Self-driving vehicles as instruments for the coordination of mobility

Autonomous cars will change the way we think about traffic. Today traffic is primarily regarded as the result of the independent actions of thousands of drivers. A view from above on any city would show large numbers of vehicles pursuing their own trajectories through the maze of roads. The cities’ traffic management systems try their best to observe, identify and somewhat channel the grand flows.

At first glance, autonomous vehicles do not seem to change this situation very much. From above, self-driving cars will not be distinguishable from human driven cars and they too, will seek their individual paths through the maze of roads. The picture changes, however, when we consider fleets of self-driving cars. Recent statements by Ford, Uber, BMW and others clearly show that fleets of self-driving cars will emerge early and have the potential to capture a significant share of individual motorized mobility.

This introduces a crucial difference: Fleet vehicles no longer pursue their local optimum; rather than completing the individual trip as quickly as possible, fleet management will seek to maximize throughput for all of its vehicles – for the fleet as a whole. The operational goals of fleet management are therefore very much aligned with the traffic flow goals of a city as a whole.

Initially, autonomous fleet vehicles will be instruments which fleet management systems can use to understand, model and predict the detailed traffic situation. The vehicles will be used as sensors and relay important information to the fleet management system.

As fleets grow, fleet managers will find that the vehicles can be used to influence the flow of traffic. Many different strategies are possible (and their effectiveness varies greatly with the ratio of fleet cars to total number cars): fleet vehicles can purposefully slow down the build-up of traffic ahead of arteries which are in danger of clogging. Fleet cars can reliably calculate and selectively or pre-emptively use alternative routes. As the percentage of fleet vehicles in relation to total traffic grows, fleet vehicles may travel part of the way in more densely packed convoys. They may even change their acceleration behavior at stop-lights (using a somewhat faster acceleration pattern than the standard acceleration pattern of human drivers) which may or may not be copied by human drivers.

Because both city traffic managers and fleet managers will recognize early on that their interests are very much aligned, we can expect many ways in which both parties will cooperate. Fleet managers will make real-time traffic information gained via their cars used as sensors available to the city traffic managers. Fleets are likely to ask city traffic managers to adjust stop light phases to improve traffic flow (and fleets will provide the data and models to prove that these changes will be beneficial). We can expect that this will lead to much more real-time traffic management for stop lights and fleet vehicles may come to very directly influence traffic signals. Eventually, as the differences in driving behavior between human-driven and autonomous vehicles become more apparent and fleet vehicles exceed 20 percent of traffic (initially mostly likely in urban centers), we may find that cities will reserve some lanes or roads for self-driving vehicles because they are more effective at providing local mobility than individual cars, or because the throughput on autonomous-vehicle-only lanes can be twice the throughput of human-driven lanes (mostly due to shorter distances between vehicles and better reaction times/acceleration behavior at stop lights, in some cases also because two conventional lanes might be re-fitted into three narrower lanes for autonomous fleet vehicles).

But this is only the tip of the iceberg. Fleet managers will understand local traffic very well and want to avoid their most valuable resources to be stuck in traffic. They will be able to predict the actual duration for a trip at any given point in time and will aim to minimize trips which incur heavy congestion. Instead of just driving a customer every day to work at a time of his choosing, they will look for ways to reduce the peak load on the fleet. Ridesharing is only one of many approaches: Fleets will provide rewards to those who stay out of the rush hour (or add congestion pricing, which in turn will drive down congestion). They may find ways to systematically phase traffic flows in certain areas, work with employers and schools to adjust working hours, provide an in-car environment that allows workers to begin their work while commuting (and ensure employers’ approval), provide a reliable forecast of trip times (and a clear indication how expected trip times can be reduced by leaving earlier or later).

Time will tell which of these many possible actions will yield the most benefit (and through which other approaches fleets of self-driving vehicles will improve the overall traffic flow in a city). But it is obvious that fleets of autonomous vehicles will lead to a very different thinking about traffic. Where today we have thousands of actors all pursuing their own little traffic goals, these fleets will start us thinking about how traffic can be optimized not just locally but as a whole. It is clear that this optimization does not necessarily start when a trip begins, but potentially already before – when a mobility demand for a trip from a certain location to another location in a certain time range  is known. Fleets will pave the way by optimizing their trips against the whole fleet. And the lessons we learn from managing trips for autonomous vehicle fleets will deeply change our thinking about traffic and how traffic should be organized.

