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Title: New driver workload prediction using clustering-aided approaches
Authors: Yi, Dewei
Su, Jinya
Liu, Cunjia
Chen, Wen-Hua
Keywords: Clustering
Classification and regression tree
Multiple model
Workload inference
Issue Date: 2018
Publisher: © Institute of Electrical and Electronics Engineers
Citation: YI, D. ... et al., 2018. New driver workload prediction using clustering-aided approaches. IEEE Transactions on Systems Man and Cybernetics: Systems, 49(1), pp. 64 - 70.
Abstract: Awareness of driver workload plays a paramount role in enhancing driving safety and convenience for intelligent vehicles. The Driver Workload Prediction Systems (DWPSs) proposed so far learn either from individual driver’s data (termed personalized system) or existing drivers’ data indiscriminately (termed average system). As a result, they either do not work or lead to a limited performance for new drivers without labelled data. To this end, we develop clustering-aided approaches exploiting group characteristics of the existing drivers’ data. Two clustering aided predictors are proposed. The first is Clustering Aided Regression (CAR) model, where the regression model for the cluster with the highest likelihood is adopted. The second is Clustering-Aided Multiple Model Regression (CAMMR) model, where the concept of multiple models is further augmented to CAR. A recent dataset from real-world driving experiments is adopted to validate the algorithms. Comparative results against the conventional average system demonstrate that by incorporating clustering information, both the proposed approaches significantly improve workload prediction performance.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under grant number EP/J011525/1 with BAE Systems as the leading industrial partner.
Version: Accepted for publication
DOI: 10.1109/TSMC.2018.2871416
URI: https://dspace.lboro.ac.uk/2134/35036
Publisher Link: https://doi.org/10.1109/TSMC.2018.2871416
ISSN: 2168-2216
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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