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Misconception 8: Self-driving cars will increase congestion in cities

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Fleets of self-driving cars will reduce the cost of individual motorized mobility and increase its accessibility to people without driver’s license. Many city planners fear that this will induce additional demand and significantly increase miles traveled with the result of even more congestion in our already heavily congested cities.

Fortunately, there are many reasons why an increase in person-miles traveled with self-driving cars will not lead to an increase in congestion. The opposite may be true: we may find that self-driving cars, while certainly increasing person-miles traveled will actually reduce the congestion in our cities. Congestion is not a direct function of the number of vehicles on a road; it depends on driver actions, routes taken, road utilization per vehicle and systems for flow optimization (traffic management systems etc.). If we increase the number of miles driven and keep all other parameters constant, then congestion will certainly increase. But with fleets of self-driving cars, all of these parameters will change, some significantly.

In the following we will first look the reasons why self-driving cars are likely to reduce congestion compared to human-driven cars. Items 1 and 2 show that there is significant potential for congestion-reduction (which in turn means that the risk of induced mobility leading to more congestion is reduced).

1. Driving behavior: The driving behavior of a self-driving car differs from the driving behavior of human drivers. Autonomous cars don’t exhibit the lane-hopping and other congestion-creating behavior. Simulations have found that even a small percentage of self-driving cars among many human-driven cars on a lane reduces congestion because the self-driving vehicles help to smoothen the traffic flow. Self-driving vehicles also reduce the typical delay of the average human driver at a stop light turning green and thus ensure that more vehicles can pass that stop light in a given time frame. A self-driving vehicle will not sit idle for a second after the car in front has started moving. This number can be further increased if the self-driving car uses an optimized acceleration pattern at a stop light. Thus, with an increasing ratio of self-driving cars, the throughput will increase at the bottlenecks which will lead to significant reduction of congestion.

2) Road capacity utilization:
 2a) Road space: Self-driving fleet cars used for urban driving will be smaller and thus use less road capacity. Self-driving cars will also systematically adhere to an optimal minimum distance to the car in front which significantly increases the number of vehicles that a given road segment can support during heavy traffic.
 2b) Parking space: Fleets of self-driving cars will be in operation most of the time, especially when mobility demands (and with it traffic) is high. Thus cities will need much less parking space and can use parking space of other purposes. In some cases, parking spaces could be turned into additional lanes, further increasing throughput. This is an option but we expect most of the parking spaces that are freed up to be put to other use. Note that self-driving car fleets may need very little dedicated parking space because they could simply use existing lanes that are no longer needed during off-peak times or at night for parking.
 2c) Convoy driving: As the ratio of self-driving cars in traffic increases, these cars will more frequently find another self-driving car in front or behind and can then coordinate their driving behavior. This can lead to further reduction of distances between the cars and can further improve reaction times at stop lights.
 2d) Lane sharing: Self-driving cars can drive consistently with more lateral precision than human drivers. Thus they can operate on narrower lanes. This also makes it possible that more self-driving cars can drive next each other than the number of lanes available. For example, three self-driving cars may ride next to each other on a two-lane highway. This could be another variant of convoy driving and would need communication between the vehicles.
 2e) Micro-cars: Very small self-driving pods could be built so that two of them fit next to each other on a single lane. An example has been proposed by Harald Buschbacher (although these two wheelers with auto-retractable stabilizer wheels are envisioned as personal rapid transit vehicles using their own very narrow lanes).

The previous 2 items (Driving behavior and road capacity utilization) ensure that the congestion-inducing effect of a self-driving car is much lower than the average human-driven car which in turn allows to significantly increase the number of person-miles traveled without increasing congestion. But the next item is the key reasons why we can be confident that self-driving car fleets will not increase congestion, even if they significantly increase the number of person-miles:

3) Internalizing the costs of congestion paves the way for combating congestion:
Today, congestion on our roads leads to enormous economic costs. Unfortunately, these costs are distributed among the many traffic participants which at the same time are cause and victims of congestion. It is difficult to unleash market forces to find ways for reducing congestion because it is difficult to set prices for congestion-free roads nor can we correctly attribute congestion-costs to those who cause it and make them pay. This changes once shared fleets of self-driving cars provide a significant share of local mobility because these fleets internalize a sufficiently large part of congestion costs.

