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

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 value-driven high-bandwidth data capture. The SBB caches short-term data in buffers and determines the compression quality for each frame by optimizing the trade-off between data value and storage cost. With finite storage, prioritized data recording discards low-value buffers to make room for new data. This paper formulates SBB data compression decision making as a constrained multi-objective optimization problem with novel data filtering and value estimation metrics. A traffic simulator generates trajectories containing events of interest. 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 recording scheme.