## 5 Computer Simulation Results  
To verify the proposed longitudinal motion scenario design method, we set appropriate parameters in the driver model to check whether the driving is like that of a human driver. The Gipps model introduced in Table 1 is used for comparison, and the parameters common to both the Gipps model and the MPC-based driver model are set to the same values [28]. The computer simulation was conducted assuming the driving situation on a dry, asphalt, straight road. The simulations are carried out with Intel(r) CoreTM i9-11900 [EMAIL_ADDRESS] GHz, 32 GB RAM. The execution time was derived with 22 ms as the maximum, 0.64 ms as the minimum, and 0.92 ms as the mean per step, which is less than the step time specified as 200 ms (0.2 s) for the overall simulation results. For this reason, real-time performance can be guaranteed during simulation. If the execution is performed with explicit MPC, it is expected to derive less than the preciously derived execution time. Generally, the driving tendency of human drivers can vary depending on the driving situation [47,48]. For this reason, the simulation results were expressed as fixed parameters and constraints for the randomly selected acceleration/deceleration section of the human driving data. The simulation results are compared with the results imitating the driving data of drivers #23, #57, #61, and #13 of SPMD, and are presented in Figures 10 and 11. The weights of the proposed MPC-based driver model were derived using Equation (12) to  
Figure 9: Scheme of the driver model for the proposed longitudinal motion scenario design. show the longitudinal driving pattern most similar to the actual driver's speed profile. The parameters set in the MPC-based driver model are specified in Table 4. It can be confirmed that the accelerations shown in Table 4 are included in the acceleration range of human drivers derived from Section 4. In these results, the proposed model has mostly lower RMSE than the Gipps model's, which means that the proposed model shows a more similar driving pattern than the Gipps model. With the simulation results from Figures 10 and 11a, it is shown that the proposed model can simulate both the acceleration and the deceleration of the target driver, as well as the timing and deceleration of the driving speed. However, the Gipps model shows a sudden acceleration/deceleration change that is physically impossible due to the absence of constraints. In other words, the Gipps model shows a large value of jerks when the vehicle starts acceleration and braking. Moreover, the sailing shown by the human driver is not considered in the Gipps model, resulting in differences between the Gipps model predictions and the actual human driver behavior. If the Gipps model is used in the verification of ADAS and ADS, the ADAS and ADS can act abnormally because the Gipps model acts in a manner that is physically impossible in the real world. For this reason, the proposed method can be reasonable in the verification of ADAS and ADS. Figure 10: Computer simulation results to compare the Gipps model (compared) and the proposed driver model (proposed): (**a**) the case of SPMD driver #23; (**b**) the case of SPMD driver #57. The proposed driver model is verified with the presence of multiple lane changes in the SPMD data, which is driver #13's driving, to confirm the driving reliability in the traffic environment (see Figure 11b). The leading vehicle with a relatively low speed changes into a lane ahead of the ego vehicle at 3040 s and 3190 s. In the case of the proposed model, the change in acceleration is similar to that of driver #13's driving, and stable driving is achieved for the leading vehicle. However, the Gipps model shows rapid acceleration changes due to the mode transition when a lane change occurs. This shows the same result as the problem shown in Figures 10 and 11a. The RMSE of the proposed method is similar to the Gipps'.