A virtual simulation environment using deep learning for autonomous vehicles obstacle avoidance

Autonomous vehicles which are capable of operating independently will be commercially available in the near future. Autonomous driving systems are becoming more complicated and must be successfully checked before implementation. Within this framework, falls our research work. The key purpose of this paper is to implement a simulation environment for autonomous vehicles. We first created this environment which is a novel high fidelity driving simulator that can connect arbitrary interfaces, build simulated worlds consisting of scenarios and incidents experienced by drivers in real-world driving, and incorporate fully autonomous driving. The simulator makes possible to clone the behavior of human driver face as well as some complex situations such as obstacle avoidance maneuvers. The work consists in creating a virtual simulation environment to collect training data used to train vehicles on how to steer themselves. The simulator is, thus, like a video game of car racing. Indeed we used the scenes to make some driving experiences. After collecting the training data, we chose to use deep learning explicitly Convolutional Neural Networks to create a model for autonomous vehicles that avoid obstacles. Clearly, the true challenge for an autonomous vehicle is to navigate without the possibility of collision. This simulator is invested to assess the performance of an autonomous vehicle and to analyze its self-driving activities. In this method, the suggested solution proves to be feasible, efficient and reliable for autonomous vehicle simulation research.