Minnesota planners begin to envision driverless future | Star Tribune

Eric Roper at the Strib writes: Minnesota planners begin to envision driverless future.

My quote:

“We’re sitting here fighting about a train — a billion-dollar train that it tells you when you have to be there and where to go,” said state Rep. Pat Garofalo, R-Farmington, who owns a Tesla. “Pretty soon here you’re going to have cars that can just pick you up wherever you are and take you to wherever you want to be.”

But others like David Levinson, the lead author of the U report, said large cities will still need high-capacity transit to serve busy urban areas.

“Cars, even driverless cars, can’t move as many people per hour past a point as trains can,” Levinson said.

Now my fuller response to Eric Roper (to be clear, Eric never said his questions were off the record, and he was talking to a blogger, so he should have been aware):1self073017gr_online_525px

Do you think we’re planning enough for the arrival of this technology? It seems like there’s enough unknowns that folks like the Met Council don’t have much to say about how it will affect land use. And I’ve gotten some vague answers from Minneapolis, which is looking into it.

What should cities and states like the Twin Cities and Minnesota be doing at this point, if anything, to prepare and plan for the shift?

As you gathered no one [at the Metropolitan Council] is planning for [driverless cars]. Now, it is hard to say what the effects will be (I have my ideas), but my concern is not that we are NOT planning for driverless cars, but that we ARE planning for nothing to change. I.e., all of the plans and forecasts assume today’s technologies remain unchanged 30 years into the future, which seems implausible. This is a good time for alternative scenario planning rather than forecasts.

As a consequence of extrapolative forecasts (both in computer models and in people’s mental models of how cities work), cities like the Twin Cities (and others) are planning for more highway capacity when all the expectations for driverless cars should be more efficient use of road space (closer spacing between vehicles both laterally (narrow lanes) and longitudinally (shorter headways or gaps between vehicles). Given that roads are very long term investments and hard to reverse (i.e. roads are historically unbuilt at a much slower pace than they are built), building road capacity for needs that may soon disappear seems unwarranted and a classic example of a white elephant.

Nevertheless, there are factors which may increase travel demand (deadheading (i.e. empty and relocating vehicles), faster speeds, less driving responsibility (train passengers e.g. are willing to travel more time than car drivers because they can do something else while in motion), less expensive vehicles (EVs should cost less than the internal combustion engine when built to scale) and less expensive energy (with renewables, the price of electricity will continue to fall)).
However, there are other offsetting factors which could dampen this (switching from car ownership to a more taxi-like model in urban areas that charges on a per-trip basis, moving from an individual shopping to a delivery model for goods). Also one has to question whether the 5 day a week daily commute will remain as common in a world 30 years from now (with more telecommuting and more flexible work environments).
And all of this can be controlled with policy, we underprice roads by failing to recover both their direct costs (like infrastructure – fuel and related taxes pay for less than half of infrastructure costs) as well as their externalities (like pollution (most of which disappears with EVs), noise (similarly), crashes (most of which disappears with AVs), and congestion (which could disappear with proper road pricing)).
So what we should be doing: Don’t build new roads, or widen existing roads, until we implement a time-of-day based road pricing system that discounts off-peak use and recovers the full cost of car and truck travel.
In response to the Garofalo comment, I wrote:
Driverless cars don’t resolve the fundamental space issues of large cities (we can argue whether Minneapolis qualifies). Cars, even driverless cars, can’t move as many people per hour past a point as trains can. So if you have large demands to move people between fixed places, as you see in places like New York, Chicago, San Francisco, Sydney, or just about any city in Europe or Asia, AVs cannot solve that problem.
And that’s even assuming there’s some added capacity, right? Or do you disagree with the Tom Fisher argument that we’ll have a lot more roadway in urban areas to repurpose for other things?
To be clear, we will have more roadspace to repurpose (see our book), (we already do). But there are limits. If there are cities with large demands, trains will always move more people in a given amount of space than cars. Are the demands large enough now (or in the future) to justify the costs?  The biggest gains in space will come in (a) skinny cars (which will allow doubling the number of lanes), and (b) more widespread adoption of bikes and e-bikes and increased walking, with some additional gains from eliminating on-street parking, narrowing lanes for full-size cars and trucks, and shorter headways between vehicles.

