期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 6, 期 2, 页码 439-446出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2020.3045925
关键词
Trajectory; Smoothing methods; Planning; Optimization; Collision avoidance; Space vehicles; Autonomous vehicles; Intelligent transportation systems; autonomous agents; nonholonomic motion planning
类别
The research introduces a free space trajectory optimization algorithm for autonomous driving, which outperforms other existing algorithms in terms of collision avoidance, control feasibility, and computation efficiency. It also meets stricter driving comfort standards in complex driving scenarios.
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 (similar to 7%) at nearly all test conditions, achieves 10x faster computational speed than other planners, fulfills 100% driving-comfort standards in complex driving scenarios, and does not induce significant time increase as boundaries or obstacles scale up.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据