Self-driving vehicles: The “platform” business model

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

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

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

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

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

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

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

Baidu expects autonomous buses to become first wave of self-driving vehicles

Chinese search engine Baidu has entered the race for self-driving vehicles in 2014. In a partnership with BMW, the company presented an early prototype of an autonomous car at the end of 2015. Baidu’s approach mimics Google in many ways: Like the first Google prototypes of 2010, the car uses the (aging) Velodyne 64 Lidar as its main sensor; Baidu’s approach also relies on detailed mapping which fits well with Baidu’s overall mapping strategy. Baidu also aims to diversify its business model by leveraging its know-how in artificial intelligence and has transferred its auto-related activities into a separate division, a move that Google started last year by restructuring into Alphabet. There are some differences: unlike Google, Baidu does not seem to put much emphasis on the sensors; they don’t seem to experiment with their own sensors and the configuration of sensors indicates that certain situations in which a car may find itself have not been considered yet.

Baidu’s vision of how self-driving vehicles will be adopted also differs somewhat from Google. Whereas Google has focused on individual cars, and is testing electric two-seaters which could easily become robotaxis, Baidu expects the first wave of self-driving vehicles to be autonomous buses or shuttles. In a recent online interview, Andrew Ng, Baid’s Chief Scientist, argued that buses which service a fixed route or a small defined region will be the best starting point. He expects a large number of such vehicles to be in operation within three years (= early 2019) and mass production to be in full swing within five years (= 2021).

Andrew Ng correctly pointed out that such autonomous buses operating on fixed routes or small regions  would have the advantage that care could be taken to ensure that the routes are well maintained, don’t have construction (or the construction site is clearly indicated in the map) etc.

Unfortunately, Andrew Ng’s argument, that driving on predefined routes would enable the vehicles to avoid “corner cases–all the strange things that happen once per 10,000 or 100,000 miles of driving” (source) is flawed. He argues, that machine learning can not prepare for these corner cases and that therefore driving in a restricted well-defined environment is the solution. Unfortunately, corner cases can happen anywhere; it is impossible to guarantee that on well-mapped and well-known routes strange situations can not occur. Pedestrians can suddenly appear in areas that are closed for pedestrians, obstacles may occur on a road, an oil spill can occur, the road can suddenly be flooded etc. Building software that can reliably handle even the most challenging situations is a hard task and needs to consist of a combination of machine learning, an enormous testing program (usually combined with knowledge acquisition and machine learning), careful and very extensive risk analysis and risk modeling, and purpose-built test scenarios which challenge the capabilities of the cars both in simulators and in staged test cases in the real world.

We have pointed out for the past five years that the switch towards shared mobility services based on fully autonomous vehicles will be the great transformation that self-driving car technology will bring. This is the reason why auto makers have been so reluctant to push fully autonomous driving and why it provides avenues for new entrants such as Google, Baidu, EasyMile, Bestmile, Zoox, potentially Apple, and others to capture a significant share of the world’s expenses for personal mobility. There are many reasons why the first fully autonomous vehicles to appear on our roads will be robo taxis or self-driving buses, not the least that many current projects focus on such autonomous mobility services. Examples are: WEPods (Netherlands), CityMobil2 (Greece and EU), One-North (Singapore), Sentosa (Singapore), EasyMile, (USA, California), Google self-driving pods (United States, California and Texas), Milton Keynes driverless pods, (United Kingdom), Ultrapods (United Kingdom), Bestmile (Switzerland), DeLijn, (Belgium), RobotTaxi (Japan), Baidu (China), Yutong Bus (China).

In summary, Baidu’s focus on self-driving buses adds weight to the expectation that shared mobility services based on driverless pods and buses will drive the initial adoption of autonomous vehicles. Both self-driving cars and buses have to solve the problem of autonomous driving and the same technology can applied for both application scenarios. This is why the technology which Google currently refines with their 53 self-driving cars can easily be transferred into self-driving buses and shuttles and why Baidu’s current prototype is not yet a bus but rather a converted BMW. Those pioneers who solve the problem of fully autonomous driving will find enormous business potential for self-driving taxis, self-driving shuttles, self-driving consumer cars, trucks and machines. The race is on!

