Application of Edge Intelligence and Vehicle-To-Vehicle Communication in Next-Generation Autonomous Driving
DOI:
https://doi.org/10.54097/hset.v38i.5935Keywords:
Edge Intelligence, Vehicle-to-vehicle Communication, Autonomous Driving.Abstract
Self-driving vehicles have been one of the most promising technologies in the 21st century; however, today’s self-driving is way behind optimal autonomous driving where the car can take complete control. This is mainly due to a lack of accuracy and speed of critical car operation decisions made by the electronic control unit from artificial intelligence learning. Using data learning, the accuracy of the calculations is strongly related to the amount of environmental data input; nevertheless, this also proposes that current onboard processors are not powerful enough. This article discusses future solutions such as vehicle-to-vehicle communication and edge intelligence, elaborating on their mechanisms involving artificial intelligence, edge computing, and the internet of vehicles. Moreover, system and mathematic models related to vehicle-to-vehicle communication and edge intelligence are summarized; together with challenges and solutions further in the article. Overall, this article has attempted to provide a theoretical basis for a more profound exploration of deeper autonomous driving techniques and possible ways to achieve so.
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References
SAE Levels of Driving AutomationTM Refined for Clarity and International Audience. (n.d.). Retrieved September 18, 2022, from https://www.sae.org/blog/sae-j3016-update
What is an Autonomous Car? – What Self-Driving Cars Work | Synopsys. (n.d.). Retrieved September 7, 2022, from https://www.synopsys.com/automotive/what-is-autonomous-car.html
Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing. Proceedings of the IEEE, 107(8), 1738–1762.
Contributor, T. (2020, September 16). edge node. WhatIs.Com. https://www.techtarget.com/whatis/definition/edge-node
Min Chen, Yuanwen Tian, Giancarlo Fortino, Jing Zhang, Iztok Humar, Cognitive Internet of Vehicles, Computer Communications, Pages 58-70
Yang, B., Cao, X., Xiong, K., Yuen, C., Guan, Y. L., Leng, S., Qian, L., & Han, Z. (2021a). Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions. IEEE Wireless Communications, 28(2), 40–47.
Mellor, C. (2020b, February 6). Autonomous vehicle data storage: We grill self-driving car experts about sensors, clouds . . . and robo taxis. Blocks and Files. Retrieved September 18, 2022, from https://blocksandfiles.com/2020/02/03/autonomous-vehicle-data-storage-is-a-game-of-guesses/
Lyu X., & Yu Z. (2021). Edge Intelligence Multi-Source Data Processing for Autonomous Driving in Internet of Vehicles. Journal of Beijing University of Posts and Telecommunications, 44(2).
Wang, J., Liu, J., & Kato, N. (2019). Networking and Communications in Autonomous Driving: A Survey. IEEE Communications Surveys &Amp; Tutorials, 21(2), 1243–1274. https://doi.org/10.1109/comst.2018.2888904W
Xu et al., “Data-Cognition-Empowered Intelligent Wireless Networks: Data, Utilities, Cognition Brain, and Architecture,” IEEE Wireless Commun.}, vol. 25, no. 1, Feb. 2018, pp. 56–63.
B. Yang et al., “Computation Offloading in Multi-Access Edge Computing: A Multi-Task Learning Approach,” IEEE Trans. Mob. Comp., Apr. 2020, pp. 1–1.
Lyu X., & Yu Z. (2021). Edge Intelligence Multi-Source Data Processing for Autonomous Driving in Internet of Vehicles. Journal of Beijing University of Posts and Telecommunications, 44(2). K. Xiong et al., “Intelligent Task Offloading for Heteroge-neous V2X Communications,” IEEE Trans. Intelligent Transportation Syst., Aug. 2020, pp. 1–13.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 1135–1143.
Zhu, G., Liu, D., Du, Y., You, C., Zhang, J., & Huang, K. (2020). Toward an Intelligent Edge: Wireless Communication Meets Machine Learning. IEEE Communications Magazine, 58(1), 19–25. https://doi.org/10.1109/mcom.001.1900103
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