When race car drivers take tight turns at high speeds, they rely on their experience and gut feeling to hit the gas pedal without spinning out. But how does an autonomous race car make the same decision?
Currently, many autonomous cars rely on expensive external sensors to calculate a vehicle’s velocity and chance of sideslipping on the racetrack. In a different approach, one research team in Switzerland has recently developed a novel a machine learning algorithm that harnesses measurements from more simple sensors. They describe their design in a study published August 14 in IEEE Robotics and Automation Letters.
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