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

• URL: http://arxiv.org/abs/2302.04634v1
• Date: Mon, 6 Feb 2023 18:56:20 GMT
• Title: Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study
• Authors: Corina S. Pasareanu, Ravi Mangal, Divya Gopinath, Sinem Getir Yaman, Calum Imrie, Radu Calinescu, and Huafeng Yu
• Abstract summary: We present a case study applying formal probabilistic analysis techniques to an experimental autonomous system. We show how to leverage local, DNN-specific analyses as run-time guards to increase the safety of the overall system.
• Score: 16.776221250574075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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