Israeli startup Imagry takes contrarian mapless approach to autonomous driving

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While it is possible to drive without highly detailed maps — that’s how humans do it — it’s safer for automated driving systems to have the extra information, said Amnon Shashua, CEO of Mobileye.

Detailed maps streaming into the vehicle provide backup information that is useful in tricky situations, such as encountering sections of roadway with no or obscured lane markings. The high-definition maps kick in and tell the car where it should be in the lane, Shashua said.

That’s why Mobileye works with the automakers that use its sensors and chips to collect data from millions of vehicles already on the road, building low-cost but detailed maps of most of the roadways in North America, Europe and other regions. Mobileye is using that information to support its development of hands-off, eyes-off automated driving systems and eventually fully autonomous systems for the robotaxi industry.

Waymo takes a more expensive approach, driving lidar-equipped vehicles through cities and elsewhere to derive even more detail about roadways.


“We’ve built an incredibly detailed set of mapping technologies that help our cars navigate places even where GPS struggles, like tunnels or between skyscrapers,” the company says on its website.

It merges that information with the sensors and perception tools onboard its robotaxis. The company said the high-definition maps provide data on what lies ahead, allowing the vehicle to drive smoother and more predictably.

Motional, the autonomous technology joint venture between Hyundai Motor Group and Aptiv that is working with Lyft to launch ride-hailing services in Las Vegas and Los Angeles, said high-definition mapping is one of the first steps it takes before operating in a city.

Balajee Kannan, vice president of autonomy at Motional, called it “critical to the safe operation of autonomous vehicles.”

But it does require “time and resources,” he said.

Longer term, Motional sees the vehicle operating “in a plug-and-play system,” in which raw sensor data from initial test drives can produce an accurate, highly detailed map quickly.

“There is no industry standard. There are multiple paradigms being worked on by multiple companies,” said Raj Rajkumar, an engineering and robotics professor at Carnegie Mellon University. “The best ideas will come out on top.”

At lower levels of automation — such as hands-free, foot-free driving systems — there’s little need for maps beyond the primary navigation system, he said.

The vehicle uses sensor and camera data to ensure it follows the car ahead at a safe distance and stays within the lane markings.

At higher levels of autonomy — with lane changes, entering and leaving highways, and more complex driving situations — a mapless system “can do a fairly reasonable job when the lane markers on the road are good and the lighting conditions are good,” Rajkumar said.

But he said high-definition maps with precise information about the size, positioning and directions of all the lanes, the location of infrastructure and buildings, and other detailed information would prove necessary, especially in inclement weather.


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