Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study

Corina S. Păsăreanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Corrie Imrie, Radu Calinescu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems as perception components processing high-dimensional image data. Formal analysis of these systems is particularly challenging due to the complexity of the perception DNNs, the sensors (cameras), and the environment conditions. We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system that guides airplanes on taxiways using a perception DNN. We address the above challenges by replacing the camera and the network with a compact probabilistic abstraction built from the confusion matrices computed for the DNN on a representative image data set. We also show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system. Our findings are applicable to other autonomous systems that use complex DNNs for perception.
Original languageEnglish
Title of host publicationComputer Aided Verification. CAV 2023
EditorsC. Enea, A. Lal
Number of pages15
ISBN (Electronic)978-3-031-37706-8
ISBN (Print)978-3-031-37705-1
Publication statusPublished - 17 Jul 2023

Publication series

NameLecture Notes in Computer Science

Bibliographical note

© The Author(s) 2023

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