---
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