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arXiv 2021年

作者： Tahir, Zaid Alexander, Rob Assuring Autonomy International Programme Department of Computer Science University of York York United Kingdom Boston University ECE Department United States

Autonomous Vehicles (AVs) i.e., self-driving cars, operate in a safety-critical domain, since errors in the autonomous driving software can lead to huge losses. Statistically, road intersections which are a part of the AVs operational design domain (ODD), have some of the highest accident rates. Hence, testing AVs to the limits on road intersections and assuring their safety on road intersections is pertinent, and thus the focus of this paper. We present a situation coverage-based (SitCov) AV-testing framework for the verification and validation (V&V) and safety assurance of AVs, developed in an open-source AV simulator named CARLA. The SitCov AV-testing framework focuses on vehicle-to-vehicle (V2V) interaction on a road intersection under different environmental conditions and intersection configuration situations (start/goal locations), using situation coverage criteria for automatic test suite generation for safety assurance of AVs. We have developed an ontology for intersection situations, and used it to generate a situation hyperspace i.e., the space of all possible situations arising from that ontology. For the evaluation of our SitCov AV-testing framework, we have seeded multiple faults in our ego AV, and compared situation coverage-based and random situation generation. We have found that both generation methodologies trigger around the same number of seeded faults, but the situation coverage-based generation tells us a lot more about the weaknesses of the autonomous driving algorithm of our ego AV, especially in edge-cases. Our code is publicly available online and since the simulation software (CARLA) is opensource, anyone can use our SitCov AV-testing framework and use it or build further on top of it. This paper aims to contribute to the domain of V&V and development of AVs, not only from a theoretical point of view, but also from the viewpoint of an open-source software contribution and releasing a flexible/effective tool for V&V and development of AVs.

关键词： Autonomous vehicles

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Bayesian Learning for the Robust Verification of Autonomous Robots

arXiv

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arXiv 2023年

作者： Zhao, Xingyu Gerasimou, Simos Calinescu, Radu Imrie, Calum Robu, Valentin Flynn, David Warwick Manufacturing Group University of Warwick Coventry United Kingdom Department of Computer Science University of York York United Kingdom Department of Computer Science and Assuring Autonomy International Programme University of York York United Kingdom Intelligent and Autonomous Systems Group Centrum Wiskunde & Informatica Amsterdam Netherlands Electrical Engineering Department Eindhoven University of Technology Eindhoven Netherlands James Watt School of Engineering University of Glasgow Glasgow United Kingdom

Autonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty. © 2023, CC BY.

关键词： Robots

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Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains

arXiv

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arXiv 2023年

作者： Gleirscher, Mario Haxthausen, E. Peleska, Jan University of Bremen Department of Mathematics and Computer Science Germany DTU Compute Technical University of Denmark Kongens Lyngby Denmark Autonomy Assurance International Programme University of York Deramore Lane YorkYO10-5GH United Kingdom

In this paper, a quantitative risk assessment approach is discussed for the design of an obstacle detection function for low-speed freight trains with grade of automation (GoA) 4. In this 5-step approach, starting with single detection channels and ending with a three-out-of-three (3oo3) model constructed of three independent dual-channel modules and a voter, a probabilistic assessment is exemplified, using a combination of statistical methods and parametric stochastic model checking. It is illustrated that, under certain not unreasonable assumptions, the resulting hazard rate becomes acceptable for specific application settings. The statistical approach for assessing the residual risk of misclassifications in convolutional neural networks and conventional image processing software suggests that high confidence can be placed into the safety-critical obstacle detection function, even though its implementation involves realistic machine learning uncertainties. © 2023, CC BY.

关键词： Stochastic systems

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Safety Controller Synthesis for Collaborative Robots

arXiv

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arXiv 2020年

作者： Gleirscher, Mario Calinescu, Radu Assuring Autonomy International Programme University of York York United Kingdom Department of Computer Science University of York York United Kingdom

—In human-robot collaboration (HRC), software-based automatic safety controllers (ASCs) are used in various forms (e.g. shutdown mechanisms, emergency brakes, interlocks) to improve operational safety. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to ASC developers and certification authorities. Key among these challenges is the need to assure the correctness of ASCs under reasonably weak assumptions. To address this need, we introduce and evaluate a tool-supported ASC synthesis method for HRC in manufacturing. Our ASC synthesis is: (i) informed by the manufacturing process, risk analysis, and regulations;(ii) formally verified against correctness criteria;and (iii) selected from a design space of feasible controllers according to a set of optimality criteria. The synthesised ASC can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

