Category Archives: Impact of driverless cars

The auto industry is channeling billions into autonomous vehicle technology

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The self-driving car industry is growing up. Valuations of self-driving car companies and private investment in these companies are exploding.  Bloomberg reports that private investment in self-driving and connected car companies in the second quarter of 2018 is more than the total private investment in this sector in the prior 4 years combined!  Morgan Stanley has raised its valuation of Waymo from 70 billion in 2017 to 175 billion.

But this is only the tip of the iceberg. Below the surface, a major restructuring of the auto industry is underway where self-driving car companies are emerging as the pivotal element in the strategies for future mobility. Over the past years, different approaches to integrating self-driving car technology into auto- and mobility companies have been tried, ranging from various types of acquisitions (GM-Cruise, Ford-ArgoAI, Aptiv(prior: Delphi)-Nutonomy, Intel-MobilEye) to partnerships (Bosch/Daimler, Daimler/BMW, Baidu/Apollo) and go-it alone strategies (Waymo, Zoox, Uber and many others).

Leaving aside Waymo, GM may have found a winning formula, which is increasingly copied by its competitors: When it acquired Cruise Automation in 2016, it allowed the new subsidiary to continue to operate in a highly autonomous mode, its growth and speed largely unencumbered by the rest of GM. Successful collaboration with GM around the electric Chevy Bolt brightened the prospects of both companies and initially led to a significant increase in in GM’s stock price (which since then has fizzled out). In 2018 Cruise attracted a 2.25 billion USD investment from Softbank’s Vision fund. Being able to attract outside investment (as well as employees through stock options in Cruise) while having close connections to the resources of the parent company should be an ideal position for Cruise to quickly shift from start-up/development mode to commercialization. At the same time Cruise is insulated from all concerns related to building legacy cars and from the headwinds that classical car companies will have to face from the revolutionary changes in the auto industry. Other auto makers seem to be copying GM’s strategy. Ford has created a self driving division (which includes ArgoAI) and will also be open for outside investment. Volkswagen seems to have been in talks to buy Aurora, but was rebuffed. Daimler who was an early leader in self-driving technology is relying on a partnership with Bosch but is also splitting the company into three separate parts (cars, trucks, mobility (which includes self-driving technology). This has the effect of insulating the less vulnerable parts of the business (trucks and mobility) from potentially dramatic changes in the auto industry. Only Toyota, which has always been late to the self-driving race has chosen a different path by investing 500 billion USD in Uber, which minimizes its ability to leverage the opportunities associated self-driving car technology.

The last 6 months have shown that the auto (and mobility) industry is now finding ways to channel billions of dollars into the commercialization of self-driving car technology. Given the extent of changes, the capital associated with these changes and the increased ability of translating advances in the technology into actual products and services (which don’t have to be full fledged drive-autonomously-anyhwere solutions but can be very targeted) it won’t take several years until we see the first real impact on the streets…

The positive risk profile of self-driving cars

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The two recent fatal accidents with self-driving cars by Uber and Tesla have not led to the major backlash which many people had predicted. While this does not come as a surprise (the predictions ignored the long history of technical innovations, where accidents have rarely slowed or even halted the advance of a technology), nevertheless, the two harrowing accidents increase the concern of the public and of regulators about the safety of self-driving cars.

Therefore this is the right time to perform a more careful analysis of the risk profile of this technology. As we will show in the following, the specific forms of risk, accident scenarios, and risk mitigation strategies for self-driving cars differ very significantly from other technologies that have been developed over the last centuries. To illustrate the differences, we will examine three key aspects of the risk profile of self-driving car technologies and contrast them with established technologies:

1) One- or two sided distribution of safety outcomes
Self-driving cars are an unusual product from the perspective of safety-related outcomes. Practically every product comes with the risk that it’s use may inflict harm under some circumstances. For most products the safety related outcomes are either harm (negative outcome) or no effect. A much smaller group of products can also lead to positive safety-related outcomes – their use increases safety. A self-driving car will prevent some accidents (positive outcome) or cause accidents (negative outcome); this two-sided distribution of safety outcomes contrasts with other product categories such as microwaves, coffee machines or electric drills which have only one-sided safety outcomes. From one perspective, products with two-sided safety distributions are preferable over products with one-sided distributions. But they present a challenge for risk analysis and for ethical considerations because uncertainty about the distribution of negative outcomes may need to be balanced against the certainty of positive outcomes. Delaying the use of self-driving cars for too long may cause harm (accidents that would not have happened).

In the health sector, this dilemma is a well-known problem for the approval of medical treatments. And the US Food and Drug Administration (FDA) has worked hard to balance both sides of the distribution (both by speeding up the approval process and by enabling critically ill patients to get access to experimental treatments in certain cases). But self-driving cars differ from medical treatments in a very positive way: Whereas the expected positive effects of a treatment often do not materialize (uncertainty on the positive part of the distribution), there is much more certainty about the positive safety outcomes of self-driving cars (accident prevention) and we already have statistical data for the safety benefits of some driver assistance systems.

Thus any legislative effort for regulating the approval of self-driving cars, needs to consider both sides of the distribution of safety outcomes.

2) Alignment of safety goals with development goals
For most products, safety is not an innate part or consequence of the development process. Over the last century we have learned the hard way that a large body of laws and regulations are needed (which then lead to well thought out internal processes) to ensure that safety is adequately addressed in all phases of the development process.

However, the situation is different for self-driving cars. For anyone developing an autonomous vehicle, the primary and overarching development goal of self-driving cars is to be able to operate the vehicle safely at all times. Driving as such is NOT the primary goal, it is a secondary concern because just navigating the car on the road and keeping control of speed and direction is only a very small part of the development problem.
The internal state of the car at any given moment is most important, because the car needs to constantly monitor its environment, identify road signs, traffic lights, predict actions of other traffic participants,  etc. Therefore the main concern of development teams is to make sure that the car has a complete and accurate internal representation (of state and probable behavior) of what is going on around it. The key metrics in the development process are not just driving errors but their much earlier cause – shortcomings in sensing, interpretation, prediction. Thus the development of self-driving cars is a constant and intensive search for failures, potential errors, potential flaws. As a consequence, even in the absence of any safety regulations, it would not be possible to develop a self-driving car for the market without being constantly focused on safety. Of course, this is not a guarantee that no mistakes will be made. And this is not a guarantee that the development process will lead to absolutely flawless vehicles (that is not possible). But the technology of self-driving cars is one of only very few technologies where safety issues are inherently the primary focus of development.

3) Efficiency of recall process for defective products
Self-driving cars are almost unique in another, third dimension of risk: For most technologies it is difficult to prevent harm once a defective model is released to the public (and this has important implications for regulation). Once an Espresso machine, a drug or another product reaches the hands of thousands or millions of users it is very difficult to ensure that a defective product model will not lead repeatedly to harm somewhere. Recalls take time and rarely reach all owners. Again, the situation is very different for self-driving cars. They incorporate wireless communication and update mechanisms that allow the near-instant grounding of defective vehicles models. A worst-case scenario where a flaw is discovered after tens of thousands of vehicles have been released to public roads is not realistic: when accidents point to the flaw, the other cars on the road will quickly be grounded and thus further accidents will be prevented from happening. Of course this does not mean that standards for approving self-driving cars should be lax but rather that we should keep the likely risk scenarios in perspective, when we consider regulations for self-driving cars.

In summary, the risk profile of self-driving cars is quite unusual because it is positive on the following three dimensions:
— With self-driving cars, safety is the primary development objective and focus, it is an inherent part of the development process and can never be just an afterthought or constraint of the development process
— Self-driving cars have double-sided safety outcomes: Besides the risk of failure, they also increase the safety of passengers. Keeping self-driving cars off the road for to long because of worries about accidents may be harmful
— Self-driving cars allow instant grounding of defective models; defects can not harm large groups of customers

In the public and regulatory discourse we need to do justice to the unique risk characteristics of self-driving cars!

