Executive Summary: A rundown of different approaches for validation of self-driving and
driver assist systems, and a recommendation to Tesla and others to have countermeasures
to detect drivers not watching the road, and permanently disable their Autopilot if they
show a pattern of inattention.
The recent fatality for a man who was allowing his car to be driven by the Tesla “autopilot”
system has ignited debate on whether it was appropriate for Tesla to allow their system to
be used as it was.
Tesla’s autopilot is a driver assist system, and Tesla tells customers it must always be
supervised by an alert driver ready to take the controls at any time. The autopilot is not
a working self-driving car system, and it’s not rated for all sorts of driving conditions,
and there are huge numbers of situations that it is not designed to handle and can’t handle. Tesla knows that, but the
public, press and Tesla customers forget that, and there are many Tesla users who are treating
the autopilot like a real self-driving car system, and who are not paying attention to the road —
and Tesla is aware of that as well. Press made this mistake as well, regularly writing
fanciful stories about how Tesla was ahead of Google and other teams.
Brown, the driver killed in the crash, was very likely one of those people, and if so, he paid for
it with his life. In spite of all the warnings Tesla may give about the system, some users
do get a sense of false security. There is debate if that means driver assist systems are
a bad idea.
There have been partial self-driving systems that require supervision since the arrival of the
cruise control. Adaptive cruise control is even better, and other car companies have released
autopilot like systems which combine adaptive cruise control with lane-keeping and forward
collision avoidance, which hits the brakes if you’re about to rear end another car. Mercedes
has sold a “traffic jam assist” like the Telsa autopilot since 2014 that only runs at low speeds
in the USA. You can even go back to a Honda demo in 2005 of an autopilot like system.
With cruise control, you might relax a bit but you know you have to pay attention. You’re steering
and for a long time even the adaptive cruise controls did not slow down for stopped cars.
The problem with Tesla’s autopilot is that it was more comprehensive and better performing than
earlier systems, and even though it had tons of things it could not handle, people started to
trust it with their lives.
Tesla’s plan can be viewed in several ways. One view is that Tesla was using customers as
“beta testers,” as guinea pigs for a primitive self-drive system which is not production ready,
and that this is too much of a risk.
Another is that Tesla built (and tested) a superior driver assist system with known and warned
limitations, and customers should have listened to those warnings.
Neither is quite right. While Tesla has been clear about the latter stance, with the knowledge that
people will over-trust it, we must face the fact that it is not only the daring drivers who
are putting themselves at risk, it’s also others on the road who are put at risk by the
over-trusting drivers — or perhaps by Tesla. What if the errant car had not gone under a truck, but
instead hit another car, or even plowed into a pedestrian when it careened off the road after the crash?
At the same time, Tesla’s early deployment approach is a powerful tool for the development and
quality assurance of self-drive systems. I have written before about how testing is the big
unsolved problem in self-driving cars. Companies like Google have spent many millions to use a
staff of paid drivers to test their cars for 1.6 million miles. This is massively expensive and
time consuming, and even Google’s money can’t easily generate the billions of miles of testing
that some feel might be needed. Human drivers will have about 12 fatalities in a billion miles,
and we want our self-driving cars to do much better. Just how we’ll get enough verification and testing done
to bring this technology to the world is not a solved problem. read more »
A Tesla blog post describes the first fatality involving a self drive system. A Tesla was driving on autopilot down a divided highway. A truck made a left turn and crossed the Tesla’s lanes. A white truck body against a bright sky is not something the MobilEye camera system in the Tesla perceives well, and it is not designed for cross traffic.
The truck trailer was also high, so when the Tesla did not stop, it went “under” it, so that the windshield was the first part of the Tesla to hit the truck body, with fatal consequences for the “driver.” Tesla notes that the autopilot system has driven 130 million miles, while human drivers in the USA have a fatality about every 94 million miles (though it’s a longer interval on the highway.) The Tesla is a “supervised” system where the driver is required to agree they are monitoring the system and will take control in the event of any problem, but this driver, a major Tesla fan named Joshua Brown, did not hit the brakes. As such, the fault for this accident will presumably reside with Brown, or perhaps the Truck driver — the accident report claims the truck did fail to yield to oncoming traffic, but as yet the driver has not been cited for this. (Tesla also notes that had the front of the car hit the truck, the crumple zones and other safety systems would probably have saved the driver — hitting a high target is the worst case situation.)
Any commentary here is preliminary until more facts are established, but here are my initial impressions:
There has been much speculation of whether Tesla was taking too much risk by releasing autopilot so early, and this will be boosted after this.
In particular, a core issue is that the autopilot works too well, and I have seen reports from many Tesla drivers of them trusting it far more than they should. The autopilot is fine if used as Tesla directs, but the better it gets, the more it encourages people to over-trust it.
Both Tesla stock and MobilEye stock were up today, with a bit of downturn after-hours. The market may not have absorbed this. The MobilEye is the vision sensor used by the Tesla to power the autopilot, and the failure to detect the truck in this situation is a not-unexpected result for the sensor.
For years, I have frequently heard it said that “the first fatality with this technology will end it all, or set the industry back many years.” My estimation is that this will not happen.
One report suggests the truck was making a left turn, which is a more expected situation, though if a truck turned with oncoming traffic it would be at fault.
Another report suggests that “friends” claim that the driver often used his laptop while driving, and some sources claim that a Harry Potter movie was playing in the car. (A portable DVD player was found in the wreckage.)
Tesla’s claim of 130M miles is a bit misleading, because most of those miles actually were supervised by humans. So that’s like reporting the record of student drivers with a driving instructor always there to take over. And indeed there are reports of many, many people taking over for the Tesla Autopilot, as Tesla says they should. So at best Tesla can claim that the supervised autopilot has a similar record to human drivers, ie. is no better than the humans on their own. Though one incident does not a driving record make.
Whatever we judge about this, the ability of ordinary users to test systems, if they are well informed and understand what they are doing is a useful one that will advance the field and give us better and safer cars, faster. Just how to do this may require more discussion, but the idea of doing it is worthwhile.
MobilEye issued a statement reminding people their system is not designed to do well on cross traffic at present, but their 2018 product will. It is also worth noting that the camera they use sees only red and gray intensity, it does not see all the colours, making it have an even harder time with the white truck and bright sky. The sun was not a factor, it was up high in the sky.
The Truck Driver claims the Tesla changed lanes before hitting him, an odd thing to happen with the Autopilot, particular if the driver was not paying attention. The lack of braking suggests the driver was not paying attention.
Camera vs. Lidar, and maps.
I have often written about the big question of cameras vs. LIDAR. Elon Musk is famously on record as being against LIDAR, when almost all robocar projects in the world rely on LIDAR. Current LIDARs are too expensive for production automobiles, but many companies, including Quanergy (where I am an advisor) are promising very low cost LIDARs for future generations of vehicles.
Here there is a clear situation where LIDAR would have detected the truck. A white truck against the sky would be no issue at all for a self-driving capable LIDAR, it would see it very well. In fact, a big white target like that would be detected beyond the normal range of a typical LIDAR. That range is an issue here — most LIDARs would only detect other cars about 100m out, but a big white truck would be detected a fair bit further, perhaps even 200m. 100m is not quite far enough to stop in time for an obstacle like this at highway speeds, however, such a car would brake to make the impact vastly less, and a clever car might even have had time to swerve or aim for the wheels of the truck rather than slide underneath the body.
Another sensor that is problematic here is radar. Radar would have seen this truck no problem, but since it was perpendicular to the travel of the car, it would not be moving away from or towards the car, and thus have the doppler speed signature of a stopped object. Radar is great because it tracks the speed of obstacles, but because there are so many stationary objects, most radars have to just disregard such signals — they can’t tell a stalled vehicle from a sign, bridge or berm. To help with that, a map of where all the fixed radar reflection sources are located can help. If you get a sudden bright radar return from a truck or car somewhere that the map says a big object is not known to be, that’s an immediate sign of trouble. (At the same time, it means that you don’t easily detect a stalled vehicle next to a bridge or sign.)
One solution to this is longer range LIDAR or higher resolution radar. Google has said it has developed longer range LIDAR. It is likely in this case that even regular range LIDAR, or radar and a good map, might have noticed the truck.
With Mobility on Demand, you don’t buy a car, you buy rides. That’s certainly Uber’s plan, and is a plan that makes sense for Google, Apple and other no-car companies. But even Daimler, with Car2Go/Car2Come, BMW with DriveNow and GM with Lyft plan to sell you a ride rather than a car, because it’s the more lucrative thing to do.