Thus, autonomous vehicles not only drive themselves; they change the cost structure of mobility, which in turn enables shared  autonomous mobility services to grab a significant part of the market for motorized individual mobility. These shared services will necessarily implement a centralized perspective on mobility which requires finding (and negotiating) ways to optimize the mobility demands of large groups, even cities. In the end, we will likely think about all mobility – whether in a fleet vehicle, in privately owned autonomous or conventional car – from a perspective of global optimization. It won’t be long before our mayors, regulators and politicians will see the potential of self-driving vehicles for traffic management and begin to develop policies that lead traffic away from today’s heavily congested local optima towards structures that come much closer to the global optimum.

Transformations 2025: How Volkswagen prepares for the (driverless?) future

Echoing a growing sentiment in the auto industry, Volkswagen’s CEO Matthias Mueller warned last week of “a rapid and hard transformation” coming to the auto industry. He presented Volkswagen’s strategy “Transform 2025+” to cope with these changes. It includes major job cuts to prepare for the transition and many new initiatives.

But his strategy also shows how difficult it is to change the direction of the tanker which all major auto makers have become. Experience accumulated in the last 100 years, shared convictions and values make it difficult to adjust the focus and prepare for fundamental changes coming the industry. Many trends are currently competing for attention: electrification, mobility services, connected vehicles, digital platforms and finally the shift towards autonomous vehicles. It does not come as a surprise, that Volkswagen wants to become a leader in most of these topics:

It plans to establish an additional (thirteenth) major brand around mobility services. It wants to become a leader in electric vehicles. It has just established a digital lab to develop cutting-edge digital services related to mobility, connectivity, its brands and its products.

But the strategy fails to consider the tectonic shift which may be caused by autonomous vehicles and the way that self-driving car technology will affect the key aspects of the auto business. Mueller plans to lay the foundation for autonomous driving in the years from 2020 to 2025 and then have the necessary business models in place around self-driving cars after 2025. Given the rapid progress of the field, he may not have that much time.

But more importantly, self-driving car technology is associated with a very specific danger (and opportunity): It changes the dynamics of each of the auto industry’s strategic topics. Mobility services based on self-driving car fleets differ fundamentally from Uber’s, Car2Go’s and other mobility services fleets on parameters such as total cost per mile, optimal car model and characteristics, volume, utilization, profitability,  etc. Similarly electrification differs greatly whether it is targeted towards autonomous vehicles (which will initially predominantly be rolled out as elements of urban self-driving car fleets) or towards the consumer. The economic justification, battery cost, vehicle range, charging infrastructure requirements, innovation diffusion path and cost-effectiveness differ fundamentally!

A little bit of everything is not the right approach. Volkswagen, like most other auto makers, suffers from the problem hat it tries to address each and every strategic topic on its own without considering the relationships and interdependence with a paradigm-changing technology. Then, when autonomous vehicle technology enters the market they will find that the original assumptions no longer hold and that very little time remains to catch up and refocus the many different aspects of their business.

It is good that the auto industry is increasing their efforts to think about a radically different future. But they extrapolate forward from today to the next 5, 10, 15 years, and their thinking remains mostly rooted in the classic automobile world with a focus on volume leadership, consumer cars as primary product, traditional branding approaches, etc. However, in the face of transformational change, a different mode of analysis is needed: First the more distant future needs to be conceptualized, a future where autonomous vehicle technology has already matured, the current doubts and questions about viability, legality and acceptance have been overcome, self-driving vehicles are in the market and where laws and regulations have been updated (as we know they will) to allow productive use of the technology. The key aspects of this future need to be considered: Mobility service markets (separately for urban and non-urban regions, for local and long distance traffic), consumer segmentation and purchase decisions, impact on road infrastructure, impact on traffic flow (which will be enormous both for urban and for long-distance roads) and fleet management algorithms, truck, bus and autonomous machine markets. For such a future key changes (including the various types of mobility service business models) need to be calculated through in detail, using quantitative models. This analysis must be unencumbered by the current “realities” of the auto market. It must include the scenarios, business models and market dynamics that may entice investors to pour funds into promising opportunities.

