The AutoSPADA platform: User-friendly edge computing for distributed learning and data analytics in connected vehicles

Contemporary connected vehicles host numerous applications, such as diagnostics and navigation, and new software is continuously being developed. However, the development process typically requires offline batch processing of large data volumes. In an edge computing approach, data analysts and developers can instead process sensor data directly on computational resources inside vehicles. This enables rapid prototyping to shorten development cycles and reduce the time to create new business values or insights. This paper presents the design, implementation, and operation of the AutoSPADA (Automotive Software Platform for Advanced Distributed Analytics) edge computing platform for distributed data analytics. The platform’s design follows scalability, reliability, resource efficiency, privacy, and security principles promoted 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, the platform is designed to be extended to support additional programming languages. AutoSPADA has been demonstrated using live vehicles in workshop sessions where external users wrote payloads exploring use cases related to durability and energy consumption in electric vehicles. This places AutoSPADA at a Technology Readiness Level (TRL) of 7 as defined by the European Union. • Design and implementation of the AutoSPADA edge computing platform. • Users define and deploy Python code to edge devices and consume their results. • Facilitates rapid prototyping and big data analytics on edge devices. • An experiment quantifies latencies in an idealized but representative environment. • The AutoSPADA platform has been deployed and demonstrated on connected vehicles.