动态环境下基于时间优化的速度剖面规划

Changliu Liu, W. Zhan, M. Tomizuka
{"title":"动态环境下基于时间优化的速度剖面规划","authors":"Changliu Liu, W. Zhan, M. Tomizuka","doi":"10.1109/IVS.2017.7995713","DOIUrl":null,"url":null,"abstract":"To generate safe and efficient trajectories for an automated vehicle in dynamic environments, a layered approach is usually considered, which separates path planning and speed profile planning. This paper is focused on speed profile planning for a given path that is represented by a set of waypoints. The speed profile will be generated using temporal optimization which optimizes the time stamps for all waypoints along the given path. The formulation of the problem under urban driving scenarios is discussed. To speed up the computation, the non-convex temporal optimization is approximated by a set of quadratic programs which are solved iteratively using the slack convex feasible set (SCFS) algorithm. The simulations in various urban driving scenarios validate the effectiveness of the method.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"44","resultStr":"{\"title\":\"Speed profile planning in dynamic environments via temporal optimization\",\"authors\":\"Changliu Liu, W. Zhan, M. Tomizuka\",\"doi\":\"10.1109/IVS.2017.7995713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To generate safe and efficient trajectories for an automated vehicle in dynamic environments, a layered approach is usually considered, which separates path planning and speed profile planning. This paper is focused on speed profile planning for a given path that is represented by a set of waypoints. The speed profile will be generated using temporal optimization which optimizes the time stamps for all waypoints along the given path. The formulation of the problem under urban driving scenarios is discussed. To speed up the computation, the non-convex temporal optimization is approximated by a set of quadratic programs which are solved iteratively using the slack convex feasible set (SCFS) algorithm. The simulations in various urban driving scenarios validate the effectiveness of the method.\",\"PeriodicalId\":143367,\"journal\":{\"name\":\"2017 IEEE Intelligent Vehicles Symposium (IV)\",\"volume\":\"164 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"44\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Intelligent Vehicles Symposium (IV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2017.7995713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Intelligent Vehicles Symposium (IV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2017.7995713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 44

摘要

为了在动态环境中为自动驾驶车辆生成安全高效的轨迹,通常考虑将路径规划和速度剖面规划分开的分层方法。本文主要研究由一组路点表示的给定路径的速度剖面规划问题。速度配置文件将使用时间优化生成,优化沿给定路径的所有航路点的时间戳。讨论了城市驾驶场景下问题的表述。为了提高计算速度,将非凸优化问题近似化为一组二次规划,并采用松弛凸可行集(slack convex viable set, SCFS)算法迭代求解。通过对不同城市驾驶场景的仿真,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speed profile planning in dynamic environments via temporal optimization
To generate safe and efficient trajectories for an automated vehicle in dynamic environments, a layered approach is usually considered, which separates path planning and speed profile planning. This paper is focused on speed profile planning for a given path that is represented by a set of waypoints. The speed profile will be generated using temporal optimization which optimizes the time stamps for all waypoints along the given path. The formulation of the problem under urban driving scenarios is discussed. To speed up the computation, the non-convex temporal optimization is approximated by a set of quadratic programs which are solved iteratively using the slack convex feasible set (SCFS) algorithm. The simulations in various urban driving scenarios validate the effectiveness of the method.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信