After such an analysis, the focus can be turned back from the future to the present and the transition period. Many likely changes will become obvious and the paths and the relationships between the different technologies being considered today will be much clearer. For Volkswagen and all other auto makers it means allocating major resources to autonomous vehicle technology today: make sure that they catch up with the leaders in the space; prepare mobility services for  the autonomous fleet scenario rather than as also-run next to all the players already established in this field and make sure that they have electric vehicle models that can be used as backbone of self-driving car fleets.  Develop, consider and prioritize business models beyond consumer cars and fleet vehicles/mobility services, for trucks, buses, autonomous machines and beyond. Each of these activities is future-proof and establishes a beachhead  in the transition towards autonomous vehicles.

This is not a call to put all eggs into one basket. But auto makers need to take the fundamental changes that will be caused by self-driving car technology seriously and prepare to adapt to these new challenges today by making them a cornerstone of their strategy.

The race for fully self-driving cars has reached a pivotal point

Several events from the last months provide a strong signal that autonomous vehicle technology has led the auto industry to a pivotal point: The first auto makers are adapting their business model for fully self-driving cars and are providing explicit time frames!

Earlier this year GM invested 500 million USD in Lyft, purchased self-driving technology startup Cruise Automation for more than 1 billion USD and announced in July that GM will build its first self-driving cars for use within the Lyft fleet as self-driving taxi. In May BMW announced that they would have a self-driving car on the market within 5 years. Next came Uber, which acquired autonomous truck startup Otto for 680 Million USD and is now beginning field trials of fully self-driving taxis in Pittsburgh. But the key change at Uber is the way that its CEO Kalanick frames the issue. He makes it clear that Uber’s survival depends on being first (or tied for first) in rolling out a self-driving taxi network.

The latest announcement comes from Ford which plans to provide mobility services with fully autonomous self-driving Fords by 2021. This is a major effort: Ford is doubling its development staff in Silicon Valley, aims to have the largest fleet of self-driving car prototypes by the end of this year and will triple the size of this fleet again next year. It has also purchased 3 companies related to autonomous driving technology and has purchased a stake in Velodyne, the leading manufacturer of LIDARs for autonomous driving.

When we started to monitor the development of self-driving car technology in 2009 we expected that this technology would turn into an avalanche that sweeps through the auto industry. There have been many signs over the past years that the avalanche is picking up speed but until now we have been reluctant to claim that it is in full swing because even though the auto industry was continually increasing their activity around self-driving car technology all players had been very reluctant to openly call this a race and to publicly position fully self-driving cars as a key element of their strategy. There was a lot of posturing, many eye-catching public demonstrations of self-driving car prototypes but very little tangible action aimed at turning fully self-driving car prototypes into a real product.

After these recent signals, this situation has changed. It is now clear that auto makers have begun competing in earnest to adapt their business models to the coming wave of fully self-driving cars. No longer is Google the only company which is stepping on the gas; auto industry executives (and Uber) are now openly competing to bring the first self-driving cars on the market. It will come as no surprise to the readers of this blog that the initial business models are not concerned with selling cars but to provide mobility services.

These signals are important in themselves. They heat up the competition and force the rest of the auto industry to decide how to adapt their business model to fully self-driving cars and to explain this strategy to their investors, journalists and analysts. They increase the value of companies in the space and increase the competition for human capital (Google has probably lost between 500 million and 1 billion USD in human capital from the exodus of key members of their self-driving car group in this year (680 mio USD Uber paid for the Otto startup founded early 2016 by 4 Googlers (including Anthony Levandowski), plus Chris Urmson.). They also increase the effort of all parties involved (auto industry, suppliers, regulators, journalists, related industries such as transport & logistics, insurance, health care etc.) to understand the implications of fully self-driving cars which gradually drives away the many misconceptions and more clearly shows risks and opportunities. We are in the middle of a global, distributed innovation process around self-driving cars and driverless mobility where all parties are learning, refining their thinking, changing their vision of the future and adapting their actions accordingly. The avalanche is in full swing now and it will be a tough ride for those who fail to adapt while there is still time…

German railways to introduce autonomous long distance trains by 2023

The CEO of Germany’s railways, Ruediger Grube, does not want to fall behind the auto industry with autonomous mobility and has announced that Deutsche Bahn (German Railways) will operate on parts of the railway network with full autonomy “by 2021, 2022, or 2023″. Test are already underway on a part of the German railway network in Eastern Germany.

