# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 4 Experiment
### Experimental Results
#### 4.3.2 Experiment Scenario 2  
The experiment in Scenario 2 is divided into two parts. The first is when the obstacle is a static obstacle, and the second evaluates a dynamic obstacle that moves more slowly than the Ego mobile robot. The experiment was conducted 10 times. (1) Static Obstacle  
This scenario compares algorithms for overtaking mobile robots as illustrated in Figure 19.  
The Ego mobile robot has a speed of 2 m/s, and the obstacle mobile robot is stationary. The static obstacle is located at \(p_{{}_{X}}=257\) and \(p_{{}_{Y}}=440\). In Figure 19, the path of ODGMPC tends to respond faster than the comparison algorithm. This trend can also be seen in acceleration (Figure 20). Because the acceleration graph tends to increase after the vehicle has changed lanes, a significant increase in the acceleration graph corresponds to a lane change. The proposed ODG MPC algorithm changes lanes at approximately 1.2 s, and PFMPC algorithm changes lanes at approximately 1.4 s. The slower the lane changes, the greater the acceleration increases, resulting in a lower Comfort score. The acceleration graph reveals that the ODG MPC algorithm acceleration value bounces less than the PF and PF MPC algorithm (Figure 20). Table 6 demonstrates that the ODG MPC algorithm has a higher comfort level score than the other algorithms. Therefore, the ODG MPC algorithm operates more safely and faster against obstacles than other algorithms. Figure 21 represents the distance from the obstacle at each time. The minimum distance between obstacles is listed in Table 7, which shows the minimum distance from the obstacle mobile robot, showing farther safety than other algorithms in Figure 21. The result reveals an 18.56% improvement over the PF and a 14.89% improvement over the PF MPC algorithm. Figure 19: Results of the path from Scenario 2 for a static obstacle. Figure 21: Results of the obstacle distance from Scenario 2 for a static obstacle. \begin{table}
\begin{tabular}{c c c c} \hline
**Comfort Level (Score)** & **ODGMPC** & **PF** & **PFMPC** \\ \hline Comfortable (10) & **18.67**\% & 10.13\% & 14.83\% \\ \hline A little uncomfortable (8) & 14.10\% & 8.10\% & **14.83**\% \\ \hline Fairly uncomfortable (6) & **17.84**\% & 10.81\% & 16.52\% \\ \hline Uncomfortable (4) & 22.40\% & 17.90\% & **22.45**\% \\ \hline Very uncomfortable (2) & 12.86\% & **23.98**\% & 18.64\% \\ \hline Extremely uncomfortable (0) & 14.10\% & **29.05**\% & 12.71\% \\ \hline Average Comfort Level Score & **5.21** & 3.50 & 4.90 \\ \hline \end{tabular}
\end{table} 
Table 6: Average result scores for 10 experiments in Scenario 2 for a static obstacle. Figure 20: Results of the acceleration from Scenario 2 for a static obstacle. (2) Dynamic Obstacle  
The Ego mobile robot path planning experiment for moving obstacles was conducted. The dynamic obstacles move at 1.5 m/s, and the Ego mobile robot moves at 2 m/s. The experimental results were similar to the experiment for the static obstacles in Figure 22. Moreover, as a result of calculating the comfort level using the acceleration graph in Figure 23, the experimental results for the ODG MPC algorithm are better than those of other algorithms and those of the static obstacles listed inTable 8. In Figure 24, the minimum distance to the moving obstacle was confirmed in Table 9, where the experiment using the ODG MPC algorithm demonstrated a longer distance with an 18.56% improvement over the PF and a 14.8% improvement over the PF MPC algorithm. Therefore, we confirmed that the ODG MPC algorithm  
Figure 23: Results of the acceleration from Scenario 2 for a dynamic obstacle.