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REVIEW 4 major objections 6 minor 1 cited by

Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A roadside unit can jointly predict every vehicle's future trajectory at a signalized intersection using traffic-light timing and maneuver priors, outperforming vehicle-centric and cooperative baselines by 30% on real-world V2I data.

desk verdict A promising infrastructure-side prediction design with overstated headline results: the SinD table itself undercuts the 15% claim, but the I2X framing and robustness work merit a serious referee. read the letter →

arxiv 2501.13461 v2 pith:BFTANDNC submitted 2025-01-23 cs.RO cs.CVcs.MA

Huilin Yin , Yangwenhui Xu , Jiaxiang Li , Hao Zhang , Gerhard Rigoll This is my paper · ORCID
classification cs.ROcs.CVcs.MA
keywords trajectorypredictionsignalizedintersectionsInfrastructure-to-Everythingmulti-agenttrafficsignalencodingmaneuverstrategypriorV2Xcommunicationjointmulti-modal
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the right place to predict what vehicles will do at a signalized intersection is the roadside, not the vehicle. It proposes an Infrastructure-to-Everything (I2X) scheme in which roadside units forecast the joint future trajectories of every vehicle in the intersection and broadcast those predictions unidirectionally to any subscribing autonomous vehicle. The accompanying model, I2XTraj, feeds real-time traffic light states, prior maneuver-strategy knowledge, and multi-agent interactions into a single attention-based network. On two real-world signalized-intersection datasets the model reports over 30% and 15% gains over existing baselines, and it degrades only gradually under data loss and communication delay. If the claims hold, infrastructure can provide a common prediction service that any autonomous driving system can use without tight coupling to specific on-board models.

What carries the argument

Two mechanisms carry the argument. The continuous signal-informed mechanism encodes each traffic light's remaining time with a nonlinear function $PE_{t,\ell} = \sin\left(\frac{t_{\text{remain}}}{T_\Omega} \cdot \left(\frac{1}{3}\right)^d\right)$, making the model sensitive to signal transitions and usable across intersection layouts. The driving strategy awareness mechanism partitions feasible maneuvers by radial distance from a reference point using the intersection diameter $D$: $[0,1)$ for stop, $[\sqrt{2}D/2, D/2)$ for right turn, $[D/2, D)$ for left turn, $[1, \sqrt{2}D/2)$ for U-turn, and $[\sqrt{2}D, \infty)$ for straight, generalized to $K$ modes as $\{[0,1), [1, D/(K-2)), \ldots, [(k-3)D/(K-2), D), [D,\infty)\}$. These intervals become mode queries through dynamic graph attention and $K$ independent decoders, producing joint trajectory proposals that an off-map loss keeps on drivable areas.

What would settle it

Train I2XTraj on a T-shaped or multi-branch signalized intersection where a single-diameter radial partition does not match the lane geometry, and check whether per-mode probability calibration and minJointFDE degrade compared with an intersection-agnostic mode clustering; a correct geometric prior should also keep the predicted mode distribution nearly unchanged when $D$ is varied by a small amount.

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Extended reading notes

Core claim

I2XTraj's central discovery is that the infrastructure can jointly predict all vehicles' future trajectories at signalized intersections by making the prediction problem knowledge-driven: a continuous encoding of remaining traffic-light times guides trajectory proposals; a driving strategy awareness mechanism maps each vehicle onto a small set of maneuver intervals derived from intersection diameter $D$ and geometry; and a spatial-temporal-mode attention network refines joint proposals across agents, time steps, and modes. The paper reports that this outperforms existing methods by more than 30% on V2X-Seq and 15% on SinD, remains accurate when half the historical data is dropped, and keeps single-agent errors nearly stable up to 400ms latency.

Load-bearing premise

The load-bearing premise is that every vehicle's feasible maneuver at an intersection can be read off from a small set of radial-distance intervals defined by a single intersection diameter $D$, so distance alone separates stop, turn, and straight maneuvers; if $D$ is poorly defined for T-shaped or multi-branch intersections, the strategy-mode assignments and probabilities are mis-specified.

