Foundational research pillars

The research we are doing in York is focused on core technical issues arising from the use of robotics and autonomous systems in critical applications.

We have been advancing our research on some of the core technical issues that remain for the safety assurance of RAS and have established a new structure to focus our work. This includes five key pillars of research:

Contact us

Assuring Autonomy International Programme

assuring-autonomy@york.ac.uk
+44 (0)1904 325345
Department of Computer Science, Deramore Lane, University of York, York YO10 5GH

Lead researchers - Richard Hawkins and Ibrahim Habli

Overview diagram of the AMLAS methodology for assuring the safety of machine learned components

The first published guidance from our research pillars is our AMLAS methodology (Assurance of Machine Learning for use in Autonomous Systems). This has been peer-reviewed by our Fellows and experienced engineers from multiple industry domains.

AMLAS comprises:

  • a set of assurance activities that integrate with the development of ML components
  • defined assurance artefacts relating to those activities
  • safety case patterns to guide the development of a compelling safety case for ML components

The integration of activities, artefacts, and safety case patterns ensures AMLAS provides a practical and coherent approach. The guidance provides practical notes, examples, and links to the Body of Knowledge, creating a complete handbook for safety engineers, developers, and regulators.

AMLAS is already used by colleagues across the globe to support their work to assure the safety of their (ML) components. The methodology is being further evaluated through the development of case studies in various domains, including healthcare, space, manufacturing and automotive.

“AMLAS provides us with a framework to integrate safety assurance of neural networks into our development process and build a compelling argument for our safety case.” Dr Matthew Carr, Co-Founder and Chief Executive Officer, Luffy AI 

Download AMLAS

Lead researchers: Richard Hawkins and Mike Parsons

The focus of SACE is on the overall system-level assurance activities, particularly considering the interactions of the autonomous system with the complex environment. Similar to AMLAS, this guidance will define a safety assurance process along with corresponding safety case patterns.

A wide range of examples is being developed to support the guidance material. The guidance has been informed by the safety assurance work being undertaken as part of the development of a team of robots that will be used in the new Institute for Safe Autonomy facility. Considerable progress has been made on the first draft and it will be ready for review in the first quarter of 2022. 

Lead researchers - John McDermid and John Molloy

The focus for SAUS is split into two principal areas of research. Firstly, is work to explain the nature of failures in the understanding component of autonomous systems, reflecting the sense-understand-decide-act (SUDA) model. This work is examining failures that exist owing to the technical and theoretical limitations of sensors and the ML algorithms, as well as those that have been observed through observation and analysis of deployed systems. 

The second step is developing an adaptation of the Hazard and Operability Analysis (HAZOP) process to the safety assurance of the understanding component informed by the theoretical and observed failure modes. Confidence in this approach is found in the observation of the hypothecated failure modes in reports relating to deployed systems (e.g. fleets of road vehicles) as they become more widespread.

This work is closely aligned with the safety assurance work on a team of robots that will be deployed in the Institute for Safe Autonomy building and will use the robots’ perception suites as a case study.

Lead researcher - Radu Calinescu

This pillar covers the elicitation and validation of safety requirements for decision-making (e.g. path planning) in autonomous systems, failure analysis and propagation for decision-making, verification of decision-making (e.g. path planning), and safety case for decision-making in autonomous systems.

Two main research strands are being explored. Firstly, the development of a process for the analysis and assurance of autonomous decision-making that builds on existing safety engineering techniques. The second research theme is focused on autonomous systems that use deep neural network (DNN) classifiers for the perception step of their decision-making. The work is developing a new method for the correct-by-construction synthesis of discrete-event controllers for these systems.

Research papers

Lead researchers - Ibrahim Habli and Zoë Porter

This pillar will cover legal acceptance, regulatory compliance, accounting for ethical considerations, risk acceptance, and public trust.

Research has so far followed two main strands. First, the development of a methodology for including ethical principles in the development and assurance of autonomous systems. A workshop held in January 2021 helped to shape the direction of this work. The event, ‘From Ethical Principles to the Ethically-Informed Engineering of Autonomous Systems’, brought together members from the engineering, ethics and regulation communities. Second, the expansion of our interdisciplinary research on responsibility. This has led to an ambitious project, funded by the UKRI’s Trustworthy Autonomous Systems Programme and commencing in January 2022, to develop a framework for tracing and allocating responsibility for autonomous systems (link to responsibility project). This project brings together engineers, lawyers, philosophers, developers and the public to address one of the most important unanswered questions about autonomous systems, and a condition of their societal acceptability: who is responsible for the decisions and outcomes of autonomous systems?

Research papers

 

Contact us

Assuring Autonomy International Programme

assuring-autonomy@york.ac.uk
+44 (0)1904 325345
Department of Computer Science, Deramore Lane, University of York, York YO10 5GH