Collision Avoidance and Direction Planning for Autonomous Vehicles. Issue 1 (1st August 2022) - Gannaram, Nithish Kumar; Nayak, Jitesh Kumar; Mahesh, Sudarsi; Anil, Pappala; Chikkala, Vamsi; Kumar, Nitin
Abstract: The rapid development of information and signal processing technology has contributed significantly to the development of autonomous driving (AD) techniques, which improve driving safety while minimizing human efforts. According to analyst predictions, automatic cars will soon surpass manual cars in number as automatic driving becomes more sophisticated. Ensuring safety and reducing the road accidents is the primary concerns in automated vehicles. Recent research has shown that computer vision, deep learning, and other fields have advanced beyond the imagination. The paper covers the deep learning algorithms and techniques necessary to make a reliable and real-time collision avoidance system using concepts such as Convolutional Neural Networks (CNN), YOLOv4 (You Look Only Once), and other deep learning architectures while also reviewing the current state of the art strategies for Advanced Driver Assistance System (ADAS). By using CNN algorithms, it is possible to detect lane markings and signboards in real time as well as estimate distances between them.