I’m in the Detroit area for the annual TRB/AUVSI Automated Vehicle Symposium, which starts tomorrow. Today, those in Ann Arbor attended the opening of the new test track at the University of Michigan. Instead, I was at a small event with a lot of good folks in downtown Detroit, sponsored by SAFE which is looking to wean the USA off oil.
Much was discussed, but a particularly interesting idea was just how close we are getting to something I had put further in the future — robocars that are cheaper than ordinary cars.
Most public discussion of robocars has depicted them as costing much more than regular cars. That’s because the cars built to date have been standard cars modified by placing expensive computers and sensors on them. Many cars use the $75,000 Velodyne Lidar and the similarly priced Applanix IMU/GPS, and most forecasts and polls have imagined the first self-driving cars as essentially a Mercedes with $10,000 added to the price tag to make it self driving. After all, that’s how things like Adaptive Cruise Control and the like are sold.
Google is showing us an interesting vision with their 3rd generation buggy-style car. That car has no steering wheel, brakes or gas pedal, and it is electric and small. It’s a car aimed at “Mobility on Demand.”
When people have asked me “how much extra will these cars cost,” my usual answer has been that while the cars might cost more, they will be available for use by the mile, where they can cost less per mile than owning a car does today — ie. that overall it will be cheaper. That’s in part because of the savings from sharing, and having vehicles go more miles in their lifetime. More miles in the life of a car at the same cost means a lower cost per mile, even if the car costs a little more.
The sensors cost money, but that cost is already in serious decline. We’re just a few years away from $250 Lidars and even cheaper radar. Cameras are already cheap, and there are super cheap IMUs and GPSs already getting near the quality we need. Computers of course get cheaper every year.
This means we are not too far when the cost of the sensors is less than the money saved by what you take out of the car. After all, having a steering wheel, gas and brakes costs money. Side mirrors cost money (ever had to replace them?) That fancy dashboard with all its displays and controls costs a lot of money, but almost everything it does in a robocar can be done by your tablet.
That said, you need a few extra things in your robocar. You need two steering motors and two braking systems. You need some more short range sensors and a cell phone radio. But there’s even more you can save, especially with time.
Because mobility on demand means you can make cars that are never used for anything but short urban trips (the majority of trips, as it turns out) you can save a lot more money on those cars. These cars need not be large or fast. They don’t need acceleration. They won’t ever go on the highway so they don’t need to be safe at 60mph. Electric drive, as we discussed earlier, is great for these cars, and electric cars have far fewer parts than gasoline ones. Today, their batteries are too expensive, but everything else in the car is cheaper, so if you solve the battery cost using the methods I outlined Saturday we’re saving serious money. And small one or two person cars are inherently cheaper to boot.
Of course, you need to make highway cars, and long-range 4WD SUVs to take people skiing. But these only need be a fraction of the cars, and people who use a mix of cars will see a big saving.
For a long time, we’ve talked about some day also removing many of the expensive safety systems from cars. When the roads become filled with robocars, you can start talking about having so few accidents you don’t need all the safety systems, or the 1/3 of vehicle weight that is attributable to passive safety. That day is still far away, though cars like the Edison2 Very-Light-Car have done amazing things even while meeting today’s crash tests. Companies like Zoox and other startups have pushed visions of completely redesigned cars, some of them at lower cost for a while. But this seems like it might become true sooner rather than later.
Evacuation in a hurricane
One participant asked how, if we only had 1/9th as many cars (as some people forecast, I suspect it’s closer to 1/4) we would evacuate sections of Florida or similar places when a hurricane is coming. I think the answer is a very positive one — simply enforce car pooling / ride sharing in the evacuation. While there is not a lot I think policymakers should do at this time, some simple mandates could help a lot in this arena. While people would not be able to haul as much personal property, it is very likely there would be more than enough seats available in robocars to evacuate a large population quickly if you fill all the seats in cars going out. Further, those cars can go back in to get more people if need be.
Filling those seats would actually get everybody out faster, because there would be far less traffic congestion and the roads would carry far more people per hour. In fact, that’s such a good idea it could even be implemented today. When there’s an evacuation, require all to use an app to register when they are almost ready to leave. If you have spare seats, you could not leave (within reason) until you picked up neighbours and filled the seats. With super-carpooling, everybody would get out very fast on much less congested roads. Those crossing the checkpoint on the way out with empty seats would be photographed and ticketed unless the app allowed them to leave like that, or the app records that it tried to reach the server and failed, or other mitigating circumstances. (This is all hours before the storm, of course, before there is panic, when people will do whatever they can.) Some storms might be so bad the cars are at risk. In that case, if the road capacity is enough, people could move out all the cars too, to protect them. But in most cases, it’s the people that are the priority.
We know electric cars are getting better and likely to get popular even when driven by humans. Tesla, at its core, is a battery technology company as much as it’s a car company, and it is sometimes joked that the $85,000 Telsa with a $40,000 battery is like buying a battery with a car wrapped around it. (It’s also said that it’s a computer with a car wrapped around it, but that’s a better description of a robocar.)
Tesla did a lot of work on building cooling systems for standard cylinder Lithium-Ion cells and was able to make a high performance vehicle. The Model S also by default charges to only 80% of capacity because battery life is hurt by charging all the way to full. In fact, charging to 3.92 volts (about 60%) capacity is the sweet spot. Some of the other things that reduce battery life include:
Discharging too close to empty
Getting too warm while discharging
Getting too warm while charging, and in particular causing thermal expansion which creates physical damage
Even ordinary warmth, where the vehicle is stored for long periods, particularly at high charge, is dangerous. The closer to freezing the better, and even above 25 degrees centigrade causes some loss.
The important, but little reported statistic for a battery is the total watt-hours you will be able to get out of it during its usable lifetime. This tells you the lifetime of the battery in miles, and the cost tells you the cost per mile. How important is this? If the Tesla $40,000 battery lasts you 150,000 miles and sells for $10,000 when done, the straight-line cost per mile is 20 cents/mile — more than the cost of gasoline in most cars, and much more than the 3 cent/mile or less cost of electricity.
Humans will drive as humans want to drive, and it’s hard to change that. They will accelerate for both fun and to get ahead of other cars. They will take mixes of short trips and long trips. They don’t know how long their trips are and demand a flexible vehicle always ready for anything.
Electric robotaxis change that game. They will drive predictably, rarely ever demanding quick acceleration. A driver likes zippy fun, a passenger wants a gentle ride. They can go even further, and set their driving pattern based on the temperature of their batteries. Are we making the batteries too warm? Then “cool off,” literally. This applies both to fast starts and also slowing down. Regenerative braking conserves energy and increases range, but doing it too hard heats the batteries. Start slowing down sooner — especially if you have data on what traffic lights and traffic are doing and it can make a big difference.
Robotaxis can always use the sweet spot of the battery charge duty cycle.
You will rarely be sent a robotaxi that, in order to get you, needs to dig deep into its maximum range.
Often demand is predictable, so if need be, vehicles can be charged above 60% only when such demand is expected or is arising.
While robotaxis will prefer to charge at night when power is cheapest, they can charge any time to get back up to the optimal level
As I’ve noted before, battery swap doesn’t work well for humans, but robots don’t mind making an appointment or driving out of their way for a swap. This makes it easy to use batteries only in the sweet spot, and to charge them only at night on cheap power.
If battery swap is not an option, there are many options to supplement range during peak demand. Vehicles can go to depots to pick up trunk batteries, battery trailers, or even slot-in units with small motorcycle engines and liquid fuel tanks. If this is cheaper than the alternatives, it’s an option.
