Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

Javier Gozalvez; Luca Lusvarghi; Miguel Sepulcre; Shahab Salehi

arxiv: 2606.21344 · v1 · pith:F5WAERXMnew · submitted 2026-06-19 · 💻 cs.AI · cs.LG· cs.RO

Mind the Noise: Sensitivity of Transformer-based Interaction-Aware Trajectory Prediction Models to Noisy Data

Shahab Salehi , Luca Lusvarghi , Miguel Sepulcre , Javier Gozalvez This is my paper

Pith reviewed 2026-06-26 14:24 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.RO

keywords trajectory predictiontransformernoisy dataautonomous vehiclesV2Xinteraction-aware modelssensitivity analysisperception uncertainty

0 comments

The pith

Noisy real-world data reduces Transformer trajectory prediction accuracy by up to 3.9 times.

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

The paper tests how noise in object position, speed, and heading affects a state-of-the-art Transformer model that uses attention to capture interactions among vehicles. Standard training and test sets are created by cleaning data from multiple sensors, but real deployments receive noisy estimates from onboard perception and V2X messages. Experiments add controlled noise levels that match reported sensor and communication errors and measure the resulting drop in prediction quality. Accuracy falls by a factor of 1.3 even at low noise and reaches 3.9 at the highest realistic levels, showing that interaction-aware models remain brittle when inputs contain the uncertainty present on the road.

Core claim

State-of-the-art Transformer-based interaction-aware trajectory prediction models, which rely on attention to model multi-agent interactions, exhibit rapid deterioration in prediction accuracy when supplied with noisy object state information that reflects real perception uncertainties and V2X localization errors.

What carries the argument

Attention mechanisms inside the Transformer that encode interactions among agents, which become unreliable once input states contain additive noise.

If this is right

Prediction error grows steadily with noise intensity rather than remaining stable up to a threshold.
Even modest noise levels already multiply error by 1.3, while the upper end of realistic noise multiplies it by 3.9.
Current clean training and evaluation sets do not expose the models to the conditions they will meet in deployment.
Explicit noise-mitigation techniques or uncertainty-aware inputs become necessary for reliable performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Adding explicit noise during training could improve robustness without changing model architecture.
The same sensitivity is likely to appear in other attention-based or graph-based predictors used for motion forecasting.
Field trials that log both predicted trajectories and ground-truth positions under live V2X conditions would provide a direct test of the reported degradation factors.

Load-bearing premise

The noise models and intensity ranges tested in the experiments match the actual uncertainties that occur in vehicle perception systems and V2X communications.

What would settle it

Running the same model on a dataset collected from actual V2X-equipped vehicles where measured localization and perception errors are recorded, then checking whether accuracy drops match the factors reported in the controlled experiments.

Figures

Figures reproduced from arXiv: 2606.21344 by Javier Gozalvez, Luca Lusvarghi, Miguel Sepulcre, Shahab Salehi.

**Figure 3.** Figure 3: Relative increase of the minADE଺, minFDE଺, and MR଺ metrics with respect to the original noiseless scenario under increasing V2X noise levels [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

read the original abstract

Trajectory prediction allows autonomous vehicles to anticipate the future behavior of surrounding objects (or agents) and, accordingly, maximize the safety and efficiency of their driving. State-of-the-art Transformed-based interaction-aware trajectory prediction models, which rely on attention mechanisms to capture multi-agent interactions and maximize prediction accuracy, are commonly trained and evaluated on long-range high-quality datasets. These datasets are typically obtained by aggregating data from multiple vehicles or drones and removing any object detection or tracking noise offline. Yet, information about a surrounding object's state (its position, speed, heading) is far from being noiseless in real-world deployments. Object state estimation is affected by perception uncertainties and localization errors that can be particularly large for objects received via Vehicle-to-Everything (V2X) communications. In this paper, we analyze the impact of noisy object state information on the trajectory prediction accuracy of a state-of-the-art Transformer-based interaction-aware trajectory prediction model. Our study demonstrates that trajectory prediction accuracy can rapidly deteriorate as the noise intensity increases. Numerical results show that the prediction accuracy can reduce by a 1.3x factor under small noise levels and by as much as a 3.9x factor under the highest (yet realistic) noise conditions. These findings reveal the strong sensitivity of trajectory prediction models to noisy data, underscoring the need for more realistic training and evaluation datasets as well as noise mitigation strategies.

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.

