Roads have well defined geometries, topologies, and traffic rules. While this has been
widely exploited in motion planning methods to produce maneuvers that obey the law, little …

This technical report presents an effective method for motion prediction in autonomous
driving. We develop a Transformer-based method for input encoding and trajectory …

In autonomous driving, goal-based multi-trajectory prediction methods are proved to be
effective recently, where they first score goal candidates, then select a final set of goals, and …

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …

Motion forecasting is a key module in an autonomous driving system. Due to the
heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low …

Motion forecasting is an important component in autonomous driving systems. One of the
most challenging problems in motion forecasting is interactive trajectory prediction, whose …

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …

Autonomous driving technology is developing rapidly and nowadays first autonomous rides
are being provided in city areas. This requires the highest standards for the safety and …

Motion Prediction (MP) of multiple surrounding agents in physical environments, and
accurate trajectory forecasting, is a crucial task for Autonomous Driving Stacks (ADS) and …

Predicting the future motion of dynamic agents is of paramount importance to ensuring
safety and assessing risks in motion planning for autonomous robots. In this study, we …