The Current Trends of Deep Learning in Autonomous Vehicles: A Review
by Raymond Ning Huang 1 
, Jing Ren2,* 
, Hossam A. Gabbar 3
1 Department of Mechanical Engineering, University of Toronto, Toronto, M5S 1A4, Canada
2 Department of Electrical and Computer Engineering, Ontario Tech University, Oshawa, L1H 7K4, Canada
3 Department of Energy and Nuclear Engineering, Ontario Tech University, Oshawa, L1H 7K4, Canada
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 10, Page # 56-68, 2022; DOI: 10.55708/js0110008
Keywords: Deep learning, Autonomous Vehicles, Control
Received: 15 August 2022, Revised: 07 October 2022, Accepted: 08 October 2022, Published Online: 31 October 2022
APA Style
Ren, J., Huang, R. N., Ren, J., & Gabbar, H. A. (2022). The Current Trends of Deep Learning in Autonomous Vehicles: A Review. Journal of Engineering Research and Sciences, 1(10), 56–68. https://doi.org/10.55708/js0110008
Chicago/Turabian Style
Ren, Jing, Raymond Ning Huang, Jing Ren, and Hossam A. Gabbar. “The Current Trends of Deep Learning in Autonomous Vehicles: A Review.” Journal of Engineering Research and Sciences 1, no. 10 (October 1, 2022): 56–68. https://doi.org/10.55708/js0110008.
IEEE Style
J. Ren, R. N. Huang, J. Ren, and H. A. Gabbar, “The Current Trends of Deep Learning in Autonomous Vehicles: A Review,” Journal of Engineering Research and Sciences, vol. 1, no. 10, pp. 56–68, Oct. 2022, doi: 10.55708/js0110008.
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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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