## 3 Results
### Simulation Results  
The MATLAB Driving Scenario Designer Toolbox created the driving simulation scenario for this investigation. In the tool box scenario editor, we first add the road model and apply a binding force to the target vehicle by setting the constants as described above. After that, we add sensors and adapt them to sense where the lane lines on either side are closest to the vehicle; artificially set target points and add functions to make them attractive to the vehicle; add road friction and set friction coefficients. In addition, during the actual simulation, the area and center point of the overlap range of the risk field of the driving operation of the target vehicle and other vehicles, calculated by the function, with the current coordinate point of the center of gravity of the target vehicle, provides the repulsive force to the target vehicle. For the purpose of algorithm verification, the environment automatically delivers all vehicle status data, including absolute/relative position, absolute/relative speed, and heading angle. The algorithm for the behavioral decision model is displayed in Algorithm 1. The primary vehicle identifies the surrounding vehicles' states. An interaction model built based on the risk field is then used to determine the size and direction of the potential field forces on the main vehicle. In order for the car to cross the intersection smoothly, the algorithm determines the lowest amount of horizontal and vertical control. We initially presummated that the cars in the simulation tests, aside from the target vehicle I, were moving like a human. The beginning conditions of vehicle speed from 7 to 24 m/s and vehicle distance from 15 to 50 m were used to replicate each of the nine aforementioned scenarios 100 times. We evaluate the proposed method from three perspectives, namely its adaptability to the scenario, its stability and its effectiveness. We generated mean S-value plots from the experimental results to reflect the sensitivity of the method to each parameter in the scenario. Transverse and longitudinal velocity and heading angle plots were generated to reflect the stability of the method's decisions, which is ensure the comfort of the passengers. Pass-by plots were generated to compare with the TTI method to reflect the effectiveness of the method in the scenario. Figure 7 illustrates the average S-values generated in nine typical scenarios for driving operational risk fields at different distances and speeds. This S-value represents the corresponding magnitude of risk that the AV responds to. We represent the reaction mechanism of the ego vehicle in this scenario by the lateral and longitudinal speed and heading angle of the ego car. Figure 8 shows a typical curve of the speed and heading angle of the vehicle when other agents enter from the left side of the intersection. It can be seen that under different initial conditions, the vehicle's lateral speed always maintains a smooth increase, which means that in the task of turning left at the intersection, the response mechanism provides a long-term effect and keeps the whole process of turning coherent. In addition, the host vehicle frequently keeps the vehicle in a relatively safe position and coasts forward in terms of longitudinal velocity by briefly setting longitudinal acceleration to zero, similar to how a human driver handles the same situation. In addition, Figures 9 and 10 show the speed curve and heading angle curve of the vehicle when other agents enter from the front and the right side of the intersection, respectively. Finally, we counted the intersection performance results obtained by applying the algorithm under these 9 scenarios, as shown in Figure 11.  
We examine the cost function graph (Figure 7) first. The cost function diagram for these nine cases demonstrates that, when the vehicle's speed is constant, the cost function S's value increases as the initial distance decreases.