Predicting Pedestrian Intention to Cross the Road

Abstract

The goal of this research is the development of a driver assistant feature, which can warn the driver in case a pedestrian is in a potential risk due to sudden intention to cross the road. The process of crossing pedestrian is defined as the changing of pedestrian orientation on the curb toward the road. We built a Convolutional Neural Network (CNN) model combined with depth sensing camera to estimate the pedestrian orientation and distance from the vehicle. The model detects the higher human body keypoints in 2D space while the depth info make it possible to translate the points into a 3D space. These info are tracked per pedestrian and any change in the pedestrian moving pattern toward the road is translated to a warning for the driver. The CNN model is end-end trained using different datasets presenting pedestrian in different configurations and scenes.