Editorial extensions

If this is right

  • Infrastructure can offer joint multi-agent trajectory prediction as a public subscription service, so heterogeneous autonomous vehicles do not need a unified on-board prediction model or shared data with the roadside.
  • Traffic-signal encoding should let predictors anticipate phase transitions, including early 'jump-start' accelerations and vehicles rushing a yellow light, rather than treating signals as static context.
  • Strategy-aware mode generation is what gives the model its robustness to missing data, because maneuver priors stay stable when vehicle-state observations are dropped.
  • Joint multi-agent predictions degrade gracefully with communication delay up to roughly 400ms, suggesting roadside or edge deployment is feasible for real-time use.
  • The combination of strategy priors and an off-map loss sharply reduces miss rate compared to baselines, which matters for downstream planning and safety.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the radial-distance partition generalizes, the same strategy-aware design could extend to roundabouts, highway ramps, or other constrained maneuver sets, though the paper only demonstrates signalized intersections.
  • A direct test of whether the geometric prior is essential would replace the $D$-based intervals with data-driven maneuver clustering and compare; if gains persist, the prior could be learned rather than assumed.
  • The I2X scheme's value for real traffic depends on how subscribing planners consume joint multi-modal scenes; the paper evaluates prediction quality but not the closed-loop safety outcome.
  • The same subscription architecture could be extended to pedestrians and cyclists by adding their state encodings and maneuver priors, since the model already handles a small shared space qualitatively.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes an Infrastructure-to-Everything (I2X) scheme in which roadside units predict the joint future trajectories of all vehicles at signalized intersections and transmit these predictions unidirectionally to subscribing vehicles. The accompanying model, I2XTraj, combines a continuous traffic-signal encoding, a driving-strategy-awareness module built from interval-based maneuver priors, and a spatial-temporal-mode attention network. The method is evaluated on two real-world datasets, V2X-Seq and SinD, in single-infrastructure and online collaborative settings, including robustness experiments against data loss and communication delay. The paper claims that I2XTraj outperforms state-of-the-art methods by more than 30% on V2X-Seq and more than 15% on SinD.

Significance. If the claims are supported, the I2X prediction scheme is a practical contribution: it decouples infrastructure-side prediction from vehicle-side models, uses traffic signal information and maneuver priors that are naturally available to infrastructure, and reports a lightweight model (3.18M parameters, 112 ms inference). The ablations show that each proposed component contributes positively, and the robustness experiments on data loss and delay address an important deployment concern. However, the headline quantitative claims are not fully supported by the paper's own tables. In particular, the SinD 12-12 result in Table III contradicts the abstract's '>15%' claim, and the V2X-Seq '>30%' claim is not uniformly supported by the multi-agent comparison in Table II. These issues are load-bearing because the paper's central contribution is the claimed empirical superiority.

major comments (4)
  1. [Abstract, Contribution (3), and Table III] The claimed 'more than 15%' improvement on SinD is not supported by Table III. In the 12-12 setting, I2XTraj's minADE is 0.09 against KI-GAN's 0.05, which is an 80% worse minADE, and its minFDE ties KI-GAN at 0.12. In the 12-18 setting, the improvement over KI-GAN is 9.09% in minADE (0.10 vs 0.11) and 19.23% in minFDE (0.21 vs 0.26), so even the better setting does not meet a uniform 15% threshold on both metrics. The abstract and contribution should state per-setting, per-metric improvements and identify the baseline for each percentage.
  2. [Section IV-B] The five-interval maneuver partition is internally inconsistent. The right-turn interval is printed as [\sqrt{2}D/2, D/2), whose lower endpoint exceeds its upper endpoint for any positive D; the U-turn interval [1, \sqrt{2}D/2) overlaps the left-turn interval [D/2, D) for typical intersection diameters; and the straight interval [\sqrt{2}D, \infty) leaves the range [D, \sqrt{2}D) unassigned. The subsequent K-mode formula {[0,1), [1, D/(K-2)), ..., [(k-3)D/(K-2), D), [D,\infty)} does not preserve the five-interval semantics and omits the \sqrt{2}D/2 boundary. Please correct the intervals and specify exactly how the interval memberships produce the labels used in L_cls (Eq. 21) and the mode queries in Eqs. (8)-(13).
  3. [Section V-B and Tables I-II] The 'more than 30% on V2X-Seq' claim is not uniformly supported. Table II, the only multi-agent V2X-Seq comparison, shows improvements over HPNet of 23.53% in minJointADE, 30.8% in minJointFDE, and 19.05% in minJointMR; only one metric exceeds 30%. In the single-agent Table I, improvements over the strongest collaborative baselines are much smaller, for example 5.9% in minADE and 4.5% in minFDE relative to Co-MTP. The authors should identify the exact baseline and metric for the 30% figure or weaken the claim.
  4. [Section IV-D and Table IV] The off-map loss is claimed in Section V-B a to be responsible for the MR improvements, but no ablation isolates it. Table IV varies CSI, MAP, and DSA only, and Eq. (22) always includes L_off-map. Please add an ablation without the off-map term, or explicitly retract the attribution.
minor comments (6)
  1. [Section V-A c] The text lists 'twelve state-of-the-art models,' but the enumeration contains ten V2X-Seq baselines and five SinD baselines; the count should be reconciled.
  2. [Table IV] The rows use only check marks without row labels or a legend, so it is not clear which components are present or absent in each row; add a configuration column or a caption that defines each row.
  3. [Section IV-B] The notation \sqrt{2}D/2 is ambiguous: it should be written as \frac{\sqrt{2}}{2}D to avoid confusion with \sqrt{2D}/2.
  4. [Section V-A d] The implementation statement 'each dynamic graph attention consists of four layers of multi-head attention' is ambiguous; please specify whether each GATv2 operation has four attention heads or four stacked layers.
  5. [Section V-B b] The sentence 'I2XTraj framework also performances improved prediction accuracy' is ungrammatical and should be rephrased.
  6. [Abstract and Section VI] After the quantitative claims are corrected, the percentages in the abstract and the conclusion should be synchronized with the revised per-setting, per-metric results.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claims are empirical comparisons on held-out benchmarks, with hand-crafted inductive biases rather than fitted outputs relabeled as predictions.

