Autonomous vehicles are coming, whether we like it or not. In a previous blog, I discussed some of the potential policy considerations related to self-driving cars. See here: How Do We Prepare for Self-Driving Cars?
But I barely even touched on the technological implications of autonomous vehicles.
By now, you’ve likely heard the term big data. You might know it as that thing that lets you track what your customers are saying on Twitter. But it’s much more than that. For a brief overview: big data.
At this year’s Indy Big Data Conference, David Glass, the CEO of LHP, estimated that autonomous vehicles will be responsible for 4,000 GB of data per day, per vehicle. That’s a lot of data. What do we do with it? Rather, what can State governments do with it?
For starters, we can use it to make our cities safer and more efficient. Autonomous vehicles can tell us where they are, how long it takes to get through traffic, and where barely missed collisions occur. This will allow us to optimize our transportation infrastructure to reduce accidents, increase the flow of traffic, and even optimize traffic lights to turn green before your car is sitting on the sensor.
And near real-time traffic information? Your car will always know the fastest route to your destination. But more importantly? Police, hospitals, and fire stations will know the fastest route to you.
Speaking of police, autonomous vehicles could improve the ability to track criminals on the roads by using cameras and optical character recognition to “read” license plates and inform police of not only where a criminal’s vehicle is, but also where it is heading.
And what if autonomous vehicles could help us fix our roads, bridges, and other infrastructure? By logging how many miles an autonomous vehicle drives in various jurisdictions over the course of a year, we could improve how taxes are collected, distributed, and used to improve our infrastructure. We could even use analytics to identify how much traffic has increased on a bridge and if repairs will be needed earlier than anticipated. We could even use it to predict how traffic will react to various road closures and where it will relocate.