Abstract Machine learning algorithms are powerful tools for data-driven tasks such as image
classification and feature detection. However, their vulnerability to adversarial examples …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Toward verified artificial intelligence Page 1 46 COMMUNICATIONS OF THE ACM | JULY
2022 | VOL. 65 | NO. 7 contributed articles ILL US TRA TION B Y PETER CRO W THER A …

We present a new approach to automated scenario-based testing of the safety of
autonomous vehicles, especially those using advanced artificial intelligence-based …

We propose a new probabilistic programming language for the design and analysis of cyber-
physical systems, especially those based on machine learning. We consider several …

Although neural networks are effective tools for processing information from image-based
sensors to produce control actions, their complex nature limits their use in safety-critical …

In high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making
high-level driving decisions such as cruising and stopping, and thus highly securitycritical. In …

This paper introduces robustness verification for semantic segmentation neural networks (in
short, semantic segmentation networks [SSNs]), building on and extending recent …

We introduce a novel notion of perception contracts to reason about the safety of controllers
that interact with an environment using neural perception. Perception contracts capture …

Deep neural networks (DNNs) are increasingly being employed in safety-critical systems,
and there is an urgent need to guarantee their correctness. Consequently, the verification …