## 5 Case Study
### Scenario 1: Unanticipated Obstacle Scenario at Intersection Inlet Road  
Scenario 1 is the unanticipated obstacle scenario illustrated in Section 2, where the obstacle vehicle is located in the center of the lane 30 m in front of the ego vehicle, and the traffic light is red. The ego vehicle enters the left-turn lane marked by solid lines on both \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Vehicle mass & \(m\) & 2271 & kg \\ Length & \(l\) & 4.796 & m \\ Width & \(w\) & 1.814 & m \\ Moment of Inertia & \(lz\) & 4600 & kg-m\({}^{2}\) \\ Distance from front axle to CG & \(l\) & 1.421 & m \\ Distance from rear axle to CG & \(l\) & 1.434 & m \\ Front cornering stiffness & \(Cf\) & 132,000 & N-rad\({}^{-1}\) \\ Rear cornering stiffness & \(Cr\) & 136,000 & N-rad\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of the ego vehicle. sides, comes to a stop behind the obstacle vehicle, and waits in the lane. The traffic light turns green at 20 s, and the front vehicle remains stationary. In the first comparison test, the ego vehicle is programmed to adhere to traffic laws strictly. The experiment's results are shown in Figure 8. The trajectories of ego (red) and obstacle (blue) vehicles are shown in Figure 8a. The speed and front wheel steering angles of the ego vehicle are shown in Figure 8b.c. The ego vehicle remains stationary and continues to wait due to the constraints of the solid-line potential field and the car-following potential field. The obstructed delay time continues to accumulate beyond the congestion threshold after two red light stages. This could lead to an infinite stop. In the second comparison test, the ego vehicle can identify the necessity of obstacle avoidance through programming, yet it completely ignores traffic laws during the obstacle avoidance process. The compliance-oriented potential field is set to zero. The simulation result is presented in Figure 9. The ego vehicle starts to overtake the obstacle vehicle when the obstructed delay time equals the congestion threshold. The vehicle only considers collision avoidance during the planning process and chooses to cross the double-solid yellow line. The maximum occupancy of the opposite-direction lane is 2.12 m. The obstructed delay time is 148.25 s. The behavior of crossing the double-solid yellow line not only has high violation penalties but also results in driving in the wrong direction, which has a serious impact on traffic environment. Figure 7: Vehicle state in normal and SIL testing. (**a**) Ego vehicle speed. (**b**) speed difference between normal and SIL testing. (**c**) ego vehicle steering angle. (**d**) angle difference between normal and SIL testing. Figure 8: Vehicle state in the first comparison test for scenario 1. (**a**) Paths of ego and obstacle vehicles; (**b**) ego vehicle speed; (**c**) ego vehicle steering angle. Figure 9: Vehicle state in the second comparison test for scenario 1. (**a**) Paths of ego and obstacle vehicles; (**b**) ego vehicle speed; (**c**) ego vehicle steering angle. After implementing the proposed algorithm, the result is shown in Figure 10 according to the obstacle avoidance motion planning framework. The compliance-oriented potential fields in the cost function are replaced by the violation cost potential fields. Crossing double-solid yellow lines results in driving in the wrong direction; thereby, the initial value of the PVCI of the double-solid yellow line is higher. The ego vehicle crosses the solid white line to pass the obstacle vehicle. As the vehicle crosses the line, the violation degree in PVCI increases as the distance by which the vehicle exceeds the compliant range increases. This prompts the ego vehicle to plan a trajectory that is closer to the original lane while maintaining safety.