Integrated Driver Pose Estimation for Autonomous Driving

Xiao Cao, Xiao Cao, Wei Hu, Hui Liu

2024

Abstract

Human-machine interaction, especially driver posture estimation is important to the development of autonomous driving, which can facilitate safe and smooth driving behaviours. Besides, it also contributes to ergonomics research and human-machine interaction design for automated vehicles. The existing studies have got great achievements in body estimation, hand pose estimation, and even face feature estimation thanks to the rapid development of deep learning approaches and the upgrade of hardware equipment. However, most existing models can only process body estimation or hand estimation separately, which will impede the research on driver-vehicle interaction in autonomous driving. This is because the driving process is highly dependent on the cooperation between the body and hands behaviours. In this study, five popular deep learning models, including Simple Faster R-CNN, RootNet, PoseNet, Yolo v3, and graph convolutional neural network, are combined through a cascade method to develop an integrated model which can estimate body and hand simultaneously during the driving process. The coordinate transform system is proposed to connect models in series. Experiment results demonstrate the proposed method can produce 2D and 3D reorganization of the human body and hands simultaneously with acceptable accuracy.

Paper Citation

in Harvard Style

Cao X., Hu W. and Liu H. (2024). Integrated Driver Pose Estimation for Autonomous Driving. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 695-702. DOI: 10.5220/0012639400003657

in Bibtex Style

@conference{biosignals24,
author={Xiao Cao and Wei Hu and Hui Liu},
title={Integrated Driver Pose Estimation for Autonomous Driving},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={695-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012639400003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Integrated Driver Pose Estimation for Autonomous Driving
SN - 978-989-758-688-0
AU - Cao X.
AU - Hu W.
AU - Liu H.
PY - 2024
SP - 695
EP - 702
DO - 10.5220/0012639400003657
PB - SciTePress