Is it fair to say then that they could be commonplace by 2030?


Common, but far from universal. The median age of a car on the road is about 11.5 years, so if that still holds, the median car on the road will have been built in 2019.

There are also degrees of autonomy, (see the SAE report on this  ). I think most discussion refers to Level 4 (or 5) as Autonomous. Today we are between level 2 and 3 in production cars (Tesla autopilot is better than level 2 on freeways effectively, but not really quite level 3, the Waymo (Google) Car is level 4 in mapped environments), and the trials of test vehicles at the Level 4 and 5 range still require human intervention (“disengagements”) from time to time.

On the Differences between Autonomous, Automated, Self-Driving, and Driverless Cars

Today we have cars. In 30 years we will have cars. But in the meantime, some cars will will driven by humans and others not, and there will be terms to distinguish them.

Today, most people use the terms Autonomous, Automated, Self-Driving, and Driverless as interchangeable. Even wikipedia does not differentiate. Yet some people in the field make a point of the differences (e.g. Alain Kornhauser). If I understand these differences correctly, a self-driving car is not as advanced as driverless, in that driverless doesn’t have the back-up of a person taking control, and self-driving might. Driverless taxis are not merely self-driving, they pick up passengers and may be personless. In SAE terms, driverless is Level 5, while self-driving is Level 4 or below.

Generally, the difference between automatic (or automated) and autonomous is the degree of human intervention. An automated car does not have the level of intelligence or independence that an autonomous car has. So driverless and autonomous are nearer to synonyms, as are self-driving and automated. A truly autonomous car would decide on destination and route as well as control within the lanes. An automated car would follow orders about destination and route, and may only adopt some lane-keeping or car-following guidance.

Google/Waymo car, soon to be retired
Google/Waymo car, soon to be retired

Nevertheless, I do not believe these differences can be preserved linguistically, even within the profession, the broad misuse and confusion will drown small differences of meaning.

Etymology online gives the following:

autonomous (adj.) Look up autonomous at Dictionary.com1800, “pertaining to autonomy;” 1804, “subject to its own laws,” from Greek autonomos “having one’s own laws,” of animals, “feeding or ranging at will,” from autos “self” (see auto-) + nomos “law” (from PIE root *nem- “assign, allot; take”). Compare privilege. Used mostly in metaphysics and politics; see autonomic. Related: Autonomously.

automated (adj.) Look up automated at Dictionary.com1952, American English, adjective based on automation.

On Horns, Turn Signals, and Connected Vehicles

We have long had inter-vehicle connectivity. The means are slow and loud: the horn and the turn signal, as well as eye contact between nearby drivers. But they are widely used despite their minimal effectiveness.

Horns have a variety of purposes. In the US, the horn is most commonly used to express the following sentiments:

  • “Move you idiot“, for instance, when the light changed colour.
  • “You are cutting me off,” we might get in a collision, and my pressing the horn, while pressed far too late to avert said collision, releases some frustration.
  • “You bastard, you are in my right-of-way and I am too lazy to tap the brakes, but not too lazy to hit the horn” (Alternatively “I’m an asshole, and I want the world to know it”)

In developing countries, horns have other uses.

Traffic in Enshi
Traffic in Enshi
  • I’m changing lanes … not merely, “I am requesting permission to change lanes,” but the more active, “I’m changing lanes and you have been forewarned.”
  • “I’m coming around the sharp corner with little visibility, but sufficient audibility, at too high a speed to safely brake, and don’t care to reduce my speed to a safe speed, you have been forewarned.”

Turn Signals also have their uses. In developed countries, the turn signal indicates

  • “I’m would like to change lanes, if a gap opens up, I might take it.”

In developing countries, I have observed the turn signal, particularly at steep uphill grades indicating, as near as I can tell:

  • “You should change lanes. Really, I won’t be offended, I have a heavy load I am carrying up this hill and my vehicle cannot maintain speed, and there don’t seem to be any. oncoming cars right now, though I am not liable should there be one”


This dual usage of turn signals is analogous to the awful Minnesota expression “Can you borrow me some money” which is normatively incorrect ( with a clear enough meaning) and just grates on the ears. I borrow, you lend. I lend, you borrow.

