Context-free Self-Conditioned GAN for Trajectory Forecasting

2022-12-01

International Conference on Machine Learning and Applications

AI Summary

This work addresses the challenge of modeling multimodal behaviors in context-free 2D trajectory prediction by proposing an unsupervised, self-conditioned generative adversarial network (GAN) that requires no external scene information. The method implicitly captures diverse motion patterns through the discriminator’s feature space and incorporates three tailored training strategies to enhance both diversity and accuracy of predictions. As the first study to apply self-conditioned GANs to context-free trajectory forecasting, the model consistently outperforms existing context-free approaches on both human motion and road-agent datasets, demonstrating particularly strong performance on sparsely labeled categories and achieving state-of-the-art results in human motion prediction.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Unsupervised & Self-Supervised Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator’s feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.

Problem

• trajectory forecasting
• context-free
• multi-modal behavior
• unsupervised learning
• motion prediction

Innovation

• self-conditioned GAN
• trajectory forecasting
• context-free learning
• unsupervised multimodal learning
• behavioral pattern modeling