Modeling and Simulation of Driving Risk Pulse Field and Its Application in Car Following Model

For assisted driving or unmanned driving, various information acquisition and comprehensive and effective information utilization will make the driving assistance system and the driving measures more reliable. With the support of advanced information acquisition, information interaction and other technologies, measuring the risk threat capability of each traffic element, and using potential field theory and risk pulse theory can effectively describe the risk distribution in the road traffic environment, which is conducive to ensuring driving safety and the implementation of driving control. In this paper, we use the risk pulse energy to measure the threat ability of each traffic element, establish the corresponding driving risk pulse field based on the risk analysis of each traffic element, measure the overall risk performance and overall level of the road traffic environment from the perspectives of vector superposition and quantity superposition, and establish a unified driving risk pulse field model. The characteristics of the established driving risk pulse field model are simulated and described, including basic risk pulse energy, random risk pulse energy and relevant parameters. Taking GM model as an example, a car following model based on driving risk pulse field is established by combining driving risk pulse field with GM model. Finally, the simulation analysis of car following model considering driving risk pulse field is carried out by taking a typical car following scene as an example. The results show that the car following model considering the impact of driving risk pulse field has the following advantages: (i) It can take into consideration the impact of the front and rear vehicle motion state changes on the current vehicle driving. (ii) The headway can be adjusted according to the change of the risk pulse energy of the front and rear vehicles. (iii) It is able to select appropriate driving strategies according to the risk pulse energy level of the front and rear vehicles. The whole process aims to minimize the risk threat to the vehicles. (iv) Based on the description of the driving risk pulse field, the visualization of the driving environment risk can be realized, and we can directly see and understand the driving risk situation and facilitate the appropriate and reasonable driving measures.

I. Introduction

How to avoid vehicle collision accidents and ensure driving safety is one of the most important research topics in the field of automobile safety assistance driving system and even unmanned driving system. The root cause of the collision accident is that two or more traffic elements have reached the same spatial coordinates at the same time, and the basic attributes and motion attributes of the units that collide at the same time determine the severity of the collision accident [1]. In order to avoiding collision accidents, two goals should be achieved: (i) the time of each traffic unit when it reaches the same spatial coordinates is different from each other, or (ii) each traffic unit should be in different spatial coordinates at the same time. The first goal can be achieved by traffic control, e.g., traffic rules at intersections, lane use settings [2]. For the second goal, how to control the distance among various traffic units within a reasonable range by a reasonable way becomes critical [3].