Abstract:

The safety potential field is utilized to characterize the distribution of safety risks around a vehicle during the driving process. However, when analyzing the safety potential field formed by moving vehicles, the existing models only focus on the vehicle motion but ignore the traffic environment information perceived by drivers. This study focuses on the construction of an improved safety potential field model and its application to the car-following model. Herein, the relative state influence factor is introduced to strengthen the influence of relative speed among vehicles, and the traffic state influence factor is introduced to reflect its influence on driving safety. Moreover, the vehicle type coefficient is introduced to adjust the distance to reflect its influence on driving safety in mixed vehicle type traffic. The car-following model is developed by using the preceptive safety potential field to establish the relationship between the motion state of the front vehicle and the behavior of the following vehicle. Furthermore, the genetic algorithm is employed to calibrate the proposed model, the intelligent driver model, and the car-following model based on the safety potential field. The results show that the root mean square errors of these three models mentioned before are 6.124, 8.515 and 7.248 respectively, which proves that the model proposed in this paper can describe car-following behavior more accurately. Therefore, this study can provide theoretical support for driving risk evaluation and vehicle control under a complex environment.

Key words: traffic and transportation engineering, car-following model, traffic environment perception, vehicle safety potential field, genetic algorithm