Computational modeling of age-differences in a visually demanding driving task: Vehicle detection

While older adults experience fewer automobile crashes than the rest of the population, their crash rate per mile driven is comparable to that of new drivers. Many crashes can be linked to visual detection problems, e.g., not seeing a car approaching at an intersection. The visual task of detecting an approaching vehicle was modeled with a neuro-physiologically motivated c omputational vision model, the National Automotive Center-Visual Perception Model (NAC-VPM), The scientific literature documenting age-related changes in early vision was reviewed in relationship to the components of the NAC- VPM, and the model was fit to lab data from older observers. The model Bt t he older observers' data adequately, particularly when the data was partiti oned into subsets based on viewing conditions. Model fits were compared to calibrations based on younger observers' data. The calibrations based on ol der observers were substantially different from calibrations based on young er observers, indicating that the model can capture age-related differences in visual perception. When calibrated to the older adults' data, the model successfully predicted conditions under which vehicle detection was partic ularly difficult for older adults.