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- Title
Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies.
- Authors
Feng, Ryan; Yao, Yu; Atkins, Ella; Radac, Mircea-Bogdan
- Abstract
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 ∼25%, 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.
- Subjects
ACQUISITION of data; DATABASES; ANOMALY detection (Computer security); FIRST in, first out (Queuing theory); DATA recorders &; recording; DATA compression; VEHICLE routing problem
- Publication
Algorithms, 2021, Vol 14, Issue 2, p57
- ISSN
1999-4893
- Publication type
Article
- DOI
10.3390/a14020057