It’s predictable that in a few years the concepts of car ownership, ridership—and even the automobile itself—will look fundamentally different. In fact, the global automobile industry itself is likely to become more instrumented, distributed and data-driven. Let’s take a spin as to what this all means.
Meet the Internet of Cars
Consider this: Ford has invested $1B in Argo AI, an artificial intelligence startup focused on autonomous cars, following its acquisition of Chariot, a startup for commuter van sharing in Silicon Valley. General Motors has invested $500M in Lyft and also acquired Cruise Automation to enable traditional cars to be more autonomous with aids of sensors and gears. Fiat Chrysler debuted its electric powered car with its own wireless network. And BMW has also shown its autonomous “ultimate driving machine” at this year’s Consumer Electronics Show.
It’s foreseeable that future models of traditional cars, electric cars and autonomous cars will be embedded with more sensors than ever. The future cars will be able to capture data to make the cars more aware of external environmental factors and make decisions accordingly. Cars then become super connected and intelligent devices empowered in the backend by artificial intelligence. This is the next-gen Internet of Cars.
The sensors not only further integrate with driver’s other devices and aid communication over voice, but also collect data about the car, its navigation, and external environmental factors such as road condition and weather. The car is its own computing machine with its own wireless computing environment with machine-to-machine, system-to-system communication capabilities. This “sensorification” of cars becomes more profound and far-reaching as the data collected by the sensors are exposed to analytics software to extract actionable insights.
The future car is not just another physical asset. It is a software-driven machine with intelligent sensors to bring data from physical endpoints to actionable insights.
The data from a car is valuable to a network of stakeholders—the driver, the car maker and the insurer all have vested interest to be informed about the state of the car—for safety, maintenance and insurance premium pricing reasons. Police would also have interest in this information, especially for information gathering purposes after car accidents. In the U.S., the Department of Motor Vehicles would also be a beneficiary of the information to keep the ledger of car registration information up to date.
From an instrumented and distributed network to an integrated intelligent infrastructure
It is conceivable to use blockchain as the backend technology to build a next generation information architecture for the Internet of Cars. Critical data about each registered car are recorded and reconciled on a massive blockchain-based distributed ledger that would be made available for multiple aforementioned stakeholders. Making use of blockchain would eliminate inefficiency in information sharing about identity of the car, car condition at a given time, accident reporting, auditing and settlement.
The ability to use blockchain as the potentially powerful backend infrastructure to transmit and orchestrate sharing of data directly from cars to a permissioned and distributed network of stakeholders actually enables integration of data by eliminating data silos.
This integration of data from the Internet of Cars is the first step to building an intelligent infrastructure of roads, traffic lights, gas stations, electric charging stations, emergency response units, and more. The integration of data from millions of cars can help data scientists in government, such as the U.S. Department of Transportation, identify patterns on traffic, accidents and externalities that might have caused delays or accidents. Furthermore, data scientists can marry this infrastructure-level insights with other data, such as city-level operational open data or satellite or drone images to create powerful data visualizations to gain additional insights into infrastructure planning and emergency response.
And it is also possible that with the Internet of Cars and integration of data, insurers and investors could further assess and calibrate the health of a city and its population, its energy consumption level, and its flow of activities to gain insights into potential macro risk exposures or into viability of large infrastructure investment projects.
Turning the ignition on autonomous vehicles
An autonomous vehicle functions as a super computer that, by definition, has to have the ability to combine and process a large amount of structured and unstructured data in real time to make split-second decisions about what direction to go and what turn to make.
An autonomous vehicle is platforms of applications – real time mapping, image recognition, computer vision, voice recognition, robotics, data integration, security and more.
In a world of autonomous vehicles, interesting questions arise:
- How would the identity information of an autonomous car evolve from a set of static and periodically updated attributes about owner, car model, and registration?
- How are car insurance premiums calculated when most, if not all, of the driving decisions are made by a machine?
- How is data about a car shared with need-to-know parties while preserving the privacy of the rider?
- If ridesharing companies begin to use mostly autonomous vehicles, what tax regulations might change?
To the outside world, much of today’s effort by the automotive industry is to transform traditional car models to the next generation of sensorified cars, while also building a new line of autonomous cars.
In essence, the automotive industry is shifting to a software and data-defined industry providing mobility.
For other stakeholders, they have the opportunity to consider how to make use of the data from this industry to collectively create sustainable infrastructures, cities, mobility networks, as well as to establish standards and regulations that can help drive us all forward.
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