论文标题
使用多代理近端策略优化，安全有效地操纵紧急车辆在自动交通中
Safe and Efficient Manoeuvring for Emergency Vehicles in Autonomous Traffic using Multi-Agent Proximal Policy Optimisation
论文作者
论文摘要
在紧急车辆在场的情况下进行操作仍然是车辆自治系统的主要问题。解决该主题的大多数研究都是基于基于规则的方法，该方法无法涵盖自动流量中可能发生的所有可能场景。多代理近端策略优化（MAPPO）最近已成为自主系统的强大方法，因为它允许在数千种不同情况下进行培训。在这项研究中，我们提出了一种基于MAPPO的方法，以确保在紧急车辆在场的情况下对自动驾驶汽车进行安全有效的操作。我们引入了风险度量标准，总结了单个指数中碰撞的潜在风险。提出的方法生成合作政策，使紧急车辆的平均速度更高，同时保持高安全距离。此外，我们探讨了安全与交通效率之间的权衡，并在竞争方案中评估绩效。
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.
