An important difference between Junior and Stanley is that Junior must be aware of fast moving objects all around it, while Stanley only had to grapple with still objects in front of it. Junior’s sensors are therefore much more sophisticated, Thrun says. They include a range-finding laser array that spins to provide a 360-degree, three-dimensional view of the surrounding environment in near real-time.

The laser array is accompanied by a device with six video cameras that “see” all around the car. Junior also uses bumper mounted lasers, radar, Global Positioning System receivers and inertial navigation hardware to collect data about where it is and what is around.

Because Junior collects much more data than Stanley did, its computational hardware must be commensurately more powerful, says Montemerlo. Using Intel Core 2 Duo processors—each chip includes multiple processing units—Junior’s “brain” is about four times more powerful than Stanley’s.

But what makes Junior truly autonomous will be its software, which is the focus of about a dozen students, faculty and researchers at the SAIL. Modules for tasks such as perception, mapping and planning give Junior the machine-learning ability to improve its driving and to convert raw sensor data into a cohesive understanding of its situation.

New software development began last fall. Montemerlo has been testing some of the team’s software modules in simulated traffic situations since the beginning of the year. The team expects to move into full-time testing and iterative improvement by the end of March.

Junior’s name is not only an implicit homage to its predecessor, but also to Stanford University’s namesake, Leland Stanford, Jr. Carrying this sense of history, Junior will set out to make technology history of its own and pave the way to a future where autonomous cars can make driving safer, more accessible and more efficient. Self-driving cars could give drivers newfound free time.

“You could claim that moving from pixelated perception, where the robot looks at sensor data to understanding and predicting the environment, is a Holy Grail of artificial intelligence,” says Thrun.

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