While autonomous vehicle (AV) technology has shown substantial progress, we still lack
tools for rigorous and scalable testing. Real-world testing, the $\textit {de-facto} $ evaluation …

Extracting interesting scenarios from real-world data as well as generating failure cases is
important for the development and testing of autonomous systems. We propose efficient …

While recent developments in autonomous vehicle (AV) technology highlight substantial
progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto …

The race for deploying AI-enabled autonomous vehicles (AVs) on public roads is based on
the promise that such self-driving cars will be as safe as or safer than human drivers …

The widescale deployment of Autonomous Vehicles (AV) seems to be imminent despite
many safety challenges that are yet to be resolved. It is well known that there are no …

As shown by recent studies, machine intelligence-enabled systems are vulnerable to test
cases resulting from either adversarial manipulation or natural distribution shifts. This has …

Autonomous vehicle (AV) software is typically composed of a pipeline of individual
components, linking sensor inputs to motor outputs. Erroneous component outputs …

Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the
safety and reliability of these systems, extensive testings are being conducted before their …

There is an urgent societal need to assess whether autonomous vehicles (AVs) are safe
enough. From published quantitative safety and reliability assessments of AVs, we know …

In recent years, autonomous vehicles (AVs), which observe the driving environment and
lead a few or all of the driving tasks, have garnered tremendous success. The field of AVs …