发表机构
University of Central Florida（中佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CAA-GMM模型，利用上下文感知注意力机制和高斯混合实现多模态轨迹预测，在nuScenes和Argoverse 2数据集上达到先进精度且计算高效。
AI 中文摘要
可靠且可解释的轨迹预测对于复杂和不确定环境下的协同驾驶与自动驾驶至关重要。本文提出了一种基于上下文感知注意力机制的高斯混合模型（CAA-GMM），用于多模态、不确定性感知的运动预测。所提出的方法将未来运动建模为以场景上下文和智能体动态为条件的概率混合，通过可解释的高斯分量捕捉多样化的行为模式。一种轻量级注意力机制自适应地编码智能体间的交互和上下文显著性，使得在密集交通场景中能够高效融合栅格化环境线索与运动历史。在nuScenes和Argoverse 2数据集上的全面评估表明，CAA-GMM在准确性上相较于最先进的基于栅格的基线方法具有竞争力或更优，同时保持较低的计算复杂度。消融分析证实了注意力模块对于稳健的上下文推理和预测精度的重要性。此外，在不完善的通信和感知条件下的评估突显了该框架对不确定性的鲁棒性，确立了CAA-GMM作为智能交通系统中协同轨迹预测的一种高效且可扩展的解决方案。
英文摘要
Reliable and interpretable trajectory prediction is critical for cooperative and autonomous driving in complex and uncertain environments. This paper introduces a Context-Aware Attention-based Gaussian Mixture Model (CAA-GMM) for multimodal, uncertainty-aware motion forecasting. The proposed approach models future motion as a probabilistic mixture conditioned on both scene context and agent dynamics, capturing diverse behavioral modes with interpretable Gaussian components. A lightweight attention mechanism adaptively encodes inter-agent interactions and contextual salience, enabling efficient fusion of rasterized environment cues and motion history in dense traffic scenes. Comprehensive evaluations on the nuScenes and Argoverse 2 datasets demonstrate that CAA-GMM achieves competitive or superior accuracy compared with state-of-the-art raster-based baselines, while maintaining low computational complexity. Ablation analyses confirm the importance of the attention module for robust contextual reasoning and predictive precision. Furthermore, evaluations under imperfect communication and perception conditions highlight the framework's resilience to uncertainty, establishing CAA-GMM as an efficient and scalable solution for cooperative trajectory prediction in intelligent transportation systems.
Comments6 pages, 3 figures. Presented at the 2026 IEEE 104th Vehicular Technology Conference (VTC2026-Fall), Boston, MA, USA, 6-9 September 2026