P.S. For more on self-driving car safety and how (not) to determine statistically whether self-driving cars are safe, see my earlier post on Misconceptions of Self-Driving cars: Misconception 7: To convince us that they are safe, self-driving cars must drive hundreds of millions of miles

 

We need a moratorium on new public transport projects!

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Across the world billions of dollars are committed every year for new public transport and road infrastructure projects: commuter rail, subways, new roads, bypasses, tunnels, bridges, etc. Committees spend years planning these projects; it often takes more than a decade until a project is implemented. Once completed, we expect the projects to yield their benefits over many, many decades. Over the last century planning and estimation processes have been refined; they work reasonably well. Unfortunately, current processes can not and do not take self-driving vehicles into account. But it is now clear that self-driving cars will fundamentally change our traffic patterns. This greatly increases the risk that public transport projects will already be obsolete at the time they are completed. In the following we will show that the most adequate action for cities and states is a temporary moratorium on new public transport projects (i.e. by systematically delaying the start of the planning phase):

At the current point in time self-driving car technology is not yet ready for widespread adoption but there can no longer by any doubt about its viability. Many companies are racing for implementation. Millions of kilometers are now routinely test-driven in self-driving vehicles; GM and Jaguar have started producing self-driving car models; Waymo is now operating self-driving cars without test-drivers inside the car. Anyone who performs an extensive analysis about the size of the self-driving car problem, the economic incentives for participants in the self-driving car space and the state of the industry must come to the conclusion that we are very likely to have large numbers of self-driving cars, buses, trucks and machines in our cities within the next decade (see the postscript of this article for a brief outline of key elements of such an analysis).

Once self-driving cars operate in cities by the thousands, we will see fundamental changes: the number of privately owned cars will fall. The higher urban density, the quicker car ownership will recede and with it parking lots. Traditional public transport will be challenged by self-driving taxis and ridesharing services. Rail-based transport solutions will suffer from their inflexibility compared to buses. The biggest problems will occur on the feeder lines; not so much on the high-capacity, high-frequency core lines. Urban and highway traffic will flow better as self-driving cars become life traffic sensors and city-wide traffic routing algorithms are applied (no, this is not science fiction, this will be a core, immanent concern of any provider of self-driving mobility services and has the benefit of being a win-win situation (identical goals) with city traffic management). We will see the distribution of traffic change significantly as trucks begin to operate 24/7, self-driving fleet vehicles are applied for delivery at night and ridesharing services increase the average occupancy per vehicle on certain routes (more likely on long distance trips as well as long commutes, less relevant for inner cities). As a consequence our road-based mobility system will change fundamentally. Of course, this will not occur over night, but the changes will greatly affect any new road infrastructure project being planned today.

Ideally, we would map out these changes today and then plan for the kind of mobility system which will be operating in the forties and fifties of this century. But there is too much uncertainty and too little chance to achieve widespread agreement on what this situation will look like. There is no established knowledge, and no agreement on how to determine the likely scenarios.

But even if we cannot yet reach agreement on what the future will look like, it should be possible to reach agreement that this future will be very, very different from the one we are planning for today. Given the uncertainties, there are three possibilities:
1) Continue planning on the basis of our current processes and knowledge.
2) Delay projects for a few years, hoping for improvements in our understanding of the effects of SDC technology
3) Design new projects with robustness or elements targeted for self-driving car scenarios

The conditions under which alternative 1 is rational are very narrow: This only makes sense for projects which are unlikely to be challenged significantly by self-driving cars. New rail-based projects certainly do not fall int his category. But bypasses, highway extensions (or new highways) and most other projects also critically depend on estimates of traffic distributions which we can no longer extrapolate from today. Therefore we must balance the disadvantages of delaying the start of such a project for a few years against the advantages of fewer expenses in the near- and medium term and possibly a better system in the long term. Because we are likely to have much better ways of managing traffic flow in 20 years it is unlikely that the congestion problems which we may fear as a result of delaying a project today will actually materialize. If we do business as usual, we may find in 20 years time that a significant share of the projects we are starting today are no longer necessary and billions of dollars have been wasted.

Given the state of our knowledge it appears difficult to design robustness for self-driving car scenarios (Alternative 3) into new projects today.

Thus the only viable option is Alternative 2. If we delay new projects for a few years, we save tax payer money but don’t have to fear enormous congestion in a few decades because self-driving car technology will give us many new levers for improving traffic flow. By delaying projects, we will increase our common knowledge and shared understanding of the impact of self-driving cars. Simulations, scientific research, experiences from the first installations of fleest of self-driving cars (such as Waymo in Phoenix) will provide us with insights that we can apply for the planning and estimation of new public transport and road infrastructure projects. We will learn how traffice changes in the first cities with self-driving cars; we will understand that fleets have an impact on the way that traffic is routed and that we can use them to detect and combat congestion. We will be much more open to consider introducing new parameters into our mobility infrastructure: some lanes might be dedicated to self-driving cars only; they could be made narrower because these cars can drive with more precision. We might change the direction of inner lanes on some roads depending on travel flows or revert parking lots on the side to be used as lanes during peak hours (only self-driving vehicles would be permitted to park there at night; they would be in use during the day or have to drive themselves to another parking space before peak hours begin).

Thus at this point, the most rational approach for new public transport and road infrastructure projects is to put the initiative on hold! This is an action for which a consensus can be found much more easily among the various stakeholders than finding consensus to plan directly for an unknown future with self-driving cars. It also has the side-benefit of increasing the pressure on the planners to seriously consider the effects of self-driving cars. We will all be better off if we place a moratorium on new public transport and road infrastructure projects today!

Postscript:
A key element of the argument presented in this article is the claim that we are likely to have very many self-driving cars, buses, trucks and machines in our cities within the next decade. This is not obvious and runs counter to the quick, intuitive assessment of many. Unfortunately, the matter is complex and requires an intensive look at the issues from multiple perspectives – technical, economic, legal, innovation theoretic. Misleading intuitions can not be eradicated with just a few sentences because they are usually based on too many self-supporting half-truths (see my paper on Misconceptions of self-driving cars). If you want to spend time to think the different aspects through, here is a brief outline of some of the issues (for more on this, attend one of my workshops or contact me directly):

1. Technology
1.1. Problem of full self-driving has been shown as solvable.
1.1.1. Problem is inherently information processing in a limited, but complex domain. Interpretation is hard but can be solved with current methods.
1.1.2. Known limitations (driving in snow / heavy rain etc.) are not fundamental limitations
1.1.3. Self-driving car does not need general world (human-like) intelligence.
1.1.4. Remote operations center can handle many of the so-called hard problems (i.e. interpreting police office hand waves)
1.1.5. Having to use pre-defined maps is not a limitation for nationwide rollout (and nationwide rollout will not be the initial use-case anyway)

 1.2. Self-driving cars will reach a state where they are much safer than the average human driver
1.2.1. Much better attention than human drivers
1.2.2. Larger field of view than human drivers (exception: highways)
1.2.3. Fast, continous learning and refinement of algorithms.
1.2.4. Human drivers make many preventable accidents.
1.2.5. Human is better at interpreting certain rare scenes
1.2.6. Self-driving cars are better at detecting common situations early
1.2.7. Vehicles have sufficient processing power and sensor mix for self-driving
1.2.8. Economic usefulness of SDC technology does not require ability to operate everywhere (-> technology can start early)