So what does that car of the future look like? There is no one answer, because in this world, the car that is sent to pick you up is tailored to your trip. The more people traveling, the bigger the car is. If your trip does not involve a highway, it may not be a car capable of the highway. If your trip is up to a mountain cabin, it’s more like an SUV, but you never use an SUV to go get a bottle of milk the way we do today. If it’s for a cruise to the beach on a sunny day, the roof may have been removed at the depot. If it’s for an overnight trip to a country home, it may be just beds.
I outlined many of these changes in this article on design changes in cars but today I will focus on the incredibly cheap and simple design of what should become the most common vehicle made, namely the car designed for a short urban trip by one person. That’s 80% of trips and around 45% of miles, so this should be a large fraction of the fleet. I predict a lot of these cars will be made every year — more than all the cars made today, even though they are used as taxis and shared among many passengers.
What does it look like?
A car for 1-2 people will be small. It will probably be around 1.5m wide, narrow enough that you can fit two in a lane, and have it park very efficiently when it has to wait. If it’s for just one person, it won’t be very long either. For two people, there will be a “face to face” configuration which is longer and an “tandem” configuration which is a bit shorter. The 2 person vehicles aren’t a lot bigger or heavier than the one person, so they might be the most common cars, since you can serve a solo rider fairly efficiently with one, even if not perfectly efficient.
A car that is so narrow can’t corner very fast. A wide stance is much more stable. There are a few solutions to that, including combinations of these:
The wheels bank independently, allowing the vehicle to lean like a motorcycle when in corners. This is the best solution, but it costs some money.
Alternately it’s a two wheeler, which is also able to lean, but has other tricks like the LIT Motors C-1 to stay upright.
It’s electric, and has all the batteries in the floor, giving it a very low center of gravity. (One extreme example of this is the Tango, which uses lead batteries deliberately to give it that stability.)
It never goes on fast roads, so it never needs to corner very fast, and its precision robot driving assures it never corners so fast as to become unstable, and it plans its route accordingly.
Not super aerodynamic
The car already has a big win when it comes to aerodynamic drag by only being half-width. The non-highway version probably gives back a bit of that because you don’t need to worry as much about that if you are not going fast. Energy lost to drag goes up with the square of velocity. So a 30mph car has 1/4 the drag of a 60mph car, and 1/8th the drag of a similar car of full width. The highway car needs to be shaped as close to a “teardrop” as you can, but the city car can get away with being a bit taller for more comfortable seating and entry/exit. read more »
When I give talks on robocars, the most common question, asked almost all the time, is the one known as the “trolley problem” question, “What will the car do if it has to choose between killing one person or another” or other related dilemmas. I have written frequently about how this is a very low priority question in reality, much more interesting to philosophy classes than it is important. It is a super-rare event and there are much more important everyday ethical questions that self-driving car developers have to solve long before they will tackle this one.
In spite of this, the question persists in the public mind. We are fascinated and afraid of the idea of machines making life or death decisions. The tiny number of humans faced with such dilemmas don’t have a detailed ethical debate in their minds; they can only go with their “gut” or very simple and quick reasoning. We are troubled because machines don’t have a difference between instant and carefully pondered reactions. The one time in billions of miles(*) that a machine faces such a question it would presumably make a calculated decision based on its programming. That’s foreign to our nature, and indeed not a task desired by programmers or vendors of robocars.
There have been calls to come up with “ethical calculus” algorithms and put them in the cars. As a programmer, I could imagine coding such an algorithm, but I certainly would not want to, nor would I want to be held accountable for what it does, because by definition, it’s going to do something bad. The programmer’s job is to make driving safer. On their own, I think most builders of robocars would try to punt the decision elsewhere if possible. The simplest way to punt the decision is to program the car to follow the law, which generally means to stay in its right-of-way. Yes, that means running over 3 toddlers who ran into the road instead of veering onto the sidewalk to run over Hitler. Staying in our lane is what the law says to do, and you are not punished for doing it. The law strongly forbids going onto the sidewalk or another lane to deliberately hit something, no matter who you might be saving.
We might not like the law, but we do have the ability to change it.
Thus I propose the following: Driving regulators should create a special panel which can rule on driving ethics questions. If a robocar developer sees a question which requires some sort of ethical calculation whose answer is unclear, they can submit that question to the panel. The panel can deliberate and provide an answer. If the developer conforms to the ruling, they are absolved of responsibility. They did the right thing.
The panel would of course have people with technical skill on it, to make sure rulings are reasonable and can be implemented. Petitioners could also appeal rulings that would impede development, though they would probably suggest answers and describe their difficulty to the panel in any petition.
The panel would not simply be presented with questions like, “How do you choose between hitting 2 adults or one child?” It might make more sense to propose formulae for evaluating multiple different situations. In the end, it would need to be reduced to something you can do with code.
Very important to the rulings would be an understanding of how certain requirements could slow down robocar development or raise costs. For example, a ruling that car must make a decision based on the number of pedestrians it might hit demands it be able to count pedestrians. Today’s robocars may often be unsure whether a blob is 2 or 3 pedestrians, and nobody cares because generally the result is the same — you don’t want to hit any number of pedestrians. Likeways, requirements to know the age of people on the road demands a great deal more of the car’s perception system than anybody would normally develop, particularly if you imagine you will ask it to tell a dwarf adult from a child. read more »
Reports from Tesla suggest they are gathering huge amounts of driving data from logs in their cars — 780 million miles of driving, and as much as 100 million miles in autopilot mode. This contrasts with the 1.6 million miles of test operations at Google. Huge numbers, but what do they mean now, and in the future?
As I’ve written before, testing is one of the biggest remaining challenges in robocar development — how do you prove to yourself and then to others that you’ve reached the desired safety goals? Tons of miles are a very important component to that. If car companies are able to get their customer to do the testing for them, that can be a big advantage. (As I wrote last week, another group which can get others to do testing are companies like Uber and even operators of large commercial and taxi fleets.) Lots of miles mean lots of testing, lots of learning, and lots of data.
Does Tesla’s quick acquisition of so many miles mean they have lapped Google? The short answer is no, but it suggests a significant threat since Google is, for now, limited to testing with its small fleet and team of professional testing drivers.
Tesla is collecting vastly less data from its cars than Google does. Orders of magnitude less. First of all, the Tesla has a lot fewer sensors and no LIDAR, and to the best of my knowledge from various sources I have spoken to, Tesla is only collecting a fraction of what their sensors gather. To collect all that they gather would be a huge data volume, not one you would send over the cell network, and even over the wifi at home it would be very noticeable. Instead, reports suggest Tesla is gathering only data on incidents and road features the car did not expect or did not handle well. However, nothing stops them in the future from logging more, though they might want to get approval from owners to use all that bandwidth.
Tesla wants to make a car for people to buy today. As such, it has no LIDAR, because a car today, and even the autopilot, can be done without LIDAR. Tomorrow’s LIDARs will be cheap but today’s production LIDARs for cars are simple and/or expensive. So while the real production door-to-door self driving car almost certainly uses LIDAR, Tesla is unable and unwilling to test and develop with it. (Of course, they can also argue that in a few years, neural networks will be good enough to eliminate the need for LIDAR. That’s not impossible, but it’s a risky bet. The first cars must be built in a safety-obsessed way, and you’re not going to release the car less safe than you could have made it just to save what will be only a few hundred dollars of cost.)
As noted, Google has being doing their driving with professional safety drivers, who are also recording a lot of data from the human perspective that ordinary drivers never will. That isn’t 100 times better but it’s pretty important.
Tesla is also taking a risk, and this has shown up in a few crashes. Their customers are beta testing a product that’s not yet fully safe. In fact, it was a pretty bold move to do this, and it’s less likely that the big car companies would have turned their customers into beta testers — at least no until forced by Tesla.
If they do, then the big automakers have even more customers than Tesla, and they can rack up even more miles of testing and data gathering.
When it comes to training neural networks, ordinary drivers can provide a lot of useful data. That’s why Commma.ai, who I wrote about earlier is even asking volunteers to put a smartphone on their dash facing out to get them more training data. At present, this app does not do much, but it will not be hard to make one that offers things like forward collision warning and lane departure warning for free, paid for by the data it gathers.
Watch me Sunday night on Dateline NBC: On Assignment
On Sunday, June 5, at 7pm (Eastern and Pacific) the news show Dateline: NBC will do a segment on self driving cars featuring Sebastian Thrun, Jay Leno and myself. I sat down for several hours with Harry Smith, but who knows how much actual airtime that turns into. Here is the promo for the episode and another more specific one.
