End-to-end system architectures in autonomous driving: Comparative analysis against modular design and technological exploration
- 1 Tongji University
* Author to whom correspondence should be addressed.
Abstract
In the context of accelerated technological advancement, artificial intelligence is becoming an increasingly pivotal enabler of autonomous driving. With the integration of machine learning into autonomous driving systems, the traditional modular architecture has been disrupted, evolving into a more advanced end-to-end architecture. The end-to-end architecture has the advantages of simple structure and no cumulative error, especially under the current situation where a large amount of data and functions are continuously inputted, it demonstrates superior performance compared to the traditional modular system. However, the implementation of end-to-end is still mostly confined to academia and has not been widely applied to actual vehicles; the mainstream on actual vehicles is still the modular system architecture. As a result, the two system architectures are compared in this paper to ascertain their relative complexity, the extent to which errors are accumulated, their interpretability, the security they afford, and the extent of their data dependency. This is achieved through a comprehensive literature review. Furthermore, the paper delineates the deployment of the end-to-end system and outlines potential avenues for addressing the challenges that emerged during the implementation phase. The results indicate that the end-to-end system exhibits a certain degree of sophistication. However, a series of challenges pertaining to interpretability and security must be addressed to fully realize the potential of the end-to-end system.
Keywords
Artificial Intelligence, Autonomous Driving, End-to-End System Architecture, Modular System Architecture.
[1]. Chib, P.S. and Singh, P. (2023) Recent advancements in end-to-end autonomous driving using deep learning: A survey. IEEE Transactions on Intelligent Vehicles, 9(1): 103-118,
[2]. Hu, S.L., Hua, X.P., Dou, M., Fei, H.L. and Fu, Z.J. (2024) Current Status and Development Trends of End-to-End Autonomous Driving for Vehicles. Times Automobile, (13): 4-6+109.
[3]. Li, K., Dai, Y.F., et al. (2017) Development Status and Trends of Intelligent Connected Vehicle (ICV) Technology. Journal of Automotive Safety and Energy Efficiency, 8(1): 1-14.
[4]. Bojarski, M., Testa, D.W., Dworakowski, D., et al. (2016) End to End Learning for Self-Driving Cars. ArXiv, abs/1604.07316.
[5]. Singh, A. (2023) End-to-end autonomous driving using deep learning: A systematic review. arXiv preprint arXiv:2311.18636.
[6]. Hu, Y., Yang, J., Chen, L., et al. (2023) Planning-oriented autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 17853-17862.
[7]. Zablocki, É., Ben-Younes, H., et al. (2022) Explainability of deep vision-based autonomous driving systems: Review and challenges. International Journal of Computer Vision, 130(10): 2425-2452.
[8]. Brüdigam, T., Olbrich, M., et al. (2021) Stochastic model predictive control with a safety guarantee for automated driving. IEEE Transactions on Intelligent Vehicles, 8(1): 22-36.
[9]. Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A. and Li, H. (2023) End-to-end autonomous driving: Challenges and frontiers. arXiv preprint arXiv:2306.16927.
[10]. Zhao, J., Zhao, W., et al. (2023) Autonomous driving system: A comprehensive survey. Expert Syst. Appl., 242: 122836.
[11]. Research In China (2024) A Comprehensive Study on End-to-End Autonomous Driving in 2024. http://www.pday.com.cn/Htmls/Report/202404/24543611.html
[12]. Cherish Capital, Autonomous Driving Branch to Shanghai Alumni Association of Nanjing University, and JiuZhang-AI. (2024) End-to-End Autonomous Driving Industry Research Report. https://img.iduodou.com/images/docs/20240612/C0EC32EF-5BFA-4BA4-9DAA-8FDA4BF4BCA7.pdf
[13]. Tian, Y., et al. (2018) DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars. 40th International Conference on Software Engineering, Gothenburg, Sweden, 303-314
[14]. Wang, W., Xie, J., Hu, C., et al. (2023) Drivemlm: Aligning multi-modal large language models with behavioral planning states for autonomous driving. arXiv preprint arXiv:2312.09245.
Cite this article
Yuan,L. (2024). End-to-end system architectures in autonomous driving: Comparative analysis against modular design and technological exploration. Applied and Computational Engineering,102,141-147.
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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