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.


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.

Google prepares for manufacturing of driverless car

Google continues to push for the introduction of their self-driving cars on public roads. After positive statements by NHTSA and overtures from the United Kingdom and Isle of Man to test their cars there, job postings show that Google aims to significantly grow their self-driving car team. The 36 job descriptions below show that Google expands activities on all aspects of their self-driving car, including manufacturing, global sourcing, automotive noise and vibration, electrical engineering etc. It remains unlikely that Google intends to manufacture their cars themselves but the job postings complete the picture that Google wants to build a manufacturing-ready reference design of a fully self-driving car which they can either use for having their cars manufactured by a supplier or which can inform licensing and cooperation discussion with OEMs from the auto industry.

The job postings below were obtained from the Google job search engine on 2016-02-13 with a reusable query. All 36 jobs are for the Self-Driving Car team at Google-X:

  1. Mechanical Global Supply Chain Manager
  2. Mechanical Manufacturing Development Engineer
  3. Manufacturing Process Engineer
  4. Manufacturing Supplier Quality Engineer
  5. PCBA and Final Assembly Global Supply Manager
  6. Automotive NVH (Noise, Vibration, Harshnees), Lead
  7. Manufacturing Test Engineer
  8. Reliability Engineer, Vehicle Test Lead
  9. Reliability Engineer
  10. Product Manager, Vehicle 
  11. Global Commmodity Manager
  12. Industrial Designer
  13. Marketing Manager
  14. Technical Program Manager, Vehicle Safety
  15. Operations Program Manager
  16. Policy Analyst
  17. Head of Real Estate and Workplace Services
  18. Product Manager, Robotics
  19. User Experience Researcher
  20. Mechatronics Engineer
  21. Electrical Engineer
  22. Mechanical Engineer, Lead
  23. Systems Engineer, Motion Control
  24. Systems Engineer, Compute and Display
  25. Reliability Engineer, Lead
  26. Vehicle Systems Engineer
  27. Perception Sensing Systems Engineer
  28. Embedded Software Engineer
  29. Electrical Validation Engineer
  30. Systems Engineer
  31. Radio-Frequency Test Engineer
  32. Researcher/ Robotics Software Engineer
  33. Radio Frequency/High Speed Digital Hardward Design Engineer
  34. Camera Hardware Engineer
  35. Mechanical Engineer, Laser
  36. HMI Displays Hardware Engineering Lead


Google restructures for its bet on self-driving cars

Google has announced a major corporate restructuring where all Google shares are transferred into Alphabet, a holding company. The new structure is much better suited for Google’s self-driving car ambitions – which may quickly grow into a billion dollar industry . This restructuring is a well calculated move to position Google for the road ahead into self-driving cars/driverless mobility, robotics etc.

It shows how serious Google is about making a major impact in fields outside of its ‘traditional’ internet-centric business.  It is also interesting that Google’s announcement carefully avoids mentioning those activities with the highest revenue potential – such as self-driving cars. Instead they just speak of much smaller activities in Life-Sciences (glucose-sensing contact lenses), longevity and drone delivery.

The Alpha-bet is indeed – as the founders indicate in their announcement - a major bet on the future. A decade from now  Alphabet’s revenues from mobility and robotics could eclipse Google’s web business.



Autonomous vehicle roadmap: 2015-2030

Two and a half years ago I wrote a note on the various views about the paths for adopting self-driving vehicles. Since then, more and more signs point towards my ‘avalanche’ model, where the adoption of self-driving cars becomes a self-sustaining, accelerating process fueled by expectations of a fundamental transformation of the auto industry and major opportunities for profit.

As a thought exercise, I have sketched a hypothetical timeline which shows how this self-accelerating global innovation process could unfold. The purpose of the timeline is to show how autonomous vehicles could come into widespread use rather quickly and what kind of market and political forces could be involved. This is an extreme of many possible futures for self-driving cars:

2015 Google launches first short-range fully autonomous vehicle service in California at NASA Ames (not on public roads) and possibly in Mountain View (small scale pilot, limited to Google employees).

2015 The first auto makers (Daimler, Honda, Nissan?) announce major strategic initiatives and major investments to counter Googles’ threat and rapidly bring vehicles capable of full autonomy (Level 4) to the market.