Executive summary: Can our emotional fear of machines and the call for premature regulation be mollified by a temporary increase in liability which takes the place of specific regulations to keep people safe?
So far, most new automotive technologies, especially ones that control driving such as autopilot, forward collision avoidance, lanekeeping, anti-lock brakes, stability control and adaptive cruise control, have not been covered by specific regulations. They were developed and released by vendors, sold for years or decades, and when (and if) they got specific regulations, those often took the form of “Electronic stability control is so useful, we will now require all cars to have it.” It’s worked reasonably well.
That there are no specific regulations for these things does not mean they are unregulated. There are rafts of general safety regulations on cars, and the biggest deterrent to the deployment of unsafe technology is the liability system, and the huge cost of recalls. As a result, while there are exceptions, most carmakers are safety paranoid to a rather high degree just because of liability. At the same time they are free to experiment and develop new technologies. Specific regulations tend to come into play when it becomes clear that automakers are doing something dangerous, and that they won’t stop doing it because of the liability. In part this is because today, it’s easy to assign blame for accidents to drivers, and often harder to assign it to a manufacturing defect, or to a deliberate design decision.
The exceptions, like GM’s famous ignition switch problem, arise because of the huge cost of doing a recall for a defect that will have rare effects. Companies are afraid of having to replace parts in every car they made when they know they will fail — even fatally — just one time in a million. The one person killed or injured does not feel like one in a million, and our system pushes the car maker (and thus all customers) to bear that cost.
Robocars change some of this equation. First of all, in robocar accidents, the maker of the car (or driving system) is going to be liable by default. Nobody else really makes sense, and indeed some companies, like Volvo, Mercedes and Google, have already accepted that. Some governments are talking about declaring it but frankly it could never be any other way. Making the owner or passenger liable is technically possible, but do you want to ride in an Uber where you have to pay if it crashes for reasons having nothing to do with you?
Due to this, the fear of liability is even stronger for robocar makers.
Robocar failures will almost all be software issues. As such, once fixed, they can be deployed for free. The logistics of the “recall” will cost nothing. GM would have no reason not to send out a software update once they found a problem like the faulty ; they would be crazy not to. Instead, there is the difficult question of what to do between the time a problem is discovered and a fix has been declared safe to deploy. Shutting down the whole fleet is not a workable answer; it would kill deployment of robocars if several times a year they all stopped working.
In spite of all this history and the prospect of it getting even better, a number of people — including government regulators — think they need to start writing robocar safety regulations today, rather than 10-20 years after the cars are on the road as has been traditional. This desire is well-meaning and understandable, but it’s actually dangerous, because it will significantly slow down the deployment of safety technologies which will save many lives by making the world’s 2nd most dangerous consumer product safer. Regulations and standards generally codify existing practice and conventional wisdom. They are very bad ideas with emerging technologies, where developers are coming up with entirely new ways to do things, and entirely new ways to be safe. The last thing you want is to tell vendors they must apply old-world thinking when they can come up with much better thinking.
Sadly, there are groups who love old-world thinking, namely the established players. Big companies start out hating regulation but eventually come to crave it, because it mandates the way they do things and understand into the law. This stops upstarts from figuring out how to do it better, and established players love that.
The fear of machines is strong, so it may be that something else needs to be done to satisfy all desires: The desire of the public to feel the government is working to keep these scary new robots from being unsafe, and the need for unconstrained innovation. I don’t desire to satisfy the need to protect old ways of doing things.
One option would be to propose a temporary rule: For accidents caused by robocar systems, the liability, if the system should be at fault, shall be double that if a similar accident were caused by driver error. (Punitive damages for willful negligence would not be governed by this rule.) We know the cost of accidents caused by humans. We all pay for it with our insurance premiums, at an average rate of about 6 cents/mile. This would double that cost, pushing vendors to make their systems at least twice as safe as the average human in order to match that insurance cost.
Victims of these accidents (including hapless passengers in the vehicles) would now be doubly compensated. Sometimes no compensation is enough, but for better or worse, we have set on values and doubling them is not a bad deal. Creators of systems would have a higher bar to reach, and the public would know it.
While doubling the cost is a high price, I think most system creators would accept this as part of the risk of a bold new venture. You expect those to cost extra as they get started. You invest to make the system sustainable.
Over time, the liability multiplier would reduce, and the rule would go away entirely. I suspect that might take about a decade. The multiplier does present a barrier to entry for small players, and we don’t want something like that around for too long.
Here is the first report of a real Tesla autopilot crash. To be fair to Tesla, their owner warnings specify fairly clearly that the autopilot could crash in just this situation — there is a stalled car partly in the lane, and the car in front of you swerves around it, revealing it with little time for you or the autopilot to react.
The deeper issue is the way that the improving quality of the Tesla Autopilot and systems like it are lulling drivers into a false sense of security. I have heard reports of people who now are trusting the Tesla system enough to work while being driven, and indeed, most people will get away with this. And as people get away with it more and more, we will see more people driving like this driver, not really prepared to react. This is one of the reasons Google decided not to make a system which requires driver takeover ever. As the system gets better, does it get more dangerous?
Some technical notes:
This is one of the things LIDAR is much more reliable at seeing than cameras. Of course, whether you can swerve once the LIDAR sees it is another matter.
On the other hand, this is where radar fails. I mean the stalled car is clear on radar, but it’s stationary, so you can’t tell it from the road or guardrail which are also stationary.
This is one of the classic V2V value propositions, but it’s not a good one. You don’t need 10ms latency to have a stalled car tell you it is stalled. Far better that car report to a server that it’s stalled and for everybody coming down that road to learn it, whether they have line of sight radio to the stall, or V2V at all. Waze already reports this just with human manual reporting and that’s a really primitive way to do it.
Declaration of Amsterdam
Last month, various EU officials gathered in Amsterdam and signed the Declaration of Amsterdam which outlines a plan for normalizing EU laws around self-driving cars. The meeting also included a truck automation demo in the Netherlands and a self-drive transit shuttle demonstration. It’s a fairly bland document, more an expression of the times, and it sadly spends a lot of time on the red herring of “connected” vehicles and V2V/V2I, which governments seem to love, and self-driving car developers care very little about.
Let’s hope the regulatory touch is light. The reality is that even the people building these vehicles can’t make firm pronouncements on their final form or development needs, so governments certainly can’t do that, and we must be careful of attempts to “help” that hinder. We already have a number of examples of that happening in draft and real regulations, and we’ve barely gotten started. For now, government statements should be limited to, “let’s get out of the way until people start figuring out how this will actually work, unless we see somebody doing something demonstrably dangerous that can’t be stopped except through regulations.” Sadly, too many regulators and commentators imagine it should be, “let’s use our limited current knowledge to imagine what might go wrong and write rules to ban it before it happens.”
Speech from the Throne
It was a sign of the times when her Majesty the Queen, giving the speech from the throne in the UK parliament, laid out some elements of self-driving car plans. The Queen drove jeeps during her military days, and so routinely drives herself at her country estates, otherwise she would be among the set of people most used to never driving.
The UK has 4 pilot projects in planning. Milton Keynes is underway, and later this year, a variation of the Ultra PRT pods in use at T5 of Heathrow airport — they run on private tracks to the car park — will go out on the open road in Greenwich. They are already signing up people for rides.
Car companies thinking differently
In deciding which car companies are going to survive the transition to robocars, one thing I look for is willingness to stop thinking like a traditional car company which makes cars and sells them to customers. Most car company CEOs have said they don’t plan to keep thinking that way, but what they do is more important than what they say. read more »
Uber has announced the official start of self-driving tests in Pittsburgh. Uber has been running their lab for over a year, and had various vehicles out there mapping and gathering data, but their new vehicle is sleeker and loaded with sensors - more than on Google’s cars or most of the other research cars I have seen. You can see several lidars on the roof and bumpers, and a seriously big array of cameras and other sensors.
In addition, recently it was announced that the GM-Lyft-Cruise combination will be offering rides in 2017 in a self-driving Chevy Bolt. Of course, there will be a safety driver in the car supervising it so it would be an empty taxi coming to pick you up, but it’s a nice step.
