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Cited by 2,466Human Motion and AnimationAutonomous Vehicle Technology and SafetyRobotic Path Planning Algorithms

Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks

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Gupta, Agrim, Johnson, Justin, Fei-Fei, Li, Savarese, Silvio, Alahi, Alexandre·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00240
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Abstract

Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the future. We tackle this problem by combining tools from sequence prediction and generative adversarial networks: a recurrent sequence-to-sequence model observes motion histories and predicts future behavior, using a novel pooling mechanism to aggregate information across people. We predict socially plausible futures by training adversarially against a recurrent discriminator, and encourage diverse predictions with a novel variety loss. Through experiments on several datasets we demonstrate that our approach outperforms prior work in terms of accuracy, variety, collision avoidance, and computational complexity.

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  • A quartic polynomial in the longitudinal direction and a quintic polynomial in the lateral direction guarantee continuity of both longitudinal and lateral accelerations, contributing to a smooth and comfortable trajectory.

    “To ensure the comfort of the generated trajectory ξ e,t at time step t , a quartic polynomial in the longitudinal direction l and a quintic polynomial in the lateral direction d are employed, such that, ξ e,t = { l e,τ , d e,τ } . This formulation follows established practice, as it guarantees the continuity of both longitudinal and lateral accelerations, thereby contributing to a smooth and comf”

    From Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving (2026) · cites this paper as [30]

    The Method (p. 3) describes a GAN-based pedestrian trajectory predictor, while the Conclusion (p. 8) discusses socially acceptable predictions. Searches for quartic and quintic polynomials, longitudinal and lateral motion, and acceleration continuity found no passage addressing the claimed formulation or its comfort guarantee. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Under the assumptions that terminal lateral acceleration and jerk are zero, the resulting trajectory minimizes jerk, thereby enhancing passenger comfort.

    “Under these assumptions, the resulting trajectory minimizes jerk, thereby enhancing passenger comfort [30].”

    From Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving (2026) · cites this paper as [30]

    The Introduction (p. 1) and Conclusion (p. 8) describe predicting socially acceptable pedestrian trajectories, not constructing minimum-jerk trajectories. Searches for terminal lateral acceleration, jerk minimization, and passenger comfort found no passage addressing those claims. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A.
Affiliations
  • Stanford University (US)
  • École Polytechnique Fédérale de Lausanne (CH)
ORCID
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
2255-2264
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2018.00240
OpenAlex
W2794787653
Cited by
2,466
References
5
Citations by year
2026: 219 · 2025: 408 · 2024: 375 · 2023: 376 · 2022: 331 · 2021: 366 · 2020: 260 · 2019: 119 · 2018: 12
FWCI
282.40
Type
conference paper
Language
en
Open access
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
Keywords
human motion prediction, generative adversarial networks, collision avoidance, social robots, autonomous vehicles, human-centric environments

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