详情

原文传递 Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification

题名：	Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification
正文语种：	eng
作者：	Armstrong Aboah;Yaw Adu-Gyamfi;Senem Velipasalar Gursoy;Jennifer Merickel;Matt Rizzo;Anuj Sharma
作者单位：	Dept. of Civil and Environmental Engineering Univ. of Missouri-Columbia E25O9 Lafferre Hall Columbia MO 65211;Dept. of Civil and Environmental Engineering Univ. of Missouri-Columbia E25O9 Lafferre Hall Columbia MO 65211;Electrical Engineering and Computer Science Syracuse Univ. Lincoln NE 68588;Dept. of Neurological Sciences Univ. of Nebraska Medical Center Omaha NE 68198-8440;Dept. of Neurological Sciences Univ. of Nebraska Medical Center Omaha NE 68198-8440;Dept. of Civil and Environmental Engineering Iowa State Univ Ames IA 50010
关键词：	Driving maneuvers; Naturalistic driving; Energy-maximization algorithm (EMA); Gyroscope; Machine learning; Annotation
摘要：	The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.
出版年：	2023
期刊名称：	Journal of Transportation Engineering
卷：	149
期：	3
页码：	04022157.1-04022157.15