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

arXiv: https://arxiv.org/abs/2610.09174 · HTML: https://mlsanity.com/paper/2610.09174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention](https://mlsanity.com/paper/2404.06351/) (2024)
- [Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM](https://mlsanity.com/paper/2503.10898/) (2025)
- [SemanticFormer: Holistic and Semantic Traffic Scene Representation for Trajectory Prediction using Knowledge Graphs](https://mlsanity.com/paper/2404.19379/) (2024)
- [Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed](https://mlsanity.com/paper/2409.16154/) (2024)
- [QML for Argoverse 2 Motion Forecasting Challenge](https://mlsanity.com/paper/2207.06553/) (2022)
- [Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving](https://mlsanity.com/paper/2107.02326/) (2021)
- [Whole-Body MPC and Dynamic Occlusion Avoidance: A Maximum Likelihood Visibility Approach](https://mlsanity.com/paper/2203.02221/) (2022)
- [Talking with Machines: A Comprehensive Survey of Emergent Dialogue Systems](https://mlsanity.com/paper/2305.16324/) (2023)

## Abstract (arXiv)

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.