metadata
language:
- en
license: mit
pretty_name: Autonomous Driving Decoherence Onset Detection v0.1
dataset_name: autonomous-driving-decoherence-onset-detection-v0.1
tags:
- clarusc64
- autonomous-driving
- multisensor
- anomaly-detection
- decoherence
- world-model
task_categories:
- tabular-classification
- time-series-forecasting
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
- split: test
path: data/test.csv
What this dataset tests
Whether a system can detect the onset of system-wide decoherence.
Decoherence means: camera, lidar, radar, and map stop supporting a unified scene narrative.
Required outputs
- decoherence_onset_timestamp
- coherence_drop_delta
- affected_modalities
- narrative_conflict_flag
- onset_confidence
- early_warning_score
Scoring conventions
- timestamp is seconds from window start
- coherence drop delta is 0 to 1
- conflict flag is 1 when the narratives diverge
- early warning is a prioritized alert score
Use case
Layer two of Anomaly Detection via System-Wide Decoherence.
Supports:
- early anomaly warning before classification
- sensor health monitoring
- policy degradation triggers