Desk Editor's Note private letter to a colleague

The paper shows transformer trajectory predictors degrade 1.3x-3.9x under added input noise but the chosen noise models lack clear ties to measured V2X or perception error stats.

read the letter

The main thing here is that these transformer-based interaction-aware trajectory predictors lose accuracy fast when position, speed, and heading inputs carry noise, with reported drops of 1.3 times at low levels and up to 3.9 times at the highest tested intensities. The work also flags that standard clean datasets do not match real deployment conditions.

The paper applies noise to an existing model on common datasets and measures the resulting prediction error growth. It does this directly without claiming new architectures or theory, which keeps the contribution focused on the sensitivity result. That is a reasonable thing to do because most prior work evaluates on aggregated high-quality data that has had detection and tracking noise removed.

The experiments appear to show consistent degradation across noise intensities, which supports the practical point that robustness matters for autonomous driving use cases where V2X and perception data are noisy.

The soft spot is the noise model. The abstract describes the higher levels as realistic, yet the paper does not appear to include citations or direct comparisons to field-measured V2X localization errors or sensor covariance data. If the injected noise is uncorrelated or has variances that do not match actual tracking outputs, the exact degradation factors become harder to translate to deployment. This is a real but not load-bearing concern; the general finding of sensitivity still stands.

This is for researchers working on trajectory prediction who want to think about moving models out of clean lab settings. A reader focused on robustness or evaluation practices would get value from the empirical numbers. It is not a big theoretical step but the observation is useful.

I would bring it to a reading group as a maybe to talk about noise modeling choices. I would not cite it in my own work in the next year because it is mainly a diagnostic note rather than a new method or dataset. It does deserve peer review. The experiments back the sensitivity claim and the topic is relevant enough that referees can address the noise grounding and suggest improvements.

Referee Report

2 major / 0 minor

Summary. The manuscript presents an empirical analysis of the sensitivity of state-of-the-art Transformer-based interaction-aware trajectory prediction models to additive noise in object state inputs (position, speed, heading). It reports that prediction accuracy degrades by a factor of 1.3 under small noise levels and up to 3.9 under the highest tested (claimed realistic) noise conditions, based on experiments with noisy data, and concludes that current models trained on clean aggregated datasets are brittle and that more realistic training/evaluation protocols plus noise mitigation are required.

Significance. If the reported degradation factors prove reproducible and the injected noise distributions are shown to match measured V2X/perception error statistics, the work would usefully document a practical limitation of attention-based predictors and motivate noise-aware training regimes. The absence of any machine-checked proofs or parameter-free derivations is expected for an empirical sensitivity study; the value would lie in the falsifiable numerical measurements themselves.

major comments (2)

[Abstract] Abstract: The central numerical claims (1.3x and 3.9x accuracy reduction) are presented without any accompanying description of the exact noise distributions, variances, correlation structure, dataset splits, error metrics (ADE/FDE), statistical significance tests, or baseline comparisons. This prevents verification of the reported degradation factors from the provided text.
[Abstract] Abstract: The statement that the highest noise conditions are “yet realistic” is unsupported by any citation to empirical V2X localization error covariances, camera/radar tracking statistics, or field measurements; without such grounding the 1.3x–3.9x factors cannot be interpreted as representative of deployment conditions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We agree that the abstract would benefit from additional details to support the reported degradation factors and the realism claim. We will revise the abstract in the next version to address these points while keeping it concise. Below we respond to each major comment.

read point-by-point responses

Referee: [Abstract] Abstract: The central numerical claims (1.3x and 3.9x accuracy reduction) are presented without any accompanying description of the exact noise distributions, variances, correlation structure, dataset splits, error metrics (ADE/FDE), statistical significance tests, or baseline comparisons. This prevents verification of the reported degradation factors from the provided text.

Authors: We agree the abstract, as a summary, omits these details. The full manuscript specifies the noise model (additive independent Gaussian perturbations to position, speed, and heading with variances scaled to match typical sensor uncertainties), the evaluation metrics (ADE and FDE), dataset splits, baseline comparisons, and reports statistical significance where applicable. To make the central claims verifiable from the abstract alone, we will revise it to briefly note the noise model, metrics used, and that full experimental details appear in Sections 3 and 4. This change will be incorporated. revision: yes
Referee: [Abstract] Abstract: The statement that the highest noise conditions are “yet realistic” is unsupported by any citation to empirical V2X localization error covariances, camera/radar tracking statistics, or field measurements; without such grounding the 1.3x–3.9x factors cannot be interpreted as representative of deployment conditions.

