Field Test of Cooperative Driving Algorithms for Lane Merging and Emergency Evasion Using V2V

초록

This paper presents a V2V-based cooperative driving framework that implements cooperative lane merging (CLM) and emergency trajectory alignment (ETrA) to improve the safety and efficiency of autonomous vehicles. The proposed method exchanges cooperative awareness messages (CAM) and collective perception messages (CPM) to coordinate planned trajectories. The CLM algorithm evaluates the feasibility of merging using shared trajectory information and enables safe integration through structured intent negotiation. The ETrA algorithm adjusts the follower vehicle trajectory to align with the lead vehicle's emergency path, allowing synchronized avoidance maneuvers and maintaining a safe distance. A field evaluation conducted on public roads in Incheon, Korea, demonstrated that the proposed approach improves safety by increasing time-to-collision margins, enhances traffic efficiency through higher target speed retention and merge success rates, and mitigates secondary collision risks. The results confirm the effectiveness of the CLM and ETrA in enabling robust cooperative behavior in real-world autonomous driving scenarios. © 2025 IEEE.

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