Deep Learning Environment Perception and Self-tracking for Autonomous and Connected Vehicles

Abstract

Autonomous and Connected Vehicle (CAV) refers to an intelligent vehicle that is capable of moving, making its own decisions without the assistance of a human driver and ensure the communication with its environment. CAVs will not only change the way we travel, their deployment will make an impact on the evolution of society in terms of safety, environment and urban planning. In the automotive industry, researchers and developers are actively pushing approaches based on artificial intelligence, in particular, deep learning to enhance autonomous driving. However, before an autonomous vehicle finds its way into the road, it must first overcome a set of challenges regarding functional safety and driving efficiency. This paper proposes an autonomous driving approach based on deep learning and computer vision, by guaranteeing the basic driving functions, the communication between the vehicle and its environment, obstacles detection and traffic signs identification. The obtained results show the effectiveness of the environment perception, the lane tracking and the appropriate decisions making.