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Pairwise learning from driving data can significantly enhance proactive collision risk assessment, outperforming conventional methods that rely on limited numerical labels.
End-to-end driving can achieve substantially improved trajectory feasibility by shifting the diffusion process to directly model feasible trajectories, rather than focusing on noise.
