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 …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Simulation is an essential tool to develop and benchmark autonomous vehicle planning
software in a safe and cost-effective manner. However, realistic simulation requires accurate …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

Many safety-critical applications of neural networks, such as robotic control, require safety
guarantees. This article introduces a method for ensuring the safety of learned models for …

End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Independent of their major contribution, they introduce changes to minor system …

We present LiDARGen, a novel, effective, and controllable generative model that produces
realistic LiDAR point cloud sensory readings. Our method leverages the powerful score …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …

Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …