## 6 Performance Evaluation on Simulator
### Performance of Path Planning
#### 6.3.3 Performance Evaluation on Campus Driving Situation Related to Hazard Scenarios  
In this section, the path planning algorithm performance considering obstacles, pedestrians, and vehicles is evaluated. For all collision scenarios that could occur in our campus, hazardous scenarios that have critical damage for the micro-EV and pedestrians are sorted. The hazardous scenarios were obtained by manually driving the micro-EV around the real CBNU campus. Moreover, for high evaluation reliability, the Euro NCAP [36] and ISO22737 r14.0 [37] were referenced. For these scenarios, the path planner would design a proper path and vehicle control commands. The seven hazardous scenarios for possible collision risk situations in the campus environment were prepared. In all the scenarios, the micro-EV sequentially followed several behavior steps to evaluate the path planning algorithm operating in the autonomous systems:  
1. When the micro-EV meets obstacles, the autonomous emergency brake system should be activated. 2. The micro-EV should determine whether the obstacle avoidance system should activate. 3. If the obstacle avoidance system is activated, the micro-EV should evade the obstacles in a collision-free operation. On the other hand, if the obstacle avoidance system is not an activated, the micro-EV should be placed in a state of braking. 4. If the micro-EV evades the obstacles, the obstacle avoidance system is deactivated. When the micro-EV meets obstacles, steps 1-4 are repeated. The seven hazardous scenarios prepared for evaluation are shown in Figure 13. In this figure, the micro-EV is marked in red, the dynamic obstacles are marked in blue, and the static obstacles are marked in black. For each scenario, objects colored in red and blue move in the direction of their arrows. A detailed description of the scenarios is as follows:  
1. For (a), this is a scenario in which pedestrians jump onto the road from a blind spot when the micro-EV is driving on a straight road. The pedestrians are set to move at 10 km/h and the pedestrians enter the roadway at a distance of 1.5 m from the front bumper of the micro-EV. The pedestrian's cut-in scenario repeats four times to see whether the micro-EV could generate a proper local path after the pedestrians disappear. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{k}_{2}\) & **0.3** & **0.2** & **0.1** \\ \hline Running Time (ms) & 21.36 & 47.73 & 627.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance measure for real time; state of curved road, \(k_{1}\) = 0.5, length 100m, and nObstacles 400. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**nObstacles** & **400** & **800** & **1400** \\ \hline Running Time (ms) & 21.36 & 29.95 & 51.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance measure for real time; state of curved road, \(k_{1}\) = 0.5, \(k_{2}\) = 0.3, and length 100 m.  
2. For (b), this is the same as (a) except with the road shape changed from straight to a right curve. This cut-in scenario of pedestrians repeats five times to see whether the micro-EV performs the behavior of avoidance or not. 3. For (c), this is the same as (a) except with the road shape changed from straight to a left curve. This cut-in scenario of pedestrians repeats four times to see whether the micro-EV performs the behavior of avoidance or not. 4. For (d), this is a scenario in which obstacle vehicles, which drive on a straight road cut into the route of the micro-EV when the micro-EV turns right. The obstacle vehicles are set to move at 40 km/h. The number of obstacle vehicles is nine. 5. For (e), this is the same as (d) except that the obstacle vehicles and the micro-EV are opposite. There are four obstacle vehicles. 6. For (f), this is the same as (d) except that the obstacle vehicles turn left. There are four obstacle vehicles. 7.