AI4CC 2024
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 dist
Stochastic trajectory prediction models start by generating a random hypothesis. In practice, a fixed number of multiple trajectories are randomly sampled using the Monte Carlo (MC) method, and all existing stochastic models follow this random sampling strategy. However, the number of samples is typically too small to represent socially-acceptable pedestrian trajectories because they are biased toward the random sampling.
We introduce a Quasi-Monte Carlo (QMC) sampling method that effectively alleviates this problem using a low-discrepancy sequence, instead of random sampling. From the view of numerical analysis, a low-discrepancy sequence can be much better than the random one, for a function with a finite variation. For QMC, as the sample comes from a quasi-random sequence, they can be uniformly spaced. This guarantees a suitable distribution for pedestrian trajectory prediction by successively constructing finer uniform p
AI4CC 2024