Driving intelligence tests are critical to the development and deployment of autonomous
vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the …

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 vehicles must be comprehensively evaluated before deployed in cities and
highways. However, most existing evaluation approaches for autonomous vehicles are static …

Deep Neural Networks (DNNs) have been widely applied in autonomous systems such as
self-driving vehicles. Recently, DNN testing has been intensively studied to automatically …

Many organizations are developing autonomous driving systems, which are expected to be
deployed at a large scale in the near future. Despite this, there is a lack of agreement on …

In recent years self-driving vehicles have become more commonplace on public roads, with
the promise of bringing safety and efficiency to modern transportation systems. Increasing …

One critical bottleneck that impedes the development and deployment of autonomous
vehicles is the prohibitively high economic and time costs required to validate their safety in …

Testing and evaluation is a crucial step in the development and deployment of connected
and automated vehicles (CAVs). To comprehensively evaluate the performance of CAVs, it …

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

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 …