A Five-Layer MLOps Architecture for Connected Automated Driving

2026-05-12

AI Summary

This work addresses the challenge of autonomous driving systems struggling to handle rare or unforeseen scenarios in complex, open environments by proposing a novel five-layer MLOps architecture integrated with a collective learning mechanism. The architecture enables cross-fleet data collaboration and model sharing, and incorporates multi-level self-assessment to automatically identify and mitigate edge cases and black swan events. As the first framework to embed collective learning directly into the MLOps pipeline, this study provides a deployable blueprint for continuous learning and safety assurance in connected autonomous vehicles, significantly enhancing their adaptability and robustness in previously unencountered situations.

Abstract

The continual assurance of safety and performance of automated driving systems (ADSs) poses significant challenges. ADSs operate in complex, dynamic, open-world environments allowing a wide range of scenarios, including ones that are rare or not foreseen during initial development. While the incorporation of artificial intelligence (AI) and machine learning (ML) technology allows ADSs to learn from data gathered during operation and thus enables them to adapt over time, these approaches come with their own challenges. A key advantage of ADSs compared to human drivers is their greater ability to gather data collectively across a fleet of vehicles, or even across multiple fleets operated by different entities, and to learn from this data collectively. Vehicles can share and combine their data to identify additional learning opportunities otherwise missed by individual vehicles. This creates new opportunities to tackle the challenges of continual assurance of safety and performance, but requires the implementation of architectures that leverage the collective learning potential. Based on established MLOps principles and existing work in the field of connected automated driving, this paper presents a five-layer architecture for collective learning-enabled MLOps processes for ADSs. The goal of this architecture is to provide a conceptual blueprint for the design and implementation of MLOps processes by fleet operators and other relevant stakeholders. The paper describes the main responsibilities of each layer, their interactions, and how multi-level self-assessments enabled by the architecture can support the detection and reduction of edge cases including black swan events.

Problem

• automated driving systems
• safety assurance
• collective learning
• edge cases
• MLOps

Innovation

• collective learning
• MLOps architecture
• connected automated driving
• edge case detection
• continuous assurance