Robocars

Robocars driving when the map is wrong

Yesterday’s note on Here’s maps brought up the question of the wisdom of map-based driving. While I addressed this a bit earlier let me add a bit more detail.

A common first intuition is that because people are able to drive just fine on a road they have never seen before that this is how robots will do it. They are bothered that present designs instead create a super-detailed map of the road by having human driven cars scan the road with sensors in advance. After all, the geometry of the road can change due to construction; what happens then?

They hope for a car that, like a human, can build its model of the road in real time while driving the road for the first time. That would be nice, of course, and gives you a car that can drive most roads right away, without needing to map them. But it’s a much harder problem to solve, and unlikely to ever be solved perfectly. Car companies are building very simple systems which can follow the lines on a freeway under human supervision without need for a map. But real city streets are a different story.

The first thing to realize is that any system which could build the correct model as you drive is a system that could build a map with no human oversight, so the situations are related. But building a map in advance is always going to have several very large advantages:

  1. You build the map from not just one scan of the road, but several, and done in different lanes and directions. As a result, you get 3-D scans of everything from different angles, and can build a superior model of the world.
  2. Using multiple scans lets you learn about things that are stationary but move one day to the next, like parked cars.
  3. You can process the data using a cloud supercomputer in as much time, memory and data storage as you want. Your computer is effectively thousands of times more capable.
  4. Humans can review the map built by the software if there’s anything it is uncertain about (or even if there is nothing) at their leisure.
  5. Humans can also test the result of the automatic and guided mapping to assure accuracy with one extra drive down the road.

In turn there are disadvantages

  1. At times, such as construction, the road will have changed from when it was mapped
  2. This process costs effort, and so the vehicle either does not drive off the map, or only handles a more limited set of simpler roads off the map.

The advantages are so great that even if you did have a system which could handle itself without a map, it is still always going to be able to do better with a map. Even with a great independent system you would want to make an effort to map the most popular roads and the most complex roads, up to the limit of your budget. The cost is an issue, but the cost of mapping roads is nothing compared to the cost of building or maintaining them. It’s a few times driving down the road, and some medium-skilled labour.

The road has changed

Let’s get to the big issue — the map is wrong, usually because construction has changed it.

First of all, we must understand that the sensors always disagree with the map, because the sensors are showing all the other cars and pedestrians etc. Any car has to be able to perceive these and drive so as not to hit them. If a traffic cone, “road closed” sign or flagman appears in the road, a car is not going to just plow into them because they are not on the map! The car already knows where not to go, the question is where it should go when the lanes have changed.

Even vehicles not rated to drive any road without a map can probably still do basic navigation and stay within their lane markers without a map. For the 10,000 miles of driving you do in a year, you need a car that does that 99.99999% of the time (for which you want a map) but it may be acceptable to have a car that’s only 99.9% able to do that for the occasional mile of restriped road. Indeed, when there are other, human-driven cars on the road, a very good strategy is just to follow them — follow one in front, and watch cars to the side. If the car has a clear path following new lane markers or other cars, it can do so.

Google, for example, has shown videos of their vehicle detecting traffic cones and changing lanes to obey the cones. That’s today — it is only going to get better at this.

But not all the time. There will be times when the lanes are unclear (sometimes the old lanes are still visible or the new ones are not well marked.) If there are no other cars to follow, there are also no other cars to hit, and no other traffic to block.

Still, there will be times when the car is not sure of where to go, and will need help. Of course, if there is a passenger in the car, as there would be most of the time, that passenger can help. They don’t need to be a licenced driver, they just need to be somebody who can point on the screen and tell the car which of the possible paths it is considering is the right one. Or guide it with something like a joystick — not physically driving but just guiding the car as to where to go, where to turn.

If the car is empty, and has a network connection, it can send a picture, 3-D scan and low-res video to a remote help station, where a person can draw a path for the car to go for its next 100 meters, and keep doing that. Not steering the car but helping it solve the problem of “where is my lane?” The car will be cautious and stop or pull over for any situation where it is not sure of where to go, and the human just helps it get over that, and confirms where it is safe to go.

If the car is unmanned and has no network connection of any kind, and can’t figure out the road, then it will pull over, or worst case, stop and wait for a human to come and help. Is that acceptable? Turns out it probably is, due to one big factor:

This only applies to the first car to encounter an unplanned, unreported construction zone

We all drive construction zones every day. But it’s much more rare that we are the first car to drive the construction zone as they are setting it up. And most of the rules I describe above are only for the first connected car to encounter a surprise change to the road. In other words, it’s not going to happen very often. Once a car encounters a surprise change to the road, it will report the problem with the map. Immediately all other cars will know about the zone.

If that first car is able to navigate the new zone, it will be scanning it with sensors, and uploading that data, where a crew can quickly build a corrected map. Within a few minutes, the map and the road will no longer differ. And that first car will be able to navigate the new zone 99.999% of the time — either because it has a human on board, remote human help or it’s a simple enough change that the car is able to drive it with an incorrect map.

In addition, the construction zone has to be a surprise. That means that, in spite of regulations, the construction crews did not log plans for it in the appropriate databases. Today that happens fairly often, but over time it’s going to happen less. In fact, there are plans to have transponders on construction equipment and even traffic cones that make it impossible to create a new construction zone without it showing up in the databases. Setting up a road change has a lot of strongly enforced safety rules, and I predict we’ll see “Get out your smartphone and make sure the zone is in the database before you create it” as one of them, especially since that’s so easy to do.

(You have probably also seen that tools like Waze, driven by ordinary human driver smartphones, are already mapping all the construction zones when they pop up.)

If a complex zone is present and unmapped, unmanned cars just won’t route through there until the map is updated. The more important the zone, the more quickly it will get updated. If need be, a mapping worker will go out in a car before work even begins. If a plan was filed, we’ll also know the plan for the zone, and whether cars can handle it with an old map or not.

Most of the time, though, a human passenger will be there to guide the car through the zone. Not to steer — there may not be a steering wheel — but to guide. The car will go slowly and stay safe.

Once a car is through, it will send the scans up to the mapping center, and all future cars will have a map to guide them until the crew changes the road again without logging it. I believe that doing so should be made against safety regulations, and be quite rare.

So look at those numbers. I will hope it’s reasonable to expect that 99% of construction zones will be logged in road authority databases before they begin. Of the 1% that aren’t, there will be a first robocar to encounter the zone. 90% of the time that car will have a passenger able to help. For the 10% unmanned cars, I predict a data network will be available 99% of the time. (Some would argue 100% of the time because unmanned cars will just not go where there is not a data connection, and we may also get new data services like Google’s Loon, or Facebook’s drone program to assure coverage everywhere.)

So now we are looking at one construction zone in 100,000 where there was no warning, there is no human, and there is no data. But we’ve rated are car as able to handle handle off-map driving 99.9% of the time. For the other .1%, it decides it can’t see a clear path, and pulls over. When it doesn’t report back in on the other side of the data dead zone, a service vehicle is dispatched and fixes the problem.

So now in one in 100,000,000 construction zones, we have a car deciding to pull over. Perhaps for half of those, it can’t figure out how to pull over, and it stops in the lane. Not great — but this is one in 200 million construction zones. In other words, it happens with much less frequency than accidents or stalled cars. And there is even a solution. If a construction worker flashes an ID card at the car’s camera when it’s in a confused state, the car can then follow that worker to a place to stop. In fact, since the confused state is so rare, there is probably not even a need for an ID card. Just walk up, make a “follow me” gesture and walk the car where it needs to go.

