mlsanityData to 7 Oct 2026

SocialCircle+: Learning the Angle-based Conditioned Interaction Representation for Pedestrian Trajectory Prediction

arXiv 2409.14984 ↗First posted 23 Sep 2024cs.CVcited by 6

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Abstract (arXiv)

Trajectory prediction is a crucial aspect of understanding human behaviors. Researchers have made efforts to represent socially interactive behaviors among pedestrians and utilize various networks to enhance prediction capability. Unfortunately, they still face challenges not only in fully explaining and measuring how these interactive behaviors work to modify trajectories but also in modeling pedestrians' preferences to plan or participate in social interactions in response to the changeable physical environments as extra conditions. This manuscript mainly focuses on the above explainability and conditionality requirements for trajectory prediction networks. Inspired by marine animals perceiving other companions and the environment underwater by echolocation, this work constructs an angle-based conditioned social interaction representation SocialCircle+ to represent the socially interactive context and its corresponding conditions. It employs a social branch and a conditional branch to describe how pedestrians are positioned in prediction scenes socially and physically in angle-based-cyclic-sequence forms. Then, adaptive fusion is applied to fuse the above conditional clues onto the social ones to learn the final interaction representation. Experiments demonstrate the superiority of SocialCircle+ with different trajectory prediction backbones. Moreover, counterfactual interventions have been made to simultaneously verify the modeling capacity of causalities among interactive variables and the conditioning capability.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 46 of its 80 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

46 of 80 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  3. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
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34 more
  1. 2022GroupNet: Multiscale Hypergraph Neural Networks for Trajectory Prediction with Relational Reasoningcited by 128
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  14. 2021Interpretable Social Anchors for Human Trajectory Forecasting in Crowdscited by 44
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  16. 2017Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detectioncited by 42
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  22. 2023Fast Inference and Update of Probabilistic Density Estimation on Trajectory Predictioncited by 27
  23. 2023SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Predictioncited by 27
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrationscited by 5
  2. 2024Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Predictioncited by 3
  3. 2025Reverberation: Learning the Latencies Before Forecasting Trajectoriescited by 1
  4. 2025User Trajectory Prediction Unifying Global and Local Temporal Informationcited by 0
  5. 2026Encore: Conditioning Trajectory Forecasting via Biased Ego Rehearsalscited by 0
  6. 2026GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Predictioncited by 0