Experimental Validation of a Steering Control System using an Adaptive Fuzzy Controller and Computer Vision

Abstract

This paper proposes an adaptive steering control strategy for self-driving cars based on a Fuzzy Expert System and Reinforcement Learning. Our objective consists in deriving an appropriate control law directly from a real vehicle that allows it to navigate on several types of lanes, by controlling the position in relation to the center of the tracks and also the translation speed of the vehicle. Using an on-line Reinforcement Learning approach, the Fuzzy expert controller is derived considering the coupling and non-linearity of the model on straight and winding tracks. To do this, an embedded camera captures the images and sends them to the computer vision algorithm responsible for performing tracks detection and recognition. From that, the control references which indicate the navigation path and direction on the lane are calculated. The main contribution of this work is to apply an online reinforcement learning approach to tune and optimize the fuzzy steering controller while the vehicle navigates through different routes. Using a real vehicle equipped with an embedded computer and also the implemented web user interface, the learning evolution of the adaptive fuzzy controller can be managed remotely during trial in actual environments. Experimental results showed that the learned fuzzy expert controller controls the self-driving car during the path tracking and precisely performs the execution of different maneuvers.