Fleet managers will focus on the bottom line and they have every incentive to maximize their return on capital. They will try to minimize the size of their fleet and to maximize the throughput of their cars. To them, congestion translates directly to cost. When they send a car through a congested area, this increases the cost of the car, reduces revenue opportunities and it also reduces the throughput for other cars of the fleet that may need to take the same route a little later. After a few months of operations, fleet controllers will be able to quantify exactly how much their bottom line would improve if the throughput in a certain bottleneck could be improved by a few percent. They would find that many investments in infrastructure, signalling algorithms, routing methods etc. would have a positive return because their costs (of congestion-reducing activities) are lower than their benefits (increased fleet revenues, lower fleet size (capital stock)).

From an economic perspective, shared fleets of self-driving cars aggregate the mobility demands and the congestion-related effects of their large group of customers. This aggregation allows the fleet to find much better ways of handling congestion – taking into account both the preferences of their customers with respect to congestion-related costs, the congestion-inducing effects of different routes and mobility solutions and internal or external potentially costly mechanisms that reduce congestion. The fleet will very clearly understand (and be able to quantify) its effect and the effect of each of their customer’s trips on congestion. In contrast to the individual driver on the way to the office very morning, who is oblivious to his share in making congestion and who simply wants to take the fastest route, the fleet will not be concerned with the speed of the individual trip but will make sure that the trips are routed in such a way that the throughput of all their vehicles will be maximized. The goals of the fleet with respect to congestion are very much aligned with the goal of the city as a whole: that throughput is maximized.

This argument may sound academic. But the effects will be very real. Fleets that are small will not have a large impact on cities. But once fleets process a significant share of local mobility, they will have the best knowledge about traffic and congestion patterns in the city. Their cars will provide them with detailed up-to-the minute traffic information for all parts of the city. Economic rationale will lead them to build complex models of traffic flow and look for ways in which throughput can be improved and they will be able to very clearly indicate what approaches in which areas of the city could lead to which level of congestion reduction. They will work with city official to optimize their signaling infrastructure, they will even be willing to invest into that infrastructure (if the cost is lower than the benefits from congestion reduction). The fleets will also look for ways to shift mobility demand (so that some people defer their trips to non-peak times) and to reduce congestion cost per trip by combining trips (through ride-sharing or by inventing new variants of ride-sharing that actually appeal to their customers).

In summary, there is no reason for city managers to worry about congestion-inducing effects of shared fleets of self-driving cars. These fleets will have large benefits for the city. They will actively combat and reduce congestion because they are the first entity that internalizes the costs of congestion. They will reduce the ecological footprint of mobility because they will be mostly electric vehicles and the average vehicle will be smaller and lighter than the vehicles today. They will accelerate the transition to electric vehicles because the shared utilization of short-range vehicles is the optimal use case for electric vehicles. They will free up parking spaces and eliminate traffic looking for parking (which can be a very significant share in inner cities).

If you are still worried about the congestion-inducing effects of self-driving car fleets, here is a simple, political argument: Self-driving car fleets won’t increase congestion in our cities because we will not let that happen. Such fleets will not populate our cities over night. They will initially service a small fraction of the population and can not immediately cause significant increases in congestion. As these fleets become larger, politicians will certainly not sit idle if congestion increased and neither would the electorate accept more and clearly attributable congestion. This in turn would increase the economic pressure on such fleets to find ways for reducing congestion (the most straightforward would be to limit their size by adding congestion charges to their pricing structure).

Note: This is part of a larger series of misconceptions related to self-driving cars. The other misconceptions are discussed here. A PDF document with all misconceptions is also available for download.

German cabinet warms to self-driving cars

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After passing a law regulating driver assistance systems recently (which unfortunately falls short of allowing fully self-driving cars) and receiving the report (PDF) from the German ethics panel on self-driving cars, the German cabinet seems to warm to a future with self-driving cars.

In June, German Chancellor Angela Merkel provided the following forecast as part of her answer to a question about what the world would look like 20 years from with the the statement that “In 20 years, we will need a special permit if we want to drive a car manually.”…”We are the biggest risk.” (Source: Die Welt, Auto Motor Sport, 2017-06-09). Also in early June, Germany’s minister for transport and infrastructure, Alexander Dobrindt, said that he wants Germany to have the world’s most modern public transport by 2025, a vision which builds on self-driving electric buses (Source: Bayernkurier, 2017-06-09).

