TomTom in Asia: Tomaso Grossi, the head of automotive marketing at map-maker TomTom, talks with Tech Wire Asia about the potential for self-driving cars in Asia. He says the big drivers will be Asia’s urban cores, aging populations, and reductions in congestion and pollution.
nuTonomy Fender-Bender: The startup nuTonomy, which has been the first to launch self-driving taxis in Singapore, recently got into a fender-bender with a truck. It sounds like the fault may have been with the self-driving taxi, as it was changing lanes when it apparently merged into the truck.
mCity China: A Chinese investment firm just signed a $27MM deal with the University of Michigan to build what sounds like a version of mCity in Shenzen, along with associated research focusing on self-driving cars in Asia.
Atieva Atvus: Electric car-maker Atieva has been quietly working on autonomous vehicles for several years. Recode reports that they are on the cusp of manufacturing a sedan similar to the Tesla Model S. It’s called the Atvus.
Trolleyology @ Mercedes: I have become a little bit tired of the trolley problem, because I think it ignores the fundamental safety problem with cars, which is that human drivers kill a lot of people. But the trolley problem won’t die, and Mercedes’ CEO Christoph von Hugo squared up and answered that Mercedes is going to prioritize the saving the lives of its passengers. And kudos to them for that.
Roboracing is Hard: The autonomous vehicle race series, Roborace, just put out a documentary profiling the ups and downs of their first season. I haven’t watched it yet, but The Verge says it’s good and doesn’t sugarcoat the difficulty of pulling this off.
Uber Freight: Contrary to my belief, Uber is not sidelining the freight side of Otto’s business. Instead, they are planning to launch it next year(!).
This afternoon I posted a long response to a question about how we will use C++ vs. Python in the Udacity Self-Driving Car Nanodegree Program, and how automotive engineers use those languages on the job.
You can read my full response, but here’s the part where I focus on how automotive engineers write software on the job:
Autonomous vehicle engineers on the job tend to use a variety of languages, depending on their team, their facility with different languages, the APIs their tools expose, and performance requirements.
C++ is a compiled, high-performance language, so most code that actually runs on the vehicle tends to be C++.
That said, many engineers spend most of their time prototyping algorithms in Python, Matlab, or even Java or other languages. Other engineers spend pretty much all of their time writing production code in C / C++.
Machine learning engineers often spend a lot of time in Python, because libraries like TensorFlow rely on Python for their primary APIs. TensorFlow does a lot of the heavy lifting in terms of compiling networks for faster performance.
Addendum: Since I have been asked several times recently, my favorite C++ book is Modern C++ Programming with Test-Driven Development, by Jeff Langr. Unfortunately, Udacity does not yet have a C++ course. There appear to be C++ courses on Coursera and edX but I have not reviewed them yet.
The improvement apparently will come largely from left turns:
This year, about 1,000 riders in the U.S. will lose their lives to the left turns of others. Cars traveling in the same direction as the motorcycle often don’t notice the bike overtaking on the left. Cars making a turn while coming from the opposite direction either fail to see the oncoming bike, or misjudge its speed.
And apparently this is a good time to buy Harley-Davidson stock:
Once every aspiring biker realizes that the driver next to him isn’t an existential threat, sales will climb in some places. Xavier Mosquet, a senior partner at Boston Consulting Group, said the bike boost will be most pronounced in markets such as the U.S., where people ride for fun, and in China and India, where many choose motorbikes because they are relatively inexpensive transportation.
Conversely, in such places as Europe. where motorcycles are often the best way to avoid traffic, self-driving cars may actually dent sales, according to Mosquet. If all goes as planned, there will be fewer tie-ups or accidents, less rubbernecking, and thus less to be gained by jumping on a bike and splitting lanes of standstill traffic.
Self-driving motorcycles, however, are still quite a ways off. Here’s a visual explanation of why.
In fairness, if I’ve heard Sebastian Thrun tell the story right, the head of that team was Anthony Levandowski, who went on to found Otto and now runs Uber’s self-driving car program. So he’s done well.
Buying and renting anything — a home, a car, a movie — involves a tradeoff between stability and flexibility. Buying provides the stability of permanent ownership and availability, whereas renting provides the flexibility of adjustment to fit changing needs and wants.
The automotive market is moving from an ownership model to a rental model, as ride-sharing services push the stability-flexibility trade in favor of renting, rather than owning. And what we’ve seen with ride-sharing is just the tip of the iceberg. Self-driving cars will push this tradeoff an order of magnitude further.
Mass Customization
As consumers come to value flexibility in transportation, we can take lessons from the manufacturing industry on the practice of mass customization.
Today, car buyers have to purchase a one-size-fits-all vehicle. If I need to drive in snow twenty days a year, I might get a four-wheel drive vehicle, even though I would be better off with a compact car the other 345 days. Similar considerations govern the purchase of a car capable of occasional carpooling, or downtown parking, or a client visit.
In the self-driving car future, we’ll be able to rent the car we want, and the companies that win will get good at doing this really fast.
Need a minivan this morning? It’ll be there in 30 seconds.
Want a convertible this evening? It’ll be there in 45 seconds.
What Do People Want?
In this world, getting the right car to somebody’s door in 60 seconds or less might be the easy part. Mass customization has been studied and optimized and is mostly a solved problem.
The harder challenge is to figure out what people want.
We have some basic starting points: sedans, vans, SUVs, pickups, sports cars.
But these are all built for human drivers in a one-size-fits-all world.
In a mass customization world, we no longer have to make tradeoffs between scenario. We can tune each vehicle option to a specific use case.
It could even be that we’ll hail one car service if we want a maneuverable short-haul vehicle, and a different service if we want a fast, long-haul vehicle.
What kinds of vehicles would you like to see in a self-driving world?
How do you envision the future of vehicle mass customization? Share your thoughts in the comments. Thanks!
The Cubs-Giants game went thirteen very long innings last night and ended in heartbreak (for me, at least), with the Giants knocking in a walk-off run in the bottom of the 13th inning.
It was also 11:45pm and the game had been going on for five hours.
As I stumbled out of the stadium, I realized my phone was totally dead. Five hours of emails and web browsing between innings had drained the battery.
If my phone had been working, I might have just hailed an Uber home and tucked into bed. But my phone wasn’t working.
No worries, though! In San Francisco, the train station is just blocks from the ballpark. I hustled on over to Caltrain, waited forever for the train to leave, and then learned I got on the wrong train. The train I was riding wouldn’t make its first stop until 8 miles past my house.
I disembarked the first chance I could and walked into an empty parking lot at the Belmont Caltrain Station at 12:45am. No taxis.
A gas station light flickered across the street and I rolled over and begged the attendant to call a cab. No cabs available.
Then I bought a charger from the station’s inventory and hailed an Uber, which took twenty minutes to arrive, being past midnight in the suburbs.
I finally tucked into bed at 1:30am.
So what’s the moral of the story?
Mostly that I shouldn’t have totally drained my phone battery, and I should look at train schedules.
But also that, in the days before ride-sharing, it was more common to have taxis circling around and you didn’t need a phone to hail them.
The world today is a better place because of Lyft and Uber, but it does require a phone to navigate.
A couple of hackers in Australia just built a miniature autonomous vehicle and published the source code.
This looks just like the Mini AV that Alex and Forrest and I built at Ford, right down to the Intel NUC!
This was such a fun project. If you have the resources to purchase the components, you should do it! It will teach you a lot about how to get a real self-driving car to work.
The self-driving car race continues, with new entrants signaling that California remains the location of choice (or at least “a” location of choice) for autonomous vehicle development.
Wheego, an Atlanta-based assembler of low-speed, electric vehicles, has applied for a testing permit.
The encouraging sign here is that both Wheego and Valeo are out of the mold of previous California autonomous vehicle licensees. Whereas previous licensees have mostly been OEMs (Ford, Mercedes), tech companies (Google, Apple), or startups (Otto, Cruise, Drive.ai), these entrants are more esoteric.
Valeo, in particular, represents wider automotive industry coming to Silicon Valley, which is terrific.
Google’s Self-Driving Cars just hit two million miles of real-world, public road driving experience.
Dmitri Dolgov, Google’s Head of Self-Driving Car Technology, explains how one of the major challenges now is to pass what I might call the “automotive Turing test”:
Over the last year, we’ve learned that being a good driver is more than just knowing how to safely navigate around people, but also knowing how to interact with them.
In a delicate social dance, people signal their intentions in a number of ways. For example, merging into traffic during rush hour is an exercise in negotiation: I’d like to cut in. May I cut in? If I speed up a little and move into the lane, will you slow down and leave me room, or will you speed up? So much of driving relies on these silent conversations conducted via gentle nudge-and-response. Because we’ve observed or interacted with hundreds of millions of vehicles, pedestrians and cyclists, our software is much better at reliably predicting the trajectory, speed, and intention of other road users. Our cars can often mimic these social behaviors and communicate our intentions to other drivers, while reading many cues that tell us if we’re able to pass, cut in or merge.
David Silver 