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.

翻译：自动驾驶技术正迅速发展，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。本文提出一种自动驾驶决策与规划框架，该框架利用动态规划（DP）进行全局路径规划，并采用二次规划（QP）实现局部轨迹优化。所提出的方法利用S-T图实现动态与静态障碍物的规避。通过建立完整的车辆动力学模型支撑控制系统，实现了精确的路径跟踪与障碍物处理。研究通过仿真实验评估了系统在多种场景下的性能，包括全局路径规划、静态障碍物规避以及涉及行人交互的动态障碍物规避。结果验证了所提出的决策与规划算法在复杂环境导航中的有效性与鲁棒性，证明了该方法在自动驾驶应用中的可行性。