In a country, where many jobs directly or indirectly depend on the auto industry, these statements may be an indication that Germany is taking a future with autonomous vehicles more seriously and begins to give some thoughts to the near- and long term implications of self-driving car technology. There are few indications, however, that the transport ministry has begun to consider the effects of self-driving cars, trucks and buses on the national road infrastructure (the recently released national traffic infrastructure plan 2030 does not take self-driving vehicles into account). Plans to reduce the current and projected shortage of truck overnight parking spaces along German highways don’t take into account that demand for such spaces may peak in the early twenties. As with many other countries (including the EU, that is currently considering a costly infrastructure requirement for EV charging stations that completely disregards the likely changes for parking, EV charging and mobility caused by autonomous vehicles) , the enabling relationship between self-driving car technology and the adoption of electric vehicles is  still not recognized. Neither are the impact on rail-based transport, which will likely see a decline on many routes and which will also loose much of its environmental advantage (so popular with many environmental thinkers and infrastructure planners) with the rise of self-driving scheduled and on-demand electric buses.

Today billions of Euros are mis-allocated in city-planning, construction, traffic infrastructure development because planners assume that mobility and transport patterns of the future will be similar today. It is time for German, European and the world’s leaders to seriously consider the changes that result from self-driving vehicles. Hopefully the recent statements from the Germany cabinet are an indication that politicians are beginning to slowly move into this direction…

Self-driving car workshops in Detroit and Frankfurt a success; next workshops in October and November

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Two full-day workshops on the strategic implications of self-driving car technology were held by Dr. Hars in Frankfurt in March and in Detroit in May. Attendees came from the auto industry including GM, Ford, Magna,  Bosch, Continental, Tenneco, as well as the insurance and telecommunications industry.  In a fast-paced day many topics as diverse as the cost structure of self-driving mobility services, decision points for consumer adoption of / transition to self-driving taxis and buses both for and long and short distance trips, emerging business models related to autonomous vehicle technology (including hte monetizability of customer data) were discussed. Two follow-on workshops are already planned for the fall, October 24 in Frankfurt and November, 2 in Detroit (Auburn Hills).

 

Baidu expects autonomous buses to become first wave of self-driving vehicles

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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

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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!

Chinese company unveils prototype of self-driving bus

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After three years of development, one of the leading Chinese bus manufacturers Yutong has sent the prototype of a self-driving city bus on a 32 km long circuit on an intercity road between Zhengzhou and Kaifeng in Henan Province. The bus drove the whole track in regular traffic without any human assistance, attained a peak speed of 68 km/h, passed 26 traffic lights and was able to change lanes and overtake autonomously. This is a significant accomplishment and clearly puts Yutong on the map for autonomous driving.

The bus is equipped with many sensors, including camera and Lidar. Two Lidar sensors are strategically placed in the middle of both sides of the car. This is the best way to monitor the adjacent lanes and mimics the approach Google has taken on their driverless pods (where the side Lidars protrude like the mirrors of conventional cars).

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Image source: Yutong, 2015.

The company’s press release points out that significant additional development is required. No further information about the timeline for the introduction of such a bus was provided.

Self-driving buses are very promising and will be a key ingredient of future mobility. On demand-buses will be able to service the complex mobility demands of our societies much better than today’s mix of scheduled buses, trains, and individual cars. They will lower the cost, resource consumption and ecological footprint of mobility. Because significantly lower costs will prompt many travelers to use buses on medium to long-distance trips instead of cars, these buses will increase the effective capacity of highways when measured in people-miles.

 

Source: Yutong Bus Company, Dailymotion video

Update 2016-02-21: The bus traveled between Zhengzhou and Kaifeng in Henan Province. The approximate route can be looked up on Google maps.

Five guiding principles for autonomous vehicle policy

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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 people with disabilities and the 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 cars: Breakthrough for electric vehicles

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Rethinking mobility

Sometimes the breakthrough for a new technology does not materialize in one of those fields which receive most attention and where everybody expects the solution. When the British needed a practical method for determining a ship’s longitude in the 18th century, they spent many decades gazing at the stars, compiling lunar tables and searching for astronomic methods for determining longitude. But the breakthrough came with an entirely different technology: an extremely precise clock! Longitude could now be determined quickly and easily by comparing the clock’s Greenwich time with local time (which can be calculated by tracking the rise of sun).

