The Smart Black Box: A Value-Driven High-Bandwidth Automotive Event Data Recorder

Abstract

Autonomous vehicles require reliable and resilient sensor suites and ongoing validation through fleet-wide data collection. This paper proposes a Smart Black Box (SBB) to augment traditional low-bandwidth data logging with valuedriven high-bandwidth data capture. The SBB caches shortterm histories of data as buffers through a deterministic Mealy machine based on data value and similarity. Compression quality for each frame is determined by optimizing the trade-off between value and storage cost. With finite storage, prioritized data recording discards low-value buffers to make room for new data. This paper formulates SBB compression decision making as a constrained multi-objective optimization problem with novel value metrics and filtering. The SBB has been evaluated on a traffic simulator which generates trajectories containing events of interest (EOIs) and corresponding first-person view videos. SBB compression efficiency is assessed by comparing storage requirements with different compression quality levels and event capture ratios. Performance is evaluated by comparing results with a traditional first-in-first-out (FIFO) recording scheme. Deep learning performance on images recorded at different compression levels is evaluated to illustrate the reproducibility of SBB recorded data.

Bridging the Gap Between Ethics and Practice

“…Similar proposals have been made for highly automated (also called self-driving or driverless) cars [86,125], which extend current work on electronic logging devices, which are installed on many cars to support better maintenance. Secondary uses of vehicle logging devices are to improve driver training, monitor environmentally beneficial driving styles, and verify truck driver compliance with work and traffic rules.…”

Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies

“…High-bandwidth data recorders address this by using prioritized data recording. In the case of [12,13], valuable data are identified using pre-defined rules for EOI detection. This paper seeks to build on the prioritized recording strategy in [12,13] by applying methods for general EOI detection.…”

“…Recently, event data recorders specialized for such high-bandwidth data capture have been explored. The smart black box (SBB) [12,13] is one such system. The SBB uses pre-defined rules for eventof-interest (EOI) detection and computes the data value according to the detected EOI.…”

“…This paper expands the smart black box to record high-priority video data and applies the SBB to a real-world driving dataset. Rather than using pre-defined rules for EOI detection as in [12,13], machine learning-based methods for generalized EOI detection are applied to derive the data value. Raw data are grouped into buffers, compressed, and stored in a priority queue in order to discard low-value data as the storage capacity is filled.…”

“…First, related work is explored in Section 2. Then, an overview of the original SBB [13] is presented, and the changes we made for application to real-world data are discussed in Section 3. Section 4 describes our adjusted data classification system, the updated SBB pipeline, and the new value estimation method.…”