The self-driving industry has a scaling problem

The question of regional driving differences may sound quaint to humans, but it poses a serious challenge to autonomous vehicles. Tech and automotive firms like Tesla and General Motors are all developing their own self-driving systems with the hopes of one day scaling them across the world. But the combined barriers of regional regulations and lack of access to local data could slow their expansion – and give local competition a leg-up.

“Self-driving car technologies, like environment perception and understanding road conditions, require local data,” said Cao Xudong, CEO of self-driving startup Momenta, at an investor event in Beijing earlier this month. To design a system that works well in China, for instance, you need Chinese data.

Outside of China, going 60 miles per hour on a highway might be considered normal. “In Beijing, even going [25 miles per hour] is considered pretty good,” he explained, describing the country’s traffic-plagued capital. Other special characteristics, like pollution and driver behavior, can also impact a system’s accuracy.

That’s the challenge the autonomous driving industry is now facing, as Silicon Valley tech giants break out of their California test sites and into more chaotic driving environments. It’s kind of like learning how to run and crawl at the same time – to date, no company has developed a commercially viable, fully autonomous vehicle. Moving somewhere new isn’t the same as starting over, but given artificial intelligence’s dependence on enormous volumes of data, it won’t be trivial either.

“Most of the autonomous vehicle technology being developed today is geo-specific, making it difficult to expand to new cities with new rules and new driving behaviors to account for,” says Doug Parker, chief operating officer at self-driving startup NuTonomy, which was acquired for US$450 million by automotive tech firm Delphi last week.