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| en:safeav:maps:summary [2026/04/23 11:29] – raivo.sell | en:safeav:maps:summary [2026/04/24 09:59] (current) – raivo.sell |
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| A second major focus is on sources of instability and validation, where the chapter connects environmental effects (weather, electromagnetic interference), infrastructure constraints, and semiconductor economics to system-level performance. It underscores that validation must be grounded in the operational design domain (ODD) and cannot rely solely on physical testing, requiring a combination of simulation, hardware-in-the-loop, and scenario-based methods. The introduction of AI further complicates verification and validation because of its probabilistic, non-deterministic nature, challenging traditional assurance techniques. As a result, safety approaches across domains are evolving toward lifecycle-based assurance, incorporating data governance, simulation-driven testing, and continuous monitoring. The chapter concludes with a structured validation framework that links perception, mapping, and localization performance to system-level safety metrics, emphasizing reproducibility, coverage, and traceability in building a credible safety case. | A second major focus is on sources of instability and validation, where the chapter connects environmental effects (weather, electromagnetic interference), infrastructure constraints, and semiconductor economics to system-level performance. It underscores that validation must be grounded in the operational design domain (ODD) and cannot rely solely on physical testing, requiring a combination of simulation, hardware-in-the-loop, and scenario-based methods. The introduction of AI further complicates verification and validation because of its probabilistic, non-deterministic nature, challenging traditional assurance techniques. As a result, safety approaches across domains are evolving toward lifecycle-based assurance, incorporating data governance, simulation-driven testing, and continuous monitoring. The chapter concludes with a structured validation framework that links perception, mapping, and localization performance to system-level safety metrics, emphasizing reproducibility, coverage, and traceability in building a credible safety case. |
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| ===== Assessment ===== | |
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| ^ # ^ Project Title ^ Description ^ Learning Objectives ^ | |
| | 1 | Multi-Sensor Perception Benchmarking | Build a perception pipeline using at least two sensor modalities (e.g., camera + LiDAR or radar). Evaluate object detection performance under varying conditions (lighting, weather, occlusion) using real or simulated datasets. | Understand strengths/limitations of different sensors. Apply sensor fusion concepts. Evaluate detection metrics (precision/recall, distance sensitivity). Analyze environmental impacts on perception. | | |
| | 2 | ODD-Driven Scenario Generation & Validation Study | Define an Operational Design Domain (ODD) for an autonomous system (e.g., urban driving, coastal navigation). Generate a set of test scenarios (including edge cases) and validate system performance using simulation tools. | Define and scope an ODD. Develop scenario-based testing strategies. Understand coverage and edge-case generation. Link scenarios to safety outcomes. | | |
| | 3 | Sensor Failure and Degradation Analysis | Simulate sensor failures (e.g., camera blackout, GNSS loss, radar noise) and analyze system-level impact on perception, localization, and safety metrics (e.g., time-to-collision). | Understand failure modes across sensor types. Evaluate system robustness and redundancy. Apply fault injection techniques. Connect sensor degradation to safety risks. | | |
| | 4 | AI vs Conventional Algorithm Validation Study | Compare a traditional perception algorithm (e.g., rule-based or classical ML) with a deep learning model on the same dataset. Analyze differences in performance, interpretability, and validation challenges. | Distinguish deterministic vs probabilistic systems. Understand validation challenges of AI/ML. Evaluate explainability and traceability. Assess implications for safety certification. | | |
| | 5 | End-to-End V&V Framework Design (Digital Twin) | Design a validation framework for perception, mapping, and localization using simulation (digital twin). Include KPIs, test conditions (e.g., ISO 26262, SOTIF), simulations, and linkage to safety standards. | Design system-level V&V strategies. Define measurable KPIs for autonomy. Understand simulation and digital twin roles. Connect numerical validation to safety standards. | | |
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