Friday’s Food for Thought: Driverless Driving

Autonomous cars fuel the race for precision mapping capabilities


By 2030, approximately half of vehicles sold globally will have some semi-autonomous driving capabilities, and 15 percent will be fully autonomous, according to MarketWatch. However, an important hurdle to jump before getting these vehicles on the market and into garages is precision mapping capabilities, which many auto and transportation companies are racing to procure.

Uber recently partnered with DigitalGlobe to take advantage of the company’s high-resolution commercial satellite imagery to improve its current customer experience, as well as to enable the company to potentially launch a fleet of self-driving vehicles in the future. At present, DigitalGlobe imagery will make Uber’s maps more precise to include side roads, parking lots, and building footprints—allowing drivers to better determine where exactly a passenger is waiting to be picked up.

Ford and Stanford University have invested in Civil Maps, which aims to crowdsource better maps for driverless vehicles, reports The Washington Post. Civil Maps’ technology uses sensors to build a map including everything from traffic patterns to construction zones. The sensor technology can read street signs, traffic lights, and warnings, then upload the data for comprehension by a driverless vehicle. According to the article, Civil Maps has drawn interest from automakers in China, Japan, and the United States.

General Motors, Volkswagen AG, and Nissan are using mapping technology from Israeli company Mobileye NV. MarketWatch reports Mobileye’s Road Experience Management product enables crowdsourced, real-time data for precise location and high-definition lane information to support autonomous driving.

Read more about autonomous vehicles in the Q2 2016 issue of trajectory.

Photo Credit: DigitalGlobe

Posted in: got geoint?   Tagged in: Machine Learning & AI

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