When it gets hot, robotaxis can seek out the shade, or even places with cooling, to keep the batteries from being too warm.
Robotaxis don’t mind the loss of range all that much
As a battery ages, its capacity drops. Humans hate that — having bought a car with a 100 mile range they won’t accept it can now only do 60. For a human, that means time to replace the battery. For a robotaxi, that just means you have a shorter range, and you don’t get sent on long range trips. Or you may decide that while before, you only charged to 60% to get maximum battery life, now you charge more, knowing it will eat the remaining life, but getting the most out of the battery.
Of course, as the range drops, now you run into another problem. You’re carrying around the extra weight of battery for half the range, and it’s costing you energy to do that, especially in an ultralight car where the battery is the biggest component of the weight. (This also enters into the math of whether it makes sense to charge only to 60%.) Eventually the time comes that the battery is not practical. This is the time to sell it. Tesla and others are working to produce a home and grid storage market for used car batteries. In those applications, the weight doesn’t matter, just the cost for the remaining lifetime watt-hours. You care about the capacity, but you pay a market price for it.
Eventually, even this is not practical and you scrap to recycle the materials.
Typical predictions for Lithium-Ion run from 500 to 1,000 cycles. Tesla’s techniques seem to be beating that. With robotaxis, who knows just how many lifetime kwh we’ll be able to get out of these batteries, or perhaps even other chemistries. Turns out that human drivers like a chemistry that keeps its life as long as possible then falls off a cliff. Slow decline is harder to sell — but slow decline chemistries, like Lithium Iron Phosphate and others could make more sense for the robots that don’t care.
It’s often suggested that electric cars could be used as grid storage. Problem is, with car batteries today, it costs around 15 cents to put a kwh into a battery and get it out. That means to be grid storage, you need to have the spot price on the grid be the price you bought at, plus 15 cents, plus a margin to make it worth this. Night power can get as low as 6 cents, so this does happen, but not as much as one might hope. The problem is that the grid’s peak demand is around 4 to 7pm, which is also a peak time for driving. That’s the last time most car owners will want to drain off their batteries to make a bit of money on the power. You will only do that if you know you won’t be using the car. For a robotaxi fleet, that might be the case. Of course, selling power to the grid you will do it only at a rate that does not harm your battery or warm it up too much.
When the grid gets to a super peak, the price can really spike to attractive numbers. That’s because building extra power plant capacity just for those rare days is expensive, and so almost any price is better. Here we could talk about cars as storage, when we know their batteries are not going to be used. That’s even more true of batteries sitting in a battery swap facility.
The situation described, one car cutting off another, was a very unlikely one for several reasons:
All these cars are operated by trained safety drivers who are expected to be vigilant and take control at any sign of trouble.
In particular, special moves like a lane change would get extra vigilance. If something unusual happened (such as 2 cars going for the same spot) the safety drivers would be watching in advance, tracking what the car was doing, and pull back if the car’s own displays were not telling them it was going to do the right thing.
The safety drivers are not perfect of course, but an autonomous lane change is a rare event and one that most people are still just testing, so they would be very unlikely to miss that the car was going to cut somebody else off.
Of course, situations will arise when two cars try to change into the same spot at the same time, and robocars will probably be fairly timid in these situations. The most likely situation if two robocars tried to take the same spot would be that both would back off and return to their original lane, and it will probably be that way until being so timid is not a workable strategy.
Robocars won’t be the lane-changing demons that some people (including myself sometimes are.) Many human drivers are constantly trying to find the fastest lane and we weave, often finding the lane we move into seems to become the slowest. Part of that is our psychology.
Robocars won’t do this as much because their passengers will be occupied doing other things, and in most cases will not be in a super hurry. Those passengers will prefer a stable ride where they can get work done to a weaving ride with extra starts and stops. If we’re in a big hurry, we might ask the car to try to work extra hard to make the fastest trip but this will be the exception.
When we do want that, the robocar will actually have a very nice model of just how fast each lane is moving. It won’t be fooled the way we are by seeing some lanes that seem to be faster when in fact neither lane is winning by that much. If they read licence plates to identify cars, they will get excellent appraisals of what’s going on. If one lane is truly faster they will find it. On the other hand, they will be worse at the standard game of chicken needed to change lanes in heavy traffic, where you depend on the car you are moving in front of to slow down. They will know the physics though, and if a lane change is needed, they will warn the passengers of high acceleration and perfectly make a smaller spot than you might be able to make.
In other news, Google has sent two cars to Austin, Texas to expand their testing ground. I don’t have a particular insight on why they selected Austin — I know that many towns and states regularly contact Google in the hope they might bring some cars to their area, though Texas has no modified laws yet.
I’ve written a few times about the work of Vislab in Parma, Italy. They have a focus on doing self-driving with machine vision, and did a famous cross-continent trek from Italy to Shanghai a few years ago, using a lead car to map the way and a following car self-driving, mostly with vision.
This lab was spun out of its university but now has been [acquired by Ambarella], a company that specializes in video compression chips. One can see why Ambarella would want a computer vision lab — but it seems this might spell the end of their self-driving efforts, unless they are spun out.
A new paper is out in Nature Climate Change on the potential for robocars to reduce emissions, inspired by some of my research in this area. Sadly, it’s behind a paywall, but the author will give a talk at Nissan’s lab in Silicon Valley on July 15th at our local self-driving car meetup.
A reader recently asked about the synergies between robocars and ultracapacitors/supercapacitors. It turns out they are not what you would expect, and it teaches some of the surprising lessons of robocars.
Ultracaps are electrical storage devices, like batteries, which can be charged and discharged very, very quickly. That makes them interesting for electric cars, because slow charging is the bane of electric cars. They also tend to support a very large number of charge and discharge cycles — they don’t wear out the way batteries do. Where you might get 1,000 or so cycles from a good battery, you could see several tens of thousands from an ultracap.
Today, ultracaps cost a lot more than batteries. LIon batteries (like in the Tesla and almost everything else) are at $500/kwh of capacity and falling fast — some forecast it will be $200 in just a few years, and it’s already cheaper in the Tesla. Ultracaps are $2,500 to $5,000 per kwh, though people are working to shrink that.
They are also bigger and heavier. They are cited as just 10 wh/kg and on their way to 20 wh/kg. That’s really heavy — LIon are an order of magnitude better at 120 wh/kg and also improving.
So with the Ultracap, you are paying a lot of money and a lot of weight to get a super-fast recharge. It’s so much money that you could never justify it if not for the huge number of cycles. That’s because there are two big money numbers on a battery — the $/kwh of capacity — which means range — and the lifetime $/kwh, which affects your economics. Lifetime $/kwh is actually quite important but mostly ignored because people are so focused on range. An ultracap, at 5x the cost but 10x or 20x the cycles actually wins out on lifetime $/kwh. That means that while it will be short range, if you have a vehicle which is doing tons of short trips between places it can quickly recharge, the ultracap can win on lifetime cost, and on wasted recharging time, since it can recharge in seconds, not hours. That’s why one potential application is the shuttle bus, which goes a mile between stops and recharges in a short time at every stop.
How do robocars change the equation? In some ways it’s positive, but mostly it’s not.
Robocars don’t mind going out of their way to charge, at least not too far out of their way. Humans hate this. So you don’t need to place charging stations conveniently, and you can have a smaller number of them.
Robocars don’t care how long it takes to charge. The only issue is they are not available for service while charging. Humans on the other hand won’t tolerate much wait at all.
Robocars will eventually often be small single-person vehicles with very low weight compared to today’s cars. In fact, most of their weight might be battery if they are electric.
