An Automotive Distributed Mobile Sensor Data Collection with Machine Learning Based Data Fusion and Analysis on a Central Backend System. (2016)

• Record Type: Journal Article
• Authors: Tiedemann, Tim; Backe, Christian; Vögele, Thomas; Conradi, Peter
• Is Part Of: Procedia technology. Volume 26(2016)
• Page Start: 570
• Page End: 579
• Publication Date: 2016
• Subjects: Sensor Cloud -- Pervasive Computing -- Distributed ML -- IoT -- Big Data; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.071
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: One of the most extensive examples for ubiquitous computing today is automotion. The equipment of sensors and independent computing devices in current vehicles is vast if not endless. Furthermore, traffic infrastructure is realized using global and local computing devices. Communication initiated by the car itself (e.g., to an emergency hotline) will be obligatory in some countries soon. And finally, by using a smart phone the driver brings an additional powerful computing device and sensor set to the vehicle. However, all these automotive sensors and computing devices are used just for fixed (and in most cases single) purposes. Data exchange between vehicles or vehicles and infrastructure is rarely done. And dynamic changes like compensating for a broken sensor with available other data, using old sensor equipment for new functions, or improving old driver assistance systems with new sensors is not possible, either. The objective of the collaborative research project Smart Adaptive Data Aggregation (SADA) is to develop technologies that enable linking data from distributed mobile on-board sensors (on vehicles) with data from previously unknown stationary (e.g., infrastructure) or mobile sensors (e.g., other vehicles, smart devices). One focus of the project is the dynamic and fully-automated switching between different sensors or sensor configurations, including the adaptation of data fusion processes. Technically, one important component for some of the SADA use cases is a central backend system that (1) collects sensor data of the vehicles and/or the infrastructure, (2) fuses these data, and (3) carries out machine learning (ML) based analysis of the data to generate new information for the drivers (sometimes refered to by the term "virtual sensors"). The article gives a short overview of the SADA project and describes in more detail the concept of the backend system architec- ture, the user interface, and the methods and processes needed for a demonstration use-case.