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stringclasses
6 values
scene_context
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driver_state_label
stringclasses
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driver_state_vector
stringclasses
6 values
candidate_policies
stringclasses
3 values
horizon_s
int64
10
15
selected_policy_id
stringclasses
3 values
policy_mode
stringclasses
5 values
predicted_coherence_trajectory
stringclasses
6 values
intervention_intensity
stringclasses
3 values
communication_strategy
stringclasses
6 values
policy_switch_trigger
stringclasses
6 values
notes
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constraints
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gold_checklist
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DVPS-001
night highway; light traffic
fatigued
fatigue 0.82; distraction 0.18; agitation 0.12
P1:assertive|P2:cautious|P3:communicative_cautious
15
P3
communicative_cautious
0.62->0.70->0.78->0.80
medium
clear_visual_prompts + gentle_speed_smoothing
if fatigue_score>0.70 and lane_drift_events>=1
Avoids overassertion and supports driver
Under 260 words
sel+mode+traj+intensity+comm+trigger
DVPS-002
night highway; moderate traffic
fatigued
fatigue 0.74; distraction 0.22; agitation 0.10
P1:assertive|P2:cautious|P3:communicative_cautious
15
P2
cautious
0.66->0.72->0.75->0.76
low
minimal_prompts + headway_widening
if fatigue_score>0.65 and traffic_density<0.4
Cautious is enough when traffic is light
Under 260 words
sel+mode+traj+intensity+comm+trigger
DVPS-003
urban; dense traffic
distracted
fatigue 0.22; distraction 0.79; agitation 0.18
P1:assertive|P2:cautious|P3:communicative_cautious
12
P3
communicative_cautious
0.58->0.66->0.73->0.74
high
salient_hud_cues + early_brake_signals
if distraction_score>0.70 and intersection_rate_high
Communication reduces surprise and override
Under 260 words
sel+mode+traj+intensity+comm+trigger
DVPS-004
urban; moderate traffic
agitated
fatigue 0.18; distraction 0.22; agitation 0.84
P1:assertive|P2:cautious|P3:firm_smoothing
10
P3
firm_smoothing
0.55->0.62->0.68->0.70
medium
steady_tone_prompts + surge_prevention
if agitation_score>0.75 and pedal_jab_events>=1
Firm smoothing contains agitation
Under 260 words
sel+mode+traj+intensity+comm+trigger
DVPS-005
urban; moderate traffic
agitated
fatigue 0.16; distraction 0.20; agitation 0.80
P1:assertive|P2:cautious|P3:firm_smoothing
10
P1
assertive
0.54->0.50->0.44->0.40
high
minimal_comm
if gap_acceptance_needed and agitation_score>0.70
Bad choice example for training
Under 260 words
sel+mode+traj+intensity+comm+trigger
DVPS-006
suburban; moderate traffic
baseline
fatigue 0.12; distraction 0.10; agitation 0.08
P1:assertive|P2:normal|P3:cautious
15
P2
normal
0.74->0.76->0.78->0.78
low
standard_signals
if no elevated driver state scores
Stable baseline needs no modulation
Under 260 words
sel+mode+traj+intensity+comm+trigger

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
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