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Today, electric mobility is in a similar situation: Billions of dollars have been invested in improving the usual components for electric vehicles: Battery technology – already quite advanced – is being perfected; charging infrastructures are being deployed; production processes are optimized and many of the legal and financial obstacles have been removed. Nevertheless, a real breakthrough is not in sight. As armies of engineers work on these problems, a much smaller group works on another technology which at first glance is not related to e-mobility: They develop autonomous vehicles that can drive themselves on regular roads and don’t require human input or modifications to the road infrastructure.
Only a deeper analysis shows how important fully autonomous vehicles will be for e-mobility: This technology changes fundamental aspects of mobility and enables alternative mobility scenarios which are more compatible with electric vehicles and where the biggest disadvantage of electric vehicles – their limited range – are much less of a concern. Range matters only in the current configuration of individual mobility which is based on individually-owned cars. If mobility is provided by fleets of driverless cars, then range limitations are no longer a problem because urban trips have an average trip length of less than 10km. They are much shorter than the range limits of current electric vehicles. Thus e-mobility may not need a breakthrough in vehicle range at all. It is sufficient to find new ways for reducing the range requirements for the vehicles!

Electric vehicles become competitive

Currently self-owned or leased cars are the cornerstone of individual mobility. Only a very small (even if growing) share relies on car-sharing and rental cars. These alternative mobility solutions currently have a big disadvantage: Every customer faces the problem of getting to the next available car and where it should be dropped off at the end of the journey. Once cars are capable of driving without any human intervention, however, this problem vanishes. Anybody will be able to request an autonomous vehicle by phone or mobile app. Within minutes a car will arrive to pick up the passenger and drop him off at the destination, where the car will then be ready to service the next customers.

Thus autonomous vehicles will initially provide the conditions for a breakthrough of car-sharing systems and autonomous mobility service providers. Compared to self-owned cars, they can provide individual mobility with a comparable level of service and comfort, and – because of better utilization and fleet optimization (see further below) – at significantly lower cost. In densely populated areas autonomous cars will therefore ensure that car-sharing systems greatly increase their share of individual motorized traffic.

This establishes the conditions for the breakthrough of electric vehicles. As part of fleets of autonomous vehicles, the advantages of electric vehicles can now be brought to bear: more robust and longer lasting motors, lower drive train complexity, lower service costs and lower emissions. Their shorter range is no longer a problem because fleet operators can dispatch vehicles that precisely match the mobility demands of their customers (local vs long-distance trips, number of passengers, baggage size etc.). As the vast majority of trips are local and short-range, most trips can be serviced with electric vehicles. A smaller number of fossil fuel vehicles can be used for long-distance trips. Thus fleets are likely to consist mostly of small electric two-seaters; only a smaller part will consist of larger vehicles or rely on fossil fuels. This potential for demand-based fleet optimization is a novelty that is only possible when cars can drive themselves to the customer. In this way total costs and resource consumption related to mobility can be reduced significantly.

Of course, fleets of autonomous cars do not have to use electric vehicles. However, there are three good reasons besides those already discussed above, why electric vehicles are particularly well suited for the first fleets of autonomous vehicles:
1) The first fleets with fully autonomous vehicles will appear in niche areas where it is easiest to control risk. Their speed and range will initially be quite low. At first, they may even travel partially on their own lanes and only later will increase their capabilities. An example is the project in Milton Keynes where 100 autonomous electric vehicles will be installed between 2015 and 2017 to ferry people between train station and city center. The requirements for these vehicles with respect to range, maximum speed, number of seats etc. differ markedly from the requirements for traditional cars. Current car models therefore do not constitute a good match for the first autonomous car fleets – even if they had been adapted for fully autonomous operation. At the same time, car manufactures are probably not eager to develop specialized low-volume models for use in early autonomous car fleets.
2) Current car models of the auto industry are not suitable for fully autonomous operation – even those with advanced driver assistance systems. They must be modified for pure fly-by-wire operation. All safety-critical components and systems have to be redundant. The modifications used in current prototypes and test vehicles are not suitable for productive use. It it is not at all trivial to adapt current car models for fully autonomous operation. Therefore the auto industry needs to develop a new vehicle platform from the ground up for fully autonomous operation. This could be a complex and time-consuming effort which will take longer than many fleet operators will be prepared to wait for.
3) The design and production of a small number of fully autonomous electric vehicles for local transport as part of fully autonomous fleets is much faster and easier than the design and production of classical cars adapted to the needs of fully autonomous mobility service providers. The complexity of electric vehicles is lower; relying on electric propulsion simplifies the redundant layout of all safety critical components. As an example, an electric motor has inherent safety benefits: It can be used for braking; if the motors are integrated into the wheels they could even play a role in emergency steering.

