state-estimation
UAV-SEAD_State_Estimation_Anomaly_DatasetUAV-SEAD_State_Estimation_Anomaly_DatasetUAV-SEAD_State_Estimation_Anomaly_Datasetautonomous-driving-driver-state-manifold-estimation-v0.1What this dataset tests
Whether a system can infer driver state
from cabin signals and driving context.
The output is a state manifold vector.
Not a single label.
Required outputs
driver_state_label
fatigue_score
distraction_score
agitation_score
confidence_estimate
state_transition_risk
Scoring conventions
all scores range 0 to 1
labels are baseline, fatigued, distracted, agitated, mixed
transition risk flags likelihood of deterioration in the next window
Use case
Layer one… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-driver-state-manifold-estimation-v0.1.aviation-pilot-vehicle-loop-coherence-state-estimation-v0.1What this dataset tests
Whether a system can estimate the coherenceof the pilot–aircraft control loopduring abnormal phases.
Key insightLoss of control beginswith loop misalignmentbefore any hard limits are exceeded.
Required outputs
loop_coherence_index
resonance_stability_band
control_lag_profile
correction_efficiency_score
baseline_deviation
Use case
Layer one of Pilot–Vehicle Loop Coherence Under Stress.Feeds attribution and adaptive intervention systems.