# 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
incorporated the average velocity of multiple preceding vehicles in the adjacent lane and introduced the difference between this average velocity and the velocity of the preceding vehicle to form an extended model, i.e., the Lane Velocity Difference (LVD) model [76]. In the work, they also proposed an average intensity coefficient to describe the relationship between the disturbance in the current lane and that in the adjacent lane and, based on this, analyzed the stability of traffic flow. Later, [PERSON] et al. extended the LVD model from a two-lane scenario to a three-lane scenario and formed the Left and Right Velocity Difference (LRVD) model [77]. The LRVD model also can be considered as an extended FVD model by introducing two items, which are the velocity difference between the current lane and the adjacent lanes. Recently, [PERSON] et al. also extended the LVD model based on the approach of [PERSON] et al. [78] to incorporate the driver heterogeneity and formed an improved model with consideration of the heterogeneity and the honk effect [79]. In this model, driver heterogeneity was considered and relative velocity information of different time steps was provided for various drivers. The results of the above research confirm that there are positive effects of the motion state of vehicles in the adjacent lanes on the car-following behavior and traffic flow. According to this, [PERSON] et al. further considered that information on the two preceding vehicles in the current lane can exert a considerable positive influence, as pointed out in the works reviewed in the previous sections, defined the two preceding vehicles in the current lane and the left/right preceding vehicles in the adjacent lanes, which contribute greatly to the car-following behavior, as the generalized preceding vehicle (GPV), and proposed an extended FVD model, i.e., the GPV model, by employing the average velocity of GPV to represent its overall motion state [80]. The incorporated information and the expressions of the above models considering the overall state of the vehicle platoon are shown in Table 3.  
The results of the studies in this and previous sections reveal that various kinds of information in the V2X environment can contribute various positive effects to optimize the car-following behavior. This kind of optimization effect referring to the system level is the enhancement of traffic flow stability. In part of the aforementioned works, the neutral stability conditions and the nonlinear evolution characteristics were derived based on the theory and methods of linear stability and nonlinear analysis. The derivation and analysis results suggest that the stability of the traffic flow is significantly enhanced with the various kinds of information in the V2X environment, which is reflected in the sinking of the neutral stability curves and the expanding of the stable region in the sensitivity-headway phase space. In general, the stability of the traffic flow will be enhanced continuously with the increasing value of the sensitivity coefficient to the information, which means the car-following behavior determined based on paying more attention to the information is better. However, the increase in sensitivity to some kinds of information, such as the motion state of following vehicles, could produce negative effects on the traffic flow stability when the value is beyond the critical value. It is of great significance for alleviating traffic congestion and guiding the development of V2X technology that systematical discussion, especially the comparison analysis, takes place on the impacts of various information sources on traffic flow stability in different scenarios.