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 …

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 …

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 …

Deep Learning (DL) systems are rapidly being adopted in safety and security critical
domains, urgently calling for ways to test their correctness and robustness. Testing of DL …

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 …

Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …

Deep Neural Networks (DNNs) are the core component of modern autonomous driving
systems. To date, it is still unrealistic that a DNN will generalize correctly to all driving …

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

There is a growing body of research on developing testing techniques for Deep Neural
Networks (DNNs). We distinguish two general modes of testing for DNNs: Offline testing …

Autonomous vehicles are complex systems that are challenging to test and debug. A
requirements-driven approach to the development process can decrease the resources …