These two announcements bring attention to two of the most important companies in the space, even though their technical efforts are much less mature than Google’s or Daimler’s. That’s because of one key forecast that I have emphasized from the start:
A large fraction of the automotive industry is going to switch to be about selling rides, not selling cars
As we all know, Uber has already become the #1 brand in the world in selling rides in just a few years. It’s a very important position to have. Lyft has #2 but other companies like Didi own China (and just got a $1B investment from Apple.)
As the owner of the ride brand, Uber has a lot of control. The brand of the car that drives you is less important and interchangeable. But that’s not the only advantage these ride companies have:
Ride companies have huge volumes of drivers on the road all day. They can be used as a resource for mapping, testing and verifying self-driving systems. Companies like Google had to pay staff and buy cars to do that.
Ride companies can combine human driven ride service with robotic taxis, to take you from anywhere to anywhere any time. It just costs more if you want to travel where the robots can’t.
Uber and Lyft can fail in their research program and still win. They just have to find somebody else to sell them the cars. Of course that does mean a power trade — it’s very nice to own the magic sauce that makes it all work, but the ride companies are among the few would could have another provider and still have a lot of control.
At the same time, Lyft is now bound to probably work with GM, and Didi possibly with Apple, which leaves Uber with more flexibility among these.
The ride companies are already doing big experiments in real ride-sharing, ie. multiple independent passengers in the same car. Today, using UberPool is popular and can save significant money. A more interesting question arises when robotic taxi service is available for 30 cents/mile. I don’t think people would share their ride to reduce the price from $1.50 to $1. Saving 50 cents does not move the needle for most people of even moderate income levels.
How will ride companies compete?
An important social question asks how many ride companies can compete in a market? Right now Uber has established a lot of dominance. In San Francisco, birthplace of Uber, Lyft and Sidecar, Sidecar shut its doors from difficulty competing with the other two. Is there room for only a few companies? That’s bad, because competition is good for the public.
The first intuition is that fleet size is a big competitive advantage because you can offer faster pickup times and more choices of vehicle. Customers will care a lot about how long they have to wait for a ride. That will vary of course based on random positions of vehicles, and also how good the predictive positioning is in the fleet management system.
At the same time, it is possible to have a successful limo company today with just one limo. You only do scheduled rides (or ad-hoc rides booked via networks like UberBlack) but you have a business. It is not the size of your fleet that fully governs your wait time, but rather the ratio of the size of your fleet to the number of customers you have. Lyft has a smaller fleet but also fewer users, so I find it can often match or beat Uber on wait time, though neither wins all the time. There is a natural balance here — the better your fleet-size/user-base ratio is, the shorter wait times you have, but that brings you more customers until the advantage starts reducing.
Today sees the un-stealthing of a new company called Otto which plans to build self-driving systems for long haul trucks. The company has been formed by a skilled team, including former members of Google’s car team and people I know well. You can see their opening blog post
My entire focus on this blog, and the focus of most people in this space, has been on cars, particularly cars capable of unmanned operation and door-to-door service. Most of those not working on that have had their focus on highway cars and autopilots. The highway is a much simpler environment so much easier to engineer, but it operates at higher speeds so the cost of accidents is worse.
That goes doubly true for trucks that are fast, big and massive. At the same time, 99% of truck driving is actually very straightforward — stay in a highway lane, usually the slow one, with no fancy moving about.
Some companies have done exploration of truck automation. Daimler/Freightliner has been testing trucks in Nevada. Volvo (trucks and cars together) has done truck and platooning experiments, notably the Sartre project some years ago. A recent group of European researchers did a truck demonstration in the Netherlands, leading up to the Declaration of Amsterdam which got government ministers to declare a plan to modify regulations to make self-driving systems legal in Europe. Local company Peloton has gone after the more tractable problem of two-truck platoons with a driver in each truck, aimed primarily at fuel savings and some safety increases.
While trucks are big and thus riskier to automate, they are also risky for humans to drive. Even though truck drivers are professionals who drive all day, there are still around 4,000 killed every year in the USA in truck accidents. More than half of those are truck drivers, but a large number of ordinary road users are also killed. Done well, self-driving trucks will reduce this toll. Just as with cars, companies will not release the systems until they believe they can match and beat the safety record of human drivers.
Self-driving trucks don’t change the way we move, but they will have a big economic effect on trucking. Driver pay accounts for about 25-35% of the cost of truck operation, but in fact early self-driving won’t take away jobs because there is a serious shortage of truck drivers in the market — companies can’t hire enough of them at the wages they currently pay. It is claimed that there are 50,000 job openings unfilled at the present time. Truck driving is grueling work, sometimes mind-numbing, and it takes the long haul driver away from home and family for over a week on every long-haul run. It’s not very exciting work, and it involves long days (11 hours is the legal limit) and a lot of eating and sleeping in truck stops or the cabin of the truck.
Average pay is about 36 cents/mile for a solo trucker on a common route. Alternately, loads that need to move fast are driven by a team of two. They split 50 cents/mile between them, but can drive 22 hours/day — one driver sleeps in the back while the first one takes the wheel. You make less per mile per driver, but you are also paid for the miles you are sleeping or relaxing.
A likely first course is trucks that keep their solo driver who drives up to 11 hours — probably less — and have the software drive the rest. Nonstop team driving speed with just one person. Indeed, that person might be an owner-operator who is paying for the system as a businessperson, rather than a person losing a job to automation. The human would drive the more complex parts of the route (including heavy traffic) while the system can easily handle the long nights and sparse heartland interstate roads.
The economics get interesting when you can do things that are expensive for human drivers and teams. Aside from operating 22 or more hours/day at a lower cost, certain routes will become practical that were not economic with human drivers, opening up new routes and business models.
Computer driven trucks will drive more regularly than humans, effectively driving in “hypermile” style as much as they can. That should save fuel. In addition, while I would not do it at first, the platooning experimented with by Peloton and Sartre does result in fuel savings. Also interesting is the ability to convert trucks to natural gas, which is domestic and burns cleaner (though it still emits CO2.) Automated trucks on fixed routes might be more willing to make this conversion.
There is strong potential to reduce the damage to roads (and thus the cost of maintaining them, which is immense and seriously in arrears) thanks to the robotruck. That’s because heavy trucks and big buses cause almost all the road wear today. A surprising rule of thumb is that road damage goes up with the 4th power of the weight per axle. As such an 80,000lb truck with 34,000lb on two sets of 2 axles and 6,000lb on the front axle does around 2,000 times the road damage of a typical car! read more »
A recent news story from Utah describes a Tesla which entered self-park (“summon”) mode and drive itself into the back of a flatbed truck raises some interesting issues.
Tesla says that the owner of the vehicle initiated auto-Summon, which requires pressing the gear selector stalk twice and then shifting into park, then leaving the vehicle. After that the car goes into its self-park mode in 3 seconds, and the driver is supposed to be watching because the feature is a beta.
The owner says he never activated the self-park, and if somehow he did by accident, he was standing by the car for 20 seconds showing it off to a stranger, and as such he claims he is absolutely certain the car did not begin moving 3 seconds after he got out. Tesla says the logs say otherwise.
Generally, one believes log files over human memory, though these stories are surprisingly at odds. When doing Summon, the Tesla is flashing its hazard lights and moving, so it’s not exactly subtle. And it’s not supposed to work unless the keyfob is close to the car. No doubt there will be back and forth on just what happened.
However, there are some things that are less disputed:
Unless the owner is out and out lying, there is a problem which allowed an owner to activate the auto-summon feature by accident, and to do so when not close to the car. (When you activate it the hazards start blinking and it shows auto-park on the screen.)
The car should not have hit the metal bars on the back of the flatbed. However, Tesla warns that the feature may not detect thin objects or hanging objects. These bars are quite low, but are sticking off the end of the truck by a large amount. Clearly the obstacle detection is indeed very “beta” if it could not see these. Apparently auto-park is done using the ultrasonic sensors, not the camera. Bumper based ultrasound is not enough.
This also adds some fuel to the ongoing debate about maps. The car was in a place where there would be no reason to initiate Tesla’s self-park, which is designed for it to drive straight into narrow parking spaces. In this case, it is not necessary to have a map of all the spaces a car might self-park, but even a fairly coarse and inaccurate map could allow the car to say, “This seems like an odd place to use the self-park feature, are you sure?” And pretty much all parallel parking spaces on the side of the road qualify as a place you would not use this particular self-park function.
So is the owner lying? Was he playing with auto-summon and screwed up? (You have to screw up royally as it drives quite slowly and any touch on the door handles or the fob will stop it.) The problem is that he claims that the car did it while he was not present, which is not supposed to happen, and if he was present, why did he not stop it?
