You may have seen a lot of press around a dashcam video of a car accident in the Netherlands. It shows a Tesla in AutoPilot hitting the brakes around 1.4 seconds before a red car crashes hard into a black SUV that isn't visible from the viewpoint of the dashcam. Many press have reported that the Tesla predicted that the two cars would hit, and because of the imminent accident, it hit the brakes to protect its occupants. (The articles most assuredly were not saying the Tesla predicted the accident that never happened had the Tesla failed to brake, they are talking about predicting the dramatic crash shown in the video.)

The accident is brutal but apparently nobody was hurt.

The press speculation is incorrect. It got some fuel because Elon Musk himself retweeted the report linked to, but Telsa has in fact confirmed the alternate and more probable story which does not involve any prediction of the future accident. In fact, the red car plays little to no role in what took place.

Tesla's autopilot uses radar as a key sensor. One great thing about radar is that it tells you how fast every radar target is going, as well as how far away it is. Radar for cars doesn't tell you very accurately where the target is (roughly it can tell you what lane a target is in.) Radar beams bounce off many things, including the road. That means a radar beam can bounce off the road under a car that is in front of you, and then hit a car in front of it, even if you can't see the car. Because the radar tells you "I see something in your lane 40m ahead going 20mph and something else 30m ahead going 60mph" you know it's two different things.

Thursday night I am heading off to CES, and it's become the main show it seems for announcing robocar news. There's already a bunch.

The California DMV got serious in their battle with Uber and revoked the car registrations for Uber's test vehicles. Uber had declined to register the cars for autonomous testing, using an exemption in that law which I described earlier. The DMV decided to go the next step and pull the more basic licence plate every car has to have if based in California. Uber announced it would take the cars to another state.

For a few months, Uber has been testing their self-driving prototypes in Pittsburgh, giving rides to willing customers with a safety driver (or two) in the front seat monitoring the drive and ready to take over.

When Uber came to do this in San Francisco, starting this week, it was a good step to study new territory and new customers, but the real wrinkle was they decided not to get autonomous vehicle test permits from the California DMV. Google/Waymo and most others have such permits. Telsa has such permits but claims it never uses them.

Google's car project (known as "Chauffeur") really kickstarted the entire robocar revolution, and Google has put in more work, for longer, than anybody. The car was also the first project of what became Google "X" (or just "X" today under Alphabet. Inside X, a lab devoted to big audacious "moonshot" projects that affect the physical world as well as the digital, they have promoted the idea that projects should eventually "graduate," moving from being research to real commercial efforts.

Robocars are broadly going to be a huge boon for many people with disabilities, especially disabilities which make it difficult to drive or those that make it hard to get in and out of vehicles. Existing disability regulations and policies were written without robocars in mind, and there are probably some improvements that need to be made.

I believe we have the potential to eliminate a major fraction of traffic congestion in the near future, using technology that exists today which will be cheap in the future. The method has been outlined by myself and others in the past, but here I offer an alternate way to explain it which may help crystallize it in people's minds.

Today many people drive almost all the time guided by their smartphone, using navigation apps like Google Maps, Apple Maps or Waze (now owned by Google.) Many have come to drive as though they were a robot under the command of the app, trusting and obeying it at every turn. Tools like these apps are even causing controversy, because in the hunt for the quickest trip, they are often finding creative routes that bypass congested major roads for local streets that used to be lightly used.

Put simply, the answer to traffic congestion might be, "What if you, by law, had to obey your navigation app at rush hour?" To be more specific, what if the cities and towns that own the streets handed out reservations for routes on those streets to you via those apps, and your navigation app directed you down them? And what if the cities made sure there were never more cars put on a piece of road than it had capacity to handle? (The city would not literally run Waze, it would hand out route reservations to it, and Waze would still do the UI and be a private company.)

The value is huge. Estimates suggest congestion costs around 160 billion dollars per year in the USA, including 3 billion gallons of fuel and 42 hours of time for every driver. Roughly quadruple that for the world.

Road metering actually works

This approach would exploit one principle in road management that's been most effective in reducing congestion, namely road metering. The majority of traffic congestion is caused, no surprise, by excess traffic -- more cars trying to use a stretch of road than it has the capacity to handle. There are other things that cause congestion -- accidents, gridlock and irrational driver behaviour, but even these only cause traffic jams when the road is near or over capacity.

