Recent progress in motion forecasting has been substantially driven by self-supervised pre-
training. However, adapting pre-trained models for specific downstream tasks, especially …

This work presents an interpretable decision-making framework for autonomous vehicles
that integrates traffic regulations, norms, and safety guidelines comprehensively and …

Human trajectory prediction is typically posed as a zero-shot generalization problem: a
predictor is learnt on a dataset of human motion in training scenes, and then deployed on …

Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-
agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and …

Due to the lack of depth cues in images, multi-frame inputs are important for the success of
vision-based perception, prediction, and planning in autonomous driving. Observations from …

This paper jointly addresses three key limitations in conventional pedestrian trajectory
forecasting: pedestrian perception errors, real-world data collection costs, and person ID …

Robot decision-making increasingly relies on expressive data-driven human prediction
models when operating around people. While these models are known to suffer from …

Traditional vehicle trajectory prediction models widely exist the generalization problem
towards unknown scenarios. In this paper, we address the generalization via the following …

To ensure the safety of autonomous vehicles, trajectory prediction is critical as it enables
vehicles to anticipate the movements of surrounding agents, thereby facilitating the planning …