Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation

Abstract

Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency vehicle. We introduce a risk metric that summarises the potential risk of collision in a single index. The proposed method generates cooperative policies allowing the emergency vehicle to go at 15% higher average speed while maintaining high safety distances. Moreover, we conduct a comprehensive evaluation of our method in a wide range of scenarios, including assessing the trade-offs between traffic efficiency and safety, measuring the scalability of the approach with respect to the number of autonomous vehicles, analysing different distributions of mixed human and autonomous traffic, and examining the various levels of cooperation and competition among agents.