Loughborough University
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Personalized driver workload inference by learning from vehicle related measurements

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journal contribution
posted on 2017-10-27, 08:42 authored by Dewei Yi, Jinya Su, Cunjia LiuCunjia Liu, Wen-Hua ChenWen-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.


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.



  • Aeronautical, Automotive, Chemical and Materials Engineering


  • Aeronautical and Automotive Engineering

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IEEE Transactions on Systems Man and Cybernetics: Systems


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.


Institute of Electrical and Electronics Engineers (IEEE)


  • VoR (Version of Record)

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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/

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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/




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