Julian Togelius writes, "At Essex, we have for some time been working on automatically learning how to race cars in simulation. It turns out that a combination of evolutionary algorithms and neural networks can learn how to beat all humans in racing games, and also come up with some quite interesting, novel behaviours, which might one day make their way into commercial racing games. While this is simulation, the race is now on for the real thing - we are setting up a competition for AI developers, where the goal is to win a race between model cars on real tracks. As the cars will be around half a meter long, the cost of participating will be a fraction of that for the famous DARPA Grand Challenge, whereas the challenges will be similar in terms of computer vision and AI." For more details, I'd suggest checking out some of the papers Julian has coauthored including Point-to-Point Car Racing: an Initial Study of Evolution Versus Temporal Difference Learning (PDF format), Sensorless but not Senseless: Prediction in Evolutionary Car Racing (PDF format), and Evolving robust and specialized car racing skills (PDF format).