Sim-to-Real Autonomous Vehicle Lane Keeping using Vision

Abstract

This paper presents the analysis of the J-Net deep neural network used for autonomous vehicle lane keeping, and its verification in simulated and real conditions. The transition from the simulated world on the personal computer to the real world in laboratory conditions is tackled. In the presented solution, autonomous lane keeping is achieved by analyzing information from visual sensors using a deep neural network. J-Net was developed with an aim to be implemented on an autonomous vehicle platform with limited hardware performance in terms of computing power and memory capacity. Verification of autonomous driving using J-Net was achieved in simulated conditions, using an open-source simulator for autonomous driving, and in real-world conditions. For the verification in real-world conditions, an autonomous driving system was designed and implemented in the Laboratory of Electronics at the School of Electrical Engineering, University of Belgrade.