# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 5 Experiments
### Results
Performance per anomaly class:Figure 4 displays the decision histograms for each anomaly class. The performance of the SBB varies heavily depending on the anomaly category. For example, the decision distribution of class OCindicates very good detection of this anomaly. In OC, an ego-vehicle collision with an oncoming vehicle, the anomalous object (the oncoming vehicle), is almost always both near the camera and largely unoccluded. However, ST, VO, LA*, VO*, and OO*have notably poor performance. ST is an extremely difficult case for OAD due to its visual similarity to AHand LA anomalies, resulting in lower OAD confidence that an anomaly has occurred. VO and VO* involve vehicles hitting obstacles in the roadway. In some scenarios, such as hitting a traffic cone or a fire hydrant, the obstacle may be blocked from view by the anomalous vehicle in a non-ego incident or outside the camera's field of view in an ego-incident. LA* often involves vehicles slowly moving closer together, making the collision relatively subtle. OO*, a non-ego vehicle leaving the roadway, can be challenging to detect simply due to the distance at which the anomaly occurs. Value estimation method comparison:Table 7 compares the decision statistics for hybrid value estimation with several parameter combinations. We note that the VAD-only method generates the largest decision difference in normal and anomalous frames; we suspect this to be a result of OAD's inability to consistently differentiate between anomalous and normal frames. Readers are directed to [15] for an in-depth discussion on the poor performance of OAD algorithms. For applications that value general EOIs, VAD-only value estimation (\(\alpha=1\), \(\beta=0\)) has the greatest ability to distinguish normal and anomalous data. However, users interested in specific EOIs may opt to use a hybrid value in order to incorporate the EOI classification offered by OAD. In terms of hybrid value parameters, Table 7 shows that lower weights result in higher decision differences. However, in situations where retaining data quality is critical, higher \(\alpha\) and \(\beta\) values may be used to achieve higher overall decision quality. Additionally, the higher decision differences as \(\alpha\) increases shown in Figure 5 indicate once again that VAD contributes more to the differentiation of normal and anomalous frames than does OAD. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(M\) & \multicolumn{1}{c}{**Normal\(\downarrow\)**} & **Anomaly\(\uparrow\)** & **Anomaly Ratio\(\uparrow\)** \\ \hline \multirow{2}{*}{25 GB} & FIFO & 1,739,855 & 7851 & 0.45\% \\  & **Priority** & **1,487,570** & **8545** & **0.57\%** \\ \hline \multirow{2}{*}{12.5 GB} & FIFO & 889,679 & 4335 & 0.49\% \\  & **Priority** & **734,625** & **5154** & **0.70\%** \\ \hline \multirow{2}{*}{6.25 GB} & FIFO & 437,673 & 2029 & 0.46\% \\  & **Priority** & **364,666** & **2898** & **0.79\%** \\ \hline \multirow{2}{*}{3.125 GB} & FIFO & 207,951 & 962 & 0.46\% \\  & **Priority** & **183,951** & **1706** & **0.93\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of prioritized recording and FIFO. Figure 4: Decision histograms per anomaly class (**ST**: collision with a starting, stopping, or stationary vehicle; **AH**: ahead collision; **LA**: lateral collision; **OC**: oncoming collision; **TC**: turning or crossing collision; **VP**: vehicle-pedestrian collision; **VO**: vehicle-obstacle collision; *****: indicates non-ego; **OO**: out-of-control leaving roadway). The \(x\)-axis represents decision \(d_{t}\in[0,1]\), and the \(y\)-axis represents the frame count per bin. Figure 5: Mean normal and anomalous decisions for \(\alpha+\beta=1\).