A driver setup tests humans’ response to a new semiautonomous safety system. Image: Sterling Anderson |
Barrels and cones dot an open field in
Saline, Mich., forming an obstacle course for a modified vehicle. A driver
remotely steers the vehicle through the course from a nearby location as a
researcher looks on. Occasionally, the researcher instructs the driver to keep
the wheel straight—a trajectory that appears to put the vehicle on a collision
course with a barrel. Despite the driver’s actions, the vehicle steers itself
around the obstacle, transitioning control back to the driver once the danger
has passed.
The key to the maneuver is a new
semiautonomous safety system developed by Sterling Anderson, a PhD student in Massachusetts
Institute of Technology (MIT)’s Department of Mechanical Engineering, and Karl
Iagnemma, a principal research scientist in MIT’s Robotic Mobility Group.
The system uses an onboard camera and laser
rangefinder to identify hazards in a vehicle’s environment. The team devised an
algorithm to analyze the data and identify safe zones—avoiding, for example,
barrels in a field, or other cars on a roadway. The system allows a driver to
control the vehicle, only taking the wheel when the driver is about to exit a
safe zone.
Anderson, who has been testing the system
in Michigan since last September, describes it as an “intelligent co-pilot”
that monitors a driver’s performance and makes behind-the-scenes adjustments to
keep the vehicle from colliding with obstacles, or within a safe region of the
environment, such as a lane or open area.
“The real innovation is enabling the car to
share [control] with you,” Anderson says. “If you want to drive, it’ll just …
make sure you don’t hit anything.”
Off the beaten path
Robotics research has focused in recent years on developing systems—from cars
to medical equipment to industrial machinery—that can be controlled by either
robots or humans. For the most part, such systems operate along preprogrammed
paths.
As an example, Anderson points to the
technology behind self-parking cars. To parallel park, a driver engages the
technology by flipping a switch and taking his hands off the wheel. The car
then parks itself, following a preplanned path based on the distance between
neighboring cars.
While a planned path may work well in a
parking situation, Anderson says when it comes to driving, one or even multiple
paths is far too limiting.
“The problem is, humans don’t think that
way,” Anderson says. “When you and I drive, [we don’t] choose just one path and
obsessively follow it. Typically you and I see a lane or a parking lot, and we
say, ‘Here is the field of safe travel, here’s the entire region of the roadway
I can use, and I’m not going to worry about remaining on a specific line, as
long as I’m safely on the roadway and I avoid collisions.'”
A utility vehicle equipped with a laser range finder drives through a field, avoiding obstacles without human intervention. Image: Sterling Anderson |
Anderson and Iagnemma integrated this human
perspective into their robotic system. The team came up with an approach to
identify safe zones, or “homotopies,” rather than specific paths of travel.
Instead of mapping out individual paths along a roadway, the researchers
divided a vehicle’s environment into triangles, with certain triangle edges
representing an obstacle or a lane’s boundary.
The researchers devised an algorithm that “constrains” obstacle-abutting edges, allowing a driver to navigate across any
triangle edge except those that are constrained. If a driver is in danger of
crossing a constrained edge—for instance, if he’s fallen asleep at the wheel
and is about to run into a barrier or obstacle—the system takes over, steering
the car back into the safe zone.
Building trust
So far, the team has run more than 1,200 trials of the system, with few
collisions; most of these occurred when glitches in the vehicle’s camera failed
to identify an obstacle. For the most part, the system has successfully helped
drivers avoid collisions.
In experiments, Anderson has also observed
an interesting human response: Those who trust the system tend to perform
better than those who don’t. For instance, when asked to hold the wheel
straight, even in the face of a possible collision, drivers who trusted the
system drove through the course more quickly and confidently than those who
were wary of the system.
And what would the system feel like for
someone who is unaware that it’s activated? “You would likely just think you’re
a dang good driver,” Anderson says. “You’d say, ‘Hey, I pulled this off,’ and
you wouldn’t know that the car is changing things behind the scenes to make
sure the vehicle remains safe, even if your inputs are not.”
He acknowledges that this isn’t necessarily
a good thing, particularly for people just learning to drive; beginners may end
up thinking they are better drivers than they actually are. Without negative
feedback, these drivers can actually become less skilled and more dependent on
assistance over time. On the other hand, Anderson says expert drivers may feel
hemmed in by the safety system. He and Iagnemma are now exploring ways to
tailor the system to various levels of driving experience.
The team is also hoping to pare down the
system to identify obstacles using a single cellphone. “You could stick your
cellphone on the dashboard, and it would use the camera, accelerometers and
gyro to provide the feedback needed by the system,” Anderson says. “I think
we’ll find better ways of doing it that will be simpler, cheaper and allow more
users access to the technology.”