Feng Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Feng Zhang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Kailong Li
School of electrical and control engineering, North China University of Technology, Beijing 100144, China
1 shared publication - Li Wang
North China University of Technology
1 shared publication - Min Li
School of electrical and control engineering, North China University of Technology, Beijing 100144, China
1 shared publication
Latest Publications
- Open Access
- 14 August 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (8) , 465
Research and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles
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.
- 14 August 2025
- Published by MDPI AG in World Electric Vehicle Journal
- Vol. 16 (8) , 465
Open AccessResearch and Quantitative Analysis on Dynamic Risk Assessment of Intelligent Connected Vehicles
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.