Whereas currently the lack of a national charging infrastructure for electric vehicles is often cited as a major problem, this problem also goes away when fleets of driverless urban cars are used. Because these vehicles are only used in local traffic in a specific region, it is sufficient to deploy the charging infrastructure for exactly that region and the actual number of electric vehicles and actual mobility demand. The infrastructure can then grow in synch with the fleet; it is no longer necessary to build up large infrastructures long before the first electric vehicles are placed into operation.

Economic pressures accelerate the transformation

Some innovations trigger intensive economic and societal changes which can advance with astonishing speed if they significantly change the cost structure and efficiency of processes. The power loom and the railroads are only two examples that highlight the potential dynamics.

Market forces work especially well, when they are brought to bear on inefficiently used capital-intensive resources. Such inefficiencies are very pronounced in transportation: Cars are among the largest single investments of private households; but their average utilization rarely exceed 6% – an incredible waste of capital. Therefore the potential for savings is enormous. The average US households spends more than 16% of its total expenses on car-based transportation.

A study of the Earth Sciences Institute at Columbia University has analyzed the savings potentials associated with fleets of self-driving cars in detail. They performed a simulation study based on the mobility patterns of Ann Arbor, a medium size city in Michigan which had about 285,000 inhabitants and 200,000 cars in 2009. 120,000 of these cars were used primarily for local traffic. Each day, 528,000 local trips occur in Ann Arbor with an average trip length of 9.3km and about 1.4 passengers per vehicle. The authors found that a fleet of 18,000 autonomous vehicles would be sufficient to satisfy the local mobility demand in Ann Arbor and ensure that no passenger would have to wait more than 60 seconds for their car – even during rush hours. This translates into a reduction in the number of cars by almost a factor of 7! Whereas a privately owned car with a range of 16,000km per year leads to costs of 0.46$/km, the fleet of driverless cars would reduce the costs per passenger-km to 0.25$. The study also examined the use of light electric vehicles instead of mid-size sedans which are typical used for car rentals. With electric vehicles, the costs would fall even further to 0,09$ per passenger-km. This is a cost reduction by a factor of five!

Although the study has not included all potential savings (not included were savings related to parking, congestion, aggregation of mobility demand, freed-up time) it clearly shows that this innovation has very high savings potential and can lead to a large increase in spending power for the individual. Even if not all consumers act rationally at all times, these calculations imply that a large number of households will choose autonomous mobility services instead of buying their own car in the future (we subsume the special case where a household purchases their own autonomous car but then rents it out to others as another variation of the fleet model). Only a smaller number of households will value the prestige of their own car or their love towards a car high enough to continue owning a car.

Another factor which will accelerate the growth of fully autonomous mobility providers is that even households which own a car will become customers of autonomous mobility services because they need their services in some situations: when a member of the family needs to be picked up somewhere, when multiple members of the household need to drive to different locations but the household does not have as many cars, when flying to other cities, etc. More and more people will then find that they can get around quite well without their own car. The number of situations in life where owning a car is almost a necessity will dwindle. Today there are many people for whom their own car is the only realistic way for getting to work. Fleets of driverless cars will greatly reduce such cases and therefore reduced the perceived need to purchase a car.

Paths towards new mobility

The transformation of mobility caused by fully autonomous cars will require some time. Despite the large advances of the last 30 years and the impressive prototypes which have been demonstrated by car manufactures (Daimler, Audi, Nissan), research institutes, Google and others, significant hurdles remain until fully autonomous cars will be able to drive on all roads without human intervention.

