Ensuring Reliable Ground Truth for Safe Autonomous Driving

The future of mobility depends on our ability to develop perception systems that can drive safely and reliably. As autonomous vehicle (AV) manufacturers increasingly adopt the Safety Element out of Context (SEooC) approach, the quality of perception has emerged as a cornerstone for building a compelling safety case.

Yet there's a critical challenge: functional safety requirements for perception can't be directly linked to ground truth data. While occasional annotation errors might not significantly impact system performance, systematic errors and inconsistencies will ultimately compromise the safety and reliability of autonomous vehicles. At Kognic, we define data free from such issues as reliable ground truth—the foundation upon which safe autonomous systems are built.

Our mission is straightforward: deliver ground truth data that's demonstrably free from systematic and random errors. Through rigorous hazard analysis and risk assessment (HARA), we've identified key challenges in this process:

• Annotation accuracy issues, including incorrectly labeled objects
• Quality control gaps that allow errors to reach production

By addressing these potential hazards head-on, we've engineered a platform that minimizes systematic error risk, allowing us to confidently deliver reliable ground truth that accelerates your path to safe autonomy.

But how does this work in practice?

The first challenge—preventing annotation errors—requires two fundamental elements: advanced annotation tools and expert annotator competence. The second challenge—preventing incorrect data delivery—demands a sophisticated quality assurance process that catches errors before they reach your systems.

Consider a real-world scenario: annotating thousands of images to train an object detection algorithm. Each image in our platform undergoes multiple verification stages before delivery. Drawing on ISO 26262-inspired methodologies, we translate high-level safety concerns into concrete platform requirements and process improvements.

Take annotation accuracy, for example. When we analyze why an object might be incorrectly annotated, our fault tree analysis reveals several potential root causes:

• Human error by annotators
• Knowledge gaps in proper annotation techniques
• Ambiguous task specifications or guidelines
• Technical limitations in the annotation toolset

This systematic approach enables us to implement targeted solutions for each failure mode. It's why we've developed a comprehensive annotator onboarding process rather than simply teaching generic box-drawing skills, and established our Guideline Agreement Process that ensures perfect alignment between Kognic, clients, and annotators on annotation standards.

This functional safety-inspired methodology extends throughout our platform development. It's visible in our advanced quality assurance process and our innovative data coverage evaluation tools currently under development.

Ready to experience how reliable ground truth can accelerate your autonomous vehicle development? Connect with our team to learn how our platform can help you build safer perception systems, faster.