Before self-driving cars can get safer, they need to push their limits

mdnaquibalam

[br] [br]Self-driving cars are known for driving conservatively. After all, automakers and tech companies design autonomous vehicles to be textbook safe drivers, not aggressive speed demons. But mechanical engineers at Stanford University have been working on a research vehicle that can drive autonomously right at the edge of the tires’ grip on the surface below them, pushing the limits of friction.[br][br]The goal isn’t to create a self-driving car that can drift around a race track. Instead, the researchers want to help autonomous cars actually drive more safely by allowing them to know their own limits and even operate at the edge of those limits. It’s a useful skill for any driver to have if a deer dashes in front of the car—you want to be able to swerve if needed, but not so hard that the tires lose all purchase on the asphalt and the car skids off the road.[br][br]“Our lab is really interested in working on self-driving cars at the limits of handling,” says Nathan Spielberg, a doctoral candidate in the mechanical engineering department at Stanford University, and first author on a new  in the journal Science Robotics on the topic. “We take a lot of inspiration from race drivers, because they’re able to skillfully use all of the available road friction to get around the track as fast as possible.”[br]To gather data for this project, the team used two test tracks: one was “near the Arctic Circle,” Spielberg says, which let them gather data (with both a human behind the wheel and in autonomous mode) about low-friction environments from a surface made out of ice and snow. Further south, Thunderhill Raceway Park in Willows, California, provided a place to get high-friction data.[br][br]They used this data to help train a neural network, which is a type of machine learning and artificial intelligence tool. Then they tested the vehicle on a oval-shaped course, giving the neural network control of the car’s steering angle.[br][br]Self-driving cars on the road today operate differently from the way this one was cruising. For one, autonomous vehicles on the streets use lidar and cameras to sense their environments, so they can react to something like a stop sign if they see it. This Stanford research vehicle, on the other hand, was using a highly accurate GPS system to know where it was, plus an onboard inertial navigation system. The researchers told it to drive around the oval, and gave it guidance on its speed, too: the vehicle goes about 46 miles per hour on the straight parts of the track, and around 25 mph on the curves. In other words, the researchers told the car what to do and how fast to go, and the neural network they’d trained was in charge of steering the car to its friction limits. They wanted the vehicle to “track the path as best as possible at the limits of the car’s capabilities,” Spielberg says. [br][br][br]

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irfan_kolkata

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