A Five-Layer MLOps Architecture for Connected Automated Driving
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...
| Authors | Bastian Lampe, Lutz Eckstein |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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