Autonomous vehicles like those being tested by Google, Uber, and major automakers rely on 3-D maps that record the position of curbstones and traffic lights with high accuracy. The maps are usually created by driving around in vehicles outfitted with expensive sensors.
Civil Maps wants to use consumer cars as a low-cost mapping workforce instead, taking advantage of the sensors being added to premium models for advanced cruise control and crash avoidance.
Those cheaper sensors can’t match those in a dedicated mapping vehicle. But pooling enough observations of the same stretch of road makes it possible to maintain high-quality maps and keep track of features such as speed bumps, road signs, and road markings, says Sravan Puttagunta, CEO of Civil Maps. “I think starting in 2017-’18 you’ll have a lot of cars that will meet our criteria for map contributions,” he says.
Puttagunta hopes to persuade automakers to add software to their vehicles so they can contribute data from cameras, radar, and lidar, which uses laser light to map objects in 3-D. Civil Maps has developed software that combines different types of sensor data, and multiple scans of the same objects, to make and update maps. Car companies that contribute data would also be able to use the maps created, says Puttagunta.
He argues that this approach is the only economical way to get rich, frequently updated maps at a continental scale so cars can drive themselves anywhere.
Carmakers have already shown they are willing to spend money—and work together—to create the maps needed for autonomous cars. Audi, BMW, and Daimler teamed up last year to acquire Nokia’s mapping business HERE for $3 billion. Civil Maps recently received $6.6 million from investors including Ford. This week Ford pledged to have fleets of autonomous vehicles on the road within five years (see “2021 May Be the Year of the Fully Autonomous Car”).
Puttagunta says he is in talks with multiple car manufacturers, but he wouldn’t comment on how close any are to signing up with the company.
Civil Maps relies on machine-learning software to interpret data from different sensors and combine it into maps. Its technology also uses data from cars to teach its software how an autonomous car should handle particular stretches of road. For example, Civil Maps software can learn that a particular lane is left turn only. It has figured out that some lanes on the Golden Gate Bridge change direction depending on the time of day.
Reilly Brennan, executive director of the Revs Program on the future of the car at Stanford University, says Civil Maps’ approach makes sense. Rapid improvements in the cost and quality of 3-D sensors make it a practical idea, he says.
One challenge for the crowdsourcing approach will be figuring out how to ensure good coverage everywhere, says Brennan. Routes that don’t get much traffic, or aren’t visited much by people with expensive new sensor-packed cars, would be mapped less frequently and in less detail.
Puttagunta says that problem is manageable. The cost of sensors is coming down so quickly that they won’t be limited to certain high-end vehicles for long, he says.