Uber ATG recently released an open-source visualization tool called AVS — Autonomous Visualization System. Many autonomous vehicle companies write their own visualization and simulation tools. The goal here is to provide an off-the-shelf, open-source solution to get teams started faster.

Check out the demo here 🚗

AVS enters a crowded field of solutions that are all trying to gain traction. MathWorks Automated Driving System Toolbox, dSPACE RTMaps, CARLA, Cognata, Microsoft AirSim, Baidu Apollo Simulation, Applied Intuition, Metamoto, RightHook, and ROS RViz are all working in the space. Some of these are focused sensor playback, some on generating simulated data, some on simulating multi-vehicle scenarios.

The weird thing is that so many teams evaluate this landscape and decide to build their own solutions. Waymo’s Carcraft is the most famous, but we made the same decision at Udacity a few years ago. We evaluated the simulation solutions on the market in the fall of 2016, found that none of them quite met our needs, and decided to build our own simulators in Unity.

Over time, maybe this decision will shift more toward using off-the-shelf solutions. When Udacity needs a simulator today, we are more likely to reach for an existing solution than we were a few years ago. Similarly, our former colleagues at Voyage have a prominent partnership with Applied Intuition. Both of those companies actually appear in the Uber blog post announcing AVS.

That struck me as pretty interesting, too. We’ve seen lots of partnerships in the traditional automotive industry, as car makers position themselves to compete with tech and ridesharing companies. Announcements from the tech and ridesharing space, however, tend to feel more like supplier relationships than partnerships. This blog post is has the feel of an Uber-Voyage-Applied Intuition partnership.

I was at work late tonight and missed my train, so I splurged for a ride home. My driver was pretty talkative, and told me he had done 8,900 rides and tracked the resulting data meticulously.

He volunteered a number of observations that struck me.

Demographics. His most common passenger is a solo female rider. He had a number of hypotheses for this, but none of them struck me as obviously correct. One hypothesis that might be incorrect in his particular case, but correct more generally, is the urban gender divide. Ridesharing is primarily an urban phenomenon, and my intuition is that women outnumber men in urban area (I’m having a surprisingly hard time finding a link that discusses this, though). San Francisco has a basically equal gender distribution, though.

Phones. Clear age divide in riders who talk to the driver. People under 25 look at their phones the whole ride.

Tipping. Older riders are more likely to tip. I think of this largely as a form of self-imposed price discrimination. Shared ride customers are less likely to tip.

Duration. Rides in San Jose (a less dense city) tend to be much longer.

Lost and Found. Charging your phone in the car increases your likelihood of leaving it behind by 5–10x, according to this driver.

I wonder which of these observations would impact self-driving cars. The male-female divide caught my attention, especially because of perceived safety. As an engineer, I think mostly about the safety of the virtual driver system, but female passengers in particular might also consider the safety issues related to entering a stranger’s car.

Tipping seems important for a few reasons. It’s a form of price discrimination that will probably vanish with autonomous driving systems. It’s also a kind of Coasean division, like franchising. This driver seemed really concerned with providing services that would generate tips in his car, in a way that I imagine would be hard to scale to a whole fleet.

This particular driver has a blog at drivingstrangers.blogspot.com, in case you’re interested to learn more.

The beginning of the calendar year means that the California DMV releases each company’s annual report on disengagements in its autonomous vehicle program. A “disengagement” is an instance in which a human safety driver takes control of the vehicle because the autonomous systems fails.

The disengagement reports are difficult to compare across companies, because different companies have different standards, such as under what circumstances a safety driver should intervene. That doesn’t stop the comparisons, though.

Waymo has the lowest disengagement rate: “1 disengage per 11,017 miles self-driven.” Cruise follows with a rate of, “once every 5,205 miles.” On top of that, Cruise likes to emphasize that it drives in San Francisco, a more challenging environment than most of Waymo’s suburban miles.

Apple and Tesla came in for shaming, although for different reasons. Apple published the worst disengagement rate of any company: once per mile. Tesla reports no self-driving miles, at all, which raises questions about the validity of its “full self-driving” feature set.

All of the disengagement data, though, elides an important fact, which is that disengagements require a human behind the wheel. That means that, almost a year after Waymo tried out fully driverless vehicles in Arizona, and several months after winning approval to go driverless in California, it’s not yet past the disengagement stage.

This is was widely reported a few months ago, so it’s not news. But it’s a reminder that there’s still a ways to go yet.

A whole bunch of autonomous vehicle companies have raised huge amounts of money in the last few days.

Aurora raised $530 million in a Series B (!!) round.

Nuro raised a $92 million Series A round.

I used to think self-driving trucks were an under-explored space, and maybe some other people thought that, too, and decided to start self-driving truck companies.

Ike was so far under the radar that it’s website doesn’t even come up when I search “Ike self-driving trucks”. They raised $52 million last week.

TuSimple, which has been around for a bit, just raised $92 million for its self-driving trucks.

Autonomous shuttle company May Mobility raised $22 million, a sum that seems relatively calm by comparison.

One thing I have not seen a good take on is whether this money is being raised to fund capital or labor. Basically, is all of this cash going to super highly-compensated robotics engineers, or is it going to purchase massive fleets of vehicles. I assume the former, because the latter might be better funded with collateralized debt. But it would be interesting to get a read on the business plans.

On Thursday, February 28th, I’ll be talking about self-driving cars with BMW at the BMW Research and Innovation Center in Munich! 🏎

Sign up here: https://www.eventbrite.de/e/connected-autonomous-driving-with-bmw-and-udacity-tickets-56111365605

There are 300 spots and we want to fill every seat!

Here’s the deal: for every 50 people who attend, I’m going to give away one piece of Udacity swag. You might win a mug! A Thermos! You won’t know unless you’re there 😉

See you in 🇩🇪