Non-Probability Sampling Network for Stochastic Human Trajectory Prediction

Inhwan Bae, Jin-Hwi Park, Hae-Gon Jeon

Abstract:

Capturing multimodal natures is essential for stochastic
pedestrian trajectory prediction, to infer a finite set of future
trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions
of pedestrians in the inference step. However, stochastic
approaches provide varying results for the same data and
parameter settings, due to the random sampling of the latent
vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively.
Through this analysis, we observe that the inferences of all
stochastic models are biased toward the random sampling,
and fail to generate a set of realistic paths from finite samples.
The problem cannot be resolved unless an infinite number of
samples is available, which is infeasible in practice. We introduce that the Quasi-Monte Carlo (QMC) method, ensuring
uniform coverage on the sampling space, as an alternative
to the conventional random sampling. With the same finite
number of samples, the QMC improves all the multimodal
prediction results. We take an additional step ahead by incorporating a learnable sampling network into the existing
networks for trajectory prediction. For this purpose, we propose the Non-Probability Sampling Network (NPSN), a very
small network (∼5K parameters) that generates purposive
sample sequences using the past paths of pedestrians and
their social interactions. Extensive experiments confirm that
NPSN can significantly improve both the prediction accuracy
(up to 60%) and reliability of the public pedestrian trajectory prediction benchmark. Code is publicly available at
https://github.com/inhwanbae/NPSN.