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 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.
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. Benjamin Saltsman, manager of intelligent truck vehicle technology and innovation at Eaton Corp., says the system has several advantages over fully autonomous variants such as the self-driving cars developed by Google and Ford. Such systems, he says, are loaded with expensive sensors, and require vast amounts of computation to plan out safe routes.
“The implications of [Anderson’s] system is it makes it lighter in terms of sensors and computational requirements than what a fully autonomous vehicle would require,” says Saltsman, who was not involved in the research. “This simplification makes it a lot less costly, and closer in terms of potential implementation.” 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 talented 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.”
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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