## 4 Experimental Evaluation
### Surrounding Vehicle Localization
#### 4.2.2 Performance Evaluation  
Figure 9(a),b show the ground truth distance to the remote vehicle obtained for the driving scenario where both vehicles are in the same lane. The longitudinal and lateral relative distance obtained from separate test runs are concatenated and presented together. The time step between two consecutive frames from the same test run corresponds to 0.1 s. The separation between the two test vehicles gradually increases while both vehicles stay in the same lane. The absolute error for the relative position in the longitudinal and lateral directions is presented in Figure 9(c),d. The results of the proposed data fusion method are presented along with those of individual remote sensing systems. Despite the fluctuations observed in the position estimates of the radar, camera, and V2X communications, the proposed method enables reliable estimation of the relative position of the remote target at varying distances. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Host Vehicle** & **Remote Vehicle** & **Remote Vehicle** \\
**Number** & **Speed (km/h)** & **Speed (km/h)** & **Driving Lane** \\ \hline \hline \(1\) & 20 & 25 & Same as HV \\
2 & 20 & 25 & Adjacent to HV \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normal driving scenario descriptions. Figure 9: Normal driving scenarios considered for the localization performance evaluation of the proposed method. (**a**) Both vehicles driving in the same lane; (**b**) remote vehicle driving in the adjacent lane. Figure 10: Relative distance to the remote vehicle during the normal driving scenario 1. (**a**) Ground truth longitudinal distance; (**b**) ground truth lateral distance; (**c**) absolute error for longitudinal distance estimates; (**d**) absolute error for lateral distance estimates. As presented in Tables 7 and 8, the performance of the proposed data fusion method is evaluated by computing the root mean squared error (RMSE) and the standard deviation of the error, and compared with those calculated for other remote sensing systems. In order to determine the positioning accuracy at varying relative distances, the results are grouped in separate 10-m bins (in the longitudinal direction). For the driving scenario with both vehicles in the same lane, the longitudinal and lateral localization accuracies of the proposed method in terms of the RMSE are found to be 0.22 m and 0.13 m, respectively, when taking into account all the results that correspond to the range of longitudinal distances between 0 and 70 m.  
The results obtained when the target vehicle is at a relative longitudinal distance of 70 m or longer are not used for this performance evaluation, considering that the lateral position accuracy of the GNSS system used for ground truth degrades at such long distances due to the limited heading accuracy such that it may not be appropriate to be utilized as a reference system. In addition, the Mobileye camera used in this work appears to suffer from significant degradation in longitudinal accuracy for remote targets located at such distances. The ground truth distance to the remote vehicle during the driving scenario where the remote vehicle is in the adjacent lane is shown in Figure 11a,b. As previously explained, the relative positions of the remote vehicle acquired from separate test runs are concatenated and presented together, and the time step between two consecutive frames from the same test run corresponds to 0.1 s. The separation between the two test vehicles gradually increases while the remote vehicle stays in the adjacent lane. The absolute error for the longitudinal and lateral distances to the remote vehicle is presented in Figure 11c,d. Similar to the aforementioned case with both vehicles in the same lane, the proposed fusion method reliably estimates the remote target position at varying distances even at times when the accuracy of individual remote sensors becomes severely degraded. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Range** & \multicolumn{2}{c}{**Camera Lon.