 1.3. Rapid evolution of technology
1.3.1. Innovation process is spread across the world; involves many companies in hardware, sensors, software, mobility, etc.
1.3.2. Enormous progress in AI algorithms
1.3.3. Sensor mix is maturing; still rapid innovation in sensor technology and rapid fall of sensor and hardware prices
1.3.4. Number of companies working on self-driving cars still increasing
1.3.5. Production of first self-driving car models has already started (GM/Jaguar/not quite there yet: Tesla)

2. Economics
 2.1. Disruptive potential of self-driving cars is eliminating the driver
2.1.1. All industries potentially affected from impact on logistics
2.1.2. SDC technology challenges competitive position of many companies (not just auto industry) and countries -> enormous competitive pressure
2.1.3. Removing the need for a driver requires rethinking all processes in the auto / transport / mobility industry
2.1.4. Diffusion of self-driving cars at a much faster pace than other auto industry innovations (won’t take decades)

 2.2. SDCs will lead to increased use of mobility as a service
2.2.1. Car ownership must fall (a detailed analysis of cost/benefit/comfort associated with owning a car / calling a self-driving taxi)
2.2.2. Self-driving taxis will slash costs for individual motorized mobility (but costs for privately owned SDCs will rise compared to current cars)
2.2.3. Vehicle stock in developed nations will fall significantly
2.2.4. Mobility as a service market exhibits network effects -> first mover advantage means winner gets all -> extreme race for being first
2.2.5. Regulation of SDC fleets by cities or countries is very likely
2.2.6. Public transport will face significant challenges from providers of self-driving mobility services
2.2.7. Rail-based networks are at a disadvantage because of their low flexibility. Only high-capacity lines can remain profitable.
2.2.8. SDCs will increase throughput in cities; increased congestion very unlikely (this is contrary to many intuitions)

 2.3. SDCs will increase person-kilometers traveled but not necessarily vehicle-kilometers traveled (impacted by occupancy rate)
2.3.1. Mobility services are not just economically viable in high density urban areas but also in many lower density rural areas (of the United States)
2.3.2. Occupancy rate for long-distance trips and longer commutes will increase
2.3.3. In short, local travel passengers will be reluctant to share rides; buses will be preferred compared to taxis for ridesharing
2.3.4. Self-driving long distance buses will multiply

 2.4. High valuation of SDC-related businesses
2.4.1. Enormous capital inflow for all business related to self-driving car technology because of high potential gains associated with market shakeups
2.4.2. High-priced human capital in self-driving car technology; rapid movement between companies (rapid transfer for knowledge from leaders to well funded followers)
2.4.3. Number of cars sold will fall. OEMs will loose significant revenue. Not all OEMs will be able to survive this transition of the industry.
2.4.4. Auto industry will change. Fleets will be powerful customers and heavily influence vehicle design.
2.4.5. SDCs will slash delivery costs.
2.4.6. Ecommerce will grow. Retail will suffer. Supermarkets will close.

 3. Legal/political
  3.1. Self-driving car technology seen as key technology affecting global balance of economic and military power
3.1.1. Countries compete to grow/protect their own self-driving car technology and related industries
3.1.2. Opposition to SDCs does not have force. Risk of job loss widely acknowledged but potential benefits to population as a whole are too large
3.1.3. Regulatory bodies have a lack of knowledge and competence in rapidly evolving SDC technology;
3.1.4. If perceived as necessary, regulatory approval for self-driving cars may occur rapidly
3.1.5. Very little attention yet on the wider regulatory implications of self-driving cars (in cities, as a business model, as a universal service, as a competitor to public transport etc.)

 4. Innovation
  4.1. Innovation process
4.1.1. Self-driving car technology close to end of fluid first phase of disruptive innovation processes
4.1.2. Shakeout and consolidation in the SDC technology likely to be observable soon
4.1.3. Self-driving car hard- and software likely to become commoditized. Not a source of long-term competitive advantage.

  4.2. Diffusion
4.2.1. Self-driving car technology will be adapted very rapidly. First mover advantage for fleets. Heavy demand by affluent consumers expected.
4.2.2. Catalyst for innovation in other areas. Transport, retail, autonomous machines and solutions.
4.2.3. Enabler for electric vehicles. Self-driving cars will greatly increase not just the number of electric vehicles but explode the number of person kilometers traveled through self-driving electric (fleet) vehicles

  4.3. New technology leads to new possibilities which will be discovered, embraced, regulated, mandated
4.3.1. Self-driving vehicles will work as traffic sensors
4.3.2. Self-driving vehicles can be used to influence and control traffic
4.3.3. Fleets of self-driving vehicles lead to a much better real-time understanding of traffic
4.3.4. Congestion-causing effect of a trip can be determined, quantified, taxed etc.
4.3.5. Building codes will reduce requirements for number of parking lots

  4.4. Changes in attitudes
4.4.1. Human error in driving will no longer be tolerated (does not mean no more human driving, could just mean that SDC algorithms oversee human driving)
4.4.2. Personal car ownership may be banned in some inner cities
4.4.3. Preferences for privately owning a car may fall
4.4.4. If global warming becomes more of a threat car ownership may be increasingly viewed critical given ubiquitous access to mobility services

 

The big squeeze: How self-driving vehicles will put pressure on the car market

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In the next five years the first fully self-driving cars will become available for purchase. This will happen after the first fleets of self-driving taxis appear in our cities. How will this affect the demand for private cars? We can expect consumers to react in multiple ways:

In the high end of the market, additional demand will be generated by price-insensitive customers who greatly value their personal time and greatly value their own life. For many affluent consumers who spend significant time at the wheel, full self-driving capability will be a must have and they will not wait until the end of the usage cycle of their current car but have high motivation to switch to a new, fully autonomous model early. People who lease their car will demand upgrades (for example via lease pull aheads) while affluent consumers who buy their own cars, will just replace their old model early. This will lead to a spike in demand for premium vehicles – which is positive for the auto industry.

At the same time it will produce a dent in demand in the run up until the first self-driving models become available. The more customers get the impression that reliable self-driving models will be available on the market soon, the more they will hold off on purchasing a non-self-driving model. Individuals and companies are likely to extend expiring leases on premium cars for a short time just to be sure to switch to fully-self-driving vehicles as early as possible. For any company the most rational path to take is to adopt fully self-driving cars as early as possible because this has a direct positive effect on the productivity and health of their employees.

It is important to recognize that this adoption path can not be incremental. Driver assistance systems are getting better and they indeed follow an incremental route. But the switch to full self-driving is a disruption: only from that moment on can the driver turn his attention away and go to sleep, go over documents, watch a movie or find other ways of using their time. For affluent people who value their time at just $50 per hour, this translates into enourmous benefits ($18250 per year with an average of 1 hour per day in a car) compared to a car model with a high performing driver assistance system.

These reasons have another consequence: The demand for new premium vehicles without fully self-driving capability will crash. The self-driving feature will be a critical benefit for almost every customer; only the exceptionally loyal will avoid switching from a brand that can not offer full self-driving to another premium brand with full self-driving. In this part of the market (excluding chauffeured cars and aficionado cars) competetion will be enormous. Brands which are late coming to the market will dramatically loose market share. We may see a very rapid shakeout in this part of the industry.

The picture looks different for more price-sensitive customers. A small part of this group will find that the obvious additional time and risk-reducing benefits of self-driving cars are reason enough to spend more on a car purchase and upgrade to a premium self-driving vehicle. This will add to the initial demand for premium self-driving cars.

