## 4 Evaluation
### Performance of Collision Risk Minimization  
Table 2 represents the average of the collision risk for the entire dataset. It can be confirmed from the table that the proposed system is significantly effective to decrease the collision risk compared to human drivers. Based on the predicted trajectory, the ego vehicle appropriately adjusted the distance and speed with respect to the adjacent vehicles. As a result, it considerably improved the driving safety with respect to that of human drivers. Figure 13 illustrates the speed of the ego vehicle under a cut-in situation. A human driver rapidly reduced the speed after the cut-in vehicle crossed the lane marking. Fortunately, a crash did not occur in this case; however, the unexpected deceleration was confirmed at ~7 s. Meanwhile, it was shown that the proposed system maintained the speed without the rapid deceleration since the lane-changing was anticipated in advance. It means that the rear collision can be prevented compared to human drivers. In addition, the two state-of-the-art ACC strategies were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Location ID** & **[PERSON] [21]** & **[PERSON] [22]** & **[PERSON] [23]** & **Proposed** \\ \hline
1 & 0.159 m & 0.215 m & 0.211 m & **0.065 m** \\
2 & 0.130 m & 0.178 m & 0.181 m & **0.062 m** \\
3 & 0.093 m & 0.150 m & 0.133 m & **0.055 m** \\
4 & 0.115 m & 0.165 m & 0.148 m & **0.062 m** \\
5 & 0.180 m & 0.216 m & 0.218 m & **0.079 m** \\
6 & 0.155 m & 0.202 m & 0.195 m & **0.070 m** \\ \hline Average & 0.139 m & 0.189 m & 0.183 m & **0.065 m** \\ Standard deviation & 0.032 m & 0.028 m & 0.034 m & **0.008 m** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the performance of the proposed method with those of the previous methods.  
implemented to compare the performance with the proposed method. The ACC system in [17] derives the acceleration of the ego vehicle by  
\[a_{ACC}=k_{1}(x_{p}-x_{e}-t_{hw}v_{e})+k_{2}(v_{p}-v_{e}), \tag{20}\]  
where \(x_{p}\) and \(x_{e}\) represent the current position of the preceding vehicle and that of the ego vehicle, respectively; \(v_{p}\) and \(v_{e}\) represent the current speed of the preceding vehicle and that of the ego vehicle, respectively; \(t_{hw}\) is the current time-gap between the two vehicles; \(K_{1}\) and \(k_{2}\) are the gains. Based on the setting in [17], the values of the two gains were determined as follows: \(k_{1}=0.23\) and \(k_{2}=0.07\). The SDM in [18] calculates the acceleration of the ego vehicle by  
\[a_{SDM}=a_{max}\bigg{[}1-\big{(}\frac{v_{e}}{v_{0}}\big{)}^{\delta}\bigg{]}- \frac{a_{max}\big{[}1-\big{(}\frac{v_{e}}{v_{0}}\big{)}^{\delta}\big{]}+\frac{ v_{e}^{2}-v_{p}^{2}}{2(x_{p}-x_{e})}}{\exp\big{(}\frac{x_{p}-x_{e}}{v_{0}+v_{e}}-1 \big{)}}, \tag{21}\]  
where \(a_{m}ax\) represents the maximum acceleration, \(v_{0}\) is the maximum speed, \(\delta\) is the acceleration exponent, and \(s_{0}\) is the standstill distance between stopped vehicles; \(T\) represents the desired time gap. The values of parameters were determined as follows, \(a_{max}=1.4\), \(v_{0}=30\), \(\delta=4\), \(s_{0}=1.5\), and \(T=1.6\), according to [18]. The AACC achieved the best performance to decrease the collision risk compared to the two previous methods. As the AACC considers not only the preceding vehicle, but also the following vehicle, the collision risk with respect to the following vehicle can be improved. Based on these results, it is demonstrated that the AACC allows improving the driving safety and outperforms both human drivers and the previous ACC systems.