The development of autonomous driving cars requires the handling of huge amounts of data produced by test vehicles and solving a number of critical challenges specific to the automotive industry. These include dealing with automotive formats and standards that are not designed for distributed processing, defragmentation of sensory data, and reprocessing the computing components running in the car inside the data center, which typically requires high performance computing.
Miha Pelko and Aleksandr Melkonyan outline these challenges and explain how BMW is overcoming them by adapting and reinventing existing big data solutions for autonomous driving—ingesting data produced by a variety of sensors into a dedicated Hadoop cluster, decoding the data, conducting quality control, processing and storing the data on the clusters, making it searchable, analyzing it, and exposing it to the engineers working on the algorithm’s development.
This session is sponsored by BMW.
Miha Pelko is a data engineer at BMW, where he focuses on big data for the company’s autonomous driving division. Previously, he was a consulting data scientist and data engineer at several German car manufacturers, where he introduced Hadoop and Spark in their data processing workflows, and was a data scientist in the fields of sport prediction and insurance. Miha holds a PhD in computational neuroscience from the University of Edinburgh.
Alexander Melkonyan is a data engineer at BMW focusing on Hadoop architecture, distributed systems, Spark, and other components of the ecosystem. Previously, he was a big data engineer at a number of cloud service and IoT companies, where he worked on establishing Hadoop as the main data platform.
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