Currently there are two different visions of the path towards full autonomy. The conventional vision  assumes that autonomous technology will gradually evolve towards more and more advanced driver assistance systems until finally reaching full autonomy. It uses the typical diffusion process of automotive innovations (such as airbag and anti-lock braking) as a reference and assumes that the technology will slowly trickle down from the premium models to the middle-class models until it becomes standard for all new cars. However, there are significant hurdles – including regulatory problems – on this path. Several generations of models with ever more advanced driver assistance systems, with complete fly-by-wire and redundant layout of all safety-critical systems will be needed until models with full autonomy will appear on the market. If we follow this line of reasoning, then it may be well after 2030 that such cars are available in larger numbers and a decade or two more until fully autonomous technology is available in most cars. In addition, electric vehicles do not feature on this path, because it is based on personally-owned vehicles where range limitations of electric vehicles will continue to be a major problem.

However, there is a second path toward full autonomy which does not adhere to the standard car industry model of technology diffusion. Instead of trying to gradually integrate the technology into consumer cars, this path seeks to capitalize on the inherent advantages of full autonomy and targets those niches where full autonomy has the largest impact and can be implemented with a minimum of risk. We have seen above that full autonomy can greatly reduce mobility costs by providing mobility as a service using fleets of self-driving cars. A natural path towards full autonomy therefore starts with small, short range and most likely electric vehicles that provide local mobility at low speeds and in increasingly less controlled environments. The challenge for the pioneers is to find those settings which are best suited for limited, low speed operations of autonomous vehicles and which provide the best environment for their growth.

There is no shortage in candidates. Several systems with very low autonomy are already in operation: The ‘UltraPods’ at Heathrow Airport are electric autonomous four-seaters which ferry passengers between Terminal 5 and a parking lot. They run on separate lanes and use transponder chips embedded every few meters in the lanes for determining their position accurately. They also rely on internal lane maps for navigation. A similar approach has been adopted in the Netherlands where 6 autonomous electric buses carry people along a stretch of about 2km. A next step for such systems is to leave the confines of separate lanes at least in some cases and merge with regular traffic. Such an approach is planned for Milton Keynes, a British city, where 100 electric autonomous vehicles will be installed between 2015 and 2017 to transport people between the train station and the city center at low speed. Initially these vehicles will run on dedicated lanes (taken from current sidewalks); by 2017 they will expand their range and will be able to share lanes with pedestrians (there are currently no plans to put these cars onto the streets). This project has the advantage of minimizing risk while at the same time advancing the envelope of autonomous vehicles: algorithms will be perfected; approaches for the operational management of distributed fleets of self-driving vehicles will be developed; customer experience, preferences and service valuation will be understood better, a vehicular platform and technology architecture will be developed.

There are many areas where autonomous electric vehicles with even this limited capability are useful and can become economically viable very quickly. Similar approaches can be implemented in many cities where electric cars or buses may be installed for specific routes. Initially some infrastructure measures (such as separate lanes, fences which keep pedestrians away from the street, external sensors at critical locations) may be adopted; as experience and intelligence of the vehicle increases, these infrastructure measures can become obsolete.

Another variant of this approach would be if car makers or Google decided to implement their autonomous technology in a fleet of electric city vehicles that would operate on carefully selected routes in a suitable city. There are many ways in which this could unfold but there is no shortage of possible approaches for starting on a growth path for such fleets of autonomous electric vehicles. As the Milton Keynes Project shows, this is possible even with limited budgets.

Another advantage of this second path is a legal issue: A fleet of self-driving cars can be regarded as an intelligent transport system where the vehicles run on exactly specified paths. For such systems the legal limitations of the Vienna Convention on traffic which requires that every vehicle must be controlled by a driver at all times do not apply. This eliminates key legal problems which probably exist in countries which have ratified the convention (not ratified by: US, UK, Spain, China, Singapore and others). Even if the question has not been finally clarified, it is clear that countries have a considerable margin of interpretation which allows the implementation of fleets of driverless vehicles already today.

Overall, this alternative path to autonomous technology based on fleets of electric vehicles used for niche applications in controlled settings for urban local mobility is much more realistic and faster to implement than the vision of a universal fully autonomous car which can be used on all roads by anybody.

