Stanford's New Robotic Audi TTS Knows How To Drift, Will Tackle Pikes Peak Next Year

Stanford unveils its latest robot car -- and it's an Audi TTS that's been modified with sensors, GPS guidance, and a trunkfull of computers

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Stanford's New Robotic Audi TTS Knows How To Drift, Will Tackle Pikes Peak Next Year

stanford autonomous car shelley audi

We were invited to Stanford on Thursday for a sneak peak at their latest robot car, from the family that includes Stanley and Junior. It’s an Audi TTS that’s been modified with sensors, GPS guidance, and a trunkfull of computers, but it’s not intended to drive you to work in the morning… It’s actually a race car, designed to push the limits of driving performance. Already, this TTS holds the unofficial world speed record for an autonomous car at 130 kph (edit- they meant to say 130 mph, which is a lot more impressive), but it’s capable of a whole lot more. Basically, Stanford is figuring out how close to the edge of control a car can be driven, and then they’re going to program their Audi to drive on that edge. They’ve set themselves a challenge of racing to the top of Pikes Peak sometime next year:

So what’s the point of all this besides being totally awesome? Simple: knowing how to drive a car to the limit gives you more options when it comes to things like accident avoidance. Most human drivers aren’t experienced enough (or have a fast enough reaction time) to take advantage of all of the potential escape routes that may be available when an accident is imminent, and research like this has the potential to teach intelligent cars how to save some of the 40,000 lives that are lost due to auto accidents every year.

stanford autonomous car shelley audi

UPDATE: The car’s name is Shelley, after Michèle Mouton, the most successful female rally driver ever and the first woman to win the Pikes Peak Hillclimb. She did it in an Audi, of course.

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Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

This article is part of our special report on AI, “The Great AI Reckoning.

"I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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