Introduction

The document offers sample curricula for Bachelor and Master level studies (see figure below) in Safe Autonomous Vehicles (SafeAV). These examples serve as a foundational framework that educational institutions worldwide can adapt and expand upon to design their unique study modules/courses.

The curriculum is structured to provide both comprehensive and in-depth learning at the undergraduate and postgraduate study levels. The modular design is highly flexible, enabling adaptation to the individual preferences and capabilities of students.

The undergraduate study program introduces students to the fundamental components of autonomous systems through six interrelated modules (1 ECTS each). Students gain an understanding of vehicle architectures and autonomy levels, complemented by perspectives on legal, ethical, and cybersecurity aspects. Core technical knowledge is developed through modules on sensing technologies, computing hardware, software systems, and middleware integration. Further emphasis is placed on perception, mapping, localization, and control strategies, where both classical and AI-based approaches to planning and decision-making are explored. Finally, the program addresses human–machine communication, highlighting interaction principles and interface concepts essential for safe and effective collaboration between users and autonomous technologies. The topics/modules are:

• Autonomous Vehicles
• Hardware and Sensing Technologies
• Software Systems and Middleware
• Perception, Mapping, and Localization
• Control, Planning, and Decision-Making
• Human Machine communication

The module structure is designed to provide flexibility, enabling educational institutions to adapt its scope to their specific needs. Universities and schools may either implement the full set of topics, amounting to 6 ECTS (European Credit Transfer and Accumulation System) credits, or select a single thematic focus, such as an introductory Autonomous vehicles, corresponding to 1 ECTS credit. This adaptable framework allows institutions to align the content and extent of study with their curricular objectives and the learning goals of their students.

The Master's level study program provides an in-depth exploration of validation, safety, and governance in autonomous systems. Through six focused modules (1 ECTS each), students examine advanced challenges in hardware and software reliability, perception and control stability, human–machine interaction, and verification and validation methods, while also engaging with emerging tools, infrastructures, and research directions shaping the future of autonomy.

• Hardware and Sensing Technologies
• Software Systems and Middleware
• Perception, Mapping, and Localization
• Control, Planning, and Decision-Making
• Human Machine communication
• Autonomy Validation Tools

The curriculum is organized into six modules, allowing students to complete up to 6 ECTS in total. Designed for flexibility, the program can be followed in a fully self-paced mode, enabling learners to engage with individual modules or the complete set according to their academic and professional goals.

The following section delineates the architecture of the curriculum module in detail:

• Study level - provides the study level for which the module is designed.
• ECTS credits - how many points can be obtained to complete the module.
• Study form - explains where the module can take place: class, online, or hybrid.
• Module aims - gives the overall goal(s) or purpose(s) of the module.
• Pre-requirements - outlines pre-requirements for the current module, which the student must meet.
• Learning outcomes - lists what students are expected to know, understand, and be able to do after completing the module.
• Topics - listed subjects taught in the module. They are based on the books that were made for the IOT-OPEN.EU Reloaded project.
• Type of assessment - a general description of how assessment is carried out in the module.
• Learning methods - describe how students are taught and how they engage with the material, ranging from traditional lectures and reading assignments to interactive, experiential, and technology-enhanced approaches.
• AI involvement - refers to the use of AI tools and technologies to create, adapt, and enhance the content and educational materials.
• References to literature - a list of books, online books, articles, etc., are given, which helps to improve knowledge in the module.
• Lab equipment - a list of equipment, software, etc., used in the module to perform local laboratory work(s).
• Virtual lab - link(s) to a virtual lab(s), which is/are used in the module to do laboratory work(s) remotely.
• MOOC course - provides a link to a massive open online course. Students from all over the world can attend it.