Category Archives: Economic issues

Monitoring the self-driving car innovation process: California AV permits

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Since 2014, many companies have applied in California for testing self-driving cars. The list of companies which have received a permit can be used as a measure for the innovation process associated with autonomous vehicle technology. The graph below shows how the number of companies active in California has only increased gradually from 2014 to the third quarter of 2016. A steep increase follows in 2017. The slope softens in the first half of 2018.

Of course it would be premature to conclude that we are already seeing the beginning of the end of the S-curve which is so typical for innovation processes. And the California AV permits can only be seen as a proxy for the larger distributed self-driving vehicle innovation process currently unfolding across the world.

Active AV permits by month

Active California AV permits by month (Data from California DMV) Updated: 2018-11-18 (you might need to reload your browser to see the latest version)

 

But neither the number of California AV permits nor the number of companies providing self-driving vehicle solutions will grow indefinitely. The time will come where the industry moves into the next phase, where the exploratory modes of development will be replaced by more systematic, managerial approaches and where commercialization will be the primary focus. A shakeout is inevitable. Time may be running out for those who still want to jump onto the self-driving car train…

Notes:
– 58 permits have been issued by the end of June 2018; One permit has not been renewed (Uber), making it 57 active permits.

Updates:

The graphic is updated from time to time. You may need to reload your this page in your browser to view the current version of the graphic.

-2018-11-18: By the end of October 2018, the number of active permits increased to 60.

– 2018-09-07: By the end of August 2018, Mando America Corporation received a permit, increasing the number of active permits to 56.

– 2018-08-07: By the end of July 2018, 2 companies did not renew their permits (Wheego Electric and Bauer’s Intelligent Transportation) reducing the number of active permits to 55.

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

 

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: 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.

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.

Five key impediments to a successful self-driving car strategy

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The auto industry increasingly recognizes the threats and opportunities associated with self-driving cars. Unfortunately several impediments stand in the way of formulating and implementing a strategy for dealing with self-driving car technology and its impacts:

1) Time: lack of urgency

Although the competition in autonomous car technology has heated up considerably over the last 2 years, most industry experts continue to expect a slow adoption curve which could easily span two to three decades. Unfortunately, adoption of self-driving car technology (level 4 and up) will be much faster than traditional adoption rates of new technologies in the auto industry. A key accelerator is the enormous net benefit of the technology not just in terms of safety but also as increase of available personal time, competitive position (for companies and countries) and a significant decrease of costs (labour, fuel, insurance, capital). As a consequence there is much less time to formulate a sound strategy for self-driving cars.

2) Shared auto industry perspective clouds impact analysis

Shared convictions and experiences make it much more difficult for the industry (including their consultants) to think through fundamental, deep, disruptive changes in the architecture of mobility. Whether it is the joy of driving, the importance of brand for the consumer, the assessment of the legislative and regulatory environment, the consumer’s propensity to use shared self-driving mobility services or the likely business models, industry insiders tend to reinforce a perspective on the impact of self-driving cars that remains much too close to the current model, experiences and structure.

3) Lack of understanding for self-driving car business models

For many years, the auto industry has recognized a trend towards shared mobility services. Automakers understand that self-driving fleets will accelerate this trend. But they seem to spend very little effort to think through the dynamics of this market (which differs fundamentally from the traditional car-sharing and mobility-brokering markets), the way that shared mobility services will operate and compete, the regulatory environment that will emerge around fleet oligopolies, the differences between urban and long distance shared self-driving mobility services or the cost structure, maintenance strategy and model mix for such services.

In addition, there are many other business models besides shared fleets which may provide opportunities related to self-driving car technology which established players need to carefully consider, evaluate and prioritize.

4) Relationship between electric vehicles and self-driving cars not understood

In parallel to the self-driving car phenomenon the auto industry is involved in the switch towards alternative propulsion modes. But the relationship between self-driving car technology and alternative fuels is widely overlooked: Because self-driving cars will change mobility patterns (increase of urban mobility services, changes in long-distance travel patterns) and self-driving fleet vehicles will be able to refuel autonomously (or nearly-autonomously), the context for the adoption of alternative fuels changes dramatically. Battery range will become much less important; rather than optimizing cars for maximum range they will be optimized for an optimal range with respect to the mobility pattern which they are used for. When fleets carry a larger share of traffic the dimensioning of an adequate charging infrastructure becomes much easier and much more economically viable. Thus autonomous vehicle technology will serve as an accelerator for the introduction of electric and alternative fuel vehicles.

