# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 7 Conclusions  
This paper proposes a novel smart black box (SBB) data processing pipeline that uses video anomaly detection and online action detection to efficiently record large-scale high-value video data. We addressed the storage and value estimation problems the SBB will face with real-world data, made adjustments to the data classification and value estimation accordingly, and presented results on a large-scale real-world driving video dataset. Value estimation is changed from an entirely information measure-based method using pre-defined EOIs to use a combination of video anomaly detection and online action detection capable of detecting more generalized EOIs. Observed decision differences between normal and anomalous data indicate that the SBB value estimation can distinguish normal and anomalous frames. In the experiments, a 24.4% increase in the anomalous-to-normal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Value Estimation** & \(\alpha\) & \(\beta\) & & **Normal** & **Anomaly** \\ \hline \multirow{2}{*}{VAD Only} & \multirow{2}{*}{1.0} & \multirow{2}{*}{0.0} & **avg. \(d_{i}\)** & 0.27 & 0.38 \\  & & & **med. \(d_{i}\)** & 0.15 & 0.38 \\  & & & **std. \(d_{i}\)** & 0.30 & 0.34 \\ \hline \multirow{2}{*}{OAD Only} & \multirow{2}{*}{0.0} & \multirow{2}{*}{1.0} & **avg. \(d_{i}\)** & 0.10 & 0.14 \\  & & & **med. \(d_{i}\)** & 0.0 & 0.07 \\  & & & **std. \(d_{i}\)** & 0.15 & 0.17 \\ \hline \multirow{6}{*}{Hybrid} & \multirow{2}{*}{1.0} & \multirow{2}{*}{1.0} & **avg. \(d_{i}\)** & 0.51 & 0.58 \\  & & & **med. \(d_{i}\)** & 0.55 & 0.63 \\  & & & **std. \(d_{i}\)** & 0.24 & 0.26 \\ \cline{1-1}  & & & **avg. \(d_{i}\)** & 0.26 & 0.37 \\  & & & **med. \(d_{i}\)** & 0.14 & 0.36 \\  & & & **std. \(d_{i}\)** & 0.29 & 0.33 \\ \cline{1-1}  & & & **avg. \(d_{i}\)** & 0.20 & 0.30 \\  & & & **med. \(d_{i}\)** & 0.10 & 0.27 \\  & & & **std. \(d_{i}\)** & 0.24 & 0.28 \\ \hline \multirow{6}{*}{Hybrid} & \multirow{2}{*}{0.1} & \multirow{2}{*}{0.9} & **avg. \(d_{i}\)** & 0.12 & 0.19 \\  & & & **med. \(d_{i}\)** & 0.02 & 0.16 \\ \cline{1-1}  & & & **std. \(d_{i}\)** & 0.16 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Compression quality decisions for hybrid value estimation.  
memory ratio was achieved compared to the raw data, in addition to a \(\sim\)25% to \(\sim\)100% increase in the anomalous-to-normal count ratio. However, we also noted that the SBB's performance increases significantly given ground-truth anomaly labels, suggesting that improved methods for general EOI detection will further improve the SBB utility. Future research in anomaly detection using sensor fusion, high-bandwidth vehicular communication networks, and intelligent event data recorders for other domains of transport can help realize prioritized data recording and storage for intelligent transportation systems.  
Conceptualization, Y.Y. and E.A.; methodology, R.F. and Y.Y.; software, R.F. and Y.Y.; validation, R.F. and Y.Y.; formal analysis, R.F. and Y.Y.; investigation, R.F. and Y.Y.; resources, E.A.; data curation, R.F.; writing--original draft preparation, R.F.; writing--review and editing, Y.Y. and E.A.; visualization, R.F.; supervision, E.A.; project administration, Y.Y. and E.A.; funding acquisition, E.A. All authors read and agreed to the published version of the manuscript.  
This research was supported by a grant from Ford Motor Company via the Ford-UM Alliance under Award N028603 and the National Science Foundation Award Number CNS 1544844.  
The BDD100K [32] and DoTA [15] datasets are available online at [[https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)]([https://bdd-data.berkeley.edu/](https://bdd-data.berkeley.edu/)) and [[https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly](https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly)]([https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly](https://github.com/MoonBlvd/Detection-of-Traffic-Anomaly)), respectively.  
The authors declare no conflict of interest.