The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

In many sequential decision-making problems (eg, robotics control, game playing,
sequential prediction), human or expert data is available containing useful information about …

Automated vehicles are expected to navigate complex urban environments safely along with
several non-cooperating agents. Therefore, accurate trajectory prediction is crucial for safe …

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …

Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …

Data-driven autonomous driving motion generation tasks are frequently impacted by the
limitations of dataset size and the domain gap between datasets, which precludes their …

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …

Reinforcement learning (RL) is a promising way to achieve human-like autonomous driving
(HAD) in complex and dynamic traffic, but faces challenges such as low sample efficiency …

Motion planning and control are crucial components of robotics applications like automated
driving. Here, spatio-temporal hard constraints like system dynamics and safety boundaries …