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 …

We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of
preference for most self-driving vehicles. We argue that, by leveraging real data, we can …

Testing the full autonomy system in simulation is the safest and most scalable way to
evaluate autonomous vehicle performance before deployment. This requires simulating …

Scalable sensor simulation is an important yet challenging open problem for safety-critical
domains such as self-driving. Current works in image simulation either fail to be …

In Autonomous Driving (AD), detection and tracking of obstacles on the roads is a critical
task. Deep-learning based methods using annotated LiDAR data have been the most widely …

We present LidarDM, a novel LiDAR generative model capable of producing realistic, layout-
aware, physically plausible, and temporally coherent LiDAR videos. LidarDM stands out with …

Different outdoor illumination conditions drastically alter the appearance of urban scenes,
and they can harm the performance of image-based robot perception systems if not seen …

Over the past few years, there has been remarkable progress in research on 3D point clouds
and their use in autonomous driving scenarios has become widespread. However, deep …

Autonomous driving datasets are often skewed and in particular, lack training data for
objects at farther distances from the ego vehicle. The imbalance of data causes a …

LiDAR sensors provide rich 3D information about their surroundings and are becoming
increasingly important for autonomous vehicles tasks such as localization, semantic …