Fuzzy Logic and Deep Steering Control based Recommendation System for Self-Driving Car

Automatic cruise control system such as steering and velocity control are complex tasks that work as fundamental to all automatic systems in self-driving cars. The challenging task is to deal with steering signals at high speeds and to manage the vehicle's velocity with significant changes in steering signals. In order to reach a destination with optimized time and stable trajectory, an autonomous vehicle system should utilize steering control and velocity signal in constraints. In this article, we propose a recommendation system based on Fuzzy logic and Deep Steering Neural Networks. Convolution neural networks(CNNs) works as a front-end stage for predicting steering control, and fuzzy logic in a back-end stage works as natural inferences for recommending velocity and adapting new steering control. The front-end stage uses CNNs with the input from raw sensory data, and the output extracts steering control prediction that feeds forward to the back-end stage. Key functions of back-end stage combine a number of dynamic vehicle information including, steering prediction to extract more effective steering control, and velocity for the autonomous car. The convolution neural network was trained on the Udacity driving datasheet with a number of hours of training and testing data, and the all system is built on MATLAB with GPU coder.