Tweak these numbers as you like. Perhaps you think there will be far more construction zones not logged in databases. Perhaps you think the car’s ability to drive a changed zone will only be 50%. Perhaps you think there will still be lots of unmanned cars running in wireless dead zones in 2020. Even so the number of cars that stop and give up will still be far fewer than the number of cars that block roads today due to accidents and mechanical problems. In other words, no big whoop.

It’s important to realize that unmanned cars are not in a hurry. They can avoid zones they are not comfortable with. If they can’t get through at all, the taxi company sending the car can just send another from a different direction in almost all cases.

It’s also important to realize that cars in an uncertain situation are also not in a big hurry. They will slow until they can be sure they are safe and able to handle the road. Slow, it turns out, is easy. Slow and heavy traffic (ie. a traffic jam) is actually also very easy — you don’t even need to see the lines on the road to handle that one; you usually can’t.

Once again this is only for the first car to encounter the surprise zone. Much more common will be a car that is the first to encounter a planned zone. This car will always have a competent passenger, because the service will not direct an unmanned car into an unknown construction zone where there is no data. This passenger will get plenty of warning, and their car may well pull over so there is no transition from full-auto to semi-auto while the car is moving. Then this person will guide the car through the zone at reduced speed. Probably just with a joystick, though possibly there will handlebars that can pop out or plug in if true semi-manual driving is needed.

New road signs

Road signs are a different problem. Already there are very decent systems for recognizing road signs captured by the camera — systems that actually do better at it than human beings. But sometimes there are road signs with text, and the system may recognize them, but not understand them. Here again we may call upon human beings, either in the vehicle, or available via a data connection. Once again, this is only for the first unmanned car to encounter the new road sign.

I will propose something stronger, though. I believe there should be a government mandated database of all road signs. Further, I believe the law should say that no road sign has legal effect until it is entered in the database. Ie. if you put up a sign with a new speed limit, it is not a violation of the limit to ignore the sign until the sign is in the database. At least not for robots. Once again, all this needs is that the crews putting in the signs have smartphones so they can plonk the sign on the map and enter what it is.

We may never need this, though, because the ability of computers to read signs is getting very good. It may be faster to just make it even better than to wait for a law that mandates the database. With a 3-D map, you will never miss a brand new sign, but you might get confused by a changed sign — you will know it changed but may need to ask for help to understand it if it is non-standard. There are already laws that standardize road signs, but only to a limited extent. Even so, the number of sign styles in any given country is still a very manageable number.

Random road events

Sometimes driving geometry changes not due to construction, but due to accidents and the environment. Trees get knocked down. Roads flood. Power lines may fall. The trees will be readily seen, and for the first car to come to a fallen tree, the procedure will be similar, though in a low traffic area the vehicles will be programmed to go around them, as they are for stalled cars and slow moving vehicles. Flooding and power lines are more challenging because they are harder to see. Flooding, of course, does not happen by surprise. That there is flooding in a region will be well known so cars will be on the lookout for it. Human guides will again be key.

A plane is not a bird

Aircraft do not fly by flapping their wings, and robocars will not see the world as people do nor drive as they do. When they have accurate maps, it gives us much more confidence in their safety, particularly the ability to pick the right path reliably at speed. But they have a number of tools open to them for driving a road that doesn’t match the map precisely without needing to have the ability to drive unmapped roads 99.999999% of the time. That’s a human level ability and they don’t need it.

Cars in the UK, China, LA, CES and Here : Robocar News Update

I see new articles on robocars in the press every day now, though most don’t say a lot new. Here, however, are some of the recent meaningful stories from the last month or two while I’ve been on the road. There are other sites, like the LinkedIn self-driving car group and others, if you want to see all the stories.

Winners chosen in UK competition

Four cities in the UK have been chosen for testing and development of robcars using the £10 million funding contest. As expected, Milton Keynes was chosen along with Coventry, and also Greenwich and Bristol. The BBC has more.

Chinese competition has another round

Many don’t know it, but China has been running its own “DARPA Grand Challenge” style race for 6 years now. The entrants are mostly academic, and not super far along, but the rest of the world stopped having contests long ago, much to its detriment. I was recently in Beijing giving a talk about robocars for guests of Baidu — my venue was none other than the Forbidden City — and the Chinese energy is very high. Many, however, thought that an announcement that Baidu would provide map data for BMW car research meant that Baidu was doing a project the way Google is. It isn’t, at least for now.

LA Mayor wants the cars

I’ve seen lots of calls from cities and regions that robocars come there first. In the fall, the mayor of Los Angeles made such a call. What makes this interesting is that LA is indeed a good early target city, with nice wide and simple roads, lots of freeways, and relatively well-behaved drivers compared to the rest of the world. And it’s in California, which is where a lot of the best development is happening, although that’s all in the SF Bay Area.

Concept designs for CES and beyond

More interesting concept cars are arising, as designers realize what they can do when freed of having a driver’s seat that faces forward and has all the controls, and as electric drivetrains allow you to move around where the drivetrain goes. Our friends at the design firm IDEO came up with some concepts that are probably not realistic but illustrate worthwhile principles. In particular, their vision of the delivery robot is quite at odds with mine. I see delivery robots as being very small, just suitcase sized boxes on wheels, except for the few that are built for very large cargo like furniture and industrial deliveries. Delivery robots will come to you on your schedule, not on the delivery company’s schedule. There will be larger robots with compartments when you can service a group of people who live together, but there is a limit to how many you can serve and still deliver at exactly the right time that people expect.

Everybody is also interested to see what Daimler will unveil at the Consumer Electronics Show. They showed off an interior with face-to-face seating and everybody wearing a VR headset, and have been testing a car under wraps.

It’s interesting to think about the VR headset. A lot of people would get sick if jostled in a car while wearing a VR headset. However, it might be possible to have the VR headset deliberately bounce the environment it’s showing you, so that it looks like you’re riding a car in that environment that’s bumping just the way you are. Or even walking.

Here (Nokia/Navteq) builds a big library of HD maps

Robocars work better if they get a really detailed map of their environment to drive with. Google’s project is heavily based on maps, and they have mapped out all the roads they test near Google HQ. Nokia’s “Here” division has decided to enter this in a big way. Nokia calls its projects “HD Maps,” which is a good name because you want to make it clear that these are quite unlike the navigation maps we are used to from Google, Here and other companies. These maps track every lane and path a car could take on the road, but also every lane marker, every curb, every tree — anything that might be seen by the cameras and 3D sensors.

Nokia makes the remarkable claim to have produced 1.2 million miles of HD Maps in 30 countries in the last 15 months. That’s remarkable because Google declared that one of their unsolved problems was that the cost of producing maps, and they were working to bring that cost down. Either Nokia/Here has made great strides in reducing that cost, or their HD Maps are not quite at the level of accuracy and detail that might be needed.

Nonetheless, the cost of the mapping will come down. In fact, many people express surprise when they learn that the cars rely so heavily on maps, as they expect a vehicle that, like a human being, can easily drive on a road they’ve never seen before, with no map. Humans can do that, but a car that could do that is also a car that could build the sort of map we’re talking about, in real time. Making the map ahead of time has several advantages, and is easier to do than doing it in real time. Perhaps some day that real-time map builder (what roboticists call Simultaneous localization and mapping) will arise, but for now, pre-mapping is the way to go.