2015 Car2Go (Daimler’s shared mobility service) announces a roadmap for autonomy in their car fleet.

2015 Automotive industry recognizes the implications of fully autonomous vehicles (transformation of mobility, significantly lowered worldwide demand). Analysts pound auto makers on their Level-4 autonomous vehicle strategy. Share prices begin a long decline.

2016 Google announces that their short range, limited-speed fully autonomous vehicle fleet will be built by Ford, Magna or others.

2016 China launches a major program to develop and deploy shared autonomous vehicles for local mobility. It recognizes that it can reduce infrastructure expenditure, jump-start their autonomous vehicle industry, reduce the ecological footprint of mobility etc.

2016 Google expands their short range autonomous vehicle service pilot to another US city that sees little rain and no snow, e.g. Las Vegas, NV or Sun City, AZ and starts their first overseas fleet.

2016 Price for semiconductor lasers used in LIDAR sensors falls below USD 150; this reduces the hardware/computing power costs for autonomous vehicles with 3D Lidars to below 10,000 USD.

2016 Transformative potential and benefits of autonomous vehicle technology are recognized widely. There is a bitter debate about the destruction of jobs.

2017 Several European countries have now adjusted their laws to allow the operation of fully autonomous vehicles on a national scale (not in international traffic).

2017 Autonomous long haul highway trucks start testing in the US, Europe or Japan.

2017 Rental car companies launch their own autonomous mobility inititiative.

2017 An international body for regulating autonomous vehicles is being formed in cooperation between the US, Europe and Japan.

2017 Google vehicles are now capable of driving in snow on pre-mapped routes.

2017 Automotive suppliers (Continental, Bosch, Valeo, or others) announce their own autonomous vehicles or special-purpose autonomous machines.

2017 Major road infrastructure projects are downsized because autonomous and connected vehicle technology have reduced the expectations on future transportation demands.

2017 Google moves their autonomous vehicle operations into a subsidiary which then merges with Uber and starts to roll out local autonomous vehicle mobility services in many more US cities.

2017 Singapore deploys the first autonomous bus for regular service. This is widely seen as a milestone for public transport and sends many transit corporations scrambling to update their strategies.

2017 The first countries mandate specific driving behavior for self-driving cars in certain driving situations.

2018 Car2Go starts to add autonomous vehicles to their fleet.

2018 The Google subsidiary/Uber merger rolls out autonomous vehicles internationally.

2018 Heavy investment into autonomous vehicle fleets and services based on autonomous vehicles. An almost unlimited amount of capital flows into startups and schemes. Countries compete trying to gain an advantage in the emerging new industries.

2018 Experience with autonomous vehicles shows that they are indeed much safer than the average human driver. People feel safe and comfortable in fully autonomous vehicles and there is no longer any question of user acceptance. No phenomenon similar to the ‘fear of flying’ can be found among users of self-driving cars.

2019 The Vienna Convention and European Laws are updated to allow the operation of fully autonomous vehicles.

2019 Autonomous vehicles now operate in over 50 cities worldwide.

2019 Rapid growth for autonomous trucks on specific routes. In many countries, truck drivers protest but this can only delay their adoption slightly.

2019 The first high-end consumer cars capable of fully autonomous driving on a large part of the national road network become available.

2020 The first countries introduce laws that prohibit bullying of autonomous vehicles (e.g. jumping in front of it to make it stop).

2020 Bleak outlook for automobile companies. Volume is down, consumers prepare for the transitioning to fully autonomous vehicles (which are not yet widely available for the consumer) or increasingly use/expect to use shared autonomous vehicle services. The fight for survival has begun: The auto industry has its “Kodak moment”.

2022 Prices for used cars decline. Too many people switch to shared autonomous vehicle schemes. Many others sell their old vehicles prematurely because they want to switch to the much safer fully autonomous models where they don’t need to drive if they don’t want to.

2022 The cost for autonomous vehicle hardware (sensors and computing power) has come down to 1500 USD.

2022 Mass transit companies increasingly rely on autonomous vehicles for transport. Transitioning the current workforce to a transit system based on autonomous vehicles is a major organizational and political challenge.

2022 Insurance rates favor operating cars in fully autonomous mode and prompt many people to stop driving on their own.

2023 Small autonomous buses are increasingly used for medium- and long distance trips. Trains have a hard time to compete on short to medium distances with autonomous buses.

2023 Most companies require that business trips with rental cars must occur in fully autonomous mode (for safety and productivity reasons).

