基于DP和QP的自动驾驶决策与规划
DP and QP Based Decision-making and Planning for Autonomous Vehicle
AI总结：
本文提出一种结合动态规划（DP）全局路径规划与二次规划（QP）局部轨迹优化的自动驾驶决策规划框架，利用S-T图实现动静态障碍物避让，仿真验证了其在复杂环境中的有效性与鲁棒性。
摘要：
自动驾驶技术正在迅速发展，并成为现代自动化系统的关键组成部分。有效的决策与规划对于确保自动驾驶车辆在复杂环境中安全高效运行至关重要。本文提出了一种面向自动驾驶车辆的决策与规划框架，利用动态规划（DP）进行全局路径规划，并利用二次规划（QP）进行局部轨迹优化。所提出的方法利用S-T图实现动态和静态障碍物的避让。一个全面的车辆动力学模型支持控制系统，能够实现精确的路径跟踪和障碍物处理。进行了仿真研究，以评估系统在多种场景下的性能，包括全局路径规划、静态障碍物避让以及涉及行人交互的动态障碍物避让。结果证实了所提出的决策与规划算法在复杂环境中导航的有效性和鲁棒性，展示了该方法在自动驾驶应用中的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
