Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling

This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle’s acceleration and steering angle. The statistical analysis of the model’s parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this strong correlation should not be ignored if one is interested in the vehicle’s microscopic motion behavior. We further develop a driving risk potential field-based car-following model (DRPFM) to remedy the failure of acceleration consideration under the conventional environment, whose parameters are calibrated by filtered I-80 NGSIM data with frequent traf?c oscillations. Simulation results indicate that our proposed DRPFM model is proved to be a good description of car-following behavior and outperforms two classical car-following models (Optimal Velocity Model and Intelligent Driver Model) in frequent oscillation phases due to our consideration of potential acceleration data acquisition in real-time under the CAVs environment. In addition, this DRPFM model is applied to deduce the safety conditions for vehicle lane-changing. The analysis results prove that this model can reasonably explain the influencing factors between driver types and lane-changing safety conditions in practice.

I. Introduction

The application of potential field theory in traffic flow modeling has existed for a long time. At the beginning of the 21st century, some scholars put forward the concept of the potential field and applied it to robot path planning. Inspired by this idea, many scholars extended the potential field theory to the field of traffic flow research and obtained some research results. Wolf and Burdick established the corresponding potential field model for different objects, creatively constructed the vehicle potential field into a wedge form, and established the mapping relationship between the potential field and traffic behavior [1]. Ni et al. demonstrated the objectivity and universality of the potential field in traffic from both macro and micro perspectives, and calibrated the potential field car-following model using NGSIM (Next Generation Simulation) data [2]–[4]. Li et al. proposed a simple car-following model based on the concept of the potential field from the perspective of stimulus-response, but the factors considered in the model are relatively simple and the potential field model established is relatively simple [5]. Wang et al. established a unified model to characterize the “driving risk field” based on the previous research, put forward the concept of “driving risk field”, and validated the model with real vehicles [6], [7]. The results show that the model can provide an effective method for evaluating the driving risk in a complex traffic environment. Based on game theory and traffic field theory Li et al. established a safety control model to evaluate the driver’s driving risk [8]. It is worth mentioning that by improving the potential field model proposed by Wang et al, they optimized the vehicle’s driving field into an elliptic structure, which is more realistic in model interpretation. In summary, the potential field theory can describe the interrelationship between various factors in a complex traffic environment and can explain various phenomena in the real traffic environment with a unified framework.