full rationale

The paper's derivation chain is not circular. The forward model in Sec. IV consists of learned encoders, attention modules, and decoders (Eqs. (3)-(18)) supervised by held-out ground-truth trajectories; none of the predicted outputs is defined as a function of the training target. The maneuver-interval partition in Sec. IV-B is a hand-crafted inductive bias based on intersection diameter D, and Pk is a learned classification probability, not a fitted parameter renamed as a prediction. The training losses (19)-(22) are standard regression/classification objectives. The only self-citation is the SinD dataset reference [40], whose authors overlap with the present paper; however, this is an external evaluation benchmark, it is not load-bearing for the method's derivation, and it does not make the benchmark comparison circular. The manuscript itself acknowledges that 'short-term prediction shows marginal limitations' on SinD (Sec. V-B-b), and Table III indeed shows I2XTraj does not uniformly beat KI-GAN in the 12-12 setting; that is an empirical-support/correctness concern explicitly outside the circularity dimension. No equation reduces a claimed result to its own input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on two hand-coded priors: the intersection-diameter-based maneuver intervals and the traffic signal encoding constants. No new physical entities are introduced. The model is an empirical architecture whose components are mostly borrowed from cited work, so the main added burden is the ad hoc maneuver partitioning and the unvalidated signal encoding parameters.

free parameters (4)
  • Intersection diameter D = not specified; per-intersection prior
    Used to define all maneuver interval boundaries in the driving strategy awareness mechanism (Section IV-B). The value is not learned or validated, and it directly determines which strategy mode a vehicle is assigned to.
  • Maximum traffic light cycle time T_Omega = not specified; text says a sufficiently large value works
    Used as the scaling denominator in the continuous signal encoding (Eq. 4). Its choice changes the sensitivity of the encoding near signal transitions.
  • Mode count K = 6
    The number of trajectory modes and maneuver intervals is set to 6 in experiments; performance is sensitive to K and no sensitivity analysis is provided.
  • Signal encoding frequency base 1/3 = 1/3
    The d-th color channel is scaled by (1/3)^d in Eq. 4; this is a hand-chosen constant that determines how quickly the encoding changes with remaining time.
assumptions (4)
  • domain assumption Roadside infrastructure has complete and synchronized visibility of all vehicles and traffic light states at the intersection.
    The I2X scheme and all inputs depend on this; stated in Section III-A and used throughout.
  • domain assumption Vehicle maneuvers at intersections are constrained to a finite set (stop, straight, left, right, U-turn) representable by radial distance intervals parameterized by a single intersection diameter D.
    Section IV-B defines all strategy modes this way; no empirical validation of the interval boundaries is provided.
  • ad hoc to paper The K-mode interval formula [ (k-3)D/(K-2), D) preserves the semantic mapping of maneuvers defined by the five-interval partition.
    The generalized formula does not obviously match the earlier specific intervals and is introduced specifically to generate K modes.
  • standard math LaneGCN, GATv2, and the HPNet-style relative spatio-temporal edge encoding provide sufficient representations for intersection prediction.
    Borrowed from cited work; the paper relies on their effectiveness without re-deriving them.

