We have been developing a Smart Black Box (SBB) to augment traditional low-bandwidth data storage logging with value-driven high-bandwidth data capture. Data time histories associated with unexpected signals, high-risk, or anomalous conditions are stored in long-term memory and later uplinked to the cloud. We developed anomaly detection metrics for four typical hazards: object detection discrepancies, challenging lighting conditions, sensor measurement outliers and traffic collision ricks. A series of driving tests with raw data capture and an off-the-shelf automotive driver assistance system enables data analysis with respect to object identification accuracy and completeness.

List of publications

1. Yao, Y., & Atkins, E. M. (2019). The Smart black box: A value-driven high-bandwidth automotive event data recorder. IEEE Transactions on Intelligent Transportation Systems, under review.
2. Yao, Y., Xu, M., Wang, Y., Crandall, D. J., & Atkins, E. M. (2019). Unsupervised traffic accident detection in first-person videos. In Proceedings of 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), accepted.
3. Yao, Y., Xu, M., Choi, C., Crandall, D. J., Atkins, E. M., & Dariush, B. (2019). Egocentric vision-based future vehicle localization for intelligent driving assistance systems. In Proceedings of 2019 IEEE International Conference on Robotics and Automation (ICRA). IEEE
4. Yao, Y., & Atkins, E. (2018). The smart black box: A value-driven automotive event data recorder. In Proceedings of 21st International Conference on Intelligent Transportation Systems (ITSC), 973-978.

The A3D dataset

The AnAn Accident Detection (A3D) dataset contains 1,500 video clips and manually labelled anomaly start/end times, and can be downloaded at: https://github.com/MoonBlvd/tad-IROS2019