Data fusion in automotive applications: Efficient big data stream computing approach

摘要

Connected vehicles are capable of collecting, through their embedded sensors, and transmitting huge amounts of data at very high frequencies. Leveraging this data can be valuable for many entities: automobile manufacturer, vehicles owners, third parties, etc. Indeed, this “big data” can be used in a large broad of services ranging from road safety services to aftermarket services (e.g., predictive and preventive maintenance). Nevertheless, processing and storing big data raised new scientific and technological challenges that traditional approaches cannot handle efficiently. In this paper, we address the issue of online (i.e., near real-time) data processing of automotive information. More precisely, we focus on the performance of data fusion to support several millions of connected vehicles. In order to face this performance challenge, we propose novel approaches, based on spatial indexation, to speed up our automotive application. To validate the effectiveness of our proposal, we have implemented and conducted real experiments on PSA Group (PSA Group is the second-largest automobile manufacturer in Europe with about 3 million sold vehicles in 2015) big data streaming platform. The experimental results have demonstrated the efficiency of our spatial indexing and querying techniques.