New_driverfinal_clean.pdf (1.18 MB)

New driver workload prediction using clustering-aided approaches

Download (1.18 MB)
journal contribution
posted on 24.09.2018, 09:51 by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen
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.

Funding

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.

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., 2018. New driver workload prediction using clustering-aided approaches. IEEE Transactions on Systems Man and Cybernetics: Systems, 49(1), pp. 64 - 70.

Publisher

© Institute of Electrical and Electronics Engineers

Version

AM (Accepted Manuscript)

Acceptance date

11/09/2018

Publication date

2018

Notes

© 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.

ISSN

2168-2216

Language

en

Exports

Logo branding

Exports