Module: Autonomy Validation Tools
| Study level | Master |
|---|---|
| ECTS credits | 1 ECTS |
| Study forms | Hybrid or fully online |
| Module aims | The aim of the course is to introduce the principles, methods and tools used for verification and validation of autonomous and other safety-critical cyber-physical systems. The course develops students’ ability to design, implement and critically assess physical, virtual and hybrid validation workflows in line with relevant industrial practices and standards, preparing them to apply these approaches in advanced engineering projects and research. |
| Pre-requirements | Solid background in control engineering, systems modelling and basic artificial intelligence or machine learning. Ability to program and familiarity with Linux-based development environments. Prior coursework in robotics, autonomous systems or cyber-physical systems is strongly recommended. |
| Learning outcomes | Knowledge • Explain the role and structure of verification and validation in the autonomy lifecycle. • Describe international standards and their influence on testing processes. • Understand the architecture of physical, virtual, and hybrid test environments for autonomous systems. • Identify limitations and emerging research trends in simulation-based validation and safety case generation. Skills • Design and execute test plans using real and simulated environments. • Apply AI-driven methods for scenario generation, coverage analysis, and failure detection. • Integrate scenario building toolchains into validation workflows. • Assess compliance and produce documentation aligned with certification processes. Understanding • Appreciate the interdependence of testing, regulation, and ethical assurance in autonomous systems. • Recognize challenges of validating stochastic, learning-based algorithms. • Demonstrate accountability, transparency, and critical thinking in evaluating safety and validation data. |
| Topics | 1. Overview of Verification and Validation: – History and evolution from traditional software testing to AI-based autonomy validation. – Key principles: verification vs validation, safety cases, and traceability. – International Standards – Harmonization of global V&V requirements. 2. Physical and Virtual Testing Environments: – Real-world validation sites and virtual tools. – HIL/SIL/MIL testing, sensor simulation, and environmental modeling. 3. Scenario-Based Validation: – Framework for scenario design and coverage. – Edge case generation, fault injection, and adversarial testing. 4. AI-Enhanced Validation: – AI for test optimization, uncertainty quantification, and robustness analysis. 5. Certification and Compliance: – Safety argumentation, data transparency, and audit readiness. – Ethical and governance challenges in autonomous validation. |
| 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 | Lecture — Present theories, standards, and frameworks governing verification and validation of autonomous systems. Lab works — Conduct simulation-based testing using CARLA, MATLAB/Simulink, and OpenSCENARIO; perform hardware-in-the-loop experiments. Individual assignments — Develop validation plans, compare standards, and write safety assurance documentation. Self-learning — Review case studies from Pegasus and ZalaZONE; analyze real certification reports and research papers. |
| AI involvement | AI tools may assist in generating test scenarios, automating fault detection, and analyzing coverage metrics. Students must document AI involvement transparently and validate all outputs against engineering and ethical standards. |
| Recommended tools and environments | MATLAB/Simulink, Scenic, CARLA, rFpro, IPG CarMaker, ASAM OpenSCENARIO, Pegasus |
| Verification and Validation focus | |
| Relevant standards and regulatory frameworks | ISO 26262, ISO 21448, DO-178C, UL 4600, IEEE P2851 |
