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New Driver Workload Prediction Using Clustering-Aided Approaches

Yi, Dewei and Su, Jinya and Liu, Cunjia and Chen, Wen-Hua (2019) 'New Driver Workload Prediction Using Clustering-Aided Approaches.' IEEE Transactions on Systems Man and Cybernetics: Systems, 49 (1). pp. 64-70. ISSN 2168-2216

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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.

Item Type: Article
Uncontrolled Keywords: Classification and regression tree (CART); clustering; multiple model; workload inference
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 19 Nov 2019 19:13
Last Modified: 18 Aug 2022 13:14

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