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en:safeav:curriculum:introduction [2025/10/30 13:46] – raivo.sellen:safeav:curriculum:introduction [2025/11/17 08:31] (current) – airi
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 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: 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 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.+  * **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. 
 + 
 +**Terminology note.** In this document, the SafeAV curriculum is the unified framework that defines the overall programme architecture, the BSc/MSc progression, and the learning flow from theory to V&V practice, aligning the SafeAV Handbook and the Hands-on Guide into a coherent, modular pathway. The subsequent chapters describe the modules as syllabi course-level maps specifying aims, learning outcomes, topics, assessment, tools, and relevant standards which constitute the formal open publication.
  
 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.   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.  
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 Each topic therefore exists in two complementary parts: 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 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.+  * **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.  +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. This two-stage progression ensures continuity between study cycles and supports lifelong learning paths in autonomous vehicle engineering.
  
-{{ :en:safeav:curriculum:SafeAV-curriculum.png?500 | SafeAV Curriculum }} +{{ :en:safeav:curriculum:SafeAV-curriculum.png?500 | SafeAV Curriculum }}
  
 The overall curriculum can be described as three integrated layers: The overall curriculum can be described as three integrated layers:
-  * Conceptual layer – theoretical foundations and system-level understanding (covered in the SafeAV Handbook)   +  * **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)   +  * **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  +  * **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. These layers are interconnected through shared terminology, datasets, and unified learning outcomes across all modules.
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 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. 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.
 +
 +It is important to note that the distinction between Bachelor (Part 1) and Master (Part 2) levels in this curriculum is conditional rather than absolute. Depending on the structure of the base study programme or the learner’s prior knowledge and competences, topics defined at the Master level in the SafeAV curriculum may also be taught within Bachelor-level courses, and vice versa. The actual implementation depends on the educational context of the university and the individual learning path of the student.
 +
 +For this reason, the SafeAV Handbook presents most topics in two levels of depth. Students who already have sufficient background or wish to advance further can continue directly to the next sub-sections, regardless of the formal level assigned to that topic in this curriculum. Conversely, in some non-technical or related engineering programmes, the same subjects might be addressed at a basic level even within Master studies, corresponding to what the SafeAV framework defines as Bachelor-level content.
 +
 +Therefore, the level designation in this curriculum should be interpreted as indicative of content depth—Basic and Advanced rather than as a strict separation between Bachelor and Master academic degrees.
 +
  
  
 ===== Learning Environments and Methods ===== ===== Learning Environments and Methods =====
  
-Each module supports flexible learning environments that allow both classroom and remote participation:+Most module supports flexible learning environments that allow both classroom and remote participation:
   * classroom teaching for theoretical foundations     * classroom teaching for theoretical foundations  
   * access to the AI-driven hybrid laboratory environment     * access to the AI-driven hybrid laboratory environment  
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 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.   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. +These learning environments are common across all modules, ensuring coherence, accessibility, and continuous feedback through AI-supported methods. 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.
- +
-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: Key features include:
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 All materials are licensed under Creative Commons (CC BY-NC), allowing reuse and modification while keeping alignment with European learning standards and ECTS principles.   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. This ensures consistency across partner universities while maintaining flexibility for local adaptation and future extension.
 +
  
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