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Towards deductive verification of control algorithms for autonomous marine vehicles

arXiv

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arXiv 2020年

作者： Foster, Simon Gleirscher, Mario Calinescu, Radu Department of Computer Science University of York York United Kingdom Assuring Autonomy International Programme University of York York United Kingdom

The use of autonomous vehicles in real-world applications is often precluded by the difficulty of providing safety guarantees for their complex controllers. The simulation-based testing of these controllers cannot deliver sufficient safety guarantees, and the use of formal verification is very challenging due to the hybrid nature of the autonomous vehicles. Our work-in-progress paper introduces a formal verification approach that addresses this challenge by integrating the numerical computation of such a system (in GNU/Octave) with its hybrid system verification by means of a proof assistant (Isabelle). To show the effectiveness of our approach, we use it to verify differential invariants of an Autonomous Marine Vehicle with a controller switching between multiple modes. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Clinicians risk becoming 'liability sinks' for artificial intelligence

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Future healthcare journal 2024年第1期11卷 100007页

作者： Tom Lawton Phillip Morgan Zoe Porter Shireen Hickey Alice Cunningham Nathan Hughes Ioanna Iacovides Yan Jia Vishal Sharma Ibrahim Habli Improvement Academy Bradford Institute for Health Research Bradford Royal Infirmary Duckworth Lane Bradford BD9 6RJ UK. Assuring Autonomy International Programme University of York Heslington York YO10 5DD UK. York Law School University of York Heslington York YO10 5DD UK. Department of Computer Science University of York Heslington York YO10 5DD UK.

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The association for the advancement of artificial intelligence 2020 workshop program

The association for the advancement of artificial intelligen...

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作者： Bang, Grace Barash, Guy Bea, Ryan Cali, Jacques Castillo-Effen, Mauricio Chen, Xin Cynthia Chhaya, Niyati Dhoopar, Rohan Dumancic, Sebastijan Espinoza, Huáscar Farchi, Eitan Fioretto, Ferdinando Fuentetaja, Raquel Galley, Michel Geib, Christopher Hernández-Orallo, José Huang, Xiaowei Keren, Sarah Kim, Seokhwan Jaidka, Kokil Liu, Xiaomo Lu, Tyler Ma, Zhiqiang Mallah, Richard McDermid, John Michalowski, Martin Mirsky, Reuth Héigeartaigh, Seán Ó. Ramachandran, Deepak Segovia-Aguas, Javier Shaban-Nejad, Arash Shehory, Onn Shwartz, Vered Srivastava, Siddharth Talamadupula, Kartik Tang, Jian Zhang, Dell Zhang, Jian S&P Global. Western Digital University of Southampton United Kingdom Blue Prism Group PLC Lockheed Martin's Advanced Technology Laboratories ArlingtonVA United States University of Hong Kong Hong Kong Adobe Research Google Research Katholieke Universiteit Leuven Belgium Commissariat à ĺÉnergie Atomique France IBM Research Department of Electrical Engineering and Computer Science Syracuse University United States Universidad Carlos III de Madrid Spain Microsoft Research AI SIFT LLC Universitat Politècnica de València Spain Leverhulme Centre for the Future of Intelligence United Kingdom Department of Computer Science University of Liverpool United Kingdom National University of Singapore Singapore Harvard School of Engineering and Applied Sciences in the of Fields of Multiagent Environment Design and Goal Recognition Design Amazon Alexa AI Future of Life Institute United States University of York Assuring Autonomy International Program United Kingdom University of Minnesota School of Nursing MinneapolisMN United States University of Texas at Austin AustinTX United States Institut de Robòtica i Informàtica Industrial Spanish Council for Scientific Research Technical University of Catalonia Spain Oak Ridge National Laboratory Center for Biomedical Informatics Department of Pediatrics University of Tennessee Health Science Center United States Bar Ilan University Israel University of Washington SeattleWA United States Autonomous Agents and Intelligent Robots lab of the School of Computing Informatics Decision Systems Arizona State University United States DiDi AI Labs School of Industrial and System Engineering Georgia Institute of Technology United States University of London United Kingdom Microsoft Azure

The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, which were submitted by most, but not all, of the workshop chairs. Copyright © 2020, Association for the Advancement of Artificial Intelligence. All rights reserved.

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