[NTC2015-SU-R-05] Minimizing Driver Errors: Detecting Unexpected Targets in Familiar Environments

Critical to the driver’s performance is the ability to detect critical items quickly, however limited cognitive resources are available for the task. For example, if a pedestrian steps into the street, the more readily the driver detects the pedestrian, the more quickly the driver can step on the breaks and avoid colliding with the pedestrian. However, the number of items that can be attended at a given time is fewer than five (Cowan, 2001) and could be as few as one item (Oztekin et al. 2010). In the current study we use a driving simulator to measure the effects of driving automaticity in a familiar environment on the ability to detect and react to pedestrians entering the roadway in expected and unexpected locations. This research is particularly relevant in times of evacuations when drivers are likely to be driving in familiar environments under unfamiliar situations (e.g., an increase in congestion). Therefore, this project will inform design and training practices with the goal of minimizing driving accidents.