5) Fear of cannibalization / resistance to change

Any organization that faces major change and must consider the effects of a disruption of its primary business model will encounter tremendous internal resistance. Those who see the writing on the wall will hesitate to become advocates of (painful) change because internal opposition is fierce, uncertainty abounds and – as a result – career risks are high. It is useful to seriously study other industries and companies which had to face disruptive change. One of many examples is Kodak, a company that had developed the first digital camera already in the Seventies and brought the first digital camera to the market in 1995. There may be some parallels to the auto industry, which has a multi-decade history of developing technologies for self-driving cars. But Kodak hesitated far too long to adapt and rethink its business models, fearing cannibalization of their very profitable film camera business. When their profits began dwindling, it was too late. The auto industry cannot afford to make the same mistake.

Learn more

For more on this topic please join us at the upcoming 1-day seminars on self-driving cars in Frankfurt (March 23) and Auburn Hills (May 16). The seminar will be run by Dr. Hars and will help to develop a better understanding and analysis of implications of self-driving cars. More info…

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

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Please join us for this 1-day workshop on March 23 in Frankfurt, Germany or on May 16 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.

 

Transformations 2025: How Volkswagen prepares for the (driverless?) future

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Echoing a growing sentiment in the auto industry, Volkswagen’s CEO Matthias Mueller warned last week of “a rapid and hard transformation” coming to the auto industry. He presented Volkswagen’s strategy “Transform 2025+” to cope with these changes. It includes major job cuts to prepare for the transition and many new initiatives.

But his strategy also shows how difficult it is to change the direction of the tanker which all major auto makers have become. Experience accumulated in the last 100 years, shared convictions and values make it difficult to adjust the focus and prepare for fundamental changes coming the industry. Many trends are currently competing for attention: electrification, mobility services, connected vehicles, digital platforms and finally the shift towards autonomous vehicles. It does not come as a surprise, that Volkswagen wants to become a leader in most of these topics:

It plans to establish an additional (thirteenth) major brand around mobility services. It wants to become a leader in electric vehicles. It has just established a digital lab to develop cutting-edge digital services related to mobility, connectivity, its brands and its products.

But the strategy fails to consider the tectonic shift which may be caused by autonomous vehicles and the way that self-driving car technology will affect the key aspects of the auto business. Mueller plans to lay the foundation for autonomous driving in the years from 2020 to 2025 and then have the necessary business models in place around self-driving cars after 2025. Given the rapid progress of the field, he may not have that much time.

But more importantly, self-driving car technology is associated with a very specific danger (and opportunity): It changes the dynamics of each of the auto industry’s strategic topics. Mobility services based on self-driving car fleets differ fundamentally from Uber’s, Car2Go’s and other mobility services fleets on parameters such as total cost per mile, optimal car model and characteristics, volume, utilization, profitability,  etc. Similarly electrification differs greatly whether it is targeted towards autonomous vehicles (which will initially predominantly be rolled out as elements of urban self-driving car fleets) or towards the consumer. The economic justification, battery cost, vehicle range, charging infrastructure requirements, innovation diffusion path and cost-effectiveness differ fundamentally!

A little bit of everything is not the right approach. Volkswagen, like most other auto makers, suffers from the problem hat it tries to address each and every strategic topic on its own without considering the relationships and interdependence with a paradigm-changing technology. Then, when autonomous vehicle technology enters the market they will find that the original assumptions no longer hold and that very little time remains to catch up and refocus the many different aspects of their business.

It is good that the auto industry is increasing their efforts to think about a radically different future. But they extrapolate forward from today to the next 5, 10, 15 years, and their thinking remains mostly rooted in the classic automobile world with a focus on volume leadership, consumer cars as primary product, traditional branding approaches, etc. However, in the face of transformational change, a different mode of analysis is needed: First the more distant future needs to be conceptualized, a future where autonomous vehicle technology has already matured, the current doubts and questions about viability, legality and acceptance have been overcome, self-driving vehicles are in the market and where laws and regulations have been updated (as we know they will) to allow productive use of the technology. The key aspects of this future need to be considered: Mobility service markets (separately for urban and non-urban regions, for local and long distance traffic), consumer segmentation and purchase decisions, impact on road infrastructure, impact on traffic flow (which will be enormous both for urban and for long-distance roads) and fleet management algorithms, truck, bus and autonomous machine markets. For such a future key changes (including the various types of mobility service business models) need to be calculated through in detail, using quantitative models. This analysis must be unencumbered by the current “realities” of the auto market. It must include the scenarios, business models and market dynamics that may entice investors to pour funds into promising opportunities.

After such an analysis, the focus can be turned back from the future to the present and the transition period. Many likely changes will become obvious and the paths and the relationships between the different technologies being considered today will be much clearer. For Volkswagen and all other auto makers it means allocating major resources to autonomous vehicle technology today: make sure that they catch up with the leaders in the space; prepare mobility services for  the autonomous fleet scenario rather than as also-run next to all the players already established in this field and make sure that they have electric vehicle models that can be used as backbone of self-driving car fleets.  Develop, consider and prioritize business models beyond consumer cars and fleet vehicles/mobility services, for trucks, buses, autonomous machines and beyond. Each of these activities is future-proof and establishes a beachhead  in the transition towards autonomous vehicles.

