Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving
- Nicholas D Cassavaugh (Central Michigan University, Mount Pleasant, MI)
- Alex Bos (Central Michigan University, Mount Pleasant, MI)
- Cole McDonlad (Central Michigan University, Mount Pleasant, MI)
- Pujitha Gunaratne (Toyota Motor Engineering & Manufacturing North America, Ann Arbor, MI)
- Richard W Backs (Central Michigan University, Mount Pleasant, MI)
Abstract
We attempted to model attention allocation of experienced drivers using the SEEV model. Unlike previous attempts, the present work looked at attention to entities (vehicles, signs, traffic control devices) in the outside world rather than considering the outside world as a unitary construct. Model parameters were generated from rankings of entities by experienced drivers. Experienced drivers drove a scenario that included a number of intersections interspersed with stretches of straight road. The intersections included non-hazard events. Eye movements were monitored during the driving session. The results of fitting the observed eye movement data to our SEEV model were poor, and were no better than fitting the data to a randomized SEEV model. A number of explanations for this are discussed.
How to Cite:
Cassavaugh, N. & Bos, A. & McDonlad, C. & Gunaratne, P. & Backs, R., (2013) “Assessment of the SEEV Model to Predict Attention Allocation at Intersections During Simulated Driving”, Driving Assessment Conference 7(2013), 334-340. doi: https://doi.org/10.17077/drivingassessment.1509
Rights: Copyright © 2013 the author(s)
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