Stanford autonomous car learns to handle unknown conditions


INTRO: Researchers at Stanford University have developed a new way of controlling autonomous cars that integrates prior driving experiences – a system that will help the cars perform more safely in extreme and unknown circumstances. [...] the system performed about as well as an existing autonomous control system and an experienced racecar driver.

“Our work is motivated by safety, and we want autonomous vehicles to work in many scenarios, from normal driving on high-friction asphalt to fast, low-friction driving in ice and snow,” said Nathan Spielberg ... lead author of the paper about this research, published March 27 in Science Robotics. “We want our algorithms to be as good as the best skilled drivers – and, hopefully, better.”

While current autonomous cars might rely on in-the-moment evaluations of their environment, the control system these researchers designed incorporates data from recent maneuvers and past driving experiences [...] Its ability to learn from the past could prove particularly powerful, given the abundance of autonomous car data researchers are producing in the process of developing these vehicles.

[...] The results were encouraging, but the researchers stress that their neural network system does not perform well in conditions outside the ones it has experienced. They say as autonomous cars generate additional data to train their network, the cars should be able to handle a wider range of conditions. (MORE - details)

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