Course content

Technological systems are becoming increasingly complex and software-intensive with more autonomous functionality. Autonomy may cause complexity and interlocks that are hard to identify and analyze. New types of hazards may be introduced, due to unforeseen interdependencies in the system design, operation, and the environment. Autonomy and shared control challenges the situation awareness and decision making between humans and the autonomous system, as well as the public perception and acceptance of such systems. This means that risk assessment and safety management of these systems should become a driver in their design and operation. This course addresses fundamental concepts and methods in system safety theory, such as STAMP, STPA and CAST, for analyzing, evaluating, verifying safety in advanced and complex systems. Simulation based probabilistic risk assessment (DPRA) is covered, as well as online risk assessment and modeling, and supervisory risk control. Online risk models utilize data from different sources, such as historical data, measurements from sensors, and experience data. The data models may vary from empirical models based on historical or online data to physics-based models. Supervisory risk control may be considered a contribution to improved artificial intelligence, supporting and enabling the autonomous system to model and plan its actions; i.e., making deliberate choices. It is important to note that autonomous systems in the course include both manned and unmanned systems with certain control functionality that may be characterized as autonomous, which means that the course topics also relevant for safety of software - intensive systems.

Learning outcome

After having completed the course, the students shall be able to understand and utilize system safety theory and methods for achieving safe operation of technological systems. The central focus will be on systems with autonomous and intelligent functionality. The students should know state-of-the-art and current challenges in the research area, and adjust this knowledge to their PhD-projects. Skills: The students shall: -Understand the content of essential concepts in system safety, systems engineering, dynamic and online risk assessment, human factors, and supervisory risk control. -Be able to utilize methods in system safety theory, including STAMP/STPA and CAST, to analyze and model risk in complex systems. -Be able to develop advanced dynamic risk models to be used for online risk management, supervisory risk control and decision support, including hardware, human, and software failures. -Develop safety and/or maintenance indicators for controlling operational safety, which may be linked to online risk models when relevant. General competence: -The students shall be able to write a course paper which could form the basis for a scientific conference article or journal article relevant for the candidate´s PhD-thesis. They should also be able to present and discuss challenges and ideas for further research in the field.

Learning methods and activities

Lectures, group discussions, presentations, scientific writing, and self study. Minimum 3 students must register for the course to be taught. To pass the course a score of at least 70 percent is required. The course will be taught in English if English speaking students take the course.

Compulsory assignments

• Oblig

Further on evaluation

Oral exam

Specific conditions

Recommended previous knowledge

TMR4130 or similar courses

Course materials

A list of relevant reading material will be provided at the start of the course.