ビヘイビアフォレスト：動的挙動のリアルタイム発見によるデータ選択
Behavior Forests: Real-Time Discovery of Dynamic Behavior for Data Selection
車両走行中の時系列データから動的挙動をリアルタイムに発見し、学習なしで解析的な記述を行うビヘイビアフォレストを提案。自動運転データの96%以上を削減しつつ重要な挙動を保持できることを示した。
著者: Philipp Reis, Philipp Rigoll, Eric Sax
分類: cs.RO, eess.SP
原文アブストラクト
Automated Driving Systems (ADS) development relies on utilizing real-world vehicle data. The volume of data generated by modern vehicles presents transmission, storage, and computational challenges. Focusing on Dynamic Behavior (DB) offers a promising approach to distinguish relevant from irrelevant information for ADS functionalities, thereby reducing data. Time series pattern recognition is beneficial for this task as it can analyze the temporal context of vehicle driving behavior. However, existing state-of-the-art methods often lack the adaptability to identify variable-length patterns or provide analytical descriptions of discovered patterns. This contribution proposes a Behavior Forest framework for real-time data selection by constructing a Behavior Graph during vehicle operation, facilitating analytical descriptions without pre-training. The method demonstrates its performance using a synthetically generated and electrocardiogram data set. An automotive time series data set is used to evaluate the data reduction capabilities, in which this method discarded 96.01% of the incoming data stream, while relevant DB remain included.
