An Analysis of the Future Trends and Challenges of Autonomous Driving Technology

Xujun Wang

doi:10.54254/2755-2721/104/20241087

Research Article

Open access

Published on 8 November 2024

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Wang,X. (2024). An Analysis of the Future Trends and Challenges of Autonomous Driving Technology. Applied and Computational Engineering,104,47-52.

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An Analysis of the Future Trends and Challenges of Autonomous Driving Technology

Xujun Wang *^,1,

¹ LONGRE-HSD FOREIGN LANGUAGE SCHOOL

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/104/20241087

Abstract

Autonomous driving technology is one of the most groundbreaking innovations in the automotive industry in recent years. This article will explore the latest trends in autonomous driving, analyzing its historical development, current status, and future prospects, while also assessing its potential across different application scenarios. With the support of data and illustrations, this article aims to provide a comprehensive perspective to understand the development and challenges of this technology. As technology progresses, autonomous driving will significantly enhance traffic safety, efficiency, and convenience, ultimately leading the transportation industry into a new era. Although current autonomous driving systems still face challenges such as boundary identification and safety concerns, ongoing research and innovation across various fields will drive continuous improvements. In the future, autonomous driving is expected to become part of mainstream transportation, not only boosting traffic efficiency but also reducing the workload of drivers and lowering the incidence of traffic accidents. Plans for its widespread global adoption are already underway.

Keywords

Autonomous Driving Technology, Technology Maturity, Transportation Infrastructure, Artificial Intelligence.

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References

[1]. Garikapati, D., Shetiya, S. S. 2024. Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape [J]. Big Data and Cognitive Computing, 8(4): 42.

[2]. CognitiveClouds. 2024. IoT in Automotive Industry: IoT-Enabled Smart Cars and Connected Vehicles [J]. CognitiveClouds Blog.

[3]. Eduarda P ,Helena M ,C. I L , et al. 2023. Trends in Motion Sickness Countermeasures for Autonomous Driving: review and future research[J]. Transportation Research Procedia, 723102-3109.

[4]. Ge Z ,Ding P ,Zhai W , et al. 2024. Multifunctional fiber-shaped flexible wearable strain sensor with high sensitivity and wide sensing range for detecting autonomous driving technology in automobiles[J]. Composites Communications, 48101909-.

[5]. Shladover, S. E., Nowakowski, C., Chan, C. Y. 2021. Automated Vehicle Technology: Liability, Insurance, and Ownership [R]. California PATH Research Report, UCB-ITS-PRR-2021-05.

[6]. University of Cambridge. 2019. Driverless cars working together can speed up traffic by 35 percent [J]. ScienceDaily.

[7]. Miklós L, Szabolcs P, Zoltán M, et al. 2023. Combining survey-based and neuroscience measurements in customer acceptance of self-driving technology[J]. Transportation Research Part F: Psychology and Behaviour, 9546-58.

[8]. Micron Technology Inc. 2021. C-V2X: A sixth sense for ADAS and autonomous vehicles [J]. Micron Blog.

[9]. Verizon Connect. 2021. What Is V2V Technology?: V2V vs V2I vs V2X Technology Systems [J]. Verizon Connect.

[10]. Milakis, D., Van Arem, B., Van Wee, B. 2017. Policy and society related implications of automated driving: A review of literature and directions for future research [J]. Journal of Intelligent Transportation Systems, 21(4): 324-348.

Cite this article

Wang,X. (2024). An Analysis of the Future Trends and Challenges of Autonomous Driving Technology. Applied and Computational Engineering,104,47-52.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation

Conference website: https://2024.confmla.org/

ISBN：978-1-83558-697-6(Print) / 978-1-83558-698-3(Online)

Conference date: 12 January 2025

Editor：Mustafa ISTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.104

ISSN：2755-2721(Print) / 2755-273X(Online)

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