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

TL;DR

This paper presents ECAM, a contrastive learning module that can be integrated into existing trajectory forecasting models to significantly reduce environmental collisions in predictions for autonomous systems.

Contribution

Introduction of ECAM, a contrastive learning-based module that improves collision avoidance with the environment in trajectory forecasting models.

Findings

Reduces collision rate by up to 50% when integrated with existing models.

Demonstrates effectiveness on ETH/UCY dataset.

Enhances collision-free trajectory predictions.

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.