Dynamic Signal Timing Optimization for Left-Turn Maneuvers via V2I and V2V Communication

In densely populated urban areas, left-turn ma-neuvers at signalized intersections are a critical bottleneck, affecting both vehicular and pedestrian flows and contributing to unsafe conditions. The integration of autonomous vehicles (AVs) adds another layer of complexity, requiring enhanced communication and coordination strategies. This study proposes an innovative approach that employs Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication to dynam-ically optimize signal timings for intersections featuring both protected and unprotected left turns. The effectiveness of this approach was validated using a benchmark traffic network. By optimizing signal phases through a reinforcement learning framework, an average of 36% improvement was observed in terms of the average speed, with only 30% connected vehicles in the hybrid traffic flow. This research not only offers a robust solution for the seamless incorporation of AVs into existing urban traffic systems but also addresses the longstanding challenges associated with left-turn maneuvers. The study's findings have profound implications for the future of smart, efficient, and safe urban mobility.