Goal-Oriented Pedestrian Motion Prediction

Abstract

Forecasting the motion of others in shared spaces is a key for intelligent agents to operate safely and smoothly. We present an approach for probabilistic prediction of pedestrian motion incorporating various context cues. Our approach is based on goal-oriented prediction, yielding interpretable results for the predicted pedestrian intention, even without the prior knowledge of goal positions. By using Markov chains, the resulting probability distribution is deterministic—a beneficial property for motion planning or risk assessment in automated and assisted driving. Our approach outperforms a physics-based approach and improves over state-of-the-art approaches by reducing standard deviations of prediction errors and improving robustness against realistic, noisy measurements.