# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 1 Introduction  
Lane changing is one of the main behaviors in vehicle driving and one of the main causes of road traffic accidents. Compared with following behavior, lane changing behavior has a higher complexity. With the development of intelligent network technology, real-time driving information can be obtained between vehicles and vehicles and between vehicles and the road environment, which puts forward higher requirements for the research of vehicle lane changing behavior. Vehicles can achieve the transmission and integration of traffic information through vehicle-to-vehicle communication and vehicle-to-road communication, and conduct real-time driving behavior judgment and operation to ensure driving efficiency and safety. Vehicle lane changing behavior is a complex decision-making process. In the study of lane changing behavior, [PERSON] [1] first constructed a vehicle lane changing model framework, which laid a theoretical foundation for subsequent lane changing research. According to the type of lane changing, [PERSON] [2] divides the lane changing behavior into three types: free lane changing, forced lane changing, and collaborative lane changing. In the study of lane changing safety conditions, [PERSON] et al. [3] used the gap acceptance model to describe the execution of lane changing behavior and established the lane changing model of forced lane changing and judged lane changing. [PERSON] et al. [4] introduced the mutualcooperation mechanism into the lane changing process and established a gap acceptance model considering the characteristics of drivers. [PERSON] et al. [5] modeled and analyzed the acceptable clearance of lane changing vehicles and surrounding conflicting vehicles, and fully considered the following safety of lane changing vehicles after lane changing. [PERSON] [6] proposed a new acceptable gap model based on different speeds and different driver types. In view of the mutual interference between vehicles in a multi-lane, [PERSON] et al. [7] established the influence model of neighboring vehicles. As for the research of the potential field, the trajectory planning method of mobile robot based on the artificial potential field concept was first proposed by [PERSON] [8]. With the development of intelligent transportation, many scholars have extended potential field theory to the field of traffic flow. Based on the gravitational and repulsive fields established by [PERSON], [PERSON] et al. [9] proposed a real-time autonomous vehicle controller based on machine learning. [PERSON] et al. [10] established potential field models for different objects based on artificial potential field theory. [PERSON] et al. [11] improved the existing safe potential field model by introducing acceleration parameters and established the safe potential field lane changing model. As for the data set used in the research, the data used can be roughly divided into three types: a natural data set, video observation data, and simulation test data. For example, [PERSON] [12] used NGSIM data to investigate gap acceptance behavior during lane changes on highways. [PERSON] et al. [13] used five GM light vehicles equipped with the SHRP2 NextGen data acquisition system to collect real-world driving data and use it for empirical analysis. Due to the limitations of equipment conditions and other aspects, some scholars use simulation experiment platforms, including Prescan 8.5 software [14], to build models of traffic scenes and vehicle control systems. It is not difficult to see that both the research on the safe distance and the research on the safe potential field are related to the safety during lane changing.