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Little Ben did not finish in the money – top honors were taken by Carnegie-Mellon, Stanford and Virginia Tech. But the Prius garnered several key honors – it was one of only six cars to complete the race and one of just five to do so in less than six hours.
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More significantly, the Ben Franklin Racing Team was the only one of the six final teams not to receive $1 million in advance funding from DARPA. Indeed, Little Ben was the only one of the original 78 Track B, or unfunded, cars to finish the race.
The total cost of outfitting Little Ben came to less than $250,000. Much of that was provided by Lockheed and Thales Communications, the team’s other funding source. (Thales’s president and CEO, Mitch Herbets, earned a B.S. in electrical engineering from Lehigh in 1979.)
“For a small team that was not externally funded,” said Dan Lee, associate professor of electrical and systems engineering at Penn and leader of the Ben Franklin Racing Team, “we gave the large engineering schools a run for the money. They had more resources, students and sponsors. We had to make a lot out of very little.
“We were not the fastest, flashiest or showiest car but we were one of the safest and most reliable.”
Necessity, mother of invention
To navigate roads and traffic by itself, says Spletzer, a robotic car must possess the same intelligence and sensing skills as human beings. It has to be able to recognize the lanes, median and shoulder of the road, and to distinguish between approaching vehicles and other obstacles.
The car’s “road-segmentation” and “lane-tracking” abilities must be particularly robust, says Spletzer. It must be able to identify and adhere to the portion of the terrain that is drivable road surface. If no lines are painted on a road, or if the car’s GPS system fails – a likelihood under bridges, overpasses and skyscrapers – the car must continue driving on the paved portion of the road.
“The car must at all times know where the road is, where the car’s half of the road is, and where the edge of the road is,” says Spletzer.
“The car not only needs to stay in its lane and remain the proper distance behind the car in front of it, it also needs to know to stop behind double-parked cars in its lane and wait for traffic ahead to clear before it proceeds.”
To equip Little Ben with human-like intelligence and sensing skills, the Ben Franklin Racing Team employed a variety of sensing devices that worked in concert to gather and crunch data. These included GPS, a stereo vision camera and lidar laser systems (lidar is an acronym for light detection and ranging).
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The stereo vision camera, located on the front of Little Ben, possessed a 50-degree field of view and functioned like human eyes, says Spletzer.
“The stereo head’s two cameras enabled Ben to estimate depth of field and to know that a traffic line was a line on the ground and not a white pole pointing up into the air. The cameras also tracked the ground plane in front of the car to estimate the car’s pitch, which oscillates when a car brakes, turns or reacts to a bump in the road. Being able to track this plane enabled us to stabilize the ‘camera in’ software.”
A series of lidar lasers, said Spletzer, worked with the stereo camera to detect obstacles and to keep Little Ben from drifting out of lane. Mounted atop the car was a high-definition lidar system developed by a California company called Velodyne, bought for the Prius by Lockheed Martin, and refined by the Penn team members. With 64 embedded lasers, this system provided a 360-degree field of view and worked with two side lidar systems to detect lane markers.
Little Ben used lidar systems developed by Sick AG, a German company, and purchased by the team with donations from Thales. Two lidars atop the car enhanced observation and road segmentation. Two under the front headlights collaborated to detect merging traffic. A third lidar in the front of the car watched for obstacles, while two lidars positioned near the side view mirrors monitored lanes and curbs.
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Jason Derenick, a Ph.D. candidate in computer science and engineering at Lehigh, refined the lidar systems so Little Ben could detect obstacles the size of a curb as well as reflections from the lane markings.
“Jason developed a robust obstacle-detection system using the Sick lidars,” said Spletzer. “The actual lane detection was accomplished by sensor fusion, by merging lidar and stereo-camera data.”
In fact, said Spletzer and Lee, integration – the ability to gather information using multiple sources and to combine and interpret that data in real time – was the overall key to Little Ben’s success.
