Show more
Data-Centric Safety Assurance for AI-Driven Autonomous Vehicles
A comprehensive introduction to data-centric safety assurance for AI-driven autonomous vehicles, with a focus on safe dataset design, validation, and continuous improvement.
Micro-credential developed in partnership with
This micro-credential introduces learners to the data side of autonomous vehicle safety, showing how dataset quality, coverage, and traceability directly influence AI reliability and safe system behaviour. Learners will explore ODD-driven dataset requirements, sensor and synthetic data acquisition, dataset verification and validation, and continuous improvement practices aligned with modern AV safety standards.
Micro-credential developed in partnership with
This micro-credential introduces learners to the data side of autonomous vehicle safety, showing how dataset quality, coverage, and traceability directly influence AI reliability and safe system behaviour. Learners will explore ODD-driven dataset requirements, sensor and synthetic data acquisition, dataset verification and validation, and continuous improvement practices aligned with modern AV safety standards.
Data-Centric Safety Assurance for AI-Driven Autonomous Vehicles
Topics
Autonomous Vehicle, Dataset Curation, Hazard Assessment
Intermediate
Price:
Included in subscription
Time to complete:
30 hours
Outcome
+3000 points
What you'll learn
-
Distinguish data-centric and model-centric approaches in autonomous vehicle safety
-
Explain how training data fidelity affects AV verifiability and safe operation
-
Define the Operational Design Domain (ODD) and relate it to dataset requirements
-
Assess dataset representativeness, diversity, and physical accuracy for AV systems
-
Examine the role of sensors, simulation, and synthetic data in AV data acquisition
-
Describe the dataset lifecycle for AI-driven autonomous vehicle systems
-
Apply requirement-driven thinking to dataset design, labeling, cleaning, and metadata tagging
-
Differentiate dataset verification and validation across levels of driving automation
-
Analyze edge cases, safety-critical scenarios, and failure modes in safe dataset design
-
Evaluate dataset coverage, empirical validation results, and continuous improvement strategies for safety assurance
Program outline
Developed with top post-secondary institutions and leading organizations, earn a credential you can share online by completing this course.
Industry-recognized
Downloadable certificate
Data-Centric Safety Assurance for AI-Driven Autonomous Vehicles
