IoT対応自動運転のための大規模言語モデル強化型微分可能軌道計画
Large Language Model Enhanced Differentiable Trajectory Planning for IoT-Enabled Autonomous Driving
自動運転の軌道計画において、大規模言語モデルを用いた意味的特徴抽出と微分可能最適化を組み合わせ、複雑な長尾相互作用や制約整合性の問題を解決するフレームワークを提案した。
詳しい要約
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著者: Shihao Zhang, Jing Yang, Ziyu Song, Zheng Lin, Sunil Prajapat, Zhaochen Xia, Hemant Ghayvat, Haitao Ding, Lip Yee Por, Ashok Kumar Das
分類: cs.RO, cs.NI
原文アブストラクト
Autonomous driving planning is a key component of IoT-enabled intelligent transportation systems, requiring vehicles to generate safe, efficient, and executable trajectories in complex urban environments from multi-source contextual information. While imitation learning (IL) has shown promise on large-scale datasets, IL-based planners still suffer from limited coverage of complex long-tail interactions, weak consistency with downstream constrained refinement, and insufficient use of high level scene semantics under real time constraints. To address these issues, this paper proposes a large language model (LLM) enhanced differentiable trajectory planning framework for IoT-enabled autonomous driving. Specifically, we introduce a surrounding agent centric data augmentation strategy to reorganize sur rounding agent trajectories as additional planning supervision, thereby improving the training distribution without collecting additional raw data. We further design a complexity-aware asyn chronous LLM-based semantic enhancement module to extract scene-related high-level semantic features with controlled online overhead. In addition, a differentiable optimization module is incorporated to refine generated trajectories with explicit residual penalties while backpropagating optimization gradients to the upstream planner. Experiments show that the proposed method achieves the best overall scores of 83.63 and 78.29 on the nuPlan closed-loop nonreactive and reactive Hard20 benchmarks, respectively, and CARLA-ROS tests further verify its online deployment and real time closed-loop execution capability.
