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

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 $F_{1}$ scores of up to 92% and 81%, respectively.Comment: Accepted for IEEE Transactions on Intelligent Vehicle

Modelling Automation–Human Driver Handovers Using Operator Event Sequence Diagrams

This research aims to show the effectiveness of Operator Event Sequence Diagrams (OESDs) in the normative modelling of vehicle automation to human drivers’ handovers and validate the models with observations from a study in a driving simulator. The handover of control from automation to human operators has proved problematic, and in the most extreme circumstances catastrophic. This is currently a topic of much concern in the design of automated vehicles. OESDs were used to inform the design of the interaction, which was then tested in a driving simulator. This test provided, for the first time, the opportunity to validate OESDs with data gathered from videoing the handover processes. The findings show that the normative predictions of driver activity determined during the handover from vehicle automation in a driving simulator performed well, and similar to other Human Factors methods. It is concluded that OESDs provided a useful method for the human-centred automation design and, as the predictive validity shows, can continue to be used with some confidence. The research in this paper has shown that OESDs can be used to anticipate normative behaviour of drivers engaged in handover activities with vehicle automation in a driving simulator. Therefore, OESDs offer a useful modelling tool for the Human Factors profession and could be applied to a wide range of applications and domains.</jats:p

Feedback in highly automated vehicles: what do drivers rely on?

SAE (Society of Automotive Engineers) Level 3 vehicles are in development by many manufacturers. In order to deliver increasing amounts and types of information, in-car information systems are becoming more varied and complex. Feedback can now be given to the driver in a wide variety of ways including text and graphics and changing colours across multiple screens, on the windscreen with a Head Up Display, vocal or other audio alerts, ambient lighting and haptics. A high-fidelity simulator study was undertaken in which participants were exposed to all of these feedback modes and then ranked them in terms of reliance. Analysis shows how the feedback modes participants relied on varies widely and how gender can influence the results

OESDs in an on-road study of semi-automated vehicle to human driver handovers

Design of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research

OESDs in an on-road study of semi-automated vehicle to human driver handovers

Design of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future researc