If you had asked me recently what big car company was the furthest behind when it came to robocars, one likely answer would be Fiat-Chrysler. In fact, famously, Chrysler ran ads several years ago during the superbowl making fun of self-driving cars and Google in particular:
Now Google has announced a minor partnership with Chyrsler where they will be getting Chrysler to build 100 custom versions of their hybrid minivans for Google’s experiments. Minivans are a good choice for taxis, with their spacious seating and electric sliding doors — if you want a vehicle to come pick you up, it probably should have something like this.
This is a pretty minor partnership, something closer to a purchase order than a partnership, but it will be touted as a great deal more. My own feeling is it’s unlikely a major automaker will truly partner with a big non-auto player like Google, Uber, Baidu or Apple. Everybody is very concerned about who will own the customer and the brand, and who will be the “Foxconn” and the big tech companies have no great reason to yield on that (because they are big) and the big car companies are unlikely to yield, either. Instead, they will acquire or do deals they control with smaller companies (like the purchase of Cruise or the partnership with Lyft from GM.)
Still, what may change this is an automaker (like FCA) getting desperate. GM got desperate and spent billions. FCA may do the same. Other companies with little underway (like Honda, Peugeot, Mazda, Subaru, Suzuki) may also panic, or hope that the Tier 1 suppliers (Bosch, Delphi, Conti) will save them.
Google custom designed a car for their 3rd generation prototype, with 2 seats, no controls and and electric NEV power train. This has taught them a lot, but I bet it has also taught them that designing a car from scratch is an expensive proposition before you are ready to make many thousands of them.
I have often written on the challenge facing existing automakers in the world of robocars. They need to learn to completely switch their way of thinking in a world of mobility on demand, and not all of them will do so. But they face serious challenges even if they are among the lucky ones who fully “get” the robocar revolution, change their DNA and make products to compete with Google and the rest of the non-car companies.
Unfortunately for the car companies, their biggest assets — their brands, their experience, their quality and their car manufacturing capacity — are no longer as valuable as they were.
Their brands are not valuable
Today if you summon a car with a company like Uber, you don’t care about what brand of car it is, as long as it’s decent. Even with the “luxury” variants of Uber, you don’t care which type of luxury car shows up, as long as it meets certain standards. For companies who have most of their value in their nameplate, this is nightmare #1. The taxi service (Uber or otherwise) becomes the brand that is seen and valued by the customer.
When you are buying a car for 5 years at the dealership, you care a lot about the brand, both for what it means, and for what it says about you when you show up driving it. When you buy a car by the ride, you don’t care a lot about the brand, because you are only going to use it for a short time.
Their brands might be tarnished
There will be accidents in Robocars, unfortunately. Those accidents will cost money, but they will also cause problems in public image. The problem is, “Mercedes runs over grandmother” is a headline that will make people less likely to buy any type of Mercedes. As such, Mercedes has plans to market self-driving car service under their Car2Go brand. You may not even know that Car2Go is Daimler, and they might like it that way. “Google car runs over grandmother” is bad news for the Google car project, but is not going to make anybody stop doing web searches with Google. (Except the grandmother…)
The non-car companies don’t have a car brand to tarnish, but they do have famous brands. They can use those brands to attract customers without the same risk. Big car companies have famous brands but may be afraid to use them.
They might just be the contract manufacturer
Companies like Uber, Google, Apple and others don’t plan to manufacture cars. Why would they? There is tons of car manufacturing capacity out there. They can just go to carmakers and say, “here’s a purchase order for 100,000 cars — built to our spec with our logo on them.” It will be very hard to turn down such an order. Still, some companies will be too proud to do this, or too unwilling to sign their own suicide note.
If they don’t accept the order, somebody else will. If nobody in the west does, somebody in China will. China is the world’s #1 car manufacturing country, but the cars are rarely exported to the west. They would love to change that.
A likely model for this is the relationship of Apple and Foxconn. Foxconn makes your iPhone, but many don’t know that. Foxconn makes good money, but Apple makes much more, designing the product and owning the customer. The car companies don’t want to be Foxconn in the world of the future, but the alternative may be to be much smaller.
(BTW, Foxconn has said it is interested in making cars.)
First-rate quality might not be that important
Chinese manufacturers don’t have the quality of the current leaders. But they may not need to. Just as Apple taught Foxconn how to make good iPhones, they might follow the same pattern here. But they don’t need to. That’s because a less reliable robocar is not the same sort of problem an unreliable personal car is. Sure, it should not break down while you are riding in it — but even then the company can quickly send you a replacement to pick you up in just a few minutes. If it breaks down otherwise, it just goes out of service. This costs the fleet manager money, but they saved a lot of money with the lower quality manufacturer. When cars can move on demand to service customers, breakdowns are not the same sort of problem. When your own car breaks down it’s a nightmare, and you will pay a lot to avoid it. For a fleet, it’s just a cost. All cars are down for maintenance some of the time. Cheaper cars will be down more, but if they are cheap enough, it still saves money.
Customer perception of quality is still important. The vehicle must maintain the level of comfort and interior quality the customer has paid for. Safety related failures are of course much less tolerable.
New car designs will be radically different
The robocar of the future will look quite different from the cars of the past. Existing car companies can handle this, but they lose some of the advantage that comes from decades of experience. The future robocars are probably electric and much simpler, with hundreds of parts rather than tens of thousands. It’s a new world and experience with the old may actually be a disadvantage. Only Nissan and Tesla have lots of electric car experience today, though GM is building it. Electric platforms are much simpler and ripe for creativity from new players.
While I’m very excited about the coming robocar world, there are still many unsolved problems. One I’ve been thinking about, particularly with my recent continued thinking on transit, is how to provide robotaxi service to the poor, which is to say people without much money and without credit and reputations.
In particular, we want to avoid situations where taxi fleet operators create major barriers to riding by the poor in the form of higher fees, special burdens, or simply not accepting the poor as customers. If you look at services like Uber today, they don’t let you ride unless you have a credit card, though in some cases prepaid debit cards will work.
Today a taxi (or a bus or Uber style vehicle) has a person in it, primarily to drive, but they perform another role — they constrain the behaviour of the rider or riders. They reduce the probability that somebody might trash the vehicle or harass or be violent to another passenger.
Of course, such things happen quite rarely, but that won’t stop operators from asking, “What do we do when it does happen? How can we stop it or get the person who does it to pay for any damage?” And further they will say, “I need a way to know that in the rare event something goes wrong, you can and will pay for it.” They do this in many similar situations. The problem is not that the poor will be judged dangerous or risky. The problem is that they will be judged less accountable for things that might go wrong. Rich people will throw up in the back of cars or damage them as much as the poor, perhaps more; the difference is there is a way to make them pay for
it. So while I use the word poor here, I really mean “those it is hard to hold accountable” because there is a strong connection.
As I have outlined in one of my examinations of privacy a taxi can contain a camera with a physical shutter that is open only between riders. It can do a “before and after” photograph, mostly to spot if you left items behind, but also to spot if you’ve damaged or soiled the vehicle. Then the owner can have the vehicle go for cleaning, and send you the bill.
But they can only send you the bill if they know who you are and have a way to bill you. For the middle class and above, that’s no problem. This is the way things like Uber work — everybody is registered and has a credit card on file. This is not so easy for the poor. Many don’t have credit cards, and more to the point, they can’t show the resources to fix the damage they might do to a car, nor may they have whatever type of reputation is needed so fleet operators will trust them. The actions of a few damn the many.
The middle class don’t even need credit cards. Those of us wishing to retain our privacy could post a bond through a privacy protecting intermediary. The robotaxi company would know me only as “PrivacyProxy 12323423” and I would have an independent relationship with PrivacyProxy Inc. which would accept responsibility for any damage I do to the car, and bill me for it or take money from my bond if I’m truly anonymous.
Options for the poor
Without the proxy, robotaxi operators will want some sort of direct accountability from passengers for any problems they might cause. Even for the middle class, it mostly means being identified, so if damage is found, you can be tracked down and made to pay. The middle class have ability to pay, and credit. The poor don’t, at least many of them don’t.
People with some level of identity (an address, a job) have ways to be accountable. If the damage rises to the level where refusing to fix it is a crime at some level, fear of the justice system might work, but it’s unlikely the police are going to knock on somebody’s door for throwing up in a car.