510 Systems story told (sort of.)

There was recently press about the kept-quiet acquisition by Google of 510 Systems. I was at Google at the time, and it involves friends of mine, so I will have to say there are some significant errors in the story, but it’s interesting to see it come out. It wasn’t really that secret. What Anthony did with PriBot was hardly secret — he was on multiple TV shows for his work — and that he was at Google working at first on Streetview and later on the car was also far from secret. But it wasn’t announced so nobody picked up on it.

Uber's legal battles and robocars

Uber is spreading fast, and running into protests from the industries it threatens, and in many places, the law has responded and banned, fined or restricted the service. I’m curious what its battles might teach us about the future battles of robocars.

Taxi service has a history of very heavy regulation, including government control of fares, and quota/monopolies on the number of cabs. Often these regulations apply mostly to “official taxis” which are the only vehicles allowed to pick up somebody hailing a cab on the street, but they can also apply to “car services” which you phone for a pick-up. In addition, there’s lots of regulation at airports, including requirements to pay extra fees or get a special licence to pick people up, or even drop them off at the airport.

Why we have Taxi regulation and monopolies

The heavy regulation had a few justifications:

  • When hailing a cab, you can’t do competitive shopping very easily. You take the first cab to come along. As such there is not a traditional market.
  • Cab oversupply can cause congestion
  • Cab oversupply can drive the cost of a taxi so low the drivers don’t make a living wage.
  • We want to assure public safety for the passengers, and driving safety for the drivers.

Most of these needs are eliminated when you summon from an app on your phone. You can choose from several competing companies, and even among their drivers, with no market failure. Cabs don’t cruise looking for fares so they won’t cause much congestion. Drivers and companies can have reputations and safety records that you can look up, as well as safety certifications. The only remaining public interest is the question of a living wage.

Taxi regulations sometimes get stranger. In New York (the world’s #1 taxi city) you must have one of the 12,000 “medallions” to operate a taxi. These medallions over time grew to cost as much as $400,000 each, and were owned by cab companies and rich investors. Ordinary cabbies just rented the medallions by the hour. To avoid this, San Francisco made rules insisting a large fraction of the cabs be owned by their drivers, and that no contractual relationship could exist between the driver and any taxi company.

This created the situation which led to Uber. In San Francisco, the “no contract” rule meant if you phoned a dispatcher for a cab, they had no legal power to make it happen. They could just pass along your desire to the cabbie. If the driver saw somebody else with their arm up on the way to get you, well, a bird in the hand is worth two in the bush, and 50% of the time you called for a cab, nobody showed up!

Uber came into that situation using limos, and if you summoned one you were sure to get one, even if it was more expensive than a cab. Today, that’s only part of the value around the world but crazy regulations prompted its birth.

The legal battles (mostly for Uber)

I’m going to call all these services (Uber, Lyft, Sidecar and to some extent Hail-O) “Online Ride” services.  read more »

The many business models for cars

When I talk about robocars, I often get quite opposite reactions:

  • Americans, in particular, will never give up car ownership! You can pry the bent steering wheel from my cold, dead hands.
  • I can’t see why anybody would own a car if there were fast robotaxi service!
  • Surely human drivers will be banned from the roads before too long.

I predict neither extreme will be true. I predict the market will offer all options to the public, and several options will be very popular. I am not even sure which will be the most popular.

  1. Many people will stick to buying and driving classic, manually driven cars. The newer versions of these cars will have fancy ADAS systems that make them much harder to crash, and their accident levels will be lower.
  2. Many will buy a robocar for their near-exclusive use. It will park near where it drops them off and always be ready. It will keep their stuff in the trunk.
  3. People who live and work in an area with robotaxi service will give up car ownership, and hire for all their needs, using a wide variety of vehicles.
  4. Some people will purchase a robocar mostly for their use, but will hire it out when they know they are not likely to use it, allowing them to own a better car. They will make rarer use of robotaxi services to cover specialty trips or those times when they hired it out and ended up needing it. Their stuff will stay in a special locker in the car.

In addition, people will mix these models. Families that own 2 or more cars will switch to owning fewer cars and hiring for extra use and special uses. For example, if you own a 2 person car, you would summon a larger taxi when 3 or more are together. In particular, parents may find that they don’t want to buy a car for their teen-ager, but would rather just subsidize their robotaxi travel. Parents will want to do this and get logs of where their children travel, and of course teens will resist that, causing a conflict.  read more »

Are today's challenges of making robocars dealbreakers?

There’s been a lot of press recently about an article in Slate by Lee Gomes which paints a pessimistic picture of the future of robocars, and particularly Google’s project. The Slate article is a follow-on to a similar article in MIT Tech Review

Gomes and others seem to feel that they and the public were led to believe that current projects were almost finished and ready to be delivered any day, and they are disappointed to learn that these vehicles are still research projects and prototypes. In a classic expression of the Gartner Hype Cycle there are now predictions that the technology is very far away.

Both predictions are probably wrong. Fully functional robocars that can drive almost everywhere are not coming this decade, but nor are they many decades away. But more to the point, less-functional robocars are probably coming this decade — much sooner than these articles expect, and these vehicles are much more useful and commercially viable than people may expect.

There are many challenges facing developers, and those challenges will keep them busy refining products for a long time to come. Most of those challenges either already have a path to solution, or constrain a future vehicle only in modest ways that still allow it to be viable. Some of the problems are in the “unsolved” class. It is harder to predict when those solutions will come, of course, but at the same time one should remember that many of the systems in today’s research vehicles were in this class just a few years ago. Tackling hard problems is just what these teams are good at doing. This doesn’t guarantee success, but neither does it require you bet against it.

And very few of the problems seem to be in the “unsolvable without human-smart AI” class, at least none that bar highly useful operation.

Gomes’ articles have been the major trigger of press, so I will go over those issues in detail here first. Later, I will produce an article that has even more challenges than listed, and what people hope to do about them. Still, the critiques are written almost as though they expected Google and others, rather than make announcements like “Look at the new milestone we are pleased to have accomplished” to instead say, “Let’s tell you all the things we haven’t done yet.”

Gomes begins by comparing the car to the Apple Newton, but forgets that 9 years after the Newton fizzled we had the success of the Palm Pilot, and 10 years after that Apple came back with the world-changing iPhone. Today, the pace of change is much faster than in the 80s.

Here are the primary concerns raised:

Maps are too important, and too costly

Google’s car, and others, rely on a clever technique that revolutionized the DARPA challenges. Each road is driven manually a few times, and the scans are then processed to build a super-detailed “ultramap” of all the static features of the road. This is a big win because big server computers get to process the scans in as much time as they need, and see everything from different angles. Then humans can review and correct the maps and they can be tested. That’s hard to beat, and you will always drive better if you have such a map than if you don’t.

Any car that could drive without a map would effectively be a car that’s able to make an adequate map automatically. As things get closer to that, making maps will become cheaper and cheaper.

Naturally, if the road differs from the map, due to construction or other changes, the vehicle has to notice this. That turns out to be fairly easy. Harder is assuring it can drive safely in this situation. That’s still a much easier problem than being able to drive safely everywhere without a map, and in the worst case, the problem of the changed road can be “solved” by just the ability to come to a safe stop. You don’t want to do that super often, but it remains the fail-safe out. If there is a human in the car, they can guide the vehicle in this. Even if the vehicle can’t figure out where to go to be safe, the human can. Even a remote human able to look at transmitted pictures can help the car with that — not live steering, but strategic guidance.

This problem only happens to the first car to encounter the surprise construction. If that car is still able to navigate (perhaps with human help,) the map can be quickly rebuilt, and if the car had to stop, all unmanned cars can learn to avoid the zone. They are unmanned, and thus probably not in a hurry.

