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

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.