Task Force 7 - Subgroup 11: Automotive

  • Posted on: 12 August 2021
  • By: Jaakko

The vehicle generates different macro categories of data, which need to be addressed with a harmonised approach for data sharing and data storage. This subgroup will address the main objectives that will give a concerted answer to the automotive needs.The Subgroup will create a common understanding between members of the BDVA interested in Big Data and Artificial Intelligence applications applied to the Automotive sector with special emphasis on Connected Cooperative Automated and Electric domains. It will also answer, among others, the needs to create meaningful strategic insights as input for policy makers as well as strengthening a critical European automotive industry during its shift towards digitalisation.The outcomes of the Automotive Subgroup will be of relevance for their application and industrial deployment, spanning data-driven product and process design, and innovative business models

Focus and activities

The automotive domain and smart mobility in general are a key industrial sector for Europe by securing 13.3 million jobs, producing 20% of the vehicle worldwide (out of 99 million vehicles produced yearly worldwide), and generating a yearly trade balance over €99 billion. At the same time, the automotive market is impacted by major regulatory challenges (such as the reduction of pollutant emissions) and societal changes (such as the reduction of traffic fatalities increased mobility for an ageing population, or reducing congestion). Parallel to that, the habits of the consumers are evolving, and new needs are emerging such as infotainment and connectivity, human-machine interaction and customization, as well as new mobility patterns (mobility as a service, multi-modal mobility, sharing services). Nowadays, five main pillars drive innovation in the automotive sector. In each of these pillars, big data is acting as a cross-cutting enabler:

  1. Electrification: With the introduction of e-mobility (hybrid, pure electric vehicle) to optimize or even completely remove the internal combustion engine, finally reducing the resulting local pollutant emissions during vehicle operation. Big data are essential to assess state of health, charging strategy and infrastructure, ageing behaviour of batteries, as well as real driving emissions.
  2. Advanced Driver Assistance Systems and Autonomous Driving: With the purpose of providing more comprehensive information to the driver for better context awareness, up to taking over specific driving manoeuvres – finally reducing the demands on the driver and lowering number and impact of possible accidents. Big data feeds the data driven artificial Intelligence systems responsible for vehicle autonomy. It also supports the validation and verification of the exploding number of driving scenarios.
  3. Connected & cooperative vehicle: Enabling optimization of vehicle’s operation or the emergence of new services while relying on external information, e.g., exploiting data from other vehicles or from the infrastructure. These tasks require reliable and scalable big data management and security.
  4. Smart Mobility: Targeting the efficient movement of people and goods with respect to different factors such that time, energy consumption, or ecological footprint. Big Data enables a new mobility service architecture and, on top of that, a new, much more flexible ecosystem of mobility offers, which will reshape the road transport in the future years.
  5. Vehicle Technology Development & Lifecycle: All above mentioned trends will have an impact on the lifecycle of the vehicle (design, production, maintenance). For example, big data plays a crucial role in virtualization of vehicle engineering and in the optimization of production and vehicle maintenance.
Subgroup Co-Lead
Bernhard Peischl
AVL List GmbH
Subgroup Co-Lead
Oihana Otaegui
Vicomtech Foundation