A much larger group will find that they can not afford a premium self-driving car. This group has two major options: It can wait until self-driving capabilities trickle down to less expensive cars. Given the significant benefits of the self-driving feature this has the consequence that they will hold off on purchasing new cars in their segment until self-driving capabilities arrive. Demand for new cars in these seqments will therefore fall and OEMs will feel the pressure to accelerate the introduction of self-driving capabilities into the lower segments of the car market.

The other option for the more price-sensitive group is to switch to mobility services where available. Self-driving taxis are likely to provide mobility at a cost per kilometer that is not significantly higher than the total cost per kilometer of the average privately owned car without self-driving capability. Because this option will be available in many cities even before self-driving cars can be purchased many customers will already experience self-driving and its benefits. In high density urban areas, where space is at a premium, reducing the number of cars per household or even eliminating all personal cars will be the obvious solution. In many such areas the marginal costs of using a private car will be higher than using a self-driving taxi. In all areas where fleet services take hold (this will include many areas with lower density) we will see that households will reduce the number of vehicles they own.

For a part of this more price-sensitive group which can not afford premium self-driving vehicles, the most rational choice will be to switch to robo taxis early – even if they are more expensive than the marginal cost of using their own car – because this will allow them to use their personal time for something better than driving and increase their safety.

Thus even before the first fully self driving cars appear on the market, we will see a drop in demand for new vehicles caused by an increasing adoption of self-driving mobility services as well as the expectation that more affordable privately owned cars will be available in the near future. In this period, the demand for non self-driving vehicles in the lower segments must fall because some consumers are reducing the number of cars in their household by switching to self-driving mobility services and others hold off buying new cars with the expectation that more affordable self-driving cars will appear on the market in the near future.

This will have an effect on the used car market: As people switch to using self-driving mobility services in densely populated areas, they will sell their current cars prematurely; this will reduce prices in the used car market. A smaller group of customers will want to hold off buying a new car until self-driving features become available in their segment. This effect will be small and not be enough to counteract the price drop for used cars.

This will lead to a dilemma for the auto industry: because demand for cars drop and more hiqh quality used cars become available on the used car market, demand for new cars without self-driving capabilities falls. However if the auto industry rapidly switches to offering self-driving cars in the lower segments, then consumers will switch even faster to self-driving cars and cars without self-driving capability will become hard to sell. Prices for traditional cars will fall and traditional cars will depreciate much faster. OEMs that don’t offer self-driving capability will rapidly loose market share.

It is inevitable, therefore, that the advent of self-driving cars will squeeze demand for privately owned cars. It is not possible to rapidly roll out cheap self-driving capability in all segments. On the path to this future, demand for new cars must shrink because for some customers it is rational to hold off on purchasing a new car to wait for the availabity of the self-driving capability, for other customers it is rational to switch to using self-driving mobility services, and last but no least every price cut in self-driving technology makes the use of fleets economically more attractive compared with the use of a privately owned self-driving car.

Thus the auto industry is in a difficult position. As long as the advent of fully self-driving private cars is only a distant vision on the horizon, everything looks like business as usual. But when the first fleets of self-driving cars provide mobility services in an increasing number of cities across the globe over the next three years and as consumers take notice that the release of the first fully self-driving private vehicles appear imminent, then the auto industry will experience a major shakeout. Time to react will then be very short and the survival of more than one OEM will be in question!

Self-driving electric fleet vehicles wont need large batteries: they solve the EV range problem

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Neither the auto industry nor politicians have yet grasped a key implication of self-driving vehicle technology: it fundamentally changes the electric vehicle range problem – which is the key limiting factor for the adoption of electric vehicles. Batteries are currently the biggest cost factor for electric vehicles; their production is costly; their enormous weight (a Tesla Model S electric battery with a capacity of about 85kWh weighs a little more than half a ton) also reduces the energy efficiency of electric cars.

But the ability to drive autonomously changes the way vehicles will be used. The first self-driving vehicles won’t be available to end users; they will provide mobility services in urban areas where most trips cover only a short to medium distance. Trips above 30km will be rare (or even impossible in a given, limited urban area) and the average trip length is unlikely to significantly exceed 10km. Thus – from the perspective of individual trips – fleet vehicles won’t require enormous battery sizes.

This means that the classical range problem for electric vehicles – which is currently seen as the major factor limiting adoption – is no longer relevant for fleets of self-driving taxis! The operators of such vehicles will not seek to maximize battery range but will want determine the optimal battery size for their usage patterns. This depends on the distance which fleet vehicles cover during the day, the geographic and temporal distribution of trips, the installed charging infrastructure and recharge speed. If we assume that the average speed achievable in urban traffic won’t exceed 30km per hour during peak times and that all vehicles will be busy servicing customers during the 3 hour morning and afternoon peaks (without any time to recharge) this means that these vehicles need to be fully charged for a range of at least 90km before the peaks. As the peak travel period ends and demand for transportation services drops off, vehicles that are idle can then drive themselves to high capacity quick charging stations and recharge so that the fleet as a whole returns to maximum battery capacity before the afternoon peak. Thus the optimal full-capacity battery range for these vehicles should be well below 200km, possibly closer to 100 than 200km. They will be able to provide mobility services with much smaller batteries than the electric vehicles that are currently being sold to private car owners, such as Tesla, Renault Zoe and others.

Self-driving cars change the fundamentals of mobility and we need to consider the effects very seriously, leaving aside our intuitions and projections which are so often based on current car-based mobility. If we examine this problem more closely, it becomes obvious that the assumption that all self-driving vehicles within a fleet should be equipped with batteries of the same size is also problematic: In urban centers a large percentage of customers only request very short trips. Thus some vehicles could be equipped with extremely small batteries and the fleet management system could channel only requests for short trips to these vehicles. Requests for longer trips could be steered towards other self-driving fleet vehicles which are equipped with larger batteries. Overall, we can expect that the total fleet battery size will be much smaller than the sum of battery capacity which a similar-sized number of privately owned electric vehicles would have.

No auto maker or new entrant in the self-driving car space has yet presented a car model that has been engineered for fleet use from ground up (going beyond the individual car design and applying a systems perspective on the fleet and its operating infrastructure). But when that happens, the architects will need to also consider whether these cars should be equipped with an ability for rapid, fully automated, battery swapping (which may also consist of adding/removing smaller battery extension packs on the order of 25 to 50km ranges). If this were feasible in small stations in a time frame of three minutes or less, then such fleets could come very close to the theoretical minimum in fleet total battery capacity with respect to given transportation demands. This would greatly reduce capital and operational costs for such fleets, increase their cost advantage over trips with privately owned vehicles, and minimize the energy costs and environmental impact of personal transportation.

It is time to recognize that self-driving cars fundamentally change many aspects of mobility and that they will be the catalyst for the electric mobility of the future. With self-driving vehicles, the classic range problem of electric vehicles vanishes (and is replaced with an entirely different problem of determining the optimal battery range for a regional mobility usage pattern). This also implies that we should be careful with our projections about the adoption rate of electric vehicles. Autonomous electric taxis could dramatically accelerate the diffusion of electric vehicles and rapidly increase the share of person-kilometers traveled in electric vehicles. Auto makers, politicians and environmentalists should take notice and move their focus from more efficient batteries to rethinking and redesigning urban (and long-distance) personal mobility in the age of self-driving vehicles!

P.S. Because fleet-based mobility services will also be available to people who still have their own (self-driving) cars, the battery range equation will also change for them. Range will no longer be such an important factor for buying a private car when it is clear that comfortable, ubiquitous fleet services are available in all cities (locally and at the destination) and that fleet vehicles can be used for long distance trips in those rare cases where the needed range is larger than the range of the electric vehicle that was purchased.

