What makes driving difficult?

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

Cabrall, C.D.D. (TU Delft Intelligent Vehicles)
de Winter, J.C.F. (TU Delft Biomechatronics & Human-Machine Control)
Happee, R. (TU Delft Intelligent Vehicles)

Alhajyaseen, Wael (editor)
Alonso, Francisco (editor)
Andersson, Jan (editor)

2017

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 numerical measurement systems [2]. Presently, we develop an ordinal visual driving scene complexity measurement based on human judgments and eye behavior. Mimicking the rebuilding of situation awareness in take-over conditions we presented 60 randomly ordered video clips (3 s duration), varying complexity factors of traffic density, road curvature, and miscellaneous visual features. Eyes of 15 participants were recorded while viewing the clips, and participants rated “how much effort for you to take control and drive within that segment?” on a 100 point scale. Effort ratings showed a monotonic increase with the number of complexity factors present. A statistically significant increase was also found for saccade amplitude, whereas a statistically significant decrease was found for fixation duration. Pupil size also showed a significant increase but only between 2 complexity levels and at a relatively less convincing strength. In conclusion, the present complexity factor coding scheme apparently corresponds to subjective effort. Further consideration should be given to relating eye tracking measures to visual driving scene components and task demands. In real-time driving systems, both human occupant(s) and computerized processes may observe the same scene at the same time, and matching the machine quantification of the situation to intuitive human judgments is expected to aid in the adherence to advisories and acceptance of automated aids.

Intelligent Vehicles
Transition of Control
Workload
Highly Automated Driving
Driver State Monitoring

http://resolver.tudelft.nl/uuid:63cbc09e-1da9-491c-b9e9-4d77465894b4

Proceedings Road Safety & Simulation International Conference 2017 (RSS2017)

RSS2017: Road Safety and Simulation International Conference 2017, 2017-10-17 → 2017-10-19, Grand Hotel Amrâth Kurhaus, The Hague, Netherlands

Paper no. 159

Institutional Repository

conference paper

© 2017 C.D.D. Cabrall, J.C.F. de Winter, R. Happee