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| en:safeav:curriculum:softsys-b [2025/11/03 09:59] – raivo.sell | en:safeav:curriculum:softsys-b [2025/11/05 09:03] (current) – airi |
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| ^ **ECTS credits** | 1 ECTS | | ^ **ECTS credits** | 1 ECTS | |
| ^ **Study forms** | Hybrid or fully online | | ^ **Study forms** | Hybrid or fully online | |
| ^ **Module aims** | Equip students with a comprehensive understanding of autonomy software stacks, middleware, and lifecycle management in cyber-physical and autonomous systems. The module covers multi-layered software architectures—from hardware abstraction and middleware to AI-driven autonomy layers—highlighting real-time performance, determinism, interoperability, safety, and maintenance challenges. Students will learn how these architectures enable reliable sensing, perception, planning, and control across distributed and safety-critical systems. | | ^ **Module aims** | The aim of the module is to introduce software architectures, middleware and lifecycle management for cyber-physical and autonomous systems. The course develops students’ understanding of how multi-layer autonomy stacks support reliable sensing, perception, planning and control under real-time, interoperability and safety constraints. | |
| ^ **Pre-requirements** | Basic programming knowledge in C/C++ or Python; understanding of operating systems, networks, and data structures; familiarity with embedded or control systems concepts; and basic linear algebra and probability. Prior exposure to Linux-based development, Git, or simulation environments (e.g., Gazebo, MATLAB/Simulink) is beneficial. | | ^ **Pre-requirements** | Basic programming skills and understanding of operating systems, computer networks and data structures. Familiarity with embedded or control systems and Linux-based development tools is recommended. | |
| ^ **Learning outcomes** | Knowledge:\\ • Explain the architecture and purpose of multi-layered autonomy software stacks (HAL, OS, Middleware, Control, AI).\\ • Describe middleware technologies such as DDS, ROS 2, and AUTOSAR Adaptive, and their role in deterministic data exchange.\\ • Identify lifecycle models (Waterfall, V-Model, Agile, DevOps) and configuration management practices for autonomous software.\\ Skills:\\ • Design modular autonomy software architectures integrating perception, localisation, planning, and control modules.\\ • Configure and deploy middleware frameworks to support real-time, distributed communication.\\ • Apply CI/CD and configuration management principles using Git, Docker, and orchestration tools.\\ Understanding/Attitudes:\\ • Evaluate safety, verification, and cybersecurity aspects of autonomy software systems.\\ • Recognize challenges in maintainability, scalability, and interoperability across heterogeneous systems.\\ • Appreciate ethical, reliable, and transparent AI integration in autonomous decision-making. | | ^ **Learning outcomes** | **Knowledge**\\ • Explain the architecture and purpose of multi-layered autonomy software stacks.\\ • Describe middleware technologies and their role in deterministic data exchange.\\ • Identify lifecycle models and configuration management practices for autonomous software.\\ **Skills**\\ • Design modular autonomy software architectures integrating perception, localisation, planning, and control modules.\\ • Configure and deploy middleware frameworks to support real-time, distributed communication.\\ • Apply CI/CD and configuration management principles and orchestration tools.\\ **Understanding**\\ • Evaluate safety, verification, and cybersecurity aspects of autonomy software systems.\\ • Recognize challenges in maintainability, scalability, and interoperability across heterogeneous systems.\\ • Appreciate ethical, reliable, and transparent AI integration in autonomous decision-making. | |
| ^ **Topics** | 1. Introduction to Autonomy Software Stacks:\\ – Functional layers: perception, localisation, planning, control, middleware, cloud.\\ – Characteristics: real-time behaviour, determinism, scalability, resilience, interoperability.\\ 2. Middleware and Communication Frameworks:\\ – DDS, ROS 2, MQTT, AUTOSAR Adaptive, CAN, Ethernet.\\ – Quality of Service (QoS), message scheduling, fault tolerance.\\ 3. Software Lifecycle and Configuration Management:\\ – Lifecycle models (Waterfall, V-Model, Agile, DevOps, Spiral).\\ – Configuration management, version control, CI/CD pipelines, baselines.\\ 4. Development and Maintenance Challenges:\\ – Real-time performance, safety, AI integration, cybersecurity, and continuous updates.\\ 5. Simulation and Testing:\\ – SIL/HIL methods, virtual environments (CARLA, Gazebo, AirSim), digital twins.\\ 6. Ethics and Human–Machine Collaboration:\\ – Transparency, accountability, and explainability in autonomy. | | ^ **Topics** | 1. Introduction to Autonomy Software Stacks:\\ – Functional layers: perception, localisation, planning, control, middleware, cloud.\\ – Characteristics: real-time behaviour, determinism, scalability, resilience, interoperability.\\ 2. Middleware and Communication Frameworks:\\ – DDS, ROS2, MQTT, AUTOSAR Adaptive, CAN, Ethernet.\\ – Quality of Service, message scheduling, fault tolerance.\\ 3. Software Lifecycle and Configuration Management:\\ – Lifecycle models (Waterfall, V-Model, Agile, DevOps, Spiral).\\ – Configuration management, version control, CI/CD pipelines, baselines.\\ 4. Development and Maintenance Challenges:\\ – Real-time performance, safety, AI integration, cybersecurity, and continuous updates.\\ 5. Simulation and Testing:\\ – SIL/HIL methods, virtual environments and digital twins.\\ 6. Ethics and Human–Machine Collaboration:\\ – Transparency, accountability, and explainability in autonomy. | |
| ^ **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation. | | ^ **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation. | |
| ^ **Learning methods** | Lectures: Cover theoretical and architectural foundations of autonomy software stacks and middleware frameworks.\\ Lab works: Practical exercises in ROS 2, DDS, and containerised deployments; simulation of autonomy software using Gazebo or CARLA.\\ Individual assignments: System design and configuration management case studies applying CI/CD and risk analysis.\\ Self-learning: Reading standards (AUTOSAR, ISO 26262), research papers, and exploring MOOC content on middleware and DevOps. | | ^ **Learning methods** | **Lecture** — Cover theoretical and architectural foundations of autonomy software stacks and middleware frameworks.\\ **Lab works** — Practical exercises in ROS2, DDS, and containerised deployments; simulation of autonomy software using Gazebo or CARLA.\\ **Individual assignments** — System design and configuration management case studies applying CI/CD and risk analysis.\\ **Self-learning** — Reading standards, research papers, and exploring MOOC content on middleware and DevOps. | |
| ^ **AI involvement** | Used for assisting code documentation, simulation setup, performance analysis, and literature review. Students must verify generated outputs, cite AI tool usage transparently, and ensure compliance with academic integrity policies. | | ^ **AI involvement** | Used for assisting code documentation, simulation setup, performance analysis, and literature review. Students must verify generated outputs, cite AI tool usage transparently, and ensure compliance with academic integrity policies. | |
| ^ **Recommended tools and environments** | | | ^ **Recommended tools and environments** | ROS2, Gazebo, CARLA, AirSim | |
| ^ **Verification and Validation focus** | | | ^ **Verification and Validation focus** | | |
| ^ **Relevant standards and regulatory frameworks** | | | ^ **Relevant standards and regulatory frameworks** | MQTT, AUTOSAR, CAN, V-Model, DevOps, ISO 26262 | |
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