Deep Learning-Based Driver’s Hands on/off Prediction System Using In-Vehicle Data

Abstract

Driver’s hands on/off detection is very important in current autonomous vehicles for safety. Several studies have been conducted to create a precise algorithm. Although many studies have proposed various approaches, they have some limitations, such as robustness and reliability. Therefore, we propose a deep learning model that utilizes in-vehicle data. We also established a data collection system, which collects in-vehicle data that are auto-labeled for efficient and reliable data acquisition. For a robust system, we devised a confidence logic that prevents outliers’ sway. To evaluate our model in more detail, we suggested a new metric to explain the events, considering state transitions. In addition, we conducted an extensive experiment on the new drivers to demonstrate our model’s generalization ability. We verified that the proposed system achieved a better performance than in previous studies, by resolving their drawbacks. Our model detected hands on/off transitions in 0.37 s on average, with an accuracy of 95.7%.

A Theoretical Framework for Creating Folk Dance Motion Templates using Motion Capture

“…According to the documentation, SKL files are "skeleton files that contain the bone hierarchy of the model and the positions and orientations of the bones in each frame of the animation" [23]. This file format is also used to capture motion data from human subjects during various exercises [24].…”