Autonomous Vehicle Use Case

Make Robotaxi Driving AI Explainable

Every perception decision, every trajectory prediction, every safety intervention — observable, queryable, and auditable. Finally answer: "Why did the robotaxi stop?"

SD.32.01 • Autonomous Vehicle Observability

Live Event Stream — Autonomous Driver

Objects Detected

2,847

per second

LiDAR Points

1.2M

per frame

Safety Score

99.97%

intervention-free

Miles Driven

40M+

autonomous

Perception Pipeline — Every Step Observable

LiDAR

3D point cloud

av.lidar.scan:1

Camera

RGB images

av.camera.frame:1

Radar

Velocity data

av.radar.return:1

Fusion

Sensor merge

av.fusion.complete:1

Predict

Trajectory forecast

av.predict.trajectory:1

Plan

Motion decision

av.planner.decision:1

Query Any Driving Decision

Why did the robotaxi stop?

Trace the exact perception chain that led to a stop decision

SELECT objects, confidence, decision
FROM events
WHERE event_id = 'av.planner.stop:1'
AND vehicle_id = 'robotaxi_sf_001'

What did it see?

Review all objects detected in a given timeframe

SELECT object_type, position, velocity
FROM events
WHERE event_id LIKE 'av.perception.%'
AND timestamp BETWEEN 'T-5s' AND 'T'

Did it predict correctly?

Compare predicted trajectories with actual outcomes

SELECT predicted, actual, error_m
FROM events
WHERE event_id = 'av.predict.evaluated:1'
AND object_id = 'pedestrian_847'

Was safety engaged?

Full audit trail of any safety system activation

SELECT trigger, response, duration_ms
FROM events
WHERE event_id LIKE 'av.safety.%'
AND ride_id = 'ride_abc123'

Before vs. After Event Model

Before: Black Box AV

Day 1 Incident reported — Robotaxi stopped abruptly

Day 3 Engineering downloads vehicle logs

Day 14 Manual log analysis begins

Day 30 NHTSA requests explanation

Day 90 Recall issued — no clear root cause

After: Observable AV

T+0 av.planner.stop:1 — full context attached

T+10ms av.perception.pedestrian_detected:1

T+50ms av.predict.collision_risk:1 — 94% conf

T+1hr Full event chain provided to NHTSA

Closed Correct behavior confirmed in minutes

Why Observable Autonomous Vehicles Win

Sensor Fusion Transparency

See exactly how LiDAR, camera, and radar data combine to form the world model. Debug perception failures instantly.

Trajectory Prediction Audit

Every predicted path for every road user is logged. Compare predictions to reality to improve models.

Safety Intervention Logs

Complete audit trail of every safety system activation. Know exactly why the vehicle took action.

Fleet-Wide Patterns

Query across all vehicles to find systemic issues. "Show me every stop caused by a misclassified object."

Regulatory Reports

Generate NHTSA incident reports automatically. Every metric backed by queryable event data.

Real-Time Debugging

Stream events live during test drives. Catch issues before they become safety incidents.

Built for Autonomous Vehicle Compliance

NHTSA

US Safety Standards

CA DMV

AV Testing Permits

ISO 26262

Functional Safety

SOTIF

Safety of the Intended Functionality

Ready to Make Your AV Stack Observable?

Join autonomous vehicle teams building trustworthy self-driving systems. Start with the Event Model today.

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