When will automated vehicles come onto the market? This question has puzzled the
automotive industry and society for years. The technology and its implementation have …

In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-
driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any …

Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …

Recent advances in Language Model (LM) agents and tool use, exemplified by applications
like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks-such …

Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …

This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving
car software in real traffic is not only expensive but also dangerous, and has already caused …

Vision-based control systems are key enablers of many autonomous vehicular systems,
including self-driving cars. Testing such systems is complicated by complex and …