… motion prediction model that enables large-scale real-time simulation and planning for
autonomous driving… GAMMA treats the prediction task as constrained optimization in traffic agents…

… We also discuss the availability of these input data in autonomous driving applications. …
Laugier, “A survey on motion prediction and risk assessment for intelligent vehicles,” …

… advanced motion planning … autonomous vehicles, we propose a crossmodal embedding
framework that demonstrates the efficacy of the use of multiple sensor data for motion prediction…

… To our knowledge, this work provides the first learning paradigm that is able to perform
pillar motion prediction in a fully self-supervised framework. We propose novel self-supervisory …

… It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a … However, future prediction is challenging due to the interaction and uncertainty in driving …

… of accurate behavior modeling in autonomous driving and analyze the key approaches and
… , its application to model behavior in autonomous driving is largely unexplored. As such, we …

… We believe it is because that the motion pattern of pedestrians are more flexible than vehicles
and bikes with non-holonomic constraint. Another remarkable finding is that simple model …

… Human motion prediction is a necessary component for many applications in robotics and
autonomous driving. Recent methods propose using sequence-to-sequence deep learning …

… In contrast, planning with a learned motion prediction model is believed to be more in… way
for autonomous driving. Our proposed approach aims to enhance the capability of prediction …

… Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting
the planner to react to predictions that … policy network learns to drive while interacting with the …