AUTOSPADA (Automotive Stream Processing and Distributed Analytics) OODIDA Phase 2
Purpose and goal: New automotive functions and services will increasingly rely on real time streaming of sensor data between vehicles and cloud infrastructures. This requires efficient and scalable data stream processing technology for low delay transmission and analytics from a distributed fleet, that also takes security and privacy issues into consideration. The aim of the AutoSPADA project is to develop and implement scalable and flexible platforms for automotive distributed analytics and data stream processing involving both in-vehicle, near-vehicle, and back-end computing resources. Expected results and effects: The project will deliver methodologies for performing efficient distributed analytics and data stream processing in the automotive industry, serving as input to the design of platforms for data capture and analysis for next-generation connected vehicle functions and services creation.The architecture developed in the project will improve the quality of next-generation products, contribute to reducing the time to market for new products and services, and give competitive advantages by having the best available technology for connectivity, data capture and analytics.
Approach and implementation: The project is divided in seven work packages with clear objectives and requirements for each.This ensures that the overall project goals are achieved. Several use cases from the automotive industry serve as basis for the research and developement of methods within the area of distributed analytics for data stream processing. Dissemination of the results will be done through implementation of proof-of-concept, demonstration of research results by applying them in the context of the specific use cases and through planned workshops and publications, among others a dissertation.
Vincenzo Massimiliano Gulisano (contact)
Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)
Alkit Communications AB
Project ID: 2019-05884
Funding Chalmers participation during 2020–2022