Self-driving cars will be a potent weapon to combat climate change

Although world leaders have reached a ‘historic’ agreement on climate change at the Paris Summit, good solutions to reduce greenhouse gas emissions remain hard to find. Fortunately – and counter-intuitively – self-driving cars have the potential to significantly reduce the ecological footprint of transportation:

The transportation sector is a major polluter and it is the economic sector with the biggest net effect on climate change. While some other sectors (such as industry and power generation) emit more greenhouse gases, these industries also emit other substances that lead to cooling (aerosols of sulfate, nitrate and others).

Of course, self-driving cars will not reduce the number of trips or kilometers traveled. On the contrary: self-driving cars have the potential to significantly lower the total cost per kilometer traveled and are thus likely to induce people to make more trips. As we have shown in other papers, self-driving taxis and buses will emerge rapidly and offer mobility services for local and long distance traffic with great convenience and at extremely competitive prices because they can achieve much higher utilization rates than private cars (which stand idle more than 94% of the time), and because autonomous fleet vehicles will be engineered for the minimization of total cost of ownership and for the maximization of useful life.

Most urban self-driving taxis will be fully electric for reasons that are not primarily environmental but that are still good for the environment: Electric motors offer safety advantages (they can be used for emergency braking and to some degree for emergency steering). They are also much more durable (an electric motor easily lasts 1 million kilometers), less expensive and less complex than conventional engines. In addition self-driving taxis that operate in local traffic will not need huge battery packs when average trip sizes rarely exceed 15 kilometers and when they can drive themselves to the next high efficiency charging station as needed. Their batteries won’t be sized to last a whole day; they will need to be just large enough to service a little more than the trips of the morning peak – after which they can recharge.

There can be no doubt that self-driving taxis and buses will change the nature of urban mobility. Much more short-distance travel than today will occur in small, lightweight, extremely energy efficient self-driving taxis. Although this may lead to a certain increase in total miles traveled, the following effects combine to reduce greenhouse gas emissions:

  1. Self-driving taxis will be mostly electric which reduces carbon emissions (approximately 25% less emissions compared to internal combustion engine)
  2. Self-driving urban taxis will be smaller and much lighter than the average car which further reduces energy consumption per kilometer
  3. Self-driving taxis reduce demand for private cars and therefore reduce the sizable greenhouse gas emissions during vehicle manufacturing which are typically more than 10% of total life-cycle emissions of a car. According to some estimates, a self-driving car-sharing vehicle or taxi can eliminate 7 to 10 private cars. What a potential for greenhouse gas reduction in auto manufacturing!
  4. Self-driving taxis facilitate multi-modal travel (taking an autonomous taxi to the train or bus station, continuing with bus or train, using an autonomous taxi for local transport at the destination)
  5. Self-driving taxis facilitate ride sharing especially during peak hours and on certain routes.

On the other hand, the effect of self-driving taxis on public transport is not yet clear. There is both the risk that some local trips which are taken by public bus today will migrate to self-driving taxis and the opportunity to capture a much larger share of the mobility demands with self-driving scheduled and on-demand buses and mini-buses – potentially in multi modal combinations. The potential benefits are large and there will certainly be a place for efficient self-driving mobility services using self-driving buses and mini-buses. Concerns that new mobility solutions centered around self-driving taxis and mini-buses will be less environmentally efficient than current scheduled buses are not warranted because today’s scheduled buses are not very good for the environment during off-peak hours when they travel near-empty.

The currently most overlooked aspect of self-driving vehicles is their effect on medium and long-distance travel in areas with sufficient population densities. Whereas today many people choose their own vehicle for distances between 100km and 500km self-driving taxis and self-driving buses make it much easier to provide excellent, extremely cost efficient long distance mobility services. When urban taxis at both origin and destination guarantee painless individual personal mobility and when small or medium-size autonomous buses provide long distance travel at extremely low rates which are much lower than the cost of traveling in a private car, then greenhouse gas emissions can be reduced very significantly. Although only a small percentage of all trips are more than 100km in length, these trips represent a large share of the total distance traveled in private cars and therefore have a large and easily overlooked potential for reducing greenhouse gas emissions.

