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Sunday, 18 February 2007 18:07

Robot cars to compete in toughest ever DARPA Challenge

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The types of robots that will be commonplace in the future are being developed today, with the Stanford Racing Team’s newest robotic car, “Junior”, just one of the competitors in the 2007 DARPA Urban Challenge, where robot cars have to drive in real world conditions – without crashing!

Although commercially available robot toys are growing in number from companies like Wow Wee with their Robosapien series, and Lego's robot building kit with 2nd-generation NXT 'brain', along with Microsoft starting up a special Robotics Division, with predictions that robotic development over the next 15-20 years will mimick the technology advances seen with PCs over a similar time period, we're still some time away from the robots seen in the Jetsons, or as seen in Asimov's many robot science fiction stories. But real robots are being worked on that will be able to deal with real-world situations - like driving through traffic!

The Stanford Racing Team has announced its latest robotic car, dubbed “Junior” (and named after Stanford University founder Leland Stanford Jr.), will be competing at the November 3 DARPA Urban Challenge – which involves the ‘real world’ conditions of handling traffic.

This is a big jump over the 2005 DARPA Grand Challenge in the still Nevada desert, where Stanford’s car, called ‘Stanley’, was the winner and one of five robot cars that completed the course. This is significant because at the previous DARPA Challenge, none of the cars that entered completed the course, meaning the year 2005 was a milestone for artificial intelligence.

But when driving on streets, the need to understand the environment, instead of just observing it, becomes paramount. On city streets, there is other traffic to deal with, pedestrians, road signs, traffic lights, road markings and more. The 2007 Urban Challenge is significantly more difficult than the challenge posed in 2005 and calls for a new generation of technology.

Enter “Junior,” the Stanford Racing Team’s new brainchild. Stanford’s Sebastian Thrun, an associate professor of computer science and electrical engineering said that: “In the last Grand Challenge, it didn’t really matter whether an obstacle was a rock or a bush because either way you’d just drive around it. The current challenge is to move from just sensing the environment to understanding the environment.”

Stanford’s release tells us that’s because in the Urban Challenge, sponsored by the Defense Advanced Research Projects Agency (DARPA), the competing robots will have to accomplish missions in a simulated city environment, which includes the traffic of the other robots and traffic laws. This means that on race day, Nov. 3, the robots not only will have to avoid collisions, but also they will have to master concepts that befuddle many humans, such as right of way.

“This has a component of prediction,” says Mike Montemerlo, a senior research engineer in the Stanford Artificial Intelligence Lab (SAIL). “There are other intelligent robot drivers out in the world. They are all making decisions. Predicting what they are going to do in the future is a hard problem that is important to driving. Is it my turn at the intersection? Do I have time to get across the intersection before somebody hits me?”

Stanford tells us that racing team leaders Thrun and Montemerlo discussed Junior for the first time Feb. 17 at the annual conference of the American Association for the Advancement of Science in San Francisco. Thrun joined fellow roboticists in a panel discussion “Robots—Our Future’s Sustainable Partner” at 8 a.m. He spoke about autonomous guidance systems and machine vision. Afterwards, he and Montemerlo participated in a press conference at noon.

The racing team, based in the School of Engineering, is supported by returning industry team members Intel, MDV-Mohr Davidow Ventures, Red Bull and Volkswagen of America and joined this year by new supporters Applanix, Google and NXP Semiconductors. DARPA also has provided $1 million of funding.

So, what’s inside of Junior that makes him so special? Well, for a start, he’s using the latest Intel Core Duo and Quad Core processors and has four times the computing power of ‘Stanley’, the 2005 winning model. But there's plenty more - read onto the next page to find out!


According to the PDF at Stanford’s web site, Junior is a 2006 Passat wagon whose steering, throttle and brakes have all been modified by engineers at the Volkswagen of America Electronics Research Lab in Palo Alto, Calif., to be completely computer-controllable. The engineers also have created custom mountings for a bevy of sophisticated sensors.

The Stanford press release contains a wealth of additional information on the challenge of navigating in a city environment, and how Junior will accomplish this feat:

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.

Want to see the detailed specs? They're on the next (and last page)...


All about Junior - here are the detailed specs:

The body

Make and model:   2006 Volkswagen Passat wagon
Engine:                 4-cylinder turbo diesel injection
Transmission:        Six-speed direct shift gearbox
Engine cubic capacity:   1968cc
Fuel Consumption: City: 25.5 mpg (9.2l/100km)
                            Highway:    42.7 mpg (5.5l/100km)
Combined:            34.6 mpg (6.8l/100km)
Power:                  140 hp (103kW) at 4000rpm
Torque:                236 lb ft (320Nm) at 1800-2500 rpm
Top speed:           126miles/h (203km/h)
Acceleration 0-100km/h:  10.1sec

Power is provided by the engine through a high-current prototype alternator and a battery-backed, electronically-controlled power system.

The senses

Position: Junior's position and orientation are determined by a cutting-edge Applanix POS LV 420 system that is optimized for adverse GPS environments. The system provides real time integration of multiple dual-frequency GPS receivers, a highperformance inertial measurement unit (IMU), wheel odometry, and Omnistar's satellite based Virtual Base Station (VBS) service. Real time accuracy exceeds 35cm and 1/50th of a degree.

Sight: is provided by several state-of-the-art sensors. A Velodyne HD Lidar looks in every direction at once. It combines 64 individual lasers into millions of 3D points per second at up to 50m range. An Ibeo ALASCA XT Lidar handles long ranges, with four scanning planes reaching as far as 200m. A Point Grey Ladybug 2 provides six video cameras that produce near-high-definition video in every direction. SICK Lidar scanners (which Stanley used in 2005) are used for precision navigation at low speeds.

The brains

Hardware: Provided by rackmount servers equipped with Intel's latest Dual and Quad Core processors. Data is processed from instruments as frequently as 200 times a second.

Software: Integrated, custom-coded modules include a planner (making decisions, choosing routes), a mapper (transforming sensor readings into environment understanding), a localizer (refining GPS position by visual observations), and a controller (actuating the planner decisions on the car).

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Alex Zaharov-Reutt

One of Australia’s best-known technology journalists and consumer tech experts, Alex has appeared in his capacity as technology expert on all of Australia’s free-to-air and pay TV networks on all the major news and current affairs programs, on commercial and public radio, and technology, lifestyle and reality TV shows. Visit Alex at Twitter here.

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