Dr. Benamar and Dr. Rachidi Publish New Paper on Deep and Reinforcement Learning Approaches in Autonomous Driving
Dr. Benamar and Dr. Rachidi, from the School of Science and Engineering (SSE), recently published a paper titled "A Comprehensive Survey on the Application of Deep and Reinforcement Learning Approaches in Autonomous Driving" in the Journal of King Saud University - Computer and Information Sciences. This journal has an impact factor of 13.473, ranking it 2 out of 162 in Computer Science, Information Systems.
This paper is an attempt to survey all recent AI-based techniques used to deal with major functions in AVs, namely scene understanding, motion planning, decision making, vehicle control, social behavior, and communication. Our survey focuses solely on deep learning and reinforcement learning based approaches. It builds a taxonomy of DL and RL algorithms that have been used so far to bring solutions to the four main issues in autonomous driving.
More details can be found at: https://authors.elsevier.com/sd/article/S1319-1578(22)00097-0