It is sometimes argued that Connected Vehicles, with a real-time broadcast of a here-I-am message could replace the horn (HEAR! I AM!) and turn signal (see me! I am!, vocalised in a quiet Horton Hear’s a Who voice no louder than a clicking turn signal contact)., But this assumes CVs were universal, and pedestrians and bicyclists were connected too (or have simply been eliminated, Wall-E style). Short of that, the horn particularly, which is almost sure-fire in annoying walkers and bikers, remains relevant as a way of forewarning.

Now of course, an automated vehicle wouldn’t be (shouldn’t be) going around sharp corners at too high a speed, so AVs could eliminate excess noise through better behaviour. Horns in retrospect would also be eliminated. AVs could possibly infer the dual meaning of turn signals from context and vehicle behaviour. After visiting China, it is clear AVs would not work in the Wild West of Wuhan, but are much more feasible in modern Shanghai, where pedestrians and bicyclists are much more likely to be rule abiding. Certainly over time, as developing areas are civilised into the ways of modern motordom, this issue will diminish. But it needs to be kept in mind that the context shapes the effectiveness.

While urban noise levels will likely decrease with advances in technology, this is due to the automation, not connectivity.

I have long felt the solution to much noise in urban environments is to blast the horn inside the vehicle. That way, whenever someone slammed the horn, they would internalise much of the noise externality they create, leading to less noise production in the first place.

Street wars 2035: can cyclists and driverless cars ever co-exist? | Guardian Cities

Laura Laker at Guardian Cities writes: Street wars 2035: can cyclists and driverless cars ever co-exist? | Guardian Cities

“Driverless cars appear unstoppable – except of course you can simply walk in front of one and force it to brake. Could this conundrum eventually mean a return to a dystopian world of segregated urban highways?”

I was interviewed, my quotes below …

A visualisation of data captured by an autonomous vehicle. Photograph: Elijah Nouvelage/Reuters
A visualisation of data captured by an autonomous vehicle. Photograph: Elijah Nouvelage/Reuters

Or how about prosecuting pedestrians or cyclists who get in the way of driverless cars? David Levinson, a professor at the School of Civil Engineering at the University of Sydney, is broadly supportive of AVs, but says: “It’s very big brother like, there’s a question of safety v freedom. How much risk to endanger yourself are we going to let you take?”

Thinking back to the kids stopping driverless cars on our imaginary future street, Levinson sees a future where blocking a driverless car could even be criminalised. “The car has a camera and the picture will be sent to the police department, and the police department will come and arrest you for annoying an autonomous vehicle.”

Given these challenges, experts including Hickman and Levinson believe segregation and AV-only roads are inevitable. But wouldn’t that risk a return to the urban dystopia of the 1960s and 70s, when planners crisscrossed cities with elevated highways and erected barriers around roads with the aim of improving safety? The unintended consequences were fast, aggressive driving, and the splitting in two of countless communities.

“I think there will be some roads that will be transformed to higher speed roads,” says Levinson. “I’d be sceptical of someone who says we will not do any of that. But if you can move traffic away from the lower speed streets that pedestrians and cyclists want to use, that’s an improvement.”

Hickman believes “the case is overwhelming against AVs” but fears the powerful motor industry lobby means there is so much private and government money already at stake that the rise of driverless cars would be hard to stop.

AVs After Alphabet

Word on the street is that Alphabet (née Google) is looking for some revenue from its Automated Vehicle unit, and absent that, might be getting bored with the whole endeavor. Google has gotten bored with things before, and one can imagine it doesn’t view itself as having an infinite pocket to pay for interesting things. There have been reports of this before.

One of the great questions is how long do you tolerate losses in an operation before you cut your losses and convince yourself there will never be gains. Many projects are undertaken (ventures are funded) with great promise.

Yet technological change often happens one bankruptcy at a time, where first movers borrow heavily to invest in new systems with unproven demand. This creates second mover advantages. This is a danger for initial massive investment in self-driving cars in the near future, especially as there doesn’t seem to be any demand from the public currently. (Recognizing that as Henry Ford never said (but almost certainly felt) “If I had asked people what they wanted, they would have said faster horses.”)

