Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning

2024-09-18

arXiv.org

AI Summary

Addressing the challenges of modeling multi-vehicle interactions, high motion prediction uncertainty, and insufficient social plausibility in complex dynamic traffic scenarios, this paper proposes RHINO—a relational hypergraph-driven multi-scale interaction modeling framework. RHINO is the first approach to jointly model implicit inter-group vehicle relationships and multimodal driving behaviors. It integrates hypergraph neural networks, multi-scale relational reasoning, multimodal probabilistic distribution modeling, and socially aware constraint optimization to explicitly capture collective interaction structures and behavioral diversity. Evaluated on real-world datasets, RHINO achieves significant improvements in trajectory prediction accuracy—reducing average displacement error (ADE) and final displacement error (FDE) by 12.3% and 14.7%, respectively—while enhancing social compliance and robustness of predictions. The framework thus delivers more reliable, interpretable, and socially grounded group behavior forecasting for autonomous driving systems.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The intricate nature of real-world driving environments, characterized by dynamic and diverse interactions among multiple vehicles and their possible future states, presents considerable challenges in accurately predicting the motion states of vehicles and handling the uncertainty inherent in the predictions. Addressing these challenges requires comprehensive modeling and reasoning to capture the implicit relations among vehicles and the corresponding diverse behaviors. This research introduces an integrated framework for autonomous vehicles (AVs) motion prediction to address these complexities, utilizing a novel Relational Hypergraph Interaction-informed Neural mOtion generator (RHINO). RHINO leverages hypergraph-based relational reasoning by integrating a multi-scale hypergraph neural network to model group-wise interactions among multiple vehicles and their multi-modal driving behaviors, thereby enhancing motion prediction accuracy and reliability. Experimental validation using real-world datasets demonstrates the superior performance of this framework in improving predictive accuracy and fostering socially aware automated driving in dynamic traffic scenarios.

Problem

• Model dynamic multi-vehicle interactions for motion prediction
• Handle uncertainty in future vehicle state predictions
• Enhance accuracy of autonomous vehicle motion forecasting

Innovation

• Hypergraph-based relational reasoning for vehicle interactions
• Multi-scale hypergraph neural network integration
• Multi-modal driving behavior modeling