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Traffic safety is important in reducing death and building a harmonioussociety. In addition to studies of accident incidences, the perception ofdriving risk is significant in guiding the implementation of appropriatedriving countermeasures. Risk assessment can be conducted in real-time fortraffic safety due to the rapid development of communication technology andcomputing capabilities. This paper aims at the problems of difficultcalibration and inconsistent thresholds in the existing risk assessmentmethods. It proposes a risk assessment model based on the potential field toquantify the driving risk of vehicles. Firstly, virtual energy is proposed asan attribute considering vehicle sizes and velocity. Secondly, the driving risksurrogate(DRS) is proposed based on potential field theory to describe the riskdegree of vehicles. Risk factors are quantified by establishing submodels,including an interactive vehicle risk surrogate, a restrictions risk surrogate,and a speed risk surrogate. To unify the risk threshold, acceleration forimplementation guidance is derived from the risk field strength. Finally, anaturalistic driving dataset in Nanjing, China, is selected, and 3063 pairs offollowing naturalistic trajectories are screened out. Based on that, theproposed model and other models use for comparisons are calibrated through theimproved particle optimization algorithm. Simulations prove that the proposedmodel performs better than other algorithms in risk perception and response,car-following trajectory, and velocity estimation. In addition, the proposedmodel exhibits better car-following ability than existing car-following models.

A Driving Risk Surrogate and Its Application in Car-Following Scenario at Expressway