Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

作     者：Chaoyue Zu Chao Yang Jian Wang Wenbin Gao Dongpu Cao Fei-Yue Wang Chaoyue Zu;Chao Yang;Jian Wang;Wenbin Gao;Dongpu Cao;Fei-Yue Wang

作者机构：the College of Computer Science and TechnologyJilin UniversityChangchun 130012China Jiangsu XCMG Construction Machinery Research Institute Ltd.Xuzhou 221004China the College of Computer Science and TechnologyKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchun 130012 Chongqing Xibu Automobile Proving Ground Management Co.Ltd.Chongqing 404100China the State Grid JIBEI Electric Power Company Limited Management Training CenterChina the Advanced Vehicle Engineering CenterCranfield UniversityCranfield MK430ALUK the State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2020年第7卷第4期

页      面：1045-1063页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 082304[工学-载运工具运用工程] 081104[工学-模式识别与智能系统] 08[工学] 080204[工学-车辆工程] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by the National Natural Science Foundation of China(61572229,6171101066) the Key Scientific and Technological Projects for Jilin Province Development Plan(20170204074GX,20180201068GX) Jilin Provincial International Cooperation Foundation(20180414015GH) 

主　　题：Collision avoidance intelligent vehicles intervehicle communication simulation testing trajectory planning 

摘      要：A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intelligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance(MVCA)algorithm is proposed by extending the reciprocal n-body collision avoidance method. MVCA enables the intelligent vehicles to choose their destinations and control inputs independently,without needing to negotiate with each other or with the coordinator. Compared to the centralized trajectory-planning algorithm, MVCA reduces computation costs and greatly improves the robustness of the system. Because the destination of each intelligent vehicle can be regarded as private, which can be protected by MVCA, at the same time MVCA can provide a real-time trajectory planning for intelligent vehicles. Therefore,MVCA can better improve the safety of intelligent vehicles. The simulation was conducted in MATLAB, including crossroads scene simulation and circular exchange position simulation. The results show that MVCA behaves safely and reliably. The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain. The results uncover that the tolerant delay should not exceed the half of deciding cycle of trajectory planning, and shortening the sending interval could alleviate the negative effects caused by the packet loss to an extent. The cases of short delay( 100100 ms) and low packet loss( 5%) can bring little influence to those trajectory planning algorithms that only depend on V2 V to sense the context, but the unpredictable collision may occur if the delay and packet loss are further worsened. The MVCA was also tested by a real intelligent vehicle, the test results prove the operability of MVCA.