Driver Maneuver Detection and Analysis Using Time Series Segmentation and Classification.

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.

Synthetic distracted driving (SynDD1) dataset for analyzing distracted behaviors and various gaze zones of a driver.

This article presents a synthetic distracted driving (SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehicle using three in-vehicle cameras positioned at locations: on the dashboard, near the rearview mirror, and on the top right-side window corner. The dataset contains two activity types: distracted activities [1], [2], [3], and gaze zones [4], [5], [6] for each participant and each activity type has two sets: without appearance blocks and with appearance blocks, such as wearing a hat or sunglasses.