Today, in many cities, highway metering is keeping the highways flowing far better than they used to. When highways stall, the metering lights stop cars from entering the freeway as fast as they want. You get frustrated waiting at the metering light but the reward is you eventually get on a freeway that's not as badly overloaded.

Another type of metering is called congestion pricing. Pioneered in Singapore, these systems place a toll on driving in the most congested areas, typically the downtown cores at rush hour. They are also used in London, Milan, Stockholm and some smaller towns, but have never caught on in many other areas for political reasons. Congestion charging can easily be viewed as allocating the roads to the rich when they were paid for by everybody's taxes.

A third successful metering system is the High-occupancy toll lane. HOT lanes take carpool lanes that are being underutilized, and let drivers pay a market-based price to use them solo. The price is set to bring in just enough solo drivers to avoid wasting the spare capacity of the lane without overloading it. Taking those solo drivers out of the other lanes improves their flow as well. While not every city will admit it, carpool lanes themselves have not been a success. 90% of the carpools in them are families or others who would have carpooled anyway. The 10% "induced" carpools are great, but if the carpool lane only runs at 50% capacity, it ends up causing more congestion than it saves. HOT is a metering system that fixes that problem.

There have been few postings this month since I took the time to enjoy a holiday in New Zealand around speaking at the SingularityU New Zealand summit in Christchurch. The night before the summit, we enjoyed a 7.8 earthquake not so far from Christchurch, whose downtown was over 2/3 demolished after quakes in 2010 and 2011. On the 11th floor of the hotel, it was a disturbing nailbiter of swaying back and forth for over 2 minutes -- but of course swaying is what the building is supposed to do; that means it's working.

Comma.ai, the brash startup attempting to make a self-driving system entirely from a neural network has announced it will cancel the "comma one" add-on box it has planned to sell to owners of certain Honda vehicles. The box stuck on the rear-view mirror and used the car's own bus commands to provide an autopilot similar to those offered by car makers, with lane-keeping and adaptive cruise control.

Of particular importance is the letter from NHTSA to comma.ai which I suggest you read. This letter creates several big issues:

1. There are many elements of this letter which would also apply to Tesla and other automakers which have built supervised autopilot functions.
2. Of particular interest is the paragraph which says: "it is insufficient to assert, as you do, that the product does not remove any of the driver's responsibilities" and "there is a high likelihood that some drivers will use your product in a manner that exceeds its intended purpose." That must be very scary for Tesla.
3. I noted before that the new NHTSA regulations appear to forbid the use of "black box" neural network approaches to the car's path planning and decision making. I wondered if this made illegal the approach being done by Comma, NVIDIA and many other labs and players. This may suggest that.
4. We now have a taste of the new regulatory regime, and it seems that had it existed before, systems like Tesla's autopilot, Mercedes Traffic Jam Assist, and Cruise's original aftermarket autopilot would never have been able to get off the ground.
5. George Hotz of comma declares "Would much rather spend my life building amazing tech than dealing with regulators and lawyers. It isn't worth it. The comma one is cancelled. comma.ai will be exploring other products and markets. Hello from Shenzhen, China."

To be clear, comma is a tiny company taking a radical approach, so it is not a given that what NHTSA has applied to them would have been or will be unanswerable by the big guys. Because Tesla's autopilot is not a pure machine learning system, they can answer many of the questions in the NHTSA letter that comma can't. They can do much more extensive testing that a tiny startup can't. But even so a letter like this sends a huge chill through the industry.

It should also be noted that in Comma's photos the box replaced the rear-view mirror, and NHTSA had reason to ask about that.

George's declaration that he's in Shenzen gives us the first sign of the new regulatory regime pushing innovation away from the United States and California. I will presume the regulators will say, "We only want to scare away dangerous innovation" but the hard truth is that is a very difficult thing to judge. All innovation in this space is going to be a bit dangerous. It's all there trying to take the car -- the 2nd most dangerous legal consumer product -- and make it safer, but it starts from a place of danger. We are not going to get to safety without taking risks along the way.

I sometimes ask, "Why do we let 16 year olds drive?" They are clearly a major danger to themselves and others. Driver testing is grossly inadequate. They are not adults so they don't have the legal rights of adults. We let them drive because they are going to start out dangerous and then get better. It is the only practical way for them to get better, and we all went through it. Today's early companies are teenagers. They are going to take risks. But this is the fastest and only practical way to let them get better and save millions.