In the future, I expect just about everybody of all income levels will have smartphones, and plans (though prepaid plans are more common at lower income levels.) One could volunteer to be accountable via the phone plan, losing your phone number if you aren’t. Indeed, it’s going to be hard to summon a car without a phone, though it will also be possible using internet terminals, kiosks and borrowing the phones of others.
More expensive rides
A likely solution, seen already in the car rental industry, is to charge extra for insurance for those who can’t prove accountability another way. Car rental company insurance is grossly overpriced, and I never buy it because I have personal insurance and credit cards to cover such issues. Those who don’t often have to pay this higher price.
It’s still a sad reality to imagine the poor having to pay more for rides than for the rich.
An option to mitigate this might be cars aimed at carrying those who are higher risk. These cars might be a bit more able to withstand wear and tear. Their interiors might be more like bus interiors, easily cleaned and harder to damage, rather than luxury leather which will probably be only for the wealthier. To get one, you might have to wait longer. While a middle-class customer ordering a cheap car might be sent a luxury car because that’s what’s spare at the time, it is less likely an untrusted and poor customer would get that.
Before we go do far, I predict the cost of robotaxi rides will get well below $1/mile, heading down to 30 cents/mile. Even with a 30% surcharge, that’s still cheaper than what we have today, in fact it’s cheaper than a bus ticket in many towns, certainly cheaper than an unsubsidized bus ticket which tends to run $5-$6. Still my hope for robotaxi service is that it makes good transportation more available to everybody, and having it cost more for the poor is a defect.
In addition, as long as damage levels remain low, as a comment points out, perhaps the added cost on every ride would be small enough that you don’t need worry about this for poor or rich. (Though having no cost to doing so does mean more spilled food, drink and sadly, vomit.)
Over time, fortunately, poor riders could develop reputations for treating vehicles well. Build enough reputation and you might have access to the same fleet and prices that the middle class do, or at least much cheaper insurance. Cause a problem and you might lose the reputation. It would be possible to build such a reputation anonymously, though I suspect most people and companies would prefer to tie it to identity, erasing privacy. Anonymous reputations in particular can be sold or stolen which presents an issue. One option is to tie the reputation to a photo, but not a name. When you get in the car, it would confirm you match the photo, but would not immediately know your name. (In the future, though, police and database companies will be able to turn the photo into a name easily enough.)
Poor riders would still have to pay more to start, probably, or suffer the other indignities of the lower class ride. However, a poor rider who develops a sterling reservation might be able to get some of that early surcharge back later. (Not if it’s insurance. You can’t get insurance back if you don’t use it, it doesn’t work that way!)
It could also be possible for the poor to get friends to vouch for them and give them some starter reputation.
Unfortunately, poor who squander their reputation (or worse, just ride with friends who trash a car) could find themselves unable to travel except at high cost they can’t afford. It could be like losing your car.
The government will have an interest in making sure the poor are not left out of this mobility revolution. As such, there might be some subsidy program to help people get going, and a safety net for loss of reputation. This of course comes with a cost. Taxes would pay for the insurance to fix cars that are damaged by riders unable to be held accountable.
The alternative, after all, is needing to continue otherwise unprofitable transit services with human drivers just for the sake of these people who can’t get private robocar rides. Transit may continue (though without human drivers) at peak times, but it almost surely vanishes off-peak if not for this. read more »
Recently a reddit user posted this short video of an amazingly lucky driver in Japan who was able to turn his car around just in time to escape the torrent of the tsunami.
The question asked was, how would a robocar deal with this? It turns out there are many answers to this question. For this particular question, as you’ll see by the end, the answer is probably “very well.”
Let’s start with the bad news. On its own, built in a world where few thought about tsunamis, there is a good chance the vehicle would not handle it well. The instinct for most developers is to be conservative and cautious when facing an unknown situation. The most cautious thing is to do nothing, to just stop and perhaps ask for help from a person in the car or a remote center. Usually if you don’t understand the situation, doing something is much riskier than doing nothing. Usually — but clearly not here.
This situation might be viewed as similar to something you might expect a car to have programming for — something is approaching fast towards you. Cars will probably have logic to deal with a car coming the wrong way down their lane, and this looks a bit like that. It’s actually stuff coming in both lanes. We can imagine the car might have logic to attempt to retreat in that situation, though this isn’t going to look too much like anything the sensors have seen before. With 3D sensors, though, it will be clear that something huge is coming fast. And with a map of what the road should look like, you will easily tell the wall of water and debris from what you should be seeing.
The best reason the car might handle this however, is the very existence of this video, and the posts about it — including this blog post here. The reason is that the developers of robocars, in order to test them, are busy building simulators. In these simulators they are programming every crazy situation they can think of, even impossible situations, just to see what each revision of the car software will do. They are programming every situation that their cars have encountered on the road. Every situation that caused their software to make an error, or anybody else to make an error.
In other words, if you can think of it after a little bit of thinking, they probably thought of it too. And if it’s in blog posts and famous news stories, they probably heard about it. Flooding and every kind of strange weather ever reported. The details of every accident from every police report that can be turned into a simulation. Earthquakes. Tornadoes. Hurricanes. Alien invasions. Oncoming tanks. If you can think of it without a major effort, and it seems like it could happen, they will put it in. And so every car will indeed be tested. In fact, the developers will probably have fun with the really strange situations which are so rare that they may not have commercial or safety justification, but still are interesting. Scenes from movies. James Bond car chases. You name it.
In this particular case, there is another thing to help with this situation. Tsunamis don’t happen by surprise, not any more. The world, having seen them like this, now has earthquake detection and tsunami warning everywhere robocars are likely to go in the near future. The warnings will be transmitted along the same data stream warning cars about traffic, weather and road conditions. We even have maps of the terrain and can even predict what areas are low and which areas cars should head to in the event of a tsunami warning, and they will take routes designed to avoid risk. With superhuman knowledge, they will not panic and do much better than people at taking the route to high ground, and so they odds of them confronting the wall of water would be very slim, unless there was no choice. The robocar simply would not have been going down that road the way the Japanese driver was.
Now we get to a final special ability of robocars — they will be just as capable in reverse gear as they are going forward, other than due to the speed limitations of reverse gear. So while you reverse timidly, a robocar need not do so. It will be able to pull off the fastest 3 point turn you can imagine if it wants to, or even just escape in reverse. Of course if it needs more speed than reverse offers, it would turn around in the best spot to do so. Stanford has even done a lot of research on drifting, and this will go into simulators too, so cars will probably know how to turn around as fast as a stunt driver if they have to. Electric cars may be able to go as fast in reverse as they can going forward to top it all off. (I should note that not all car designs feature sensors that see the same forward and back, so this may not be true for all vehicles, but all vehicles that can reverse at all need not be timid about it the way people are.)
So for this situation, and anything else we know about, robocars should do a superhuman job. That doesn’t mean there aren’t things nobody ever thought of. But the more videos and stories like this that get recorded, the less and less probable unknown events will be, and thus an unknown event where the software does the wrong thing becomes not impossible, but very low probability.
My recent article on a future vision for public transit drew some ire by those who viewed it as anti-transit. Instead, the article broke with transit orthodoxy by suggesting that smaller vehicles (including cars and single person pods) might produce more efficient transit than big vehicles. Transitophiles love big vehicles for reasons beyond their potential efficiency, so it’s a hard sell.
Let’s look at the factors which determine what vehicle size makes the best transit.
Before the robocar future arrives, vehicle size is partly dominated by the need for drivers. Consider a bus route which could have one 40 person bus every 30 minutes or a 20 person bus every 15 minutes. The smaller vehicles have the same capacity, and but they will use a little more energy, a little more road space and cost somewhat more to buy. This leads to the intuition that bigger must be better.
At the same time the smaller vehicles need twice as many drivers. Labour is more than half the operating budget of many transit agencies. Look at the Chicago Transit Authority and you see labour listed as 69% — and much labour is actually in other subcontractor categories — while fuel and electricity are only 7% — the capital costs like vehicles are not even included here. Needing twice the drivers dominates the equation.
Riders of course would have an easy time deciding. They would of course love having vehicles every 15 minutes! Indeed they would be very pleased to get a 7 person van every 5 minutes if they could, the difference would be qualitative, not just quantitative, because when you get to that frequency you start thinking about it more like a car. In addition, the 2 small vehicles do about 1/8th the damage to the road as the one large vehicle.
Taking the cost of drivers out, what is the optimum size? More to the point, what provides the optimum balance between rider demand (which would love more frequent service in smaller vehicles) and efficiency (which pushes for larger vehicles, up to a point?) In particular, more smaller vehicles does not just have to mean more frequent service on one route, it can also mean more routes. More routes can both mean getting places you could not get to before, and also getting there faster because you don’t need as many transfers.
