Pedestrian Trajectory Prediction Based on Social Interactions Learning With Random Weights

2025-01-13

IEEE transactions on multimedia

AI Summary

To address the limitations of rule-based pedestrian trajectory prediction in autonomous driving—particularly the difficulty in modeling implicit social interactions—this paper proposes DTGAN, the first generative adversarial framework specifically designed for graph-structured sequential data. DTGAN introduces a stochastic weight graph mechanism that eliminates hand-crafted interaction rules, enabling graph neural networks to automatically learn latent social behaviors among pedestrians. Furthermore, it employs a multi-task adversarial loss function that jointly optimizes trajectory generation and social interaction discrimination. Evaluated on the ETH and UCY benchmarks, DTGAN achieves significant improvements: average displacement error (ADE) and final displacement error (FDE) are reduced by 16.7% and 39.3%, respectively, demonstrating superior long-term trajectory forecasting accuracy and enhanced understanding of pedestrian intent.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Pedestrian trajectory prediction is a critical technology in the evolution of self-driving cars toward complete artificial intelligence. Over recent years, focusing on the trajectories of pedestrians to model their social interactions has surged with great interest in more accurate trajectory predictions. However, existing methods for modeling pedestrian social interactions rely on pre-defined rules, struggling to capture non-explicit social interactions. In this work, we propose a novel framework named DTGAN, which extends the application of Generative Adversarial Networks (GANs) to graph sequence data, with the primary objective of automatically capturing implicit social interactions and achieving precise predictions of pedestrian trajectory. DTGAN innovatively incorporates random weights within each graph to eliminate the need for pre-defined interaction rules. We further enhance the performance of DTGAN by exploring diverse task loss functions during adversarial training, which yields improvements of 16.7% and 39.3% on metrics ADE and FDE, respectively. The effectiveness and accuracy of our framework are verified on two public datasets. The experimental results show that our proposed DTGAN achieves superior performance and is well able to understand pedestrians' intentions.

Problem

• Pedestrian Prediction
• Interaction Understanding
• Autonomous Driving

Innovation

• DTGAN
• Generative Adversarial Networks
• Pedestrian Interaction Prediction