Personalized Driver Workload Inference by Learning From Vehicle Related Measurements

Dewei Yi, Jinya Su*, Cunjia Liu, Wen-Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Citations (Scopus)
2 Downloads (Pure)

Abstract

Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers' workload can enhance both 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 an appropriate 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 (FCM) 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 type 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 FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F 1 -score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness.
Original languageEnglish
Pages (from-to)159-168
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume49
Issue number1
Early online date1 Dec 2017
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Fuzzy C-means (FCM) clustering
  • personalized aiding
  • support vector machine (SVM)
  • workload recognition

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