Self-driving vehicles: outlook for 2018

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After the race for fully self-driving cars heated up in 2016, 2017 became a year with exciting developments – many billions of dollars changed hands for self-driving car related acquisitions(1) and many collaborations were started(2). But besides progress, 2017 also showed some limits: Tesla was plagued by defections from their SDC team and had to cancel their fully autonomous coast to coast test drive planned for the end of 2017 and shift the target date for their fully self-driving capability back by 2 years. Volvo effectively cancelled their planned Gothenburg self-driving car trials (by changing the scope to a test of driver assistance technologies).

Nevertheless an enormously important milestone for the adoption of self-driving cars has been reached in 2017: Waymo is now operating self-driving cars without test driver on public roads in Phoenix, Arizona. Five years ago we had expected this milestone to be reached around 2018. This unequivocally demonstrates to the world that self-driving cars are viable and that they can no longer be considered a technology that is half a decade or more away.

This milestone (and the multitude of achievements of the many actors involved up to the end of 2017) also change the dynamics of the global distributed innovation process around autonomous vehicles. It is beginning to shift from the typical chaotic process involving many different actors with little formal organization trying out different paths and approaches to a more mature process. The acquisitions we have seen in 2016 and 2017 are an indicator that the global innovation process is consolidating and getting closer to move from the early stage of an innovation process (called ‘fluid phase’ in innovation theory) to the ‘transitional phase’. This is a major step typically associated with deep structural changes in the innovation process. We may reach a peak in the number of companies competing to develop self-driving car technology in 2018 or 2019 before seeing a market shakeout thereafter.

For the auto-industry, 2018 will be a crucial year because the time is running out for most OEMs to ensure that they can weather the changes caused by self-driving cars and – maybe even more importantly – that they can identify, understand and profit from new opportunities. There can be no doubt that car sales will come under pressure in the early 2020ies as autonomous mobility services (both for local and long-distance travel) grab a significant share of the mobility market, consumers fundamentally change their car-buying behavior and some emerging markets adjust their traffic infrastructure policies to take advantage of self-driving car technology.

OEMS that have not yet committed to a serious self-driving car strategy risk their medium-term competitive position. With every year that passes, it will become more difficult to adjust to the changes coming to the auto industry. It is unlikely that OEMs will be able to offset losses in demand for privately owned cars by building self-driving cars and selling or leasing them to mobility service providers (or operating them themselves). When the industry gradually comes to accept the reality of shrinking demand for automobiles, it will become more and more difficult to adjust because profitability will fall rapidly and with it the ability to change. Several automakers are likely to fall into the Kodak trap: Kodak was the first company to develop a digital camera. It always understood digital cameras but it failed to reinvent its business model in time and then was unable to turn around the already sinking ship which was bleeding from all sides. The European, Korean and Japanese auto makers need to strongly accelerate their self-driving car activities if they want to survive the coming turmoils of the next decade. General Motors seems to be the only OEM which currently is well positioned in this space. It is pity that Daimler, one of the earliest pioneers of self-driving cars, appears to be content to mostly watch from the sidelines.

In 2018, we can expect another change in the maturing innovation process: The focus will start to move away from the core technical issues towards the implications for the automobile as a whole (its interior, exterior and structural design, its supporting and sales infrastructure etc.) and towards the business models associated with self-driving cars. There are many more use cases for self-driving technology than just ferrying people around; many of these use cases have strong services components which OEMs (or their challengers) need to embrace. 2018 may also be the year where players beyond the auto industry start to seriously consider the implications, opportunities and risks. Retail will be deeply affected by dramatically falling local distribution costs. In the next decade nany supermarkets will have to close their doors as products can be delivered conveniently (and with very customer-flexible timing) to the doorstep. Hospitals, care and emergency services will need to adjust to fewer traffic related injuries. Most industries will need to consider the implications and opportunities associated with significantly lower transportation costs (affecting both inbound and outbound logistics and possibly providing new product or service opportunities). Cities, countries, architects, construction firms need to start planning for a future where mobility is provisioned differently and where space and capacity requirements for transportation are changing. Railways and transportation companies need to consider the challenges which will be raised by autonomous mobility services providers. Self-driving cars and machines will also have major impact on construction and agriculture industries and provide new opportunities there.

2018 may also be the year where the opposition to self-driving cars finds their voice. While self-driving cars have enormous benefits they will eliminate many jobs (not just professional drivers but also in the auto industry and many other industries). Society needs to find ways to cope with the fundamental changes that result from software-based devices with capabilities which some call ‘artificial’ intelligence and we all need to consider in depth how the fabric of society will be impacted and what changes on the different subsystems of society will be necessary. This process should not be underestimated and requires a major, multi-disciplinary effort.

In 2018, every business, organization, political actor, and any forward-thinking individual should take the time to look beyond the technicalities of self-driving cars and carefully consider their implications, opportunities and risks!

Update: 2018-01-16: Removed a sentence stating that BMW seemed to have reduced the extents of its targets for autonomy in 2021.

(1) Acquisitions: Intel/MobilEye, Delphi/Nutonomy, Cruise Automation/Strobe, Ford/ArgoAI (Ford majority stakeholder), ArgoAI/Princeton Lightwave

2) Cooperations: Waymo with Lyft, Avis and others, Daimler/Bosch, Baidu/Apollo platform, Intel Alliance, Uber/Daimler

Fleets of self-driving cars will not be limited to high-density urban areas

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Self-driving mobility services are likely to be adopted quickly in high density urban areas. In these regions, car ownership is likely to fall significantly. Several studies have shown that one autonomous taxi might provide sufficient transport capacity to service the mobility needs which are currently fulfilled with 6 to 10 privately owned vehicles. These studies have considered local motorized mobility in large cities such as Ann Arbor, Lisbon, Austin and others.

But how will autonomous fleets impact mobility and car ownership in less densely populated areas? About 86% of the US population live in metropolitan statistical areas (i.e. areas that have a relatively high population density at its core). These are not limited to the great cities and agglomerations on the west and east coast but include much smaller areas such as the Grand Forks metropolitan area which comprises 2 adjacent counties in North Dakota and Minnesota with about 100,000 inhabitants (in 2014) and a population density of 11 people per km square. Of course, self-driving mobility services will be very viable in the urban core of this metro area where about 60,000 people live. The remaining 40,000 people living in rural parts of this area have significant, predictable mobility demands for trips towards and back from the urban core. Thus there is a potential for self-driving mobility services even in the outer, less densely populated parts of metropolitan statistical areas. A further 8.6% of the US population live in in micropolitan statistical areas (i.e. areas which are centered around an urban cluster with at least 10,000 but less than 50,000 people). The remaining 6% of the US population live neither in a metropolitan nor a micropolitan statistical area (see the white area in the map of metropolitan and micropolitan areas in US). It is instructive to consider their situation.

Let’s take Sidney, Montana as an example (Google maps): This is a small town with just about 5,000 inhabitants in eastern Montana. It is far away from more populated centers. The nearest larger city is Williston, ND with about 20,000 inhabitants at a distance of 70km. The next city with more than 100,000 inhabitants is Billings, MT at a distance of about 430km. There seems to be a significant mobility demand for trips to Billings: more than four flights leave for Billings every day (airfare about 40 USD). Uber is already active in this town and popular destinations/pick up spots include the airport, high school, health center and Holiday Inn Express.

