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Achieving 66.9 MOTA in 3D tracking without LiDAR reveals a new frontier in roadside infrastructure understanding.
Training with local visual cues can dramatically enhance MLLMs' ability to extract fine-grained visual details without altering their inference interface.
Time-to-collision metrics miss critical collision risk information, but a new 2D acceleration-based metric anticipates collisions far better.
LLMs can now handle autonomous driving tasks with greater precision and efficiency thanks to DriveCode, which replaces discrete number tokens with continuous embeddings.
