SocialCVAE: Predicting Pedestrian Trajectory via Interaction Conditioned
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Wei Xiang Haoteng Yin He Wang Xiaogang Jin
Pedestrian trajectory prediction is the key technology in many applications
for providing insights into human behavior and anticipating human future
motions. Most existing empirical models are explicitly formulated by observed
human behaviors using explicable mathematical terms with a deterministic
nature, while recent work has focused on developing hybrid models combined with
learning-based techniques for powerful expressiveness while maintaining
explainability. However, the deterministic nature of the learned steering
behaviors from the empirical models limits the models' practical performance.
To address this issue, this work proposes the social conditional variational
autoencoder (SocialCVAE) for predicting pedestrian trajectories, which employs
a CVAE to explore behavioral uncertainty in human motion decisions. SocialCVAE
learns socially reasonable motion randomness by utilizing a socially
explainable interaction energy map as the CVAE's condition, which illustrates
the future occupancy of each pedestrian's local neighborhood area. The energy
map is generated using an energy-based interaction model, which anticipates the
energy cost (i.e., repulsion intensity) of pedestrians' interactions with
neighbors. Experimental results on two public benchmarks including 25 scenes
demonstrate that SocialCVAE significantly improves prediction accuracy compared
with the state-of-the-art methods, with up to 16.85% improvement in Average
Displacement Error (ADE) and 69.18% improvement in Final Displacement Error
(FDE).