  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
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 random forest model at 97.71%, and
the support vector machine model at 97.65%. These model accuracies where
consistent across different data sources. The study concludes that implementing
a segmentation-classification pipeline significantly improves both the accuracy
for driver maneuver detection and transferability of shallow and deep ML models
across diverse datasets.
