ClarusC64/autonomous-driving-driver-vehicle-coherence-optimal-policy-selection-v0.1
What this dataset tests Whether a system can choose a vehicle policy that maximizes coherence across: driver state vehicle behavior scene context. This is not a single driving style. It is policy manifold navigation. Required outputs selected_policy_id policy_mode predicted_coherence_trajectory intervention_intensity communication_strategy policy_switch_trigger Scoring conventions trajectory is a sequence of coherence values 0 to 1 intensity is low, medium, or high switch… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-driver-vehicle-coherence-optimal-policy-selection-v0.1.
What this dataset tests
Whether a system can choose a vehicle policy that maximizes coherence across: driver state vehicle behavior scene context.
This is not a single driving style. It is policy manifold navigation.
Required outputs
- selectedpolicyid
- policy_mode
- predictedcoherencetrajectory
- intervention_intensity
- communication_strategy
- policyswitchtrigger
Scoring conventions
- trajectory is a sequence of coherence values 0 to 1
- intensity is low, medium, or high
- switch trigger must name the measurable condition that forces change
Use case
Layer three of Driver-State and Vehicle-Response Coupling Manifold.
Supports:
- adaptive human-in-the-loop driving
- trust-preserving vehicle behavior
- safe fallback and takeover management