The technology for autonomous long distance trains differs greatly from the technology for autonomous metro-trains and subways which already operate in many cities of the world. In the latter case, most of the intelligence for autonomous driving is embedded into the railroad infrastructure and a centralized controller that is in constant communication with all trains; the trains themselves, in contrast have little intelligence; they don’t operate autonomously. This approach is not viable for long-distance networks because upgrading thousands of kilometers of the network with controllers and sensors would be much to costly. Therefore most of the intelligence has to be embedded within the locomotive. Fully autonomous long distance trains therefore need to be equipped with sensors and algorithms that are very similar to those used in self-driving cars.

The advantage of self-driving trains does not lie so much in cost reduction but in the ability to increase network capacity because trains can be operated with higher frequencies and at shorter distances. This also increase the flexibility of rail-based transportation solutions and makes new services possible. These capabilities are essential if railroads want to survive against the greatly intensifying competition from fully autonomous self-driving cars, trucks and buses.

German unions immediately criticized their plans. But they fail to understand that fully autonomous road-based transportation will provide an enormous challenge for the railroads. Deutsche Bahn is on the right track. They should do everything to accelerate their introduction of autonomous long distance trains.

Cities around the world jump on the self-driving car bandwagon

Autonomous vehicles will have a major impact on urban transportation. Mayors, transportation companies and urban planners are increasingly taking notice. The number of cities which recognize the benefits of self-driving cars and buses increases rapidly. Below is a list of some cities around the world which have launched or are working to launch activities focused on self-driving cars and buses:

San Francisco, Austin, Columbus, Denver, Kansas City, Pittsburgh, Portland (Oregon):These seven cities strive to be pioneers in integrating self-driving car technology into their transportation network. Each of these cities has already received a 100.000 USD grant from the US Department of Transportation (Smart City Challenge) to refine their earlier proposals on how to transform their urban transportation systems. In June, Secretary of Transportation Anthony Foxx will award a 50 million USD grant to one of these 7 cities to become the first city to implement self-driving car and related technology into their urban transportation system. San Francisco, for example, has proposed phased plans to deploy autonomous buses and neighborhood shuttles. The city has also gathered pledges of an additional 99 million USD from 40 companies in case it receives the 50 million USD grant.

Milton Keynes, UK: Trials of self-driving pods have already begun in this British city. The electric pods will transport people at low speed between the train station and the city center. Additional UK cities which are experimenting with self-driving car technologies are London (self-driving shuttles, Volvo Drive Me London), Coventry and Bristol.

Singapore: This may be the most active and visionary city with respect to driverless transportation. Several years ago it has launched the Singapore Autonomous Vehicle Initiative, partnered with MIT on future urban mobility and initiated several projects aimed at improving urban transportation systems through self-driving car technology. The city has already set up a testing zone for self-driving cars and is conducting several trials in 2016.

Wageningen / Dutch Province Gelderland (Netherlands): A project with driverless shuttles is already underway. The self-driving Wepods aim to revolutionize public transport and provide a new, cost-effective way to bring public transportation to under-served areas.

Wuhu, China: According to Baidu’s head of self-driving cars, self-driving cars and buses will be introduced into the city of Wuhu over the next five years.

Beverly Hills, USA: The city council of Beverly Hills has just passed a resolution aimed at the long-term adoption of self-driving cars. The resolution starts first activities towards achieving that goal but does not yet commit major resources.

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

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

Chinese search engine Baidu has entered the race for self-driving vehicles in 2014. In a partnership with BMW, the company presented an early prototype of an autonomous car at the end of 2015. Baidu’s approach mimics Google in many ways: Like the first Google prototypes of 2010, the car uses the (aging) Velodyne 64 Lidar as its main sensor; Baidu’s approach also relies on detailed mapping which fits well with Baidu’s overall mapping strategy. Baidu also aims to diversify its business model by leveraging its know-how in artificial intelligence and has transferred its auto-related activities into a separate division, a move that Google started last year by restructuring into Alphabet. There are some differences: unlike Google, Baidu does not seem to put much emphasis on the sensors; they don’t seem to experiment with their own sensors and the configuration of sensors indicates that certain situations in which a car may find itself have not been considered yet.

