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

2025-06-11

AI Summary

Existing pedestrian trajectory prediction methods often neglect environmental constraints, leading to predicted trajectories that frequently collide with static obstacles. To address this, we propose the Environment Collision Avoidance Module (ECAM), the first approach to introduce contrastive learning into environment-aware trajectory prediction. ECAM explicitly models spatial relationships between pedestrians and obstacles via an environment attention mechanism, obstacle-distance-aware encoding, and multi-scale spatial constraint modeling. Designed as a plug-and-play module, ECAM integrates seamlessly with existing backbone networks without architectural modifications. Evaluated on the ETH/UCY benchmark, ECAM reduces the environment collision rate of state-of-the-art methods by 40–50%, significantly enhancing the physical plausibility, safety, and feasibility of predicted trajectories.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Motion & Tracking
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Avoiding environmental collisions in trajectory forecasting
• Enhancing collision-free predictions in existing models
• Reducing collision rates in human trajectory forecasting

Innovation

• Contrastive learning for collision avoidance
• Integration with existing trajectory models
• Reduces collision rate by 40-50%