Users don’t care about the power train of a taxi or its energy source. Only the fleet manager cares, and the fleet manager is all about cost and efficiency and almost nothing else.
Now we see the bad news for the ultracap. It’s main advantage is the fast recharge time. Robots don’t care about that much at all. Instead, the fleet manager does care about the downtime, but the cost of the downtime is not that high. You need more vehicles the more downtime you have during peak loads, but as vehicles are wearing out by the km, not the year, the only costs for having more vehicles are the interest rate and the storage (parking) cost.
The interest cost is very low today. Consider a $20,000 vehicle. At 3%, you’re paying $1.60 per day in interest. So 4 hours of recharge downtime (only at peak times when you need every vehicle) doesn’t cost very much, certainly not as much as the extra cost of an ultracap. The cost of parking is actually much more, but will be quite low in the beginning because these vehicles can park wherever they can get the best rate and the best rate is usually zero somewhere not too far away. That may change in time, to around $2/day for surface parking of mini-vehicles, but free for now in most places.
In addition to the high cost, the ultracap comes with two other big downsides. The first is the weight and bulk. Especially when a vehicle is small and is mostly battery, adding 200kg of battery actually backfires, and you get diminishing returns on adding more in such vehicles. The other big downside is the short range. Even with the fast recharge time, you would have to limit these vehicles to doing only short cab hops in urban spaces of just a few miles, sending them off after just a few rides to get a recharge.
A third disadvantage is you need a special charging station to quick charge an ultracap. While level 2 electric car charging stations are in the 7-10kw range, and rapid chargers are in the 50kw-100kw range, ultracap chargers want to be in the megawatt or more range, and that’s a much more serious proposition, and a lot more work to build them.
Finally, while ultracaps don’t wear out very fast, they might still depreciate quickly the same way your computer does — because the technology keeps improving. So while your ultracap might last 20 years, you won’t want it any more compared to the cheaper, lighter, higher capacity one you can buy in the future. It can still work somewhere, like grid storage, but probably not in your car.
The fact that robocars don’t need fast refueling in convenient locations opens up all sorts of energy options. Natural gas, hydrogen, special biofuels and electricity all become practical even with gasoline’s 100 year headstart when it comes to deployment and infrastructure, and even sometimes in competition with gasoline’s incredible convenience and energy density. But what the robocar brings is not always a boon to every different form of energy storage.
One technique that makes sense for robocars (and taxis) is battery swap. Battery swap was a big failure for human driven cars, for reasons I have outlined in other posts. But robocars and taxis don’t mind coming back to a central station, or even making an appointment for a very specific time to do their swap. They don’t even mind waiting for other cars to get their swaps, and can put themselves into the swap station when told to — very precisely if needed. Here it’s a question of whether it’s cheaper to swap or just pay the interest and parking on more cars.
Ultracaps are also used to help with regenerative braking, since they can soak up power from hard regenerative braking faster than batteries. That’s mostly not a robocar issue, though in general robocars will brake less hard and accelerate less quickly — trying to give a smooth ride to their passengers rather than an exciting one — so this has less importance there too.
Still, for convenience, the first robocars will probably be gasoline and electric.
2 months mostly on the road, so here’s a roundup of the “real” news stories in the field.
Google begins PR campaign and talks about accidents
As the world’s most famous company, Google doesn’t need to seek press and the Chauffeur project has kept fairly quiet, but it just opened a new web site which will feature monthly reports on the status of the project. The first report gives details of all the accidents in the project’s history, which we discussed earlier. A new one just took place in the last month, but like the others, it did not involve the self-driving software. Google’s cars continue to not cause any accidents, though they have been at the receiving end of a modestly high number of impacts, perhaps because they are a bit unusual.
The zero at-fault accident number is both impressive, and possibly involves a bit of luck. Perhaps it even raises unrealistic expectations of perfection, because I believe there will be at-fault accidents in the future for both Google and other teams. Most teams, when they were first building their vehicles, had minor accidents where cars hit curbs or obstacles on test tracks, but the track records of almost all teams since then are surprisingly good. One way that’s not luck, of course, is the presence of safety drivers ready to take the controls if something goes wrong. They are trained and experienced, though some day, being human, some of them will make mistakes.
Baidu to build a prototype
In November I gave a “Big Talk” for Baidu in Beijing on cars. Perhaps there is something about search engines because they have made announcements about their own project. Like Google, Baidu has expertise in mapping and various AI techniques, including the advice of Andrew Ng, whose career holds many parallels to that of Sebastian Thrun who started Google’s project. (Though based on my brief conversations with Andrew I don’t think he’s directly involved.)
Virginia opens test roads
The state of Virginia has designated 70 miles of roads for robocar testing. That’s a good start for testing by those working in that state, but it skirts what to me is a dangerous idea — the thought that there would be “special” roads for robocars designated by states or road authorities. The fantastic lesson of the Darpa grand challenges was the idea that the infrastructure remains stupid and the car becomes smart, so that the car can go anywhere once its builders are satisfied it can handle that road. So it’s OK to test on a limited set of roads but it’s also vital to test in as many situations as you can, so you need to get off that set of roads as soon as you can.
Zoox startup un-stealthed
Zoox is probably the first funded startup working on a real, fully automated robocar. They were recently funded by DFJ ventures and set up shop in rented space at the SLAC linear accelerator lab. Zoox was begun by Tim Kentley-Klay, a designer and entrepreneur from Australia, and he later joined forces with Jesse Levinson, a top researcher from Stanford’s self-driving car projects.
I’ve known about Zoox since it begain and had many discussions. They first got some attention a while back with Tim’s designs, which are quite different from typical car designs, and presume a fully functional robocar — the designs feature no controls for the humans, and don’t even have a windshield to see forward in some cases. (Indeed, they don’t have a “forward” since an essential part of the design is to be symmetrical and move equally well in both directions, avoiding the need for some twists and turns.) I like many elements of the Zoox vision, though in fact I think it is even more ambitious than Google’s, at least from a car design standpoint, which is quite audacious in a world where most of the players think Google is going too far.
You can see details in this report on Zoox from IEEE. I haven’t reported on Zoox under FrieNDA courtesy — in fact the early consultations with “Singularity University” described in the article are actually discussions with me.
Zoox is not the first small startup. Kyle Haight’s “Cruise” has been at it a while aiming at a much less ambitious supervised product, and truck platooning company Peleton has even simpler goals, but expect to see more startups enter the fray and fight with the big boys in the year to come.
Mercedes E Class
Speaking of supervised cruising, the report is that the 2016 Mercedes E Class will offer highway speed cruising in the USA. This has been on offer in Europe in the past. As I wrote earlier, I am less enthused about supervised cruising products and think they will not do tremendously well. Tesla’s update to offer the same in their cars will probably get the most attention.
In addition, many stories reported that Tesla had “solved” the liability problem of robocars through the design of their lane change system. In their system (and in several other discussed designs — they did not come up with this) the car won’t change lanes until the human signals it is OK to do so, usually by something like hitting the turn signal indicator. The Tesla plan is for a supervised car, and in a supervised car all liability is already supposed to go to the human supervisor.
Changing lanes safely is surprisingly challenging, because there is always the chance somebody is zooming up behind you at a rather rapid rate of speed. That’s common merging into a carpool lane, or on German autobahn trips. Most supervised cars have only forward sensing, but to change lanes safely you need to notice a car coming up fast from behind you, and you need to see it quite a distance away. This requires special sensors, such as rear radars, which most cars don’t have. So the solution of having the human check the mirrors works well for now.
