The connected vehicle microscopic behavior modeling base on risk field theory: Theoretical developments, methodological overview and future trends

This study explores the applications of risk field in modeling connected vehicles’ microscopic behaviors within the “Vehicle-Road-Cloud Integration (VRCI)” framework, aiming to enhance driving safety and traffic efficiency. It addresses four key dimensions through bibliometric analysis. First, it examines how risk field integrates safety principles, motion dynamics, and environmental interactions to address existing modeling limitations. Second, it systematically reviews the evolution of risk field theories, including conceptual frameworks, quantitative modeling methods, and calibration techniques. Third, it evaluates strengths and weaknesses of current approaches across four sub-modules: risk perception, behavioral decision-making, trajectory planning, and tracking control. The analysis emphasizes hybrid methodologies combining physical models with machine learning, outlining their problem-solving potential, application conditions, and limitations. Finally, the study proposes a future research agenda informed by the challenges of applying risk field to vehicle microscopic behavior modeling, aligning it with the concept of “VRCI” to offer a feasible direction for future development.