Reinforcement learning‐based bird‐view automated vehicle control to avoid crossing traffic. (9th July 2020)

• Record Type: Journal Article
• Authors: Wang, Yipei; Hou, Shuaikun; Wang, Xin
• Other Names: Qu Xiaobo guestEditor.; van Arem Bart guestEditor.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 36:Number 7(2021)
• Page Start: 890
• Page End: 901
• Publication Date: 2020-07-09
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12572
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: This paper presents an innovative bird‐view control framework for connected automated vehicles (CAV). Most recently tested automated vehicles are based on sensing systems equipped on the car body, which require the self‐driving policy to be robust and adaptive to various environmental uncertainties. Inspired by the vehicle to infrastructure technologies, the self‐driving technology can also be achieved through the communication between road infrastructure and the vehicle, where sensors are mainly installed on the road in a high position, which can collect traffic information from a bird‐view. To this end, we developed a fusion‐based Q‐learning method to yield an optimal bird‐view control policy for a CAV on a multi‐lane road. With our control policy, the CAV can drive smartly under complicated traffic environment, interacting with leading vehicles and crossing traffic simultaneously. A series of case studies show our CAV control policy is string stable and can avoid collisions under various scenarios.