用大模型动态生成避障安全走廊，提升自动驾驶安全性与效率
CorrA: Leveraging Large Language Models for Dynamic Obstacle Avoidance of Autonomous Vehicles
- 大模型推理生成障碍物周边安全走廊参数
- 实时调整边界，确保无碰撞且计算高效
- 适合关注自动驾驶避障与智能决策的研究者
本文提出走廊代理（CorrA）框架，将大语言模型（LLMs）与模型预测控制（MPC）结合，解决自动驾驶中动态障碍物避让问题。该方法利用大模型的推理能力，生成基于sigmoid函数的边界参数，定义障碍物周围的动态安全走廊，有效缩减车辆状态空间。框架根据实时车辆数据动态调整边界，保证轨迹无碰撞的同时兼顾计算效率与最优性。问题被建模为带约束的最优控制问题，通过微分动态规划（DDP）求解，并嵌入MPC框架。大量仿真与真实世界实验表明，相比基线MPC方法，本框架在复杂动态环境中显著提升了安全性与运行效率。
原文摘要 · Abstract (English)
In this paper, we present Corridor-Agent (CorrA), a framework that integrates large language models (LLMs) with model predictive control (MPC) to address the challenges of dynamic obstacle avoidance in autonomous vehicles. Our approach leverages LLM reasoning ability to generate appropriate parameters for sigmoid-based boundary functions that define safe corridors around obstacles, effectively reducing the state-space of the controlled vehicle. The proposed framework adjusts these boundaries dynamically based on real-time vehicle data that guarantees collision-free trajectories while also ensuring both computational efficiency and trajectory optimality. The problem is formulated as an optimal control problem and solved with differential dynamic programming (DDP) for constrained optimization, and the proposed approach is embedded within an MPC framework. Extensive simulation and real-world experiments demonstrate that the proposed framework achieves superior performance in maintaining safety and efficiency in complex, dynamic environments compared to a baseline MPC approach.
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