"...some drivers will use your product in a manner that exceeds its intended purpose"

This sentence, though in the cover letter and not the actual legal demand, looks at the question asked so much after the Tesla fatal crash. The question which caused Consumer Reports to ask Tesla to turn off the feature. The question which caused MobilEye, they say, to sever their relationship with Tesla.

The paradox of the autopilot is this: The better it gets, the more likely it is to make drivers over-depend on it. The more likely they will get complacent and look away from the road. And thus, the more likely you will see a horrible crash like the Tesla fatality. How do you deal with a system which adds more danger the better you make it? Customers don't want annoying countermeasures. This may be another reason that "Level 2," as I wrote yeterday is not really a meaningful thing.

NHTSA has put a line in the sand. It is no longer going to be enough to say that drivers are told to still pay attention.

Black box

Comma is not the only company trying to build a system with pure neural networks doing the actual steering decisions (known as "path planning".) NVIDIA's teams have been actively working on this, as have several others. They plan to make commentary to NHTSA about these element of the regulations, which should not be forbidding this approach until we know it to be dangerous.

It's no secret that I've been a critic of the NHTSA "levels" as a taxonomy for types of Robocars since the start. Recent changes in their use calls for some new analysis that concludes that only one of the levels is actually interesting, and only tells part of the story at that. As such, they have become even less useful as a taxonomy. Levels 2 and 3 are unsafe, and Level 5 is remote future technology. Level 4 is the only interesting one and there is thus no taxonomy.

I had hoped I was done ranting about our obsession with what robocars will do in no-win "who do I hit?" situations, but this week, even Barack Obama in his interview with Wired opined on the issue, prompted by my friend Joi Ito from the MIT Media Lab. (The Media Lab recently ran a misleading exercise asking people to pretend they were a self-driving car deciding who to run over.)

After my initial reactions and Overall Analysis here is a point by point consideration of second set of elements from NHTSA's 15 point certification list for robocars. See my series for other articles or the first half of the list.

Crashworthiness

In this section, the remind vendors they still need to meet the same standards as regular cars do. We are not ready to start removing heavy passive safety systems just because the vehicles get in fewer crashes. In the future we might want to change that, as those systems can be 1/3 of the weight of a vehicle.

They also note that different seating configurations (like rear facing seats) need to protect as well. It's already the case that rear facing seats will likely be better in forward collisions. Face-to-face seating may present some challenges in this environment, as it is less clear how to deploy the airbags. Taxis in London often feature face-to-face seating, though that is less common in the USA. Will this be possible under these regulations?

The rules also call for unmanned vehicles to absorb energy like existing vehicles. I don't know if this is a requirement on unusual vehicle design for regular cars or not. (If it were, it would have prohibited SUVs with their high bodies that can cause a bad impact with a low-body sports-car.)

Consumer Education and Training

This seems like another mild goal, but we don't want a world where you can't ride in a taxi unless you are certified as having taking a training course. Especially if it's one for which you have very little to do. These rules are written more for people buying a car (for whom training can make sense) than those just planning to be a passenger.

Registration and Certification

This section imagines labels for drivers. It's pretty silly and not very practical. Is a car going to have a sticker saying "This car can drive itself on Elm St. south of Pine, or on highway 101 except in Gilroy?" There should be another way, not labels, that this is communicated, especially because it will change all the time.

Post-Crash Behavior

This set is fairly reasonable -- it requires a process describing what you do to a vehicle after a crash before it goes back into service.

Federal, State and Local Laws

This section calls for a detailed plan on how to assure compliance with all the laws. Interestingly, it also asks for a plan on how the vehicle will violate laws that human drivers sometimes violate. This is one of the areas where regulatory effort is necessary, because strictly cars are not allowed to violate the law -- doing things like crossing the double-yellow line to pass a car blocking your path.

After my initial reactions and Overall Analysis here is a point by point consideration of the elements from NHTSA's 15 point certification list for robocars. See also the second half and the whole series

Let's dig in:

Data Recording and Sharing

These regulations require a plan about how the vehicle keep logs around any incident (while following privacy rules.) This is something everybody already does -- in fact they keep logs of everything for now -- since they want to debug any problems they encounter. NHTSA wants the logs to be available to NHTSA for crash investigation.

NHTSA also wants recordings of positive events (the system avoided a problem.)