This is not a call to put all eggs into one basket. But auto makers need to take the fundamental changes that will be caused by self-driving car technology seriously and prepare to adapt to these new challenges today by making them a cornerstone of their strategy.

The race for fully self-driving cars has reached a pivotal point

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Several events from the last months provide a strong signal that autonomous vehicle technology has led the auto industry to a pivotal point: The first auto makers are adapting their business model for fully self-driving cars and are providing explicit time frames!

Earlier this year GM invested 500 million USD in Lyft, purchased self-driving technology startup Cruise Automation for more than 1 billion USD and announced in July that GM will build its first self-driving cars for use within the Lyft fleet as self-driving taxi. In May BMW announced that they would have a self-driving car on the market within 5 years. Next came Uber, which acquired autonomous truck startup Otto for 680 Million USD and is now beginning field trials of fully self-driving taxis in Pittsburgh. But the key change at Uber is the way that its CEO Kalanick frames the issue. He makes it clear that Uber’s survival depends on being first (or tied for first) in rolling out a self-driving taxi network.

The latest announcement comes from Ford which plans to provide mobility services with fully autonomous self-driving Fords by 2021. This is a major effort: Ford is doubling its development staff in Silicon Valley, aims to have the largest fleet of self-driving car prototypes by the end of this year and will triple the size of this fleet again next year. It has also purchased 3 companies related to autonomous driving technology and has purchased a stake in Velodyne, the leading manufacturer of LIDARs for autonomous driving.

When we started to monitor the development of self-driving car technology in 2009 we expected that this technology would turn into an avalanche that sweeps through the auto industry. There have been many signs over the past years that the avalanche is picking up speed but until now we have been reluctant to claim that it is in full swing because even though the auto industry was continually increasing their activity around self-driving car technology all players had been very reluctant to openly call this a race and to publicly position fully self-driving cars as a key element of their strategy. There was a lot of posturing, many eye-catching public demonstrations of self-driving car prototypes but very little tangible action aimed at turning fully self-driving car prototypes into a real product.

After these recent signals, this situation has changed. It is now clear that auto makers have begun competing in earnest to adapt their business models to the coming wave of fully self-driving cars. No longer is Google the only company which is stepping on the gas; auto industry executives (and Uber) are now openly competing to bring the first self-driving cars on the market. It will come as no surprise to the readers of this blog that the initial business models are not concerned with selling cars but to provide mobility services.

These signals are important in themselves. They heat up the competition and force the rest of the auto industry to decide how to adapt their business model to fully self-driving cars and to explain this strategy to their investors, journalists and analysts. They increase the value of companies in the space and increase the competition for human capital (Google has probably lost between 500 million and 1 billion USD in human capital from the exodus of key members of their self-driving car group in this year (680 mio USD Uber paid for the Otto startup founded early 2016 by 4 Googlers (including Anthony Levandowski), plus Chris Urmson.). They also increase the effort of all parties involved (auto industry, suppliers, regulators, journalists, related industries such as transport & logistics, insurance, health care etc.) to understand the implications of fully self-driving cars which gradually drives away the many misconceptions and more clearly shows risks and opportunities. We are in the middle of a global, distributed innovation process around self-driving cars and driverless mobility where all parties are learning, refining their thinking, changing their vision of the future and adapting their actions accordingly. The avalanche is in full swing now and it will be a tough ride for those who fail to adapt while there is still time…

Shared autonomous vehicles could increase urban space by 15 percent

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A recent UK study has looked at the transformative implications of self-driving vehicles on cities. The authors found that shared autonomous vehicles could increase available urban space by 15 to 20 percent, largely through the elimination of parking spaces. Today central London has about 6.8 million parking spaces and a parking coverage of around 16%! Many large cities have even larger coverage ratios for parking space of up to 30%. Freeing up this space would make our cities greener, increase quality of life and also create the potential for additional housing.

Autonomous vehicles will also make the rural communities more attractive because shared travel to nearby cities becomes widely available, affordable and does not lead to loss of productive time.

The authors also consider autonomous vehicle only development areas and highways that are limited to autonomous vehicles. This could reduce costs as lane markings and signage would no longer be needed, the lanes could be narrower and throughput per lane would be higher.

Overall the authors from a cooperation between professional services firm WSP Parsons Brinckerhoff and architect planners Farrells conclude that autonomous vehicles will be transformational:  Future mobility may be headed to a shared pay-as-you-go transport system. The study provides many key points which infrastructure planners and legislators need to consider!

Source: “Making better places: Autonomous vehicles and future opportunities“, 2016 by WSP | Parsons Brinckerhoff, Farrells