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

## TL;DR

This paper introduces a Smart Black Box system for autonomous vehicles that intelligently captures high-value data with optimized compression, enhancing data relevance and storage efficiency for validation and analysis.

## Contribution

It presents a novel value-driven, high-bandwidth data logging method using a deterministic Mealy machine and multi-objective optimization, improving over traditional FIFO recording.

## Key findings

- SBB achieves higher event capture ratios than FIFO.
- Optimized compression balances data value and storage cost effectively.
- Deep learning models perform reliably on SBB-recorded data at various compression levels.

## 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 value-driven high-bandwidth data capture. The SBB caches short-term 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.