Convolutional Neural Network Based Steering Direction Estimation for Self-Driving Car

Abstract

With technological advancements in automation and artificial intelligence, automated vehicle systems, fully autonomous vehicles, self-driving cars are becoming the part of news constantly. In order to assist drivers in critical moments, for fast response and decision making, a methodology is required to conciliate the burden on human drivers. A recent research by MIT shows that number of fatalities can be lessen to as much as 1% by using Autopilot (self-driving cars). In order to provide full alternative to human driver CNNs are being used to steer the car perfectly and understanding the internal representations of the required processing steps on its own without optimization of human-selected features like detecting the outline of road, lane-following, traffic signals etc. This thesis work targets to propose an end-to-end solution in which images will be given as an input to CNN model and the output is steering angle. The datasets being used are provided publicly by autonomous driving startups. Initially two main designs were evaluated i.e. Nvidia Approach (real-time environment model) and Udacity Approach (simulated environment model). The major network proposed in this thesis performed training using real-time dataset and testing was done on simulated dataset. So it overcomes the problem of state of the art implementations like over-fitting and domain adaption. During experimentation it is observed that domain adaption model outperforms with an accuracy of 97% approximately and a loss of 0.017. The network trained on real-time dataset was able to drive in simulated environment’s representative track without any inconvenience. However, an even more interesting approach would be to perform network’s training on simulated (artificial) driving dataset and testing/validating it on real-time dataset. This would be beneficial since the networks can be trained on data that can be effortlessly and cost effectively produced in huge quantities.