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Cited by 24Privacy-Preserving Technologies in DataTraffic control and managementAdvanced Control Systems Optimization

The DOI matches its Crossref record, with a small difference in title or year; 1 of the 1 references it cites were found in a registry or an index.

Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

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Zhang, Kaixiang, Chen, Kaian, Li, Zhaojian, Chen, Jun, Zheng, Yang·2024·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2023.3329484
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Abstract

Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage and might further lead to malicious activities. In this paper, we develop a privacy-preserving data-enabled predictive control scheme for CAVs in a mixed traffic environment, where human-driven vehicles (HDVs) and CAVs coexist. We tackle external eavesdroppers and honest-but-curious central unit eavesdroppers who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs’ state and input information. An affine masking-based privacy protection method is designed to conceal the true state and input signals, and an extended form of the data-enabled predictive leading cruise control under different data matrix structures is derived to achieve privacy-preserving optimal control for CAVs. Numerical simulations demonstrate that the proposed scheme can protect the privacy of CAVs against attackers without affecting control performance or incurring heavy computations.

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Metadata

Authors
Zhang, K., Chen, K., Li, Z., Chen, J., & Zheng, Y.
Affiliations
  • Michigan State University (US)
  • Oakland University (US)
  • University of California San Diego (US)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024-05
Volume
25
Issue
5
Pages
3467-3482
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2023.3329484
OpenAlex
W4388878626
Cited by
24
References
1
Citations by year
2026: 7 · 2025: 13 · 2024: 2
FWCI
2.04
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (2045436, CMMI-2320698, CMMI-2320697)
  • NSF (ECCS-2154650)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
Keywords
privacy-preserving control, data-enabled predictive control, connected and automated vehicles, mixed traffic, eavesdropper, privacy leakage, human-driven vehicles

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