ADAS and autonomous driving development are increasingly challenged by growing data volumes, edge-case complexity, expanding Operational Design Domains (ODDs), and the need for scalable AI validation workflows. This workshop presents a holistic, end-to-end approach to automotive test drive validation, covering the complete pipeline from real-world data collection to virtual validation, dataset preparation for machine learning, and fleet orchestration. Attendees will gain insights into: · Efficient and scalable automotive data collection · Reuse of recorded driving data in HiL and virtual validation environments · AI dataset quality assurance and automated annotation workflows · Test fleet management as the orchestration layer across the entire validation process Using practical examples from real driving scenarios, the workshop demonstrates how integrated validation workflows help engineering teams accelerate development cycles, improve data quality, reduce redundancy, and scale validation efforts efficiently. The session combines strategic industry insights, practical workflows, and interactive audience engagement.

• Why data quality and orchestration are becoming key bottlenecks in ADAS/AD development
• How real-world driving data can be efficiently collected, reused, and validate
• How virtual validation and HiL environments enable scalable edge-case testing
• Why AI performance strongly depends on dataset quality and annotation workflows
• How fleet management and process orchestration improve efficiency across validation campaigns