# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.5 Information of Vehicles Platoon  
In the aforementioned exploring process, it is pointed out that the motion state information of two preceding vehicles can produce considerable positive effects on car-following behavior and traffic flow. In contrast, considering the motion state information of more vehicles will lead to a surge in resource consumption, such as communication bandwidth and computing power. The positive effects that they produce are relatively less and subject to the marginal effect. Furthermore, it is difficult for human drivers to receive and effectively process a large amount of information at the same time, and there is a distinct primary and secondary priority for drivers when processing information. According to this, some researchers assumed that the drivers pay attention to the overall motion state of multiple preceding vehicles, which are not close to them, rather than the individual motion state of these vehicles. This assumption is based on the actual characteristics of the drivers, in that they utilize the information of vehicles not close to them to acquire the traffic state in the front segment of the road, and do not focus on the value of the individual motion state of these vehicles. Therefore, the researchers employed the average motion state of the platoon to represent the overall motion state of the vehicles that are not close to the object vehicle and incorporated it to extend or improve the car-following model. [PERSON] et al. incorporated the average velocity of the arbitrary number of preceding vehicles, which can represent the overall operating state of vehicles ahead, and, based on this, extended the velocity difference item to improve the FVD model [70]. Considering that the average headway of the arbitrary number of preceding vehicles can assist the driver with acquiring the congestion degree of the road ahead, [PERSON] et al. incorporated the average headway and proposed an extended FVD model [71]. [PERSON] et al. introduced the average field velocity of the arbitrary number of preceding vehicles and established an extended FVD model [72]. In the same period, [PERSON] et al. constructed an extended FVD model with consideration of the average optimal velocity of those vehicles [73]. Soon after, based on the previous research [70; 73], [PERSON] et al. comprehensively incorporated average velocity and average optimal velocity of the arbitrary number of preceding vehicles and further extended the FVD model [74]. In the above research, multiple kinds of average motion state information were incorporated. However, the individual motion state of the vehicles close to the object vehicle was not considered comprehensively. According to this, [PERSON] et al. further extended [PERSON]'s model [71] by introducing the acceleration of the preceding vehicle [75]. The results of the above research reveal that the overall motion state of the vehicles platoon can assist the driver with understanding the traffic operating state in the front segment of the road, and thus contribute to the optimization of car-following behavior as well as the traffic flow. In fact, with driving experience, not only the overall motion state of vehicles in the current lane but also the overall motion state of vehicles in the adjacent lanes can represent the traffic operating state and thus contribute to the car-following behavior of the object vehicle in the current lane. For instance, when the average velocity of the vehicles in the adjacent lane is lower than that of the current lane, the driver is likely to decelerate according to their judgment, which is that the traffic state in the front segment is poor and the vehicles in the adjacent lane decelerated due to this, even if the (average) velocity of the vehicles in the current lane is relatively high. According to this, [PERSON] et al.