Keeping Up

It can be hard keeping up with all the different companies working on autonomous vehicles!

I recently came across two lists of autonomous vehicle companies that I found helpful: “The State of the Self-Driving Car Race 2020” (Bloomberg) and “Factbox: Investors Pour Billions Into Automated Delivery Startups” (New York Times).

The Bloomberg article summarizes the larger, better-funded efforts, whereas the Times covers fundraising by smaller startups.

Between the two of them, how many of these companies are you keeping up with?

Teleoperations From Home

Kirsten Korosec has a story out in TechCrunch about the partnership between Postmates and Phantom Auto to teleoperate Serve, the small autonomous delivery vehicle that Postmates has launched.

Postmates teleoperations staff is now working from home, as are so many office workers during the COVID-19 pandemic. Postmates has provisioned its teleoperations staff with the necessary equipment to remotely operate vehicles from home.

Postmates says that by moving this job to a work-from-home setup, it’s opened the role to many more possible operators.

The interesting question, for me, is whether Postmates and Phantom Auto can make this setup economical enough for massive scale.

One of the huge advantages of that Tesla is reaping is the ability to use its vehicle owners as free data labelers.

Teleoperators can be free data labelers, as well. If Postmates and Phantom can make the teleoperators economical at scale, that would be a huge data advantage.

Graph Neural Networks

A Waymo blog post caught my eye recently, “VectorNet: Predicting behavior to help the Waymo Driver make better decisions.”

The blog post describes how Waymo uses deep learning to tackle the challenging problem of predicting the future. Specifically, Waymo vehicles need to predict what everyone else on the road is going to do.

As Mercedes-Benz engineers teach in Udacity’s Self-Driving Car Engineer Nanodegree Program, approaches to this problem tend to be either model-based or data-driven.

A model-based approach relies on our knowledge (“model”) of how actors behave. A car turning left through an intersection is likely to continue turning left, rather than come to a complete stop, or reverse, or switch to a right-turn.

A data-driven approach uses machine learning to process data from real world-observations and apply the resulting model to new scenarios.

VectorNet is a data-driven approach takes relies heavily on the semantic information from its high-definition maps. Waymo converts semantic information — turn lanes, stop lines, intersections — into vectors, and then feeds those vectors into a hierarchical graph neural network.

I’m a bit out of touch with the state-of-the-art in deep learning, so I followed a link from Waymo down a rabbit hole. First I read “An Illustrated Guide to Graph Neural Networks,” by a Singaporean undergrad named Rishabh Anand.

That article led me to an hour-long lecture on GNNs by Islem Rekik at Istanbul Technical University.

It was a longer rabbit hole than I anticipated, but this talk was just right for me. It has a quick fifteen minute review of CNNs, followed by a quick fifteen minute review of graph theory. About thirty-minutes in she does a really nice job covering the fundamentals of graph neural networks and how they allow us to feed structured data from a graph into a neural network.

Now that I have a bit of an understanding of GNNs, I’ll need to pop all the way back up to the Waymo blog post and follow it to their academic paper, “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation.”

The Waymo team is scheduled to present that paper at CVPR 2020 next month.

Brian Salesky, CEO

Wired has a recent and very flattering profile of Brian Salesky, founder and CEO of Argo, Ford’s self-driving car venture.

The piece has more information than I’ve read elsewhere about the early history of the Google Self-Driving Car Project, now known as Waymo. There’s also a good description about the friendship and rivalry between Salesky and Aurora CEO Chris Urmson.

Recommended.

Super Batteries and Terafactories

Paul Leinert reports an amazing read in Reuters. Tesla is planning to launch breakthrough electric batteries that will make the cost of electric powertrains as economical or even better than gasoline.

As if that’s not enough, Tesla is going to build “Terafactories” in China to produce these batteries. Terafactories will be at least an order of magnitude bigger than Tesla’s Nevada “gigafactory”.

There’s also some battery chemistry in the piece, as well as an academic laboratory from Nova Scotia, of all places.

It’s almost too out there to believe, but it seems just plausible and Paul is one of the automotive industries best reporters on future technology.

Read the whole thing.

The Middle Mile

Gatik, a Palo Alto start-up, made the news recently with autonomous box trucks focused on “middle mile” logistics.

I confess I was previously unfamiliar with the term “middle mile”, which apparently refers to the fixed routes between centralized distribution centers and dispersed retail locations.

The advantage, I take it, is that “middle mile” routes are limited and fixed, which would dramatically simplify technical challenge.

The box trucks should also be simpler to handle than articulated tractor-trailers.

The middle “swivel” point between a tractor and trailer adds a huge degree of complexity for control systems — not only laterally, but also vertically. The back of the trailer literally bounces up and down.

Box trucks are essentially rigid bodies in that way, like cars.

One of the big questions in autonomy is how and how much it is possible to simplify the technical challenge of autonomy. Tesla approaches this by limiting self-driving mainly to highways. Waymo limits its vehicles to specific geofences in a few metro areas. Voyage tackles this deploying relatively low-speed vehicles in gated retirement communities. Other vehicles work on sidewalks, or farms, or warehouses, or mines.

Gatik is betting that “middle mile” logistics will be a favorable niche.

I’d love to see some way to quantify how simple an environment is.

Rivian, Amazon, and Ford

Rivian, which operated in stealth mode for nearly a decade, continues to quietly fly under the radar as one of the most intriguing startups in mobility.

The average American has never heard of them, and probably will never hear of them until they launch their first electric vehicles next year.

They’ve raised a ton of money since their founding in 2009, and they’ve made some recent news as a possible pawn in a (possibly non-existent?) competition between Jeff Bezos and Elon Musk.