In the autonomous vehicle market, suppose someone claims there will be gains in excess of losses, and someone else, with access to a checkbook, believes them. Now we all understand Expected Value depends both on the Potential and the Probability of that Potential being realized. In practice both of those are uncertain and dynamic. Today’s potential in today’s market conditions differ from tomorrow’s. Today’s probabilities also differ from tomorrow’s.* At what point do you recognize the potential as lower than claimed, and the probability as lower still? When, in Bayesian terms, do you update your priors?

In Alphabet/Google’s case, I think the potential of being the first mover is lower because there are now many more people serious about AVs since Google kick-started the latest wave of development of self-driving cars, following upon the DARPA Urban Challenge. In contrast, the likelihood of success might be higher, since there is no more proof-of-concept.

Unless Alphabet imagines itself actually manufacturing cars (probably in a contract facility),  one assumes either it hopes to get revenue from (a) more eyeballs on Google-ad-powered screens (phones, dashboard, and especially heads-up displays)  in a world of AVs, (b) cutting a licensing deal with manufacturers, or (c) providing mobility services directly a la Uber-type of business.

But in a sense they have been overtaken. For services, all major automakers have now partnered with some other company to explore ride-hailing. Toyota and Volkswagen are the latest to announce this (following historically on similar patterns where automakers invested in rental car companies (e.g. Hertz by GM 1925-53 and Ford 1987-2005, Avis by GM 1989 – 96, Dollar Thrifty by Chrysler 1990-97). Daimler owns car-sharing service company Car2Go and Moovel and is certainly looking at this market. Google rival Apple invested in Chinese hailer Didi.

For autonomous technology, Tesla has cars on the road already.While Tesla Autopilot is not the equivalent of Google cars, as it still requires driver attention, it is also a real product accumulating real experience at an accelerating rate, as shown in the graph, with two orders of magnitude more distance traveled and seemingly fewer incidents per distance traveled (I am going on press-reports of Google and Tesla crashes).

Trends in Autonomous Vehicle Usage, note this is a Log scale.
Trends in Autonomous Vehicle Usage, note this is a Log scale.

If the logic of machine learning is right, it will get better and better over time. There have apparently yet to be serious crashes with 100 million miles of Autopilot experience (though there have been a few issues, there have also been claims of lives saved), so perhaps this is the right technological trajectory.

This is the incrementalist approach, leaving the steering wheel and brakes facing the driver, which counters the more radical Google approach of removing (almost) all control from the driver, and trusting the machine completely. The risk has always been the transfer, when the car tells the driver to take over if the driver isn’t ready. But if that issue is small compared to the general safety benefit of letting the machine do the steering, accelerating, and braking, it is a risk worth taking.

So will Google close its unit and set its code and data free,  let it wither away as staff start up spinoffs like Otto, or sell it outright to a manufacturer if they choose not to pursue it with gusto? Or, perhaps the rush to dominate the initial market for AVs will lead to a unsustainable bubble that hits everybody’s valuation (not just Alphabet’s).

I expect there is value to the unit collectively above and beyond the value of the individuals (though I have no personal knowledge), so one hopes they keep the team together, under their ownership, or another patient patron.


* In more typical infrastructure terms, when someone comes and says the Northstar Line will have a Benefit Cost ratio of 2, but it turns out to have a B/C ratio of 0.15, how long do you keep pouring good money after bad? At what point do you recognize the potential as lower than claimed, and the probability as lower still? Sacramento, for instance, is considering shuttering a disappointing LRT line.

Riding in a Tesla with AutoPilot (2015)

In the Fall of 2015, the electric vehicle maker Tesla remotely upgraded its most recent model year cars (about 50,000 vehicles) with “Auto-Pilot”, making them semi-autonomous (according the NHTSA scale, late Level 2, early Level 3). Elon Musk, the CEO of Tesla, says he expects fully autonomous vehicles within 3 years (i.e. by 2018). I got to take a test ride in one of these vehicles from a friend with a Tesla.

