Measuring Pedestrian Collision Detection With Peripheral Field Loss and the Impact of Peripheral Prisms

Citation:

Qiu, C., Jung, J. - H., Tuccar-Burak, M., Spano, L., Goldstein, R., & Peli, E. (2018). Measuring Pedestrian Collision Detection With Peripheral Field Loss and the Impact of Peripheral Prisms. Translational Vision Science & Technology , 7 (5), 1-1. Copy at https://tinyurl.com/yagdtws6
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Abstract:

Peripheral field loss (PFL) due to retinitis pigmentosa, choroideremia, or glaucoma often results in a highly constricted residual central field, which makes it difficult for patients to avoid collision with approaching pedestrians. We developed a virtual environment to evaluate the ability of patients to detect pedestrians and judge potential collisions. We validated the system with both PFL patients and normally sighted subjects with simulated PFL. We also tested whether properly placed high-power prisms may improve pedestrian detection. A virtual park-like open space was rendered using a driving simulator (configured for walking speeds), and pedestrians in testing scenarios appeared within and outside the residual central field. Nine normally sighted subjects and eight PFL patients performed the pedestrian detection and collision judgment tasks. The performance of the subjects with simulated PFL was further evaluated with field of view expanding prisms. The virtual system for testing pedestrian detection and collision judgment was validated. The performance of PFL patients and normally sighted subjects with simulated PFL were similar. The prisms for simulated PFL improved detection rates, reduced detection response times, and supported reasonable collision judgments in the prism-expanded field; detections and collision judgments in the residual central field were not influenced negatively by the prisms. The scenarios in a virtual environment are suitable for evaluating PFL and the impact of field of view expanding devices. This study validated an objective means to evaluate field expansion devices in reproducible near-real-life settings.

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Last updated on 01/24/2022