GUIDE: Generalizable Trajectory Prediction Using Intention-Aware Diffusion With Efficiency

Abstract

The ability to predict the future trajectories of surrounding vehicles is crucial for the safe and efficient operation of autonomous vehicles. However, most existing data-driven trajectory prediction models lack a deep understanding of driving intentions, which complicates the prediction task and often leads to suboptimal results. In this paper, we introduce GUIDE, a framework that decomposes driving intention into driver personality traits and driving maneuvers. This approach incorporates these inferred intentions into a diffusion-based trajectory prediction model, which better models the probability distribution of future trajectories in a generative manner. Furthermore, to address the limited generalization ability of existing models in unseen scenarios, we focus on increasing the data scale and diversity during training. To this end, we have designed a unified data processing framework and evaluation benchmark that integrates multiple autonomous driving datasets. Upon this foundation, we build our diffusion-based, high-generalization trajectory prediction system. Experimental results demonstrate that by training on this unified dataset, our model learns more robust scene representations, leading to significant improvements in both prediction performance and generalization ability.