Most interesting is a requirement for a data sharing plan. NHTSA wants companies to share their logs with their competitors in the event of incidents and important non-incidents, like near misses or detection of difficult objects.

This is perhaps the most interesting element of the plan, but it has seen some resistance from vendors. And it is indeed something that might not happen at scale without regulation. Many teams will consider their set of test data to be part of their crown jewels. Such test data is only gathered by spending many millions of dollars to send drivers out on the roads, or by convincing customers or others to voluntarily supervise while their cars gather test data, as Tesla has done. A large part of the head-start that leaders have in this field is the amount of different road situations they have been able to expose their vehicles to. Recordings of mundane driving activity are less exciting and will be easier to gather. Real world incidents are rare and gold for testing. The sharing is not as golden, because each vehicle will have different sensors, located in different places, so it will not be easy to adapt logs from one vehicle directly to another. While a vehicle system can play its own raw logs back directly to see how it performs in the same situation, other vehicles won't readily do that.

Instead this offers the ability to build something that all vendors want and need, and the world needs, which is a high quality simulator where cars can be tested against real world recordings and entirely synthetic events. The data sharing requirement will allow the input of all these situations into the simulator, so every car can test how it would have performed. This simulation will mostly be at the "post perception level" where the car has (roughly) identified all the things on the road and is figuring out what to do with them, but some simulation could be done at lower levels.

These data logs and simulator scenarios will create what is known as a regression test suite. You test your car in all the situations, and every time you modify the software, you test that your modifications didn't break something that used to work. It's an essential tool.

In the history of software, there have been shared public test suites (often sourced from academia) and private ones that are closely guarded. For some time, I have proposed that it might be very useful if there were a a public and open source simulator environment which all teams could contribute scenarios to, but I always expected most contributions would come from academics and the open source community. Without this rule, the teams with the most test miles under their belts might be less willing to contribute.

Such a simulator would help all teams and level the playing field. It would allow small innovators to even build and test prototype ideas entirely in simulator, with very low cost and zero risk compared to building it in physical hardware.

This is a great example of where NHTSA could use its money rather than its regulatory power to improve safety, by funding the development of such test tools. In fact, if done open source, the agencies and academic institutions of the world could fund a global one. (This would face opposition from companies hoping to sell test tools, but there will still be openings for proprietary test tools.)

Privacy

This section demands a privacy policy. I'm not against that, though of course the history of privacy policies is not a great one. They mostly involve people clicking "I agree" to things they don't read. More important is the requirement that vendors be thinking about privacy.

The requirement for user choice is an interesting one, and it conflicts with the logging requirements. People are wary of technology that will betray them in court. Of course, as long as the car is not a hybrid car that mixes human driving with self-driving, and the passenger is not liable in an accident, there should be minimal risk to the passenger from accidents being recorded.

The rules require that personal information be scrubbed from any published data. This is a good idea but history shows it is remarkably hard to do properly.

The recent Federal Automated Vehicles Policy is long. (My same-day analysis is here and the whole series is being released.) At 116 pages (to be fair, less than half is policy declarations and the rest is plans for the future and associated materials) it is much larger than many of us were expecting.

The policy was introduced with a letter attributed to President Obama, where he wrote:

There are always those who argue that government should stay out of free enterprise entirely, but I think most Americans would agree we still need rules to keep our air and water clean, and our food and medicine safe. That’s the general principle here. What’s more, the quickest way to slam the brakes on innovation is for the public to lose confidence in the safety of new technologies. Both government and industry have a responsibility to make sure that doesn’t happen. And make no mistake: If a self-driving car isn’t safe, we have the authority to pull it off the road. We won’t hesitate to protect the American public’s safety.

This leads in to an unprecedented effort to write regulations for a technology that barely exists and has not been deployed beyond the testing stage. The history of automotive regulation has been the opposite, and so this is a major change. The key question is what justifies such a big change, and the cost that will come with it.

Make no mistake, the cost will be real. The cost of regulations is rarely known in advance but it is rarely small. Regulations slow all players down and make them more cautious -- indeed it is sometimes their goal to cause that caution. Regulations result in projects needing "compliance departments" and the establishment of procedures and legal teams to assure they are complied with. In almost all cases, regulations punish small companies and startups more than they punish big players. In some cases, big players even welcome regulation, both because it slows down competitors and innovators, and because they usually also have skilled governmental affairs teams and lobbying teams which are able to subtly bend the regulations to match their needs.

