Dave Barry once wrote that there is a federal law that no two people on a plane can pay the same price for their seat. Airlines use complex systems to manage ticket prices, constantly changing them based on expected demand and competition, and with over a dozen fare classes with different rules.
When I talk about robocars, I often get quite opposite reactions:
I predict neither extreme will be true. I predict the market will offer all options to the public, and several options will be very popular. I am not even sure which will be the most popular.
In addition, people will mix these models. Families that own 2 or more cars will switch to owning fewer cars and hiring for extra use and special uses. For example, if you own a 2 person car, you would summon a larger taxi when 3 or more are together. In particular, parents may find that they don't want to buy a car for their teen-ager, but would rather just subsidize their robotaxi travel. Parents will want to do this and get logs of where their children travel, and of course teens will resist that, causing a conflict.
In August, I attended the World Science Fiction Convention (WorldCon) in London. I did it while in Coeur D'Alene, Idaho by means of a remote Telepresence Robot(*). The WorldCon is half conference, half party, and I was fully involved -- telepresent there for around 10 hours a day for 3 days, attending sessions, asking questions, going to parties. Back in Idaho I was speaking at a local robotics conference, but I also attended a meeting back at the office using an identical device while I was there.
On Saturday I wrote about how we're now capturing the world so completely that people of the future will be able to wander around it in accurate VR. Let's go further and see how we might shoot the video resolutions of the future, today.
Recently I tried Facebook/Oculus Rift Crescent Bay prototype. It has more resolution (I will guess 1280 x 1600 per eye or similar) and runs at 90 frames/second. It also has better head tracking, so you can walk around a small space with some realism -- but only a very small space. Still, it was much more impressive than the DK2 and a sign of where things are going. I could still see a faint screen door, they were annoyed that I could see it.
I'm waiting at CDG in Paris, so it's time to add a new article to my series about fixing money in politics by looking at another thing campaigns spend money on (and thus raise money for), namely management of their campaigns.
A modern campaign is a complex thing. And yes, most of the money is spent on advertising, GOTV, events and staff. But there's also a lot of logistics, and a fair amount of software.
There's been a lot of press recently about an article in Slate by Lee Gomes which paints a pessimistic picture of the future of robocars, and particularly Google's project. The Slate article is a follow-on to a similar article in MIT Tech Review
Gomes and others seem to feel that they and the public were led to believe that current projects were almost finished and ready to be delivered any day, and they are disappointed to learn that these vehicles are still research projects and prototypes. In a classic expression of the Gartner Hype Cycle there are now predictions that the technology is very far away.
Both predictions are probably wrong. Fully functional robocars that can drive almost everywhere are not coming this decade, but nor are they many decades away. But more to the point, less-functional robocars are probably coming this decade -- much sooner than these articles expect, and these vehicles are much more useful and commercially viable than people may expect.
There are many challenges facing developers, and those challenges will keep them busy refining products for a long time to come. Most of those challenges either already have a path to solution, or constrain a future vehicle only in modest ways that still allow it to be viable. Some of the problems are in the "unsolved" class. It is harder to predict when those solutions will come, of course, but at the same time one should remember that many of the systems in today's research vehicles were in this class just a few years ago. Tackling hard problems is just what these teams are good at doing. This doesn't guarantee success, but neither does it require you bet against it.
And very few of the problems seem to be in the "unsolvable without human-smart AI" class, at least none that bar highly useful operation.
Gomes' articles have been the major trigger of press, so I will go over those issues in detail here first. Later, I will produce an article that has even more challenges than listed, and what people hope to do about them. Still, the critiques are written almost as though they expected Google and others, rather than make announcements like "Look at the new milestone we are pleased to have accomplished" to instead say, "Let's tell you all the things we haven't done yet."
Gomes begins by comparing the car to the Apple Newton, but forgets that 9 years after the Newton fizzled we had the success of the Palm Pilot, and 10 years after that Apple came back with the world-changing iPhone. Today, the pace of change is much faster than in the 80s.
Here are the primary concerns raised:
Google's car, and others, rely on a clever technique that revolutionized the DARPA challenges. Each road is driven manually a few times, and the scans are then processed to build a super-detailed "ultramap" of all the static features of the road. This is a big win because big server computers get to process the scans in as much time as they need, and see everything from different angles. Then humans can review and correct the maps and they can be tested. That's hard to beat, and you will always drive better if you have such a map than if you don't.
Any car that could drive without a map would effectively be a car that's able to make an adequate map automatically. As things get closer to that, making maps will become cheaper and cheaper.
Naturally, if the road differs from the map, due to construction or other changes, the vehicle has to notice this. That turns out to be fairly easy. Harder is assuring it can drive safely in this situation. That's still a much easier problem than being able to drive safely everywhere without a map, and in the worst case, the problem of the changed road can be "solved" by just the ability to come to a safe stop. You don't want to do that super often, but it remains the fail-safe out. If there is a human in the car, they can guide the vehicle in this. Even if the vehicle can't figure out where to go to be safe, the human can. Even a remote human able to look at transmitted pictures can help the car with that -- not live steering, but strategic guidance.
This problem only happens to the first car to encounter the surprise construction. If that car is still able to navigate (perhaps with human help,) the map can be quickly rebuilt, and if the car had to stop, all unmanned cars can learn to avoid the zone. They are unmanned, and thus probably not in a hurry.
In the interests of safety, a lot of work is put into today's maps. It's a cost that somebody like Google or Mercedes can afford if they need to, (after all, Google's already scanned every road in many countries multiple times) but it would be high for smaller players.
A recent newspaper column where people complained about carpool cheats got me thinking -- could cheating actually be a solution to some carpool problems?
In late August, I visited Singapore to give an address at a special conference announcing a government sponsored collaboration involving their Ministry of Transport, the Land Transport Authority and A-STAR, the government funded national R&D centre. I got a chance to meet the minister and sit down with officials and talk about their plans, and 6 months earlier I got the chance to visit A-Star and also the car project at the National University of Singapore.
Some recent announcements have caused lots of press stir, and I have not written much about them, both because of my busy travel schedule, but also because there is less news that we might imagine.
