erl

Test driving the Audi ERL tech of tomorrow, today

This week, we paid a visit to Audi's Electronics Research Laboratory (ERL) in northern California. Rather, it paid a visit to us, bringing along its Audi Urban Intelligence Assist demo vehicle to San Francisco and taking me for a spin.

The Audi Urban Intelligence Assist (AUIA) vehicle is, essentially, an Audi A6 3.0T that has been outfitted as a test bed for a variety of technologies being developed by Audi ERL and its partners at the University of California at Berkeley, the University of California at San Diego, and the Center for Advanced Transportation Technologies at USC. During … Read more

Shelley the robot car laps a dirt oval

Watching a self-parking car turn the wheel as it backs into a parallel parking spot is a delightfully eerie experience. Sitting in Stanford's driverless Audi TTS as it races up straight-aways and shuffles the steering wheel through turn after turn on a dirt oval makes you believe there's a ghost in the machine.

Stanford's Center for Automotive Research invited us out to a test day, where Professor Chris Gerdes and his team of graduate students sent the driverless TTS, named Shelley, around and around an oval track in an open field. Besides the sheer entertainment value, the team used the laps to collect data on how well the car stuck to its programmed path.

The car is a 2009 Audi TTS, a sport-tuned version of the standard Audi TT, featuring a 2-liter turbocharged direct injection four-cylinder engine, dual clutch transmission, and Audi's Quattro all-wheel drive. Normally that engine produces 265 horsepower, but as the students involved in the project are automotive enthusiasts, they chipped it to 320 horsepower.

High-tech gear sits under the back hatch of Shelley, although it uses surprisingly little computing power. The main processor is a 1.6GHz Pentium 3 housed in a ruggedized case sending commands to individual boards that control steering, braking, transmission, and acceleration. Unlike the DARPA competitors built by Stanford's AI lab for the Grand Challenge and Urban Challenge, Shelley doesn't take in external sensor input to see the landscape. Rather, it uses GPS and an inertial sensor to know where it is in the world.

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