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Cite this review

Pith. "Pith review of Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything." pith.science (2026). https://pith.science/paper/BFTANDNC

@misc{pith2026250113461,
  author       = {Pith},
  title        = {Pith review of: Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BFTANDNC}},
  note         = {Machine review of arXiv:2501.13461}
}
read the original abstract

Multi-agent trajectory prediction at signalized intersections is crucial for developing efficient intelligent transportation systems and safe autonomous driving systems. Due to the complexity of intersection scenarios and the limitations of single-vehicle perception, the performance of vehicle-centric prediction methods has reached a plateau. In this paper, we introduce an Infrastructure-to-Everything (I2X) collaborative prediction scheme. In this scheme, roadside units (RSUs) independently forecast the future trajectories of all vehicles and transmit these predictions unidirectionally to subscribing vehicles. Building on this scheme, we propose I2XTraj, a dedicated infrastructure-based trajectory prediction model. I2XTraj leverages real-time traffic signal states, prior maneuver strategy knowledge, and multi-agent interactions to generate accurate, joint multi-modal trajectory prediction. First, a continuous signal-informed mechanism is proposed to adaptively process real-time traffic signals to guide trajectory proposal generation under varied intersection configurations. Second, a driving strategy awareness mechanism estimates the joint distribution of maneuver strategies by integrating spatial priors of intersection areas with dynamic vehicle states, enabling coverage of the full set of feasible maneuvers. Third, a spatial-temporal-mode attention network models multi-agent interactions to refine and adjust joint trajectory outputs.Finally, I2XTraj is evaluated on two real-world datasets of signalized intersections, the V2X-Seq and the SinD drone dataset. In both single-infrastructure and online collaborative scenarios, our model outperforms state-of-the-art methods by over 30\% on V2X-Seq and 15\% on SinD, demonstrating strong generalizability and robustness.

Figures

Figures reproduced from arXiv: 2501.13461 by the authors.

Figure 1
Figure 1. Schematic illustration of Infrastructure-to-Everything (I2X) trajectory [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. A comparison between the Vehicle-Infrastructure Cooperation (VIC) pipeline and our proposed Infrastructure-to-Everything (I2X) cooperative prediction [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The overall framework of our I2XTraj. Our architecture is an infrastructure-based method, which comprises three parts: (a) Knowledge-Informed Scene [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Schematic illustration of the continuous signal-informed mechanism. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Schematic illustration of dynamic graph attention. FFN donates Feed [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Qualitative comparison of the influence of traffic signal lights. Target [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Qualitative results of I2XTraj on six driving scenarios from V2X-Seq (SI) validation set. (a [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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Forward citations

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Reference graph

Works this paper leans on

46 extracted references · 43 canonical work pages · cited by 1 Pith paper

  1. [1]

    Effects of road width, radii and speeds on collisions at three-arm priority intersections,

    M. Ekmekci, L. Woods, and N. Dadashzadeh, “Effects of road width, radii and speeds on collisions at three-arm priority intersections,” Accid. Anal. Prev., vol. 199, no. 10, p. 107522, May. 2024

    work page 2024

  2. [2]

    Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction,

    F. Zeng, Z. Zhang, and J. Wu, “Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction,”Digital Commun. Networks, Aug. 2024

    work page 2024

  3. [3]

    Bat: Behavior-aware human-like trajectory prediction for autonomous driving,

    H. Liao et al., “Bat: Behavior-aware human-like trajectory prediction for autonomous driving,” in Proc. AAAI Conf. Artif. Intell. (AAAI) , vol. 38, no. 9, Feb. 2024, pp. 10 332–10 340

    work page 2024

  4. [4]

    Densetnt: End-to-end trajectory prediction from dense goal sets,

    J. Gu, C. Sun, and H. Zhao, “Densetnt: End-to-end trajectory prediction from dense goal sets,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Oct. 2021, pp. 15 303–15 312

    work page 2021

  5. [5]

    Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic inter- action graphs,

    L. Rowe, M. Ethier, E.-H. Dykhne, and K. Czarnecki, “Fjmp: Factorized joint multi-agent motion prediction over learned directed acyclic inter- action graphs,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2023, pp. 13 745–13 755

    work page 2023

  6. [6]

