Next-generation in-vehicle data loggers are evolving from passive recorders into intelligent edge computing platforms. With AI-capable hardware embedded at the sensor interface, ADAS and automated driving validation programs can fundamentally rethink how data is captured, filtered, and utilized.
In this interactive round table, we will explore how AI-driven triggering, semantic pre-labeling, adaptive compression, and real-time scenario detection can dramatically reduce data volumes while increasing relevance and insight density. How do we move from “record everything” to “record what matters”? What is the impact on storage architecture, cloud pipelines, and homologation evidence chains?
Join us to discuss concrete use cases, architectural trade-offs, and the practical ROI of edge intelligence in large-scale validation fleets. Let’s redefine data logging from a cost driver to a strategic accelerator.
By engineers, for engineers: A technically grounded guide to the rapidly evolving in-cabin technology industry and companies.