2025 Fleets of autonomous vehicles now operate in most cities of developed nations.

2025 Automotive companies shut down more and more plants. Major automotive countries including Germany, Sweden and Japan desperately try to prop up their OEMs.

2030 Car ownership has declined dramatically. Only 20% of the US population still own a car (200 cars for 1000 people, today: 439 cars for 1000 people).  90% of all trips now happen in fully autonomous mode. Traffic accidents and fatalities have declined dramatically.

Google’s self-driving cars: Implications for the auto industry and the key role of machine perception

Google’s self driving car effort is a threat to the auto industry. The company is the clear leader in autonomous vehicle technology and several years ahead of all other auto makers, including Daimler and Volvo. By presenting an all-electric prototype of a fully autonomous two-seater in May, Google has also made clear that it is serious to become a player in individual mobility and intent on reaping the rewards of its investment in this project (which so far has likely cost a few hundred million Dollars – not an enormous amount by the standards of the auto industry for developing a new car model).

What are the implications for the auto industry? They have much more experience in all aspects of mobility and are also working on autonomous vehicles. Could Google really be a signficant threat?

The standard answer to this question has been denial: Last year the main argument was something like: They may be able to build great software but they don’t know how to design a car. Now that they have designed a steer-by-wire two-seater with redundant layout of all safety-critical components and skillfully navigated the regulations – including limiting the speed to 25mph – , the argument is updated: They may be able to build a slow-moving two seater, but they can’t build a real car. And even if they could, they could not produce it in any meaningful volume.

As they overcome each objection, denial becomes harder, and additional time is lost. The argument that Google would not be able ramp up production is misguided. Google has no intention to challenge the auto makers on their playing field. It will change the game by providing autonomous mobility services rather than selling cars. Each Google autonomous car will then reduce the demand for privately owned cars by a factor of 5 to 10. This will have an impact on auto makers. It will affect their strategies, stock prices and make production capacity much easier to acquire.

Instead of denial, auto makers need to understand the magnitude of the threat. Self-driving cars will be a disruptive force; they will change the business model of the auto industry and bring hard times to most auto makers because demand for passenger cars will fall significantly. From a global perspective this is a good thing because resources will be used much more efficiently, alternative propellants can be used much more readily within autonomous mobility services and the strain on the environment (both pollution and land-use) will fall.

But it will be hard for the auto industry to adapt to these changes. Cars have been produced for more than a century. The requisite knowledge is widely available. The same does not apply to a key ingredient for self-driving cars: Teaching a machine to perceive its environment. Perception is the core problem which determines the success of a self-driving car.

Perception is a multi-faceted problem. It has to do with sensors, with prior knowledge, machine learning and is sensitive to action and context. Unfortunately, perception is not limited to the context of driving. Self-driving cars need to understand the behavior of people and things that may be relevant to the driving context – even if their behavior has nothing to do with driving a car (e.g. kicking a ball).

Because perception is hard, it requires considerable financial and human resources to solve the problem. Google has not only the financial resources but has recruited many leading experts in this field. Even the leading auto makers would find it hard to build teams that match Google’s expertise. Joining forces with other auto makers may be the only viable strategy.

Because perception is a general capability, it is applicable to many fields beyond driving and consequently it can generate returns in many fields besides driving. This is an advantage for Google because it allows cross-fertilization with its other business areas. Google has recently bought several leading robotics companies. Advances in perception by the self-driving car group could also benefit these business areas and vice versa. Google has also started a mobile phone project (Tango) which aims to use a high end Android mobile phone to create 3D maps of the environment in real time. Advances in this space may also be useful for the self-driving car project.

As a consequence, auto manufacturers who want to beat Google to a fully autonomous car, will need to carefully consider the additional opportunities which advanced perception could bring and determine how to integrate these opportunities into their strategy. Instead of narrowing the perception task to specific driving scenarios, auto makers should consider whether they could leverage their perception activities in additional ways.

Machine perception is the core competence for succeeding with autonomous cars. Auto makers need to give this capability top priority if they want to recover the ground already lost to Google.

Google’s electric self-driving two seaters: A milestone towards autonomous mobility services

With the unveiling of their new electric-mini cars Google’s self driving car strategy is becoming more and more evident. By the standards of the auto industry, these cars have many obvious drawbacks: they are very small, can only seat two persons, speed is limited to 25mph, range is also quite limited, the big sensor on top is seen by some (including Daimler’s CEO Zetsche) as an eyesore. They will be hard to sell.

