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The Review of AI Efficiency in Autonomous Driving
- 1 Shenzhen MSU-BIT University of Electronic & Computer Engineering, Shenzhen, P.R. China
* Author to whom correspondence should be addressed.
Abstract
This paper explores the impact of Artificial Intelligence (AI) on the development of autonomous driving, highlighting advancements, challenges, and future directions. The roles of adaptive learning, sensor fusion, and energy efficiency in improving vehicle autonomy are analyzed, drawing on case studies from Tesla and Waymo to illustrate real-world applications. Despite progress, issues such as data management and system integration persist. The study anticipates benefits such as enhanced safety and traffic efficiency, alongside increased adoption of autonomous vehicles. Emphasis is placed on the need for innovation, collaboration, and standardization in overcoming these challenges.
Keywords
Artificial Intelligence, Autonomous Driving, Adaptive Learning, Energy Efficiency, Traffic Efficiency
[1]. Wang, L., Zhang, X., Song, Z., & Bi, J. (2023). Multi-modal 3D object detection in autonomous driving: A survey and taxonomy. IEEE Transactions on Intelligent Vehicles, PP(99), 1-19. https://doi.org/10.1109/TIV.2023.3264658
[2]. Dong, H., Zhuang, W., Chen, B., et al. (2022). A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections. Applied Energy, 306, 118524. https://doi.org/10.1016/j.apenergy.2022.118524
[3]. Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2019). A survey of deep learning techniques for autonomous driving. Journal of Field Robotics. https://doi.org/10.1002/rob.21918
[4]. Uçar, A., Demir, Y., & Güzeliş, C. (2017). Object recognition and detection with deep learning for autonomous driving applications. SAGE Journal, 93(9). https://doi.org/10.1177/00375497177099
[5]. K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, "Deep Reinforcement Learning: A Brief Survey, " in IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26-38, Nov. 2017, doi: 10.1109/MSP.2017.2743240.
[6]. Dwivedi, R., Dave, D., Naik, H., et al. (2023). Explainable AI (XAI): Core ideas, techniques, and solutions. ACM Computing Surveys, 55(9), Article 194, 1-33. https://doi.org/10.1145/3561048
[7]. Zhao, J., Zhao, W., Deng, B., et al. (2023). Autonomous driving system: A comprehensive survey. Expert Systems with Applications, 122836. https://doi.org/10.1016/j.eswa.2023.122836
[8]. Caesar, H. (n.d.). NuScenes: A Multimodal dataset for Autonomous Driving. ar5iv. https://www.arxiv-vanity.com/papers/1903.11027/.
[9]. Sun, P., Kretzschmar, H., Dotiwalla, X., et al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. Affiliation: Waymo LLC, Google LLC.
[10]. Petrou, A. (2023). AI-driven systems for autonomous vehicle traffic flow optimization and control. Journal of AI-Assisted Scientific Discovery, 3(2), 221–241. https://scienceacadpress.com/index.php/jaasd/article/view/98
[11]. Vermesan, O., John, R., Pype, P., et al. (2021). Automotive intelligence embedded in electric connected autonomous and shared vehicles technology for sustainable green mobility. Frontiers in Future Transportation, 2. https://doi.org/10.3389/ffutr.2021.688482
[12]. Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2024). Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3431437
[13]. Cohen, S. A., & Hopkins, D. (2019). Autonomous vehicles and the future of urban tourism. Annals of Tourism Research, 74, 33-42. https://doi.org/10.1016/j.annals.2018.10.009
Cite this article
Wang,Y. (2024). The Review of AI Efficiency in Autonomous Driving. Applied and Computational Engineering,113,92-100.
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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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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