论文标题
基于学习的高级决策，以在自动驾驶汽车中可流产的超车
Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles
论文作者
论文摘要
自动驾驶汽车是一种不断发展的技术，旨在通过自动操作从车道变更到超车来提高安全性，可访问性，效率和便利性。超车是自动驾驶汽车最具挑战性的操作之一，当前的自动超车技术仅限于简单情况。本文研究了如何通过允许动作流产来提高自主超车的安全性。我们提出了一个基于深Q网络的决策过程，以确定是否以及何时需要流产超车的操作。拟议的算法在与交通情况不同的模拟中进行了经验评估，表明所提出的方法可以在超车行动中提高安全性。此外，使用自动班车Iseauto在现实世界实验中证明了该方法。
Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overtaking. Overtaking is one of the most challenging maneuvers for autonomous vehicles, and current techniques for autonomous overtaking are limited to simple situations. This paper studies how to increase safety in autonomous overtaking by allowing the maneuver to be aborted. We propose a decision-making process based on a deep Q-Network to determine if and when the overtaking maneuver needs to be aborted. The proposed algorithm is empirically evaluated in simulation with varying traffic situations, indicating that the proposed method improves safety during overtaking maneuvers. Furthermore, the approach is demonstrated in real-world experiments using the autonomous shuttle iseAuto.
