It could’ve happened to anyone. Just as the Land Rover began passing a Chevy Tahoe that had stopped at the entrance to a traffic circle, the Tahoe started moving. The resulting collision was ruled “no fault.” But it was also “no driver”: it occurred in the finals of the Urban Challenge robotic-car race on November 3 in Victorville, CA, supplying one of the event’s biggest thrills. The automated Land Rover, known as Talos, was MIT’s first faculty-student entry in a U.S. Defense Advanced Research Projects Agency (DARPA) Challenge race. Talos took the fender bender with Cornell’s vehicle in stride and went on to a respectable fourth-place finish behind vehicles designed by veteran teams from Carnegie Mellon, Stanford, and Virginia Tech.
The initial DARPA Grand Challenge races, held in 2004 and 2005, tested robotic cars mainly on lonely desert roads. In a testament to the advances in robotics in the past three years, DARPA staged the Urban Challenge on an abandoned military base to assess how well driverless cars could navigate through a mock city while obeying traffic laws and interacting safely with other bots and human drivers.
To enable Talos to drive on its own, the MIT team outfitted it with hundreds of thousands of dollars’ worth of equipment, bolting on laser range finders, GPS hardware, and video cameras to capture information about the vehicle’s surroundings. To crunch all that data, the researchers installed a miniature supercomputer with 40 cores, stowing it behind the back seats. They also added an extra generator to keep the hardware up and running and a second air conditioner to cool the computers.
During the race, each vehicle appeared to drive with its own personality: some accelerated quickly, while others were more hesitant. Talos seemed confident at some points and tentative, as though pausing to “think,” at others. Team leader John Leonard, a professor of mechanical and ocean engineering, says that Talos hesitated when GPS data conflicted with sensor observations, which its designers thought should be able to overrule the navigation system. While other cars relied heavily on GPS receivers, the MIT team wanted Talos to be able to use other means to find its way along the course. “In a military setting, GPS can be jammed,” says Leonard. “Our vehicle might have looked cautious, but what’s going on inside is that our vehicle was looking at the road and traffic with other sensors.”
Leonard says the race was a learning experience for him and his team. “I’m tremendously proud of our team for creating such an impressive machine that qualified for this race,” he says. DARPA invited 35 teams out of 89 entrants to compete in the semifinals; of the 11 vehicles qualifying for the finals, MIT’s was one of just six to complete the challenging 55-mile course. The team plans to eventually share a large portion of its software online, along with the gigabytes of data collected during the race, so anyone can use it to develop future robotic cars.
The dark secret behind those cute AI-generated animal images
Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.
Inside Charm Industrial’s big bet on corn stalks for carbon removal
The startup used plant matter and bio-oil to sequester thousands of tons of carbon. The question now is how reliable, scalable, and economical this approach will prove.
The hype around DeepMind’s new AI model misses what’s actually cool about it
Some worry that the chatter about these tools is doing the whole field a disservice.
How Charm Industrial hopes to use crops to cut steel emissions
The startup believes its bio-oil, once converted into syngas, could help clean up the dirtiest industrial sector.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.