Capturing Subtle Motion Differences of Pedestrian Street Crossings

Abstract

The pedestrian intention is not only signalized by their past trajectory and head gaze direction, but by their whole body movement. In order to improve existing algorithms, analysis of pedestrian motions before entering a shared environment, like a street crossing, is necessary. In addition, more accurate human models are required for a digital reality in order to enable the scalable evaluation of autonomous systems using synthetic data, most of all in the area of autonomous driving. In this work, we present a first approach to capturing pedestrian locomotion in a field experiment using an existing motion capture solution. The motion of 20 participants in 80 crossing trials was recorded and numerically evaluated. The evaluation suggests, that the hip and shoulder rotation with respect to the street is more pronounced before crossing the street compared to other, non-critical behavior.