Obstacle Detection in Crowded Spaces

Moving through a station concourse at rush hour without a guiding arm.

Crowds are the hardest case: the obstacles move, they move unpredictably, and a cane finds them a step too late. We ran Phoenix Eye through four weeks of peak-hour testing in a mainline station, tuning how early a warning arrives and — just as importantly — when the glasses stay quiet. Constant narration is worse than none. The device now speaks only when something on your path changes: a suitcase left standing, a person cutting across, a queue that has formed since you last passed.

Delivered

Obstacle model tuned for dense pedestrian traffic, with warning distance and alert frequency set from tester feedback.

Use cases

Each case is a real route, a real problem, and what changed once Phoenix Eye was on.

Use cases

Each case is a real route, a real problem, and what changed once Phoenix Eye was on.

Use cases

Each case is a real route, a real problem, and what changed once Phoenix Eye was on.