关于自动车辆可中止接管的基于学习的高级别决策 (Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles)

from arxiv, 10 pages, 13 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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.

翻译：自主车辆是一种不断增长的技术,目的是通过从更换车道到超车的自主操作,提高安全性、可及性、效率和方便性。超载是自主车辆最具挑战性的操作方法之一,目前自主超载的技术仅限于简单的情况。本文研究如何通过允许中止操作来提高自动超载的安全性。我们提议了一个基于深度Q-Network的决策过程,以确定超载操作是否和何时需要中止。提议的算法是在模拟各种交通情况时经过经验评估的,这表明拟议方法改善了超载操作的安全性。此外,使用自动穿梭飞机的实际世界实验也证明了这一方法。