Problem Statement

Inside today’s vehicles about 4000 signals are processed per second in comparison to only very few signals in smart phones and alike. The amount of CAN-Bus data produced by a single vehicle accumulates to about ~500MB/hour. With the increasing number of connected sensors and actuators within vehicles, this number will most probably rise in short-term. This large amount of continuously gathered data by road vehicles represents major big data-driven business potentials, not only for the automotive industry but in particular also for cross-sectorial industries with interdisciplinary applications. Today, this major business potential is still locked since the automotive industry was not yet able to establish an open service ecosystem – inspired by the smart phone and mobile internet industry domain – that leverages such potentials. This situation is based on the following three major difficulties:

  1. Current offerings in the connected vehicle domain are driven by brand-specific business approaches. Those individual companies provide customer-centric services and try to establish new businesses with their proprietary solutions. By doing so they enter entirely new markets that do not necessarily correlate with their actual core business. The result are brand- specific service solutions hindering long-term value creation by service providers due to fragmented environments and the lack of brand-independent vehicle data. Due to this high level of market fragmentation, the situation today is characterised by far too complex and individual value chains resulting in economic inefficiency.
  2. Connected vehicle services provided today primarily focus on the individual vehicle customer, which inevitably results in privacy concerns. There is a lack of consideration how anonymized vehicle data may be used in other cross-sectorial contexts, which are explicitly not addressing the individual vehicle customer but provide other socially beneficial and economically relevant services. Overall, the customer’s legitimate privacy concerns in the context of connected vehicles are not sufficiently addressed and transparently communicated.
  3. The associated risk of collaboration on vehicle data between competitive OEMs can certainly be considered as one of the major obstacles, why such ecosystems have not yet evolved. However, in order to retain and advance the leading position of the European automotive industry, it is essential to leverage already emerging cross-border Big Data service solutions on a broader, brand-independent scale.

AutoMat Key Objectives:

  • Enable QoS-adaptive data processing and data aggregation inside the vehicle
  • Define user transparent data access handling for OEM data harvesting and crowd sensing
  • Collaborate and excel in OEM data analytics with big data technology and data anonymisation
  • Develop and publish the Common Vehicle Information Model (CVIM) and corresponding SDK as incubator of the open ecosystem
  • Define and prototyping of Vehicle Big Data Marketplace concept as mediator between anonymised vehicle data and cross-sectorial service providers
  • Validate the AutoMat concept by development of exemplary cross-sectorial (nonautomotive) big data services (Meteorological Data based Hyper Local Services & Extended Innovative Enterprise Services)
  • Define and validate the market business model and its value chain and stimulate the market participation by an open call for services and applications