What opportunities exist for AI and Distributed Ledger Technology in Mobility and how can emerging companies capture the short and long term value?

The digital transformation in the Automotive Industry is creating more data than ever before. According to a study conducted by McKinsey, the value pool of car-data-monetization could be as large as $750 billion by 2030. According to the study, the opportunity for auto manufacturers hinges on their ability to 1) quickly build and test automotive data-driven products and services and 2) develop new business models built on technological innovation, advanced capabilities, and partnerships that push the boundaries of the automotive industry. Given these two goals, auto manufacturers will be creating a myriad of opportunities for technology developers, startups, insurance providers, data management servers and many more stakeholders.

The demand for data capturing by automotive manufacturers is creating a shift in the traditional mobility business model. To capture this data, vehicles are starting to have their own digital identity and digital wallets, built on distributed ledger technology (DLT). For instance, the transactional data from re-fuelling at a gas station is currently on a credit card, but soon will be housed on a digital wallet owned and operated by a car. This car could generate its own income through a service model and pay for its own fuel, maintenance, and other services. Distributed ledger technology enables a new level of secure data communication between vehicles and infrastructure, like charging stations. With a new layer of infrastructure powered by DLT, Tier 1 Auto manufacturers who have operated independently with silo’ed data, are embracing standardized DLT based protocols that enable interoperability between vehicle-to-vehicle interactions / transactions (V2V) and vehicle-to-infrastructure interactions (V2I). This level of standardization opens up the opportunity to collect data pools that can be can be fed to AI service providers to create additional “intelligent” services for vehicles, such as fleet management, crash prevention, traffic prediction, fleet energy optimization, etc. There are many different opportunities and use cases that auto manufacturers are currently implementing and looking to implement in the near future. To get an understanding of how technology companies can leverage their position to take a piece of the $750 billion pie let’s dig a bit deeper.

How is the industry reacting?

To propel innovative data-driven solutions forward, original equipment manufacturers (OEMs), such as Volkswagen, Toyota, BMW, Ford, GM, Porsche, etc. partner with technology developers to create proof of concept projects (PoCs) implemented in testbeds. Successful PoCs either continue development in house or continue to be outsourced to the technology developers for long term implementation and mass market adoption. For instance the International Transportation Innovation Center (ITIC) has partnered with the IOTA Foundation to build a global alliance of smart mobility testbeds. ITIC’s key focus is to build a global network of open and closed testbeds to incubate and validate AI-based sustainable mobility services in smart city environments using virtual, augmented and physical testing methods in selected testbed sites, as well as generating a test data pool that can be utilized by the whole smart mobility ecosystem. To learn more about specific projects, check out the Toyota Research Institute’s collaboration with the MIT Media Lab on autonomous vehicle fleets, Volkswagen’s partnership with the city of Hamburg to collaborate on mobility testbeds, EY’s blockchain based shared mobility platform, or Jaguar’s investment into a blockchain based startup called Dovu.

These partnerships signal the value that the transportation industry puts on AI and distributed ledger technology (DLT) services as a means to empower meaningful insights and autonomy built on secure communication of data transfer, transactions, and audit trails. Some examples of AI services that could be provided in conjunction with DLT are predictive maintenance, predictive EV charging, energy optimization, and fleet optimization, among many others. To show the overlap of the technologies in a potential testbed, let’s work through a few use cases.

Use Case I — Car-Wallet and Payments in Fleet Management & Energy Optimization

With an integrated digital wallet or wallet app, cars are enabled to make payments on their own. With DLT, payments concerning every aspect of the car’s mobility can be executed quickly, securely and automatically. The importance of this functionality increases with future car generations that have more advanced autonomous functions, including the automation of payments and the development of usage-based insurance (we will talk about that more in our second use case). Currently, DLT enables secure communication of data between entities. This technology allows for a smarter understanding of an entire fleet, leading to an increase in performance of AI powered fleet management and energy optimization. For an example, let’s look at a freight company who is interested in optimizing their fleet through something called platooning.

Platooning. The digitalization and networking of automated vehicles is a key element in the increase of efficiency. Cars need to communicate with each other to, for example, buy data for the optimization of their operational strategy. With Platooning, several vehicles drive behind each other in close proximity, typically used in trucking and freight transportation. Safety is ensured by the communication of the involved vehicles and the real-time exchange of sensor data, enabled by DLT technology. The reduction of the distance between the vehicles leads to significant savings in terms of consumptions due to reduced wind resistance of the following vehicles.

The leading platoon vehicle creates greater energy savings for the trailing fleet while sharing sensor data with other vehicles to detect and bypass potential dangers . With DLT, the trailing fleet could compensate the leading truck for its services and energy savings. Furthermore, with smart contracts, the payment process could be securely automated with real-time data.

In addition to this, the transportation infrastructure could negotiate with the vehicles on the streets in order to optimize the traffic situation using complex algorithms. A future economy could see such a platoon buy a green wave as a premium product to further increase energy savings and reduce traffic volume.