Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant
systems. While existing approaches may sample from a predicted distribution of vehicle …

Prior studies in the field of motion predictions for autonomous driving tend to focus on finding
a trajectory that is close to the ground truth trajectory, which is highly biased toward straight …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …

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 …

Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …

We propose advances that address two key challenges in future trajectory prediction:(i)
multimodality in both training data and predictions and (ii) constant time inference …

In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …

Predicting diverse yet admissible trajectories that adhere to the map constraints is
challenging. Graph-based scene encoders have been proven effective for preserving local …