With the continuous maturation and application of autonomous driving technology, a
systematic examination of open-source autonomous driving datasets becomes instrumental …

Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …

Prediction, decision-making, and motion planning are essential for autonomous driving. In
most contemporary works, they are considered individual modules or combined into a multi …

This letter presents a S imple and eff I cient M otion P rediction base L ine (SIMPL) for
autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but …

Accurate prediction the future movements of surrounding traffic participants is crucial for
autonomous driving. Among various strategies, learning complex interactive behaviors …

Most prior motion prediction endeavors in autonomous driving have inadequately encoded
future scenarios, leading to predictions that may fail to accurately capture the diverse …

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

Trajectory prediction is crucial for autonomous driving as it aims to forecast the future
movements of traffic participants. Traditional methods usually perform holistic inference on …

Data about vehicle trajectories assumes a crucial role in applications such as intelligent
connected vehicles. However, missing values resulting from sensors and other factors …

In autonomous driving tasks, trajectory prediction in complex traffic environments requires
adherence to real-world context conditions and behavior multimodalities. Existing methods …