원호 스플라인 근사를 이용한 주행 기동 이벤트의 감지 및 분류

다국어 초록 (Multilingual Abstract)

Classification of driving maneuver events is essential for generating lane-level maps, developing advanced driver assistance systems, and analyzing driver behavior. Typically, maneuver events classification involves three key stages: segmentation of driving data, extraction of features, and classification process. However, existing studies that used thresholdbased or sliding window approaches for segmentation have not adequately considered the various durations of maneuvers and face difficulties in distinguishing consecutively occurring events. This paper presents a novel algorithm that exhibits a high classification performance through segmentation using arc spline approximation and classification employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), enabling the precise identification of consecutively occurred events. The effectiveness of this algorithm was validated using sensor data from real vehicle test drives in Sejong City, South Korea.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 주행 기동 이벤트 감지, 분류 및 할당
• 3. 데이터 기반 검증
• 4. 결론
• References