The Current Trends of Deep Learning in Autonomous Vehicles: A Review

Abstract

Autonomous vehicles are the future of road traffic. In addition to improving safety and efficiency from reduced errors compared to conventional vehicles, autonomous vehicles can also be implemented in applications that may be inconvenient or dangerous to a human driver. To realize this vision, seven essential technologies need to be evolved and refined including path planning, computer vision, sensor fusion, data security, fault diagnosis, control, and lastly, communication and networking. The contributions and the novelty of this paper are: 1) provide a comprehensive review of the recent advances in using deep learning for autonomous vehicle research, 2) offer insights into several important aspects of this emerging area, and 3) identify five directions for future research. To the best of our knowledge, there is no previous work that provides similar reviews for autonomous vehicle design.

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“…The integration of advanced computational methods, multidimensional analysis of impacts, and the development of enabling technologies are key areas driving the future of AVs. Ren, Huang, and Gabbar (2022) provide an insightful review of the current trends of deep learning in autonomous vehicles. Deep learning, a subset of machine learning, has become increasingly significant in the development of AV systems.…”