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Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification by Aboah, Armstrong; Adu-Gyamfi, Yaw; Gursoy, Senem Velipasalar; Merickel, Jennifer; Rizzo, Matt; Sharma, Anuj is a scholarly article available to read on EtoBox.

What is Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification about?

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 is continuous. Our objective is to develop an end-to-end pipeline for 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 were c

Author

Aboah, Armstrong; Adu-Gyamfi, Yaw; Gursoy, Senem Velipasalar; Merickel, Jennifer; Rizzo, Matt; Sharma, Anuj

Published

2022

Language

EN