---
language:
  - en
license: mit
pretty_name: Autonomous Driving Driver-Vehicle Coherence-Optimal Policy Selection v0.1
dataset_name: autonomous-driving-driver-vehicle-coherence-optimal-policy-selection-v0.1
tags:
  - clarusc64
  - autonomous-driving
  - driver-state
  - policy-manifold
  - human-in-the-loop
  - coupling
  - trust
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 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 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