Crossing Traffic Avoidance of Automated Vehicle Through Bird-View Control, a Reinforcement Learning Approach

13 Pages Posted: 16 Dec 2019

See all articles by Yipei Wang

Yipei Wang

University of Wisconsin - Madison

Shuaikun Hou

University of Wisconsin - Madison

Xin Wang

University of Wisconsin-Madison

Date Written: October 28, 2019

Abstract

This paper presents an innovative bird-view control framework for connected automated vehicles (CAV). Most recent 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 fusionbased Q-learning method to yield an optimal birdview control policy for a CAV on a single lane. 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.

Keywords: bird-view control, connected automated vehicles, reinforcement learning

JEL Classification: L, R

Suggested Citation

Wang, Yipei and Hou, Shuaikun and Wang, Xin, Crossing Traffic Avoidance of Automated Vehicle Through Bird-View Control, a Reinforcement Learning Approach (October 28, 2019). Available at SSRN: https://ssrn.com/abstract=3495727 or http://dx.doi.org/10.2139/ssrn.3495727

Yipei Wang (Contact Author)

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Shuaikun Hou

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Xin Wang

University of Wisconsin-Madison ( email )

Madison, WI Wisconsin 53706
United States
2178982195 (Phone)

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