This paper presents a novel trajectory planning pipeline for complex driving scenarios like autonomous lane changing, by integrating risk-aware planning with guaranteed collision avoidance into a unified optimization framework. We first construct a dynamic risk fields (DRF) that captures both the static and dynamic collision risks from surrounding vehicles. Then, we develop a rigorous strategy for generating time-varying convex feasible spaces that ensure kinematic feasibility and safety requirements. The trajectory planning problem is formulated as a finite-horizon optimal control problem and solved using a constrained iterative Linear Quadratic Regulator (iLQR) algorithm that jointly optimizes trajectory smoothness, control effort, and risk exposure while maintaining strict feasibility. Extensive simulations demonstrate that our method outperforms traditional approaches in terms of safety and efficiency, achieving collision-free trajectories with shorter lane-changing distances (28.59 m) and times (2.84 s) while maintaining smooth and comfortable acceleration patterns. In dense roundabout environments the planner further demonstrates robust adaptability, producing larger safety margins, lower jerk, and superior curvature smoothness compared with APF, MPC, and RRT based baselines. These results confirm that the integrated DRF with convex feasible space and constrained iLQR solver provides a balanced solution for safe, efficient, and comfortable trajectory generation in dynamic and interactive traffic scenarios.

翻译：本文提出了一种用于复杂驾驶场景（如自主变道）的新型轨迹规划流程，通过将风险感知规划与有保障的碰撞避免集成到统一的优化框架中。我们首先构建了一个动态风险场（DRF），用于捕捉来自周围车辆的静态和动态碰撞风险。随后，我们开发了一种严谨的策略来生成时变凸可行空间，以确保运动学可行性和安全性要求。轨迹规划问题被表述为一个有限时域最优控制问题，并使用约束迭代线性二次型调节器（iLQR）算法进行求解，该算法在保持严格可行性的同时，联合优化了轨迹平滑度、控制能耗和风险暴露。大量仿真实验表明，我们的方法在安全性和效率方面优于传统方法，能够实现无碰撞轨迹，且具有更短的变道距离（28.59 米）和变道时间（2.84 秒），同时保持平滑舒适的加速度模式。在密集环岛环境中，该规划器进一步展现出强大的适应性，与基于人工势场法（APF）、模型预测控制（MPC）和快速探索随机树（RRT）的基线方法相比，能产生更大的安全裕度、更低的加加速度以及更优的曲率平滑度。这些结果证实，集成了凸可行空间和约束iLQR求解器的DRF方法，为动态交互交通场景下安全、高效且舒适的轨迹生成提供了一个均衡的解决方案。