TDWI Upside - Where Data Means Business

Autonomous Vehicles: A World of New Data and Analytics (Part 2 of 4)

The data gathered by today's cars has already formed the foundation of new applications and novel businesses. The driverless car will only accelerate this trend.

With the introduction of autonomous vehicles, will human drivers become as inconsequential to travel as horses are now? Before we get excited about being driven everywhere, we need to examine just what kind of information these vehicles will gather and how that data will be used and by whom.

Data is the New Motor Oil

The data gathered by today's cars has already formed the foundation of new applications and novel businesses. So far, changes are incremental rather than substantive. An in-car navigation system may advise, but a driver still turns the wheel. An insurance company may try to influence driving behavior through monitoring and premium pricing, but the driver remains the ultimate arbiter of action.

The coming transition to driverless vehicles is pushing us into a whole new world.

As autonomous vehicles take responsibility for the deeply operational process of getting people from Point A to Point B, an array of new information beyond that already described in Part 1will be collected, including

  • The identity of the "driver" (the person initiating travel) and everyone in the car
  • Details of the start and end points
  • The travel route
  • Time and date of travel
  • The speed of travel (continuously monitored)
  • Payment method (if, as appears likely, rental displaces ownership of automobiles)

Given the propensity of the big data industry to save everything possible at the greatest level of detail, it seems likely that we may see such vehicles saving video and audio records of everything around and inside the vehicle to protect the company from any and all liability claims. All of this collected and generated data will be transmitted continuously to the cloud and reside in the hands of a small number of manufacturers and/or car-sharing companies.

Data collection isn't restricted to the autonomous vehicle and its own passengers. Understanding and predicting the behavior of other vehicles (and their passengers) is important for safe operation. Monitoring pedestrians, bicyclists -- and perhaps the last remaining horse rider -- on the roads is vital, too. Identifying people through deep-learning facial recognition algorithms is already feasible. In the event of dangerous behavior (by the person, of course) or an unavoidable accident, knowing who to blame is always useful.

Consider also the interim phase, when roads are occupied by both human-driven and driverless cars. While you are physically driving a "regular" car -- an old Ford Taurus as dumb as they come -- your behavior is being monitored by default by an increasing number of autonomous vehicles. Any and all driving irregularities could be recorded and passed back to the data collector (whoever that may be).

Who Will Use the Data?

To improve road safety, such vehicles will likely be sharing information with each other. After all, two (or more) cars sharing a road need to know what the other vehicles are doing.

Beyond the vehicles themselves, the data is gold to businesses and governments alike. It seems reasonable to assume that automakers and rental companies, as the original collectors, will be obliged to give what they gather to government agencies and will most likely sell data to other businesses as well.

What are those businesses and agencies doing with the data? Imagine these scenarios:

  • Speeding? Autonomous vehicles can clearly see if you exceed the speed limit by even a few miles per hour and for a few moments. For a fee or by law, the data gathered is likely to be made available to police, government, business (especially insurance companies), and city transportation departments.

  • In the case of an accident, autonomous vehicles can share data directly with law enforcement in real time, which can immediately dispatch their autonomous police vehicles, ambulances, and (driverless?) tow trucks.

  • Bad driver? Data analytics may reveal a pattern of poor driving in "regular" vehicles. Data will likely go to your automobile insurance company, which can increase your premium payment on the spot. It seems reasonable to assume that your driving license might be revoked after a few infractions, removing another potential problem driver from the roads and putting them in a safer autonomous vehicle. Your only hope may be the institutional will to live of the Department of Motor Vehicles and the insurance companies: they may have a vested interest in keeping you on the road to preserve their income.

  • In their quest to secure the streets and protect the country, law enforcement and national security agencies will want to know where potential criminals and terrorists are going and with whom they are associating.

  • What streets are used most and where is maintenance likely to be needed next? By analyzing street-use data, transportation departments at all levels can predict which streets need repaving or which intersections have the most accidents to help prioritize road safety improvements.

  • Where is traffic slowing? Information about traffic patterns can be coordinated with traffic managers to adjust the timing of signals (if still needed), vehicle routing, etc. to maximize traffic flow.

  • Entertainment options aren't just for back-seat passengers any more. Proponents of autonomous vehicles point to the enormous amount of time spent driving that could be put to productive use in self-driving cars. With no need for attention to the exterior world, the occupants of autonomous vehicles -- as is already the case in airplanes -- are a captive audience for entertainment offerings. This industry, as well as the advertising industry, stands to benefit significantly, particularly as the identities of the passengers will be known and validated. To a lesser extent, we could see the education and office productivity industries targeting travelers with time on their hands.

Next in This Series

How will employment change with the advent of driverless cars? I examine this impact in Part 3.

About the Author

Dr. Barry Devlin defined the first data warehouse architecture in 1985 and is among the world’s foremost authorities on BI, big data, and beyond. His 2013 book, Business unIntelligence, offers a new architecture for modern information use and management.


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