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, engineering and social. Among the technical challenges, we will explore the nature of decision-making technologies and will consider the implications for data management, model learning, verification and deployment and understanding of the interaction between AS and the 'outside world', including humans. Engineering challenges include 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, implications for incident report and investigation etc.. Social challenges include 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.

The module will be taught in a blended fashion, using a combination of pre-recorded lectures and live exercises sessions in which students will be taught in small groups. After the taught part of the module, students will select a topic and conduct a short critical literature review (formative). They will use this as a basis for a short talk, in a small group session, on which they will receive feedback both from other members of the group and from the course tutor. There will also be an open assessment (summative), undertaken over 7 weeks following the taught part of the module.

Course Description

Module aims:

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, engineering and social. Among the technical challenges, we will explore the nature of decision-making technologies and will consider the implications for data management, model learning, verification and deployment and understanding of the interaction between AS and the 'outside world', including humans. Engineering challenges include 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, implications for incident report and investigation etc.. Social challenges include 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.

The module will be taught in a blended fashion, using a combination of pre-recorded lectures and live exercises sessions in which students will be taught in small groups. After the taught part of the module, students will select a topic and conduct a short critical literature review (formative). They will use this as a basis for a short talk, in a small group session, on which they will receive feedback both from other members of the group and from the course tutor. There will also be an open assessment (summative), undertaken over 7 weeks following the taught part of the module.


Module learning outcomes:

- Identify and describe the disruptors - technical, engineering and social - to existing system safety engineering practices generated by RASS.

- Describe and evaluate the implications for and changes required in safety assessment and assurance practices to accommodate RASS and emerging technologies such as ML.

- Communicate consistently and clearly concepts and issues relating to RASS engineering and safety.

- Identify the societal and regulatory impact of RASS and implications for risk acceptance in a range of safety-critical domains.

- Develop a safety case for RASS.