Stanford scientists have developed a way for self-driving cars that to learn from previous experiences, helping the autonomous vehicles perform more safely in extreme and unknown circumstances. The researchers tested the technology on a racetrack using two self-driving cars Niki and Shelley. Making cars safeThe system performed about as well as an existing autonomous control system and an experienced driver, they said. “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, a graduate student at Stanford University in the U.S.“We want our algorithms to be as good as the best skilled drivers — and, hopefully, better,” said Mr. Spielberg. While current autonomous cars might rely on in-the-moment evaluations of their environment, the control system these researchers designed incorporates data from recent manoeuvres and past driving experiences — including trips Niki took around an icy test track near the Arctic Circle. 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.Control systems for autonomous cars need access to information… Read full this story
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