Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive
prediction and plan-ning framework that can enable more efficient and less conser-vative …

Behavior prediction plays an important role in integrated autonomous driving software
solutions. In behavior prediction research, interactive behavior prediction is a less-explored …

Interactive driving scenarios, such as lane changes, merges and unprotected turns, are
some of the most challenging situations for autonomous driving. Planning in interactive …

This paper aims to tackle the interactive behavior prediction task, and proposes a novel
Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene …

Generative modeling of complex behaviors from labeled datasets has been a longstanding
problem in decision making. Unlike language or image generation, decision making …

Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by the …

Motion forecasting in highly interactive scenarios is a challenging problem in autonomous
driving. In such scenarios, we need to accurately predict the joint behavior of interacting …

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are
separated, limiting the planner to react to predictions that are not informed by the planned …

Humans have a remarkable ability to accurately reason about future events, including the
behaviors and states of mind of other agents. Consider driving a car through a busy …

Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …