Introduction

The document presents a structured and adaptable curriculum for Bachelor and Master level studies in Safe Autonomous Vehicles (SafeAV), with a strong focus on Verification and Validation (V&V) of autonomous systems. The framework serves as a foundation that higher education institutions can adapt and expand when designing their own study modules or programmes related to the safety, reliability, and governance of autonomous technologies.

The curriculum follows a modular structure combining theoretical foundations, applied engineering knowledge, and hands-on experimentation. It is supported by two complementary educational resources developed within the SafeAV project:

• SafeAV Handbook – provides the theoretical and methodological background, including system architectures, sensing, software, and formal V&V methods.
• SafeAV Hands-on Guide – offers practical laboratory and simulation exercises that allow students to perform verification and validation tasks using real and virtual autonomous platforms.

The SafeAV curriculum architecture defines the overall structure, modular hierarchy, and learning flow that connects theoretical knowledge, simulation-based validation, and experimental practice. It ensures coherence between study levels and provides a clear path from basic understanding to advanced assurance of autonomous vehicle safety. Modules are organised in pairs: Part 1 (Bachelor) introduces the concepts, while Part 2 (Master) deepens the same topic through practical verification and validation methods. This two-level structure enables a stepwise learning progression across study cycles and gives universities the flexibility to adopt the curriculum or parts of it into existing educational programs.

Each topic therefore exists in two complementary parts:

• Part 1 (Bachelor level) – introduces the fundamental principles, technologies, and system interactions. Emphasis is on conceptual understanding, component function, and system-level awareness.
• Part 2 (Master level) – deepens the focus toward verification and validation, including analytical, experimental, and regulatory methods used to demonstrate safety, reliability, and trustworthiness.

For example, in Hardware and Sensing Technologies Part 1, students learn sensor types, signal processing basics, and data acquisition. In Part 2, they perform calibration, fault analysis, redundancy testing, and scenario-based validation using V&V tools and simulation environments. This two-stage progression ensures continuity between study cycles and supports lifelong learning paths in autonomous vehicle engineering.

The overall curriculum can be described as three integrated layers:

• Conceptual layer – theoretical foundations and system-level understanding (covered in the SafeAV Handbook)
• Practical layer – hands-on experiments, data analysis, and verification in laboratory environments (based on the SafeAV Hands-on Guide)
• Digital layer – self-study materials, MOOC courses, and AI-supported assistants that guide learning and track individual progress

These layers are interconnected through shared terminology, datasets, and unified learning outcomes across all modules.

The curriculum consists of six interrelated modules that together form a complete 6 ECTS study block but can also be used independently. Each module represents approximately 25–30 hours of student work, combining lectures, laboratory tasks, and self-study. The modular design allows multiple implementation strategies:

• full six-module SafeAV course (6 ECTS)
• selected modules as independent 1 ECTS units
• integration into existing robotics, AI, or control courses
• use for lifelong learning or professional training

Each module includes theoretical reading, guided experiments, simulation exercises, and assessment through a report, presentation, or quiz. The same structure is followed in all modules to maintain coherence across institutions.

The undergraduate programme introduces the building blocks of autonomous systems and their relation to safety assurance. The emphasis is on understanding system components and basic verification of function. Six modules (1 ECTS each) provide foundational knowledge of vehicle architecture, autonomy levels, sensing, computing, software systems, and human–machine interaction.

Modules – Part 1:

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

Each module combines reading assignments from the SafeAV Handbook with laboratory or simulation tasks from the Hands-on Guide, such as sensor calibration, perception benchmarking, or control-loop validation. The recommended full scope equals 6 ECTS, yet the modular design allows partial adoption depending on local curricula and student pathways.

The Master’s programme deepens the same thematic areas into Part 2 modules that focus on validation, verification, and system governance. Students explore how safety and reliability are demonstrated through structured testing, scenario generation, formal methods, and compliance with standards. Modules are directly linked to the advanced chapters of the SafeAV Handbook and the experimental work described in the Hands-on Guide.

Modules – Part 2:

• Hardware and Sensing Technologies (Validation and Reliability)
• Software Systems and Middleware (Safety and Verification)
• Perception, Mapping, and Localization (Scenario-based Testing)
• Control, Planning, and Decision-Making (Formal and Simulation-backed Validation)
• Human–Machine Communication (HMI Safety and V&V)
• Autonomy Verification and Validation Tools (Integrated Frameworks and Methods)

Students build validation pipelines from model design to field testing, using digital twins and simulation environments. The progression mirrors the V-model lifecycle introduced in the handbook — from design to verification, validation, and governance.

Each module supports flexible learning environments that allow both classroom and remote participation:

• classroom teaching for theoretical foundations
• access to the AI-driven hybrid laboratory environment
• virtual experiments linked to the MOOC platform
• hybrid sessions combining on-site instruction with online validation tasks

The SafeAV Hands-on Guide defines equipment lists, hybrid lab configurations, and step-by-step procedures. Remote setups ensure that students can conduct verification and validation exercises even without physical access to hardware.