Conclusion

We are at the start of a development which could very quickly become an avalanche. Two years ago autonomous vehicles were widely regarded as utopian; by now it is clear that it is just a question of a few years until these vehicles appear on our roads. Right now, the view is still predominant that autonomous technology will do little to change the nature of cars and the nature of mobility. But a closer look reveals that the technology will lead to fundamental transformation from individually owned cars to mobility as a service and from mostly fossil fuels to electric vehicles. These fundamental changes hold many opportunities. Now is the time to take them…

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RAND report on autonomous vehicles

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As policy makers are waking up to the potential and issues related autonomous vehicle technology, the US think tank RAND Corporation has released a comprehensive report which analyzes driverless car technology from a policy perspective. The report looks at costs and benefits, the state of the technology, current legislation, liability and concludes with recommendations for policy makers.

Rand-Study

The report wavers between more incremental, functional perspectives (where autonomous technology is seen as an incremental improvement of current cars with little change to patterns of car use and ownership) and transformational perspectives (transformation of mobility, fully autonomous vehicles, fleets of driverless taxis). On one hand, the authors point out that the technology may enable developing countries to  “leapfrog autonomous vehicle technology” and “skip some aspects of conventional, human-driver centered travel infrastructure”. They point out that much urban space may be freed up because much fewer parking space is needed when fleets of self-driving cars provide mobility services (which actually may increase the density of urban centers) and argue that the declining costs of mobility may increase the number of vehicles miles traveled.

On the other hand, they warn that the adoption of self-driving car technology could be hindered because the individual car owner does not profit sufficiently from the reduction in external costs of the technology (an argument that seems quite far-fetched) and fail to point out the obvious incompatibility of new regulations that would impose the “Irrebuttable presumption of driver control of vehicle” with fully autonomous mobility services. While the report warns about job losses in the transportation industry, they neither mention the risks for the auto industry nor their potential lack of incentives for deploying fully autonomous vehicles.

In the same vein, the sections on liability are mostly focused on the various alternatives of assigning liability to either the driver or the automotive manufacturer. The car’s owner and entity responsible for maintenance is not seen as another alternative. Neither are the additional concerns addressed that may arise when autonomous fleets are operated by mobility service providers.

The study is an excellent read and brings together many important aspects of autonomous vehicles. For those interested in the legal issues it also contains a good discussion of the theories of liability which are relevant in the United States. As their final conclusion, the authors recommend that policy makers let the technology evolve and abstain from premature regulation.

Driverless car white paper by KPMG: Why own a car?

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When KPMG released their first white paper on self-driving cars in late 2012, we were surprised at how little thought they had given to the disruptive potential of fleets of driverless vehicles.They have corrected this now and a major headline and probably the guiding question of the report is: “Mobility on Demand: Why own a car?”

This year’s report begins with the realization that the “momentum around self-driving vehicles is astonishing”. The authors acknowledge that the “industry is moving even faster than we predicted”. They look at the variety and number of autonomy-related news events during the preceding months. They conclude that “the technology is evolving at a rapid pace”. While the industry is definitely gaining momentum, the statement may be a bit on the optimistic side with respect to the technology: If we sort through all the announcements and public demonstrations of some cars driving in some state of quite limited autonomy, we can not see that many significant advances have occurred in the past 12 months.

They then look at the history of the first automotive revolution and proceed to report insights from focus groups. This is useful but yields few real surprises (even auto enthusiasts are willing to use self-driving cars; tech brands such as Google are a little more trusted even than premium auto brands with respect to the technology).

The most interesting section deals with a potential decrease in car ownership: “If half of all American families who currently own two or more cars were to give up one of their vehicles, how would that affect the automotive industry”. They point out that the ratio between fleet and retail sales could “change dramatically”. Mobility costs could decrease for the consumer and big data and dynamic pricing will be a key capability for autonomous mobility providers. They also hint at adverse consequences for mass transit systems.

It is great that the major consulting companies are beginning to realize how disruptive this technology will be (and it is nice that they are coming around to a view that we already had published in early 2010 (Autonomous cars – the next revolution looms).

If the momentum continues to increase – which is very likely – next year’s report will be very different. Then they may be at their best and provide a much more detailed look at business models, competitive space, impact, and strategic positioning for automotive companies and other industries. We will be looking forward to the next report!