Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication
In modern transportation systems, the management of traffic safety has become increasingly critical as both the number and complexity of vehicles continue to rise. These systems frequently encounter multiple challenges. Consequently, the effective assessment and management of collision risks in vari...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2846.pdf |
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| author | Min Zeng Mohd Sani Mohamad Hashim Mohd Nasir Ayob Abdul Halim Ismail Qiling Zang |
| author_facet | Min Zeng Mohd Sani Mohamad Hashim Mohd Nasir Ayob Abdul Halim Ismail Qiling Zang |
| author_sort | Min Zeng |
| collection | DOAJ |
| description | In modern transportation systems, the management of traffic safety has become increasingly critical as both the number and complexity of vehicles continue to rise. These systems frequently encounter multiple challenges. Consequently, the effective assessment and management of collision risks in various scenarios within transportation systems are paramount to ensuring traffic safety and enhancing road utilization efficiency. In this paper, we tackle the issue of intelligent traffic collision prediction and propose a vehicle collision risk prediction model based on vehicle-to-vehicle (V2V) communication and the graph attention network (GAT). Initially, the framework gathers vehicle trajectory, speed, acceleration, and relative position information via V2V communication technology to construct a graph representation of the traffic environment. Subsequently, the GAT model extracts interaction features between vehicles and optimizes the vehicle driving strategy through deep reinforcement learning (DRL), thereby augmenting the model’s decision-making capabilities. Experimental results demonstrate that the framework achieves over 80% collision recognition accuracy concerning true warning rate on both public and real-world datasets. The metrics for false detection are thoroughly analyzed, revealing the efficacy and robustness of the proposed framework. This method introduces a novel technological approach to collision prediction in intelligent transportation systems and holds significant implications for enhancing traffic safety and decision-making efficiency. |
| format | Article |
| id | doaj-art-755a5c63ce68450e8ed564650b70fc2a |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-755a5c63ce68450e8ed564650b70fc2a2025-08-20T03:09:32ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e284610.7717/peerj-cs.2846Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communicationMin Zeng0Mohd Sani Mohamad Hashim1Mohd Nasir Ayob2Abdul Halim Ismail3Qiling Zang4Mechanical Department, Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, MalaysiaMechanical Department, Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, MalaysiaMechatronic Department, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, MalaysiaMechatronic Department, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, MalaysiaSchool of Business Administration, JiangXi University of Finance and Economics, Nanchang, Jiangxi, ChinaIn modern transportation systems, the management of traffic safety has become increasingly critical as both the number and complexity of vehicles continue to rise. These systems frequently encounter multiple challenges. Consequently, the effective assessment and management of collision risks in various scenarios within transportation systems are paramount to ensuring traffic safety and enhancing road utilization efficiency. In this paper, we tackle the issue of intelligent traffic collision prediction and propose a vehicle collision risk prediction model based on vehicle-to-vehicle (V2V) communication and the graph attention network (GAT). Initially, the framework gathers vehicle trajectory, speed, acceleration, and relative position information via V2V communication technology to construct a graph representation of the traffic environment. Subsequently, the GAT model extracts interaction features between vehicles and optimizes the vehicle driving strategy through deep reinforcement learning (DRL), thereby augmenting the model’s decision-making capabilities. Experimental results demonstrate that the framework achieves over 80% collision recognition accuracy concerning true warning rate on both public and real-world datasets. The metrics for false detection are thoroughly analyzed, revealing the efficacy and robustness of the proposed framework. This method introduces a novel technological approach to collision prediction in intelligent transportation systems and holds significant implications for enhancing traffic safety and decision-making efficiency.https://peerj.com/articles/cs-2846.pdfMachine learningData scienceV2VIntelligent transportationGAT |
| spellingShingle | Min Zeng Mohd Sani Mohamad Hashim Mohd Nasir Ayob Abdul Halim Ismail Qiling Zang Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication PeerJ Computer Science Machine learning Data science V2V Intelligent transportation GAT |
| title | Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication |
| title_full | Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication |
| title_fullStr | Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication |
| title_full_unstemmed | Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication |
| title_short | Intersection collision prediction and prevention based on vehicle-to-vehicle (V2V) and cloud computing communication |
| title_sort | intersection collision prediction and prevention based on vehicle to vehicle v2v and cloud computing communication |
| topic | Machine learning Data science V2V Intelligent transportation GAT |
| url | https://peerj.com/articles/cs-2846.pdf |
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