Rivian also has an interesting relationship with Ford. Ford invested over half a billion dollars in Rivian, despite (because of?) Rivian targeting Ford’s profit-center, the pick-up truck.

Nonetheless, Ford is building its Mustang Mach E on its own platform, and recently canceled a Lincoln vehicle that was supposed to be built on Rivian’s “skateboard” platform.

If you want to learn a bit more about Rivian and listen to highly-energetic take on why first Ford and now Amazon should buy Rivian, check out this video from HyperChange last year.

Capital-Intensive Businesses

Yesterday, I wrote about my old boss, Oliver Cameron, and his company’s partnership with FCA to build customized driverless vehicles.

Oliver is back in the news today, quoted extensively in an honest and sobering New York Times article about the ups and downs of the self-driving car industry.

“That was a clear moment in time where the whole industry went from being a bull market to a bear market,” Mr. Cameron said. “Covid has taken us even further into the bear market.”

The Times closes with the high capital needs of self-driving companies.

With autonomous vehicles, “you may find yourself in a company that requires billions of dollars of capital,” with no clear timeline for building a large business or seeing a return on the investment, said Aaron Jacobson, a partner at NEA.

This is true, and it is one reason robotics companies are not super attractive generally, at the moment. But it would be a mistake to get too spooked by the industry’s capital needs.

The other side of that coin, as Warren Buffett has preached for years, is that capital intensive businesses offer the opportunity to deploy huge amounts of capital with attractive returns. Capital requirements also form a durable moat around the business.

I noticed the reverse perspective in today’s post on Fred Wilson’s blog, AVC. Fred describes profitability and low capital needs of online learning companies, which is also an industry of obvious interest to me.

“They [Duolingo, Quizlet, Skillshare, Codecademy, and Outschool] have all been very capital efficient and most are cash flow positive at this point.

What this tells me is that direct to learner businesses are very attractive. They can serve a very large number of learners very efficiently, they can lightly monetize and yet produce massive revenues because of their scale, and they don’t require a huge amount of capital to build.”

Autonomous vehicles and online education are both attractive industries, but they are very different industries, with distinct capital needs. Companies in each industry have to tailor their business plans to that reality.

Voyage Partners with FCA

My friends and former colleagues at Voyage announced today that they have partnered with FCA to create custom-built driverless Pacifica minivans for use in Voyage’s fleet.

This is so amazing!

I remember less than four years ago meeting the whole Voyage team was at Udacity and had never worked on self-driving cars. Now they’re following the same path and building on the same platform as Waymo.

This team is truly an inspiration that if you work hard you won’t believe what you can accomplish.

And check out this video!

Tesla’s Return to Work Playbook

Yesterday Tesla published a blog post, “Getting Back to Work,” that emphasized the company’s desire to resume automotive production at its Fremont, California, plant.

The post links to Tesla’s “Return to Work Playbook”, a polished 38-page report on Tesla’s plans for regulation and compliance.

The document is clearly intended for government relations and press purposes, not as a how-to manual for staffing the plant properly. Those documents exist, too, and this playbook shows some examples, but this playbook is for the public, not Tesla staff.

Nonetheless, the playbook serves as a good read for those of us wondering what the near-term future of work will look like, whether in manufacturing or retail or offices.

The main steps include:

The most notable changes to me are the use of PPE, temperature checks, and eliminating large meetings.

Cleaning

There’s quite a bit of emphasis on cleaning and disinfectant. Most of what I’ve read in the news about COVID indicates person-to-person transmissions, not so much person-to-surface-to-person. Cleaning is always welcome, of course, but I wonder how much of an effect rigorous cleaning will have.

Eating

“Break/lunch areas — occupancy reduced by removing some chairs and table, posting signage and installing barriers in some areas.”

Temperature Screening

The playbook references both thermal cameras and thermometers. It sounds like the thermal cameras are passive sensors that alert whenever somebody passes by with a temperature. That seems genius, and is also much more invasive than Americans are typically used to in the workplace.

Fun

“Ensure common areas are closed (e.g. game areas, gym, bean bags, etc.).”

Fun is about to become a lot less important at work.

Carpooling

“Verify signs promoting carpooling have been removed.”

Beyond Tesla, I wonder if state-regulated HOV lanes will also go away.

Handshakes

“Do not shake hands or engage in any unnecessary physical contact”

We’ve all already stopped shaking hands, but it’s interesting to see a company explicitly prohibit the practice. I assume this will come back eventually, but I wonder how long.

Testing

“By entering into our work location, you agree that:
…
YOU have NOT tested positive or have been tested and are awaiting the results for COVID-19 in the last 14 days [my emphasis]”

I wonder how rigorously this will be followed. This means if the tests ever become widespread enough that you could just take them on-demand, you might not want to take them, lest you get banished from work until you receive a result. Perhaps there is a legal liability issue that drives this.

Travel

“All international business-related travel is currently curtailed and requires VP’s approval…Please refrain from booking and staying at alternative or home sharing rentals.”

Further evidence that Airbnb has a tough road ahead.

Self-Care

“Hearing about the pandemic repeatedly can cause undue stress, so consider taking a break from it.”

Sound advice, I suppose, but let’s not cross the line into willful ignorance.

Conference Rooms

“Conference Rooms — occupancy is reduced to 1/3 capacity until further notice.”

That probably means that in order to have a 2-person meeting you’ll need a room originally designed for at least six people. That’s going to be hard to come by at most companies (I don’t know about Tesla specifically). I would guess this winds up being a major factor in causing white collar employees to continue to work from home. A meeting with a colleague sitting at the next desk might be easier to arrange via web chat from home, rather than scrounging a six-person conference room in the office and wearing masks.

It’s a brave new world out there.