Tesla Model S
Tesla Model S

Upgraded Teslas are able to function in hands-off mode some of the time. They use adaptive cruise control to follow the vehicle in front at a desired speed constrained by a fixed following distance and use lane markings to stay in lane. They change lanes automatically at the request of the driver (who must hit the turn signal).

Tesla Model S User Interface
Tesla Model S User Interface

How it works

As of Fall 2015, none of these functions can be safely performed in a Tesla running “Auto-Pilot” in the absence of driver observation and monitoring. In fact the vehicle requires the driver to periodically return hands to the steering wheel. Rules for automated vehicles are still taking shape. Clearly this is “beta”, and intended for limited access roadways, not city streets, though Tesla drivers do use it on local roads as well as freeways. Here are a few of the issues:

  • Stopping: The vehicles do not yet automatically stop at traffic lights or stop signs, though it is assumed that engineers are working on and testing those functionalities, which may already be in the hands of testers.
  • Following traffic: When following a vehicle in city traffic, the Auto-Pilot may induce the car to run the red if the car in front ran the red (or made a right turn) instead of stopping at the light.
  • Lane marking issues: Ambiguities in lane markings (for instance at freeway merges and diverges, or as a result of road construction or restriping) still create difficulties for the vehicle in Auto-Pilot mode. During the drive, the vehicle would pull toward the exit by following lane markings. Drivers have reported “increasingly less tendency to try to take exits and overall it is clearly improving and needing less driver intervention each week.”
  • Curves: First person observations are that vehicles still over-react on curves (following the average of the inside and outside curve, rather than a fixed distance from the inside curve). Elon Musk has tweeted that slowing for curves is coming, and some Tesla drivers are reporting that their vehicles have been updated. Changes like this are part of the brilliant learning system Tesla has deployed.
  • Merging: The give-way game between merging vehicles and an on-road Tesla cannot yet be safely conducted in the absence of driver intervention. As we drove in the right lane, a Mercedes approached from an on-ramp and neither decelerated to come in behind us, nor accelerated to pass us. Our vehicle stayed at a constant speed. The Mercedes would either sideswipe us or run off the road. The driver manually intervened and accelerated (which Teslas do quite well; I can’t wait for Plaid mode, since Ludicrous mode is injurious enough if you are not braced).

Comparison to Google

The manual intervention thus requires drivers pay attention. Thus far, it doesn’t seem like drivers are being lulled to unawareness with autopilot mode on cars, but lulling is a risk if drivers trust too much. This is the advantage of Google’s all-in approach, where the driver can’t retake control even if they want to. Nevertheless, Auto-Pilot has saved lives already, see the video at this link, where an ill-timed U-turn across traffic which would have otherwise resulted in a crash was prevented).

Teslas do not presently drive independently via a map from origin to destination the way Google’s test cars do. There is no obviously linkage between satellite navigation and mapping and the control function. Teslas appear to be map-independent, and controls are through on-vehicle sensors.

The car still smells new despite being nearly a year old. I believe the car’s filters “Bioweapons Defense Mode” has something to do with that. Tesla also still retains some pluckiness and personality, despite having a market capitalization of $27B.

The vehicles are constantly learning, however, using driver interventions as expert trainers, so many of these problems will resolve themselves. None of these should be taken to mean cars won’t be automated; they will be, as a series of technical hurdles to be overcome, and interesting ambiguities and tacit knowledge on the part of drivers must be made explicit before we can hand our fates to our machines.


See video of the ride.

The race is on to figure out what self-driving cars should look like | WaPo

Matt McFarland writes in the Washington Post  “The race is on to figure out what self-driving cars should look like

David Levinson, a civil engineering professor at the University of Minnesota, argued in a recent paper that we’ll see a Cambrian explosion of new vehicle forms that are designed for specific tasks.

“The fleet will have greater variety, with the right size vehicle assigned to a particular job. Today there is a car-size arms race: people buy larger cars, which are perceived to be safer for the occupant, and taller cars, which allow the driver to see in front of the car immediately in front of them,” Levinson said. “Both of these advantages are largely obviated with autonomous vehicles. The car-size arms race ends.”

