Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification

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Aboah, Armstrong
Adu-Gyamfi, Yaw
Velipasalar Gursoy, Senem
Merickel, Jennifer
Rizzo, Matt
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American Society of Civil Engineers
Sharma, Anuj
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Civil, Construction and Environmental Engineering

The Department of Civil, Construction, and Environmental Engineering seeks to apply knowledge of the laws, forces, and materials of nature to the construction, planning, design, and maintenance of public and private facilities. The Civil Engineering option focuses on transportation systems, bridges, roads, water systems and dams, pollution control, etc. The Construction Engineering option focuses on construction project engineering, design, management, etc.

The Department of Civil Engineering was founded in 1889. In 1987 it changed its name to the Department of Civil and Construction Engineering. In 2003 it changed its name to the Department of Civil, Construction and Environmental Engineering.

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  • Department of Civil Engineering (1889-1987)
  • Department of Civil and Construction Engineering (1987-2003)
  • Department of Civil, Construction and Environmental Engineering (2003–present)

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Institute for Transportation
InTrans administers 14 centers and programs, and several other distinct research specialties, and a variety of technology transfer and professional education initiatives. More than 100 Iowa State University faculty and staff work at InTrans, and from 200 to 250 student assistants from several ISU departments conduct research while working closely with university faculty. InTrans began in 1983 as a technical assistance program for Iowa’s rural transportation agencies.
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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.
This article is published as Aboah, Armstrong, Yaw Adu-Gyamfi, Senem Velipasalar Gursoy, Jennifer Merickel, Matt Rizzo, and Anuj Sharma. "Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification." Journal of Transportation Engineering, Part A: Systems 149, no. 3 (2023): 04022157. DOI: 10.1061/JTEPBS.TEENG-7312. Copyright 2022 American Society of Civil Engineers. Posted with permission.