Fault-Tolerant Cooperative Driving at Signal-Free Intersections
Cooperative driving shows great potential to improve traffic safety and efficiency and has been well discussed in recent years. However, most existing studies focus on ideal traffic environments and ignore potential vehicle failures in traffic systems, which pose significant threats to traffic safety. Therefore, the fault-tolerant capacity of the existing cooperative driving strategies is questionable. To fill this research gap, this paper proposes a fault-tolerant cooperative driving strategy for signal-free intersections by modeling potential vehicle failure types, aiming to keep a good balance between traffic safety and efficiency. Notably, a rule-based fault-tolerant model is constructed to mitigate the threat of potential vehicle failures to traffic safety and efficiency, and to effectively recover the cooperative driving system after vehicle failures occur. Theoretical analysis and simulation results jointly demonstrate the promising performance of the proposed model in achieving fault tolerance and improving traffic efficiency.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
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Supplemental Notes:
- Copyright © 2023, IEEE.
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Authors:
- Pei, Huaxin
- Zhang, Jiawei
- Zhang, Yi
- Pei, Xin
- Feng, Shuo
- Li, Li
- Publication Date: 2023-1
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 121-134
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Serial:
- IEEE Transactions on Intelligent Vehicles
- Volume: 8
- Issue Number: 1
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2379-8858
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857
Subject/Index Terms
- TRT Terms: Connected vehicles; Driving; Fault tolerance; Traffic safety; Unsignalized intersections
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01875413
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 13 2023 10:23AM