The big advantage of self-driving car technology is that it can accomplish several benefits at the same time: It increases the options for individual mobility and lowers the cost of individual mobility because of new driverless mobility services which through increased sharing, more efficient use and quicker adoption of alternative fuels reduces greenhouse gas emissions. Nobody will have to abandon their cherished car but the joint actions of the large group of less or only moderately affluent consumers who value the flexibility and cost-saving associated with self-driving mobility services will inexorably lead to a reduction of greenhouse car emissions. It is time for the political leaders searching for solutions to combat climate change to take notice!

Volvo’s liability promise for autonomous mode may cut out insurance companies and independent repair shops

Volvo has recently stated that they will accept full liability for accidents that happen while the car drives in fully autonomous mode. This takes the heat away from the discussion about liability issues for self-driving cars. But it also has side effects that strengthen the business model of the auto maker: By accepting full liability the auto maker in effect shoulders the liability not only for all defects of the software (which no auto maker can evade anyhow) but also for all other accidents that may occur in autonomous mode. Some accidents can not be prevented: Obstacles may suddenly appear on the way (animals, pedestrians, other objects) and make an accident unavoidable. Defects of the roadway, certain weather conditions, and certain questionable behaviors of other traffic participants may lead to accidents that even the best software can not prevent.

Therefore the acceptance of full liability contains both a promise regarding the quality of the software and an insurance element: Volvo must either add the total, non-zero, lifetime risk of driving in autonomous mode to the purchase price of their self-driving cars. This could have the disadvantage of making their cars more expensive. Or they could duplicate the insurance industry’s business model and request that their customers subscribe to a (low) supplementary insurance policy. The latter has the advantage that risk profiles – total number of miles driven per year and the area where the cars are driven (urban, country, highway) can be taken into account. But the insurance industry would surely mobilize against the latter approach and decry it as anti-competitive.

In the following we therefore examine the first case where Volvo decides to include the cost of insurance as a hidden element in the purchase price in more detail: It is hard to provide a good estimate of the risks but there are some numbers we can build from: In 2012 US insurance expenditures for a car had an average value of $815 per year. If we take this as a proxy for the risk of human driving, then factoring in the risk of human driving for a 12 year life expectancy of a car would increase the purchase price by $9780. How much lower will the risk of autonomous mode driving be? A representative study of more than 5000 severe accidents in the United States published by the NHTSA which was carried out between 2005 and 2007 provides some clues: The study found that human errors were the most critical factor in more than 93% of the accidents. In less severe accidents human error probably plays an even bigger, but certainly not smaller role. Other factors were: Technical failures: 2.0%, road conditions: 1.8%, atmospheric conditions (including glare): 0.6%. If we assume that autonomous vehicles do not add significant additional modes of error, then they should be able to reduce the number of accidents by at least a factor of 10 ( 1/(1-0.93) = 14.2). Because the vehicles drive more defensively, break earlier in critical situations, are much more consistent in their behavior in critical situations than humans (some of whom will not react at all in a critical situation, not even step on the brakes) the average damage per accident is likely to be significantly smaller than the average current damage. Therefore the costs of vehicle accidents are likely to fall even further; we estimate that autonomous vehicles have the potential of reducing accident costs by a factor between 15 and 50. This assumes that autonomous vehicles do not create major additional risks and don’t somehow cause rare but unusually enormous accidents. Under these assumptions, Volvo’s liability promise can be added into the purchase price: If we assume a reduction of damages by a factor of 15, the life-span risk (12 years) translates into 652$ of additional costs for each fully autonomous car which Volvo sells.

Accepting full liability for all accidents in autonomous mode may therefore indeed be a viable strategy for Volvo and other makers of fully autonomous vehicles. This move cuts out the insurance industry and – if copied by other auto makers – should not be a competitive disadvantage, because the risks are unlikely to differ greatly from auto maker to auto maker. In addition, auto makers might use this approach to open additional revenue streams for more risky use of vehicles where they might request additional fees – for example for heavily used fleet vehicles.

There is another side-effect of assuming liability for accidents in autonomous mode. Accidents are more likely if the cars are not maintained properly. Therefore auto makers may place more stringent requirements on maintenance, shorten maintenance intervals and require that the cars be maintained in certified repair shops only – which eliminates the business of independent repair shops. By increasing maintenance revenues, auto makers may be able to offset the costs of assuming liability for accidents.

In summary, Volvo’s shrewd move to assume liability may extend their revenue streams while cutting out insurance companies and independent repair shops.