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Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus crucial for delivering adaptive human machine interaction solutions with reduced impact on the primary task of driving. In this article, 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 $F_{1}$ scores of up to 92% and 81%, respectively.

Most research into haptic feedback for in-car applications has used vibrotactile feedback. In this article, two simulator studies investigate novel thermal feedback during driving for a lane change task. The distraction and time differences of audio and thermal feedback were investigated in the first, with results showing that thermal feedback does not increase lane deviation, but the time to completed lane change is 1.82s longer in the thermal than the audio condition. The second experiment explored the difference in variable changes of the thermal stimuli on the recognition rate and false positive recognition at the return to the neutral temperature. Variable alterations can have different effects on these tasks and are not mirrored for the directions of temperature change. This suggests that the design of thermal stimuli is highly dependent on what result should be maximized: recognition rate or minimal additional changes at the return to the neutral temperature.

Two studies investigated the concept of following a lead vehicle as a navigational aid. The first video-based study (n=34) considered how drivers might use a real-world lead vehicle as a navigational aid, whist the second simulator-based study (n=22) explored how an Augmented Reality (AR) virtual car, presented on a head-up display (HUD), may aid navigation around a complex junction. Study 1 indicated that a lead vehicle is most valued as a navigation aid just before/during a required maneuver. During the second study the dynamic virtual car (which behaved like a real vehicle) resulted in greater confidence and lower workload than a static virtual car that “waits” at the correct junction exit, but resulted in more gaze concentration. It is concluded that a virtual car may be a valuable element of a navigation system, in combination with other forms of information, to completely fulfil all a driver's navigational task requirements.