Crossing Traffic Avoidance of Automated Vehicle Through Bird-View Control, a Reinforcement Learning Approach
13 Pages Posted: 16 Dec 2019
Date Written: October 28, 2019
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
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