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Title: Personalized driver workload inference by learning from vehicle related measurements
Authors: Yi, Dewei
Su, Jinya
Liu, Cunjia
Chen, Wen-Hua
Keywords: Fuzzy C-means clustering
Personalized aiding
Support vector machine
Workload recognition
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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.
Abstract: 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.
Description: 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/
Sponsor: 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.
Version: Published
DOI: 10.1109/TSMC.2017.2764263
URI: https://dspace.lboro.ac.uk/2134/27149
Publisher Link: https://doi.org/10.1109/TSMC.2017.2764263
ISSN: 2168-2216
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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