Five guiding principles for autonomous vehicle policy

As self-driving car technology matures, politicians and regulators find themselves called to action. But the technology is a moving target and views about the technology’s path and impact vary widely. So how should policy makers approach the subject? Here are five guiding principles proposed by Marc Scribner,  a transportation and telecommunications policy specialist and research fellow at the Competitive Enterprise Institute. Scribner only discussed the principles briefly at a recent presentation at the Cato Institute. In the following I supplement each of his five bullet points with my interpretation:

1. Recognize and promote the huge potential benefits of self-driving cars

Policy makers need to familiarize themselves with the potential benefits of self-driving cars. First, they need to get the concepts right and clearly distinguish self-driving cars (which can drive without human supervision, even empty, and don’t need additional infrastructure) from other technologies such as driver assistance systems and connected cars. Connected cars and driver assistance systems are certainly also interesting topics but their benefits pale in comparison to the benefits of cars that drive themselves. Besides greatly reducing accidents, self-driving cars also bring individual motorized mobility to those who do not have a driver’s license – including the handicapped and elderly. They reduce energy consumption, simplify the introduction of alternative fuels and reduce the load on the road infrastructure.
Policy makers need to recognize that self-driving cars can solve or greatly reduce many longstanding problems. This is not a technology where a wait-and-see attitude is warranted. Politicians need to actively promote this technology. Of course, this does not mean that the technology’s risk should be ignored.

2. Reject the precautionary principle

Safety is a key concern and a key benefit of self-driving cars. There is good reason to expect mature self-driving cars to drive much safer than humans. They are equipped with 360 degree sensors, including cameras, radar and Lidar, are always alert, never tired, don’t drink and adopt a defensive, risk-minimizing driving strategy. But letting the first such cars drive by themselves on public streets is a difficult decision: what if anything goes wrong?
The application of the precautionary principle avoids this situation by requiring the developer to prove that the car is harmless. Unfortunately, proving that a self-driving car is safe is a hard problem and strict application of the principle could significantly delay the introduction of self-driving vehicles.
This weakness of the precautionary principle is well-known: There is the risk that erring on the side of caution when certifying self-driving cars prolongs the current carnage on our  on our roads. Unfortunately, we don’t have the luxury to delay a well-functioning self-driving car for a few more years to be extra-sure that everything is perfect when 33,000 people die in traffic accidents per year in the US alone and more than 1 million per year worldwide.
As much as it is not acceptable to let first prototypes roam the streets unsupervised it is not acceptable to delay and delay just to be on the safe side. A middle ground must be found. This is not an easy task for policy makers but one on which lives depend.

3. Don’t presume to know how the technology and law will evolve

Will autonomous vehicle technology gradually evolve from driver assistance systems? Will they first appear on the highway or in low-speed local settings? What new business models will emerge and what role will machines play? Will the US be the first to legalize fully autonomous vehicles or does the Vienna Convention on road traffic really prevent many European Countries from adopting self-driving vehicles? There are so many paths that this technology can take, so many changes in many different areas of business and society, so many proponents and possibly opponents that it is hard to be right about the path of technology and – consequently – of law. It is very dangerous to assume that the technology will evolve in one way, then regulate for this situation and subsequently find that the technology evolves very differently.

4. Let the innovators innovate

This section was originally entitled ‘minimize legislative and regulatory intervention’ and included the goal to give the innovators the space to innovate. But here I differ with Scribner: Unfortunately, transportation law is so much based on the concept of vehicles driven by humans that many laws do need to be changed. Current traffic laws contain so many elements that inhibit progress for this new and safer technology. Autonomous vehicles change the concept of what a car is and the laws need to be updated accordingly. Otherwise innovators will find it hard to make progress. This is a task that should be started immediately – before fully autonomous vehicles are ready for public roads.

5. Preserve technology neutrality

Laws and regulations should be technologically neutral. As much possible, they should avoid favoring a specific technical approach.

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…).

EU wraps up first autonomous bus demonstration in Italy with mixed results

The European CityMobil2 project aims to demonstrate automated road transport systems in Europe, develop guidelines to design and implemented such systems and propose a legal framework for certifying such systems.

One of their key activities is to demonstrate autonomous buses operating in various European cities. From July until today (September 4) two autonomous electric buses supplied by French company Robosoft carried passengers on a 1.3km pedestrian stretch next ta a beach near Oristano in southern Italy. The small-scale demonstration operated on 38 days and transported 1600 persons in 3000 trips.

