The conditioned anticipation of people (CAP) model of driving in urban spaces

Abstract

Despite significant gains in overall collision rates, pedestrian and bicycle safety in complete street environments remains an on-going challenge. However, urban areas with the most risk exposure for pedestrians continue to maintain lower incident rates than their suburban counterparts (“Dangerous by Design” 2021). Under most driving circumstances, the over-rehearsed nature of driving leads to a psychological state similar to self-hypnosis, where attention is subconsciously maintained on the driving task while metacognitive awareness is minimized or eliminated. This state continues until some type of conflict, uncertainty, and/or novel stimulus is presented. The primary difference in driver attention in urban environments is postulated to result from the Conditioned Anticipation of People (CAP). Based on the human neurological predisposition to recognize and fixate on human faces and figures, we hypothesize that in areas where drivers have been conditioned to expect human presence, low-level metacognition is preemptively re-engaged to address their presence, resulting in higher attentional resource expenditure and increased distraction management. Such conditioning may be generated by contextual features common to pedestrian-friendly environments but, necessarily, must be reinforced over time by the Actual Presence of People (APP). CAP driver engagement is limited by the perceptual abilities of the driver such that at higher speeds or within wider corridors, the presence of pedestrians is more difficult to perceive. This results in a non-CAP attention pattern, exhibiting minimal metacognitive activity, high levels of automaticity, and reduced attention. This model was generated based on the observation that vulnerable user presence and the roadway contextual features that support the driver's ability to see vulnerable users were related to attention data measured during the SHRP2 Naturalistic Driving Study. Visually discernable features that were associated with vulnerable user presence had relationships with attention and large effect sizes (η2 > 0.5). Contextual features that had relationships with vulnerable user presence, but minimal visual impact or interfered with the driver’s ability to see vulnerable users had no relationship to driver attention. This behavioral pattern provides supportive evidence for the proposed model.