Autonomous Drifting Using Reinforcement Learning

Author:

Orgován László,Bécsi Tamás,Aradi Szilárd

Abstract

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

Publisher

Periodica Polytechnica Budapest University of Technology and Economics

Subject

Mechanical Engineering,Aerospace Engineering,Automotive Engineering,Modelling and Simulation

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