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

Autor:	Yao, Yu, Atkins, Ella M.
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to IEEE Transactions on Intelligent Transportation Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1903.01450 Zobrazit plný text záznamu View this record from Arxiv