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Enhancing Decision-Making in Highway Overtaking Scenarios with Graph Convolution Reinforcement Learning

Sam M. K. Weiguo Gee Sofia Arkhstan Hakan Khan Lima Hongou
Volume 3

Abstract

Autonomous vehicles have a number of open challenges, one of which is decision-making
regarding motion, particularly while operating in an environment that is both complex and
diverse. Within the scope of this work, a model for motion decision-making that is founded on
deep reinforcement learning (DRL) is proposed. In order to achieve the best possible results from
the driving policy, a multi-objective reward function has been developed. This function is
intended to direct the autonomous driving system toward the investigation of the best possible
decision policy, with the objectives of maximizing both safety and efficiency. As the backbone
network of the DRL model, a convolutional neural network, also known as a CNN, has been
created. This allows the model to make optimal use of the information gleaned from observations
of the surrounding environment. In order to improve overall safety, a safety check module that
helps users identify and prevent potentially hazardous behaviors has been developed. It is shown
that the suggested method gives good model performance when used in the DeepTraffic
simulation environment by doing several tests that compare everything. The tests showed that the
DRL-based model works well at making safe, efficient, and smooth decisions about mobility,
and it also works well in a wide range of traffic situations.

Keywords

decision-making , deep reinforcement learning , graph neural network , autonomous vehicles , multi-agent

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