IEEE_CC-BY-08125589.pdf (1.75 MB)
0/0

Personalized driver workload inference by learning from vehicle related measurements

Download (1.75 MB)
journal contribution
posted on 27.10.2017 by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen
Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual drivers’ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI) system considering individual drivers’ driving characteristics is developed using machine learning techniques via easily accessed Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers’ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.

Funding

This work was supported by the U.K. Engineering and Physical Sciences Research Council Autonomous and Intelligent Systems Programme with BAE Systems as the leading industrial partner under Grant EP/J011525/1. The work of D. Yi was supported by the Chinese Scholarship Council.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Systems Man and Cybernetics: Systems

Citation

YI, D. ...et al., 2017. Personalized driver workload inference by learning from vehicle related measurements. IEEE Transactions on Systems Man and Cybernetics: Systems, 49 (1), pp.159-168.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 International (CC BY 3.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by/3.0/

Acceptance date

12/10/2017

Publication date

2017

Notes

This paper was published as Open Access by IEEE. It is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

ISSN

2168-2216

Language

en

Licence

Exports

Logo branding

Licence

Exports