Prediction of effort and eye movement measures from driving scene components

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos ...

Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic

Automated driving systems are increasingly prevalent on public roads, but there is currently little knowledge on the level of workload and stress of drivers operating an automated vehicle in a real environment. The present study aimed to measure driver workload and stress during partially automated driving in real traffic. We recorded heart...

What makes driving difficult?: Perceived effort and eye measures follow visible semantic complexity factors

A majority (95%) of crashes can be attributed to humans, with the highest cause category (41%) involving errors of recognition (i.e., inattention, distraction, inadequate surveillance) [1]. Driving safety research often claims that as much as 90% of the information that drivers use is visual. However, these claims have been hampered by a lack of...

The effects of time pressure on driver performance and physiological activity: A driving simulator study

Speeding because of time pressure is a leading contributor to traffic accidents. Previous research indicates that people respond to time pressure through increased physiological activity and by adapting their task strategy in order to mitigate task demands. In the present driving simulator study, we investigated effects of time pressure on...