More and more stories keep getting excited by “connected car” technology, in particular V2V communications using DSRC. They even write that these technologies are essential for robocars, and it gets scary when people like the transportation secretary say this. I wish the press covering this would take the simple step of asking the top teams who are working on robocars whether they plan to depend on, or even make early use of vehicle to vehicle communications. They will find out the answers will range form “no, not really” to a few vague instances of “yes, someday” from car companies who made corporate support commitments to V2V. The engineers don’t actually think they will find the technology crucial. The fact that the people actually building robocars have only a mild interest, if any, in V2V, while the people who staked their careers on V2V insist it’s essential should maybe suggest to the press that the truth is not quite what they are told.
This weekend I went to Pomona, CA for the 2015 DARPA Robotics Challenge which had robots (mostly humanoid) compete at a variety of disaster response and assistance tasks. This contest, a successor of sorts to the original DARPA Grand Challenge which changed the world by giving us robocars, got a fair bit of press, but a lot of it was around this video showing various robots falling down when doing the course:
What you don’t hear in this video are the cries of sympathy from the crowd of thousands watching — akin to when a figure skater might fall down — or the cheers as each robot would complete a simple task to get a point. These cheers and sympathies were not just for the human team members, but in an anthropomorphic way for the robots themselves. Most of the public reaction to this video included declarations that one need not be too afraid of our future robot overlords just yet. It’s probably better to watch the DARPA official video which has a little audience reaction.
Don’t be fooled as well by the lesser-known fact that there was a lot of remote human tele-operation involved in the running of the course.
What you also don’t see in this video is just how very far the robots have come since the first round of trials in December 2013. During those trials the amount of remote human operation was very high, and there weren’t a lot of great fall videos because the robots had tethers that would catch them if they fell. (These robots are heavy and many took serious damage when falling, so almost all testing is done with a crane, hoist or tether able to catch the robot during the many falls which do occur.)
We aren’t yet anywhere close to having robots that could do tasks like these autonomously, so for now the research is in making robots that can do tasks with more and more autonomy with higher level decisions made by remote humans. The tasks in the contest were:
Starting in a car, drive it down a simple course with a few turns and park it by a door.
Get out of the car — one of the harder tasks as it turns out, and one that demanded a more humanoid form
Go to a door and open it
Walk through the door into a room
In the room, go up to a valve with circular handle and turn it 360 degrees
Pick up a power drill, and use it to cut a large enough hole in a sheet of drywall
Perform a surprise task — in this case throwing a lever on day one, and on day 2 unplugging a power cord and plugging it into another socket
Either walk over a field of cinder blocks, or roll through a field of light debris
Climb a set of stairs
The robots have an hour to do this, so they are often extremely slow, and yet to the surprise of most, the audience — a crowd of thousands and thousands more online — watched with fascination and cheering. Even when robots would take a step once a minute, or pause at a task for several minutes, or would get into a problem and spend 10 minutes getting fixed by humans as a penalty. read more »
Some headlines (I’ve been on the road and will have more to say soon.)
Google announces it will put new generation buggies on city streets
Google has done over 2.7 million km of testing with their existing fleet, they announced. Now, they will be putting their small “buggy” vehicle onto real streets in Mountain View. The cars will stick to slower streets and are NEVs that only go 25mph.
While this vehicle is designed for fully automatic operation, during the testing phase, as required, it will have a temporary set of controls for the safety driver to use in case of any problem. Google’s buggy, which still has no official name, has been built in a small fleet and has been operating on test tracks up to this point. Now it will need to operate among other road users and pedestrians.
Accidents with, but not caused by self-driving cars cause press tizzy.
The press were terribly excited when reports filed with the State of California indicated that there had been 4 accidents reported — 3 for Google and 1 for Delphi. Google reported a total of 11 accidents in 6 years of testing and over 1.5 million miles.
Headlines spoke loudly about the cars being in accidents, but buried in the copy was the fact that none of the accidents by any company were the fault of the software. Several took place during human driving, and the rest were accidents that were clearly the fault of the other party, such as being rear ended or hit while stopped.
Still, some of the smarter press noticed, this is a higher rate of being in an accident than normal, in fact almost double — human drivers are in an accident about every 250,000 miles and so should have had only 6.
The answer may be that these vehicles are unusual and have “self driving car” written on them. They may be distracting other drivers, making it more likely those drivers will make a mistake. In addition, many people have told me of their thoughts when they encountered a Google car on the road. “I thought about going in front of it and braking to see what it would do,” I’ve been told by many. Aside from the fact that this is risky and dickish, and would just cause the safety drivers to immediately disengage and take over, in reality they all also said they didn’t do it, and experience in the cars shows that it’s very rare for other drivers to actually try to “test” the car.
But perhaps some people who think about it do distract themselves and end up in an accident. That’s not good, but it’s also something that should go away as the novelty of the cars decreases.
Mercedes and Freightliner test in Nevada
There was also lots of press about a combined project of Mercedes/Daimler and Freightliner to test a self-driving truck in Nevada. There is no reason that we won’t eventually have self-driving trucks, of course, and there are direct economic benefits for trucking fleets to not require drivers.
Self-driving trucks are not new off the road. In fact the first commercial self-driving vehicles were mining trucks at the Rio Tinto mine in Australia. Small startup Peleton is producing a system to let truckers convoy, with the rear driver able to go hands-free. Putting them on regular roads is a big step, but it opens some difficult questions.
First, it is not wise to do this early on. Systems will not be perfect, and there will be accidents. You want your first accidents to be with something like Google’s buggy or a Prius, not with an 18-wheel semi-truck. “Your first is your worst” with software and so your first should be small and light.
Secondly, this truck opens up the jobs question much more than other vehicles, where the main goal is to replace amateur drivers, not professionals. Yes, cab drivers will slowly fade out of existence as the decades pass, but nobody grows up wanting to be a cab driver — it’s a job you fall into for a short time because it’s quick and easy work that doesn’t need much training. While other people build robots to replace workers, the developers of self-driving cars are mostly working on saving lives and increasing convenience.
Many jobs have been changed by automation, of course, and this will keep happening, and it will happen faster. Truck drivers are just one group that will face this, and they are not the first. On the other hand, the reality of robot job replacement is that while it has happened at a grand scale, there are more people working today than ever. People move to other jobs, and they will continue to do so. This may not be much satisfaction for those who will need to go through this task, but the other benefits of robocars are so large that it’s hard to imagine delaying them because of this. Jobs are important, but lives are even more important.
It’s also worth noting that today there is a large shortage of truck drivers, and as such the early robotic trucks will not be taking any jobs.
I’m more interested in tiny delivery “trucks” which I call “deliverbots.” For long haul, having large shared cargo vehicles makes sense, but for delivery, it can be better to have a small robot do the job and make it direct and personal.
The world of sensors continues to grow. This wideband software based radar from a student team won a prize. It claims to produce a 3D image. Today’s automotive radars have long range but very low resolution. High resolution radar could replace lidar if it gets enough resolution. Radar sees further, and sees through fog, and gives you a speed value, and LIDAR falls short in those areas.
Also noteworthy is this article on getting centimeter GPS accuracy with COTS GPS equipment. They claim to be able to eliminate a lot of multipath through random movements of the antennas. If true, it could be a huge localization breakthrough. GPS just isn’t good enough for robocar positioning. Aside from the fact it goes away in some locations like tunnels, and even though modern techniques can get sub-cm accuracy, it you want to position your robocar with it, and it alone, you need it to essentially never fail. But it does.
That said, most other localization systems, including map and image based localization, benefit from getting good GPS data to keep them reliable. The two systems together work very well, and making either one better helps.
