This question sprawled in every direction.
Start by clarifying the problem scope and constraints, then break the system into two main components: drop-off location selection and maneuver planning. For each component, discuss data sources, ML models, and evaluation metrics, emphasizing safety, comfort, and regulatory compliance. Conclude by addressing integration, trade-offs, and potential failure modes.
Pro tip: Emphasize safety as the top priority and discuss how you would validate the system with simulation and real-world testing, including edge cases like passenger urgency or construction zones.
Ask questions to understand the operational design domain, passenger preferences, regulatory constraints, and safety requirements. Define success metrics such as drop-off accuracy, passenger comfort, and time efficiency.
Propose a system that uses map data, traffic rules, passenger preferences, and real-time conditions to generate and rank candidate drop-off spots. Discuss ML models for scoring candidates, such as learning-to-rank or reinforcement learning.
Outline a planning module that takes the selected drop-off location and generates a safe, comfortable trajectory. Mention techniques like optimization-based planning, behavior prediction, and motion control.
Explain how the two components interact, including feedback loops. Describe offline evaluation with logged data, simulation, and online A/B testing, focusing on safety and comfort metrics.
Highlight trade-offs between optimality and safety, latency constraints, and handling of edge cases. Propose mitigation strategies for failures like sensor noise or unexpected obstacles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.