Competing with the Giants in Race to Build Self-Driving Cars

Competing with the Giants in Race to Build Self-Driving Cars

Mirroring the work at Waymo, Aurora is building algorithms that can recognize objects on the road and anticipate and react to what other vehicles and pedestrians will do next. As Mr. Urmson explained, the software can learn what happens when a driver turns the vehicle in a particular direction at a particular speed on a particular type of road.

Learning from human drivers in this way is an evolution of an old idea. In the early 1990s, researchers at Carnegie Mellon University built a car that learned relatively simple behavior. Last year, a team of researchers at Nvidia, the computer chip maker, published a paper showing how modern hardware can extend the idea to more complex behavior. But many researchers question whether carmakers can completely understand why neural networks make particular decisions and rule out unexpected behavior.

“For cars or flying aircraft, there is a lot of concern over neural networks doing crazy things,” said Mykel Kochenderfer, a robotics professor who oversees the Intelligent Systems Laboratory at Stanford University.

Some researchers, for instance, have shown that neural networks trained to identify objects can be fooled into seeing things that aren’t there — though many, including Mr. Kochenderfer, are working to develop ways of identifying and preventing unexpected behavior.


A Chrysler Pacifica outfitted with Waymo’s self-driving sensors.

Jason Henry for The New York Times

Like Waymo, Toyota, and others, Aurora says that its approach is more controlled than it might seem. The company layers cars with backup systems, so that if one system fails, another can offer a safety net. And rather than driving the car using a single neural network that learns all behavior from one vast pool of data — the method demonstrated by Nvidia — they break the task into smaller pieces.

One system detects traffic lights, for example. Another predicts what will happen next on the road in a particular kind of situation. A third chooses a response. And so on. The company can train and test and retrain each piece.

“How do you get confidence that something works?” asked Drew Bagnell, a machine learning specialist who helped found Aurora after leaving the self-driving car program at Uber. “You test it.”

Mr. Goodhall, the Virginia Department of Transportation researcher, said car designers must reassure both regulators and the public that these methods are reliable.

“The onus is on them,” he said.

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