This is the kind of question where you can go a hundred directions and that's exactly what tripped me up at first.
Start by clarifying requirements: what defines 'similar' (e.g., near-collision, heavy rain), latency needs, and dataset scale. Then propose a two-stage retrieval system: first, extract compact embeddings from video clips using a video encoder; second, use approximate nearest neighbor search to retrieve top-k similar clips, followed by re-ranking if needed.
Pro tip: Emphasize the importance of a well-defined similarity metric and the trade-off between embedding dimensionality and retrieval speed; also mention the need for a robust evaluation protocol using human judgments or proxy tasks.
Ask about the definition of 'similar', dataset size, latency constraints, and whether the system should handle multimodal queries (e.g., text + video).
Choose a video encoder (e.g., 3D CNN, Transformer) to map clips to fixed-dimensional vectors; consider self-supervised pretraining on driving data for better representations.
Use approximate nearest neighbor (ANN) libraries (e.g., FAISS, ScaNN) to index embeddings for fast retrieval; discuss trade-offs between index size, speed, and recall.
Optionally re-rank top candidates with a more expensive model (e.g., cross-attention) to improve precision, especially for subtle similarities.
Define metrics (e.g., recall@k, mAP) and create a validation set with human labels; iterate on model and index parameters based on performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.