metadata
language:
- en
license: mit
pretty_name: Autonomous Driving Multi-Sensor Coherence Baseline Modeling v0.1
dataset_name: autonomous-driving-multisensor-coherence-baseline-modeling-v0.1
tags:
- clarusc64
- autonomous-driving
- multisensor
- coherence
- perception
- world-model
task_categories:
- tabular-regression
- 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 model the expected coherence of a sensor suite for a given driving context.
The output is a baseline and tolerance band. This is the reference for later decoherence detection.
Required outputs
- baseline_coherence_score
- expected_sensor_alignment
- cross_modal_correlation
- stability_band
- drift_tolerance
- baseline_confidence
Scoring conventions
- all scores range 0 to 1
- stability band is a low-high interval
- drift tolerance encodes how much map/sensor mismatch is normal in context
Use case
Layer one of Anomaly Detection via System-Wide Decoherence.
Supports:
- early decoherence onset detection
- sensor health monitoring
- graceful degradation triggers