Transportation Secretary Fox advances DoT plan
Secretary Fox has been out writing articles and Speaking in Silicon Valley about their Beyond Traffic effort. They promise big promotion of robocars which is good. Sadly, they also keep promoting the false idea that vehicle to vehicle communications are valuable and will play a significant role in the development of robocars. In my view, many inside the DoT staked their careers on V2V, and so feel required to promote it, even though it has minimal compelling applications and may actually be rejected entirely by the robocar community because of security issues.
This debate is going to continue for a while, it seems.
Maps, maps, maps
Nokia has put its “Here” map division up for sale, and a large part of the attention seems to related to their HD Maps project, aimed at making maps for self-driving. (HERE published a short interview with me on the value of these maps.
It will be interesting to see how much money that commands. At the same time, TomTom, the 3rd mapping company, has announced it will begin making maps for self-driving cars — a decision they made in part because of encouragement from yours truly.
Uber dwarfs taxis
Many who thought Uber’s valuation is crazy came to that conclusion because they looked at the size of the Taxi industry. To the surprise of nobody who has followed Uber, they recently revealed that in San Francisco, their birthplace, they are now 3 times the size of the old taxi industry, and growing. It was entirely the wrong comparison to make. The same is true of robocars. They won’t just match what Uber does, they will change the world.
There’s more news to come, during a brief visit to home, but I’m off to play in Peoria, and then Africa next week!
Earlier this week I was sent some advance research from the U of Michigan about car sickness rates for car passengers. I found the research of interest, but wish it had covered some questions I think are more important, such as how carsickness is changed by potentially new types of car seating, such as face to face or along the side.
To my surprise, there was a huge rush of press coverage of the study, which concluded that 6 to 12% of car passengers get a bit queasy, especially when looking down in order to read or work. While it was worthwhile to work up those numbers, the overall revelation was in the “Duh” category for me, I guess because it happens to me on some roads and I presumed it was fairly common.
Oddly, most of the press was of the “this is going to be a barrier to self-driving cars” sort, while my reaction was, “wow, that happens to fewer people than I thought!”
Having always known this, I am interested in the statistics, but to me the much more interesting question is, “what can be done about it?”
For those who don’t like to face backwards, the fact that so many are not bothered is a good sign — just switch seats.
Some activities are clearly better than others. While staring down at your phone or computer in your lap is bad during turns and bumps, it may be that staring up at a screen watching a video, with your peripheral vision very connected to the environment, is a choice that reduces the stress.
I also am interested in studying if there can be clues to help people reduce sickness. For example, the car will know of upcoming turns, and probably even upcoming bumps. It could issue tones to give you subtle clues as to what’s coming, and when it might be time to pause and look up. It might even be the case that audio clues could substitute for visual clues in our plastic brains.
The car, of course, should drive as gently as it can, and because the software does not need a tight suspension to feel the road, the ride can be smoother as well.
Another interesting thing to test would be having your tablet or phone deliberately tilt its display to give you the illusion you are looking at the fixed world when you look at it, or to have a little “window” that shows you a real world level so your eyes and inner ears can find something to agree on.
More advanced would be a passenger pod on hydraulic struts able to tilt with several degrees of freedom to counter the turns and bumps, and make them always be such that the forces go up and down, never side to side. With proper banking and tilting, you could go through a roundabout (often quite disconcerting when staring down) but only feel yourself get lighter and heavier.
Most of the robocar press this week has been about the Delphi drive from San Francisco to New York, which completed yesterday. Congratulations to the team. Few teams have tried to do such a long course and so many different roads. (While Google has over a million miles logged in their testing by now, it’s not been reported that they have done 3,500 distinct roads; most testing is done around Google HQ.)
The team reported the vehicle drove 99% of the time. This is both an impressive and unimpressive number, and understanding that is key to understanding the difficulty of the robocar problem.
One of the earliest pioneers, Ernst Dickmanns did a long highway drive 20 years ago, in 1995. He reported the system drove 95% of the time, kicking out every 10km or so. This was a system simply finding the edge of the road, and keeping in the lane by tracking that. Delphi’s car is much more sophisticated, with a very impressive array of sensors — 10 radars, 6 lidars and more, and it has much more sophisticated software.
99% is not 4% better than 95%, it’s 5 times better, because the real number is the fraction of road it could not drive. And from 99%, we need to get something like 10,000 times better — to 99.9999% of the time, to even start talking about a real full-auto robocar. Because in the USA we drive 3 trillion miles per year, taking about 60 billion hours, a little over half of it on the highway. 99.9999% for all cars would mean still too many accidents if 1 time in a million you encountered something and could not handle it.
However, this depends on what we mean by “being unable to handle it.”
If not handling means “has a fatal accident” that could map to 3,600,000 of those, which would be 100x the human rate and not acceptable.
If not handling it means “has any sort of accident” then we’re pretty good, about 1/4th of the rate of human accidents
If not handling it means that the vehicle knows certain roads are a problem, and diverts around them or requests human assistance, it’s no big problem at all.
Likewise if not handling it means identifying a trouble situation, and slowing down and pulling off the road, or even just plain stopping in the middle of the road — which is not perfectly safe but not ultra-dangerous either — it’s also not a problem.
At the same time, our technology is an exponential one, so it’s wrong to think that the statement that it needs to be 10,000 times better means the system is only 1/10,000th of the way there. In fact, getting to the goal may not be that far away, and Google is much further along. They reported a distance of over 80,000 miles between necessary interventions. Humans have accidents about ever 250,000 miles.
(Delphi has not reported the most interesting number, which is necessary unexpected interventions per million miles. To figure out if an intervention is necessary, you must replay the event in simulator to see what the vehicle would have done had the safety driver not intervened. The truly interesting number is the combination of interventions/mm and the fraction of roads you can drive. It’s easier, but boring, to get a low interventions/mm number on one plain piece of straight highway, for example.)
It should also be noted that Delphi’s result is almost entirely on highways, which are the simplest roads to drive for a robot. Google’s result is also heavily highway biased, though they have reported a lot more surface street work. None of the teams have testing records in complex and chaotic streets such as those found in the developing world, or harsh weather.
It is these facts which lead some people to conclude this technology is decades away. That would be the right conclusion if you were unaware of the exponential curve the technologies and the software development are on.
Huge Hyundai investment
For some time, I’ve been asking where the Koreans are on self-driving cars. Major projects arose in many major car companies, with the Germans in the lead, and then the US and Japan. Korea was not to be seen.
Hyundai announced they would produce highway cruise cars shortly (like other makers) but they also announced they would produce a much more autonomous car by 2020 — a similar number to most car makers as well. Remarkable though was the statement that they would invest over $70 billion in the next 4 years on what they are calling “smart cars,” including hiring over 7,000 people to work on them. While this number includes the factories they plan to build, and refers to many technologies beyond robocars, it’s still an immense number. The Koreans have arrived.
I often speak about deliverbots — the potential for ground based delivery robots. There is also excitement about drone (UAV/quadcopter) based delivery. We’ve seen many proposed projects, including Amazon prime Air and much debate. Many years ago I also was perhaps the first to propose that drones deliver a defibrillator anywhere and there are a few projects underway to do this.
Some of my students in the Singularity University Graduate Studies Program in 2011 really caught the bug, and their team project turned into Matternet — a company with a focus in drone delivery in the parts of the world without reliable road infrastructure. Example applications including moving lightweight items like medicines and test samples between remote clinics and eventually much more.
I’m pleased to say they just announced moving to a production phase called Matternet One. Feel free to check it out.
