Explainability in Self-Driving Cars: Lessons from Tesla and Waymo

Explainability in Self-Driving Cars: Lessons from Tesla and Waymo

Explainability in Self-Driving Cars: Lessons from Tesla and Waymo

Introduction

Self-driving cars integrate perception, prediction, and planning to navigate complex road environments. But when accidents occur, one urgent question emerges: Why did the AI act that way? This is where XAI (Explainable Artificial Intelligence) plays a crucial role—helping engineers, regulators, and the public understand the decision-making of autonomous vehicles.

Key Concepts

  • Perception: Sensors like cameras, radar, and LiDAR detect objects and lane markings.
  • Prediction: Estimating how other vehicles, cyclists, or pedestrians will move.
  • Planning: Deciding the car’s own trajectory—when to brake, accelerate, or change lanes.
  • Black box risk: Opaque models that prevent clear accountability after crashes.

Tesla vs. Waymo: Different Design Philosophies

Tesla relies on a vision-centric approach using cameras and neural networks, while Waymo uses a multi-sensor fusion system combining LiDAR, radar, and cameras. Their differences illustrate trade-offs in explainability:

  • Tesla: Provides camera-derived attention maps but less redundancy when sensors fail.
  • Waymo: Multi-sensor logs allow cross-verification and clearer reconstructions.

Case Study A: Post-Incident Reconstruction

When a crash occurs, investigators need to reconstruct the timeline. XAI tools can show which objects were detected, how the system classified them, and why the planner chose its actions (e.g., braking due to a predicted collision risk). Event recorders with explainable outputs make accountability faster and fairer.

Case Study B: Human-in-the-Loop Handoffs

Some self-driving systems require drivers to retake control in edge cases. XAI can improve safety by explaining why a handoff was requested (e.g., sensor occlusion, ambiguous lane markings). Clear, timely explanations improve driver trust and response times.

Comparison: Explainability Features

System ElementVision-Centric (Tesla)Multi-Sensor Fusion (Waymo)
PerceptionCamera-based attention maps3D point cloud overlays and cross-sensor checks
PredictionTrajectory estimates from video patternsJoint trajectory models using multiple sensors
PlanningNeural network outputs, less interpretableCost-function breakdowns with redundancy triggers
LoggingLimited video and telemetryComprehensive multi-sensor event records

Regulatory Spotlight

Some regulators propose requiring event data recorders for all autonomous vehicles, capturing not just sensor inputs but also model explanations. This would make post-crash investigations more reliable and help assign responsibility more fairly.

Pause & Probe Questions

  1. Should real-time explanations be visible to drivers, or is post-crash analysis enough?
  2. How might too much transparency risk exposing system vulnerabilities to hackers?
  3. What minimum explainability standards should regulators enforce for public-road testing?

© 2025 • Explainability in Self-Driving Cars • Part of the Industry Case Studies in XAI Series

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