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A large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving is proposed and a surrounding agent centric data augmentation strategy is introduced to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training distribution without collecting additional raw data.
