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Monitoring drivers' mental workload facilitates initiating and maintainingsafe interactions with in-vehicle information systems, and thus deliversadaptive human machine interaction with reduced impact on the primary task ofdriving. In this paper, we tackle the problem of workload estimation fromdriving performance data. First, we present a novel on-road study forcollecting subjective workload data via a modified peripheral detection task innaturalistic settings. Key environmental factors that induce a high mentalworkload are identified via video analysis, e.g. junctions and behaviour ofvehicle in front. Second, a supervised learning framework usingstate-of-the-art time series classifiers (e.g. convolutional neural network andtransform techniques) is introduced to profile drivers based on the averageworkload they experience during a journey. A Bayesian filtering approach isthen proposed for sequentially estimating, in (near) real-time, the driver'sinstantaneous workload. This computationally efficient and flexible method canbe easily personalised to a driver (e.g. incorporate their inferred averageworkload profile), adapted to driving/environmental contexts (e.g. road type)and extended with data streams from new sources. The efficacy of the presentedprofiling and instantaneous workload estimation approaches are demonstratedusing the on-road study data, showing $F_{1}$ scores of up to 92% and 81%,respectively.

Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data