This Self-Driving Car Is Programmed To Learn From Its Mistakes
A robot car that learns from experience, like a human motorist with years of experience behind the wheel, has been tested for the first time.
The Volkswagen GTI was put through its paces at Stanford University in the U.S. and performed as well as a skilled racing car driver.
The idea was to create a control system that allowed the driverless car to handle unexpected conditions, such as ice and snow.
Current autonomous cars are good at making on-the-spot assessments of their environment.
But the new system incorporates data from past driving experiences.
As part of the project, one of the Stanford team's autonomous vehicles, a Volkswagen GTI known as "Niki", was let loose on an icy test track near the Arctic Circle.
Skidding around corners, the car's computer "brain" learned from its mistakes and stored the information away.
"Our work is motivated by safety, and we want autonomous vehicles to work in many scenarios, from normal driving on high-friction asphalt to fast, low-friction driving in ice and snow," lead researcher Nathan Spielberg said.
A major hurdle facing autonomous cars is dealing with unexpected emergencies. Safely recovering from a skid on ice, for instance, requires planning based on advanced information about the way the car is likely to behave.
To overcome this problem, the Stanford researchers built an artificially intelligent neural network that "remembered" past driving experiences.
Tests of the system were conducted at Thunderhills Raceway, a motor sports complex in Willows, California.
Niki performed as well as another autonomous car, an Audi TTS nicknamed "Shelley", which had been pre-loaded with information about the course and conditions.
Unlike Shelley, Niki had no pre-programming and had to rely on memories of its earlier driving experience.
Both cars achieved similar lap times to a skilled amateur racing driver.