{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:38:33Z","timestamp":1765546713407,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:00:00Z","timestamp":1612828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100002427","name":"Ford Motor Company","doi-asserted-by":"publisher","award":["N028603"],"award-info":[{"award-number":["N028603"]}],"id":[{"id":"10.13039\/100002427","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation, United States","award":["CNS 1544844"],"award-info":[{"award-number":["CNS 1544844"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The smart black box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with an FIFO strategy. The results show that SBB data compression can increase the anomalous-to-normal memory ratio by \u223c25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to an FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance.<\/jats:p>","DOI":"10.3390\/a14020057","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T23:43:16Z","timestamp":1612914196000},"page":"57","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies"],"prefix":"10.3390","volume":"14","author":[{"given":"Ryan","family":"Feng","sequence":"first","affiliation":[{"name":"Robotics Institute, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2329-2355","authenticated-orcid":false,"given":"Yu","family":"Yao","sequence":"additional","affiliation":[{"name":"Robotics Institute, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2132-6256","authenticated-orcid":false,"given":"Ella","family":"Atkins","sequence":"additional","affiliation":[{"name":"Robotics Institute, University of Michigan, Ann Arbor, MI 48109, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tranpol.2020.02.009","article-title":"Future local passenger transport system scenarios and implications for policy and practice","volume":"90","author":"Enoch","year":"2020","journal-title":"Transp. Policy"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot Multibox Detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2018). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_4","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully Convolutional One-Stage Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017). Simple Online and Realtime Tracking with a Deep Association Metric. arXiv.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Choi, W. (2015). Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. arXiv.","DOI":"10.1109\/ICCV.2015.347"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Alahi, A., and Savarese, S. (2015, January 11\u201318). Learning to Track: Online Multi-object Tracking by Decision Making. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.534"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Li, F.-F., and Savarese, S. (2016, January 27\u201330). Social lstm: Human trajectory prediction in crowded spaces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yao, Y., Xu, M., Choi, C., Crandall, D.J., Atkins, E.M., and Dariush, B. (2019). Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems. arXiv.","DOI":"10.1109\/ICRA.2019.8794474"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yao, Y., Atkins, E., Johnson-Roberson, M., Vasudevan, R., and Du, X. (2020). BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation. arXiv.","DOI":"10.1109\/LRA.2021.3056339"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Salzmann, T., Ivanovic, B., Chakravarty, P., and Pavone, M. (2020). Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control. arXiv.","DOI":"10.1007\/978-3-030-58523-5_40"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yao, Y., and Atkins, E. (2018, January 4\u20137). The smart black box: A value-driven automotive event data recorder. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569253"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yao, Y., and Atkins, E. (2020). The Smart Black Box: A Value-Driven High-Bandwidth Automotive Event Data Recorder. IEEE Trans. Intell. Transp. Syst.","DOI":"10.1109\/TITS.2020.2971385"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yao, Y., Xu, M., Wang, Y., Crandall, D.J., and Atkins, E.M. (2019). Unsupervised Traffic Accident Detection in First-Person Videos. arXiv.","DOI":"10.1109\/IROS40897.2019.8967556"},{"key":"ref_15","unstructured":"Yao, Y., Wang, X., Xu, M., Pu, Z., Atkins, E., and Crandall, D. (2020). When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, M., Gao, M., Chen, Y.T., Davis, L.S., and Crandall, D.J. (2019). Temporal Recurrent Networks for Online Action Detection. arXiv.","DOI":"10.1109\/ICCV.2019.00563"},{"key":"ref_17","unstructured":"DaSilva, M. (2014). Analysis of Event Data Recorder Data for Vehicle Safety Improvement."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gabler, H.C., Hampton, C.E., and Hinch, J. (2004, January 8\u201311). Crash Severity: A Comparison of Event Data Recorder Measurements with Accident Reconstruction Estimates. Proceedings of the SAE 2004 World Congress & Exhibition, Detroit, MI, USA.","DOI":"10.4271\/2004-01-1194"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1109\/TITS.2012.2205917","article-title":"Self-Coaching System Based on Recorded Driving Data: Learning From One\u2019s Experiences","volume":"13","author":"Takeda","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TITS.2016.2582208","article-title":"Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques","volume":"18","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.aap.2006.05.001","article-title":"The development of a naturalistic data collection system to perform critical incident analysis: An investigation of safety and fatigue issues in long-haul trucking","volume":"38","author":"Dingus","year":"2006","journal-title":"Accid. Anal. Prev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, N., Misu, T., and Miranda, A. (2016, January 1\u20134). Driver behavior event detection for manual annotation by clustering of the driver physiological signals. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.","DOI":"10.1109\/ITSC.2016.7795971"},{"key":"ref_23","unstructured":"Chan, F.H., Chen, Y., Xiang, Y., and Sun, M. (2016). Anticipating Accidents in Dashcam Videos. Asian Conference on Computer Vision, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Herzig, R., Levi, E., Xu, H., Gao, H., Brosh, E., Wang, X., Globerson, A., and Darrell, T. (2019). Spatio-Temporal Action Graph Networks. arXiv.","DOI":"10.1109\/ICCVW.2019.00288"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., and Gool, L.V. (2016). Temporal Segment Networks: Towards Good Practices for Deep Action Recognition. arXiv.","DOI":"10.1007\/978-3-319-46484-8_2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., and Paluri, M. (2018). A Closer Look at Spatiotemporal Convolutions for Action Recognition. arXiv.","DOI":"10.1109\/CVPR.2018.00675"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., and He, K. (2019). SlowFast Networks for Video Recognition. arXiv.","DOI":"10.1109\/ICCV.2019.00630"},{"key":"ref_28","unstructured":"Lewis, V., Dingus, T., Klauer, S., and Sudweeks, J. (2005). An Overview of the 100-Car Naturalistic Study and Findings."},{"key":"ref_29","unstructured":"Bezzina, D., and Sayer, J. (2015). Safety Pilot Model Deployment: Test Conductor Team Report 2015, Tech. Rep. DOT HS 812 171, 2014."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. arXiv.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_32","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T. (2018). BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling. arXiv."},{"key":"ref_33","unstructured":"Fang, J., Yan, D., Qiao, J., and Xue, J. (2019). DADA: A Large-scale Benchmark and Model for Driver Attention Prediction in Accidental Scenarios. arXiv."},{"key":"ref_34","unstructured":"Espi\u00e9, E., Guionneau, C., Wymann, B., Dimitrakakis, C., Coulom, R., and Sumner, A. (2020, December 13). TORCS, The Open Racing Car Simulator. Available online: http:\/\/torcs.sourceforge.net\/."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2016). FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. arXiv.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., and Davis, L.S. (2016, January 27\u201330). Learning temporal regularity in video sequences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.86"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chong, Y.S., and Tay, Y.H. (2017). Abnormal event detection in videos using spatiotemporal autoencoder. International Symposium on Neural Networks, Springer.","DOI":"10.1007\/978-3-319-59081-3_23"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., and Gao, S. (2018, January 18\u201323). Future frame prediction for anomaly detection\u2014A new baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ionescu, R.T., Khan, F.S., Georgescu, M.I., and Shao, L. (2019, January 15\u201320). Object-centric auto-encoders and dummy anomalies for abnormal event detection in video. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00803"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Morais, R., Le, V., Tran, T., Saha, B., Mansour, M., and Venkatesh, S. (2019, January 15\u201320). Learning regularity in skeleton trajectories for anomaly detection in videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01227"},{"key":"ref_41","first-page":"1","article-title":"Holistic tactical-level planning in liner shipping: An exact optimization approach","volume":"5","author":"Pasha","year":"2020","journal-title":"J. Shipp. Trade"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s12293-019-00292-3","article-title":"An Adaptive Island Evolutionary Algorithm for the berth scheduling problem","volume":"12","author":"Dulebenets","year":"2020","journal-title":"Memetic Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101818","DOI":"10.1016\/j.jairtraman.2020.101818","article-title":"The determinants of air passenger traffic at Turkish airports","volume":"86","author":"Can","year":"2020","journal-title":"J. Air Transp. Manag."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Tr\u00f6sterer, S., Meneweger, T., Meschtscherjakov, A., and Tscheligi, M. (2017, January 24\u201327). Transport companies, truck drivers, and the notion of semi-autonomous trucks: A contextual examination. Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Adjunct, Oldenburg, Germany.","DOI":"10.1145\/3131726.3131748"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/2\/57\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:22:05Z","timestamp":1760160125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/2\/57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,9]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["a14020057"],"URL":"https:\/\/doi.org\/10.3390\/a14020057","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2021,2,9]]}}}