@inbook{bd2a31c51550412080897d55db420f33,
title = "Formal Verification Techniques for Vision-Based Autonomous Systems – A Survey",
abstract = "Providing safety guarantees for autonomous systems is difficult as these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs), for visual perception. DNNs are hard to formally verify due to their size (they can have billions of parameters), lack of formal specifications, and sensitivity to slight changes in the surrounding environment. Furthermore, the high-dimensional inputs to the DNNs come from sensors such as high-fidelity cameras that are themselves complex and hard to model – they bear complex relationships to the system states and are subject to random environmental perturbations. We present a survey of verification techniques that aim to provide quantitative or qualitative formal guarantees for such autonomous systems.",
author = "Sayan Mitra and Corina P{\u a}s{\u a}reanu and Pavithra Prabhakar and Seshia, {Sanjit A.} and Ravi Mangal and Yangge Li and Christopher Watson and Divya Gopinath and Huafeng Yu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.",
year = "2025",
doi = "10.1007/978-3-031-75778-5_5",
language = "English (US)",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "89--108",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}