Pedestrian trajectory prediction aims to predict future trajectories based on observed trajectories. Current state-of-the-art methods often assume that the observed sequences of agents are complete, which is a strong assumption that overlooks inherent uncertainties. Understanding pedestrian behavior when dealing with missing values in the observed sequence is crucial for enhancing the performance of predictive models. In this work, we propose the MultiScale hypergraph for Trajectory Imputation and Prediction (MS-TIP), a novel approach that simultaneously addresses the imputation of missing observations and the prediction of future trajectories. Specifically, we leverage transformers with diagonal masked self-attention to impute incomplete observations. Further, our approach promotes complex interaction modeling through multi-scale hypergraphs, optimizing our trajectory prediction module to capture different types of interactions. With the inclusion of scenic attention, we learn contextual scene information, instead of sole reliance on coordinates. Additionally, our approach utilizes an intermediate control point and refinement module to infer future trajectories accurately. Extensive experiments validate the efficacy of MS-TIP in precisely predicting pedestrian future trajectories. Code is publicly available at https://github.com/Pranav-chib/MS-TIP.

Trajectory prediction is an important task that involves modeling the indeterminate nature of agents to forecast future trajectories given the observed trajectory sequences. The task of predicting trajectories poses significant challenges, as agents not only move individually through time but also interact spatially. The learning of complex spatio-temporal representations stands as a fundamental challenge in trajectory prediction. To this end, we propose a novel approach called SSWDP (Self-Supervised Waypoint Distortion Prediction). We propose a simple yet highly effective self-supervised task of predicting distortion present in the observed trajectories to improve the representation learning of the model. Our approach can complement existing trajectory prediction methods. The experimental results highlight a significant improvement with relative percentage differences of 22.7%/38.9%, 33.8%/36.4%, and 16.60%/23.20% in ADE/FDE for the NBA, TrajNet++, and ETH-UCY datasets, respectively, compared to the baseline methods. Our approach also demonstrates a significant improvement over baseline methods with relative percentage differences of 76.8%/82.5% and 61.0%/36.1% in ADE/FDE for TrajNet++ and NBA datasets in distorted environments, respectively.

Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://github.com/Pranav-chib/TrajImpute.