用动态规划与二次规划实现自动驾驶路径规划与避障
DP and QP Based Decision-making and Planning for Autonomous Vehicle
- 结合动态规划与二次规划，分层优化全局路径与局部轨迹
- 通过S-T图实现静态与动态障碍物避让，仿真验证效果良好
- 适合研究自动驾驶决策规划的工程师与研究人员
自动驾驶技术快速发展，成为现代自动化系统的关键。本文提出一种自动驾驶车辆的决策与规划框架，采用动态规划（DP）进行全局路径规划，二次规划（QP）实现局部轨迹优化。利用S-T图完成动态与静态障碍物避让，基于完整的车辆动力学模型支持控制系统，实现精确路径跟踪与障碍物处理。通过多场景仿真评估系统性能，涵盖全局路径规划、静态障碍物避让及涉及行人交互的动态障碍物避让。结果表明该决策与规划算法在复杂环境中的有效性与鲁棒性，验证了该方法在自动驾驶应用中的可行性。
原文摘要 · Abstract (English)
Autonomous driving technology is rapidly evolving and becoming a pivotal element of modern automation systems. Effective decision-making and planning are essential to ensuring autonomous vehicles operate safely and efficiently in complex environments. This paper introduces a decision-making and planning framework for autonomous vehicles, leveraging dynamic programming (DP) for global path planning and quadratic programming (QP) for local trajectory optimization. The proposed approach utilizes S-T graphs to achieve both dynamic and static obstacle avoidance. A comprehensive vehicle dynamics model supports the control system, enabling precise path tracking and obstacle handling. Simulation studies are conducted to evaluate the system's performance in a variety of scenarios, including global path planning, static obstacle avoidance, and dynamic obstacle avoidance involving pedestrian interactions. The results confirm the effectiveness and robustness of the proposed decision-making and planning algorithms in navigating complex environments, demonstrating the feasibility of this approach for autonomous driving applications.
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