Most autonomous vehicles use LIDAR sensors and/or cameras to figure out where they are on the road, but cameras can be thrown off by lighting conditions or snow-covered signs and lane markings, and LIDAR often becomes less accurate in inclement weather. GPR, on the other hand, sends electromagnetic pulses into the ground to measure the specific combination of soil, rocks and roots. That data is turned into a map for self-driving vehicles.

The system, which uses a type of GPR called Localizing Ground Penetrating Radar developed at the MIT Lincoln Laboratory, offers a few benefits. For starters, it doesn’t matter if the road is snow-covered or if visibility is blocked by fog. And conditions under the road tend to change less often than features like lane striping and signage.

“If you or I grabbed a shovel and dug it into the ground, all we’re going to see is a bunch of dirt,” says CSAIL PhD student Teddy Ort. “But LGPR can quantify the specific elements there and compare that to the map it’s already created, so that it knows exactly where it is, without needing cameras or lasers.”

So far, the CSAIL team has only tested the system at low speeds on a closed country road, but the researchers believe it could be easily extended to highways and other high-speed areas. They admit that the system doesn’t work as well in rainy conditions, when water has seeped into the ground below the road, and that it is far from road-ready. It would also have to be used in combination with other technology, as it wouldn’t detect hazards on the road.

A paper on the project will be published in the IEEE Robotics and Automation Letters journal later this month. The team plans to continue refining the hardware, so that it is less bulky — it’s currently six feet wide — and improving LGPR mapping techniques.

While human depth perception isn’t duped by object motion, AI isn’t quite there yet. But generating enough clips to train AI would be difficult, and gathering enough people of different ages and genders creates an added challenge. That’s why the team turned to YouTube. If you participated in the Mannequin Challenge, there’s a chance your video is one of the 2,000 the researchers scraped together to build their dataset. They plan to share that data with the larger scientific community.

To train the neural network, the researchers converted the clips into 2D images, estimated the camera pose and created depth maps. The AI was then able to predict the depth of moving objects in videos with higher accuracy than previously possible. With this enhanced ability, neural networks could help self-driving cars and robots better navigate unfamiliar areas.

The fact that the researchers used videos without notifying the people in them could raise some privacy concerns. As Technology Review points out, it’s not uncommon for researchers to glean publicly available data from sources like Twitter and Flickr, and as neural networks grow dependent on larger data sets, the practice will likely continue. That might make you think twice about which YouTube fads you join.

GM hasn’t specified exactly how all the hiring will break down, but the company reportedly plans to use the majority of those new job openings to attract engineers. The hiring spree may be in response to the company’s recent struggles. As of last year, reports indicated Cruise was behind schedule, missing mileage targets and milestones. The company’s tech has also had some nagging issues, including an inability to reliably recognize pedestrians and a habit of stopping for bicyclists who aren’t actually there. Overhauling the engineering team appears to be an attempt to get Cruise back on track.

Despite troubles getting up to speed, GM has some lofty ambitions for Cruise and wants to get a self-driving taxi service up and running before the end of the year. The company has already partnered up with Lyft to offer rides from its autonomous fleet and just recently Cruise announced plans with DoorDash to make deliveries with self-driving cars.