Expected Outcome:
The project is expected to contribute to the following outcomes:
- Advanced AI-assisted methods and tools, including generative AI, for the automation of software engineering tasks, from enhancing human efficiency and optimizing resource utilization to enabling complex data/problems analysis/interpretation and supporting intelligent decision-making. Such engineering tasks often involve multiple domains (e.g., modelling, control, data management, communication, mechatronics, etc.) and stakeholders, with the burden of daunting legacy integration, refactoring (e.g., to re-design and replace obsolete technology), and the compliance with specific standards, regulations and certifications.
- Open and extensible AI-assisted integrated platform, based on methodologies including AI-support, AI-based tools and toolchains, following a well-defined engineering process, including the integration with legacy tools. The platform shall provide flexible usage in small and large multi-domain and multi-stakeholder engineering teams, impacting existing and upcoming ECS engineering automation tools and their usage.
- Showcasing and evaluation for software-defined vehicles of efficiency enhancements in terms of cost and time for complex data/knowledge management, resource optimization, energy consumption, interoperability, product/process quality/trustworthiness, learning curve and usability, over the whole lifecycle, from design, through deployment, operations, and maintenance, to the product end-of-life and recycling, and its evolution.
- Best practices and small proof-of-concept studies for other sectors, e.g. medical/pharmaceutical and/or digital industry.