The US currently has a stock of about 240 million light duty cars, which translates to about 750 cars for a thousand people. Because this ratio is higher in areas with lower population density, there should be significantly more than 5*750=3750 light duty cars in Sidney. Because a large share of the daily trips are local, and because their average speed is high compared to the speed in congested cities, autonomous fleets should be able to provide high-yield mobility services with a relatively small fleet. With a replacement rate of 1 to 7, about 535 self-driving vehicles could theoretically replace the town’s entire vehicle stock. The local mobility demands of 5000 people are also large enough that a mobility services provider can start with the smallest economically viable fleet size of probably somewhere between 10 and 20 cars and then grow the fleet as demand picks up. The low regional population density has an interesting consequence for non-local trips: The number of typical destinations is small; the number of routes people can travel from/to Sidney is quite limited. Therefore the potential for on-demand shuttles is high; Williston, with it’s Walmart (about a 1 hour drive) is an obvious target. Such shuttles have another side effect: they can provide the same mobility service to all locations which they pass on their route. Such shuttles therefore effectively will bring access to self-driving mobility services to some very rural dwellings.

Today, households in low density areas of the US have much higher car ownership rates than the rest of the population: there simply are  no viable alternatives. Self-driving cars fundamentally change this situation. Wherever there is a minimum of demand for personal mobility, self-driving mobility services become economically viable. The number of persons needed to sustain a self-driving taxi resource is rather small; towns with just a few thousand of inhabitants should always provide enough demand to allow a small fleet of self-driving taxis to operate. Initially it may only be the seniors who use these services but then households will start to think about the number of cars they really need and gradually demand for these services (and with it, supply) will increase.

In many lower density areas of the United States, car ownership is a prerequisite for finding work and – as a consequence – people without cars suffer and economic opportunities are lost. For seniors access to medical services and just getting around can be extremely difficult. The young face similar problems. These examples show that we can expect sufficient demand for self-driving mobility services in most parts of the United States – including many small towns and even in many areas that have low population densities. The impact of fleets of self-driving cars will not at all be limited to big cities!

Self-driving vehicles: The “platform” business model

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How will autonomous car technology generate profits? Among the many different business models – from self-driving mobility services to models centered on data, advertising or entertainment – platform-oriented business models are currently receiving much attention, not the least because Waymo seems to be leaning towards them.

The term “platform” can be understood in different ways: In the automotive context it is usually understood as a car platform where many different models share the same technology under the hood which reduces development costs and allows economies of scale. In a more general, wider interpretation platform business models aim to build a unique competitive position through a complex technology or service which is combined with an ecosystem of users and partners. Ideally the platform exhibits network effects: the larger the ecosystem, the more attractive it becomes to its users and partners and the harder it becomes for competitors to challenge the position.

Waymo’s integrated hard- and software platform

When Waymo’s CEO John Krafcik talks about Waymo’s strategy he emphasizes the integrated hard- and software platform which Waymo is building. Currently this platform is embodied in the ugly white box  on top of Waymo’s self-driving Chrysler Pacificas which are occasionally driving around Phoenix. Most of the self-driving hard- and software in the box has been engineered by Waymo/Google: Not just the software, also a novel 360 degree spinning Lidar (with better performance than the Velodyne Lidar, costs reduced by almost an order of magnitude); radar sensors (with better short range detection of stationary objects); the computing platform (developed from scratch in collaboration with Intel); cameras, microphones. Ideally, this box, Waymo’s “better driver”, could be integrated easily into other car models. However, this will always require more work than just adding the box because some sensors will still need to be mounted on the car; more importantly, the car must be ready for self-driving (e.g. redundant safety components) and must be able to communicate with the box by reporting its physical conditions to the box and accepting driving instructions from it.

Can there be much doubt that such a universal driving module would be a highly profitable product? There are many application scenarios (vehicles for commercial use: taxis, buses, trucks, logistics) where self-driving modules would be economically viable for the customer even if priced at very high margins. Startups and established companies should see much opportunity for quickly bringing self-driving vehicles of many kinds onto the market. The technology provider could realize economies of scale while still keeping the total cost for the customer significantly below the alternatives (i.e. where self-driving technology is self-developed or sourced from a variety of vendors).

Platform economics in the consumer car space

Unfortunately, this calculation does not apply to the consumer car space: Consumers are not willing to pay a significant premium for self-driving car technology because they value their own time differently than commercial users of self-driving car technology. In addition, the equation changes for auto makers selling large volumes of vehicles: with a century of experience in managing and cutting costs auto makers will look for every way they can find to slash the price of the self-driving car technology and bring margins down. The larger the sales volume, the higher is the incentive to find other, more cost-effective solutions. Even if they initially agree to source the universal self-driving hard- and software modules, they will work hard to reduce their dependency on it. And they will find many ways to scale back the size of the external self-driving car module: they will want sensors to be integrated into the car – rather than to come with the self-driving platform – and they will want to source them independently. They will clamor to structure and compartmentalize the interface between the self-driving module and their vehicles and they will fight to standardize and take over some of those functions, so that they get control over them. There will be fights over access to the data, over controlling the interface with the user. And it will be hard for the universal self-driving module provider to beat all of those demands back because the OEMs have experience and market knowledge and their car models have special use cases in various segments that the self-driving module provider is not familiar with, does not own and therefore can not easily implement independently. If the provider of the SDC technology platform can not impose lasting, full control over the whole extent of the self-driving platform (prohibiting partial sourcing of components, keeping all modifications to the platform under their own control (even those developed in the context of a particular customer relationship) etc., avoiding any replacement of functionality by the OEM) his power position and margins are likely to deteriorate significantly over time. In the other extreme, the OEM risks losing their established central position in the market to a newcomer who now controls the ‘heart’ of the vehicles. The middle ground is a slippery slope characterized by an uneasy, highly unstable and competitive relationship between both partners where each continually tries to boost their power position to the detriment of the other.

Thus Waymo’s apparent lack of success at finding partners in the auto industry does not come as a big surprise. Why should companies that are used to investing billions for  designing a new car model  succumb to a company that has invested not much more than a billion dollars (approximately 1.1 bio $ between 2009 and 2015) into self-driving car technology? Shouldn’t they just follow the same path, jump-start their own efforts and ensure that they reduce the gap?

Self-driving software can’t establish a lasting competitive advantage

For anyone who examines the technology and its potential there can be little doubt that many actors will eventually master self-driving car technology. There are many commercial players who have every incentive and sufficient resources to solve the problem. This includes General Motors which has spent 581 million dollars to acquire Cruise Automation and is making a concerted effort to reach manufacturing readiness on the first self-driving car model. There are big European OEMs which are determined to solve the self-driving equation but there are also countries which regard the technology as vital to their economic and military interests. There are investors who understand the economic potential of the technology. Furthermore, although the self-driving car problem is exceptionally hard, it has a ceiling; it will not keep increasing and becoming more and more difficult. Over time, algorithms, simulation environments, tools test data collection and test case generation, hard- and software will become more refined and more easily available. Thus it is very unlikely that a provider of self-driving car technology will be able to establish a lasting advantage over the competition just on the basis of the technology. On the contrary: the time will come where the technology will be mastered by many and be commoditized. The time will come where self-driving car technology will be seen as a natural part of every vehicle, where cars will no longer be differentiated on the basis of their self-driving car technology and where customers will no longer care very much what kind of self-driving car technology is inside. Because safety requirements will be very stringent, vendors of self-driving car technology will have a hard time making the case that their technology is significantly better than the competing products.

Platform models with network effects?

But couldn’t there be a way for the first market entrant to establish a platform position in the wider sense where the technical self-driving car solution forms the base for a self-sustaining ecosystem of customers and partners which exerts a pull on the market and erects a powerful barrier against entry for competitors?

There are several strategies which could be applied toward this end: those who enter the market first and expand quickly can realize economies of scale, which keeps costs down and can discourage competitors by keeping prices low. But keeping prices down means foregoing much of the rents associated with significant productivity increases due to reduced costs of mobility. It is more than questionable whether this would discourage competitors or whether it would be interpreted as a play towards dominance in a lucrative market – an economic signal that might actually entice competitors to redouble their efforts.

