You are interviewing for a Senior Machine Learning Engineer role on a self-driving car team. They ask you to design a machine learning system for **obstacle detection and collision avoidance** in urban driving. The system should run **on-vehicle** and must: - Take input from multiple sensors (e.g., cameras, LiDAR, radar). - Detect relevant objects (cars, pedestrians, cyclists, traffic cones, etc.) and free space. - Support real-time decision-making to help avoid collisions. Latency and reliability requirements (you may refine/adjust them during clarifying questions): - End-to-end perception latency: **≤ 100 ms** per frame. - Very high recall on obstacles within 50 meters in front of the car. - System must be robust to weather, lighting, and rare edge cases. **Design a complete ML system**, covering at least: 1. **Problem definition and metrics** - What exactly the system should output. - Offline and online metrics you would use. 2. **Data and labeling** - What data you will collect from the fleet. - How you will label obstacles, free space, and other relevant signals. - How you will handle class imbalance and rare events. 3. **Modeling choices** - What model architectures you would use for perception (e.g., object detection, segmentation, sensor fusion). - How you would fuse multi-modal sensor information. 4. **Training pipeline** - Data preprocessing. - Train/validation/test splitting strategy. - Handling distribution shift (e.g., new cities, weather conditions). 5. **Serving / inference architecture** - How models are deployed on the car. - How you meet latency and reliability constraints. - Any model compression or optimization techniques. 6. **Monitoring, feedback loop, and continuous improvement** - How you monitor model quality in production. - How you detect and mitigate degradation or new failure modes. - How you use new data to retrain and update models safely. Walk through your design step by step, explaining trade-offs and key decisions.