Human motion trajectory prediction: a survey

By Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, Kai Oliver Arras. 2019

905 citations recorded by the source corpus.

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Social Psychology

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Citations in the corpus

Attention Based Vehicle Trajectory Prediction (2020, background): “We follow Rudenko et al. [2] who divide the motion prediction problem into three main components.”
Transformer Networks for Trajectory Forecasting (2020, background): “The TrajNet Dataset: At the moment of writing, the TrajNet Challenge1 [1] does represent the largest multi-scenario forecasting benchmark [32]; the challenge requests to predict 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.”
A Survey on Trajectory-Prediction Methods for Autonomous Driving (2022, background): “[11], [12] survey human motion prediction, which is obviously different from the topic of this study.”
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction (2020, result): “There have been many previous studies [8] on how to forecast pedestrians’ trajectories and predict their behaviors.”
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting (2021, methodology): “A thorough review of these deterministic methods is provided in [43].”
Trajectron++: Dynamically-Feasible Trajectory Forecasting with Heterogeneous Data (2020, background): “An excellent review of such methods can be found in [40].”
Core Challenges of Social Robot Navigation: A Survey (2021, background): “Note that human motion prediction is a relevant and active field that has received much attention recently [2, 90, 178, 214, 270, 287].”