## 3 Experiments and Discussion
### Performance of Collision Avoidance Method  
Safety is the most important measure for autonomous vehicles and should be ensured during the collision avoidance process. In addition, comfort is another measure that reflects the practicability for passengers. To evaluate the performance of our collision avoidance method, a comparison was conducted between our method and the method used in [8], which ignores the driving intent of other vehicles. Figure 13 shows the distance between the autonomous vehicle and the detected collision point when the collision point is detected on straight roads. It can be seen that collisions will be detected at a longer distance with our method. Under our method, the average distance is 31.6 m, whereas it is 23.2 m under the method used in [8]. The difference of the average collision detection distance for the two methods is 8.4 m. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Intent Types**} & **True Positive** & **False Positive** & **False Negative** & **Precision Rate** & **Recall Rate** & \multirow{2}{*}{**F-Score**} \\ \cline{3-4} \cline{6-7}  & Normal moving & 221 & 9 & 12 & 96.1\% & 94.8\% & 95.4\% \\ Method & Left lane changes & 33 & 7 & 5 & 82.5\% & 86.8\% & 84.6\% \\ Right lane change & 25 & 5 & 4 & 83.3\% & 86.2\% & 84.7\% \\ \hline \multirow{2}{*}{Method in [11]} & Normal moving & 176 & 36 & 57 & 83.0\% & 75.5\% & 79.1\% \\  & Left lane change & 17 & 33 & 21 & 34\% & 44.7\% & 38.6\% \\ Right lane change & 14 & 24 & 15 & 36.8\% & 48.3\% & 41.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance on winding roads. Figure 13: Collision detection distance on straight roads. The detection distance of the intent-estimation- and motion-model-based (IEMMB) method on a straight road is longer than the method used in [8]. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Intent Types**} & **True Positive** & **False Positive** & **False Negative** & **Precision Rate** & **Recall Rate** & \multirow{2}{*}{**F-Score**} \\ \cline{3-4} \cline{6-7}  & Normal moving & 270 & 8 & 8 & 97.1\% & 97.1\% & 97.1\% \\ Method & Left lane changes & 34 & 5 & 4 & 87.2\% & 89.5\% & 88.6\% \\ Right lane change & 30 & 3 & 4 & 90.9\% & 88.2\% & 89.5\% \\ \hline \multirow{2}{*}{Method in [11]} & Normal moving & 249 & 19 & 29 & 92.9\% & 89.6\% & 91.2\% \\ Left lane change & 28 & 16 & 10 & 63.6\% & 73.7\% & 68.3\% \\ Right lane change & 25 & 13 & 9 & 65.8\% & 73.5\% & 69.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance on straight roads. Figure 14 shows the collision detection distance on winding roads. Compared with the distance on straight roads, it is shorter on winding roads, and the general tendency is similar. The average distance of our method is 27.4 m, and the shortest distance is 19.2 m; in contrast, these values are 15.6 m and 8.1 m under the method used in [8]. The difference of average collision detection distance for the two methods is 11.8 m. Potential collisions can be detected at longer distances with our method, because the trajectories of the detected vehicles can be predicted by combining the estimated intent and motion model. Then, evasive actions can be performed to avoid collisions with a sufficient distance. In addition to safety, comfort is another factor that must be considered when evaluating the collision avoidance method. It can be inferred from [22] that the yaw rate and acceleration play import roles in driving comfort. With increasing yaw rate and acceleration, comfort decreases. Figure 15 shows the peaks of the yaw rate during collision avoidance maneuvers when applying the IEMMB collision avoidance method and the method used in [8]. It can be seen that the yaw rate of the IEMMB method is mainly concentrated in the range of \(-\)10 to 10, whereas a considerable portion of the yaw rate of the other method exceeds the range of \(-\)10 to 10. A smaller yaw rate indicates that our method improves the comfort of autonomous driving.