Data grading for intelligent connected vehicles: An inference strength-driven framework✩

Shichun Yang; Bowen Zheng; Yi Shi; Haoran Guang; Tianyang Gong; Weifeng Gong; Xinjie Feng; Mingjie Chen; Yaoguang Cao; Hao Wu; Tian Liu; Jia Zhao

doi:10.1016/j.dcan.2026.02.004

›› 2026, Vol. 12 ›› Issue (4) :698 -716. DOI: 10.1016/j.dcan.2026.02.004

research-article

Data grading for intelligent connected vehicles: An inference strength-driven framework^✩

Author information +

^a State Key Laboratory of Intelligent Vehicle Safety Technology , Chongqing 401133, China

^b School of Transportation Science and Engineering, Beihang University , 100091, Beijing, China

^c College of Automotive Engineering, Jilin University , 130022, Changchun Jilin, China

^d School of Vehicle and Mobility, Tsinghua University , 100084, Beijing, China

^e Hangzhou International Innovation Institute, Beihang University , 311115, Hangzhou Zhejiang, China

^f State Key Lab of Intelligent Transportation System, Beihang University , 102206, Beijing, China

^g Network Information Security Institute, CNCERT/CC , 100094, Beijing, China

^* E-mail addresses: yangshichun@buaa.edu.cn (S. Yang),

zbw94@buaa.edu.cn (B. Zheng),

yi_shi@buaa.edu.cn (Y. Shi),

haoranguang@buaa.edu.cn (H. Guang),

gongty22@mails.jlu.edu.cn (T. Gong),

gwfgriffin@163.com (W. Gong),

bhfengxinjie@buaa.edu.cn (X. Feng),

chenmj@buaa.edu.cn (M. Chen),

caoyaoguang@buaa.edu.cn (Y. Cao),

wuhao@cert.org.cn (H. Wu),

liutian@changan.com.cn (T. Liu),

zhaojia@changan.com.cn (J. Zhao).

Shichun Yang is the Dean of School of Transportation Science and Engineering in Beihang University. In 2004, he graduated from School of Automotive Engineering, Jilin University. In 2008, he worked as a postdoctoral in State Key Laboratory of Auto motive Dynamic Simulation. His research mainly lies in scientific and technological research for EV power system safety, Safety of The Intended Functionality and cybersecurity.

Bowen Zheng received his M.Sc. degree in Technological Systems Management from SUNY-Stony Brook University and B.Sc. degree in Electrical Engineering from Ohio State University. He is an Ph.D. candidate in Vehicle Engineering at Beihang University. Prior to that, he was an electrical architecture engineer and diagnostic development engineer at BAIC Motor Inc.. His interests include hardware and software architecture, cybersecurity and data security.

Yi Shi received her Bachelor of Engineering degree from Wuhan University of Technology and is currently pursuing postgraduate studies in transportation at Beihang University. Her primary research focus lies in the field of data security for intelligent connected vehicles, where she specializes in explainable artificial intelligence and privacy preservation. She is dedicated to advancing the data security industry by applying her professional knowledge and expertise in this area.

Haoran Guang received the M.S. degree in Automatic Control and Systems Engineering from the University of Sheffield, Sheffield, UK, in 2020. He is currently working toward the Ph.D. degrees in Vehicle Engineering, School of Transportation Science and Engineering, Beihang University, Beijing, China. His research interests include automotive cyber security, intrusion detection system, intelligent and connected vehicles, and intelligent transportation systems.

Tianyang Gong received the B.S. degree in energy and power engineering from Jilin University, Changchun, China, in 2020, where he is currently pursuing the Ph.D. degree in power engineering and engineering thermophysics with the College of Automotive Engineering. His research interests include trajectories and behavioral predictions of unmanned vehicles in a variety of traffic environments, prediction-based decision making and planning, vehicle dynamics and control for Autonomous Vehicle.

Weifeng Gong received the M.S. degree in Automotive Engineering from School of Transportation Science and Engineering, Beihang University, Beijing, China, in 2025. He is currently pursuing the Ph.D. degrees in vehicle engineering at School of Vehicle and Mobility, Tsinghua University. His research interests include generative artificial intelligence and the evaluation of connected and automated vehicles.

Xinjie Feng received the B.S. degree in Vehicle Engineering from Shenyang Ligong University and the M.S. degree in Transportation Engineering from Beihang University, China, in 2017 and 2022. He is currently a Ph.D. candidate in School of Transportation Science and Engineering, Beihang University. His research interests include decision planning and safety for autonomous vehicles.