Authors: The noise levels were selected to reflect reported ranges of V2X and perception errors discussed in the introduction and related-work sections of the full paper. We acknowledge that the abstract itself provides no citations for this claim. We will revise the abstract to include a brief supporting reference to representative V2X localization error statistics (e.g., position errors on the order of several meters under urban conditions) and add the corresponding citations. This will allow the degradation factors to be interpreted against deployment-relevant conditions. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical sensitivity study

full rationale

The paper conducts an empirical analysis by injecting noise into input data and measuring resulting prediction accuracy drops on a Transformer model. No derivations, equations, fitted parameters presented as predictions, or self-citation chains are present. All reported factors (1.3x–3.9x) are direct experimental measurements, not outputs of a closed model or self-referential construction. The noise model grounding concern is a validity issue, not circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the representativeness of the chosen noise model and the assumption that the tested Transformer architecture is a fair proxy for current SOTA interaction-aware predictors; no free parameters are explicitly fitted in the abstract, and no new entities are postulated.

free parameters (1)

noise intensity levels
Specific small and high noise values selected to represent realistic conditions; exact distributions and magnitudes not stated in abstract.

axioms (1)

domain assumption The selected Transformer model and dataset are representative of state-of-the-art interaction-aware trajectory predictors.
Invoked when generalizing the observed sensitivity to the broader class of models.

pith-pipeline@v0.9.1-grok · 5796 in / 1261 out tokens · 14916 ms · 2026-06-26T14:24:56.111165+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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It is worth highlighting that, in both Fig

The relative increase of all metrics ranges from 1.3x to 3.9x (observed in the minADE଺ case). It is worth highlighting that, in both Fig. 2 and Fig. 3, minADE௄ exhibits a larger relative increase than minFDE௄, across all V2X noise levels. This indicates that noisy object state information affects the overall shape and temporal consistency of predicted tra...
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and the INTERACTION dataset [8], respectively. These datasets rely on offline post-processing pipelines to: (i) aggregate observations from multiple viewpoints and collect a larger number of surrounding objects, including objects beyond the line-of-sight range of the ego-vehicle’s onboard sensors; and (ii) remove detection and tracking noise to obtain hig...

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Argoverse 2: Next Generation Datasets for Self- Driving Perception and Forecasting,

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INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps,

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Y. Huang, et al., “A Survey on Trajectory-Prediction Methods for Autonomous Driving,” IEEE Transactions on Intelligent Vehicles, vol. 7, no. 3, Sep. 2022, pp. 652–674

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SIMPL: A Simple and Efficient Multi-Agent Motion Prediction Baseline for Autonomous Driving,

L. Zhang, P. Li, S. Liu, and S. Shen, “SIMPL: A Simple and Efficient Multi-Agent Motion Prediction Baseline for Autonomous Driving,” IEEE Robot. Autom. Lett., vol. 9, no. 4, Apr. 2024, pp. 3767–3774

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nuscenes: A multimodal dataset for autonomous driving,

H. Caesar et al., “nuscenes: A multimodal dataset for autonomous driving,” in Proc. IEEE/CVF CVPR 2020 , Seattle, WA, USA, June 2020, pp. 11621–11631

2020

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OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising,

H. Zhang et al., “OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising,” in Proc. IEEE/CVF CVPR 2024 , Seattle, WA, USA, Jun. 2024, pp. 14802–14811

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2021

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J. Houston et al., “One Thousand and One Hours: Self-driving Motion Prediction Dataset,” in Proc. CoRL 2020, Cambridge, MA, USA, Jan. 2021, pp. 409–418

2020

[17] [17]

How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles,

A. Mohammadisarab et al., “How the Fusion of Onboard Sensors and V2X Data can Improve (or not) the Cooperative Perception of Connected Automated Vehicles,” in Proc. VTC2025-Spring , Oslo, Norway, 2025, pp. 1-5

2025

[18] [18]

Generation of Cooperative Perception Messages for Connected and Automated Vehicles,

G. Thandavarayan, et al., “Generation of Cooperative Perception Messages for Connected and Automated Vehicles,” IEEE Transactions on Vehicular Technology , vol. 69, no. 12, Dec. 2020, pp. 16336-16341

2020

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Maximum non-bounded difference method for overbounding Global Navigation Satellite System errors,

M. M. S. Alghananim and W. Y. Ochieng, “Maximum non-bounded difference method for overbounding Global Navigation Satellite System errors,” GPS Solutions, vol. 29, no. 1, 2025, pp. 1–14. Fig. 3. Relative increase of the minADE଺, minFDE଺, and MR଺ metrics with respect to the original noiseless scenario under increasing V2X noise levels. Fig. 2. Relative incr...

2025