That the whole sector of automotive is heading towards a disruption, is a given, no one is disputing it. Opinions may differ on when, surely not on its occurrence. Actually, it will not be a clear cut thresholds rather a fuzzy area experiencing and accelerated rate of change:
- shift from internal combustion engine to electric motors (with a significant decrease in the number of components and an impact on the value chain);
- shift towards autonomous cars with an expected decrease of the numbers of cars being sold (from over 70 million a year to 40 million, a sharp decrease that will have a tremendous impact on today’s players -car manufacturers- compounded by the entrance of new players in the arena) and a concurrent upswing of car-as-a-service that again will change the value chain..
As cars morph into computers they become part of a network and more and more of their functionalities will be controlled from remote they will be mirrored in the cyberspace through a Digital Twin (a few models already have a flanking digital twin, like Tesla models).
Fujitsu has released a Digital Twin Collector, see image, that serves as a point of data accrual and processing. Interestingly, this platform is receiving data from single digital twins (digital twin instances of a same car model as well as digital twins instances of different car models -each can model has its own digital twin that gets instantiated on each car of that model) and as such support services (like monitoring and proactive maintenance) to that specific car, and at the same time support the sharing of data:
- among different instances of digital twins, allowing global data analytics thus gathering intelligence from each instances and creating a global intelligence (like: cars reaching a certain mileage are likely to incur in a specific problem: this global intelligence is applied to each single instance taking corrective measures to avoid the emergence of the problem);
- among different service providers thus increasing the potential offer towards the end user and increasing the potential market to service providers;
- sharing local intelligence to allow prompt (or better) delivery of services, as an example by sharing information with insurance companies. The platform can also collect video streaming from cars (from their related digital twins) and this can be used to evaluate an accident situation.
- sharing the global intelligence to third parties, like information of road status to foster proactive road maintenance.
Fujitsu represents an example of a company entering the automotive field through the service provisioning door.
As shown in the graphic on the side cars will be producing more and more data, as part of their “operation”. At the same time each car is moving around and act as a sensor collecting data on its environment, from bumps in the road to external temperature, traffic information (this can be derived from the average speed of several cars in an area) and “interest” information (unusual traffic patterns…).
All this information can be provided by the car’s sensors or can be resulting from data analytics performed on the raw data.
Obviously, some of these data (or the information derived from them) may be sensitive (like associating the position of people through the day). Others may not present (privacy) problems and can be used without any specific concerns. Some of the “problematic” data can be made less problematic (and more usable) through anonymization, and some through an explicit opt-in sought from the car’s owner/user.
One way or the other, there is no doubt that a new game to leverage on data generated by car is on and will just get more intense in the coming years.