{"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}
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.