首页|Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

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A global planning algorithm for intelligent vehicles is designed based on the A* algorithm, which provides intel-ligent vehicles with a global path towards their destinations. A distributed real-time multiple vehicle collision avoidance (MVCA) algorithm is proposed by extending the reciprocal nnn-body colli-sion 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 im-proves 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 inves-tigated 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 (<<<111000000 ms) and low packet loss (<<<555％％％) can bring little influence to those trajectory planning algorithms that only depend on V2V 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.

Collision avoidanceintelligent vehiclesinter-vehicle communicationsimulationtestingtrajectory planning

Chaoyue Zu、Chao Yang、Jian Wang、Wenbin Gao、Dongpu Cao、Fei-Yue Wang

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College of Computer Science and Technology, Jilin University, Changchun 130012, China

Jiangsu XCMG Construction Machinery Research Institute Ltd., Xuzhou 221004, China

College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012

State Grid JIBEI Electric Power Company Limited Management Training Center, China

Advanced Vehicle Engineering Center, Cranfield University, Cranfield MK430AL, UK

State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences,Beijing 100190, China

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This work was supported by the National Natural Science Foundation of ChinaKey Scientific and Technological Projects for Jilin Province Development Planand Jilin Provincial International Coop

617110106620180201068GX

2020

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDCSCDSCIEI
ISSN：2329-9266
年,卷(期)：2020.7(4)
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