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Self-driving cars are returning to work too

Shelter-in-place orders have forced many companies that are developing self-driving cars to stop testing their vehicles. That’s bad news for the nascent industry because the machine learning that the cars rely on learns best from road testing.

Consider ride-hailing company Lyft, among the many businesses experimenting with self-driving cars. Last week, the company said that it would resume test driving its autonomous vehicles on a test track in Palo Alto, Calif. after a three-month pause due to the pandemic.

During those three months, Lyft didn’t leave its self-driving car project stuck in idle. Instead of driving on pavement, it trained its machine-learning systems on simulated roads intended to mimic the real world.

The idea of using simulated driving to train autonomous vehicles isn’t new. But the coronavirus pandemic has led to Lyft and others like Alphabet’s Waymo and GM’s Cruise self-driving car subsidiary to beef up their simulation technology.

Sameer Qureshi, a Lyft self-driving car director, told Fortune that the next few weeks are important because Lyft will be able to compare its simulated testing to real driving. In the process, Lyft will learn how to improve its simulated testing to be more like the physical world.

A typical simulation may require an autonomous vehicle to properly react to a pedestrian crossing the street (i.e. it must recognize the individual as a human and then stop). Despite the relatively simple scenario, it involves numerous variables that cars could fail to take into account and therefore cause a collision.

For instance, a real-life self-driving car’s brakes may be subjected to “wear and tear” after repeated use, thus impacting how that car stops, Qureshi said. Driving simulators have difficulty taking into account such a nuance, but it’s a crucial issue that can mean the difference between a self-driving car breaking too hard (causing passengers to spill their coffee) or too soft (ending with a collision).  

“Sometimes simulation does not 100% match what the cars do in real life,” Qureshi said.

Returning to the road is a big deal for Lyft, whose self-driving car technology is behind other companies like Waymo and Cruise, according to many analysts. Qureshi acknowledged that “self-driving cars are hard,” but he argued that Lyft has some advantages that may be overlooked.

One involves the data the company collects from its ride-hailing service that it can use to fine tune its autonomous driving technology, he said. For example, it knows which streets are the busiest in certain cities and how rainstorms affect traffic patterns.

“So I can’t argue that we are way ahead of everyone else,” Qureshi said. “But we made some significant amounts of progress in the last three years we’ve been around.”

Still, don’t expect Lyft, or any other company to put its self-driving taxis into a commercial service anytime soon—the technology still needs to be further developed. As Qureshi says, “It will be a long time before autonomous vehicles completely replace human drivers.”

Source: Fortune

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