Estimating pedestrian intentions from trajectory data

Abstract

In this paper, several machine learning methods are used to train classifiers capable of estimating the intention of a pedestrian to cross a zebra crossing. Their results are compared to a Bayesian network-an approach commonly used in autonomous driving.

The data used for the estimation contain only position and heading of the pedestrians.

Keywords

• Intention estimation
• Machine learning
• Automated driving