EMERGENCY VEHICLE PRIORITIZATION USING RL AND V2X AIDED, SUMO SIMULATIONS

Abstract: Rapid and reliable movement of emergency vehicles is critical for saving lives, yet conventional traffic signal systems often fail to provide timely right-of-way under congested urban conditions. This work presents a reinforcement learning (RL) based emergency vehicle prioritization framework enhanced by Vehicle-to-Everything (V2X) communication and evaluated using the SUMO traffic simulator. The proposed system enables traffic signals to dynamically adapt their phases based on real-time information exchanged between emergency vehicles, roadside units, and intersections. An RL agent is trained to minimize emergency vehicle delay while maintaining overall traffic efficiency by observing traffic density, queue lengths, and emergency vehicle proximity. V2X communication ensures early detection of approaching emergency vehicles, allowing proactive signal control rather than reactive pre-emption. Simulation results demonstrate that the proposed approach significantly reduces emergency vehicle travel time and intersection delay compared to fixed-time and conventional priority strategies, while limiting negative impacts on non-emergency traffic.

Keywords: Emergency Vehicle Prioritization, Reinforcement Learning, V2X Communication, Intelligent Traffic Signal Control, SUMO Simulation, Smart Transportation Systems