The cost of maps

In the interests of safety, a lot of work is put into today’s maps. It’s a cost that somebody like Google or Mercedes can afford if they need to, (after all, Google’s already scanned every road in many countries multiple times) but it would be high for smaller players.  read more »

Live public test in Singapore

In late August, I visited Singapore to give an address at a special conference announcing a government sponsored collaboration involving their Ministry of Transport, the Land Transport Authority and A-STAR, the government funded national R&D centre. I got a chance to meet the minister and sit down with officials and talk about their plans, and 6 months earlier I got the chance to visit A-Star and also the car project at the National University of Singapore. At the conference, there were demos of vehicles, including one from Singapore Technologies, which primarily does military contracting.

Things are moving fast there, and this week, the NUS team announced they will be doing a live public demo of their autonomous golf carts and they have made much progress. They will be running the carts over a course with 10 stops in the Singapore Chinese and Japanese Gardens. The public will be able to book rides online, and then come and summon and direct the vehicles with their phones. The vehicles will have a touch tablet where the steering wheel will go. Rides will be free. Earlier, they demonstrated not just detecting pedestrians but driving around them (if they stay still) but I don’t know if this project includes that.

This is not the first such public demo - the CityMobil2 demonstration in Sardinia ran in August, on a stretch of beachfront road blocked to cars but open to bicycles, service vehicles and pedestrians. This project slowed itself to unacceptably slow speeds and offered a linear route.

The Singapore project will also mix with pedestrians, but the area is closed to cars and bicycles. There will be two safety officers on bicycles riding behind the golf carts, able to shut them down if any problem presents, and speed will also be limited.

Singapore is interesting because they have a long history of transportation innovation, and good reason for it. As a city-state, it’s almost all urban, and transportation is a real problem. That’s why congestion charging was first developed in Singapore, along with other innovations. Every vehicle in Singapore has a transponder, and they use them not just for congestion tolling, but to pay for parking seamlessly in almost all parking lots and a few other tricks.

In spite of this history of innovation, Singapore is also trending conservative — this might dampen truly fast innovation, but this joint project is a good start. Though I advised them that private projects will be able to move faster than public sector ones, in my view.

The NUS project is a collaboration with MIT, involving professor Emilio Frazzoli. Their press release has more details, including maps showing the route is non-linear but the speed is slow.

Tesla, Audi and other recent announcements

Some recent announcements have caused lots of press stir, and I have not written much about them, both because of my busy travel schedule, but also because there is less news that we might imagine.

Tesla is certainly an important company to watch. As the first successful start-up car company in the USA, they are showing they know how to do things differently, taking advantage of the fact that they don’t have a baked in knowledge of “how a car company works” the way other companies do. Tesla’s announcements of plans for more self-driving are important. Unfortunately, the announcements around the new dual-motor Model S involve offerings quite similar to what can be found already in cars from Mercedes, Audi and a few others. Namely advanced ADAS and the combination of lane-keeping and adaptive cruise control to provide a hands-off cruise control where you must keep your eyes on the road.

One notable feature demonstrated by Tesla is automatic lane change, which you trigger by hitting a turn signal. That’s a good interface, but it must be made clear to people that they still have the duty to check that it’s safe to change lanes. It’s not that easy for a robocar’s sensors, especially the limited sensor package in the Telsa, to see a car coming up fast behind you in the next lane. On some highways relative speeds can get pretty high. You’re not likely to be hit by such cars, but in some cases that’s because they will probably brake for you, not because you did a fully safe lane change.

Much more interesting are Elon Musk’s predictions of a real self-driving car in 5 to 6 years. He means one where you can read a book, or even, as he suggests, go to sleep. Going to sleep is one of the greatest challenges, almost as hard as operating unmanned or carrying a drunk or disabled person. You won’t likely do that just with cameras — but 5 to 6 years is a good amount of time for a company like Tesla.

Another unusual thing about Tesla is that while they are talking about robocars a lot, they have also built one of the finest driver’s cars ever made. The Model S is great fun to drive, and has what I call a “telepathic” interface sometimes — the motors have so much torque that you can almost think about where you want to go and the vehicle makes it happen. (Other examples of telepathic interfaces include touch-typing and a stickshift.) In some ways it is the last car that people might want to automate. But it’s also a luxury vehicle, and that makes self-driving desirable too.

Audi Racing

Another recent announcement creating buzz is Audi’s self-driving race car on a test track in Germany. Audi has done racing demos several times now. They are both important but also unimportant. It definitely makes sense to study how to control a car in extreme, high performance situations. To understand the physics of the tires so fully that you can compete in racing will teach lessons of use in danger situations (like accidents) or certain types of bad weather.

At the same time, real-world driving is not like racing, and nobody is going to be doing race-like driving on ordinary streets in their robocar. 99.9999% of driving consists of “staying in your lane” and some other basic maneuvers and so racing is fun and sexy but not actually very high on the priority list. (Not that teams don’t deserve to spend some of their time on a bit of fun and glory.) The real work of building robocars involves putting them through all the real-world road situations you can put them through, both real and in some cases simulated on a track or in a computer.

Google first showed its system to many people by having it race figure-8s on the roof parking lot at the TeD conference. The car followed a course through a group of cones at pretty decent speed and wowed the crowd with the tight turns. What most of the crowd didn’t know was that the cones were only there for show, largely. The car was guiding itself from its map of all the other physical things in the parking lot — line markers, pavement defects and more. The car is able to localize itself fine from those things. The cones just showed the public that it really was following the planned course. At the same time, making a car do that is something that was accomplished decades ago, and is used routinely to run “dummy cars” on car company test tracks.

A real demo turns out to be very boring, because that’s how being driven should be. I’m not saying it’s bad in any way to work on racing problems. The only error would be forgetting that the real-world driving problems are higher priority and success in them is less dramatic but more impressive in the technical sense.

This doesn’t mean we won’t see more impressive demos soon. Many people have shown off automatic braking. Eventually we will see demos of how vehicles respond in danger situations — accidents, pedestrians crossing into the road and the like. A tiny part of driving but naturally one we care about. And we will want them to understand the physics of what the tires and vehicle are capable of so that they perform well, but not so they can find the most efficient driving line on the track.

There was some debate about having a new self-driving car contest like the DARPA grand challenges, and a popular idea was man vs. machine, including racing. That would have been exciting. We asked ourselves whether a robot might have an advantage because it would have no fear of dying. (It might have some “fear” of smashing its owners very expensive car.) Turns out this happens on the racetrack fairly often with new drivers who try to get an edge by driving like they have no fear, that they will win all games of chicken. When this happens, the other drivers get together to teach that new driver a lesson. A lesson about cooperating and reciprocation in passing and drafting. So the robots would need to be programmed with that as well, or their owners would find a lot of expensive crashes and few victories.

Robocar Retirement

Here’s an interview with me in the latest Wall Street Journal on the subject of robocars and seniors.

This has always been a tricky question. Seniors are not early adopters, so the normal instinct would be to expect them to fear a new technology as dramatic as this one. Look at the market for simplified cell phones aimed at seniors who can’t imagine why they want a smartphone. Not all are like this, but enough are to raise the question.

Sometimes this barrier is broken. Pictures of grandchildren in e-mail brought grandparents online, as did video calls with them. Necessity overcomes the fear of change.

As people get older, they start losing driving ability. They die more often in accidents, eventually surpassing the rates of reckless teens, because they are more fragile, and they make mistakes that cause other people to hit them. Many seniors report troubles with vision at night, and they stop driving at night. In some cases, they get their licences taken away by the state — though the AARP and others fight this so it’s rare — or their kids take away their keys when things get really dangerous. And the kids become a taxi service for their parents.

