Rapid Autonomous Vehicle Drifting with Deep Reinforcement Learning

Abstract

Abstract: Now days all types of vehicles run on road are upgrading to automated self-driving techniques. Drifting is a complicated procedure for autonomous vehicle control which is used for high-speed sideslip cornering. It is difficult to drift a vehicle at a high speed of 80-120 km/hr by using traditional techniques like two-state single-track model and three-state singletrack models, which depend on the knowledge of tire or road forces that are not to be known accurately due to the real-world environmental complexity. Because of these inaccuracies in these parameters there will be poor control performance and may lead to road accidents. In this scenario, this project presents a strong drift control algorithm, which is based on the most recent model-free deep reinforcement learning algorithm called soft actor-critic (SAC). SAC is used to control front-wheel drive (FWD) vehicles to drive at high speed (80– 120 km/h), and to drift through sharp corners quickly and stability which in turn helps to prevent road accidents at the time of drifting. The efficiency of this algorithm is evaluated by conducting experiments on Trajectories dataset taken from Git-Hub repository, which consists of some road maps with reference drift trajectories. The SAC algorithm can also deal with vehicle types with various actual properties, like mass, tyre friction, and so on to show its notable generalization ability.