Motion (physics)

Human behavior

Context (archaeology)

Scale (ratio)

Pedestrian

Engineering

Anticipation (artificial intelligence)

Transport engineering

Cartography

Computer security

Systems engineering

Archaeology

Data mining

Computer science

Estimation

Physics

Trajectory

Astronomy

Geography

Artificial intelligence

Machine learning

Key (lock)

Pedestrian behavior anticipation is a key challenge in the design of assistive and autonomous driving systems suitable for urban environments. An intelligent system should be able to understand the intentions or underlying motives of pedestrians and to predict their forthcoming actions. To date, only a few public datasets were proposed for the purpose of studying pedestrian behavior prediction in the context of intelligent driving. To this end, we propose a novel large-scale dataset designed for pedestrian intention estimation (PIE). We conducted a large-scale human experiment to establish human reference data for pedestrian intention in traffic scenes. We propose models for estimating pedestrian crossing intention and predicting their future trajectory. Our intention estimation model achieves 79% accuracy and our trajectory prediction algorithm outperforms state-of-the-art by 26% on the proposed dataset. We further show that combining pedestrian intention with observed motion improves trajectory prediction. The dataset and models are available at http://data.nvision2.eecs.yorku.ca/PIE_dataset/.

PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction