This diversity of partnerships presumably helps Waymo on both offense and defense. Deploying across multiple partners on different continents will move Waymo further into a position as the industry-standard for autonomous vehicle software. And maintaining alternative vehicle suppliers protects Waymo against any one supplier leaving the fold.
On the other hand, managing multiple supplier relationships is a huge time investment. There are parallels between Waymo’s new position in the automotive supply chain and the current positions of the automotive manufacturers, who spend a lot of time managing their own supply chains.
NVIDIA (ahem, one of Udacity’s partners in developing the Udacity Self-Driving Car Engineer Nanodegree Program) announced their new DRIVE Constellation Autonomous Vehicle Simulator a few weeks ago at their annual GPU Technology Conference.
NVIDIA is taking the next step toward the holy grail of autonomous vehicle simulation: training autonomous driving software in the simulator and deploying it straight to the real world.
“Deploying production self-driving cars requires a solution for testing and validating on billions of driving miles to achieve the safety and reliability needed for customers,” said Rob Csongor, vice president and general manager of Automotive at NVIDIA. “With DRIVE Constellation, we’ve accomplished that by combining our expertise in visual computing and datacenters. With virtual simulation, we can increase the robustness of our algorithms by testing on billions of miles of custom scenarios and rare corner cases, all in a fraction of the time and cost it would take to do so on physical roads.”
Testing and validation is hugely important to the autonomous vehicle industry. Every time a software developer tweaks a model or a the autonomous vehicle source code, the developer needs to verify that nothing else broke. The fastest way to do this is testing in simulation. NVIDIA is releasing what looks like the world’s best simulation platform for testing.
The biggest leap, though, is training in simulation. It’s one thing to write code, put it in a car, collect data about different scenarios, and then later test in simulation that the your self-driving software still handles those scenarios correctly.
It’s a whole different animal to be able to train self-driving software in simulation, without ever putting a car out on the road. Whoever cracks that nut will have a huge leg up in the autonomous vehicle race. Keep an eye on NVIDIA.
Back home on vacation this weekend, my brother was telling me about paying up to $30 one-way to drive Interstate 66 inside the DC Beltway. One the one hand, the pricing (which varies with traffic) is outrageous. On the other hand, people will pay it. My brother, coming off a 12- or 16- or 20-hour hospital shift as a medical resident, will pay whatever he has to pay to get home and sleep.
The latest round of self-driving car projections offer conflicting predictions about the effect self-driving cars will have on congestion. Self-driving cars should lower the psychological costs of vehicular transportation, which should in turn encourage demand and ultimately lead to more congestion.
On the other hand, self-driving cars may be more efficient drivers, and will encourage ride-sharing. Ride-sharing, in turn, has the potential to reduce parking needs and vehicle sizes. All of this should lead to less congestion.
Where all of this ultimately shakes out is to be seen, but congestion pricing has potential to reduce a lot of the zero-sum friction. With a few exceptions, most roads are free to use, paid for by tax dollars. Moving to a world in which we pay to use roads will lower our tax burdens, allocate space on the road more efficiently, and hopefully reduce congestion.
McCormick makes the point that mobility is at least as plausible an industry for CFIUS to monitor as semiconductors:
“There are two ways that autonomous vehicles might fall under CFIUS’ broad “national security” jurisdiction. The first is that this jurisdiction includes homeland security, and there have been growing concerns about the potential of autonomous vehicles for terrorism — for example, a cyber-hacker who took control of an autonomous vehicle could potentially crash it into any number of targets.
The second is based on the idea that some “critical technologies” provide economic advantages that are so great they can impact national security, and the U.S. must protect its dominance in them.”
I worry about this type of government regulation, as it seems ripe for abuse. Politicians or CEOs or others that have their own reasons for blocking a transaction can shout “National Security” and kill a perfectly legitimate business transactions.
But regardless of my own personal misgivings, CFIUS is becoming more prominent and its something for lawyers and executives and entrepreneurs in the industry to keep in mind.
The state of California recently updated its self-driving car regulations to allow completely driverless vehicles on public roads — no safety driver at all. This has already been happening in Arizona with Waymo’s pilot there, and it’s good to see this come to California, as well.
Part of this rollout is the requirement that driverless vehicles have the capacity for teleoperation, so that if they get into trouble, there is somebody remote who can move the vehicle to a safe space while help is on the way.
Our partners at Phantom Auto specialize in this, and they are alumni of Udacity’s self-driving car program. Check out what they do:
“ Brian Holt, Head of Autonomous Driving at Parkopedia, added, “Autonomous Valet Parking will be one of the first fully autonomous features available to the general public. Parkopedia is uniquely positioned to provide the full range of parking services including the detailed indoor parking maps required to power autonomous vehicles globally and the CCAV grant will enable us to accelerate the on-going development of this exciting new technology.””
NVIDIA is the world’s leading deep learning company and a terrific Udacity partner, including for our Intro Self-Driving Cars Nanodegree Program. It’s a great relationship, because we are both excited to train lots of engineers to work on autonomous vehicles.
Here’s NVIDIA’s Danny Shapiro, talking about the NVIDIA’s BB8, and the potential for anybody to learn to become a self-driving car engineer.
TechCrunch reports a number of interesting factoids. There was a safety driver. The safety driver had to take control of the vehicle many times (“usually only a few seconds”). Embark is not using high-definition maps, although they appear to be using lidar. Their goal is not to replace, but to augment, the human driver.
Uber’s promotional video has a few interesting notes of its own. They appear to be very careful to stay within Arizona state lines. They’re also making a big deal out of the human interest angle — the self-driving truck works in tandem with human truck drivers and makes their work more enjoyable. They can be home in time for dinner.
Sort of. It was seven miles and the highway was totally closed to other traffic.
As somebody with a lot of extended family spread across Florida, I loved this observation:
“In true Florida Man fashion, founder and CEO Stefan Seltz-Axmacher decided to do something much bolder and a bit scarier: In mid-February, in the Sunshine State (where regulations are as lax as those in Arizona), he sent his truck down the road for a 7-mile journey — with nobody inside.”
Note that practically the first thing that appears in their promo video is an alligator. Florida!
David Silver 