🤖 AI Summary
Current AI model development for functional safety–critical domains (e.g., automotive and industrial control) lacks a systematic workflow that simultaneously ensures stability, certifiability, and adaptability.
Method: This paper proposes a tool-certifiability–driven lightweight AI workflow paradigm. It introduces an extended ONNX-based, cross-stage verifiable AI model representation to unify modeling, verification, and deployment. The workflow integrates tool qualification (per ISO 26262/IEC 61508), the V-model development lifecycle, and static/dynamic AI verification techniques to enable end-to-end qualifiability of AI models in mixed-criticality systems.
Contribution/Results: The approach significantly reduces tool qualification effort and supports reliable deployment across heterogeneous runtimes—including AUTOSAR and ROS 2. In representative use cases, it achieves 100% verification pass rate for model behavioral consistency.
📝 Abstract
The development and deployment of safe and dependable AI models is crucial in applications where functional safety is a key concern. Given the rapid advancement in AI research and the relative novelty of the safe-AI domain, there is an increasing need for a workflow that balances stability with adaptability. This work proposes a transparent, complete, yet flexible and lightweight workflow that highlights both reliability and qualifiability. The core idea is that the workflow must be qualifiable, which demands the use of qualified tools. Tool qualification is a resource-intensive process, both in terms of time and cost. We therefore place value on a lightweight workflow featuring a minimal number of tools with limited features. The workflow is built upon an extended ONNX model description allowing for validation of AI algorithms from their generation to runtime deployment. This validation is essential to ensure that models are validated before being reliably deployed across different runtimes, particularly in mixed-criticality systems. Keywords-AI workflows, safe-AI, dependable-AI, functional safety, v-model development