In automotive and ADAS development, hidden errors in labeled datasets can cause major setbacks in system reliability. This session addresses the validation gap, showing how scalable auditing, automation and correction frameworks can de-risk data pipelines. Attendees will gain practical strategies to improve data trust, reduce validation cycles and accelerate safe deployment of AI-driven perception systems.

• Understand why validation, not just annotation, is critical for reliable AI performance
• Discover common pitfalls in current label validation workflows – and how they impact model accuracy
• Learn how scalable auditing and correction strategies can lower costs while increasing dataset trustworthiness
• Explore frameworks to benchmark label quality across internal teams, vendors and automated pipelines
• Walk away with actionable strategies to build faster, safer and more transparent AI systems