A self-driving car has learned to make high-speed turns without spinning out. The skill could come in useful during emergency manoeuvres. J Christian Gerdes and colleagues at Stanford University used a type of artificial intelligence algorithm called a neural network, which is loosely based on the neural networks in our brains, to create the self-driving system. They trained the neural network on data from more than 200,000 motion samples taken from test drives on a variety of surfaces, including on a mix of snow and ice at a track near the Arctic Circle. The team equipped a Volkswagen GTI with the algorithm and tested it on an oval-shaped race track.

Where we're going we don't need roads, just a large database of pictures. A team at New York University have taught a machine learning system to drive off-road by showing it photos of different landscapes. It's one thing to train driverless cars to navigate city streets or highways. But taking robot drivers off-road is tricky because the surroundings are highly variable, says Karl Iagnemma at the Massachusetts Institute of Technology, who wasn't involved in the work. It is hard to give a robot an exhaustive set of driving rules for every environment.