No abstract

This study quantified drivers' ability to recognize pedestrians at night. Ten young and 10 older participants drove around a closed road circuit and responded when they first recognized a pedestrian. Four pedestrian clothing and two beam conditions were tested. Results demonstrate that driver age, clothing configuration, headlamp beam, and glare all significantly affect performance. Drivers recognized only 5% of pedestrians in the most challenging condition (low beams, black clothing, glare), whereas drivers recognized 100% of the pedestrians who wore retroreflective clothing configured to depict biological motion (no glare). In the absence of glare, mean recognition distances varied from 0.0 m (older drivers, low beam, black clothing) to 220 m (722 feet; younger drivers, high beam, retroreflective biomotion). These data provide new motivation to minimize interactions between vehicular and pedestrian traffic at night and suggest garment designs to maximize pedestrian conspicuity when these interactions are unavoidable.

A technique was developed to automate subjective measurement of the resting states of the eyes. This technique, the Modified Binary Search (MOBS), evolved from the binary search and a commonly used manual bracketing technique. The procedure is mathematically and logically simple, and it requires minimal storage and computation capabilities. Monte Carlo simulations indicate that the MOBS procedure provides more precise measures with fewer stimulus presentations than conventional staircases. It is also relatively insensitive to response errors.

Drivers' ability to recognize pedestrians at night is degraded by common visual impairments, even when the drivers' mean visual acuity meets licensing requirements. To maximize drivers' ability to see pedestrians, drivers should wear their optimum optical correction, and cataract surgery should be performed early enough to avoid potentially dangerous reductions in visual performance.