The boomer generation, which took over the suburbs and exurbs have nice houses with minimal transit. Some find themselves leaving that home because they can’t drive any more and they will become a shut-in if they don’t do something.

The robocar offers answers to many of these problems. Safe transportation for those with disabilities. (Eventually even mild dementia.) Inexpensive taxi transportation anywhere, including those low-transit suburbs. And a chance to video chat with the grandchildren while on the way.

It’s no surprise that retirement communities are discussed as an early deployment zone for robocars. In those communities, you have a controlled street environment — often with heavy use of NEVs/golf carts already. You have people losing the ability to drive who have limited mobility needs. If they can get to basic shopping and a few other locations (including transit hubs to travel further) they can do pretty well.

Until the robocar came along, we were all doomed to lose the freedom cars gave us. This is no longer going to happen.

Talking soon on robocars and insurance

I’ve been on the road a lot, talking in places like Singapore, Shenzen and Hong Kong, and visiting Indonesia which is a driving chaos eye-opener. In a bit over 10 hours I will speak at Swiss Re’s conference on robocars and insurance in Zurich. While the start will be my standard talk, in the latter section we will have some new discussion of liability and insurance.

A live stream of the event should be available at http://swissre.adobeconnect.com/theautonomouscar/ I talk at 8:45am Central European Summer Time.

A lot of news while I’ve been on the road — driving permits in California, new projects and the Singapore effort I was there at the announcement of. And lots of non-news that got people very excited like the “revelation” that Google’s car doesn’t drive in snow (nobody thought it could) or on all roads (nobody even suggested this) or that it was forced to add a steering wheel for testing (this was always planned, Google participating in the hearings writing those laws.) And lots of car company announcements from the ITS world congress (a conference that 2 years ago barely acknowledged the presence of self-driving cars.)

More to come later.

Short Big Think video piece on Privacy vs. Security

There’s another video presentation by me that I did while visiting Big Think in NYC.

This one is on The NSA, Snowden and the “tradeoff” of Privacy and Security.

Earlier, I did a 10 minute piece on Robocars for Big Think that won’t be news to regular readers here but was reasonably popular.

The Neighbourhood Elevator and a new vision of urban density

I’ve been musing more on the future of the city under the robocar, and many visions suggest we’ll have more sprawl. Earlier I have written visions of Robocar Oriented Development and outlined all the factors urban planners should look at.

In the essay linked below, I introduce the concept of a medium density urban neighbourhood that acts like a higher density space thanks to robocars functioning like the elevators in the high-rises of high density development.

Read The Neighbourhood Elevator and 21st century urban density at robocars.com.

Robocar News: UK Legalization, MobilEye IPO, Baidu, new Lidar, Nissan pullback, FBI Weapons, Navia, CityMobil2

A whole raft of recent robocar news.

UK to modify laws for full testing, large grants for R&D

The UK announced that robocar testing will be legalized in January, similar to actions by many US states, but the first major country to do so. Of particular interest is the promise that fully autonomous vehicles, like Google’s no-steering-wheel vehicle, will have regulations governing their testing. Because the US states that wrote regulations did so before seeing Google’s vehicle, their laws still have open questions about how to test faster versions of it.

Combined with this are large research grant programs, on top of the £10M prize project to be awarded to a city for a testing project, and the planned project in Milton Keynes.

Jerusalem’s MobilEye going public in largest Israeli IPO

The leader in doing automated driver assist using cameras is Jerusalem’s MobilEye. This week they’re going public, to a valuation near $5B and raising over $600 million. MobilEye makes custom ASICs full of machine vision processing tools, and uses those to make camera systems to recognize things on the road. They have announced and demonstrated their own basic supervised self-driving car with this. Their camera, which is cheaper than the radar used in most fancy ADAS systems (but also works with radar for better results) is found in many high-end vehicles. They are a supplier to Tesla, and it is suggested that MobilEye will play a serious role in Tesla’s own self-driving plans.

As I have written, I don’t believe cameras are even close to sufficient for a fully autonomous vehicle which can run unmanned, though they can be a good complement to radar and especially LIDAR. LIDAR prices will soon drop to the low $thousands, and people taking the risk of deploying the first robocars would be unwise to not use LIDAR to improve their safety just to save a few thousand for early adopters.

Chinese search engine Baidu has robocar (and bicycle) project

Baidu is the big boy in Chinese search — sadly a big beneficiary of Google’s wise and moral decision not to be collaborators on massive internet censorship in China — and now it’s emulating Google in a big way by opening its own self-driving car project.

Various stories suggest a vehicle which involves regular handoff between a driver and the car’s systems, something Google decided was too risky. Not many other details are known.

Also rumoured is a project with bicycles. Unknown if that’s something like the “bikebot” concept I wrote about 6 years ago, where a small robot would clamp to a bike and use its wheels to deliver the bicycle on demand.

Why another search engine company? Well, one reason Google was able to work quickly is that it is the world’s #1 mapping company, and mapping plays a large role in the design of robocars. Baidu says it is their expertise in big data and AI that’s driving them to do this.

Velodyne has a new LIDAR

The Velodyne 64 plane LIDAR, which is seen spinning on top of Google’s cars and most of the other serious research cars, is made in small volumes and costs a great deal of money — $75,000. David Hall, who runs Velodyne, has regularly said that in volume it would cost well under $1,000, but we’re not there yet. He has released a new LIDAR with just 16 planes. The price, while not finalized, will be much higher than $1K but much lower than $75K (or even the $30K for the 32 plane version found on Ford’s test vehicle and some others.)

As a disclaimer, I should note I have joined the advisory board of Quanergy, which is making 8 plane LIDARs at a much lower price than these units.

Nissan goes back and forth on dates

Conflicting reports have come from Nissan on their dates for deployment. At first, it seemed they had predicted fairly autonomous cars by 2020. A later announcement by CEO Carlos Ghosn suggested it might be even earlier. But new reports suggest the product will be less far along, and need more human supervision to operate.

FBI gets all scaremongering

Many years ago, I wrote about the danger that autonomous robots could be loaded with explosives and sent to an address to wreak havoc. That is a concern, but what I wrote was that the greater danger could be the fear of that phenomenon. After all, car accidents kill more people every month in the USA than died at the World Trade Center 13 years ago, and far surpass war and terrorism as forms of violent death and injury in most nations for most of modern history. Nonetheless, an internal FBI document, released through a leak, has them pushing this idea along with the more bizarre idea that such cars would let criminals multitask more and not have to drive their own getaway cars.  read more »

The two cultures of robocars

I have many more comments pending on my observations from the recent AUVSI/TRB Automated Vehicles Symposium, but for today I would like to put forward an observation I made about two broad schools of thought on the path of the technology and the timeline for adoption. I will call these the aggressive and conservative schools. The aggressive school is represented by Google, Induct (and its successors) and many academic teams, the conservative school involves car companies, most urban planners and various others.

The conservative (automotive) view sees this technology as a set of wheels that has a computer.

The aggressive (digital) school sees this as a computer that has a set of wheels.

The conservative view sees this as an automotive technology, and most of them are very used to thinking about automotive technology. For the aggressive school, where I belong, this is a computer technology, and will be developed — and change the world — at the much faster pace that computer technologies do.

Neither school is probably entirely right, of course. It won’t go as gung-ho as a smartphone, suddenly in every pocket within a few years of release, being discarded when just 2 years old even though it still performs exactly as designed. Nor will it advance at the speed of automotive technology, a world where electric cars are finally getting some traction a century after being introduced.

