Control of Emergency Vehicles with Deep Q-Learning

Abstract

In contemporary times, the issue of traffic congestion has become a paramount concern affecting a broad spectrum of society. However, when it comes to emergency vehicles, particularly ambulances, this matter takes on even greater significance. This study addresses a research endeavor aimed at mitigating traffic risks for emergency situations. The primary objective of the research is to employ Deep Q-Learning methodology to ensure that ambulances transport patients to hospitals in the quickest and most optimal routes. Factors such as urgency levels, traffic density, and distances between patients and ambulances are modeled using state vectors. The Deep Q-Learning algorithm utilizes these vectors to select the most effective actions, determining the most efficient routes for ambulances to transport patients. The reward function is transformed into a penalty function by prioritizing patients based on their waiting times.The study evaluates the learning outcomes of the agent created with Deep Q-Learning, demonstrating the successful completion of the learning process. This method represents a significant step in optimizing the intra-city mobility of emergency vehicles.