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arxiv: 2506.14831 · v3 · submitted 2025-06-13 · 💻 cs.CV · cs.LG· cs.RO

Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review

C\'eline Finet , Stephane Da Silva Martins , Jean-Bernard Hayet , Ioannis Karamouzas , Javad Amirian , Sylvie Le H\'egarat-Mascle , Julien Pettr\'e , Emanuel Aldea This is my paper

Pith reviewed 2026-05-19 08:55 UTC · model grok-4.3

classification 💻 cs.CV cs.LGcs.RO
keywords multi-agent trajectory predictiondeep learninghuman trajectory predictionsurveyETH/UCY benchmarkautonomous drivingsocial robot navigation
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The pith

A review organizes recent deep learning methods for multi-agent human trajectory prediction by architecture, inputs, and strategies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper establishes a structured categorization of deep learning-based methods for multi-agent human trajectory prediction published between 2020 and 2025. It groups them according to architectural design, input representations, and prediction strategies, with special attention to those tested on the ETH/UCY benchmark. A sympathetic reader would care because accurate modeling of agent interactions supports safer robot navigation, autonomous driving, and crowd simulations. The survey also identifies main challenges and points to future research directions.

Core claim

The authors review recent advancements in deep learning for multi-agent human trajectory prediction. They categorize existing methods based on architectural design, input representations, and overall prediction strategies. Particular emphasis is placed on models evaluated on the ETH/UCY benchmark. Key challenges and future research directions in multi-agent HTP are highlighted as part of the contribution.

What carries the argument

The categorization of methods by architectural design, input representations, and prediction strategies, with emphasis on ETH/UCY evaluations.

Load-bearing premise

The assumption that papers selected between 2020 and 2025 together with the focus on ETH/UCY evaluations provide a sufficiently complete and unbiased picture of the field.

What would settle it

Publication or identification of a significant multi-agent human trajectory prediction method from 2020-2025 that uses a primary evaluation other than ETH/UCY or falls outside the architectural and input categories would test the survey's completeness.

Figures

Figures reproduced from arXiv: 2506.14831 by C\'eline Finet, Emanuel Aldea, Ioannis Karamouzas, Javad Amirian, Jean-Bernard Hayet, Julien Pettr\'e, Stephane Da Silva Martins, Sylvie Le H\'egarat-Mascle.

Figure 1
Figure 1. Figure 1: A color-coded overview of publications used in this survey. Blue indicates multi-agent models, while purple [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The PRISMA methodology flow chart. The criteria for exclusion in the first-round screening include [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The five scenes of the benchmark ETH/UCY. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An overview of the global framework for HTP algorithms that also describes the structure of this paper. The [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Structure of the Section 2. Static Contextual Information FAdvantages FDisadvantages → Understanding the Environment → Lack of Dynamics in Complex Situations Static information, such as semantic maps derived from RGB images, provides a detailed representation of the environment (stationary obstacles). Static information does not account for dynamic elements like moving pedestrians. In crowded or highly dyn… view at source ↗
Figure 6
Figure 6. Figure 6: The Zara2 scene from the ETH/UCY dataset and its corresponding semantic map. Purple pixels are obstacles, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Structure of the Backbone architecture Section. “TF" is for Transformers architecture, we mean not the input part nor the output one, but the layers in between and in charge of treating all the information. The key modules of interest are sequence and generative modules [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Deep-learning modules used in HTP. This figure takes inspiration from [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualisation of different prediction strategies. Direct prediction is efficient and straightforward, often relying on sequence-to-sequence networks or fully connected layers to output all future positions simultaneously. For example, PTP-STGCN [89] relies on two-dimensional convolutional layers for producing bivariate Gaussian distributions from which the predicted trajectories are deduced. Similarly, IMP… view at source ↗
read the original abstract

With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with important implications in areas such as social robot navigation, autonomous driving, and crowd modeling. This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction, focusing on studies published between 2020 and 2025. We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies, placing a particular emphasis on models evaluated using the ETH/UCY benchmark. Furthermore, we highlight key challenges and future research directions in the field of multi-agent HTP.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper is a survey reviewing recent deep learning-based methods for multi-agent human trajectory prediction from 2020 to 2025. It categorizes these methods by architectural design, input representations, and prediction strategies, emphasizing models tested on the ETH/UCY benchmark, and discusses key challenges and future directions.

Significance. If the paper selection is representative, the survey provides a structured categorization that can help organize the literature in multi-agent HTP, facilitating better understanding of interaction modeling in applications such as autonomous driving and social navigation.

major comments (1)
  1. [Introduction] The abstract and introduction state a focus on papers from 2020 to 2025 evaluated on ETH/UCY but provide no details on the literature search strategy, databases queried, keywords, or inclusion/exclusion criteria. This omission directly affects the reliability of the claimed categorization and the highlighted challenges/future directions, as omitted papers could alter the synthesis.
minor comments (1)
  1. [Categorization sections] The taxonomy figures would benefit from more detailed captions explaining how sub-categories map to specific architectural choices.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on improving the transparency of our survey. We agree that explicit details on the paper selection process will strengthen the reliability of the categorization and future directions discussion. We will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Introduction] The abstract and introduction state a focus on papers from 2020 to 2025 evaluated on ETH/UCY but provide no details on the literature search strategy, databases queried, keywords, or inclusion/exclusion criteria. This omission directly affects the reliability of the claimed categorization and the highlighted challenges/future directions, as omitted papers could alter the synthesis.

    Authors: We acknowledge this point. Although the survey is framed as a review of representative recent advancements (as indicated by the use of 'some' in the abstract) rather than an exhaustive systematic review, the absence of search methodology details is a valid concern for transparency. In the revised manuscript, we will add a dedicated subsection in the Introduction describing the literature search strategy. This will include the primary databases consulted (arXiv, Google Scholar, IEEE Xplore), key search terms (e.g., 'multi-agent human trajectory prediction', 'social interaction modeling', 'ETH/UCY benchmark'), the 2020-2025 time frame, and inclusion criteria focused on deep learning methods with ETH/UCY evaluations. Exclusion criteria will note the omission of non-deep-learning or single-agent-only works. This addition will clarify the scope without changing the core categorization or challenges identified. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive survey without derivations or self-referential reductions

full rationale

This paper is a literature review that organizes recent deep learning methods for multi-agent human trajectory prediction by architecture, inputs, and strategies, with emphasis on ETH/UCY evaluations from 2020-2025. No equations, fitted parameters, predictions, or derivation chains appear in the provided text or abstract. Claims rest on external citations rather than reducing to internal definitions, self-citations as load-bearing premises, or renaming of results by construction. Paper selection criteria constitute a methodological scope decision but do not create circularity in any claimed derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey, the paper introduces no free parameters, axioms, or invented entities; it relies on the prior literature it cites.

pith-pipeline@v0.9.0 · 5681 in / 925 out tokens · 30458 ms · 2026-05-19T08:55:55.741636+00:00 · methodology

discussion (0)

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Reference graph

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