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Volume 7 Issue 4
Jun.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Chaoyue Zu, Chao Yang, Jian Wang, Wenbin Gao, Dongpu Cao and Fei-Yue Wang, "Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045-1063, July 2020. doi: 10.1109/JAS.2020.1003246
Citation: Chaoyue Zu, Chao Yang, Jian Wang, Wenbin Gao, Dongpu Cao and Fei-Yue Wang, "Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045-1063, July 2020. doi: 10.1109/JAS.2020.1003246

Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms

doi: 10.1109/JAS.2020.1003246
Funds:

the National Natural Science Foundation of China 61572229

the National Natural Science Foundation of China 6171101066

the Key Scientific and Technological Projects for Jilin Province Development Plan 20170204074GX

the Key Scientific and Technological Projects for Jilin Province Development Plan 20180201068GX

Jilin Provincial International Cooperation Foundation 20180414015GH

More Information
  • 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 (${ < 100}$ ms) and low packet loss (${ < 5\%}$) 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.

     

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    Highlights

    • A distributed real-time multiple vehicles collision avoidance (MVCA) algorithm is proposed by extending the reciprocal n-body collision avoidance method and enables the intelligent vehicles to choose their destinations and control inputs independently. MVCA behaves safely and reliably by the simulation.
    • The effects of latency and packet loss on MVCA are also statistically investigated through theoretically formulating broadcasting process based on one-dimensional Markov chain and 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 MVCA was tested by a real intelligent vehicle, the information on obstacles and the latitude and longitude of the vehicle were input into the algorithm, MVCA outputted the control quantity, and finally made the vehicle arrive at the destination. The test results prove the operability of MVCA.

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