Journal of Machine and Computing

A Discussion of Key Aspects and Trends in Self Driving Vehicle Technology

Journal of Machine and Computing

Received On : 20 April 2023

Revised On : 28 July 2023

Accepted On : 26 August 2023

Published On : 05 October 2023

Volume 03, Issue 04

Pages : 556-565


Autonomous vehicles use remote-sensing technologies such as radar, GPS, cameras, and lidar to effectively observe their immediate environment and construct a comprehensive three-dimensional representation. The conventional constituents of this particular environment include structures, additional vehicles, people, as well as signage and traffic indicators. At now, a self-driving car is equipped with a wide array of sensors that are not found in a traditional automobile. Commonly used sensors include lasers and visual sensors, which serve the purpose of acquiring comprehensive understanding of the immediate environment. The cost of these sensors is high and they exhibit selectivity in their use requirements. The installation of these sensors in a mobile vehicle also significantly diminishes their operational longevity. Furthermore, the issue of trustworthiness is a matter of significant concern. The present article is structured into distinct parts, each of which delves into a significant aspect and obstacle pertaining to the trend and development of autonomous vehicles. The parts describing the obstacles in the development of autonomous vehicles define the conflict arising from the use of cameras and LiDAR technology, the influence of social norms, the impact of human psychology, and the legal complexities involved.


Camera Technology, Autonomous Vehicles, Advanced Driver Assistance Systems, Light Detection and Ranging, Connected and Autonomous Vehicles.

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Dong Jo Kim, “A Discussion of Key Aspects and Trends in Self Driving Vehicle Technology”, Journal of Machine and Computing, vol.3, no.4, pp. 556-565, October 2023. doi: 10.53759/7669/jmc202303047.


© 2023 Dong Jo Kim. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.