  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 .