The conservative school embraces the 4 NHTSA Levels or 5 SAE levels of technology, and expects these levels to be a path of progress. Car companies are starting to sell “level 2” and working on “level 3” and declaring level 4 or 5 to be far in the future. Google is going directly to SAE level 4.

The two cultures do agree that the curve of deployment is not nearly-instant like a smartphone. It will take some time until robocars are a significant fraction of the cars on the road. What they disagree on is how quickly that has a big effect on society. In sessions I attended, the feeling that the early 2020s would see only a modest fraction of cars being self-driving meant to the conservatives that they would not have that much effect on the world.

In one session, it was asked how many people had cars with automatic cruise control (ACC.) Very few hands went up, and this is no surprise — the uptake of ACC is quite low, and almost all of it is part of a “technology package” on the cars that offer it. This led people to believe that if ACC, now over a decade old, could barely get deployed, we should not expect rapid deployment of more complete self-driving. And this may indeed be a warning for those selling super-cruise style products which combine ACC and lanekeeping under driver supervision, which is the level 2 most car companies are working on.

To counter this, I asked a room how many had ridden in Uber or its competitors. Almost every hand went up this time — again no surprise. In spite of the fact that Uber’s cars represent an insignificant fraction of the deployed car fleet. In the aggressive view, robocars are more a service than a product, and as we can see, a robocar-like service can start affecting everybody with very low deployment and only a limited service area.

This dichotomy is somewhat reflected in the difference between SAE’s Level 4 and NHTSA’s. SAE Level 4 means full driving (including unmanned) but in a limited service area or under other limited parameters. This is what Google has said they will make, this is what you see planned for services in campuses and retirement communities. This is where it begins, and grows one region at a time. NHTSA’s levels falsely convey the idea that you slowly move to fully automated mode and immediately do it over a wide service area. Real cars will vary as to what level of supervision they need (the levels) over different times, streets and speeds, existing at all the levels at different times.

Follow the conservative model and you can say that society will not see much change until 2030 — some even talk about 2040. I believe that is an error.

Another correlated difference of opinion lies around infrastructure. Those in the aggressive computer-based camp wish to avoid the need to change the physical infrastructure. Instead of making the roads smart, make the individual cars smart. The more automotive camp has also often spoken of physical changes as being more important, and also believes there is strong value in putting digital “vehicle to vehicle” radios in even non-robocars. The computer camp is much more fond of “virtual infrastructure” like the detailed ultra-maps used by Google and many other projects.

It would be unfair to claim that the two schools are fully stratified. There are researchers who bridge the camps. There are people who see both sides very well. There are “computer” folks working at car companies, and car industry folks on the aggressive teams.

The two approaches will also clash when it comes to deciding how to measure the safety of the products and how they should be regulated, which will be a much larger battle. More on that later.

Robotics: Science and Systems and Automated Vehicles Symposium this week

It’s a big week for Robocar conferences.

In Berkeley, yesterday I attended and spoke at the “Robotics: Science and Systems” conference which had a workshop on autonomous vehicles. That runs to Wednesday, but overlapping and near SF Airport is the Automated Vehicles Symposium — a merger of the TRB (Transportation Research Board) and AUVSI conferences on the same topic. 500 are expected to attend.

Yesterday’s workshop was pretty good, with even a bit of controversy.

Yesterday saw:

  • Ed Olson on more of the lessons from aviation on handoff between automation and manual operation. This keeps coming up a a real barrier to some of the vehicle designs that have humans share the chores with the system.
  • Jesse Levinson of Stanford’s team showed some very impressive work in automatic calibration of sensors, and even fusion of LIDAR and camera data, aligning them in real time in spite of movement and latency. This work will make sensors faster, more reliable and make fusion accurate enough to improve perception.
  • David Hall, who runs Velodyne, spoke on the history of their sensors, and his plans for more. He repeated his prediction that in large quantities his sensor could cost only $300. (I’m a bit skeptical of that, but it could cost much, much less than it does today.) David made the surprising statement that he thinks we should make dedicated roads for the vehicles. (Surprising not just because I disagree, but because you could even get by without much LIDAR on such roads.)
  • Marco Panove of Stanford showed research they did on Taxi models from New York and Singapore. The economics look very good. Dan Fagnant also presented related research assuming an on-demand semi shared system with pickup stations in every TAZ. It showed minimal vacant miles but also minimal successful rideshare. The former makes sense when it’s TAZ to TAZ (TAZs are around a square mile) but I would have thought there would be more rideshare. The conclusion is that VMT go up due to empty miles, but that rideshare can partially compensate, though not as much as some might hope.
  • Ken Laberteaux of Toyota showed his research on the changing demographics of driving and suburbs. Conclusion: We are not moving back into the city, suburbanization is continuing. Finding good schools continues to drive people out unless they can afford private school are are childless.

The event had a 3-hour lunch break, where most went to watch some sporting event from Brazil. The Germans at the conference came back happier.

Some good technical talks presented worthwhile research

  • Sheng Zhao and a team from UC Riverside showed a method to get cm accuracy in position and even in pose (orientation) from cheap GPS receivers, by using improved math on phase-matching GPS. This could also be combined with cheap IMUs. Most projects today use very expensive IMUs and GPSs, not the cheap ones you find in your cell phone. This work may lead to being able to get reliable data from low cost parts.
  • Matthew Cornick and a team from Lincoln Lab at MIT showed very interesting work on using ground penetrating radar to localize. With GPR, you get a map of what’s below the road — you see rocks and material patterns down several feet. These vary enough, like the cracks and lines on a road, and so you can map them, and then find your position in that map — even if the road is covered in snow. While the radar units are today bulky, this offers the potential for operations in snow.
  • A team from Toyota showed new algorithms to speed up the creation of the super-detailed maps needed for robocars. Their algorithms are good at figuring out how many lanes there are and when they start and stop. This could make it much cheaper to build the ultramaps needed in an automatic way, with less human supervision.

The legal and policy sessions got more heated.

  • Bryant Walker Smith laid out some new proposals for how to regulate and govern torts about the vehicles.
  • Eric Feron of Georgia Tech made proposals for how to do full software verification. Today formally proving and analysing code for correctness takes 0.6 hours per line of code — it’s not practical for the 50 million line (or more) software systems in cars today. Jonathan argues it can be made cheaper, and should be done. Note that fully half the cost of developing the 787 aircraft was software verification!

The final session, on policy included:

  • Jane Lappin on how DoT is promoting research.
  • Steve Shladover on how we’re all way to optimistic on timelines, and that coming up with tests to demonstrate superior safety to humans is very far away, since humans run 65,000 hours between injury accidents.
  • Myself on why regulation should keep a light touch, and we should not worry too much about the Trolley Problem — which came up a couple of times.
  • Raj Rajkumar of CMU on the success they have had showing the CMU/GM car to members of congress.

Now on to the AVS tomorrow.

Robocars 101 on Big Think, NPR Interview and many talks

Some recent press and talks:

Earlier in June I sat down with “Big Think” for an interview they have titled “Robocars 101” explaining some of the issues around the cars.

I also did a short interview on NPR’s “All Things Considered” not long after Google’s new car was announced. What you might find interesting is how I did it. I was at a friend’s house in Copenhagen and went into a quiet room where they called me on my cell phone. However, I also started a simple audio recorder app on my phone. When we were done, I shared the mp3 of a better sample from the same microphone with them, which they mixed in.

As a result, the interview sounds almost like it was done in-studio instead of over an international cell phone call.

