This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (PJSO) problem. The work leads to remarkable driving performance improvements including more robust and precise collision avoidance, higher control feasibility, higher computation efficiency and stricter driving comfort guarantee, compared with other existing algorithms. The advantages of our algorithm are attributed to our fast iterative collision checks with exact vehicle/obstacle shapes, strict non-holonomic dynamic constraints and accurate kinematics-based speed optimization. It has been validated that, through batch simulation and road experiments, compared with prior works, our algorithm is with the highest robustness and capable to maintain the lowest failure rate ($\sim\!\text{7}\%$) at nearly all test conditions, achieves 10x faster computational speed than other planners, fulfills $\text{100}\%$ driving-comfort standards in complex driving scenarios, and does not induce significant time increase as boundaries or obstacles scale up.

中文翻译：

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采用双环迭代锚定路径平滑和分段加加速度优化的自动驾驶轨迹优化

这封信提出了一种用于自动驾驶的自由空间轨迹优化算法，该算法将无碰撞轨迹生成问题解耦为双环迭代锚定路径平滑 (DL-IAPS) 问题和分段加速速度优化 (PJSO) 问题。与其他现有算法相比，该工作带来了显着的驾驶性能改进，包括更稳健和精确的防撞、更高的控制可行性、更高的计算效率和更严格的驾驶舒适性保证。我们算法的优势归功于我们对精确车辆/障碍物形状的快速迭代碰撞检查、严格的非完整动态约束和基于运动学的精确速度优化。已经验证，通过批量模拟和道路实验，与之前的工作相比，$\sim\!\text{7}\%$) 在几乎所有的测试条件下，实现比其他规划器快 10 倍的计算速度，满足 $\text{100}\%$ 复杂驾驶场景中的驾驶舒适度标准，并且不会随着边界或障碍物的扩大而导致显着的时间增加。