Here’s where big vehicles are better:
When near full, or overfull, they use:
Less energy per passenger-mile
Less road space per passenger
Less vehicle cost (depreciation, maintenance etc.) per passenger
Less frequent service forces people to bunch their travel together with others, allowing the advantages above.
Fewer stops also forces people to bunch together, to live near transit and to walk more.
Here are some of the advantages of more, smaller vehicles
As noted, road damage is roughly as the 4th power of vehicle weight per axle.
More frequent and/or ubiquitous service as described above
Less likely to be lightly loaded (smaller vehicle is sent when demand is light.)
When lightly loaded, much more efficient in all factors than large vehicle
While the whole fleet takes more total road space than the large vehicles, each vehicle causes much less obstruction of traffic.
Able to use smaller bus-stops and navigate tighter turns and narrower roads.
Able to park in smaller spaces including many lots for cars (though still taking as much or slightly more total space.)
Stops are sometimes fewer, and take less time (fewer people getting on/off any given vehicle.)
Each vehicle is considerably less expensive.
The big trade-off comes because the load varies. The full 40 person bus is an efficiency and cost win over two full 20 person buses (or 10 full 4 person cars) but not as much of a win as you might imagine. But the real question involves the frequent issue of a half-full 40 person bus vs. a full 20 person bus. In this case, the smaller vehicle is quite a bit more efficient. Even worse is the 1/4 full 10 person bus vs. the half full 20 person bus or 3 4-person cars. Here the winner is probably the cars, and this is important, because the average bus in the USA actually has just under 10 people on it.
The ideal situation would be to send out a fleet of 40 or even 60 person buses at the peak of rush hour, and then put those in garages, and send out small buses during the off-peak takes and just cars in the off-off-peak times like the night. Have every vehicle run as close to full as possible and you get your greatest efficiency. This is not an option for a few reasons:
To do that with buses, you must lower frequency to keep them full, and riders will reject that
Agencies usually can’t afford huge fleets of large vehicles as well as huge fleets of medium vehicles to keep the large vehicles idle for most of the day. They are better off choosing with a loss of efficiency.
In the robocar world, they will be able to call upon a large fleet of small vehicles (cars for 1-4 people) at all times and they won’t need to own them. But the transit companies and agencies still must own these larger (8 to 60) person vehicles.
In some cities, it may be practical to keep a fleet of large vehicles for use only at rush hour. In fact, that’s what some commuter train lines use, and they are the most efficient transportation lines in the USA. The rush-hour-only commuter trains run full out to the suburbs, spend the night in the suburbs and run full back into town. That’s really efficient. The commuter trains with daytime service are not nearly as good. Train lines that can drop cars off-peak get a win here as well.
How practical it is depends on how long you need the big bus to last. Transit vehicles tend to be robust, heavy and expensive, and they are well maintained to maximize their lifetime. A bus that only works rush hour will last more years than one that works all day. The problem is it may last too many years, to the point that it becomes obsolete or wears out from time rather than just miles. Leaving vehicles idle also means tying up capital for longer, so even if you find a good schedule for depreciation of the vehicles, the cost of money makes it difficult to have two or three different fleets.
So in the end, cities have to choose. Because of the labour cost of drivers, they almost always choose the bigger vehicles. Without that cost, the advantages of the smaller vehicles win out because of the variability of load. If the line regularly runs low-load vehicles, it has chosen a size that is larger than optimal.
This is all general analysis. The next step I would like to see from the transportation research community is to build these models with the actual numbers from real transit systems. For each city, for each route, the optimal size will be different. And of course, the existence of the robocars will change demand, which also changes load. They can change demand down (by being a superior solution) or up (by making it easier to get to the shared vehicle.) They can also replace the big vehicles entirely at off-peak times. That sounds like competition, but it actually can be enabling. One reason transit agencies run their big vehicles all day long (erasing their efficiency) is that riders want assurance they can come in at rush hour and then decide to leave early or late. Thus there has to be off-peak service. If riders can be assured that something else (like a robotic taxi or even an Uber) can get them home inexpensively off-peak, they are more willing to take the transit in.
Indeed, it could make sense for transit agencies to say, “we will have low service after 8pm, but if you can show you rode with us in the morning, we will subsidize a private car for you after hours 10 times a month.” They might actually save money by offering this rather than running a mostly empty bus.
Perhaps the world’s most exciting new technology today are deep neural networks, in
particular the convolutional neural networks such as “Deep Learning.” These networks
are conquering some of the most well known problems in artificial intelligence and pattern
matching, and since their development just a few years ago, milestones in AI have been
falling as computer systems that match or surpass human capability have been demonstrated. Playing Go
is just the most recent famous example.
This is particularly true in image recognition. Over the past several years, neural
network systems have gotten better than humans at problems like recognizing street
signs in camera images and even beating radiologists at identifying cancers in
These networks are having their effect on robocar development. They are allowing
significant progress in the use of vision systems for robotics and driving, making
those progress much faster than expected. 2 years ago, I declared that the time when
vision systems would be good enough to build a safe robocar without lidar was still
fairly far away. That day has not yet arrived, but it is definitely closer, and it’s
much harder to say it won’t be soon. At the same time, LIDAR and other sensors are
improving and dropping in price. Quanergy (to whom I am an advisor) plans to ship $250
8-line LIDARS this year, and $100 high resolution LIDARS in the next couple of years.
The deep neural networks are a primary tool of MobilEye, the Jerusalem company which
makes camera systems and machine-vision ASICs for the ADAS (Advanced Driver Assistance
Systems) market. This is the chip used in Tesla’s autopilot, and Tesla claims it has
done a great deal of its own custom development, while MobilEye claims the important magic sauce is still mostly them. NVIDIA has made a big
push into the robocar market by promoting their high end GPUs as the supercomputing tool
cars will need to run these networks well. The two companies disagree, of course, on
whether GPUs or ASCICs are the best tool for this — more on that later.
In comes comma.ai
In February, I rode in an experimental car that took this idea to the extreme. The small
startup comma.ai, lead by iPhone hacker George Hotz, got some press by building an autopilot
similar in capability to many others from car companies in a short amount of time. In January, I wrote an introduction to their approach
including how they used quick hacking of the car’s network bus to simplify having the computer control the car.
did it with CNNs, and almost entirely with CNNs. Their car feeds the images from a camera
into the network, and out from the network come commands to adjust the steering and speed to
keep a car in its lane. As such, there is very little traditional code in the system, just
the neural network and a bit of control logic.
Here’s a video of the car taking us for a drive:
The network is built instead by training it. They drive the car around, and the car learns
from the humans driving it what to do when it sees things in the field of view. To help
in this training, they also give the car a LIDAR which provides an accurate 3D scan of the
environment to more absolutely detect the presence of cars and other users of the road. By letting
the network know during training that “there is really something there at these coordinates,”
the network can learn how to tell the same thing from just the camera images. When it is
time to drive, the network does not get the LIDAR data, however it does produce outputs of
where it thinks the other cars are, allowing developers to test how well it is seeing things.
This approach is both interesting and frightening. This allows the development of a credible
autopilot, but at the same time, the developers have minimal information about how it works,
and never can truly understand why it is making the decisions it does. If it makes an
error, they will generally not know why it made the error, though they can give it more training
data until it no longer makes the error. (They can also replay all other scenarios for which
they have recorded data to make sure no new errors are made with the new training data.) read more »
Most of our focus these days is on self-driving personal cars. In spite of that focus, the effects on mass transit will also be quite dramatic, in ways far beyond taking the driver out of the bus. Indeed, for various reasons, I believe traditional approaches to mass transit (large vehicles on fixed routes and schedules, sometimes with private right-of-way) will be obsoleted by robocar technology, and that the result will be almost 100% good — transportation that is better, faster, more convenient and even more sustainable. (The latter shocks people, who think that anything with small vehicles is inherently less energy efficient.)
I have a new special article on Robocars.com outlining potential visions for the future of transit, and what they might mean. The vision is a work in progress, but I invite debate.
I frequently see people claim that one effect of robocars is that because we’ll share the cars (when they work as taxis) and most cars stay idle 95% of the time, that a lot fewer cars will be made — which is good news for everybody but the car industry. I did some analysis of why that’s not necessarily true and recent analysis shows the problem to be even more complex than I first laid out.
