What Waymo’s new leadership means for its self-driving cars

Waymo, Alphabet’s self-driving car subsidiary, is reshuffling its top executive lineup. On April 2, John Krafcik, Waymo’s CEO since 2015, declared that he will be stepping down from his role. He will be replaced by Tekedra Mawakana and Dmitri Dolgov, the company’s former COO and CTO. Krafcik will remain as an advisor to the company.

“[With] the fully autonomous Waymo One ride-hailing service open to all in our launch area of Metro Phoenix, and with the fifth generation of the Waymo Driver being prepared for deployment in ride-hailing and goods delivery, it’s a wonderful opportunity for me to pass the baton to Tekedra and Dmitri as Waymo’s co-CEOs,” Krafcik wrote on LinkedIn as he declared his departure.

The change in leadership can have significant implications for Waymo, which has seen many ups and downs as it continues to develop its driverless car business. It can also hint at the broader state of the self-driving car industry, which has failed to live up to its hype in the past few years.

The deep learning hype

In 2015, Krafcik joined Google’s self-driving car effort, then called Project Chauffeur. At the time, there was a lot of excitement around deep learning, the branch of artificial intelligence that has made great inroads in computer vision, one of the key components of driverless cars. The belief was that, thanks to continued advances deep learning, it was only a matter of time before self-driving cars became the norm on streets.

The <3 of EU tech

The latest rumblings from the EU tech scene, a story from our wise ol' founder Boris, and some questionable AI art. It's free, every week, in your inbox. Sign up now!

Deep learning models rely on vast amounts of training data to develop stable behavior. And if the AI algorithms were ready, as it seemed at the time, reaching deployment-level self-driving car technology was only a question of having a scalable data-collection strategy to train deep learning models. While some of this data can be generated in simulated environments, the main training of deep learning models used in self-driving cars comes from driving in the real world.

The limits of self-driving technology

Like the executives of other companies working on driverless car technology, Krafcik promised time and again that fully autonomous vehicles were on the horizon. In Waymo’s 2020 Web Summit, Krafcik presented a video of a Waymo self-driving car driving in streets without a backup driver.

“We’ve been working on this technology a long time, for about eight years,” Krafcik said. “And every company, including Waymo, has always started with a test driver behind the wheel, ready to take over. We recently surveyed 3,000 adults across the U.S. and asked them when they expected to see self-driving vehicles, ones without a person in the driver’s seat, on their own roads. And the common answer we heard was around 2020… It’s not happening in 2020. It’s happening today.”

But despite Krafcik’s leverage in the automotive industry, Google’s crack AI research team, and Alphabet’s deep pockets, Waymo—like other self-driving car companies—has failed to produce a robust driverless technology that can run on any road without rigorous testing and tuning. And aside from areas where Waymo’s self-driving technology has been fully tested and approved, the cars still require backup drivers to monitor and take control as soon as the AI starts to act erratically.

The AI technology for self-driving cars is not ready, and despite the lidar, radar, and other sensor technologies that many companies use to complement deep learning models, self-driving cars still can’t handle unknown conditions in the same way as humans do. Self-driving cars can run thousands of miles without making errors, but they might suddenly make very dumb and dangerous mistakes when they face corner cases, such as an overturned truck on the highway or a fire truck parked at the wrong angle.

So far, Waymo has fared better than its competitors and has avoided major self-driving scandals such as Tesla and Uber’s fatal accidents. But it has yet to deliver a technology that can be deployed at scale. Waymo One, the company’s fully driverless robo-taxi service, is only available in limited parts of Phoenix, AZ. The company is in the process of expanding the service to more crowded and volatile urban areas.

The company is still far from becoming profitable. Alphabet’s Other Bets segment, which includes Waymo, had an operating cost of $4.48 billion in 2020, against $657 million in revenue. And Waymo’s valuation has seen a huge drop amid cooling sentiments surrounding self-driving cars, going from nearly $200 billion in 2018 to $30 billion in 2020.

The AI and legal challenges of self-driving cars

Driverless technology has come a long way, but a lot more needs to be done, and the past few years have shown that the “fully self-driving cars are here” narrative is a bit fallacious. It’s clear that just putting more miles on your deep learning algorithms will not make them more robust against unpredictable situations. We need to address some of the fundamental problems of deep learning, such as lack of causality, poor transfer learning, and intuitive understanding of physics. These are active areas of research, and no one has still provided a definitive answer to them.

The self-driving car industry also faces several legal complications. For instance, if a driverless car becomes involved in an accident, how will culpability be defined? How will self-driving cars share roads with human-driven cars? How do you define whether a road or environment is stable enough for driverless technology? These are some of the questions that the self-driving car community will have to solve as the technology continues to develop and prepare for mass adoption.

In this regard, the new co-CEOs of Waymo are well-positioned to face these challenges. Dologov, who was Waymo’s CTO before his new role, has a PhD in computer science with a focus on artificial intelligence and has a long history of working on self-driving car technology. As a postdoc researcher, he was part of Stanford’s self-driving car team that won second place in DARPA’s 2007 Urban Challenge. He was also a researcher at Toyota’s Research Institute in Ann Arbor, MI. And since 2009, he has been among the senior engineers in Google’s self-driving car outfit that later became Waymo. In a nutshell, he’s as good a leader you can have to deal with the AI software, algorithm, and hardware challenges that a driverless car company will face in the coming years.

Mawakana, on the other hand, is a Doctor of Law. She had led policy teams at Yahoo, eBay, and AOL before joining Waymo and becoming the COO. She’s now well-positioned to tackle the legal and policy challenges that Waymo will face as self-driving cars gradually find try to find their way in more jurisdictions.

The dream of self-driving cars is far from dead. In fact, in his final year as CEO, Krafcik managed to secure more than $3 billion in funding for Waymo. There’s still a lot of interest in self-driving cars and their potential value. But Waymo’s new lineup suggests that self-driving cars still have a bumpy road ahead.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.