This need not even be nefarious, though it often is. Companies that can devote a large team to dealing with regulations, those who can always send staff to meetings and negotiations and public comment sessions will naturally do better than those which can't.

The US has had a history of regulating after the fact. Of being the place where "if it's not been forbidden, it's permitted." This is what has allowed many of the most advanced robocar projects to flourish in the USA.

The attitude has been that industry (and startups) should lead and innovate. Only if the companies start doing something wrong or harmful, and market forces won't stop them from being that way, is it time for the regulators to step in and make the errant companies do better. This approach has worked far better than the idea that regulators would attempt to understand a product or technology before it is deployed, imagine how it might go wrong, and make rules to keep the companies in line before any of them have shown evidence of crossing a line.

In spite of all I have written here, the robocar industry is still young. There are startups yet to be born which will develop new ideas yet to be imagined that change how everybody thinks about robocars and transportation. These innovative teams will develop new concepts of what it means to be safe and how to make things safe. Their ideas will be obvious only well after the fact.

Regulations and standards don't deal well with that. They can only encode conventional wisdom. "Best practices" are really "the best we knew before the innovators came." Innovators don't ignore the old wisdom willy-nilly, they often ignore it or supersede it quite deliberately.

What's good?

Some players -- notably the big ones -- have lauded these regulations. Big players, like car companies, Google, Uber and others have a reason to prefer regulations over a wild west landscape. Big companies like certainty. They need to know that if they build a product, that it will be legal to sell it. They can handle the cost of complex regulations, as long as they know they can build it.

The long awaited list of recommendations and potential regulations for Robocars has just been released by NHTSA, the federal agency that regulates car safety and safety issues in car manufacture. Normally, NHTSA does not regulate car technology before it is released into the market, and the agency, while it says it is wary of slowing down this safety-increasing technology, has decided to do the unprecedented -- and at a whopping 115 pages.

Some people have wondered about my forecast in the spreadsheet on Robotaxi economics about the very low parking costs I have predicted. I wrote about most of the reasons for this in my 2007 essay on Robocar Parking but let me expand and add some modern notes here.

The Glut of Parking

Today, researchers estimate there are between 3 and 8 parking spots for every car in the USA. The number 8 includes lots of barely used parking (all the shoulders of all the rural roads, for example) but the value of 3 is not unreasonable. Almost all working cars have a spot at their home base, and a spot at their common destination (the workplace.) There are then lots of other places (streets, retail lots, etc.) to find that 3rd spot. It's probably an underestimate.

We can't use all of these at once, but we're going to get a great deal more efficient at it. Today, people must park within a short walk of their destination. Nobody wants to park a mile away. Parking lots, however, need to be sized for peak demand. Shopping malls are surrounded by parking that is only ever used during the Christmas shopping season. Robocars will "load balance" so that if one lot is full, a spot in an empty lot too far away is just fine.

Small size and Valet Density

When robocars need to park, they'll do it like the best parking valets you've ever seen. They don't even need to leave space for the valet to open the door to get out. (The best ones get close by getting out the window!) Because the cars can move in concert, a car at the back can get out almost as quickly as one at the front. No fancy communications network is needed; all you need is a simple rule that if you boxed somebody in, and they turn on their lights and move an inch towards you, you move an inch yourself (and so on with those who boxed you in) to clear a path. Already, you've got 1.5x to 2x the density of an ordinary lot.

I forecast that many robotaxis will be small, meant for 1-2 people. A car like that, 4' by 12' would occupy under 50 square feet of space. Today's parking lots tend to allocate about 300 square feet per car. With these small cars you're talking 4 to 6 times as many cars in the same space. You do need some spare space for moving around, but less than humans need.

When we're talking about robotaxis, we're talking about sharing. Much of the time robotaxis won't park at all, they would be off to pick up their next passenger. A smaller fraction of them would be waiting/parked at any given time. My conservative prediction is that one robotaxi could replace 4 cars (some estimate up to 10 but they're overdoing it.) So at a rough guess we replace 1,000 cars, 900 of which are parked, with 250 cars, only 150 of which are parked at slow times. (Almost none are parked during the busy times.)