To summarize, in a world of robotic taxis, just like today’s taxis, they don’t wear out by the year any more, they wear out by the mile (or km.) Taxis in New York last about 5 years and about 250,000 miles, for example. Once cars wear out by the mile, the number of cars you need to build per year is equal to:
Total Vehicle Miles per year Avg Car Lifetime in Miles
As you can see, the simple equation does not involve how many people share the vehicle at all! As long as the car is used enough that the car isn’t junked before it wears out from miles, nothing changes. It’s never that simple, however, and some new factors come into play. The actual model is very complex with a lot of parameters — we don’t know enough to make a good prediction.
People travel more in cars.
It’s likely that the number of miles people want to travel goes up for a variety of reasons. Robocars make car travel much more pleasant and convenient. Some people might decide to live further from work now that they can work, read, socialize or even sleep on the commute. They might make all sorts of trips more often. Outside of rush hour, they might also be more likely to switch from other modes, such as public transit, and even flying. Consider two places about a 5 hour drive apart — today flying is going to take just under 3 hours due to all the hassles we’ve added to flying, even with the improvements robocars make to those hassles. Many might prefer an uninterrupted car ride where they can work, watch videos or sleep.
Vehicles run empty to reposition
Regular taxis have wasted miles between rides. Indeed, a New York taxi has no passenger 38% of the time. Fortunately, robocars will be a lot more efficient than that, since they don’t need to cruise around looking for rides. Research suggests a more modest 10% “empty mile” cost, but this will vary from situation to situation. If you need the robotaxi fleet to constantly run empty in the reverse commute direction, it could get worse. Among those who believe robocars will be more personally owned than used as taxis, we often see a story painted of how a household has a car that takes one person to work, and returns home empty to take the 2nd person, and then returns again to take others on daytime errands. This is possible, but pretty inefficient. I think it’s far more likely that in the long term, such families will just use other taxi services rather than have their car return home to serve another family member.
Cars last longer
The bottom part of the equation is likely to increase, which reduces the number of cars made. Today, cars are engineered for their expected life-cycle — 19 years and 190,000 miles in California, for example. Once you know your car is going to have a high duty cycle, you change how you engineer it. In particular, you combine engineering of parts for your new desired life cycle with specific replacement schedules for things that will wear out sooner. You want to avoid junking a car with lots of life in the engine just because the seats are worn out, so you make it easy to replace the seats, and you have the car bring itself to a service center where that’s fast and easy. read more »
General Motors has purchased “Cruise,” a small self-driving startup in San Francisco. Rumours suggest the price was over one billion dollars. In addition, other rumours have come to me suggesting that at least one other startup has been seeking a new round of funding at that valuation, but did not succeed due to the market downturn.
I gave Cruise some small assistance when they were getting started, and wrote about them when they showed off
their first prototype. Since then, Cruise, as expected, moved away from highway autopilot retrofit into making a proper robocar, and their test Leaf has been running around SF with 4 velodyne LIDARs and other sensors for a while.
Even in my wildest dreams, I did not imagine startup valuations this high, this soon. (Time to get my own startup going.) Let’s consider why:
First, GM, as the world’s 2nd largest car company, is way behind on robocars. They were one of the first companies to announce a highway autopilot (called, ironically, “Super Cruise”) for the 2014 Cadillac. However, they quickly pulled back on that announced, and for the last few years have continued to delay it, recently announcing it would not even appear in the 2017 car, even though Mercedes, Tesla and several other companies had products like that.
GM’s main academic partner was CMU. They sponsored Boss, the CMU team that won the Darpa Urban Challenge, headed by Chris Urmson (who now leads the Google car project.) Recently, Uber moved into Pittsburgh in a big way and poached many of the top people from CMU for their project. This left GM with very little, a poor position for the world’s 2nd largest car company.
Next, we have Kyle Vogt, founder of Cruise. Kyle was on the founding team for justin.tv, and also for Twitch, which had a billion dollar acquisition — in other words, Kyle is not precisely hurting for money. He has not confirmed this to me, but I suspect when GM showed up at his door, he was not interested in joining a big car company, and his resources meant he was not in any hurry. I then presume GM took that as negotiation and bumped the price to where you would have to be crazy to say no.
GM will let cruise be independent, at least for now. That’s the only sane path. We’ll see where this goes.
Reports released reveal that one of Google’s Gen-2 vehicles (the Lexus) has a fender-bender (with a bus) with some responsibility assigned to the system. This is the first crash of this type — all other impacts have been reported as fairly clearly the fault of the other driver.
This crash ties into an upcoming article I will be writing about driving in places where everybody violates the rules. I just landed from a trip to India, which is one of the strongest examples of this sort of road system, far more chaotic than California, but it got me thinking a bit more about the problems.
Google is thinking about them too. Google reports it just recently started experimenting with new behaviours, in this case when making a right turn on a red light off a major street where the right lane is extra wide. In that situation it has become common behaviour for cars to effectively create two lanes out of one, with a straight through group on the left, and right turners hugging the curb. The vehicle code would have there be only one lane, and the first person not turning would block everybody turning right, who would find it quite annoying. (In India, the lane markers are barely suggestions, and drivers — which consist of every width of vehicle you can imagine) — dynamically form their own patterns as needed.)
As such, Google wanted their car to be a good citizen and hug the right curb when doing a right turn. So they did, but found the way blocked by sandbags on a storm drain. So they had to “merge” back with the traffic in the left side of the lane. They did this when a bus was coming up on the left, and they made the assumption, as many would make, that the bus would yield and slow a bit to let them in. The bus did not, and the Google car hit it, but at very low speed. The Google car could have probably solved this with faster reflexes and a better read of the bus’ intent, and probably will in time, but more interesting is the question of what you expect of other drivers. The law doesn’t imagine this split lane or this “merge.” and of course the law doesn’t require people to slow down to let you in.
But driving in so many cities requires constantly expecting the other guy to slow down and let you in. (In places like Indonesia, the rules actually give the right-of-way to the guy who cuts you off, because you can see him and he can’t easily see you, so it’s your job to slow. Of course, robocars see in 360 degrees, so no car has a better view of the situation.)
While some people like to imagine that important ethical questions for robocars revolve around choosing who to kill in an accident, that’s actually an extremely rare event. The real ethical issues revolve around this issue of how to drive when driving involves routinely breaking the law — not once in a 100 lifetimes, but once every minute. Or once every second, as is the case in India. To solve this problem, we must come up with a resolution, and we must eventually get the law to accept it the same what it accepts it for all the humans out there, who are almost never ticketed for these infractions.
So why is this a good thing? Because Google is starting to work on problems like these, and you need to solve these problems to drive even in orderly places like California. And yes, you are going to have some mistakes, and some dings, on the way there, and that’s a good thing, not a bad thing. Mistakes in negotiating who yields to who are very unlikely to involve injury, as long as you don’t involve things smaller than cars (such as pedestrians.) Robocars will need to not always yield in a game of chicken or they can’t survive on the roads.
In this case, Google says it learned that big vehicles are much less likely to yield. In addition, it sounds like the vehicle’s confusion over the sandbags probably made the bus driver decide the vehicle was stuck. It’s still unclear to me why the car wasn’t able to abort its merge when it saw the bus was not going to yield, since the description has the car sideswiping the bus, not the other way around.
Nobody wants accidents — and some will play this accident as more than it is — but neither do we want so much caution that we never learn these lessons.
It’s also a good reminder that even Google, though it is the clear leader in the space, still has lots of work to do. A lot of people I talk to imagine that the tech problems have all been solved and all that’s left is getting legal and public acceptance. There is great progress being made, but nobody should expect these cars to be perfect today. That’s why they run with safety drivers, and did even before the law demanded it. This time the safety driver also decided the bus would yield and so let the car try its merge. But expect more of this as time goes forward. Their current record is not as good as a human, though I would be curious what the accident rate is for student drivers overseen by a driving instructor, which is roughly parallel to the safety driver approach. This is Google’s first caused accident in around 1.5M miles.
It’s worth noting that sometimes humans solve this problem by making eye contact, to know if the other car has seen you. Turns out that robots can do that as well, because the human eye flashes brightly in the red and infrared when looking directly at you — the “red eye” effect of small flash cameras. And there are ways that cars could signal to other drivers, “I see you too” but in reality any robocar should always be seeing all other parties on the road, and this would just be a comfort signal. A little harder to read would be gestures which show intent, like nodding, or waving. These can be seen, though not as easily with LIDAR. It’s better not to need them.