Driver mental workload is an important factor in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA R-TLX) and objectively (auditory detection-response task) on Dutch public highways ($150 km) comparing manual and supervised automated driving in a Tesla Model S with moderators automation experience and traffic complexity. Participants (N = 16) were either automation-inexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. When using the automation, automationexperienced drivers perceived a lower workload, while automation-inexperienced drivers perceived their workload to be similar to manual driving. However, the detection-response task indicated an increase in cognitive load with automation, in particular in complex traffic. This indicates that drivers underestimate the actual task load of attentive monitoring. The findings also highlight the relevance of using system-experienced participants and the importance of incorporating both objective and subjective measures when examining workload.
Objective To investigate how well gaze behavior can indicate driver awareness of individual road users when related to the vehicle’s road scene perception. Background An appropriate method is required to identify how driver gaze reveals awareness of other road users. Method We developed a recognition-based method for labeling of driver situation awareness (SA) in a vehicle with road-scene perception and eye tracking. Thirteen drivers performed 91 left turns on complex urban intersections and identified images of encountered road users among distractor images. Results Drivers fixated within 2° for 72.8% of relevant and 27.8% of irrelevant road users and were able to recognize 36.1% of the relevant and 19.4% of irrelevant road users one min after leaving the intersection. Gaze behavior could predict road user relevance but not the outcome of the recognition task. Unexpectedly, 18% of road users observed beyond 10° were recognized. Conclusions Despite suboptimal psychometric properties leading to low recognition rates, our recognition task could identify awareness of individual road users during left turn maneuvers. Perception occurred at gaze angles well beyond 2°, which means that fixation locations are insufficient for awareness monitoring. Application Findings can be used in driver attention and awareness modelling, and design of gaze-based driver support systems.
This paper contains a study to find faster numerical methods for Hamilton-Jacobi Isaacs partial differential equations in application to model-based flight envelope estimation. The aim is to identify new methods capable of providing the basic information needed for flight envelope protection. Useful insights have been obtained through assessing the reachable set theory associated to the problem, which permits integration of depth-first and breadth first estimation methods and the estimation of the flight envelope as a minimal time problem. The applicability of a class of non-iterative schemes, known as the fast marching methods, has been evaluated. The behavior of the studied methods is demonstrated on different example problems, including a simplified aircraft model.
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