But this is not the point. Google has set their sight on reinventing mobility, not just on building a self-driving car. These cars no longer need to be tethered to a person; they can roam freely and provide shared mobility services to anyone at prices that are significantly lower than individually-owned cars. This is the picture, that Google’s project leader Chris Urmson has in mind when he envisions cities without parking lots (no more need to park these cars, they can transport others in the mean time).

Google’s investment in Uber, their clear focus on fully autonomous driving are all parts of the same picture. It will be very hard for the auto industry to compete on this field because it means cannibalizing their own products, completely transforming their purchase-oriented business model which has served them well for more than a century towards a service-oriented model and fundamentally rethinking the concept of a car.

Google won’t need to sell these cars. They will organize mobility. They already excel at mapping and travel planning, but in the future they will send a car to pick you up wherever you are and bring you where you want to go. They will predict, balance and aggregate mobility demand. Billions are spent for individual mobility. Google should be able to grab a significant share of this market once their mobility-on-demand services are ready.

Decisions on the path to future mobility – Research Forum

The 6th research forum on mobility took place on May 8 in Duisburg, Germany. With a good mix of presentations from academia and industry, a wide array of topics was covered. Electric mobility was regarded with much more enthusiasm as many speakers saw battery prices coming down faster than anticipated by most think tanks.

Futurist Lars Thomsen discussed many tipping points for the auto industry; he expects autonomous cars to perform better than the average driver by 2017 and also noted that fleets of autonomous pods will become the dominant medium for transport in mega cities, where most people will no longer own a car. At the same time he did not connect the dots and consider the impact on the demand side and the implications for OEMs.

This fit well with the topic of my presentation which focused on fleets of self-driving taxis. I presented detailed cost calculations for a fleet of urban autonomous vehicles. The data shows that driverless mobility services could halve the costs per person-kilometer compared to car-ownership. One of the key sources of savings is professional life-cycle management for all vehicle components which will greatly increase the economic life of the cars and thereby decrease the capital cost per kilometer traveled.


Sergey Brin on driverless car future

Californias driverless car law was signed by Governor Brown at Googles headquarters last week. During the ceremony, Sergey Brin talked about Google’s driverless car efforts.

Some highlights:
– Brin expects driverless cars to be available to the public in not more than 6 years
– Driverless cars to become available for testing to a larger subset of Google employees within one year
– Safety continues to be the core issue. Google is looking at all issues including hardware failures, tires blowing out, electronics failures, etc.
– Three pronged approach to testing: a) actual driving on the road, b) lab testing, c) testing on closed circuits
– Google working on improving the sensor arrangements (It would be interesting to know whether they have found ways to reduce the dependence on the extremely expensive LIDAR)
– Google does not intend to manufacture vehicles
– Google intends to work with partners in commercializing the technology
– Driverless cars will greatly reduce the waste of land for parking spaces because driverless cars can drop someone of and then transport another passenger (reading between the lines, this was the only hint about the business model which Google may follow. Fewer parking lots also means fewer privately owned cars and an increasing share of trips using driverless taxi service (think: ‘Google mobile’ (Brin did not use this term!)).
– Nobody else is as far ahead as Google in this field.

Below is a video of the signing event:

Google cooperates with German rails for travel planning

Do you think of Google as a mobility service provider? While it does not yet offer its own transportation services, it is establishing itself as the leading provider of travel planning services. The cooperation with Deutsche Bahn, Germany’s leading rail network, which was just announced last week, is only one of many examples which show that Google  relentlessly expands its travel-related services. By establishing interfaces to many established transportation services – including mass transit systems, trains and airlines – it raises the barrier to entry for potential competitors and the value of its competitive position.

Google’s autonomous vehicles fit nicely into this scenario. Once they are ready to be released to the public, Google can become a full-service mobility provider: It then has the technology and all information to pick its customers up anywhere and bring them to their destination in the most time- and cost-effective way. It will be able to anticipate transportation demand, to react to delays of trains and flights and to optimize the placement of its vehicles. Its intelligent fleet will change the economics of transportation and significantly lower the costs of personal mobility.

Taken together, Google maps, its travel planing services and its autonomous vehicle technology, provide a strong foundation for becoming one of the key players in transportation in the next decade. Google is preparing itself to capture a much larger share of the transportation industry’s revenue than the great auto makers anticipate today.