Mingjie Chen received the B. Eng. degree in automotive engineering from Tsinghua University, Beijing, China in 2021. He is working toward his master’s degree in automotive engineering at Beihang University, Beijing, China. His current research interests include vehicle chassis control, fusion security and simulation.

Yaoguang Cao received the B.S. degree in Thermal Energy and Power Engineering in 2010 from Beihang University, China. He received the M.S. and Ph.D. degree in Automotive Engineering in 2012 and 2020 from Beihang University. During 2013-2015, he had a work experience serving in the EV project management office of High Technology R&D Center, Ministry of Science and Technology of China. His research interests include intelligent decision and planning strategy for autonomous vehicles, automotive cybersecurity, and intelligent ADAS sensors. Dr. Cao’s awards and honors include Second Prize of The National Science and Technology Progress Award, First Prize of Technical Invention by China Automatic S&T.

Hao Wu was born in Shandong, China, in 1985. He received the bachelor’s degree in mathematics from Shandong University in 2008, and the Ph.D. degree in automation from Tsinghua University, China, in 2014. Since 2014, he has been a Senior Engineer with the Network Information Security Institute, CNCERT/CC. His research interests include intrusion detection, IoT security, and state estimation.

Tian Liu received his Bachelor of Engineering degree from the School of Computer Science and Technology at Harbin Institute of Technology in 2014. He is currently pursuing a part-time master’s degree at the School of Artificial Intelligence, University of Chinese Academy of Sciences, while serving as a cybersecurity engineer at Changan Automobile Co., Ltd. His primary research focus lies in cybersecurity and artificial intelligence, with a particular commitment to enhancing the security of intelligent vehicles through artificial intelligence technology.

Jia Zhao received her M.S. degree from Beijing Normal University. She is an engineer and currently serves as a Senior Manager at Changan Automobile Co., Ltd., where she leads key initiatives in intelligent connected vehicle cybersecurity. Her research focuses on foundational technologies for automotive cybersecurity, including threat modeling, intrusion detection, and secure onboard communication protocols.

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Abstract

Intelligent Connected Vehicles (ICVs) generate massive heterogeneous multi-modal data during operation, and due to the limited computing resources on board, graded data encryption protection is of great significance for balancing data security and efficient utilization. However, the current data grading processes struggle to address the evolving inference attacks and dynamic operational environments, and traditional grading approaches relying on static expert judgment or information-theoretic metrics. To bridge this gap, this paper proposes a novel inference strength-driven data grading framework, where inference strength quantifies the susceptibility of one dataset to infer another through adversarial reasoning. The framework employs a systematic methodology combining graph theory, optimization, and Large Language Models to construct an inference library and calculate inference strength. The framework also provides a PageRank-based algorithm to generate interpretable data grading lists for both static policy and vehicle-end application, prioritizing core data protection while respecting computational constraints. Validated on the Audi A2D2 dataset and real vehicle controller, our approach demonstrates improved protection utility compared to default grading baselines. The results highlight its potential to enhance data security in ICVs through prioritized protection of core data under computational constraints.

Keywords

Intelligent vehicles / Data security / Risk analysis / Data handling

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Shichun Yang, Bowen Zheng, Yi Shi, Haoran Guang, Tianyang Gong, Weifeng Gong, Xinjie Feng, Mingjie Chen, Yaoguang Cao, Hao Wu, Tian Liu, Jia Zhao. Data grading for intelligent connected vehicles: An inference strength-driven framework^✩. , 2026, 12 (4) : 698-716 DOI:10.1016/j.dcan.2026.02.004

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Acknowledgments

This work is supported by National Key R&D Program of China (No. 2022YFB3104900), the National Natural Science Foundation of China (No. U22A202101), and State Key Laboratory of Intelligent Vehicle Safety Technology program (No. IVSTSKL-202439).

CRediT authorship contribution statement

Shichun Yang: Supervision, Project administration, Conceptualization; Bowen Zheng: Writing – review & editing, Writing – original draft, Methodology, Formal analysis; Yi Shi: Writing - review & editing, Writing – original draft; Haoran Guang: Visualization, Software; Tianyang Gong: Validation; Weifeng Gong: Validation; Xinjie Feng: Writing - review & editing; Mingjie Chen: Validation, Software; Yaoguang Cao: Supervision, Funding acquisition; Hao Wu: Resources, Funding acquisition, Formal analysis, Data curation; Tian Liu: Resources, Formal analysis, Data curation; Jia Zhao: Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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