#DUNDEE STRESS STATE QUESTIONNAIRE PDF DRIVER#
On-road research on real vehicles can be costly in terms of money and driver safety and may not afford adequate experimental control and data collection. Any new automation device must therefore be carefully tested to adequately weigh its benefits and shortcomings. Drivers’ attention may be diverted elsewhere and they could have troubles at refocusing on the driving task in case of emergency. However, this condition may result in low demand for action and an underload. On the other hand, effective automation could perform both longitudinal and lateral control of the car, thus leaving to the driver the monitoring task. However, research has shown that making automation available to the driver may fail to alleviate the workload, or, somehow ironically, it may even introduce a further source of workload by creating additional task activities. Driving automation at different SAE (Society of Automotive Engineers) levels is seen as having the potential to reduce road fatalities, in particular by mitigating physical demand or improving awareness of the driving environment. Technology solutions have been proposed to reduce such a complex activity, alleviating the driver from demanding tasks and shifting them to automation. Indeed, car driving is a complex activity, consisting of both psychological and physiological demands, and workload appears inevitable. On the other hand, underload can cause reduced alertness, focused attention could be lowered, and the reaction to events could be slow or inaccurate. Therefore, overload would affect the capacity to focus attention on right task or pieces of information. The former can lead to distraction, diverted attention, and insufficient capacity and time for adequate information processing. De Waard and Brookhuis argued that both overload and underload could impair attention. Mental workload can impair driving performance either when it is too high or when it is too low. Taking into account the characteristics of driving, physical workload is less relevant than the mental one, which results from mental processes when performing driving tasks, depending on the users' capabilities and the task demands.
It is related to an unbalance between mental and/or physical resources and task demands. The term workload can refer to a number of different yet related entities. Distraction and poor understanding of traffic situation could be generated by inefficient attentional resource allocation and workload level has been identified as one of the main factors affecting attention and performance. Although many environmental factors, such as roadway type, vehicle characteristics, weather, road conditions, traffic density, and flow may jeopardize road safety, a vast majority of traffic accidents are caused by, or at least can be related to, poor human performance (e.g., mainly, distraction, and risk misperception). Reconciling mobility needs with efficient and more sustainable transportation, while increasing levels of road safety, is a key objective in the transportation sector.
#DUNDEE STRESS STATE QUESTIONNAIRE PDF SIMULATOR#
Simulator sickness also emerged as a critical issue for the generalizability of the results. Consistent with previous studies, mixed support was found for both types of validity, although results suggest that allowing more and/or longer familiarization sessions with the simulator may be needed to increase its validity. In this study we investigated the validity of a driving-simulator-based experimental environment for research on mental workload measures by comparing behavioral and subjective measures of workload of the same large group of participants in a simulated and on-road driving task on the same route. However, for each driving simulator to be a useful research tool, for each specific task an adequate correspondence must be established between the behavior in the simulator and the behavior on the road, namely, the simulator absolute and relative validity. Driving simulators have become a viable alternative to on-the-road tests, since they allow optimal experimental control and high safety. However, each new functionality can also introduce a new source of workload, due to the need to attend to new tasks and thus requires careful testing before being implemented in vehicles. Automated in-vehicle systems and related human-machine interfaces can contribute to alleviating the workload of drivers.
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