Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components

• URL: http://arxiv.org/abs/2202.03360v2
• Date: Mon, 27 Mar 2023 15:51:56 GMT
• Title: Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components
• Authors: Radu Calinescu (1), Calum Imrie (1), Ravi Mangal (2), Gena\'ina Nunes Rodrigues (3), Corina P\u{a}s\u{a}reanu (2), Misael Alpizar Santana (1), and Gricel V\'azquez (1) ((1) University of York, (2) Carnegie Mellon University, (3) University of Bras\'ilia)
• Abstract summary: We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems. The synthesised models correspond to controllers guaranteed to satisfy the safety, dependability and performance requirements of the autonomous system. We use the method in simulation to synthesise controllers for mobile-robot collision mitigation and for maintaining driver attentiveness in shared-control autonomous driving.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present DeepDECS, a new method for the synthesis of correct-by-construction discrete-event controllers for autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making processes. Despite major advances in deep learning in recent years, providing safety guarantees for these systems remains very challenging. Our controller synthesis method addresses this challenge by integrating DNN verification with the synthesis of verified Markov models. The synthesised models correspond to discrete-event controllers guaranteed to satisfy the safety, dependability and performance requirements of the autonomous system, and to be Pareto optimal with respect to a set of optimisation objectives. We use the method in simulation to synthesise controllers for mobile-robot collision mitigation and for maintaining driver attentiveness in shared-control autonomous driving.

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