Each bus was overseen by an experienced bus driver at all times; for legal and insurance reasons all passengers had to register as ‘testers’ before boarding. Participation and acceptance – also on part of the professional bus drivers recruited for the demo (who could have been worried that the buses were an early step towards replacing them) – were very positive.

Valuable lessons were learned during the demo. Not everything worked as expected. For safety purposes, the car’s maximum speed was reduced from the planned 15 to 20km/h to 12km/h. This was due to the large number of pedestrians which were on the road at peak times and technical issues that had to do with sensor range.

The autonomous operation was also limited because of problems with GPS reception. Localization was uniquely based on GPS – which is not a very practical approach for autonomous vehicles (fortunately the next demonstrators will use additional localization mechanisms). Before the demonstrator started, trees had been cut back to ensure good GPS reception but nevertheless during todays live demonstration in a webinar GPS reception was spotty and the driver had to manually override the vehicle.

Another critical problem has hampered the project in the last few days: The sensors started to report non-existing obstacles. This causes the bus to stop immediately. Because of this problem,  the bus had to be driven manually for the live demonstration. Surprisingly the team did not have an explanation for this problem. Robosoft is epxected to analyze the problem to determine the cause. But it is hard to understand that such a critical issue is neither analyzed nor fixed when it arises.

We applaud the hard work that has been put into these demonstrators. But the demonstrator also shows that Europe needs to become much more serious in its efforts to develop autonomous vehicles if  it does not want to get completely outdistanced by the American competition.

Sources: CityMobil2 webinar on 2014-09-04, CityMobil2

Sony enters the market for automotive imaging sensors

Increasing demand for driver assistance systems and the need for better sensors has prompted Sony to enter the market for automotive imaging sensors. Beginning in 2015, Sony will make a new sensor available that performs much better in low-light situations. Even in moonlight the sensor can produce color images, the company claims. Sony, which is a leading supplier of image sensors but so far has not entered the automotive imaging market hopes to grab significant market share from the leading automotive imaging sensor suppliers such as US-based Omnivision and ON Semiconductor (formerly Aptina).

Better sensors are crucial for the success of fully autonomous vehicles. Advanced image sensors could reduce the dependence of autonomous vehicles on costly 3D Lidar systems. Better image sensors could reduce the number of Lasers within the rotating LIDAR systems. Google’s current LIDAR sensors currently contains 64 lasers. However, it is not likely that fully autonomous vehicles operating in urban contexts will be able to operate without any LIDAR sensors within the next few years.

Sony’s entry into this market shows the potential of this market and may increase the incentives for innovative start-up companies to developing even more advanced sensors (e.g. ASCar, Inc: Flash Lidar, LeddarTech: LED flash sensor, Quanergy: 3D Lidar).

Source: Nikkei Asian Review

Singapore to start autonomous vehicle testing on public roads in 2015

Singapore clearly realizes the potential of autonomous vehicles for revolutionizing road transport. They already have several projects in place – including autonomous golf carts and a Navia shuttle. Now they have set up an oversight committee on Autonomous Road Transport which will support guidance on the research and implementation of self-driving cars. Besides government officials the board includes representatives from MIT, Nissan, Toyota and Continental.

Singapore wants to understand, shape and apply the technology to improve the road infrastructure. It envisions a greener future where a much smaller pool of cars provides urban mobility. In a first step, Singapore will allow testing driverless vehicles on select public roads of its one-north business district starting January of 2015. Of course, stringent safety measures must be in place. Another application of the technology for testing could be driverless buses that operate on fixed routes.
Singapore is the first city that systematically works towards a future with driverless cars. It recognizes that it needs to incorporate driverless technology into its long-term infrastructure plans already today. In addition, becoming a pioneer of this technology could lead to important competitive advantages for this city-state in the future.

Sources: Channel Asia 1,2

Proceedings of the Automated Vehicles Symposium available online

This year’s Automated Vehicles Symposium took place in San Francisco in mid-July. Most presentations are available in PDF format with some notable exceptions (both presentations by Google and one by Daimler are not available). The presentations provide a wide range of insights into thoughts of the auto industry and policy makers. I recommend the presentations from Bosch (Tue), Department of Energy (Thur) and theCalifornia Department of Motor Vehicles (Thur).

Many presentations reported statistical insights about attitudes toward autonomous vehicles, autonomous driving etc. Unfortunately, such data is not reliable to any degree because the attitudes are likely to change significantly as the technology matures and awareness about the technology increases.

Level 3 automation was still a major topic. But a quick poll among the participants seemed to indicate that the majority of participants now believe that full autonomy (level 4) is the way to go.

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.