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.
M. R. Kılınç, J. Linderoth, and J. Luedtke, “Lift-and-project cuts for convex mixed integer nonlinear programs: Linear programming based separation and extended formulations,” Math. Program. Comput., vol. 9, no. 4, pp. 499–526, 2017.
“Road rage: How to avoid aggressive driving,” Aaa.com. [Online]. Available: https://exchange.aaa.com/wp-content/uploads/2013/06/Road-Rage-Brochure.pdf. [Accessed: 20-Aug-2023].
M. Kyriakidis, C. van de Weijer, B. van Arem, and R. Happee, “The deployment of advanced driver assistance systems in Europe,” SSRN Electron. J., 2015.
S. Regev, J. J. Rolison, and S. Moutari, “Crash risk by driver age, gender, and time of day using a new exposure methodology,” J. Safety Res., vol. 66, pp. 131–140, 2018.
A. Haleem, M. Javaid, M. A. Qadri, and R. Suman, “Understanding the role of digital technologies in education: A review,” Sustainable Operations and Computers, vol. 3, pp. 275–285, 2022.
M. P. Biernacki and R. Lewkowicz, “How do older drivers perceive visual information under increasing cognitive load? Significance of personality on-road safety,” Accid. Anal. Prev., vol. 157, no. 106186, p. 106186, 2021.
N. A. Stanton and M. S. Young, “Driver behaviour with adaptive cruise control,” Ergonomics, vol. 48, no. 10, pp. 1294–1313, 2005.
L. Madl and T. Radebner, “Technology transfer for social benefit: Ten principles to guide the process,” Cogent Soc. Sci., vol. 7, no. 1, 2021.
R. M. Trimpop, “Risk homeostasis theory: Problems of the past and promises for the future,” Saf. Sci., vol. 22, no. 1–3, pp. 119–130, 1996.
V. Knoop and S. Hoogendoorn, “Free flow capacity and queue discharge rate: Long-term changes,” Transp. Res. Rec., vol. 2676, no. 7, pp.483–494, 2022.
Q. Guo and X. (jeff) Ban, “A multi-scale control framework for urban traffic control with connected and automated vehicles,” Trans. Res. Part B: Methodol., vol. 175, no. 102787, p. 102787, 2023.
M. Faheem, Ridwan, R. Muneer, M. Aneeque, and S. Afghan Khan, “Effect of expansion level on the flow development with sudden expansion at high Mach numbers,” Mater. Today, vol. 46, pp. 2714–2725, 2021.
S. Gössling and A. Humpe, “The global scale, distribution and growth of aviation: Implications for climate change,” Glob. Environ. Change, vol. 65, no. 102194, p. 102194, 2020.
F. Chen, J. Wu, X. Chen, and J. Wang, “Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: Evidence from Shanghai City,” Transp. Res. D Transp. Environ., vol. 55, pp. 227–245, 2017.
C. Dominguez-Péry, L. N. R. Vuddaraju, I. Corbett-Etchevers, and R. Tassabehji, “Reducing maritime accidents in ships by tackling human error: a bibliometric review and research agenda,” J. Shipp. Trade, vol. 6, no. 1, 2021.
Z. Wang and M. Menenti, “Challenges and opportunities in lidar remote sensing,” Front. Remote Sens., vol. 2, 2021.
C. Kelly, B. Wilkinson, A. Abd-Elrahman, O. Cordero, and H. A. Lassiter, “Accuracy assessment of low-cost lidar scanners: An analysis of the velodyne HDL–32E and livox mid–40’s temporal stability,” Remote Sens. (Basel), vol. 14, no. 17, p. 4220, 2022.
Q. Chen et al., “Modeling accident risks in different lane-changing behavioral patterns,” Anal. Methods Accid. Res., vol. 30, no. 100159, p.100159, 2021.
C. Arranz, “Determining the Number of Ants in Ant Colony Optimization,” Journal of Biomedical and Sustainable Healthcare Applications, pp. 76–86, Jan. 2023, doi: 10.53759/0088/jbsha202303008.
T. Li, X. Han, J. Ma, M. Ramos, and C. Lee, “Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior,” Proc. Inst. Mech. Eng. O. J. Risk Reliab., vol. 237, no. 2, pp. 355–366, 2023.
T. Arakawa, R. Hibi, and T.-A. Fujishiro, “Psychophysical assessment of a driver’s mental state in autonomous vehicles,” Transp. Res. Part A Policy Pract., vol. 124, pp. 587–610, 2019.
“UNECE paves the way for automated driving by updating UN international convention,” Unece.org. [Online]. Available: https://unece.org/press/unece-paves-way-automated-driving-updating-un-international-convention. [Accessed: 20-Aug-2023].
The author(s) received no financial support for the research, authorship, and/or publication of this article.
No funding was received to assist with the preparation of this manuscript.
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Dong Jo Kim
Dong Jo Kim
Department Image design, Sunchon National University, Sunchon, Korea.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
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.