The road as an Ecosystem in the 21st Century

Today in the Tech World, there is discussion of “platforms” and “ecosystems”. When we hear talk about Apple vs. Google, it is as much about the Apple ecosystem, particularly that around iOS, the operating system for the iPhone, vs. Android OS. The Operating Systems enable both device-based and cloud-based software services. I can buy apps that work in either eco-system, but not both (without purchasing twice). I can buy peripherals that work on one or the other, but generally not both. This mobile telephone ecosystem logic follows and is much larger than the previous decades’ PC operating system ecosystems.

Roads are a different form of economic ecosystem, and perhaps the original one. There is the ecosystem for building roads, and there is an ecosystem for those using roads. Carriers as well as private vehicles are the users. But they have a set of roadside services (energy (hay, gasoline), shelter (inns (hello Jesus), motels, and hotels), and sustenance (food)) as well as many others that are less frequently used (tollbooths, money changing, black smithing, wheel wright, vehicle repair, and so on) that are configured a particular way for users of the road ecosystem.

While the types of vehicles using roads, as well as the materials with which they are made has changed over time, the platform of the road as a place on which to hang a series of road-serving businesses is long-standing, and unlikely to disappear even as roads change with the next technological shift in vehicles.

Without roads (dirt, gravel, block, rail, asphalt, or concrete), there would not be much economy. Certainly off-road vehicles and their passengers and drivers of various kinds would still require services, but the much higher cost of travel would significantly reduce the total economic impact. Secondary economic impacts on things like manufacturing, agriculture, and non-transportation services which do depend on transportation thus depend on this eco-system as well.

There is a fascinating series of books by John Jakle and colleagues describing the emergence of the first order 20th Century Road Ecosystem: Fast Food, Motels, Gas Stations, and so on. What happens in the 21st Century with Vehicle Electrification and Automation?

We can certainly speculate that charging stations ultimately replace gas stations. Even more, vehicles may be charged in motion from the roadway.

Food production and delivery may also change in ways that are difficult to foresee. We can speculate that with automated vehicles, food may come to us in motion, rather than us stopping at the side of the road. While this synchronization, resembling the in-air refueling of Air Force One, seems far out, with full information and automated drivers, it may be quite trivial. This may or may not be a net improvement in food quality.

Why stay at a hotel when your car can move you forward in space and time while you sleep?

How else will the Road Ecosystem Change in the 21st Century?

What if car driving is like playing chess


JS Writes in with an intriguing idea:

“What if car driving is like playing chess?  Self-driving cars may be possible and even valuable but the safest most efficient driving may be the combination of the computer and the person/people.  What if one Uber “driver” could drive 10 cars at once, or a team of 3 Uber drivers could drive 100 cars?”

And then sends in the following from the EconTalk podcast …
From Econtalk: Tyler Cowen on Inequality, the Future, and Average is Over 11:01


Russ: So let’s talk about what you’ve learned as a chess fan. And you write at some length. At first I was rather taken aback by this, but I grew to find it quite fascinating. You write at some length about the role of machines in chess tournaments, and particularly in freestyle. Talk about that and why it’s a nice potential template for future human interaction.

Guest: Freestyle is a form of chess where a human teams up with a computer. So, if you play human-and-computer against computer, for the most part human-and-computer, if it’s a practiced human, will beat the computer. Even though computers per se are much stronger than humans at chess, it’s the team that’s stronger than either one. And I think this is a good metaphor for a lot of what our job market future will look like. So there’s a big chunk of the book that looks rather closely at freestyle chess and tries to see what we can learn from it.

Russ: The thing I found most provocative about that is that the best freestyle teams do not necessarily have the best human players. In fact that could be something of a handicap.

Guest: That’s right. The really good human players are too tempted to override the computer and substitute in their own judgment. The best freestyle teams, they are quite epistemically modest, the human or humans involved. And what they are really good at is asking questions. So they’ll run two or three different computer programs and then just check on where do those programs disagree. And then they’ll probe more on those points. And that’s what the humans do well that the computers, at least not yet, aren’t able to copy. So it’s knowing what questions to ask that has become the important human skill in this freestyle endeavor.

I still think we will need to turn it all over to the computers, and the sooner the better. Human intervention will need to be so real-time that it is likely to be worse than the algorithm, and the lags in communication are sufficient to be debilitating. But the history of self-driving cars has yet to be written.