Many more spaces available for use

Robocars don't park, they "stand." Which means we can let them wait all sorts of places we don't let you park. In front of hydrants. In front of driveways. In driveways. A car in front of a hydrant should be gone at the first notification of a fire or sound of a siren. A car in front of your driveway should be gone the minute your garage opens or, if your phone signals your approach, before you get close to your house. Ideally, you won't even know it was there. You can also explicitly rent out your driveway space for money if you wish it. (You could rent your garage too, but the rate might be so low you will prefer to use it to add a new room to your house unless you still own a car.)

In addition, at off-peak times (when less road capacity is needed) robocars can double park or triple park along the sides of roads. (Human cars would need to use only the curb spots, but the moment they put on their turn signal, a hole can clear through the robocars to let them out.)

So if we consider just these numbers -- only 1/6 of the time spent parking and either 4 times the density in parking lots or 2-3 times the volume of non-lot parking (due to the 2 spots per car and loads of extra spots) we're talking about a huge, massive, whopping glut of parking. Such a large glut that in time, a lot of this parking space very likely will be converted to other uses, slowly reducing the glut.

Ability to move in response to demand

To add to this glut, robocars can be the best parking customers you could ever imagine. If you own a parking lot, you might have sold the space at the back or top of your lot to the robocars -- they will park in the unpopular more remote sections for a discount. The human driver customers will prefer those spots by the entrance. As your lot fills up, you can ask the robocars to leave, or pay more. If a high paying human driver appears at the entrance, you can tell the robocars you want their space, and off they can go to make room. Or they can look around on the market and discover they should just pay you more to keep the space. The lot owner is always making the most they can.

If robocars are electric, they should also be excellent visitors, making little noise and emitting no soot to dirty your walls. They will leave a tiny amount of rubber and that's about it.

The "spot" market

All of this will be driven by what I give the ironic name of the "spot" market in parking. Such markets are already being built by start-ups for human drivers. In this market, space in lots would be offered and bid for like any other market. Durations will be negotiated, too. Cars could evaluate potential waiting places based on price and the time it will take to get there and park, as well as the time to get to their likely next pickup. A privately owned car might drive a few miles to a super cheap lot to wait 7 hours, but when it's closer to quitting time, pay a premium (in competition with many others of course) to be close to their master.

Tesla's spat with MobilEye reached a new pitch this week, and Tesla announced a new release of their autopilot and new plans. As reported here earlier, MobilEye announced during the summer that they would not be supplying the new and better versions of their EyeQ system to Tesla. Since that system was and is central to the operation of the Telsa autopilot, they may have been surprised that MBLY stock took a big hit after that announcement (though it recovered for a while and is now back down) and TSLA did not.

The vision of many of us for robocars is a world of less private car ownership and more use of robotaxis -- on demand ride service in a robocar. That's what companies like Uber clearly are pushing for, and probably Google, but several of the big car companies including Mercedes, Ford and BMW among others have also said they want to get there -- in the case of Ford, without first making private robocars for their traditional customers.

In this world, what does it cost to operate these cars? How much might competitive services charge for rides? How much money will they make? What factors, including price, will they compete on, and how will that alter the landscape?

Here are some basic models of cost. I compare a low-cost 1-2 person robotaxi, a higher-end 1-2 person robotaxi, a 4-person traditional sedan robotaxi and the costs of ownership for a private car, the Toyota Prius 2, as calculated by Edmunds. An important difference is that the taxis are forecast to drive 50,000 miles/year (as taxis do) and wear out fully in 5 years. The private car is forecast to drive 15,000 miles/year (higher than the average for new cars, which is 12,000) and to have many years and miles of life left in it. As such the taxis are fully depreciated in this 5 year timeline, and the private car only partly.

Some numbers are speculative. I am predicting that the robotaxis will have an insurance cost well below today's cars, which cost about 6 cents/mile for liability insurance. The taxis will actually be self-insured, meaning this is the expected cost of any incidents. In the early days, this will not be true -- the taxis will be safer, but the incidents will cost more until things settle down. As such the insurance prices are for the future. This is a model of an early maturing market where the volume of robotaxis is fairly high (they are made in the low millions) and the safety record is well established. It's a world where battery prices and reliability have improved. It's a world where there is still a parking glut, before most surplus parking is converted to other purposes.

Fuel is electric for the taxis, gasoline/hybrid for the Prius. The light vehicle is very efficient.

