用超图建模车辆间多模式交互，提升自动驾驶预测准确性。
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
- 构建超图神经网络捕捉多车群体交互关系
- 在真实数据集上实现更精准的多模态轨迹预测
- 适合自动驾驶决策与行为预测研究者使用
真实驾驶环境中的车辆动态多样，彼此交互复杂且未来状态不确定，给自动驾驶车辆运动状态预测带来挑战。本文提出一种基于超图的集成框架——RHINO，通过多尺度超图神经网络建模多个车辆间的群体交互及其多模态驾驶行为，增强运动预测的准确性和可靠性。该方法能有效捕捉车辆间隐含关系和多样化行为模式。在真实世界数据集上的实验表明，该框架显著提升了预测性能，支持更符合社会规范的自动化驾驶决策。代码已公开于 https://github.com/keshuw95/RHINO-Hypergraph-Motion-Generation。
原文摘要 · Abstract (English)
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.
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