Shelley is the product of collaboration between Stanford’s Dynamic Design Lab and the Volkswagen Electronics Research Lab. Photo: Steve Fyffe / Stanford News Service
than some decals and a few extra antennas, there’s nothing outwardly
remarkable about the white Audi TTS zipping around the track at
Thunderhill Raceway, north of Sacramento, Calif. Its tires squeal as it
zigs through chicanes. Its engine growls as it tops 120 mph on the
straights. The car gets around the 3-mile course in less than 2-1/2
minutes, a time that rivals those posted by professional drivers.
What is remarkable about this car is its driver: There isn’t one.
as the self-driving car is known, is the product of collaboration
between Stanford’s Dynamic Design Lab, led by mechanical engineering
Associate Professor Chris Gerdes, and the Volkswagen Electronics
Research Lab. Earlier this summer, Gerdes’ group brought Shelley to
Thunderhill for high-speed tests of the latest tweaks to the software
that tells her when to brake, how tight to take turns and when to punch
experience and data gathered by running Shelley around the track could
one day lead to fully autonomous cars that safely drive you and your
loved ones from Point A to Point B on public roads. In the nearer term,
the technology could show up as a sort of onboard co-pilot that helps
the driver steer out of a dangerous situation. And while Gerdes and crew
clearly enjoy racing Shelley, the truth is that pushing the car to its
limits on the racetrack—its brake pads melted on its last Thunderhill
run—is the best way to learn what type of stress a car is under in a
crisis, and what it takes to get the car straightened out.
example, the math involved in getting a spinning wheel to grip the
pavement is very similar to recovering from a slide on a patch of ice.
“If we can figure out how to get Shelley out of trouble on a race track,
we can get out of trouble on ice,” Gerdes said.
The human element
very little difference between the path a professional driver takes
around the course and the route charted by Shelley’s algorithms. And
yet, the very best human drivers are still faster around the track, if
just by a few seconds.
drivers are very, very smooth,” Gerdes said. Shelley computes the
fastest line around a course and executes the exact corrections required
to stick to it. A person relies more on feel and intuition, and thus
may, for example, allow the car to swing too wide in one turn if he
knows it sets him up better for the next.
drivers are OK with the car operating in a comfortable range of
states,” Gerdes said. “We’re trying to capture some of that spirit.”
and his students will have the opportunity to do just that Aug. 17-19
at the Rolex Monterey Motorsports Reunion races at the Laguna Seca
Raceway. The group has enlisted two professional drivers to wear a suite
of biological sensors as they race around the track; among other
things, the sensors will record the drivers’ body temperature and heart
rate. And in an effort to determine which driving maneuvers require the
most concentration and brainpower, scalp electrodes will register
drivers’ brain activity as they race against other humans.
biological data will be paired with mechanical performance data from
the car—a 1966 Ford GT40, the only American-built automobile to finish
first overall at the 24 Hours of Le Mans race—which Stanford has kitted
out with feedback sensors similar to those on Shelley.
need to know what the best drivers do that makes them so successful,”
Gerdes says. “If we can pair that with the vehicle dynamics data, we can
better use the car’s capabilities.”
Source: Stanford University