A Proactive Collision Avoidance Model for Connected and Autonomous Vehicles in Mixed Traffic Flow

• Published in: World electric vehicle journal Vol. 16; no. 7; p. 394
• Main Authors: Hu, Guojing, Li, Kun, Lu, Weike, Chen, Ouchan, Sun, Chuan, Zhao, Yuanqi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Adaptive control; Automation; Autonomous vehicles; Collision avoidance; collision avoidance model; Collisions; connected and autonomous vehicle; Control algorithms; Cooperation; Cooperative control; Cruise control; Decision making; Driving conditions; Efficiency; lane change; Lane changing; mixed traffic; Speed limits; Traffic flow; Vehicles
• ISSN: 2032-6653; 2032-6653
• DOI: 10.3390/wevj16070394

Collision avoidance between vehicles is a great challenge, especially in the context of mixed driving of connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs). Advances in automation and connectivity technologies provide opportunities for CAVs to drive cooperatively. This paper proposes a proactive collision avoidance model, aiming to avoid collisions by controlling the speed and lane-changing behavior of CAVs. In the model, the subject vehicle first collects information about surrounding lanes and judges the traffic conditions; it then chooses to decelerate or change lanes to avoid collisions. The subject vehicle also searches for the optimal vehicle in the surrounding lanes for cooperation. The effectiveness of the proposed collision avoidance model is verified through the Python-SUMO platform. The experimental results show that the performance of the collision avoidance model is better than that of the cooperative adaptive cruise control (CACC) model in terms of average speed, lost time and the number of vehicle conflicts, proving the advantages of the proposed model in safety and efficiency.