Steering Angle Estimation for an Autonomous Car

Abstract

The number of traffic accidents and fatalities has increased dramatically in the modern period. Every minute, a catastrophic traffic accident occurs in India, and every hour, 16 people die on Indian roadways. The driver’s errors such as negligence, drowsiness, and wrong driving decisions lead to advancement of the autonomous vehicle industry. Such a dire situation necessitates decision-making approaches that are both computationally efficient and quick to respond. A convolutional neural network (CNN) model could be used to map the raw pixels from a single front-facing camera directly to calculate the steering commands.In this paper, a CNN methodology has been implemented for the estimation of steering angles. Here the concepts of deep learning and convolutional neural networks are applied to teach the computer to drive car autonomously. The revolutionary aspect of CNNs is that characteristics are automatically learned from training samples. Because the convolution procedure captures the 2D aspect of images. Images from the cameras (input to CNN) are fed into a CNN which then computes a proposed steering command. The command thus obtained from CNN is compared with desired command, and the CNN weights are changed accordingly to get the CNN output closer to the desired output. Back propagation algorithm is used for weight adjustments. Once trained, the network can generate steering angles from the video images of a single center camera. The data collection is performed using Udacity self-driving car simulator designed by Unity (game engine). The CNN model is able to learn meaningful road features from training signal (steering alone).