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.

California to become the engine of the driverless revolution

We don’t know whether the Champagne corks will be flying at Google when California’s Gov. Jerry Brown signs the bill to legalize autonomous cars which was almost unanimously approved by the legislature on Wednesday. But Google’s intense lobbying efforts which started in Nevada have paid off: Now the internet giant is a big step closer to introduce autonomous cars on its home turf.

California is almost perfect for the introduction of autonomous cars. It is large and diverse enough to allow testing in almost all environments. There are regions  with almost optimal weather conditions such as Southern California (snow being very unlikely except in the mountains). This is where Google could launch their first commercial trials for car-sharing, taxi or truck/logistics services.

The second great asset of California is its hotbed of innovation and technology in Silicon Valley which Google is part of and which it profits from. But driverless technology could give another boost to the valley and lead to many new products and services based on autonomous technology. When driverless technology becomes the next big thing, Silicon Valley will be at the center.

Driverless cars are a golden opportunity for the ‘Golden State’. The legislature was right to ignore the objections from the auto industry (which is worried about their business model).