Awareness of driver workload (DW) plays a paramount role in enhancing driving safety and convenience for intelligent vehicles. The DW prediction systems 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 labeled 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 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.
|Number of pages
|IEEE Transactions on Systems, Man, and Cybernetics: Systems
|Early online date
|9 Oct 2018
|Published - Jan 2019
Bibliographical noteThis 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.
D. Yi would like to thank Chinese Scholarship Council for supporting his study in the U.K