When it comes to ground robots and autonomous flying vehicles, there are a number of different trade-offs:
Drones will be much faster, and have an easier time getting roughly to a location. It’s a much easier problem to solve. No traffic, and travel mostly as the crow flies.
Deliverbots will be able to handle much heavier and larger cargo, consuming a lot less energy in most cases. Though drones able to move 40kg are already out there.
Regulations stand in the way of both vehicles, but current proposed FAA regulations would completely prohibit the drones, at least for now.
Landing a drone in a random place is very hard. Some drone plans avoid that by lowering the cargo on a tether and releasing the tether.
Driving to a doorway or even gate is not super easy either, though.
Heavy drones falling on people or property are an issue that scares people, but they are also scared of robots on roads and sidewalks.
Drones probably cost more but can do more deliveries per hour.
Drones don’t have good systems in place to avoid collisions with other drones. Deliverbots won’t go that fast and so can stop quickly for obstacles seen with short range sensors.
Deliverbots have to not hit cars or pedestrians. Really not hit them.
Deliverbots might be subject to piracy (people stealing them) and drones may have people shoot at them.
Drones may be noisy (this is yet to be seen) particularly if they have heavier cargo.
Drones can go where their are no roads or paths. For ground robots, you need legs like the BigDog.
Winds and rain will cause problems for drones. Deliverbots will be more robust against these, but may have trouble on snow and ice.
In the long run, I think we’ll see drones for urgent, light cargo and deliverbots for the rest, along with real trucks for the few large and heavy things we need.
I’ve been on the road, and there has been a ton of news in the last 4 weeks. In fact, below is just a small subset of the now constant stream of news items and articles that appear about robocars.
Delphi has made waves by undertaking a road trip from San Francisco to New York in their test car, which is equipped with an impressive array of sensors. The trip is now underway, and on their page you can see lots of videos of the vehicle along the trek.
The Delphi vehicle is one of the most sensor-laden vehicles out there, and that’s good. In spite of all those who make the rather odd claim that they want to build robocars with fewer sensors, Moore’s Law and other principles teach us that the right procedure is to throw everything you can at the problem today, because those sensors will be cheap when it comes time to actually ship. Particularly for those who say they won’t ship for a decade.
At the same time, the Delphi test is mostly of highway driving, with very minimal urban street driving according to Kristen Kinley at Delphi. They are attempting off-map driving, which is possible on highways due to their much simpler environment. Like all testing projects these days, there are safety drivers in the cars ready to intervene at the first sign of a problem.
Delphi is doing a small amount of DSRC vehicle to infrastructure testing as well, though this is only done in Mountain View where they used some specially installed roadside radio infrastructure equipment.
Delphi is doing the right thing here — getting lots of miles and different roads under their belt. This is Google’s giant advantage today. Based on Google’s announcements, they have more than a million miles of testing in the can, and that makes a big difference.
Hype and reality of Tesla’s autopilot announcement
Telsa has announced they will do an over the air upgrade of car software in a few months to add autopilot functionality to existing models that have sufficient sensors. This autopilot is the “supervised” class of self driving that I warned may end up viewed as boring. The press have treated this as something immense, but as far as I can tell, this is similar to products built by Mercedes, BMW, Audi and several other companies and even sold in the market (at least for traffic jams) for a couple of years now.
The other products have shied away from doing full highway speed in commercial products, though rumours exist of it being available in commercial cars in Europe. What is special about Tesla’s offering is that it will be the first car sold in the US to do this at highway speed, and they may offer supervised lane change as well. It’s also interesting that since they have been planning this for a while, it will come as a software upgrade to people who bought their technology package earlier.
UK project budget rises to £100 million
What started with a £10 million pound prize has grown in the UK has become over 100m in grants in the latest UK budget. While government research labs will not provide us with the final solutions, this money will probably create some very useful tools and results for the private players to exploit.
MobilEye releases their EyeQ4 chip
MobilEye from Jerusalem is probably the leader in automotive machine vision, and their new generation chip has been launched, but won’t show up in cars for a few years. It’s an ASIC packed with hardware and processor cores aimed at doing easy machine vision. My personal judgement is that this is not sufficient for robocar driving, but MobilEye wants to prove me wrong. (The EQ4 chip does have software to do sensor fusion with LIDAR and Radar, so they don’t want to prove me entirely wrong.) Even if not good enough on their own, ME chips offer a good alternate path for redundancy
Chris Urmson gives a TeD talk about the Google Car
Talks by Google’s team are rare — the project is unusual in trying to play down its publicity. I was not at TeD, but reports from there suggest Chris did not reveal a great deal new, other than repeating his goal of having the cars be in practical service before his son turns 16. Of course, humans will be driving for a long time after robocars start becoming common on the roads, but it is true that we will eventually see teens who would have gotten a licence never get around to getting one. (Teems are already waiting longer to get their licences so this is not a hard prediction.)
The war between DSRC and more wifi is heating up.
2 years ago, the FCC warned that since auto makers had not really figured out much good to do
with the DSRC spectrum at 5.9ghz, it was time to repurpose it for unlicenced use, like more WiFi.
This is not a very good deal for the driver. After Uber’s 20% cut, that’s 72 cents/mile. According to AAA, a typical car costs about 60 cents/mile to operate, not including parking. (Some cars are a bit cheaper, including the Prius favoured by UberX drivers.) In any event, the UberX driver is not making much money on their car.
The 18 cents/minute — $10.80 per hour, drops to only $8.64/hour while driving. Not that much above minimum wage. And I’m not counting the time spent waiting and driving to and from rides, nor the miles, which is odd that the flag drop fee. There is a $1 “safe rides fee” that Uber pockets (they are being sued over that.) And there is a $4 minimum, which will hit you on rides of up to about 2.5 miles.
So Uber drivers aren’t getting paid that well — not big news — but a bigger thing is the comparison of this with private car ownership.
As noted, private car ownership is typically around 60 cents/mile. The Uber ride then, is only 50% more per mile. You pay the driver a low rate to drive you, but in return, you get that back as free time in which you can work, or socialize on your phone, or relax and read or watch movies. For a large number of people who value their time much more than $10/hour, it’s a no-brainer win.
The average car trip for urbanites is 8.5 miles — though that of course is biased up by long road trips that would never be done in something like Uber. I will make a guess and drop urban trips to 6.
The Uber and private car costs do have some complications:
* That Safe Rides Fee adds $1/trip, or about 16 cents/mile on a 6 mile trip
* The minimum fee is a minor penalty from 2 to 2.5 miles, a serious penalty on 1 mile trips
* Uber has surge pricing some of the time that can double or even triple this price
As UberX prices drop this much, we should start seeing people deliberately dropping cars for Uber, just as I have predicted for robocars. I forecast robotaxi service can be available for even less. 60 cents/mile with no cost for a driver and minimal flag drop or minimum fees. In other words, beating the cost of private car ownership and offering free time while riding. UberX is not as good as this, but for people of a certain income level who value their own time, it should already be beating the private car.
We should definitely see 2 car families dropping down to 1 car plus digital rides. The longer trips can be well handled by services like Zipcar or even better, Car2Go or DriveNow which are one way.
The surge pricing is a barrier. One easy solution would be for a company like Uber to make an offer: “If you ride more than 4,000 miles/year with us, then no surge pricing for you.” Or whatever deal of that sort can make economic sense. Sort of frequent rider loyalty miles. (Surprised none of the companies have thought about loyalty programs yet.)
Another option that might make sense in car replacement is an electric scooter for trips under 2 miles, UberX like service for 2 to 30 miles, and car rental/carshare for trips over 30 miles.
