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--- en:safeav:maps:validation [2026/03/31 10:04] – [Multi-Fidelity Workflow and Scenario-to-Track Bridge] airi
+++ en:safeav:maps:validation [2026/03/31 10:14] (current) – [Assessment] airi
@@ Line 85: / Line 85: @@
 ====== Assessment ======
+^ # ^ 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. |

en/safeav/maps/validation.txt · Last modified: 2026/03/31 10:14 by airi