InnoGuard: Hybrid and Generative Intelligence for Trustworthy Autonomous Cyber-Physical Systems

InnoGuard addresses novel challenges imposed by quality assurance of Autonomous Cyber-Physical Systems (ACPS), which have integrated Artificial Intelligence (AI) components.

Their use in our daily lives is increasing, such as in public transport systems, highlighting the need for novel, robust development methodologies to ensure their dependability, whose compromise could have severe consequences, as exemplified by various incidents (e.g., accidents caused by Tesla's autopilot system).

To ensure ACP dependability, InnoGuard targets developing novel methods for ACPS quality assurance by creating a tailored training program for Early-Stage Researchers (ESRs), with scientific objectives including methods to automate ACPS quality assessment and behavior evolution using AI techniques and enhancing ACPS dependability through real-time security, privacy, and uncertainty handling solutions.

Additionally, InnoGuard seeks to improve the trustworthiness of AI methods, enhance environmental sustainability by increasing the energy efficiency of ACPSs with the usage of AI methods, including Large Language Models (LLMs), and validate such techniques in open-source contexts such as Robot Operating Systems (ROS)-based systems.

Ultimately, InnoGuard will deliver novel techniques and methodological principles for ACPS quality assurance, ensuring high trustworthiness, reliability, and legal compliance.

The project's holistic approach encompasses technical advancements and training initiatives, with a broad plan to disseminate results to industry stakeholders, associations, and the wider public. Through these efforts, InnoGuard strives to elevate the status of ESRs as future experts in ACPS engineering.