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.

This graphic shows the future of the auto industry

It may have taken professional auto industry analysts some time to understand the impact of autonomous vehicles. But now Morgan Stanley’s Adam Jonas has come up with another ingenious 2-by-2 chart so much en vogue with international strategy consultants which highlights the core transformative forces at work. This is a major intellectual feat because it compresses the problem space and helps reason about changes, challenges and opportunities associated with self-driving cars. The chart is shown below. Because I don’t have access to the original Morgan Stanley report the following explanations may not exactly reflect Morgan Stanley’s reasoning.

End-of-the-auto-industry

Quadrant (1) shows the auto industry today which is exposed to to major forces of change: The sharing economy leads to the emergence of companies which provide mobility as a service. Uber, Car2Go, DriveNow, Lyft and others are examples for this trend. In parallel, the auto industry faces the trend toward autonomous driving. Several companies, including Daimler, Nissan, and others are working on models targeted toward the consumer which can drive autonomously. The fourth quadrant shows the confluence of both trends: The shared autonomy. Autonomous pods such as the Google electric autonomous 2-seater, the Lutz Pathfinder currently being deployed in the UK and CityMobil2 autonomous buses fall into this category.

The future of the auto industry can be found in this fourth quadrant. Economic reasons clearly show that this quadrant will capture the lion’s share of individual motorized mobility. Neither of the other quadrants will be able provide individual mobility at competitive prices compared with the providers of autonomous mobility services of quadrant (4). Of course the other quadrants – particularly the 1st and third quadrant will not disappear entirely. There will still be some privately owned cars but they will represent a much smaller share of the mobility market than today.

Adam Jonas conclusions about the future of the auto industry are in line with the scenarios I have outlined over the past years in many articles – including five years ago in my first paper on this topic:  ‘Autonomous cars: The next revolution looms‘ .

Source: Morgan Stanley, Los Angeles Times

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.

Passenger cars in 2040: New Shell & Prognos study fails to consider the impact of autonomous vehicles

shell-prognos-study-cover
Since 1958 Shell has been publishing scenario analyses of the German passenger vehicle market. Looking 25 years into the future until 2040, Shell and Prognos have just released a detailed analysis of the evolution of the stock of passenger cars, travel patterns and fuel consumption for this time frame. Although they look at an alternative scenario with an accelerated switch to zero emission vehicles, they conclude that “no revolution” is likely to occur until 2040. The only revolution they consider are engine-related changes: in neither scenario will electric or other alternative engine types overtake the internal combustion engine.

Unfortunately, their analysis completely overlooks the emergence of autonomous vehicles. This is more than an unfortunate oversight, because even a cursory analysis should show that fully autonomous vehicles could greatly change travel patterns: Significant parts of the population that currently don’t have access to individual motorized mobility could considerably increase the number of miles traveled. Autonomous mobility services could reduce car ownership and the stock of cars and could accelerate the adoption of electric vehicles for local trips.

How can this happen to a Shell – a company that has pioneered scenario analysis and has always emphasized that – rather than extrapolating the current situation into the future – scenario analysis aims to detect and think about alternative futures? How can their analysis miss a potential game changer for the auto industry?

For more than a year the media have bombarded the public with news about autonomous cars. There can be no doubt that the technology has made enormous progress in the last 10 years and continues to make progress at a rapid pace. No professional who looks at long-term socio-economic trends related to mobility can ignore the potential implications of autonomous vehicles any longer. There is no excuse! Of course, there is room for scepticism about the speed at which the technology will mature. But there is no room for scepticism about the speed at which self-driving cars will be adopted once they are mature (a little careful scenario analysis which looks at business models and transformative aspects of fully autonomous vehicles will quickly yield this insight…).

PwC predicts collapse of car sales because of self-driving cars

PricewaterhouseCoopers – the world’s largest professional services firm – has just released an analyst note about the effects of autonomous cars on the auto industry. While the report is extremely positive about the technology (predicting a reduction of traffic accidents by a factor of 10) it cautions that the fleet of vehicles in the  United States may collapse from 245 million to just 2.4 million. This is a reduction by the factor of 100 and significantly higher than the factor of 10 provided in a recent study by  the Earth Institute which we highly recommend.

It is encouraging that the major consulting firms and think tanks are beginning to take note of the tectonic shifts which will occur in the auto industry within a few years – and which we have emphasized for the last 3 years. The study contrasts with a recent report by KPMG on “Self-driving cars – the next revolution“. While KPMG’s analysts briefly mentioned on-demand mobility services (autonomous car sharing), they failed to see its disruptive potential.

It is time for the auto industry to seriously plan for this future. Contact us – we can help!