Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Proximal Policy Optimisation

TL;DR

This paper presents a multi-agent reinforcement learning approach using MAPPO to enable autonomous vehicles to safely and efficiently maneuver around emergency vehicles, balancing safety and traffic flow in complex scenarios.

Contribution

It introduces a MAPPO-based method with a novel risk metric for cooperative autonomous vehicle maneuvers near emergency vehicles, improving speed and safety.

Findings

Emergency vehicle speed increased by 15% with safety maintained

Proposed method outperforms rule-based approaches in safety and efficiency

Trade-off analysis between safety and traffic flow demonstrated

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 explore the trade-off between safety and traffic efficiency and assess the performance in a competitive scenario.