    Learning cooperative trajectory representations for motion forecasting,

    H. Ruan, H. Yu, W. Yang, S. Fan, Y . Tang, and Z. Nie, “Learning cooperative trajectory representations for motion forecasting,” in Proc. Adv. Neural Inf. Process. Syst. (NIPS) , Dec. 2024, pp. 13 430–13 457

    work page 2024

  7. [7]

    On the road to aiot: A framework for vehicle road cooperation,

    D. Annunziata, D. Chiaro, P. Qi, and F. Piccialli, “On the road to aiot: A framework for vehicle road cooperation,” IEEE Internet Things J. , vol. 12, no. 5, pp. 5783–5791, Mar. 2025

    2025

  8. [8]

    Advancing vulnerable road users safety: Interdisciplinary review on v2x commu- nication and trajectory prediction,

    B. Abdi, S. Mirzaei, M. Adl, S. Hidajat, and A. Emadi, “Advancing vulnerable road users safety: Interdisciplinary review on v2x commu- nication and trajectory prediction,” IEEE Trans. Intell. Transp. Syst. , vol. 26, no. 3, pp. 2921–2943, Mar. 2025

    work page 2025

Show all 46 references
  1. [9]

    Enhancing task efficiency in vehicular fog computing: Leveraging mobility prediction and min-max optimization for reduced latency,

    I. Sarkar and A. Taherkordi, “Enhancing task efficiency in vehicular fog computing: Leveraging mobility prediction and min-max optimization for reduced latency,” in Proc. Veh. Technol. Conf. (VTC), Jul. 2023, pp. 1–7

    2023

  2. [10]

    Proactive bandwidth allocation for v2x networks with multi-attentional deep graph learning,

    C. Wang et al., “Proactive bandwidth allocation for v2x networks with multi-attentional deep graph learning,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8542–8555, Aug. 2024

    2024

  3. [11]

    Conformal trajectory prediction with multi-view data integration in cooperative driving,

    X. Chen, R. Bhadani, and L. Head, “Conformal trajectory prediction with multi-view data integration in cooperative driving,” arXiv preprint arXiv:2408.00374, 2024. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 12

    2024 arXiv

  4. [12]

    Co-mtp: A cooperative trajectory prediction framework with multi-temporal fusion for autonomous driving,

    X. Zhang, Z. Zhou, Z. Wang, Y . Ji, Y . Huang, and H. Chen, “Co-mtp: A cooperative trajectory prediction framework with multi-temporal fusion for autonomous driving,” arXiv preprint arXiv:2502.16589 , Feb. 2025

    2025

  5. [13]

    Co-http: Cooperative trajectory prediction with heterogeneous graph transformer for autonomous driving*,

    X. Zhang, Z. Zhou, Y . Ji, J. Xing, Z. Wang, and Y . Huang, “Co-http: Cooperative trajectory prediction with heterogeneous graph transformer for autonomous driving*,” in Proc. 27th Int. Conf. Intell. Transp. Syst. (ITSC), Sep. 2024, pp. 3033–3039

    2024

  6. [14]

    Large scale interactive motion forecasting for au- tonomous driving: The waymo open motion dataset,

    S. Ettinger et al. , “Large scale interactive motion forecasting for au- tonomous driving: The waymo open motion dataset,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV)) , 2021, pp. 9710–9719

    2021

  7. [15]

    nuscenes: A multimodal dataset for autonomous driving,

    H. Caesar et al. , “nuscenes: A multimodal dataset for autonomous driving,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Aug. 2020, pp. 11 621–11 631

    2020

  8. [16]

    Argoverse: 3d tracking and forecasting with rich maps,

    M.-F. Chang, J. W. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays, “Argoverse: 3d tracking and forecasting with rich maps,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2019, pp. 8748–8757

    2019

  9. [17]

    Argoverse 2: Next generation datasets for self-driving perception and forecasting,

    B. Wilson et al., “Argoverse 2: Next generation datasets for self-driving perception and forecasting,” in Proc. Adv. Neural Inf. Process. Syst. Track Datasets Benchmarks (NIPS) , vol. 1, Dec. 2021

    2021

  10. [18]

    Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,

    T. Wang et al. , “Deepaccident: A motion and accident prediction benchmark for v2x autonomous driving,” in Proc. AAAI Conf. Artif. Intell. (AAAI), vol. 38, no. 6, Feb. 2024, pp. 5599–5606

    2024

  11. [19]

    V2x-seq: A large-scale sequential dataset for vehicle- infrastructure cooperative perception and forecasting,

    H. Yu et al. , “V2x-seq: A large-scale sequential dataset for vehicle- infrastructure cooperative perception and forecasting,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2023, pp. 5486–5495

    2023

  12. [20]

    Int2: Interactive trajectory prediction at intersections,

    Z. Yan et al., “Int2: Interactive trajectory prediction at intersections,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV) , Oct. 2023, pp. 8536–8547

    2023

  13. [21]

    V2xpnp: Vehicle-to-everything spatio-temporal fu- sion for multi-agent perception and prediction,

    Z. Zhou et al. , “V2xpnp: Vehicle-to-everything spatio-temporal fu- sion for multi-agent perception and prediction,” arXiv preprint arXiv:2412.01812, 2024

    2024 arXiv

  14. [22]

    A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,

    Z. Bai et al., “A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 11, pp. 15 191–15 209, Nov. 2024

    2024

  15. [23]

    V2v4real: A real-world large-scale dataset for vehicle- to-vehicle cooperative perception,

    R. Xu et al. , “V2v4real: A real-world large-scale dataset for vehicle- to-vehicle cooperative perception,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , Aug. 2023, pp. 13 712–13 722

    2023

  16. [24]

    Survey on cooperative perception in an automotive context,

    A. Caillot, S. Ouerghi, P. Vasseur, R. Boutteau, and Y . Dupuis, “Survey on cooperative perception in an automotive context,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 9, pp. 14 204–14 223, Mar. 2022

    2022

  17. [25]

    Segment-based trajectory prediction and risk assessment for rsu- assisted cavs at signalized intersections,

    Y . Cao, W. Shangguan, A. Visser, J. Chen, L. Chai, and B. Cai, “Segment-based trajectory prediction and risk assessment for rsu- assisted cavs at signalized intersections,” IEEE Trans. Intell. Veh. , pp. 1–19, Jun. 2024, early access

    2024

  18. [26]

    The effects of green signal countdown timer and retiming of signal intervals on dilemma zone related crash risk at signalized intersections under heterogeneous traffic conditions,

    M. Paul, I. Ghosh, and M. M. Haque, “The effects of green signal countdown timer and retiming of signal intervals on dilemma zone related crash risk at signalized intersections under heterogeneous traffic conditions,” Saf. Sci., vol. 154, p. 105862, Oct. 2022

    2022

  19. [27]

    D2-tpred: Discontinuous dependency for trajectory prediction under traffic lights,

    Y . Zhang et al. , “D2-tpred: Discontinuous dependency for trajectory prediction under traffic lights,” in Proc. Eur. Conf. Comput. Vis. (ECCV), Oct. 2022, pp. 522–539

    2022

  20. [28]

    A data- driven approach for probabilistic traffic prediction and simulation at signalized intersections,

    A. Wu, Y . Ranjan, R. Sengupta, A. Rangarajan, and S. Ranka, “A data- driven approach for probabilistic traffic prediction and simulation at signalized intersections,” in Proc. IEEE Intell. Veh. Symp. (IV), Jul. 2024, pp. 3092–3099

    2024

  21. [29]

    Ki- gan: Knowledge-informed generative adversarial networks for enhanced multi-vehicle trajectory forecasting at signalized intersections,

    C. Wei, G. Wu, M. J. Barth, A. Abdelraouf, R. Gupta, and K. Han, “Ki- gan: Knowledge-informed generative adversarial networks for enhanced multi-vehicle trajectory forecasting at signalized intersections,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) ...

