Context-aware model improves vehicle path predictions in traffic
Context-aware Attention-based Gaussian Mixture Models for Vehicular Trajectory Prediction
Machine Learning
Summary
Predicting where cars will go next is important for self-driving and cooperative vehicles. The authors propose a new model that looks at both the surrounding environment and how cars have moved before to forecast multiple possible future paths along with their chances. Their approach uses attention to focus on important parts of the scene and interactions between vehicles, making predictions efficient and reliable even when data is imperfect. Tests show this method matches or beats current models while being faster and more interpretable.
What this means in practice
- •For autonomous vehicle engineers: Develop efficient and interpretable systems to predict multiple possible future paths of vehicles in crowded and complex traffic environments.
- •For traffic management teams: Incorporate resilient trajectory forecasts that handle imperfect communication and perception to improve cooperative traffic control strategies.
Authors
Arash Raftari, Babak Ebrahimi Soorchaei, Yaser P. Fallah
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.