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Volume 16
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles

by
Kailong Li
1,2,3,*,
Feng Zhang
4,
Min Li
1,5 and
Li Wang
1,3,*
1
Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China
2
Beijing Jidao Technology Co., Ltd., Beijing 100114, China
3
Beijing Key Laboratory of Cooperative and Autonomous Intelligent Control Technology for Ground Transportation, North China University of Technology, Beijing 100144, China
4
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330100, China
5
School of Digital Industry, North China University of Technology, Beijing 100144, China
*
Authors to whom correspondence should be addressed.
World Electr. Veh. J. 2025, 16(8), 465; https://doi.org/10.3390/wevj16080465
Submission received: 17 June 2025 / Revised: 8 August 2025 / Accepted: 11 August 2025 / Published: 14 August 2025
Review Reports Versions Notes

Abstract

Ensuring dynamic risk management for intelligent connected vehicles (ICVs) in complex urban environments is critical as autonomous driving technology advances. This study presents three key contributions: (1) a comprehensive risk indicator system, constructed using entropy-based weighting, extracts 13-dimensional data on abnormal behaviors (e.g., speed, acceleration, position) to enhance safety and efficiency; (2) a multidimensional risk quantification method, simulated under single-vehicle and platooning modes on a CARLA-SUMO co-simulation platform, achieved >98% accuracy; (3) a cloud takeover strategy for high-level autonomous vehicles, directly linking risk assessment to real-time control. Analysis of 56,117 risk data points shows a 32% reduction in safety risks during simulations. These contributions provide methodological innovations and substantial data support for ICV field testing.
Keywords: automatic driving; decision aid; risk assessment; simulation experiment automatic driving; decision aid; risk assessment; simulation experiment

Share and Cite

MDPI and ACS Style

Li, K.; Zhang, F.; Li, M.; Wang, L. Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles. World Electr. Veh. J. 2025, 16, 465. https://doi.org/10.3390/wevj16080465

AMA Style

Li K, Zhang F, Li M, Wang L. Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles. World Electric Vehicle Journal. 2025; 16(8):465. https://doi.org/10.3390/wevj16080465

Chicago/Turabian Style

Li, Kailong, Feng Zhang, Min Li, and Li Wang. 2025. "Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles" World Electric Vehicle Journal 16, no. 8: 465. https://doi.org/10.3390/wevj16080465

APA Style

Li, K., Zhang, F., Li, M., & Wang, L. (2025). Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles. World Electric Vehicle Journal, 16(8), 465. https://doi.org/10.3390/wevj16080465

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