Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Distribution shift is a major source of failure for machine learning models. However,
evaluating model reliability under distribution shift can be challenging, especially since it …

We introduce 3DB: an extendable, unified framework for testing and debugging vision
models using photorealistic simulation. We demonstrate, through a wide range of use cases …

Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an
effective way to evaluate the robustness of autonomous driving systems. However, the …

The application of machine learning (ML) in computer systems introduces not only many
benefits but also risks to society. In this paper, we develop the concept of ML governance to …

This paper introduces CRITICAL, a novel closed-loop framework for autonomous vehicle
(AV) training and testing. CRITICAL stands out for its ability to generate diverse scenarios …

Generating adversarial scenarios, which have the potential to fail autonomous driving
systems, provides an effective way to improve the robustness. Extending purely data-driven …

The lack of well-calibrated confidence estimates makes neural networks inadequate in
safety-critical domains such as autonomous driving or healthcare. In these settings, having …

Rendered images have been used to debug models, study inductive biases, and
understand transfer learning. To scale up rendered datasets, we construct a pipeline with 40 …

Before autonomous systems can be deployed in safety-critical applications, we must be able
to understand and verify the safety of these systems. For cases where the risk or cost of real …