Learning a Pedestrian Social Behavior Dictionary
This addresses the challenge of semantic behavior modeling for autonomous agents in human-populated environments, though it is incremental as it builds on existing trajectory analysis methods.
The paper tackles the problem of understanding pedestrian behavior for autonomous navigation by learning an unsupervised dictionary of pedestrian behaviors from trajectory data, achieving results comparable to state-of-the-art on ETH and UCY datasets.
Understanding pedestrian behavior patterns is a key component to building autonomous agents that can navigate among humans. We seek a learned dictionary of pedestrian behavior to obtain a semantic description of pedestrian trajectories. Supervised methods for dictionary learning are impractical since pedestrian behaviors may be unknown a priori and the process of manually generating behavior labels is prohibitively time consuming. We instead utilize a novel, unsupervised framework to create a taxonomy of pedestrian behavior observed in a specific space. First, we learn a trajectory latent space that enables unsupervised clustering to create an interpretable pedestrian behavior dictionary. We show the utility of this dictionary for building pedestrian behavior maps to visualize space usage patterns and for computing the distributions of behaviors. We demonstrate a simple but effective trajectory prediction by conditioning on these behavior labels. While many trajectory analysis methods rely on RNNs or transformers, we develop a lightweight, low-parameter approach and show results comparable to SOTA on the ETH and UCY datasets.