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

TL;DR

This paper presents RHINO, a hypergraph-based neural network framework for autonomous vehicle motion prediction that models complex multi-vehicle interactions and multi-modal behaviors to improve accuracy and safety.

Contribution

It introduces a novel hypergraph neural network approach for modeling multi-vehicle interactions and multi-modal behaviors in motion prediction for autonomous driving.

Findings

Enhanced prediction accuracy demonstrated on real-world datasets.

Improved social awareness in automated driving scenarios.

Superior performance compared to existing methods.

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. The source code is publicly available at https://github.com/keshuw95/RHINO-Hypergraph-Motion-Generation.