Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation (2307.02663v1)
Abstract: Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information collected by sensors and wireless systems for decision-making on longitudinal and lateral movements. However, enabling autonomous navigation for CAVs requires a convergent integration of communication, control, and learning systems. The goal of this article is to explicitly expose the challenges related to this convergence and propose solutions to address them in two major use cases: Uncoordinated and coordinated CAVs. In particular, challenges related to the navigation of uncoordinated CAVs include stable path tracking, robust control against cyber-physical attacks, and adaptive navigation controller design. Meanwhile, when multiple CAVs coordinate their movements during navigation, fundamental problems such as stable formation, fast collaborative learning, and distributed intrusion detection are analyzed. For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation. Preliminary simulation results are provided to show the merits of proposed solutions.
- R. Hussain and S. Zeadally, “Autonomous cars: Research results, issues, and future challenges,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1275–1313, Secondquarter 2019.
- I. W. Damaj et al., “Future trends in connected and autonomous vehicles: Enabling communications and processing technologies,” IEEE Access, vol. 10, pp. 42 334–42 345, Apr. 2022.
- W. Liu et al., “A systematic survey of control techniques and applications: From autonomous vehicles to connected and automated vehicles,” arXiv preprint arXiv:2303.05665, 2023.
- J. Zhang and K. B. Letaief, “Mobile edge intelligence and computing for the internet of vehicles,” Proc. IEEE, vol. 108, no. 2, pp. 246–261, Feb. 2020.
- X. Sun et al., “A survey on cyber-security of connected and autonomous vehicles (CAVs),” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6240–6259, Jul. 2021.
- Z. Ju et al., “A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 4, pp. 815–837, Dec. 2022.
- J. Zhu et al., “Merging control strategies of connected and autonomous vehicles at freeway on-ramps: A comprehensive review,” Journal of Intelligent and Connected Vehicles, vol. 5, no. 2, pp. 99–111, May 2022.
- T. Zeng et al., “Joint communication and control system design for connected and autonomous vehicle navigation,” in Proc. of IEEE ICC, Shanghai, China, May 2019.
- A. Ferdowsi et al., “Robust deep reinforcement learning for security and safety in autonomous vehicle systems,” in Proc. of IEEE ITSC, Maui, HI, USA, Nov. 2018.
- Konečnỳ et al., “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527, 2016.
- F. Yu et al., “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in Proc. of IEEE CVPR, Seattle, WA, Jun. 2020.
- T. Zeng et al., “Joint communication and control for wireless autonomous vehicular platoon systems,” IEEE Trans. Commun., vol. 67, no. 11, pp. 7907–7922, Nov. 2019.
- T. Li et al., “Federated optimization in heterogeneous networks,” in Proc. of ACM MLSys, Austin, TX, USA, Mar. 2020.
- A. Ferdowsi and W. Saad, “Generative adversarial networks for distributed intrusion detection in the internet of things,” in Proc. of IEEE GLOBECOM, Waikoloa, HI, USA, Dec. 2019.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.