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Contemporary connected vehicles host numerous applications, such asdiagnostics and navigation, and new software is continuously being developed.However, the development process typically requires offline batch processing oflarge data volumes. In an edge computing approach, data analysts and developerscan instead process sensor data directly on computational resources insidevehicles. This enables rapid prototyping to shorten development cycles andreduce the time to create new business values or insights. This paper presentsthe design, implementation, and operation of the AutoSPADA edge computingplatform for distributed data analytics. The platform's design followsscalability, reliability, resource efficiency, privacy, and security principlespromoted through mature and industrially proven technologies. In AutoSPADA,computational tasks are general Python scripts, and we provide a library to,for example, read signals from the vehicle and publish results to the cloud.Hence, users only need Python knowledge to use the platform. Moreover, theplatform is designed to be extended to support additional programminglanguages.

The AutoSPADA Platform: User-Friendly Edge Computing for Distributed Learning and Data Analytics in Connected Vehicles