Videos of my talks at Next Berlin at at Dutch Media Future Week 2014 are also up. And a shortened talk at Ontario Centers for Excellence Discovery 2014 in Toronto May 12. There we had the Governor General of Canada as our opening act. :-) That’s just 3 of the 11 events I was at on that trip.

Completely off the Robocar track is a short interview with CNBC where I advise people to invest in Bitcoin related technology, not in bitcoins.

Small startup "Cruise" plans to sell modification kits for highway driving

So far it’s been big players like Google and car companies with plans in the self-driving space. Today, a small San Francisco start-up named Cruise, founded by Kyle Vogt (a founder of the web video site Justin.tv) announces their plans to make a retrofit kit that will adapt existing cars to do basic highway cruise, which is to say, staying in a lane and keeping pace behind other cars while under a driver’s supervision.

I’ve been following Cruise since its inception. This offering has many similarities to the plans of major car companies, but there are a few key differences:

  • This is a startup, which can be more nimble than the large companies, and having no reputation to risk, can be bolder.
  • They plan to make this as a retrofit kit for a moderate set of existing cars, rather than custom designing it to one car.

They’re so dedicated to the retrofit idea that the Audi A4 they are initially modifying does not even have drive-by-wire brakes like the commonly used hybrid cars. Their kit puts sensors on the roof, and puts a physical actuator on the brake and another physical actuator on the steering wheel — they don’t make use of the car’s own steering motor. They want a kit that can be applied to almost any car the market tells them to target.

They won’t do every car, though. All vendors have a strong incentive to only support cars they have given some solid testing to, so most plans don’t involve retrofit at all, and of course Google has now announced their plans to design a car from scratch. Early adopters may be keen on retrofit.

I rode in the car last week during a demo at Alemeda air station, a runway familiar to viewers of Mythbusters. There they set up a course of small orange cones, which are much easier to see than ordinary lane markings, so it’s hard to judge how well the car does on lane markings. It still has rough edges, to be sure, but they don’t plan to sell until next year. In the trial, due to insurance rules, it kept under 40mph, though it handled that speed fine, though drifted a bit in wider parts of the “lane.”

On top is an aerodynamic case around a sensor pack which is based on stereo cameras and radar from Delphi. Inside is just a single button in the center arm console to enable and disable cruise mode. You take the car to the lane and push the button.

All stuff we’ve seen before, and not as far along, but the one key difference — being a nimble startup — may make all the difference. Only early adopters will pay the $10,000 for a product where you must (at least for now) still watch the road, but that may be all that is needed.

Live Google transit directions seriously changes the value of transit

On my recent wanderings in Europe, I became quite enamoured by Google’s latest revision of transit directions. Google has had transit directions for some time, but they have recently improved them, and linked them in more cities to live data about where transit vehicles actually are.

The result not a mere incremental improvement, it’s a game-changing increase in the utility of decent transit. In cities like Oslo and London, the tool gives the user the ability to move with transit better than a native. In the past, using transit, especially buses, as a visitor has always been so frustrating that most visitors simply don’t use it, in spite of the much lower cost compared to taxis. Transit, especially when used by an unfamiliar visitor, is slow and complex, with long waits, missed connection and confusion about which bus or line to take during shorter connections, as well as how to pay.

Not so any more. With a superhuman ability, your phone directs you to transit stops you might not figure out from a map, where the right bus usually appears quite quickly. Transfers are chosen to be quick as well, and directions are given as to which direction to go, naming the final destination as transit signs often do, rather than the compass direction. It’s optimized by where the vehicles actually are and predicted to be, and this will presumably get even better.

By making transit “just work” it becomes much more useful, and gives us a taste of the robocar taxi world. That world is even easier, of course — door to door with no connections and no need for you to even follow directions. But while Uber also shows us that world well in user experience, Uber is expensive, as are cabs, while transit is closer in cost to the anticipated robocar cost of well below $1/mile.

It also helps to have transit systems with passes or contactless pay cards, to avoid the hassles of payment.

Why does this work so well? In the transit-heavy cities, it turns out there are often 2, 3 or even 4 ways to get to your destination via different transit lines and connections. The software is able to pick among them in a way even a native couldn’t, and one is often leaving soon, and it finds it for you.

In some cities, there is not live data, so it only routes based on schedules. This cuts the utility greatly. From a user experience standpoint, it is often better to give people a wait they expect than to do a better job but not give accurate expectations.

What’s clear now is that transit agencies should have done this a lot sooner. Back in the 1980s a friend of mine built one of the first systems which tracked transit vehicles and gave you a way to call to see when the bus would come, or in some cases signs on the bus stops. Nice as those were they are nothing compared to this. There is not much in this technology that could not have been built some time ago. In fact, it could have been built even before the smartphone, with people calling in by voice and saying, “I am at the corner of X and Y and I need to get to Z” with a human helper. The cost would have actually been worth it because by making the transit more useful it gets more riders.

That might be too expensive, but all this needed was the smartphone with GPS and a data connection, and it is good that it has come.

In spite of this praise, there is still much to do.

  • Routing is very time dependent. Ask at 1:00 and you can get a very different answer than you get asking at 1:02. And a different one at 1:04. The product needs a live aspect that updates as you walk and time passes.
  • The system never figures out you are already on the bus, and so always wants to route you as though you were standing on the road. Often you want to change plans or re-look up options once you are on the vehicle, and in addition, you may want to do other things on the map.
  • Due to how rapidly things change, the system also needs to display when multiple options are equivalent. For example, it might say, “Go to the train platform and take the B train northbound.” Then due to how things have change, you see a C train show up — do you get on it? Instead, it should say, “Take a B, C or E train going north towards X, Y or Z, but B should come first.”
  • For extra credit, this should get smarter and combine with other modes. For example, many cities have bikeshare programs that let you ride a bike from one depot to another. If the system knew about those it could offer you very interesting routings combining bikes and transit. Or if you have your own bike and transit lines allow it on, you could use that.
  • Likewise, you could combine transit with cabs, getting a convenient route with low walking but with much lower cab expense.
  • Finally, you could also integrate with one-way car share programs like car2go or DriveNow, allowing a trip to mix transit, car, bike and walking for smooth movement.
  • Better integration with traffic is needed. If the buses are stuck in traffic, it’s time to tell you to take another method (even cycling or walking) if time is your main constraint.
  • Indoor mapping is needed in stations, particularly underground ones. Transit agencies should have beacons in the stations or on the tracks so phones can figure out where they are when GPS is not around. Buses could also have beacons to tell you if you got on the right one.
  • The systems should offer an alert when you are approaching your stop. Beacons could help here too. For a while the GPS map has allowed the unfamiliar transit rider to know when to get off, but this can make it even better.
  • This is actually a decent application for wearables and things like Google glass, or just a bluetooth earpiece talking in your ear, watching you move through the city and the stations and telling you which way to go, and even telling you when you need to rush or relax.
  • In some cities going onto the subway means loss of signal. There, storing the live model for relevant lines in a cache would let the phone still come up with pretty good estimates when offline for a few minutes.

A later stage product might let you specify a destination and a time, and then it will buzz you when it’s time to start walking, and guide you there, through a path that might include walking, bike rides, transit lines and even carshare or short cab rides for a fast, cheap trip with minimal waiting, even when the transit isn’t all that good.

Conan O'Brien's Google Car, Nissan in 2018 and more

I’m in the home stretch of a long international trip — photos to follow — but I speak tomorrow at Lincoln Center on how computers (and robocars) will change the worlds of finance. In the meantime, Google’s announcement last month has driven a lot of news in the Robocar space worthy of reporting.

