Modeling of driving risk fields considering vehicle-lane interactions and its application in car-following model

Driving risk situation awareness is essential for the decision-making and planning of connected vehicles. However, existing driving risk models are difficult to comprehensively describe real-time dynamic evolution and spatial migration of the risk distribution. We propose a unified dynamic driving risk field (DDRF) model, which comprehensively considers the motion changes of vehicles, lane markings constraints and inter-vehicle interaction factors. Firstly, a dynamic vehicle driving risk field model based on asymmetric risk attenuation is proposed to quantify the risk impacts arising from vehicle’s inherent attributes and motion states. Then, lane risk fields considering lane curvature are constructed for different types of lane markings. Finally, the inter-vehicle interaction risk field model is built by integrating the pseudo-distance model that considers dynamic interaction between vehicles and the extended social force model. To verify the effectiveness of the DDRF model, the model is applied to existing car-following models, with verification and analysis conducted on open-source datasets and special car-following scenarios, while the risk changes during the vehicle car-following process are analyzed. Experimental results show that: (i) In straight and curved lane environments, the car-following models based on the DDRF model can select appropriate driving strategies according to the risk impacts between vehicles, ensuring a safe inter-vehicle distance. Compared with existing car-following models, the DDRF-based car-following models exhibit reductions of 55.39 %, 46.45 %, and 25.57 % in the root mean square error (RMSE) of position, RMSE of speed, and RMSE of acceleration, respectively. (ii) During the vehicle car-following process, the DDRF model is compared with existing risk models, it exhibits higher sensitivity to changes in risk.