# Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential
## 4 Model Verification
### Model Evaluation  
In order to verify that the vehicle interaction potential lane change model can effectively improve the speed of traffic flow and reduce the speed fluctuation of traffic flow, SUMO software (win64-1.11.0) was used to conduct simulation experiments. The experiment scene was set as a 2 km long one-way, three-lane road with a speed limit of 60 km/h. This was the simulation interface of the lane changing experiment, as shown in the Figure 13. After the vehicle generates the intention of lane changing, it will turn on the left turn signal to signal, which well reflects the environmental state of the lane changing vehicle in the model.  
In reference to the calibration results of the vehicle action potential model parameters, the values \(u=0.713\), \(v=1.648\), \(\gamma=0.032\), and \(s_{0}=2.11\) are taken, and combined with the parameters determined in 4.1, so that the model is determined.  
The traffic flow operating parameters output by the experiment were graphically analyzed, as shown in Figure 14, reflecting the changes of the average speed and average acceleration of the traffic flow. In the intelligent network environment, the vehicle can obtain the operating parameters of the surrounding vehicles in real time, and then judge the environment in which it is driving to achieve uninterrupted speed adjustment. Figure 14a reflects the change in the average speed of the traffic flow. It can be seen from the figure that the traffic flow on the whole is traveling at a relatively ideal speed, and the average speed fluctuates at 0.6 m\(\cdot\)s\({}^{-1}\), which is relatively gentle. Figure 14b reflects the average acceleration of traffic flow. It can be seen from the figure that the overall average acceleration is distributed within the range of \(-0.7\) m\(\cdot\)s\({}^{-2}\)\(\sim 0.7\) m\(\cdot\)s\({}^{-2}\), and the acceleration state is obviously more than the deceleration state, which is also the reason why the average speed of traffic flow is relatively ideal.  
In order to better verify the effectiveness of the vehicle action potential lane changing model, a set of comparison experiments between the vehicle action potential lane changing model, molecular dynamics lane changing model, and SL2015 lane changing model are designed. Among them, SL2015 is a built-in model in SUMO software, which can well simulate the lane changing behavior of vehicles. The molecular dynamics lane changing model is based on the Lennard-Jones 6-12 potential function. The vehicle action potential proposed in this study is modified on the basis of the Lennard-Jones molecular interaction potential, and the comparison of the three parameters has a certain significance.  
Figure 13: Simulation interface of lane changing experiment.  
Figure 15 shows the average output speed of the three models when the traffic flow is roughly stable. It can be seen that compared with the molecular dynamics lane changing model and SL2015 lane changing model, the average traffic flow speed of the vehicle action potential lane changing model is greatly improved, the average speed fluctuation is relatively gentle, and the driving comfort of the vehicle is higher.