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arxiv: 2604.03649 · v1 · submitted 2026-04-04 · 💻 cs.CV · cs.AI

ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations

Ruochen Li , Ziyi Chang , Junyan Hu , Jiannan Li , Amir Atapour-Abarghouei , Hubert P. H. Shum This is my paper

Pith reviewed 2026-05-13 17:40 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords pedestrian trajectory predictionrelational transformertemporal-aware relation graphadaptive interaction pruninghuman interaction modelingETH/UCY benchmarkNBA trajectory datasettransformer efficiency
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The pith

The Adaptive Relational Transformer improves pedestrian trajectory prediction by explicitly modeling how pairwise interactions change over time while pruning unnecessary computations.

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

Predicting where pedestrians will walk next is essential for robots navigating crowds safely. Existing graph and transformer methods either add too much computation or fail to capture how human interactions shift from moment to moment. This paper introduces the Adaptive Relational Transformer, which builds a temporal-aware relation graph to track evolving interactions and uses adaptive pruning to skip redundant calculations. On standard benchmarks like ETH/UCY and NBA, the model reaches higher accuracy than prior work while running more efficiently. A reader should care because better trajectory forecasts could enable smoother robot-human interactions in real environments.

Core claim

The Adaptive Relational Transformer (ART) introduces a Temporal-Aware Relation Graph (TARG) to explicitly capture the evolution of pairwise interactions among pedestrians and an Adaptive Interaction Pruning (AIP) mechanism to reduce redundant computations. This combination allows the model to represent diverse and time-varying human interactions more effectively than previous graph-based or transformer-based approaches, leading to state-of-the-art accuracy and computational efficiency on the ETH/UCY and NBA benchmarks.

What carries the argument

Temporal-Aware Relation Graph (TARG) combined with Adaptive Interaction Pruning (AIP), which together model changing pairwise relations and eliminate unnecessary interaction computations.

Load-bearing premise

The assumption that the temporal-aware graph and pruning mechanism can consistently identify and preserve all critical time-varying interactions without introducing bias or discarding essential data on the tested scenarios.

What would settle it

A new dataset featuring highly complex, rapidly changing group interactions where the pruned model produces significantly higher prediction errors than a full-interaction baseline.

Figures

Figures reproduced from arXiv: 2604.03649 by Amir Atapour-Abarghouei, Hubert P. H. Shum, Jiannan Li, Junyan Hu, Ruochen Li, Ziyi Chang.

Figure 1
Figure 1. Figure 1: Framework overview. Framework overview. The relation between [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of ART. Left: Temporal-Aware Relation Graph (TARG) leverages pairwise attention to model agent interactions across time steps, assigning higher weights to informative moments. Right: Adaptive Interaction Pruning (AIP) uses top-p filtering to adaptively retain informative neighbors based on cumulative interaction strength, producing a sparsified graph for trajectory prediction. The proposed ART ach… view at source ↗
Figure 3
Figure 3. Figure 3: Ablation study of Top-p threshold on the ETH/UCY dataset. TABLE III ABLATION STUDY OF RELATION WEIGHTING STRATEGIES ON THE ETH/UCY DATASET. Weighting Strategies ETH/UCY Dataset min ADE20 min FDE20 Cosine Similarity 0.22 0.36 Random Weighting 0.23 0.37 Uniform Weighting 0.23 0.36 Ours 0.20 0.32 relations leads to more effective interaction representations. 2) Ablation Study on Top-p Threshold [PITH_FULL_IM… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparisons with MART [17] on the ETH/UCY [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparisons with MART [17] on the NBA dataset. Past [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

Accurate prediction of real-world pedestrian trajectories is crucial for a wide range of robot-related applications. Recent approaches typically adopt graph-based or transformer-based frameworks to model interactions. Despite their effectiveness, these methods either introduce unnecessary computational overhead or struggle to represent the diverse and time-varying characteristics of human interactions. In this work, we present an Adaptive Relational Transformer (ART), which introduces a Temporal-Aware Relation Graph (TARG) to explicitly capture the evolution of pairwise interactions and an Adaptive Interaction Pruning (AIP) mechanism to reduce redundant computations efficiently. Extensive evaluations on ETH/UCY and NBA benchmarks show that ART delivers state-of-the-art accuracy with high computational efficiency.

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

0 major / 3 minor

Summary. The paper proposes an Adaptive Relational Transformer (ART) for pedestrian trajectory prediction. It introduces a Temporal-Aware Relation Graph (TARG) to explicitly model the evolution of time-varying pairwise interactions and an Adaptive Interaction Pruning (AIP) mechanism to reduce redundant computations. The approach is evaluated on the ETH/UCY and NBA benchmarks, where it reports state-of-the-art accuracy alongside high computational efficiency.

Significance. If the results hold, the work is significant for trajectory prediction in robotics applications. It improves upon prior graph- and transformer-based methods by explicitly handling diverse, time-varying interactions while maintaining efficiency. Strengths include internally consistent architecture and loss formulation, ablations that isolate the contribution of TARG and AIP, and direct efficiency comparisons against comparable baselines on standard benchmarks.

minor comments (3)
  1. [Abstract] Abstract: The claim of SOTA accuracy would benefit from a brief quantitative summary (e.g., ADE/FDE deltas on ETH/UCY) to allow readers to assess the magnitude of improvement without reading the full results section.
  2. [§4] §4 (Experiments): While ablations are reported, include a short error analysis or failure-case discussion (e.g., crowded scenes or long-horizon predictions) to strengthen the claim that AIP does not discard critical interactions.
  3. [§3] Notation: Ensure consistent use of symbols for temporal relations in TARG across equations and figures; a small table of key symbols would improve readability.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive review and recommendation of minor revision. We are pleased that the significance for robotics applications, internal consistency of the architecture and loss, ablations isolating TARG and AIP, and efficiency comparisons are recognized. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces an Adaptive Relational Transformer (ART) architecture consisting of a Temporal-Aware Relation Graph (TARG) and Adaptive Interaction Pruning (AIP) mechanism for modeling pedestrian interactions. Its claims rest entirely on empirical evaluation against standard external benchmarks (ETH/UCY and NBA), with reported SOTA accuracy and efficiency metrics. No equations, derivations, or parameter-fitting steps are described that would reduce any prediction or result to the inputs by construction. Ablations isolate component contributions independently of the final numbers, and the work is self-contained against those benchmarks without load-bearing self-citation chains or self-definitional reductions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted from the provided text.

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Reference graph

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