在复杂且不确定环境中，可靠且可解释的轨迹预测对于协同自动驾驶至关重要。本文提出了一种基于上下文感知注意力的 Gaussian Mixture Model (CAA-GMM)，用于多模态、不确定性感知的运动预测。该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分布，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交互及上下文显著性，从而在密集交通场景中实现栅格化环境线索与运动历史的高效融合。在 nuScenes 和 Argoverse 2 数据集上的综合评估表明，CAA-GMM 在保持低计算复杂度的同时，其精度优于或媲美最先进的基于栅格的基线方法。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，在非理想通信和感知条件下的评估凸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高效且可扩展的协同轨迹预测解决方案的地位。

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.