LLM駆動のシナリオ認識型自動運転プランニング
LLM-Driven Scenario-Aware Planning for Autonomous Driving
大規模言語モデルを活用してシーンを理解し、モード切替と運動計画を統合最適化する自動運転プランナーLAPを提案。高密度交通下での走行効率と安全性を両立。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: He Li, Zhaowei Chen, Rui Gao, Guoliang Li, Qi Hao, Shuai Wang, Chengzhong Xu
分類: cs.RO
原文アブストラクト
Hybrid planner switching framework (HPSF) for autonomous driving needs to reconcile high-speed driving efficiency with safe maneuvering in dense traffic. Existing HPSF methods often fail to make reliable mode transitions or sustain efficient driving in congested environments, owing to heuristic scene recognition and low-frequency control updates. To address the limitation, this paper proposes LAP, a large language model (LLM) driven, adaptive planning method, which switches between high-speed driving in low-complexity scenes and precise driving in high-complexity scenes, enabling high qualities of trajectory generation through confined gaps. This is achieved by leveraging LLM for scene understanding and integrating its inference into the joint optimization of mode configuration and motion planning. The joint optimization is solved using tree-search model predictive control and alternating minimization. We implement LAP by Python in Robot Operating System (ROS). High-fidelity simulation results show that the proposed LAP outperforms other benchmarks in terms of both driving time and success rate.
