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Advanced Topics in Safety (ADTS)

Course details

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Robotics and Autonomous Systems (RAS) are being increasingly used as elements in safety-critical applications in a variety of domains. These technologies provide many challenges to current system safety engineering methods and assurance techniques. In this module, we will identify the nature of the safety challenges - technical, engineering and social - posed by RAS and consider their implications for legislation and regulatory guidance for engineering practice. 

In this module, we will consider the challenges posed to safety engineering techniques and praxis by Robotics and Autonomous Systems in three broad areas:

  • Technical challenges - we will explore the nature of decision-making technologies and will consider the implications for data management, model learning, verification and deploment and understanding of the interaction between AS and the "outside world", including humans. 
  • Engineering challenges - including the elicitation and validation of safety requirements, identifying and analysing new classes of hazard and understanding how failures propagate in systems with an autonomous component, implication for incident report and investigation, etc. 
  • Social challenges - including the role and expectations of the human in interactions with RAS, ethical concerns, acceptance and communication of risk and challenges for the law, governance and regulatory regimes in a number of domains. Implications for the safety case, particularly with reference to machine understanding and decision-making, will be considered throughout the module. 

By the end of this module, students will be able to:

  • Identify the disruptors - technical, engineering and social - to existing system safety engineering practices generated by RAS;
  • Describe the core elements of RAS systems engineering, sufficient for safety engineering and assurance understanding;
  • Discuss the validation and verification aspects of machine learning;
  • Describe and evaluate the implications for and changes required in safety assessment and assurance practices to accommodate RAS as emerging technologies;
  • Use consistent and clear terminology in communications about RAS engineering and safety;
  • Identify the societal impact of RAS and implications for risk acceptance;
  • Identify the potential impact of RAS on current regulatory requirements and guidance in a variety of safety-critical domains.

Who is the course for?

This course is suitable for:

  • practitioners across all domains including aerospace, military, railway, automotive, civil nuclear, civil maritime, medical devices, healthcare, and so on;
  • developers of equipment safety cases during design for software, hardware, procedures, systems and/or platforms;
  • developers of safety cases for operational safety and disposal;
  • reviewers of safety cases within an organisation or as an independent activity;
  • developers and reviewers of changes to existing safety-critical / safety-related equipment and operations;
  • project managers where development of a safety case is a significant element of projects they manage;
  • regulators of safety critical domains.


A basic understanding of system safety terminology and lifecycle via prior learning or industrial experience. It is useful for you to have taken our Foundations of System Safety Engineering and Computers and Safety courses, but if you have not, please email us with your details so we can assess your suitability for taking this course.

No prior knowledge of RAS is required for this module - we will provide an introduction to the technologies sufficient for understanding of the safety aspects during the module. 

How is the course taught?

We are hoping to be back on campus for some element of face-to-face teaching in the 2021/22 academic year but the University will only be able to make a final decision on this based on official advice closer to the time.

As a general guide we would advise you to bear in mind that some preparatory work or reading will need to be completed before the start of the teaching week. 
During the teaching week there will be a combination of lecture materials and case studies to explore. The case studies give you the chance to work through an example to reinforce your learning from the lectures. We expect you to put in around 30 hours of study.
Students will have full access to the benefits of the York approach: experienced, knowledgeable lecturing staff present in the groups and accessible for comment, as well as the opportunity to gain insights from the experience of industry peers attending as delegates.
The module ends with an assessed exercise, which you have the option of completing. It takes approximately 35 hours in addition to the scheduled teaching time and can be completed on or off site. All assessed exercises are open (so you won't take an exam in supervised conditions), and comprise a report, case study, or documented piece of software.
If you choose to take and pass your assessment, your results can count towards the completion of the MSc in Safety Critical Systems Engineering. Our MSc in Safety Critical Systems Engineering is an accredited course, recognised by both the BCS, the Chartered Institute for IT and the Institution of Engineering and Technology (IET) for the purposes of partial fulfilment of the educational requirement for CEng registration. 

Recommended reading


Russell, S.J. and Norvig, P. Artificial Intelligence: a modern approach Malaysia: Pearson Education Limited 2016
Marcus, G. and Davis, E. Robooting AI: Building artificial intelligence we can trust Pantheon 2019
Goodfellow, I. et al Deep learning (Vol 1) Cambridge: MIT press 2016
Géron, Aurélien. Hands-on machine learning with Scikit-Kearn, Keras, and TensorFlow: Concepts, tools and techniques to build intelligent systems O'Reilly Media 2019
Topol, Eric Deep medicine: how artificial intelligence can make healthcare human again Hachette UK 2019
Liu, Yun et al "How to read articles that use machine learning: users' guides to the medical literature" Jama 322 18 2019
Chen, Po-Hsuan Cameron, Yun Liu and Lily Peng "How to develop machine learning models for healthcare" Nature materials 18.5:410 2019
Assuring Autonomy Body of Knowledge    



Key dates & Book your place

Key Dates

The next instance of ADTS will take place in the 2021/22 academic year, the teaching week for this module is planned for the w/c 25/04/2022.

Book your place

Before booking, please read our Booking Conditions (PDF , 104kb).

To book your place, please complete the booking form and payment form below and return to our Admissions Team at Payment can be made online via credit/debit card

CPD Booking Form 2020/21 (MS Word , 70kb)

CPD Payment Form 2020-21 (MS Word , 66kb)

If you have any queries, please contact our Admissions Team at