Start by clarifying the assumptions and constraints of the scenario, then walk through a structured reasoning process that combines physics-based prediction with ML model considerations. Emphasize safety and uncertainty handling, and conclude with a clear outcome and potential mitigations.
Pro tip: Show that you think beyond the immediate collision: discuss how you would validate the prediction with simulation and real-world data, and how you'd handle edge cases. This demonstrates a safety-first mindset valued at Waymo.
Ask clarifying questions about the objects (e.g., vehicles, pedestrians), their speeds, masses, and the environment. Confirm that the diagram is to scale and that no other factors (e.g., weather, road conditions) are at play.
Discuss the physical principles (momentum, energy) that govern the collision outcome. Then, explain how ML models (e.g., trajectory prediction, collision detection) would be used to predict the outcome given sensor data.
Use the diagram to estimate trajectories and impact point. Apply physics to determine post-collision velocities or deformation, and consider uncertainties in the prediction.
Explain how the system would use this prediction to make decisions (e.g., braking, evasive maneuvers) and prioritize safety. Mention redundancy and fail-safes.
Describe how you would validate the prediction using simulation and real-world data, and how you would handle edge cases or model uncertainties.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.