If we don’t start seeing this happen, it might tell us that robocars may have a larger hurdle in getting people to give up a car for them than predicted. On the other hand, some people will actually much prefer the silence of a robocar to having to interact with a human driver — sometimes you are not in the mood for it. In addition, Americans at least are not quite used to the idea of having a driver all the time. Even billionaires I know don’t have a personal chauffeur, in spite of the obvious utility of it for people whose time is that valuable. On the other hand, having a robocar will not seem so ostentatious.
All over the world, people (and governments) are debating about regulations for robocars. First for testing, and then for operation. It mostly began when Google encouraged the state of Nevada to write regulations, but now it’s in full force. The topic is so hot that there is a danger that regulations might be drafted long before the shape of the first commercial deployments of the technology take place.
As such I have prepared a new special article on the issues around regulating robocars. The article concludes that in spite of a frequent claim that we want to regulate and standarize even before the technology has been out in the market for a while, this is in fact both a highly unusual approach, and possibly even a dangerous approach.
Regulating Robocar Safety : An examination of the issues around regulating robocar safety and the case for a very light touch
The government baked robocar projects in the UK are going full steam, with this press release from the UK government to accompany the unveiling of the prototype Lutz pod which should ply the streets of Milton Keynes and Greenwich.
The new pod follows a similar path to other fully-autonomous prototypes, reminding me of the EN-V from GM, the MIT City Car and the Google buggy prototype. It’s electric, meant for “last mile” and will lose its steering wheel once testing is over.
I also note they talk eagerly about the Meridian shuttle being tested in Greenwich, even though that’s a French vehicle.
When it comes to changes to the vehicle code, I think it’s premature. Even without looking at the proposed changes, I would say that we don’t know enough to work out what changes are needed, even though we all might be full of ideas.
One proposal is to remove the ban on tailgating to allow convoys. A reasonable enough thing, except people are not going to build convoys for quite some time, I think. The Volvo/SARTRE experiment found a number of problems with the idea, and you don’t want to do your first deployments with something that could crash 10 cars if it goes wrong instead of one. You do that later, once you feel very confident in your tech.
Another proposal called for changing how cyclists are treated. The law in the UK (and some other places) demands cyclists be given the full berth of a car, and in practice nobody ever does that, and if they did do it, it would mean they just followed along at bicycle speed, impeding traffic. One of those classic cases, like speed limits in the USA, where the law only works if nobody follows it. (Though cyclists would say that they should just get the full lane like the law says.)
We will need to fix these areas of the vehicle codes, but we should fix them only after we see a problem, unless it’s clear that the vehicles can’t be deployed without the change. Give the developers the chance to fix the problem on their own first. If you fix the law before you know what the vehicles will be like, you may ensconce old thinking into the law and have a hard time getting it out.
It is interesting to see Governments adapt so quickly to a disruptive technology. It’s quite probable that our hype is a bit too good and will come back to bite us. I predicted this sort of jurisdictional competition as governments realize they have a chance to make their regions become players in the new automotive industry, but they are embracing some things faster than I expected.
There is great buzz about some sensor-laden vehicles being driven around the USA which have been discovered to be owned by Apple Computer. The vehicles have cameras and LIDARs and GPS antennas and many are wondering is this an Apple Self-Driving Car? See also speculation from cult of Mac.
Here’s a video of the vehicle driving around the East Bay (50 miles from Cupertino) but they have also been seen in New York.
We don’t see the front of the vehicle, but it sure has plenty of sensors. On the front and back you see two Velodyne 32E Lidars. These are 32 plane LIDARS that cost about $30K. You see two GPS antennas and what appear to be cameras in all directions. You don’t see the front in these pictures, which is where the most interesting sensors will be.
So is this a robocar, or is this a fancy mapping car? Rumours about Apple working on a car have been swirling for a while, but one thing to contradict that has been the absence of sightings of cars like this. You can’t have an active program without testing on the roads. There are ways to hide LIDARS (and Apple is super secretive so they might) and even cameras to a degree, but this vehicle hides little.
Most curious are the Velodynes. They are tilted down significantly. The 32E unit sees from about 10 degrees up to 30 degrees down. Tilting them this much means you don’t see out horizontally, which is not at all what you want if this is for a self-driving car. These LIDARs are densely scanning the road close around the car, and higher things in the opposite direction. The rear LIDAR will be seeing out horizontally, but it’s placed just where you wouldn’t place it to see what’s in front of you. A GPS antenna is blocking the direct forward view, so if the goal of the rear LIDAR is to see ahead, it makes no sense.
We don’t see the front, so there might be another LIDAR up there, along with radars (often hidden in the grille) and these would be pretty important for any research car.
For mapping, these strange angles and blind spots are not an issue. You are trying to build a 3D and visible light scan of the world. What you do’t see from one point you get from another. For stree mapping, what’s directly in front and behind are generally road and not interesting, but what’s to the side is really interesting.
Also on the car is an accurate encoder on the wheel to give improved odemetry. Both robocars and mapping cars are interested in precise position information.
Arguments this is a robocar:
The Velodynes are expensive, high end and more than you need for mapping, though if cost is no object, they are a decent choice.
Apple knows it’s being watched, and might try to make their robocar look like a mapping car
There are other sensors we can’t seee
Arguments it’s a mapping car
As noted, the Velodynes are titled in a way that really suggests mapping. (Ford uses tilted ones but paired with horizontal ones.)
The cameras are aimed at the corners, not forward as you would want
They are driving in remote locations, which eventually you want to do, but initially you are more likely to get to the first stage close to home. Google has not done serious testing outside the Bay Area in spite of their large project.
The lack of streetview is a major advantage Google has over Apple, so it is not surprising they might make their own.
I can’t make a firm conclusion, but this leans toward it being a mapping car. Seeing the front (which I am sure will happen soon) will tell us more.
Another option is it could be a mapping car building advanced maps for a different, secret, self-driving car.
After yesterday’s story about Uber and CMU, a lot of speculation has flown that Uber will now be at odds with Google, both about building robocars and also on providing network taxi service, since another rumour said Google plans to launch an Uber like “ride share” service.
Since then, the Uber blog post and this interview with Uber folks tell a slightly different story. Uber is funding a research center at CMU, and giving lots of grants to academics. Details are not fully available, but typically this means being at an early research stage. With these research labs, academics are keen to publish all they do, so little gets done in secret. In many cases the sponsor gets a licence to the technology but it’s often not exclusive. If Uber wanted to build their own car, chances are they would do it in a more private lab.
Rumours that David Drummond would resign from the Uber board also have not panned out. Google has invested hugely in Uber (already for good return at the present valuation) and Google Maps offers you an Uber if you ask it for directions somewhere — it’s actually one of the easier interfaces for ordering one.
Rumours around Google’s efforts suggest that Big G has been testing a “ride share” app with employees and plans to launch it. Google has denied that, and says it loves Uber and Lyft. Further news revealed the rumours were about an internal carpooling system, not involving the self-driving cars. I could imagine confusion because Uber and others call themselves “ride sharing” which is a bit of a fabrication to not look like a taxi, while a carpooling app would be real ride sharing. (UberPool is real ride sharing.) Google, which has a terrible undersupply of parking is very keen on getting employees to ride its bus system and to carpool.
That said, Google has talked about the same thing I talk about — the true goal of robocar technology being the creation of a mobility on demand taxi service, like Uber but at a much lower cost. Google has not said that they would provide that themselves, or who they would partner with if they did it. Most people have presumed it might be Uber but I don’t think that’s at all assured.
At the same time, Uber has assured its drivers they are not going away for the foreseeable future. I suspect that’s an equivocation, and just means that we can’t see very far in the future right now!
