arXiv:2004.01130 (2020).

[47] T. Buhet, E. Wirbel, A. Bursuc and X. Perrotton. ‘PLOP: Probabilistic poLynomial Objects trajectory

Planning for autonomous driving’. In: Conference on Robot Learning (CoRL). 2020.

[48] T. Buhet, E. Wirbel and X. Perrotton. Conditional Vehicle Trajectories Prediction in CARLA Urban

Environment. 2019. arXiv: 1909.00792 [cs.AI].

[49] H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan and O.
Beijbom. ‘nuScenes: A Multimodal Dataset for Autonomous Driving’. In: Conference on Computer
Vision and Pattern Recognition (CVPR). 2020.

[50] A. Q. Cao, G. Puy, A. Boulch and R. Marlet. ‘PCAM: Product of Cross-Attention Matrices for Rigid

Registration of Point Clouds’. In: Submitted for publication (2021).

[51] D. Chang, A. Sain, Z. Ma, Y.-Z. Song and J. Guo. ‘Mind the Gap: Enlarging the Domain Gap in Open

Set Domain Adaptation’. In: arXiv preprint arXiv:2003.03787 (2020).

[52] M.-F. Chang, J. W. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey,
D. Ramanan and J. Hays. ‘Argoverse: 3D Tracking and Forecasting with Rich Maps’. In: Conference
on Computer Vision and Pattern Recognition (CVPR). 2019.

[53] C. Chen, A. Seff, A. L. Kornhauser and J. Xiao. ‘DeepDriving: Learning Affordance for Direct

Perception in Autonomous Driving’. In: CoRR (2015). eprint: 1505.00256.

[54] Y. Choi, Y. Uh, J. Yoo and J.-W. Ha. ‘Stargan v2: Diverse image synthesis for multiple domains’. In:

Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

[55] D. Christie, A. Koymans, T. Chanard, J.-M. Lasgouttes and V. Kaufmann. ‘Pioneering Driverless
Electric Vehicles in Europe: The City Automated Transport System (CATS)’. In: Transportation
Research Procedia 13 (2016). Towards future innovative transport: visions, trends and methods,
43rd European Transport Conference Selected Proceedings, pp. 30–39. DOI: 10.1016/j.trpro.2
016.05.004. URL: http://www.sciencedirect.com/science/article/pii/S2352146516
300047.

[56] L. Claussmann, A. Carvalho and G. Schildbach. ‘A path planner for autonomous driving on
highways using a human mimicry approach with Binary Decision Diagrams’. In: European Control
Conference (ECC). 2015.

[57] L. Claussmann, M. O’Brien, S. Glaser, H. Najjaran and D. Gruyer. ‘Multi-Criteria Decision Mak-
ing for Autonomous Vehicles using Fuzzy Dempster-Shafer Reasoning’. In: Intelligent Vehicles
Symposium (IV). 2018.

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Tech. Rep. FHWA-HRT07-030. 2007.

J. Colyar and J. Halkias. ‘Us highway i-80 dataset.’ In: Federal Highway Administration (FHWA),
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[60] C. Corbière, N. Thome, A. Bar-Hen, M. Cord and P. Pérez. ‘Addressing Failure Prediction by
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[61]

S. Cui, B. Seibold, R. Stern and D. B. Work. ‘Stabilizing traffic flow via a single autonomous vehicle:
possibilities and limitations’. In: Intelligent Vehicles Symposium (IV). 2017.

[62] R. F. Cunha, T. R. Gonçalves, V. S. Varma, S. E. Elayoubi and M. Cao. ‘Reducing fuel consumption
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[63] T. Denoux, N. E. Zoghby, V. Cherfaoui and A. Jouglet. ‘Optimal object association in the dempster-
shafer framework’. In: IEEE Transactions on Cybernetics 44.12 (2014), pp. 2521–2531. DOI: 10.110
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[64] G. Fayolle and J.-M. Lasgouttes. ‘Asymptotics and scalings for large closed product-form networks

via the Central Limit Theorem’. In: Markov Processes and Related Fields 2.2 (1996), pp. 317–348.

[65] G. Fayolle and P. Mühlethaler. ‘A Markovian Analysis of IEEE 802.11 Broadcast Transmission
Networks with Buffering’. In: Probability in the Engineering and Informational Sciences. Probability
in the Engineering and Informational Sciences 30.3 (June 2016), p. 19. DOI: 10.1017/S02699648
16000036. URL: https://inria.hal.science/hal-01166082.

[66] C. Flores. ‘Control architecture for adaptive and cooperative car-following’. Theses. Université
Paris sciences et lettres, Dec. 2018. URL: https://pastel.archives-ouvertes.fr/tel-0227
5824.

