The design paradigm for automotive systems faces significant challenges from the evolution towards the vision of the future car as a mobile smart device that is substantively defined by its symbiotic interaction and integration with its environment, supporting increased levels of autonomy as well as user-centric service innovation. This is further compounded by the pressure to develop systems in an environment and cost conscious manner, with uncompromised levels of safety and reliability.
Model-based tools have been introduced to support multi-disciplinary automotive systems development and verification. However, the current methodologies are unlikely to be effective in addressing key technical and methodological challenges arising from the increasing use of open architectures to support evolvability of automotive systems to serve the autonomy and servitisation / personalization agenda. This brings significant uncertainties to be managed by the system during operation (i.e. inputs and parameters uncertainty), as well as models / modelling uncertainties early in product development and throughout the vehicle lifecycle to certify system safety, dependability and robustness. This needs to be addressed in a structured and systematic way across the domains and disciplines involved.
The main aim is to carry out research towards a model based ecosystem of processes and tools to support engineering system analysis of complex automotive systems with higher levels of intelligence and supervisory autonomy.
Specific research objectives include:
• Development of a structured and systematic approach for capturing and modelling uncertainties affecting the design and operation of complex autonomous systems, summarised as a taxonomy / ontology as well as a mathematical framework. • Develop a “resilience engineering” framework for automotive systems, underpinned by model based systems engineering tools, to support a coherent approach for the development and validation of resilient robust systems. • Develop a framework for effective integration of big data streams and the associated methods (data analytics and mining) with the engineering systems analysis of complex systems at the design stage as well as in operation / through life. • Validate the frameworks and methodologies / tools ecosystem developed through application to complex automotive case studies (real world – through collaboration with industrial partners) – focusing on intelligent autonomous systems.
This interdisciplinary research will be carried out in the Automotive Research Centre, and in partnership with our industrial collaborators, which include Jaguar Land Rover and Ford.
Applications and expressions of interest are invited from prospective researchers with background in engineering (mechatronic, robotics, mechanical, electrical) or computer science and applied mathematics.