Autonomous Vehicle for Obstacle Detection and Avoidance Using Reinforcement Learning

Abstract

Obstacle detection and avoidance during navigation of an autonomous vehicle is one of the challenging problems. Different sensors like RGB camera, Radar, and Lidar are presently used to analyze the environment around the vehicle for obstacle detection. Analyzing the environment using supervised learning techniques has proven to be an expensive process due to the training of different obstacle for different scenarios. In order to overcome such difficulty, in this paper Reinforcement Learning (RL) techniques are used to understand the uncertain environment based on sensor information to make the decision. Policy free, model-free Q-learning based RL algorithm with the multilayer perceptron neural network (MLP-NN) is applied and trained to predict optimal vehicle future action based on the current state of the vehicle. Further, the proposed Q-Learning with MLP-NN based approach is compared with the state of the art, namely, Q-learning. A simulated urban area obstacles scenario is considered with the different number of ultrasonic radar sensors in detecting obstacles. The experimental result shows that Q-learning with MLP-NN along with the ultrasonic sensors is proven to be more accurate than conventional Q-learning technique with the ultrasonic sensors. Hence it is demonstrated that combining Q-learning with MLP-NN will improve in predicting obstacles for autonomous vehicle navigation.