Annual report warns that driverless cars could disrupt AllState’s insurance business

In the annual report for 2015, which was just filed with the SEC, US-insurance company AllState warns that autonomous cars could disrupt their business model. This is the first time that such a risk has been mentioned in the risk section of their annual report.

The following statement appears on page 20 of AllState Corporation’s annual report for fiscal year 2015 as filed with the SEC using form 10-K on 2016-02-19 (link to download page):

Other potential technological changes, such as driverless cars or technologies that facilitate ride or home sharing could disrupt the demand for our products from current customers, create coverage issues or impact the frequency or severity of losses, and we may not be able to respond effectively.

The company clearly sees the combined risk of the introduction of autonomous vehicles – which will significantly reduce accidents – and increased adoption of mobility services (which will become much more convenient and cost-effective through autonomous vehicle technology). The company also realizes that it will be very difficult to compensate for the resulting losses to their business model.

Sources: AllState,, Kargas

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


Netherlands first to operate a self-driving shuttle in public traffic?

The competition for low-speed self-driving vehicles in public traffic is heating up. Now the executive council of Dutch ministers has given the green light for running two driverless shuttles in the Dutch city of Wageningen starting in December 2015. The electric shuttles will carry up to 8 persons from a train station to the university on a stretch of approximately 6km on public roads with a maximum speed of 50km/h. Although these will be tests, the shuttles will operate autonomously without safety drivers on board. The shuttles’ operations will be monitored remotely. Before the shuttles be placed in service both chambers of the Dutch parliament need to amend Dutch traffic law. If everything goes according to plan, the world’s first fully autonomous shuttles without backup driver on board could make history in the Netherlands in December!

© Ligier Group

Image: EZ-10 Autonomous Shuttle of Ligier Group, Easymile

Sources: de Gelderlander,

Global technical regulations for autonomous vehicles: Informal working group established

As regulators grapple with autonomous technology, conflicts between country-specific laws could impede the adoption of this technology. The United Nations has a forum (“WP29“) which aims to avoid such problems by harmonizing vehicle regulations. Many aspects of technical regulations for wheeled vehicles are discussed in a broad range of (informal) working groups. Because of the rapid progress of autonomous technology, the informal working group on Intelligent Transport Systems has recently been renamed and refocused as informal working group on ITS/Automated Driving.

The participants are now laying the ground work for future regulations. They have discussed various approaches to frame levels of autonomy and seem to be leaning toward SAE’s 6 levels of automated driving. Unfortunately, this framework is not very useful because most of the interest lies in just 2 of the six levels, because it can be misinterpreted as conveying a linear progression of technology from level to level and because it is based on a limited, somewhat mechanistic perspective but fails to see the full complexity of the software-based self-driving vehicle and the complexity of the context in which it operates, which it interacts with and constantly learns about.

Fortunately, the group decided against addressing highly automated first and fully automated driving only beginning in 2016 (see annotated working group document). Both topics will now be considered somewhat in parallel, although the group still leans more toward highly automated driving. One of their future discussion items will be usage scenarios for highly automated driving. Maybe they will also consider some scenarios for fully automated driving and then begin to understand the extent to which mobility and with it the role of passenger vehicles will change.
An excellent source for information about this process is, which maintains an up-to date list of cross-referenced documents related WP9.


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.

Convoys of self-driving trucks for the military

Since 2012 Lockheed Martin has been working on an autonomous driving kit for military trucks. An early but already quite impressive version has been demonstrated in January at Fort Hood, Texas. The ‘Automous Mobility Applique System’ can be quickly fitted to most tactical vehicles. It contains sensors – including a rotating 3D LIDAR and actuators to control the various car components. At the demonstration, several autonomous trucks drove in convoy mode, negotiated obstacles and had to dynamically re-plan their route.The system seems to be capable of negotiating some traffic including pedestrians and bicyclists.

Although the demonstration was impressive, there was no word on when the technology would be available in the field. Another demonstration is scheduled for later this year. The demonstration clearly shows that military is determined to make use of this technology and that military applications may not lag far behind the civilian.


Source: Lockheed Martin

Nissan tests autonomous Leaf on Japanese highway

Underscoring their intention to develop vehicles which are capable of full autonomy, Nissan has shown one of their autonomous Leaf prototypes on a public Japanese highway to the press. The auto-pilot system not only kept lane and distance; it also was also able to switch lanes, overtake other cars and merge into traffic at on-ramps. Although the highway segment was short and no details were provided on the quality of the lane markings (or localization algorithms) and merging capabilities, this event certainly shows that Nissan is committed to its vision of fully autonomous driving and aims to be perceived as an innovation leader. The event does not yet show, however, that it prototypes are more advanced than similar prototypes by other auto makers (e.g. Daimler, BMW etc.).

Sources: Nissan, Japan-News

DARPA virtual robotics challenge: Lessons for driverless cars?

DARPA has just announced the winning teams of their virtual robotics challenge. Each team had to develop software to enable a very advanced humanoid robot to perform disaster-response tasks such as 1) walking to a standard utility vehicle and entering it, 2) driving the vehicle using steering wheel and pedals, 3)  walking on straight, uneven and obstacle-laden surfaces, 4) picking up a fire hose, connecting it and opening the valve. The winning teams will be provided with actual physical versions of the simulated robots and move on to the next level of the Darpa Robotics Challenge.

DARPA used the open source simulation environment Gazebo to create a virtual environment where the software of more than 100 competing teams could be tested. They have created a complete virtual world with a street, houses, obstacles etc. and developed a model of the robot which could then be placed into the virtual world and controlled by the team’s software. This approach has many advantages: Different scenarios can be tested, no physical damage can occur when the software does not behave as expected, the teams can use the simulation to optimize their algorithms etc.

A similar approach could be used to test driverless cars before they are released to the public. Official simulation environments would be needed with standard interfaces for the driverless car software. This would have to include standardized models which describe the behaviour of the car’s sensors (and translate the state of the simulation environment into sensor readings).

A  standard simulation environment for driverless cars would enable a testing body to verify that a software-based driverless car behaves as expected in many difficult cases. It would allow quantitative claims about the performance of driverless car software and could be used to compare different driverless car systems. Driverless car companies could use such test environments to prove that their software has been properly and carefully developed.

Of course test cases would have to be challenging and representative of the actual scenarios which a driverless car will encounter. This requires significant effort. It is likely over time the number of test scenarios which a driverless car software will have to pass will increase. In the event of driverless car accidents, the testing agency could rapidly analyze the causal chain for the accident and then build additional simulation test cases which the updated driverless car software would have to pass.

As we have argued in our white paper three years ago, it is extremely important to develop a public common simulation platform for driverless cars now!

Developments in Road Transport Automation – EU Workshop

On March 7 another pan-European workshop on Automation in Road Transport took place in Brussels. Europe is and has been very active in automated and autonomous cars with numerous projects having received large amounts of funding over the last 20 years.

Several projects have concluded in the last years, including SARTRE (road trains of networked cars) and CityMobil. Work has begun on CityMobil2 (no website yet) which will implement autonomous transportation system demonstrators in five European cities and which also will work on the legal aspects and industrial potential of this technology.

A key topic at the workshop was the 1968 Geneva Convention on Road Traffic which limits the adoption of autonomous cars because it specifies that a driver must be in control at all times (paragraphs 8.1,8.5 and 13.1). The US and Japan did not sign the convention and therefore may have a crucial head start in the adoption of this technology (we can already see this happening in California and Nevada).

More details about the workshop can be found here.