Traffic safety is important in reducing death and building a harmonious
society. In addition to studies of accident incidences, the perception of
driving risk is significant in guiding the implementation of appropriate
driving countermeasures. Risk assessment can be conducted in real-time for
traffic safety due to the rapid development of communication technology and
computing capabilities. This paper aims at the problems of difficult
calibration and inconsistent thresholds in the existing risk assessment
methods. It proposes a risk assessment model based on the potential field to
quantify the driving risk of vehicles. Firstly, virtual energy is proposed as
an attribute considering vehicle sizes and velocity. Secondly, the driving risk
surrogate(DRS) is proposed based on potential field theory to describe the risk
degree 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 for
implementation guidance is derived from the risk field strength. Finally, a
naturalistic driving dataset in Nanjing, China, is selected, and 3063 pairs of
following naturalistic trajectories are screened out. Based on that, the
proposed model and other models use for comparisons are calibrated through the
improved particle optimization algorithm. Simulations prove that the proposed
model performs better than other algorithms in risk perception and response,
car-following trajectory, and velocity estimation. In addition, the proposed
model exhibits better car-following ability than existing car-following models