论文标题
DL-IAP和PJSO：路径/速度解耦轨迹优化及其在自动驾驶中的应用
DL-IAPS and PJSO: A Path/Speed Decoupled Trajectory Optimization and its Application in Autonomous Driving
论文作者
论文摘要
本文提出了自动驾驶车辆的自由空间轨迹优化算法，该算法将无碰撞的轨迹计划问题解散为双环迭代锚定路径平滑（DL-IAP）和零件的混蛋速度速度优化（PJSO）。这项工作可实现显着的驾驶性能改进，包括更精确的避免碰撞，更高的控制可行性和更好的驾驶舒适性，因为在其他现有的路径/速度解耦轨迹优化方法中通常很难实现。我们的算法的效率，鲁棒性和对复杂驾驶场景的适应性得到了模拟和实际的公路测试的验证。
This paper presents a free space trajectory optimization algorithm of autonomous driving vehicle, which decouples the collision-free trajectory planning problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) and a Piece-wise Jerk Speed Optimization (PJSO). The work leads to remarkable driving performance improvements including more precise collision avoidance, higher control feasibility and better driving comfort, as those are often hard to realize in other existing path/speed decoupled trajectory optimization methods. Our algorithm's efficiency, robustness and adaptiveness to complex driving scenarios have been validated by both simulations and real on-road tests.
