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

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 …

Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to
positively affect the quality of life of millions of people. To unlock this potential one of the …

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

With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …

Trajectory generation and prediction are two in-terwoven tasks that play important roles in
planner evaluation and decision making for intelligent vehicles. Most existing methods focus …

Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …

Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the
industry and research communities have acknowledged the need for such a pillar by …

Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …