Click For Photo: https://regmedia.co.uk/2019/03/28/screenshot_stanford_niki_car.jpg
Video Researchers claim to have trained an autonomous vehicle to drive as well as an amateur race-car driver, a skill set that could be used to build safer artificially intelligent motorists, in theory.
Boffins at Stanford University in the US taught the computer brain of a driverless Volkswagen GTI to negotiate the Thunderhill Raceway Park in California at speeds of up to 95 miles per hour. Its skill level is said to be roughly comparable to that of a good amateur driver – well, one with an otherwise entirely empty race track to itself.
Researchers - Work - Cars - Network - Experience
More practically, the researchers said their work could be used to make future self-driving cars safer, because they have essentially built a neural network that has experience with a range of road conditions, from rough asphalt to ice. This knowledge, we're told, allows the software to intuitively keep control of the vehicle in situations it hasn't experienced before. If that's the case, this competence could be built into next-gen self-driving systems to make them better at driving than their human owners.
“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, first author of a paper describing the project, which was published in Science Robotics on Wednesday.
Algorithms - Drivers
“We want our algorithms to be as good as the best skilled drivers – and, hopefully, better.”
Car crashes are, after all, mostly down to human error, Spielberg, a graduate student in mechanical engineering at Stanford, noted. The academics reckon 94 per cent of them are the result of “human recognition, decision, or performance error.” If an autonomous car can take over in extraordinary situations, such as when a car needs to suddenly swerve, speed up, or brake, crashes could be...
Wake Up To Breaking News!
With God all things are possible, but not probable.