A Method of Generating a Composite Dataset for Monitoring of Non-Driving Related Tasks Tongji University

Format:

Book

Conference/Event

Author/Creator:

Wu, Xian, author.

Contributor:

Gou, Junjie

Shao, Jianwang

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Recently, several datasets have become available for occupant monitoring algorithm development, including real and synthetic datasets. However, real data acquisition is expensive and labeling is complex, while virtual data may not accurately reflect actual human physiology. To address these issues and obtain high-fidelity data for training intelligent driving monitoring systems, we have constructed a hybrid dataset that combines real driving image data with corresponding virtual data generated from 3D driving scenarios. We have also taken into account individual anthropometric measures and driving postures. Our approach not only greatly enriches the dataset by using virtual data to augment the sample size, but it also saves the need for extensive annotation efforts. Besides, we can enhance the authenticity of the virtual data by applying ergonomics techniques based on RAMSIS, which is crucial in dataset construction. This paper presents the process and content of generating a hybrid dataset for the monitoring of driver's high risk NDRTs monitoring, serving as a potential alternative to existing datasets and addressing their limitations

Notes:

Vendor supplied data

Publisher Number:

2024-01-2640

Access Restriction:

Restricted for use by site license