Maintenance is also speculative. Today's cars spend about 6 cents/mile, including 1 cent/mile for the tires. Electric cars are expected to have lower maintenance costs, but the totals here are higher because the car is going 250,000 miles not 75,000 miles like the Prius. With this high level of maintenance and such smooth driving, I forecast low repair cost.

Parking is cheaper for the taxis for several reasons. First, they can freely move around looking for the cheapest place to wait, which will often be free city parking, or the cheapest advertised parking on the auction "spot" market. They do not need to park right where the passenger is going, as the private car does. They will park valet style, and so the small cars will use less space and pay less too. Parking may actually be much cheaper than this, even free in many cases. Of course, many private car owners do not pay for parking overtly, so this varies a lot from city to city.

(You can view the spreadsheet directly on Google docs and download it to your own tool to play around with the model. Adjust my assumptions and report your own price estimates.)

The Prius has one of the lowest costs of ownership of any regular car (take out the parking and it's only 38 cents/mile) but its price is massively undercut by the electric robotaxi, especially my estimates for the half-width electric city car. (I have not even included the tax credits that apply to electric cars today.) For the taxis I add 15% vacant miles to come up with the final cost.

The price of the Prius is the retail cost (on which you must also pay tax) but a taxi fleet operator would pay a wholesale, or even manufacturer's cost. Of course, they now have the costs of running a fleet of self-driving cars. That includes all the virtual stuff (software, maps and apps) with web sites and all the other staff of a big service company ranging from lawyers to marketing departments. This is hard to estimate because if the company gets big, this cost will not be based on miles, and even so, it will not add many cents per mile. The costs of the Prius for fuel, repair, maintenance and the rest are also all retail. The taxi operator wants a margin, and a big margin at first, though with competition this margin would settle to that of other service businesses.

The past period has seen some very big robocar news. Real news, not the constant "X is partnering with Y" press releases that fill the airwaves some times.

Uber has made a deal to purchase Otto, a self-driving truck company I wrote about earlier founded by several friends of mine from Google. The rumoured terms of the deal as astronomical -- possibly 1% of Uber's highly valued stock (which means almost $700M) and other performance rewards. I have no other information yet on the terms, but it's safe to say Otto was just getting started with ambitious goals and would not have sold for less than an impressive amount. For a company only 6 months old, the rumoured terms surpass even the amazing valuation stories of Cruise and Zoox.

While Otto has been working on self-driving technology for trucks, any such technology can also move into cars. Uber already has an active lab in Pittsburgh, but up to now has not been involved in long haul trucking. (It does do local deliveries in some places.) There are many startups out there calling themselves the "Uber for Trucks" and Otto has revealed it was also working on shipping management platform tools, so this will strike some fear into those startups. Because of my friendship with Otto's team, I will do more commentary when more details become public.

In other Uber news, Uber has announced it will sell randomly assigned Uber rides in their self-driving vehicles in Pittsburgh. If your ride request is picked at random (and because it's in the right place) Uber will send one of their own cars to drive you on your ride, and will make the ride free, to boot. Of course, there will be an Uber safety driver in the vehicle monitoring it and ready to take over in any problem or complex situation. So the rides are a gimmick to some extent, but if they were not free, it would be a sign of another way to get customers to pay for the cost of testing and verifying self-driving cars. The free rides, however, will probably actually cause more people to take Uber rides hoping they will win the lottery and get not simply the free ride but the self-driving ride.

GM announced a similar program for Lyft -- but not until next year.

Ford also goes all-in, but with a later date

Ford has announced it wants to commit to making unmanned capable taxi vehicles, the same thing Uber, Google, Cruise/GM, Zoox and most non-car companies want to make. For many years I have outlined the difference between the usual car company approaches, which are evolutionary and involve taking cars and improving their computers and the approaches of the non-car companies which bypass all legacy thinking (mostly around ADAS) to go directly to the final target. I call that "taking a computer and putting wheels on it." It's a big and bold move for Ford to switch to the other camp, and a good sign for them. They have said they will have a fleet of such vehicles as soon as 2021.

At the recent AUVSI/TRB conference in San Francisco, there was much talk of upcoming regulation, particularly from NHTSA. Secretary of Transportation Foxx and his NHTSA staff spoke with just vague hints about what might come in the proposals due this fall. Generally, they said good things, namely that they are wary of slowing down the development of the technology. But they said things that suggest other directions.