Rare real-world driving events are recreated on a closed course so AVs can collect repeatable training data and improve edge-case responses.
A two-stage shared-backbone planner generates high-recall trajectories and ranks them with full scene context to cut latency and energy use.
Fast-memory tiling and precomputed relative embeddings cut transformer attention latency for more accurate object and trajectory prediction.
Multi-agent Co-DMPC coordinates steering, braking, and suspension to handle strong coupling with lower computational burden.