PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning
<|reference_start|>PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle Motion Planning: Vehicle motion planning is an essential component of autonomous driving technology. Current rule-based vehicle motion planning methods perform satisfactorily in common scenarios but struggle to generalize to long-tailed situations. Meanwhile, learning-based methods have yet to achieve superior performance over rule-based approaches in large-scale closed-loop scenarios. To address these issues, we propose PlanAgent, the first mid-to-mid planning system based on a Multi-modal Large Language Model (MLLM). MLLM is used as a cognitive agent to introduce human-like knowledge, interpretability, and common-sense reasoning into the closed-loop planning. Specifically, PlanAgent leverages the power of MLLM through three core modules. First, an Environment Transformation module constructs a Bird's Eye View (BEV) map and a lane-graph-based textual description from the environment as inputs. Second, a Reasoning Engine module introduces a hierarchical chain-of-thought from scene understanding to lateral and longitudinal motion instructions, culminating in planner code generation. Last, a Reflection module is integrated to simulate and evaluate the generated planner for reducing MLLM's uncertainty. PlanAgent is endowed with the common-sense reasoning and generalization capability of MLLM, which empowers it to effectively tackle both common and complex long-tailed scenarios. Our proposed PlanAgent is evaluated on the large-scale and challenging nuPlan benchmarks. A comprehensive set of experiments convincingly demonstrates that PlanAgent outperforms the existing state-of-the-art in the closed-loop motion planning task. Codes will be soon released.<|reference_end|>
@article{zheng2024planagent:,
    title={PlanAgent: A Multi-modal Large Language Agent for Closed-loop Vehicle
  Motion Planning},
    author={Yupeng Zheng, Zebin Xing, Qichao Zhang, Bu Jin, Pengfei Li, Yuhang
  Zheng, Zhongpu Xia, Kun Zhan, Xianpeng Lang, Yaran Chen, Dongbin Zhao},
    journal={arXiv preprint arXiv:2406.01587},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.01587},
    primaryClass={cs.RO}
}