## 5 Simulation and Experimental Results
### Experimental Result 2: Sensing Failover  
The next scenario concerns a detection failover through V2V cooperation. As shown in Figure 14, V1 with constant speed of 50 km/h is moving in a narrow space. However, V2 driving in 5 m/s\({}^{2}\) cannot  
Figure 13: Snapshots of the scenario modeling: (**a**) Traces at \(t=0\) s. (**b**) Traces at \(t=1.2\) s without the proposed system. (**c**) Traces at \(t=1.2\) s with the proposed system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **V1** & **V2** & **Output** \\ \hline \multirow{3}{*}{Existence} & Existence & 0.0 & 0.85 & 0.66 \\  & Non-existence & 0.0 & 0.05 & 0.03 \\  & Uncertainty & 1.0 & 0.1 & 0.31 \\ \hline \multirow{3}{*}{Class} & Person & - & 0.153 & 0.153 \\  & Other 15 classes (car, bus, \(\cdots\)) & - & 5.64 \(\times 10^{-2}\) & 5.64 \(\times 10^{-2}\) \\ \hline \multirow{3}{*}{Latency} & Detection time [ms] & 24.09 & 22.46 & \multirow{3}{*}{28.02} \\  & Communication delay [ms] & - & 4.68 & 28.02 \\ \cline{1-1}  & (message size [bytes]) & (-) & (1053) & (totally) \\ \cline{1-1}  & Estimation+Decision [ms] & 0.88 & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for the anti-collision case. obtain a clear video of V1 due to broken sensor. Therefore, in this case, V1 is regarded as \"nonexistence\" rather than \"uncertainty\" by V2, which represents a _false negative_ error [44]. In addition, we assume that V1 cannot communicate with other vehicles due to a malfunction of the communication tools or lack of communication capability. We assume that there are similar problem at V3, i.e., it is hard to obtain clear image on V1. Under this set-up, V2 cannot perceive the movement of V1 at all, and the crash of V1 and V2 is imminent. However, the other cars (V4, V5) located at nearby V1 can recognize the movement of V1 as presented in Figure 14e,f. They transmit this information to the V2 from which the accident can be avoided. The cameras equipped at V2-V5 record V1's movement simultaneously. We manipulated cameras at V2 and V3 so that they produce blurred videos. Figure 14a,c shows the original clear front views, and Figure 14b,d shows the blurred images at the same time. Figure 14: The video images captured at vehicles and detection results: (**a**) The original image at V2. (**b**) The blurred image at V2. (**c**) The original image at V3. (**d**) The blurred image at V4. (**e**) The front view of V4. (**f**) The front view of V5. (**g**) Top view illustration. We compare the measurements and estimation of V1 in Figure 15. Due to the dynamics of V1, the angles between nearby vehicles are changed, which results in inaccurate measurements as shown in Figure 15b. However, Figure 15a-c demonstrates that we obtain precise estimation values by the proposed scheme, and the predicted collisions are avoided. We demonstrate how the confidences change as data sources are added in Figure 16. Especially, we compare the performance of the proposed scheme with the conventional weighted data fusion method exploiting Jousselme's distance. For abnormal states of V2 and V3, we assume that they have high confidence in \"nonexistence\" about V1, and the other vehicles are set to have confidence in existence of V1 as 0.7 and 0.9, respectively. In Figure 16a,b, we plot changes of confidences for the proposed and existing weighted DS schemes, respectively. The detailed parameters and experimental results are presented in Table 4. As the data are received sequentially, we can find that the confidence in existence is changed in both figures. However, under the baseline scheme, the final confidence on existence is 0.48, which in turn makes V2 decide \"nonexistence\". Contrary to this, the proposed scheme shows 0.58 confidence in existence, thus V2 can recognize V1 successfully. From this result, it is shown that the proposed scheme can reduce the false negative error as compared to the conventional data fusion method. Next, we consider latency.