Autonomous vehicles — fully or partially — will rely on a large variety of data types. And guess who is best positioned to take advantage of this enormous new business? Yep, Google is. 

The Google driveless car is an extraordinary technical achievement. To grasp the its scope, watch this video featuring a near-blind man sitting behind the wheel of an autonomous Prius as the car does the driving. Or, to get an idea of the complexity of the system, see this presentation by Sebastian Thrun (one of the main architects of Google’s self-driving car project) going through the multiple systems running inside the car.

Spectacular as it is, this public demonstration is merely the tip of the iceberg. For Google, the economics of self-driving cars lie in a vast web of data that will become a must to operate partially or fully self-driving vehicles on a massive scale. This network of data will require immense computational and storage capabilities. Consider the following needs in the context of Google’s current position in related fields.

Maps. Since the acquisition of Where2 Technologies and Keyhole Inc. in 2004, Google has been refining its mapping system over and over again (see this brief history of Google Maps). After a decade of work, Google Maps feature a rich set of layers and functions. Their mapping of the world has been supplemented by crowdsourcing systems that allow corrections as well as the creation of city maps where data do not exist. Street View has been launched in 2007 and more than 5 million miles of metropolitan area have been covered. Today, maps are augmented with satellite imagery, 3D, 45-degree aerial views, buildings and infrastructure renderings. All this is now merged, you can plunge from a satellite view to the street level.

Google’s goal is building the most complete an reliable map system in the world. Gradually, the company replaces geo-data from third party suppliers with data collected by its own crews around the world. To get an idea of how fast Google progresses, consider the following: In 2008, Google mapping covered 22 countries and offered 13 million miles with driving directions. In 2012, 187 countries where covered, 26 million miles with driving directions, including 29 countries with turn-by-turn directions. On the chart below, you can also see the growing areas of Google-sourced maps (in green) as opposed to licensed data (in red):

Apple’s failure in maps shows that, regardless of the amount of money invested, experience remains a key element. In California and India, Google maintains a staff of hundreds if not thousands of people manually checking key spots in large metropolitan areas and correcting errors. They rely on users whose individual suggestions are manually checked, using Street View imagery as shown here (the operator drags the 360° Street View image to verify signs at an intersection — click to enlarge.)

Google’s engineers even developed algorithms aimed at correcting slight misalignments between “tiles” (pieces of satellite imagery stitched together) that could result from… tectonic plates movement — it could happen when two pictures are taken two years apart. Such accuracy is not a prerequisite for current navigation, but it could be important for autonomous cars that will depend heavily on ultra-precise (think what centimers/inches mean when cars are close on the road) mapping of streets and infrastructures.

But, one might object, Google is not the only company providing geo-data and great mapping services. True: The Dutch company Tom-tom, or the Chicago-based Navteq have been doing this for years. As geo-data became strategically important, Tom-tom acquired TeleAtlas for $2.9bn in 2008, and Nokia bought Navteq in 2007. But Google intends to move one step ahead by merging its mapping and imagery technologies with its search capabilities. Like in this image:

Accurate, usable and data-rich maps are one thing. Now, when you consider the variety of data needed for autonomous or semi-autonomous vehicles, the task becomes even more enormous. The list goes on:

Traffic conditions will be a key element. It’s pointless to envision fleets of self-driving, or assisted-driving cars without systems to manage traffic. These goes along with infrastructure development. For instance, as  Dr. Kara Kockelman, professor of transportation engineering at the University of Texas at Austin explained to me, in the future, we might see substantial infrastructure renovation aimed at accommodating autonomous vehicles (or vehicles set on self-driving mode). Dedicated highway corridors would be allocated to “platoons” of cars driving close together, in a faster and safer way, than manned cars. Intersections, she said, are also a key challenge as they are responsible for most traffic jams (and a quarter of accidents). With the advent of autonomous vehicles, we can see cars taken over by intersection management systems that will regroup them in platoons and feed them seamlessly in intersecting traffic flows, like in this spectacular simulation. If traffic lights are still needed, they will change every five or six seconds just to optimize the flow.

Applied to millions of vehicles, traffic and infrastructure management will turn into a gigantic data and communication problem. Again, Google might be the only entity able to write the required software and to deploy the data centers to run it. Its millions of servers will be of great use to handle weather information, road conditions (as cars might be able to monitor their actual friction on the road and transmit the data to following vehicles, or detect humidity and temperature change), parking data and fuel availability (gas or electricity). And we can even think of merging all this with day-to-day life elements such as individual calendars, commuting patterns and geolocating people through their cell phones.

If the data collection and crunching tasks can conceivably be handled by a Google-like player, communications remain an issue. “There is not enough overlap between car-to-car communication and in other fields”, Sven Beiker, director Center for Automotive Research  (CARS) at Stanford told me (see his recent lecture about The Future if the Car). He is actually echoing executives from Audi (who made a strategic deal with Google), BMW and Ford; together at the Mobile World Congress, they were critical of cell phone carriers’ inability to provide the right 4G (LTE) infrastructure to handle the amount of data required by future vehicles.

Finally, there is the question of an operating system for cars. Experts are divided. Sven Beiker believes the development of self-driving vehicles will depend more on communication protocols than on an OS per se. Others believe that Google, with its fleet of self-driving Priuses criss-crossing California, is building the first OS dedicated to autonomous vehicles. At some point, the search giant could combine its mapping, imagery and local search capabilities with the accumulation of countless self-driven miles, along with scores of specific situations “learned” by the cars’ software. The value thus created would be huge, giving Google a decisive position in yet another field. The search company could become the main provider of both systems and data for autonomous or semi autonomous cars.

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