To make the best autonomous cars, we’ll have to teach their A.I. how to navigate in the the worst possible conditions. That’s why the most daring innovation in the field may wind up taking place far from the sun-soaked streets of California, and instead in less forgiving environments.

“No one will purchase a self-driving car to ride it in California only. This is a question of the next level industrial systems,” Olga Uskova, president of Russia’s Cognitive Technologies and founder of the C-Pilot autonomous driving system, tells Inverse. “For example in our system, we use such a tech called ‘virtual tunnel’. The vehicle moves not only by the road marking, but it defines the road scene the same way the human brain does, by analyzing the lateral situations — the location of trees, buildings, the horizon line etc.”

Uskova notes that 70 percent of the world’s roads are nothing like the ones found in California. But instead of working their way up from empty test tracks to more real-world situations, Uskova’s team decided to use these harsh conditions as a starting point. Driving in bad weather, they determined, was using an estimated 35 to 40 percent of testing time anyway.

“Climate in most parts of Russia is presented by a large number of days per year when drivers must travel in bad weather conditions — on the roads with snow, mud, lack of road marking and poor visibility,” Uskova says.

It’s this deep-end-first approach that characterizes a great deal of the autonomous car development on the international stage. In the United Kingdom, for example, there are no laws against jaywalking. Some startups have argued this is an ideal venue for teaching car-driving A.I. how to deal with pesky pedestrians. One, based at Imperial College London, has already developed a system capable of understanding over 150 behaviors to judge whether a pedestrian is about to step out into the road.

“We are very confident that we are able to predict if someone is going to cross or not,” Leslie Noteboom, co-founder of Humanising Autonomy, told the Evening Standard. “Cars need to understand the full breadth of human behavior before they’re ever going to be implemented into urban environments. The current technology is able to understand whether something is a pedestrian and not a lamp post, and where that pedestrian is moving, framing them as a box. We’re looking inside that box to see what the person is doing, where they’re looking, are they aware of the car, are they on the phone or running — does this mean they are distracted, or risky?”

London is expected to host its first autonomous taxis in 2021, courtesy of Oxford-based developer Oxbotica and taxi firm Addison Lee. Oxbotica has completed a series of limited grocery deliveries as part of its tests, while preparing for a London-to-Oxford autonomous drive in the second half of 2019. The 60-mile journey has patchy cellular service, which will make car communications difficult. The country as a whole has around 75 percent geographic 3G and 4G coverage. The team will have to work out how the car should react when it loses internet connectivity.

An Oxbotica trial vehicle.
An Oxbotica trial vehicle.

In the case of Cognitive Pilot, it’s had to develop new sensors capable of handling the road come what may. It has developed a radar capable of creating a 3D projection of objects from 300 meters away. While Silicon Valley largely focuses on lidar solutions that struggle with harsh weather, radar is better equipped for all seasons. In bad weather conditions, the range of the team’s radar falls by just 50 to 100 meters to reach between 200 to 250 meters. Lidar, which uses a spinning laser to bounce off objects and read their distance, can fail in snow when their lasers instead bounce off of falling flakes.

Silicon Valley is not blind to these issues. Waymo tested its autonomous driving system trekking through snow in South Lake Tahoe back in March 2017. And Tesla, which considers lidar as having too many flaws, has already opted for a combination of cameras and radar for its “Hardware 2” suite designed to support autonomy at a later date. Even CEO Elon Musk, however, notes that it’s “extremely difficult” to develop an all-purpose autonomous driving solution.

Technology firms have recently had to scale back their expectations, as Waymo’s trials in Arizona struggle with complex intersections. Drive.AI has even suggested redesigning roads to support these new cars. While Musk is still confident that Tesla could achieve a point-to-point solution sometime next year, the challenges faced by international developers show it’s unclear how these systems will work elsewhere.

Photos via Oxbotica, Cognitive Technologies