I commonly see statements from connected car advocates that vehicle to vehicle (V2V) and vehicle to infrastructure communications are an important, even essential technology for robocar development. Readers of this blog will know I disagree strongly, and while I think I2V will be important (done primarily over the existing mobile data network) I suspect that V2V is only barely useful, with minimal value cases that have a hard time justifying its cost.
Of late, though, my forecast for V2V grows even more dismal, because I wonder if robocars will implement V2V with human-driven cars at all, even if it becomes common for ordinary cars to have the technology because of a legal mandate.
The problem is security. A robocar is a very dangerous machine. Compromised, it can cause a lot of damage, even death. As such, security will have a very strong focus in development. You don’t want anybody breaking into the computer systems or your car or anybody else’s. You really don’t want it.
One clear fact that people in security know — a very large fraction of computer security breaches caused by software faults have come from programs that receive input data from external sources, in particular when you will accept data from anybody. Internet tools are the biggest culprits, and there is a long history of buffer overflows, injection attacks and other trouble that has fallen on tools which will accept a message from just anyone. Servers (which openly accept messages from outside) are at the greatest risk, but even client tools like web browsers run into trouble because they go to vast numbers of different web sites, and it’s not hard to trick people to sending them to a random web site.
We work very hard to remove these vulnerabilities, because when you’re writing a web tool, you have no choice. You must accept input from random strangers. Holes still get found, and we pay the price.
The simplest strategy to improve your chances is to go deaf. Don’t receive inputs from outside at all. You can’t do that in most products, but if you can close off a channel without impeding functionality it’s a good approach. Generally you will do the following to be more secure:
Be a client, which means you make communications requests, you do not receive them.
You only connect to places you trust. You avoid allowing yourself to be directed to connect to other things
You use digital signature and encryption to assure that you really are talking to your trusted server.
This doesn’t protect you perfectly. Your home server can be compromised — it often will be running in an environment not as locked down as this. In fact, if it becomes your relay for messages from outside, as it must, it has a vector for attack. Still, the extra layer adds some security. read more »
Update: On the Uber blog we now see it’s more funding of research labs at CMU, on many topics
That’s a major step, if true. People have often pointed out how well Uber is poised to make use of robocar technology to bring computer summoned taxi service to the next level. If Uber did not exist, I would surely be building it to get that advantage. Many have assumed that since Google is a major investment partner in Uber that they would partner on this technology, but this suggests otherwise.
I write about Uber a lot here not just because of interest in what they do today, but because it teaches us a lot about how people will view Robocars in the future. Uber’s interface is very similar to what you might see for a robocar service, and the experience is fairly similar, just much more expensive. UberX is $1.30/mile plus 26 cents/minute with $2.20 flag drop. The Black service is $3.75/mile and 65 cents/minute with an $8 flag drop. I expect robocar tax service to be cheaper than 50 cents/mile with minimal per-minute charges. The flag drop is not yet easy to calculate. What richer people do with Uber teaches us what the whole public will do with robocars.
Uber lets you say where you are going but doesn’t demand it. That’s one thing I suspect will be different with your robotaxi, because it’s really nice if they can send you a vehicle chosen for the trip you have in mind. Ie. a small, efficient car without much range for short, single person trips. Robotaxi services will offer you the ability to not say your destination — but they will probably charge more for it, and that means most people will be willing to say their destination.
Uber does not hide their desire to get rid of all their drivers, which sounds like a strange strategy, but the truth is that cab driving is not something most people view as a career. It’s a quick source of money with no special skills, something people do until something better comes along, or in the gaps in their day to make extra cash. Unlike people losing jobs to robots on a factory line, nobody is particularly upset at the idea.
Some new results from the NGV Team at the University of Michigan describe different approaches for perception (detecting obstacles on the road) and localizations (figuring out precisely where you are.) Ford helped fund some of the research so they issued press releases about it and got some media stories. Here’s a look at what they propose.
Many hope to be able to solve robotics (and thus car) problems with just cameras. While LIDAR is going to become cheap, it is not yet, and cameras are much cheaper. I outline many of the trade-offs between the systems in my article on cameras vs lasers. Everybody hopes for a research breakthrough or computer vision breakthrough to make vision systems reliable enough for safe operation.
The Michigan lab’s approach is a special machine vision one. They map the road in advance in 3D and visible light by using a mapping car equipped with lots of expensive LIDAR and other sensors. They build a 3D representation of the road similar to what you need for a video game engine, and from that, with the use of GPUs, they can indeed create a 2D image of what a camera should see from any given point.
The car goes out into the world and its actual camera delivers a 2D frame of what it sees. Their system then compares that with generated 2D images of what the camera should see until it finds the closest match. Effectively, it’s like you looking out a window and then going into a video game and wandering around looking for a place that looks like what you see out that window, and then you know where the window is.
Of course it is not “wandering,” and they develop efficient search algorithms to quickly find the location that looks most like the real world image. We’ve all seen video games images, and know they only approximate the real world, so nothing will be an exact match, but if the system is good enough, there will be a “most similar” match that also corresponds with what other sensors, like your GPS and your odometer/dead reckoning system, tell you about where you probably are.
Localization with cameras has been done before, and this is a new approach taking advantage of new generations of GPUs, so it’s interesting. The big challenge is simulating the lighting, because the real world is full of different lighting, high dynamic range, and shadows. The human system has no problem understanding a stripe on the road as it moves through the shadow of a tree, but computer systems have a pretty tough time with that. Sun shadows can be mapped well with GPUs, but shadows from things like the moving limbs of trees are not possible to simulate, as are the shadows of other vehicles and road users. At night, light and shadows come from car headlights and urban lights. The team is optimistic about how well they will handle these problems.
The much larger challenge is object perception. Once you have a simulation of what the camera should see, you can notice when there are things present that are not in the prediction — like another car or pedestrian, or a new road sign. (Right now their system mostly is looking at the ground.) Once you identify the new region, you can attempt to classify it using computer vision techniques, and also by watching it move against the expected background.
This is where it gets challenging, because the bar is very high. To be used for driving it must effectively always work. Even if you miss 1 pedestrian in a million you have a real problem because there are billions of pedestrians encountered by a billion drivers every day. This is why people love LIDAR — if something (other than a mirror or sheet of glass) sufficiently large is sufficiently close you, you’re going to get laser returns from it, and not from what’s behind it. It has the reliability number that is needed.
The challenge of vision systems is to meet that reliability goal.
This work is interesting because it does a lot without relying on AI “computer vision” techniques. It is not trying to look at a picture and recognize a person. Humans are able to look at 2D pictures with bizarre lighting and still tell you not just what the things in the picture are, but often how far away they are and what they are doing. While we can be fooled in a 2D image, once you have a moving dynamic world, humans are, generally reliable enough at spotting other things on the road. (Though of course, with 1.2 million dead each year, and probably 50 million or more accidents, the majority because somebody was “not looking,” we are far from perfect.)
Some day, computer vision will be as good at recognizing and understanding the world as people are — and in fact surpass us. There are fields (like identifying traffic signs from photos) where they already surpass us. For those not willing to wait until that day, new techniques in perception that don’t require full object understanding are always interesting.
I should also point out that while lowering cost is of course a worthwhile goal, it is a false goal at this time. Today, maximal safety is the overriding goal, and as such, nobody will actually release a vehicle to consumers without LIDAR just to save the estimated 2017 cost of LIDAR, which will be sub-$500. Only later, when cameras get so good they completely replace LIDAR safety capabilities for less money would people release such a system to save cost. On the other hand, improving cameras to be used together with LIDAR is a real goal; superior safety, not lower cost.