[67] C. Flores, V. Milanés and F. Nashashibi. ‘Using Fractional Calculus for Cooperative Car-Following
Control’. In: Intelligent Transportation Systems Conference 2016. IEEE. Rio de Janeiro, Brazil, Nov.
2016. URL: https://hal.inria.fr/hal-01382821.

[68] M. Forster, R. Frank, M. Gerla and T. Engel. ‘A Cooperative Advanced Driver Assistance System to
mitigate vehicular traffic shock waves’. In: INFOCOM - Conference on Computer Communications.
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[69] G. Franchi, A. Bursuc, E. Aldea, S. Dubuisson and I. Bloch. ‘Encoding the latent posterior of
Bayesian Neural Networks for uncertainty quantification’. In: arXiv preprint arXiv:2012.02818
(2020).

[70] G. Franchi, A. Bursuc, E. Aldea, S. Dubuisson and I. Bloch. ‘TRADI: Tracking deep neural network

weight distributions’. In: European Conference on Computer Vision (ECCV). 2020.

[71] C. Furtlehner, Y. Han, J.-M. Lasgouttes, V. Martin, F. Marchal and F. Moutarde. ‘Spatial and Tem-
poral Analysis of Traffic States on Large Scale Networks’. In: Intelligent Transportation Systems
Conference (ITSC). 2010.

[72] C. Furtlehner and J.-M. Lasgouttes. ‘A queueing theory approach for a multi-speed exclusion
process.’ In: Traffic and Granular Flow ’07. Ed. by C. Appert-Rolland et al. Springer, 2009, pp. 129–
138. URL: http://hal.archives-ouvertes.fr/hal-00175628/en/.

[73] C. Furtlehner, J.-M. Lasgouttes, A. Attanasi, L. Meschini and M. Pezzulla. Spatio-temporal Probab-

ilistic Short-term Forecasting on Urban Networks. Research Report. INRIA, 2018.

[74] C. Furtlehner, J.-M. Lasgouttes and A. Auger. ‘Learning Multiple Belief Propagation Fixed Points

for Real Time Inference’. In: Physica A: Statistical Mechanics and its Applications (2010).

[75] C. Furtlehner, J.-M. Lasgouttes and A. de La Fortelle. ‘A belief propagation approach to traffic
prediction using probe vehicles’. In: Intelligent Transportation Systems Conference (ITSC). 2007.

[76] C. Furtlehner, J.-M. Lasgouttes and M. Samsonov. ‘One-dimensional Particle Processes with
Acceleration/Braking Asymmetry’. Anglais. In: Journal of Statistical Physics 147.6 (June 2012),
pp. 1113–1144. DOI: 10.1007/s10955-012-0521-y. URL: http://hal.inria.fr/hal-00743
369.

[77] C. Furtlehner, J.-M. Lasgouttes and M. Samsonov. ‘The Fundamental Diagram on the Ring Geo-
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<scratchpad>
- Main topic: Research Centre ASTRA (Inria Paris Centre) focusing on Automated and Safe Transportation systems
- Key points: 
  - Research program: Vision and 3D Perception for Scene Understanding, Localization & Mapping, Decision making, motion Planning & vehicle Control, Large scale modeling and deployment of mobility systems in Smart Cities
  - Four research topics: Perception and understanding of the scene, Localization & Mapping, Decision systems and vehicle control, Cooperative driving and system modeling
  - ASTRA team: Inria Paris Centre, Valeo, and Valeo.ai collaboration
  - Partnerships: Industry collaborations, bilateral contracts, grants, and international initiatives
- Structure: Document is divided into sections, including Project-Team, Activity Report, Research program, and Application domains
- Potential artifacts to ignore: Web-related elements such as links, HTML tags, and unnecessary text
</scratchpad>

<final_summary>
The ASTRA research centre, located at Inria Paris Centre, focuses on the development of Automated and Safe Transportation systems. The research program is divided into four main topics: Perception and understanding of the scene, Localization & Mapping, Decision systems and vehicle control, and Cooperative driving and system modeling. The ASTRA team is a collaboration between Inria Paris Centre, Valeo, and Valeo.ai, and has established partnerships with various industry entities, including bilateral contracts, grants, and international initiatives. The centre's research aims to improve the safety, efficiency, and comfort of transportation systems, and has the potential to contribute to the development of autonomous vehicles and smart cities.
The ASTRA research centre, located at Inria Paris Centre, focuses on the development of Automated and Safe Transportation systems. The research program is divided into four main topics: Perception and understanding of the scene, Localization & Mapping, Decision systems and vehicle control, and Cooperative driving and system modeling. The ASTRA team is a collaboration between Inria Paris Centre, Valeo, and Valeo.ai, and has established partnerships with various industry entities, including bilateral contracts, grants, and international initiatives. The centre's research aims to improve the safety, efficiency, and comfort of transportation systems, and has the potential to contribute to the development of autonomous vehicles and smart cities.