CASIA OpenIR  > 学术期刊  > IEEE/CAA Journal of Automatica Sinica
Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms
Chaoyue Zu; Chao Yang; Jian Wang; Wenbin Gao; Dongpu Cao; Fei-Yue Wang
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractA 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.
KeywordCollision avoidance intelligent vehicles inter-vehicle communication simulation testing trajectory planning
Citation statistics
Cited Times:27[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Chaoyue Zu,Chao Yang,Jian Wang,et al. Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms[J]. IEEE/CAA Journal of Automatica Sinica,2020,7(4):1045-1063.
APA Chaoyue Zu,Chao Yang,Jian Wang,Wenbin Gao,Dongpu Cao,&Fei-Yue Wang.(2020).Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms.IEEE/CAA Journal of Automatica Sinica,7(4),1045-1063.
MLA Chaoyue Zu,et al."Simulation and Field Testing of Multiple Vehicles Collision Avoidance Algorithms".IEEE/CAA Journal of Automatica Sinica 7.4(2020):1045-1063.
Files in This Item: Download All
File Name/Size DocType Version Access License
JAS-2018-0129.pdf(40115KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Chaoyue Zu]'s Articles
[Chao Yang]'s Articles
[Jian Wang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Chaoyue Zu]'s Articles
[Chao Yang]'s Articles
[Jian Wang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Chaoyue Zu]'s Articles
[Chao Yang]'s Articles
[Jian Wang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: JAS-2018-0129.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.