Context-aware Attention-based Gaussian Mixture Models for Vehicular Trajectory Prediction

A new arXiv paper (arXiv:2610.09174v1) introduces the Context-Aware Attention-based Gaussian Mixture Model (CAA-GMM), a multimodal, uncertainty-aware framework for vehicular trajectory prediction that models future motion as a probabilistic mixture conditioned on scene context and agent dynamics. Evaluations on the nuScenes and Argoverse 2 datasets show CAA-GMM achieves competitive or superior accuracy versus state-of-the-art raster-based baselines while maintaining low computational complexity, with ablation analyses confirming the attention module's role in contextual reasoning and predictive precision. The authors report the framework remains resilient under imperfect communication and perception conditions, positioning CAA-GMM as an efficient and scalable solution for cooperative trajectory prediction in intelligent transportation systems.

arXiv:2610.09174v1 Announce Type: new Abstract: 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.