mlsanityData to 14 Oct 2026

ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting

arXiv 2506.09626 ↗First posted 11 Jun 2025cs.CVcited by 2

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Abstract (arXiv)

Human trajectory forecasting is crucial in applications such as autonomous driving, robotics and surveillance. Accurate forecasting requires models to consider various factors, including social interactions, multi-modal predictions, pedestrian intention and environmental context. While existing methods account for these factors, they often overlook the impact of the environment, which leads to collisions with obstacles. This paper introduces ECAM (Environmental Collision Avoidance Module), a contrastive learning-based module to enhance collision avoidance ability with the environment. The proposed module can be integrated into existing trajectory forecasting models, improving their ability to generate collision-free predictions. We evaluate our method on the ETH/UCY dataset and quantitatively and qualitatively demonstrate its collision avoidance capabilities. Our experiments show that state-of-the-art methods significantly reduce (-40/50%) the collision rate when integrated with the proposed module. The code is available at https://github.com/CVML-CFU/ECAM.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 11 Jun 2025.

Builds on

30 of 42 references.

References · 30

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18 more
  1. 2021Multimodal Motion Prediction with Stacked Transformerscited by 208
  2. 2022Stochastic Trajectory Prediction via Motion Indeterminacy Diffusioncited by 185
  3. 2020Transformer Networks for Trajectory Forecastingcited by 181
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  13. 2022MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Predictioncited by 59
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  15. 2023EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecastingcited by 41
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  17. 2022Under the Hood of Transformer Networks for Trajectory Forecastingcited by 8
  18. 2024TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Predictioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negativescited by 8
  2. 2026MTF-Net: Multi-Modal Temporal Feature Fusion Network for Pedestrian Intention Predictioncited by 0