There are many of us that work with systems where data is processed in real-time and then used to make decisions. These might be humans viewing reports and then taking action, or some automated system that might react based on a value changing. In many cases, however, the amount of data, timing in which to react with a decision, and the implications for mistakes aren't that critical. We have some leeway for the processing not being perfect.

For vehicles traveling at 180mph, the tolerance for mistakes is low, with the chance of a catastrophic crash looming constantly. That is what is happening with a competition at the Indianapolis Motor Speedway, where university students are competing to develop race cars that can move around that track at these speeds. This would be quite a challenge for vehicles moving around the track by themselves, but in this case, it's a race with multiple cars.

This might seem silly, but it's a step towards understanding just how much data needs to be processed and how the results can deal with the chaos in the real world. Each car is independent, so has to react to the other vehicles and make decisions on how to adjust its own operation in real-time, with a sub-second response to prevent accidents. This isn't different from the decisions human drivers have to make in a race, and there are plenty of mistakes that result in crashes. However, humans can think in new situations and react. They don't need to have every possible response programmed in.

These AI-driven race cars will be similar, but how well they perform remains to be seen. This is the type of test environment that will help us move forward in using technology and AI models in less constrained environments, like a public highway. Lots of technology was tried on race tracks before it became available to consumers, and I think this will make its way to retail cars as well at some point.

There are already some companies trying to build this into cars. Tesla famously has Full Self Driving, although this has been in beta for a long time, and its results are less than stellar in some cases. Waymo has been working on the problem, and I actually had the chance to ride in a self-driving Uber in Las Vegas, though that experience was less than thrilling. The driver had to take control a number of times, so this wasn't quite self-driving.

There is a lot of work still to be done here, and I don't know how quickly this will become safe enough for general driving. I suspect that we'll see this in very limited areas first, like a zone in a city that only allows these types of cars, or maybe specific highways, like HOV lanes. Somewhere the problem domain is simpler, with less decisions that need to be made.

There are already lots of safety features in modern cars that help prevent mistakes and accidents, but most of these are simple systems that aren't making decisions in many ways. Moving to more complex driving operations will require some heavy data gathering, processing, and analysis, something that should be of interest to data professionals. This is a problem domain that will be fascinating to watch in the next few years.