Another approach would be to use current dominance in the technology to establish a hard-to-assail business position, a self-growing platform, around the technology. Self-driving car technology requires much more than the car’s hard- and software. There are many legal aspects which require substantial effort. Various service infrastructures need to be established – some to fulfill legal requirements, others out of practical necessity – and might become key parts of the platform ecosystem: California self-driving car regulations already mandate that operators of self-driving cars ensure that high-definition maps are kept up to date and are regularly distributed to the cars. The same regulations describe a remote operations service which assists fully self-driving cars in challenging situations (i.e. a 24/7 remote operations center). Infrastructures are needed for cleaning and maintenance, accident handling, secure over-the-air updates of self-driving car software. The scope of platform services could be extended further to include services for managing fleets of self-driving taxis, trucks and buses as well as associated customer facing services (reservation, payment processing etc.).

Companies which provide the full breadth of such services (or manage access to it) certainly have a favorable competitive position, but it is questionable to what degree this can protect the platform and establish a barrier against entry of competitors. Precursors to most of the platform services described above already exist today and companies exist already that would be willing to extend their services to the self-driving car market. Today many OEMs already operate remote assistance centers (GM OnStar, LexusLink, BWM Assist etc.)  which could easily be extended to provide assistance to fully-self driving cars. Several companies are focused on building and maintaining high definition maps (among others  Here which was purchased by the German OEMs). Rental car and mobility services companies already have experience with some of the additional services needed and would certainly aim extend their business models to the self-driving car space. Thus it is unlikely that such a Waymo self-driving platform could not be replicated with a determined effort by some of the OEMs or other players.

SDC platforms not similar to operating system or marketplace platforms

The market for self-driving car technology is not similar to other markets where we have seen platform models succeed. This is not like some of the operating system (Windows, Android) which have grown into a platform, where this platform is the base for millions of different applications and uses, where the platform grows because with more users the breadth of applications and uses increase. In contrast, self-driving mobility is a much more specific – and for safety and security reasons – limited application domain where scale effects matter but the diversity and number of applications will be comparatively low. A software platform for self-driving cars can never be as open as Windows or Android. A self-driving software platform will most likely evolve in a way that the platform has a very limited external application programming interface which partners may latch onto. But this also means that competitors which provide their own universal self-driving car modules or platforms should find ways to expose similar interfaces to their partners and these partners could more easily support multiple self-driving car platforms with their services and applications. Thus a self-driving hard- and software is not likely to achieve an operating-system like lock-in effect for its partners and customers.

The market also does not resemble an Airbnb, Ebay or Uber, domain-specific optimized marketplaces which link a large number of product or service providers to a large number of customers and which increase in value and attractiveness with an increasing number of participants, thus quickly erecting barriers to competition. Yes, self-driving car technology can be the basis for establishing mobility services which will tend to rapidly establish a dominant, hard-to-assail position in a region. This mobility-as-a-service business model does have a lock-in effect but this is a very different type of business model than the self-driving hard- and software platform model which we are currently examining.

Thus, the pioneers of self-driving hard- and software can base their business models on viable platform strategies centered around a universal self-driving hard- and software model complemented with associated services and business relationships. Given the economic value that can be realized in many markets and business scenarios with self-driving vehicle technology the business model will initially be very profitable. As in many other markets the pioneers have the potential of establishing a leading and hopefully lasting market position. But their competitive advantage will fall over time as the market becomes commoditized and it will be hard to keep competitors out – unlike the platform models in other markets which enjoy considerable network effects.

The problems with Waymo’s focus on a platform business model

Thus Waymo’s apparent focus on a universal self-driving platform-based business model seems to be questionable. When Waymo decided to shelve the activities related to their self-driving firefly electric two-seaters, they seem to have made a decision against squarely focusing on the mobility services model, the one business model in the self-driving car space that exhibits strong network effects and which would provide a permanent advantage for the first mover.

A side problem of Wamo’s universal self-driving platform is that it does not seem to be well executed. To make their platform truly universal, they would need to expose themselves to many different use cases and ensure that the platform works for cars, trucks, buses, even self-driving machines of different types. Many startups are currently working on products and services in the self-driving space and would be keen to cooperate with a provider of a self-driving car modules but there is no evidence, that Waymo is branching out to them. Companies such as EasyMile, Navya, LocalMotors, truck manufacturers, and many others would be more than willing to jump on the bandwagon and thus ensure that the platform really becomes universal. Waymo would profit from learning about differing requirements in different application scenarios which would necessarily lead to a more customizable structure of the self-driving “box” which Waymo envisions placing on top of a vehicle. That the top box may not be the best idea can easily be seen when we consider the context of trucks where a top box is much less compelling because it would not achieve full 360 degree unobstructed sensor vision. Another worry about Waymo’s approach to a universal driving platform is the reliance on their own sensors. With the current innovation in the automotive sensor market it is not very likely that their sensor suite can remain ahead of the competition for long. A universal self-driving car platform needs the ability to rapidly incorporate new sensors and even new sensor types. Impressive as Waymo’s self-developed sensors may be, there is also the risk of paying less attention to external innovations.

Conclusion

For the market as a whole, Waymo’s detour focusing on a business model based on some incarnation of a universal self-driving hard- and software platform (“the better driver”) may be a positive development. It reduces the risk that one player will dominate the field, has given auto makers time to understand the nature of the challenges better and increase their determination to close the gap. Most auto makers have now understood the dimension of the challenge (although some have difficulties balancing their priorities between autonomous driving and electric vehicles). General Motors is an excellent example of an auto-maker getting up to speed: their acquisition of Cruise Automation is a win-win for both companies and both companies together are not plagued by the competitive stalemate that a collaboration between a universal self-driving module provider and established auto makers would engender. Being the most advanced player, Waymo is likely to profit greatly from its self-driving car technology but a problematic platform-focused commercialization strategy may be giving its competitors some welcome breathing space for catching up.

Workshop: Self-driving cars – strategic implications for the auto industry

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Please join us for this 1-day workshop on October 24 in Frankfurt, Germany or on November 2 in Auburn Hills, USA. The workshop examines the disruptive implications of self-driving car technology and the strategic consequences for the auto industry, its suppliers and related industries. The workshop will be led by Dr. Alexander Hars.

Program highlights

  • The workshop begins with a review of the current state of the global, distributed innovation process related to self-driving cars, and examines the underlying technical, economic, legal and geopolitical factors upon which it depends.
  • Key implications for the mobility space will be discussed through an in-depth analysis of the many facets of the economics of self-driving mobility services.
  • We will examine how fully self-driving cars will affect different aspects of personal mobility – the propensity to use self-driving mobility services for local or long distance travel, the decision to purchase a car, buyer preferences for specific car models and features as well as the transition towards electric vehicles.
  • We will then focus on the various players in the SDC field, including leading OEMs, new entrants such as Google, Uber, key suppliers, including sensor and hardware providers as well as various governments, including the US, UK, Singapore, Japan and China.
  • We explore four potential strategic responses for the auto industry and discuss business models associated with self-driving vehicles and their suitability for the various players.
  • We review key implications for model mix, volume, as well as sales and design processes.

Who should attend?
This workshop is intended for executives who need to think through the consequences of self-driving cars on the automotive sector. It offers frameworks and insights to help them develop their understanding and analysis of the threats and opportunities of SDCs for the industry.  It will help them to understand the implications of SDCs and to formulate appropriate strategies for their business.

More information, event agenda and registration
This event is organized by Autelligence. Further details are available on Autelligence site.

