A Novel Graph based Trajectory Predictor with Pseudo Oracle

Abstract

Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challenges concentrate on capturing pedestrians' social interactions and handling their future uncertainties. Pedestrians' head orientations can be used as an oracle that indicates relevant pedestrians [1], thus is beneficial to model social interactions. Moreover, latent variable distributions of pedestrians' future trajectories can be termed as another oracle. However, few works fully utilize these oracle information for an improved prediction performance. In this work, we propose GTPPO (Graph-based Trajectory Predictor with Pseudo Oracle), which is a generative model-based trajectory predictor. Pedestrians' social interactions are captured by the proposed GA 2 T (Graph Attention & social Attention neTwork) module. Social attention is calculated on the basis of pedestrians' moving directions, which are termed as a pseudo oracle. Moreover, we propose a latent variable predictor to learn the latent variable distribution from observed trajectories. Such latent variable distribution reflects pedestrians' future trajectories, and therefore can be taken as another pseudo oracle. We compare the performance of GTPPO with several recently proposed methods on benchmarking datasets. Quantitative evaluations demonstrate that GTPPO outperforms state-of-theart methods with lower average and final displacement errors. Qualitative evaluations show that GTPPO successfully recognizes the sudden motion changes since the estimated latent variable reflects the future trajectories.

A federated pedestrian trajectory prediction model with data privacy protection

“…Pedestrian trajectory prediction suffers from future uncertainties from pedestrians' internal and external factors, such as potential destination, surrounding pedestrian interference, and scene constraints. To better handle the uncertainty of future pedestrian movements and improve the trajectory prediction performance, Yang et al [36] proposed a POP (pseudo oracle predictor) module to generate an informative potential variable by learning the future behavior of pedestrians. It could be better used in the testing phase and facilitates the broad application of trajectory prediction.…”

“…Moreover, the model contains two LSTMs and two fully connected (FC) layers to predict destinations. Similar to [36], pedestrians' relative displacements are fed into the encoding module to obtain the hidden states that represent pedestrians' motion patterns from their observed trajectories, as follows:…”

“…Enlightened by GTPPO [36], we propose a destination prediction strategy without scene semantic segmentation to maintain lightweight. Specifically, we use two LSTMs to extract sequential position information from observed and future predicted spatial coordinates.…”

“…The proposed method is evaluated on three public datasets, including ETH [45], UCY [46], and Stanford Drone Dataset (SDD) [47], which are widely used for pedestrian trajectory prediction [32,33,35,36,48]. These datasets are taken from diverse scenes, including the hotel, university, zara, and different places at Stanford.…”