Cornell’s Self-Driving Car will get Smarter (Oct. 9, 2009) — Cornell’s self-driving car will soon be a test bed for new research in robotics and artificial intelligence (AI).

selfdrivingc“We’ll be looking at the next level of intelligence in the vehicle,” said Mark Campbell, the lead researcher and associate professor of mechanical and aerospace engineering.

Campbell and Daniel Huttenlocher, co-principal investigators were advisers to the team of Cornell students who built Skynet, Cornell’s entry in the DARPA (Defense Advanced Research Projects Agency) Urban Challenge.

The car, a converted Chevy Tahoe named for the AI in the Terminator movies, was one of six of 11 finalists to successfully complete the DARPA race by driving autonomously, obeying traffic laws and merging with and passing other robotic cars as well as cars driven by humans.

DARPA hopes to develop self-driving vehicles to replace human drivers in hazardous environments such as war zones. Civilian applications are in mining, farming, disaster response and remote exploration.

But autonomous vehicles still aren’t smart enough, Campbell said. The DARPA challenge was run on a closed, controlled course. “They didn’t have pedestrians or people on bicycles,” Campbell pointed out. “Our car could not drive autonomously through Collegetown right now.”

The present car uses sensor data from radar, lidar and video cameras to build a “model” of its environment — something human drivers also do, Campbell pointed out — but the model is still quite simple. It’s designed to avoid collisions, but it still can make mistakes, such as failing to distinguish between another car and a cement barrier — a mistake Skynet made in the DARPA challenge.

To intelligently drive through a place like Collegetown, the environmental model must be created in real time, Campbell said, and distinguish between small cars and big trucks, not to mention pedestrians and even pedestrians towing rolling suitcases.

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