Digital tools, Dokuwiki materials, and the MOOC environment allow integration with AI-based assistants that support self-learning, answer technical questions, and provide feedback on simulation or validation tasks. These learning environments are common across all modules, ensuring coherence, accessibility, and continuous feedback through AI-supported methods.

The SafeAV MOOC platform acts as a transversal framework that supports all curriculum modules equally. It is a shared digital infrastructure that enables self-paced learning, international collaboration, and AI-assisted study across all SafeAV topics.

The MOOC platform provides unified access to course materials, exercises, simulations, and AI tutoring functions. It ensures inclusivity and flexibility, enabling personalized learning paths and supporting students with different needs and backgrounds. The same platform is used by all modules, ensuring a consistent digital experience throughout the entire curriculum. Each course component is accessed through the same environment, which connects theoretical materials, laboratory tasks, and evaluation.

Key features include:

• AI tutoring and feedback – AI assistants answer questions, explain concepts, and provide formative feedback.
• Accessibility and inclusion – automatic transcription, summarisation, translation, and adaptive pacing to support all learners.
• Integration with laboratories – seamless connection between online content and hybrid laboratory activities.
• Open-access collaboration – materials and results can be shared, reused, and expanded across institutions.

The MOOC environment also functions as the central tool for monitoring student progress and competence development. It is continuously updated with new content and integrated with AI analysis to track engagement, learning efficiency, and V&V-related skills.

The SafeAV curriculum builds upon the remote and virtual laboratory infrastructure previously developed within earlier Erasmus+ projects (Interstudy, SimLab, Autonomian, IoT.Open Reloaded). This existing framework enables students to perform practical experiments not only in traditional classroom settings but also remotely, even when physical equipment and autonomous platforms are involved.

The hybrid laboratory integrates real test environments, such as sensor and control systems, with cloud-based and virtual simulation platforms. Through this setup, learners can connect to remote hardware, collect data, and carry out validation tasks in real time, regardless of their location. The same infrastructure also supports collaborative use between partner universities, allowing shared access to experiments, datasets, and learning tools.

SafeAV enhances this environment by introducing an AI component that expands the capabilities of the virtual laboratories. AI-based modules enable advanced simulation, automated data analysis, and model validation within digital twin environments. Intelligent assistants help students interpret results, identify anomalies, and generate experiment documentation automatically.

This AI-driven hybrid environment forms the backbone of the SafeAV practical learning concept. It bridges physical and virtual domains, connects theoretical understanding to verification and validation processes, and provides a unified experimental framework for both Bachelor and Master level studies.

The integration of artificial intelligence (AI) tools into the SafeAV curriculum is a central element for enabling modern, personalized learning experiences. In addition to supporting individualized study paths for typical learners, it also enhances accessibility and provides improved educational opportunities for students with special needs.

AI technologies are implemented at two levels:

• integration within the learning content to illustrate how AI supports autonomous vehicle V&V (e.g., AI in perception, planning, or safety analysis)
• integration as pedagogical tools to assist students and lecturers throughout the learning process

The following AI-based methods are used within the SafeAV ecosystem:

• AI-powered virtual assistants – LLM-based agents embedded in the MOOC and Dokuwiki environment answer course-related questions, explain theoretical concepts, and provide V&V-related guidance.
• AI-driven interactive simulations and virtual labs – intelligent digital twins and scenario generators support sensor fusion validation, control-loop testing, and human–machine communication studies.
• Personalized AI tutors – adaptive learning systems analyse student progress and recommend additional materials, exercises, or simulations based on performance.
• AI-supported content summarization – automatic generation of concise summaries of lectures, reports, and laboratory documentation helps students prepare for assessment and supports accessibility.
• Automated peer review and feedback – integrated AI tools assist in assessing reports and coding exercises, providing constructive feedback and reducing lecturer workload.

AI-based tools play a significant role in SafeAV by reducing repetitive communication tasks, offering continuous learning support, and improving the overall organization of study activities. These systems provide students with round-the-clock access to guidance and feedback, allowing instructors to focus on higher-level mentoring and project supervision.

To ensure trustworthy and responsible use of AI in education, all implementations follow privacy-by-design principles and comply with relevant data protection regulations. Student data are processed transparently and securely, with anonymized interaction records and clear options to opt out of AI-assisted learning when preferred.

In the long term, the SafeAV approach aims to develop a shared and open AI learning framework that promotes accessibility, multilingual support, and collaboration between partner universities, ensuring sustainable and equitable use of AI technologies in higher education.

The SafeAV architecture is open and adaptable. Educational institutions may:

• adopt the complete curriculum as a dedicated SafeAV course
• integrate selected modules into existing study programmes
• use materials in non-formal education or industrial training

All materials are licensed under Creative Commons (CC BY-NC), allowing reuse and modification while keeping alignment with European learning standards and ECTS principles. This ensures consistency across partner universities while maintaining flexibility for local adaptation and future extension.