As much as I want self-driving cars to become a reality, I worry that it simply isn’t possible via the pathway Waymo, Cruise, Uber ATG, Argo, Zoox, Voyage, and others are trying to take. At least not without new fundamental advances in machine learning that allow for vastly better data efficiency.
The pathway of large-scale data collection that Tesla is trying to take (and other companies like Mobileye may try) might not work either. I worry even more about that because right now it looks like our best hope.
Without large-scale data, you can’t leverage machine learning to its fullest extent. Without leveraging machine learning to its fullest extent, it might be impossible to develop software that is superhuman at the constituent tasks of autonomous driving: computer vision, behaviour prediction, and path planning/driving policy. Even with large-scale data, it might be impossible, but it’s more likely to be possible with large-scale data than without it.
If it isn’t possible, then self-driving cars just won’t work. Not until we make new breakthroughs that push robotics and AI closer to general intelligence.
It’s disheartening to see Kyle Vogt’s optimism crash against the rocks. Especially after the same thing happened to John Krafcik; Waymo was supposed to have a robotaxi service with no safety drivers last year, and now Krafcik downplays robotaxis and emphasizes freight trucking instead.
Uber ATG’s culture was so bad that they disabled a critical safety feature — braking for pedestrians — to impress the new CEO with a smooth demo ride. I’m not even sure Uber should be allowed to continue public road testing. I definitely don’t trust Uber to do a competent job at self-driving car development. I’m sure they have some fantastic engineers, but the culture is bad. The problems at Uber ATG may not be isolated from the cultural problems at Uber more broadly, like HR and management covering up sexual harassment.
I don’t know much about Argo or Ford Autonomous Vehicles. I don’t think they are doing anything fundamentally different or better than Waymo or Cruise. So I don’t see why Argo/Ford would be any more successful. Same for other startups like Zoox.
Voyage and Nuro are trying to solve somewhat more limited and easier problems first (gated retirement communities and grocery delivery, respectively), but I don’t see how that ultimately makes it easier to solve the robotaxi problem.
Wayve is taking a fundamentally different approach — end-to-end learning — but end-to-end learning seems to require large-scale data in the same way that all machine learning requires large-scale data. Uber ATG tried doing end-to-end learning for a year and abandoned it. If this approach works, I think it will be at the scale of millions of cars, not hundreds.
So, I see Waymo’s and Cruise’s inability to meet their goals as telling us that small data self-driving car companies in general will not be able to meet their goals. Not until Waymo and Cruise do.
Big data self-driving car companies (of which Tesla is the only confirmed example so far) might find more success. Let’s hope so because if not, it bodes poorly for everyone.
The worst thing for everyone would be if self-driving cars aren’t possible.
If Tesla (and/or others) succeed with the big data approach, that would be a good thing for Waymo and Cruise. GM could start equipping its production cars with Cruise hardware. Alphabet could acquire a car company to vertically integrate with Waymo, or forge partnerships to integrate Waymo’s hardware and software with mass produced vehicles. Or possibly even sell Waymo to a car company or a consortium of car companies, although it would be hard to get $100 billion (which is what Waymo reportedly values itself at).
These companies are competing with each other, but more importantly they are also collectively competing against time to deploy a commercial product before the people providing capital give up. So Waymo and Cruise should root for Tesla’s success. And vice versa, since if Waymo and Cruise can do it with small data, surely Tesla can do it with big data.