A Comprehensive Study on Implementation of Deep Learning on Autonomous Vehicle for Steering Angle Prediction and Stability

Abstract

The development of autonomous vehicles has recently enhanced the transportation industry and opened up a variety of opportunities and problems that can be solved with the aid of current methods and technology. In this study, three separate algorithms were used to predict the steering angle with a track image: Artificial Neural Network (ANN), Convolution Neural Network (CNN), and a combination of CNN and Long-Short Term Memory (CNN-LSTM). The PID controller was employed for benchmarking, which takes Cross-Track Error (CTE) provided by the simulation to steer the vehicle. To achieve improved performance, the standard NVIDIA CNN self-driving model was slightly altered by feeding it with sequential frames. The comparison analysis was conducted using the OpenAI Gym Donkey Simulator.