Li Gao, Duanfeng Chu, Yongxing Cao, Liping Lu, Chaozhong Wu
{"title":"基于分布图和势场的自动驾驶车辆多车道护航控制","authors":"Li Gao, Duanfeng Chu, Yongxing Cao, Liping Lu, Chaozhong Wu","doi":"10.1109/ITSC.2019.8917409","DOIUrl":null,"url":null,"abstract":"Generally, multi-vehicle cooperative driving mainly refers to the vehicle platoon control in a single lane. However, due to the limits of queue length, communication distance and time delay, the traditional vehicle platoon may encounter string instability. This paper extends the traditional single-lane platoon and proposes a multi-lane convoy with better capacity and stability. Specifically, based on the distributed graph method, a formation strategy is proposed to improve its obstacle avoidance ability and stability. Moreover, the traffic field model is built by using the potential field approach to complete motion planning. Simulation has been carried out to show the performance of the proposed algorithm.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"36 1","pages":"2463-2469"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Multi-lane Convoy Control for Autonomous Vehicles based on Distributed Graph and Potential Field\",\"authors\":\"Li Gao, Duanfeng Chu, Yongxing Cao, Liping Lu, Chaozhong Wu\",\"doi\":\"10.1109/ITSC.2019.8917409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generally, multi-vehicle cooperative driving mainly refers to the vehicle platoon control in a single lane. However, due to the limits of queue length, communication distance and time delay, the traditional vehicle platoon may encounter string instability. This paper extends the traditional single-lane platoon and proposes a multi-lane convoy with better capacity and stability. Specifically, based on the distributed graph method, a formation strategy is proposed to improve its obstacle avoidance ability and stability. Moreover, the traffic field model is built by using the potential field approach to complete motion planning. Simulation has been carried out to show the performance of the proposed algorithm.\",\"PeriodicalId\":6717,\"journal\":{\"name\":\"2019 IEEE Intelligent Transportation Systems Conference (ITSC)\",\"volume\":\"36 1\",\"pages\":\"2463-2469\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Intelligent Transportation Systems Conference (ITSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITSC.2019.8917409\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2019.8917409","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-lane Convoy Control for Autonomous Vehicles based on Distributed Graph and Potential Field
Generally, multi-vehicle cooperative driving mainly refers to the vehicle platoon control in a single lane. However, due to the limits of queue length, communication distance and time delay, the traditional vehicle platoon may encounter string instability. This paper extends the traditional single-lane platoon and proposes a multi-lane convoy with better capacity and stability. Specifically, based on the distributed graph method, a formation strategy is proposed to improve its obstacle avoidance ability and stability. Moreover, the traffic field model is built by using the potential field approach to complete motion planning. Simulation has been carried out to show the performance of the proposed algorithm.