Advanced Driving Assistance System using distributed computation on single-board computers

Due to increasing use of vehicles and road traffic, drive safety has become an important issue. Therefore, an Advanced Driving Assistant System (ADAS) can be a convenient option to increase driving safety. This paper presents a model of an ADAS that is capable of driving from one place to another in different paths such as curved, straight and straight line followed by curved lines. This area of research is divided into several sub-domains, such as deep learning, hardware platform, computer vision, and control. Every self-driving car must be aware of its surroundings and act accordingly. Advances in neural networks and deep learning made it possible to extract information from cameras easily and robustly. In this paper, a neural network is trained to identify various objects such as traffic lights and pedestrians. In addition, image processing is used to detect road lines. due to high computational costs of image processing operations, a network of embedded systems are utilized. Furthermore, MPC control method is used for automated and intelligent steering that makes the right decisions in a timely manner with a small error.