On the lighter side, this video from the Conan O’Brien show highlights the issues around people’s deep fear of being injured by machines. While the video is having fun, this is a real issue that will dominate the news when the first accidents and injuries happen. I cover that in detail in my article about accidents but the debate will be a major one.

Nissan announced last year that it would sell cars in 2020. Now that Tesla has said 2016, Google has said civilians will be in their small car within a year and Volvo has said the same will happen in Sweden by 2017, Nissan CEO Carlos Ghosn has said they might do it 2 years earlier.

As various locations rush to put in robocar laws, in Europe they are finally getting around to modifying the Vienna convention treaty, which required a human driver. However, the new modifications, driven by car companies, still call for a steering wheel that a driver can use to take over (as do some of the US state laws.) These preclude Google’s new design, but perhaps with a bit of advance warning, this can be fixed. Otherwise, changing it again will be harder. Perhaps the car companies — none of whom have talked about anything like Google’s car with no controls — will be happy with that.

The urban test course at the University of Michigan, announced not very long ago, is almost set to open — things are moving fast, as they will need to if Michigan is to stay in the race. Google’s new prototype, by the way, is built in Michigan. Google has not said who but common speculation names not a major car company, but one of their big suppliers.

The Ernst & Young auto research lab (in Detroit) issued a very Detroit style forecast for autonomous vehicles which said their widespread use was 2 decades away. Not too surprising for such a group. Consultants are notoriously terrible at predictions for exponential technology. Their bad smartphone predictions are legendary (and now erased, of course.) A different study predicts an $87 billion market — but the real number is much larger than that.

This article where top car designers critique Google’s car illustrates my point from last week how people with car company experience are inclined to just not get it. But at the same time some of the automotive press do get it.

Why Google's "ridiculous" looking car is brilliant

It’s not too surprising that the release of images of Google’s prototype robocar have gotten comments like this:

Revolutionary Tech in a Remarkably Lame Package from Wired

A Joy Ride in Google’s Clown Car says Re/Code

I’ve also seen comparisons to the Segway, and declarations that limited to 25 mph, this vehicle won’t get much adoption or affect the world much.

Google’s own video starts with a senior expressing that it’s “cute.”

I was not involved in the specifics of design of this vehicle, though I pushed hard as I could for something in this direction. Here’s why I think it’s the right decision.

First of all, this is a prototype. Only 100 of this design will be made, and there will be more iterations. Google is all about studying, learning and doing it again, and they can afford to. They want to know what people think of this, but are not scared if they underestimate it at first.

Secondly, this is what is known as a “Disruptive Technology.” Disruptive technologies, as described in the Silicon Valley bible “The Innovators Dilemma” are technologies that seem crazy and inferior at first. They meet a new need, not well understood by the incumbent big companies. Those big companies don’t see it as a threat — until years later, they are closing their doors. Every time a disruptive technology takes over, very few of the established players make it through to the other side. This does not guarantee that Google will dominate or crush those companies, or that everything that looks silly eventually wins. But it is a well established pattern.

This vehicle does not look threatening — not to people on the street, and not to existing car companies and pundits who don’t get it. Oh, there are many people inside those car companies who do get it, but the companies are incapable of getting it in their bones. Even when their CEOs get it, they can’t steer the company 90 degrees — there are too many entrenched forces in any large company. The rare exception are founder-led companies (like Google and Facebook and formerly Apple and Microsoft) where if the founder gets it, he or she can force the company to get it.

Even large companies who read this blog post and understand it still won’t get it, not most of the time. I’ve talked to executives from big car companies. They have a century of being car companies, and knowing what the means. Google, Tesla and the coming upstarts don’t.

One reason I will eventually move away from my chosen name for the technology — robocar — along with the other popular names like “self-driving car” is that this future vehicle is not a car, not as we know it today. It is no more a “driverless car” than a modern automobile is a horseless carriage. 100 years ago, the only way they could think of the car was to notice that there was no horse. Today, all many people notice about robocars is that no human is driving. This is the thing that comes after the car.

Some people expected the car to look more radical. Something like the Zoox or ATMBL by Mike and Maaike (who now work in a different part of Google.) Cars like those will come some day, but are not the way you learn. You start simple, and non threatening, and safe. And you start expensive — the Google prototype still has the very expensive Velodyne LIDAR on it, but trust me, very soon LIDAR is going to get a lot less expensive.

The low speed is an artifact of many things. You want to start safe, so you limit where you go and how fast. In addition, US law has a special exception from most regulations for electric vehicles that can’t go more than 25mph and stick to back roads. Some may think that’s not very useful (turns out they are wrong, it has a lot of useful applications) but it’s also a great way to start. Electric vehicles have another big advantage in this area. Because you can reverse electric motors, they can work as secondary brakes in the event of failure of the main brake system, and can even be secondary steering in case of failure of the steering system at certain speeds. (Google has also said that they have two steering motors in order to handle the risk of failure of one steering motor.) Electric vehicles are not long-range enough to work as taxis in a large area, but they can handle smaller areas just fine.

If you work in the auto industry, and you looked at this car and saw a clown car, that’s a sign you should be afraid.

Google to custom make its own car with no steering wheel

In what is the biggest announcement since Google first revealed their car project, it has announced that they are building their own car, a small low-speed urban vehicle for two with no steering wheel, throttle or brakes. It will act as a true robocar, delivering itself and taking people where they want to go with a simple interface. The car is currently limited to 25mph, and has special pedestrian protection features to make it even safer. (I should note that as a consultant to that team, I helped push the project in this direction.)

This is very different from all the offerings being discussed by the various car companies, and is most similar to the Navia which went on sale earlier this year. The Navia is meant as a shuttle, and up to 12 people stand up in it while it moves on private campus roads. It only goes 20 km/h rather than the 40 km/h of Google’s new car. Google plans to operate their car on public roads, and will have non-employees in test prototype vehicles “very soon.”

This is a watershed moment and an expression of the idea that the robocar is not a car but the thing that comes after the car, as the car came after the horse. Google’s car is disruptive, it seems small and silly looking and limited if you look at it from the perspective of existing car makers. That’s because that’s how the future often looks.

I have a lot to say about what this car means, but at the same time, very little because I have been saying it since 2007. One notable feature (which I was among those pushing for inside) is a soft cushion bumper and windshield. Clearly the goal is always to have the car never hit anybody, but it can still happen because systems aren’t perfect and sometimes people appear in front of cars quickly making it physically impossible to stop. In this situation, cars should work to protect pedestrians and cyclists. Volvo and Autoliv have an airbag that inflates on the windshield bars, which are the thing that most often kills a cyclist. Of the 1.2 million who are killed in car accidents each year, close to 500,000 are pedestrians, mostly in the lower income nations. These are first steps in protecting them as well as the occupants of the car.

The car has 2 seats (side-by-side) and very few controls. It is a prototype, being made at first in small quantities for testing.

More details, and other videos, including a one of Chris Urmson giving more details, can be found at the new Google Plus page for the car. Also of interest is this interview with Chris.

I’m in Milan right now about to talk to Google’s customers about the car — somewhat ironic — after 4 weeks on the road all over Europe. 2 more weeks to go! I will be in Copenhagen, Amsterdam, London and NYC in the coming weeks, after having been in NYC, Berlin, Krakow, Toronto, Amsterdam, Copenhagen, Oslo, the fjords and Milan. In New York, come see me at Singularity U’s Exponential Finance conference June 10-11.

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