Misconception 8: Self-driving cars will increase congestion in cities

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Fleets of self-driving cars will reduce the cost of individual motorized mobility and increase its accessibility to people without driver’s license. Many city planners fear that this will induce additional demand and significantly increase miles traveled with the result of even more congestion in our already heavily congested cities.

Fortunately, there are many reasons why an increase in person-miles traveled with self-driving cars will not lead to an increase in congestion. The opposite may be true: we may find that self-driving cars, while certainly increasing person-miles traveled will actually reduce the congestion in our cities. Congestion is not a direct function of the number of vehicles on a road; it depends on driver actions, routes taken, road utilization per vehicle and systems for flow optimization (traffic management systems etc.). If we increase the number of miles driven and keep all other parameters constant, then congestion will certainly increase. But with fleets of self-driving cars, all of these parameters will change, some significantly.

In the following we will first look the reasons why self-driving cars are likely to reduce congestion compared to human-driven cars. Items 1 and 2 show that there is significant potential for congestion-reduction (which in turn means that the risk of induced mobility leading to more congestion is reduced).

1. Driving behavior: The driving behavior of a self-driving car differs from the driving behavior of human drivers. Autonomous cars don’t exhibit the lane-hopping and other congestion-creating behavior. Simulations have found that even a small percentage of self-driving cars among many human-driven cars on a lane reduces congestion because the self-driving vehicles help to smoothen the traffic flow. Self-driving vehicles also reduce the typical delay of the average human driver at a stop light turning green and thus ensure that more vehicles can pass that stop light in a given time frame. A self-driving vehicle will not sit idle for a second after the car in front has started moving. This number can be further increased if the self-driving car uses an optimized acceleration pattern at a stop light. Thus, with an increasing ratio of self-driving cars, the throughput will increase at the bottlenecks which will lead to significant reduction of congestion.

2) Road capacity utilization:
 2a) Road space: Self-driving fleet cars used for urban driving will be smaller and thus use less road capacity. Self-driving cars will also systematically adhere to an optimal minimum distance to the car in front which significantly increases the number of vehicles that a given road segment can support during heavy traffic.
 2b) Parking space: Fleets of self-driving cars will be in operation most of the time, especially when mobility demands (and with it traffic) is high. Thus cities will need much less parking space and can use parking space of other purposes. In some cases, parking spaces could be turned into additional lanes, further increasing throughput. This is an option but we expect most of the parking spaces that are freed up to be put to other use. Note that self-driving car fleets may need very little dedicated parking space because they could simply use existing lanes that are no longer needed during off-peak times or at night for parking.
 2c) Convoy driving: As the ratio of self-driving cars in traffic increases, these cars will more frequently find another self-driving car in front or behind and can then coordinate their driving behavior. This can lead to further reduction of distances between the cars and can further improve reaction times at stop lights.
 2d) Lane sharing: Self-driving cars can drive consistently with more lateral precision than human drivers. Thus they can operate on narrower lanes. This also makes it possible that more self-driving cars can drive next each other than the number of lanes available. For example, three self-driving cars may ride next to each other on a two-lane highway. This could be another variant of convoy driving and would need communication between the vehicles.
 2e) Micro-cars: Very small self-driving pods could be built so that two of them fit next to each other on a single lane. An example has been proposed by Harald Buschbacher (although these two wheelers with auto-retractable stabilizer wheels are envisioned as personal rapid transit vehicles using their own very narrow lanes).

The previous 2 items (Driving behavior and road capacity utilization) ensure that the congestion-inducing effect of a self-driving car is much lower than the average human-driven car which in turn allows to significantly increase the number of person-miles traveled without increasing congestion. But the next item is the key reasons why we can be confident that self-driving car fleets will not increase congestion, even if they significantly increase the number of person-miles:

3) Internalizing the costs of congestion paves the way for combating congestion:
Today, congestion on our roads leads to enormous economic costs. Unfortunately, these costs are distributed among the many traffic participants which at the same time are cause and victims of congestion. It is difficult to unleash market forces to find ways for reducing congestion because it is difficult to set prices for congestion-free roads nor can we correctly attribute congestion-costs to those who cause it and make them pay. This changes once shared fleets of self-driving cars provide a significant share of local mobility because these fleets internalize a sufficiently large part of congestion costs.

Fleet managers will focus on the bottom line and they have every incentive to maximize their return on capital. They will try to minimize the size of their fleet and to maximize the throughput of their cars. To them, congestion translates directly to cost. When they send a car through a congested area, this increases the cost of the car, reduces revenue opportunities and it also reduces the throughput for other cars of the fleet that may need to take the same route a little later. After a few months of operations, fleet controllers will be able to quantify exactly how much their bottom line would improve if the throughput in a certain bottleneck could be improved by a few percent. They would find that many investments in infrastructure, signalling algorithms, routing methods etc. would have a positive return because their costs (of congestion-reducing activities) are lower than their benefits (increased fleet revenues, lower fleet size (capital stock)).

From an economic perspective, shared fleets of self-driving cars aggregate the mobility demands and the congestion-related effects of their large group of customers. This aggregation allows the fleet to find much better ways of handling congestion – taking into account both the preferences of their customers with respect to congestion-related costs, the congestion-inducing effects of different routes and mobility solutions and internal or external potentially costly mechanisms that reduce congestion. The fleet will very clearly understand (and be able to quantify) its effect and the effect of each of their customer’s trips on congestion. In contrast to the individual driver on the way to the office very morning, who is oblivious to his share in making congestion and who simply wants to take the fastest route, the fleet will not be concerned with the speed of the individual trip but will make sure that the trips are routed in such a way that the throughput of all their vehicles will be maximized. The goals of the fleet with respect to congestion are very much aligned with the goal of the city as a whole: that throughput is maximized.

This argument may sound academic. But the effects will be very real. Fleets that are small will not have a large impact on cities. But once fleets process a significant share of local mobility, they will have the best knowledge about traffic and congestion patterns in the city. Their cars will provide them with detailed up-to-the minute traffic information for all parts of the city. Economic rationale will lead them to build complex models of traffic flow and look for ways in which throughput can be improved and they will be able to very clearly indicate what approaches in which areas of the city could lead to which level of congestion reduction. They will work with city official to optimize their signaling infrastructure, they will even be willing to invest into that infrastructure (if the cost is lower than the benefits from congestion reduction). The fleets will also look for ways to shift mobility demand (so that some people defer their trips to non-peak times) and to reduce congestion cost per trip by combining trips (through ride-sharing or by inventing new variants of ride-sharing that actually appeal to their customers).

In summary, there is no reason for city managers to worry about congestion-inducing effects of shared fleets of self-driving cars. These fleets will have large benefits for the city. They will actively combat and reduce congestion because they are the first entity that internalizes the costs of congestion. They will reduce the ecological footprint of mobility because they will be mostly electric vehicles and the average vehicle will be smaller and lighter than the vehicles today. They will accelerate the transition to electric vehicles because the shared utilization of short-range vehicles is the optimal use case for electric vehicles. They will free up parking spaces and eliminate traffic looking for parking (which can be a very significant share in inner cities).

If you are still worried about the congestion-inducing effects of self-driving car fleets, here is a simple, political argument: Self-driving car fleets won’t increase congestion in our cities because we will not let that happen. Such fleets will not populate our cities over night. They will initially service a small fraction of the population and can not immediately cause significant increases in congestion. As these fleets become larger, politicians will certainly not sit idle if congestion increased and neither would the electorate accept more and clearly attributable congestion. This in turn would increase the economic pressure on such fleets to find ways for reducing congestion (the most straightforward would be to limit their size by adding congestion charges to their pricing structure).

Note: This is part of a larger series of misconceptions related to self-driving cars. The other misconceptions are discussed here. A PDF document with all misconceptions is also available for download.