    2024

  22. [30]

    A hierarchical vehicle behavior prediction framework with traffic signals and interactive agents,

    Z. Yang, R. Zhang, G. Pandey, N. Masoud, and H. X. Liu, “A hierarchical vehicle behavior prediction framework with traffic signals and interactive agents,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 10, pp. 11 066–11 079, Oct. 2023

    2023

  23. [31]

    Incorporating driving knowledge in deep learning based vehicle trajectory prediction: A survey,

    Z. Ding and H. Zhao, “Incorporating driving knowledge in deep learning based vehicle trajectory prediction: A survey,” IEEE Trans. Intell. Veh., vol. 8, no. 8, pp. 3996–4015, Apr. 2023

    2023

  24. [32]

    Physics-informed trajectory prediction for autonomous driving under missing observation,

    H. Liao et al., “Physics-informed trajectory prediction for autonomous driving under missing observation,” in Proc. Int. Jt. Conf. Artif. Intell. (IJCAI), Aug. 2024, pp. 6841–6849

    2024

  25. [33]

    Vehicle trajectory prediction by integrating data-driven and knowledge-guided technique,

    J. Guo, Z. He, H. Wang, J. Wang, and K. Li, “Vehicle trajectory prediction by integrating data-driven and knowledge-guided technique,” IEEE Trans. Intell. Transp. Syst. , vol. 26, no. 5, pp. 5888–5898, May. 2025

    2025

  26. [34]

    Hpnet: Dynamic trajectory forecasting with historical prediction attention,

    X. Tang, M. Kan, S. Shan, Z. Ji, J. Bai, and X. Chen, “Hpnet: Dynamic trajectory forecasting with historical prediction attention,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2024, pp. 15 261–15 270

    2024

  27. [35]

    Query-centric trajectory prediction,

    Z. Zhou, J. Wang, Y .-H. Li, and Y .-K. Huang, “Query-centric trajectory prediction,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2023, pp. 17 863–17 873

    2023

  28. [36]

    Learning lane graph representations for motion forecasting,

    M. Liang et al. , “Learning lane graph representations for motion forecasting,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , Aug. 2020, pp. 541–556

    2020

  29. [37]

    End-to-end object detection with transformers,

    N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , Aug. 2020, pp. 213–229

    2020

  30. [38]

    How attentive are graph attention networks?

    S. Brody, U. Alon, and E. Yahav, “How attentive are graph attention networks?” in Proc. Int. Conf. Learn. Represent (ICLR) , Jan. 2022, pp. 1–26

    2022

  31. [39]

    Tnt: Target-driven trajectory prediction,

    H. Zhao, J. Gao, T. Lan, C. Sun, B. Sapp, B. Varadarajan, Y . Shen, Y . Shen, Y . Chai, C. Schmid et al. , “Tnt: Target-driven trajectory prediction,” in Proc. of the Conference on Robot Learning (CoRL) , Nov. 2021, pp. 895–904

    2021

  32. [40]

    Sind: A drone dataset at signalized intersection in china,

    Y . Xu, W. Shao, J. Li, K. Yang, W. Wang, H. Huang, C. Lv, and H. Wang, “Sind: A drone dataset at signalized intersection in china,” in Proc. 21st Int. Conf. Intell. Transp. Syst. (ITSC) , Oct. 2022, pp. 2471–2478

    2022

  33. [41]

    Hivt: Hierarchical vector transformer for multi-agent motion prediction,

    Z. Zhou, L. Ye, J. Wang, K. Wu, and K. Lu, “Hivt: Hierarchical vector transformer for multi-agent motion prediction,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2022, pp. 8823– 8833

    2022

  34. [42]

    Hsti: A light hierarchical spatial-temporal interaction model for map-free trajectory prediction,

    X. Luo, S. Fu, B. Gao, Y . Zhao, H. Tan, and Z. Song, “Hsti: A light hierarchical spatial-temporal interaction model for map-free trajectory prediction,” IEEE Trans. Intell. Transp. Syst. , pp. 1–10, 2025, early access

    2025

  35. [43]

    Social gan: Socially acceptable trajectories with generative adversarial networks,

    A. Gupta, J. Johnson, L. Fei-Fei, S. Savarese, and A. Alahi, “Social gan: Socially acceptable trajectories with generative adversarial networks,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR) , Jun. 2018, pp. 2255–2264

    2018

  36. [44]

    Social lstm: Human trajectory prediction in crowded spaces,

    A. Alahi, K. Goel, V . Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog- nit. (CVPR), Jun. 2016, pp. 961–971

    2016

  37. [45]

    Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,

    T. Salzmann, B. Ivanovic, P. Chakravarty, and M. Pavone, “Trajectron++: Dynamically-feasible trajectory forecasting with heterogeneous data,” in Proc. Eur. Conf. Comput. Vis. (ECCV) , Aug. 2020, pp. 683–700

    2020

  38. [46]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. Int. Conf. Learn. Represent (ICLR) , May. 2019, pp. 1–18

    2019

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.