Monitoring drivers' mental workload facilitates initiating and maintaining
safe interactions with in-vehicle information systems, and thus delivers
adaptive human machine interaction with reduced impact on the primary task of
driving. In this paper, we tackle the problem of workload estimation from
driving performance data. First, we present a novel on-road study for
collecting subjective workload data via a modified peripheral detection task in
naturalistic settings. Key environmental factors that induce a high mental
workload are identified via video analysis, e.g. junctions and behaviour of
vehicle in front. Second, a supervised learning framework using
state-of-the-art time series classifiers (e.g. convolutional neural network and
transform techniques) is introduced to profile drivers based on the average
workload they experience during a journey. A Bayesian filtering approach is
then proposed for sequentially estimating, in (near) real-time, the driver's
instantaneous workload. This computationally efficient and flexible method can
be easily personalised to a driver (e.g. incorporate their inferred average
workload profile), adapted to driving/environmental contexts (e.g. road type)
and extended with data streams from new sources. The efficacy of the presented
profiling and instantaneous workload estimation approaches are demonstrated
using the on-road study data, showing F1​ scores of up to 92% and 81%,
respectively.Comment: Accepted for IEEE Transactions on Intelligent Vehicle