Baidu’s vision of how self-driving vehicles will be adopted also differs somewhat from Google. Whereas Google has focused on individual cars, and is testing electric two-seaters which could easily become robotaxis, Baidu expects the first wave of self-driving vehicles to be autonomous buses or shuttles. In a recent online interview, Andrew Ng, Baid’s Chief Scientist, argued that buses which service a fixed route or a small defined region will be the best starting point. He expects a large number of such vehicles to be in operation within three years (= early 2019) and mass production to be in full swing within five years (= 2021).

Andrew Ng correctly pointed out that such autonomous buses operating on fixed routes or small regions  would have the advantage that care could be taken to ensure that the routes are well maintained, don’t have construction (or the construction site is clearly indicated in the map) etc.

Unfortunately, Andrew Ng’s argument, that driving on predefined routes would enable the vehicles to avoid “corner cases–all the strange things that happen once per 10,000 or 100,000 miles of driving” (source) is flawed. He argues, that machine learning can not prepare for these corner cases and that therefore driving in a restricted well-defined environment is the solution. Unfortunately, corner cases can happen anywhere; it is impossible to guarantee that on well-mapped and well-known routes strange situations can not occur. Pedestrians can suddenly appear in areas that are closed for pedestrians, obstacles may occur on a road, an oil spill can occur, the road can suddenly be flooded etc. Building software that can reliably handle even the most challenging situations is a hard task and needs to consist of a combination of machine learning, an enormous testing program (usually combined with knowledge acquisition and machine learning), careful and very extensive risk analysis and risk modeling, and purpose-built test scenarios which challenge the capabilities of the cars both in simulators and in staged test cases in the real world.

We have pointed out for the past five years that the switch towards shared mobility services based on fully autonomous vehicles will be the great transformation that self-driving car technology will bring. This is the reason why auto makers have been so reluctant to push fully autonomous driving and why it provides avenues for new entrants such as Google, Baidu, EasyMile, Bestmile, Zoox, potentially Apple, and others to capture a significant share of the world’s expenses for personal mobility. There are many reasons why the first fully autonomous vehicles to appear on our roads will be robo taxis or self-driving buses, not the least that many current projects focus on such autonomous mobility services. Examples are: WEPods (Netherlands), CityMobil2 (Greece and EU), One-North (Singapore), Sentosa (Singapore), EasyMile, (USA, California), Google self-driving pods (United States, California and Texas), Milton Keynes driverless pods, (United Kingdom), Ultrapods (United Kingdom), Bestmile (Switzerland), DeLijn, (Belgium), RobotTaxi (Japan), Baidu (China), Yutong Bus (China).

In summary, Baidu’s focus on self-driving buses adds weight to the expectation that shared mobility services based on driverless pods and buses will drive the initial adoption of autonomous vehicles. Both self-driving cars and buses have to solve the problem of autonomous driving and the same technology can applied for both application scenarios. This is why the technology which Google currently refines with their 53 self-driving cars can easily be transferred into self-driving buses and shuttles and why Baidu’s current prototype is not yet a bus but rather a converted BMW. Those pioneers who solve the problem of fully autonomous driving will find enormous business potential for self-driving taxis, self-driving shuttles, self-driving consumer cars, trucks and machines. The race is on!

Self-driving cars will be a potent weapon to combat climate change

Although world leaders have reached a ‘historic’ agreement on climate change at the Paris Summit, good solutions to reduce greenhouse gas emissions remain hard to find. Fortunately – and counter-intuitively – self-driving cars have the potential to significantly reduce the ecological footprint of transportation:

The transportation sector is a major polluter and it is the economic sector with the biggest net effect on climate change. While some other sectors (such as industry and power generation) emit more greenhouse gases, these industries also emit other substances that lead to cooling (aerosols of sulfate, nitrate and others).

Of course, self-driving cars will not reduce the number of trips or kilometers traveled. On the contrary: self-driving cars have the potential to significantly lower the total cost per kilometer traveled and are thus likely to induce people to make more trips. As we have shown in other papers, self-driving taxis and buses will emerge rapidly and offer mobility services for local and long distance traffic with great convenience and at extremely competitive prices because they can achieve much higher utilization rates than private cars (which stand idle more than 94% of the time), and because autonomous fleet vehicles will be engineered for the minimization of total cost of ownership and for the maximization of useful life.

Most urban self-driving taxis will be fully electric for reasons that are not primarily environmental but that are still good for the environment: Electric motors offer safety advantages (they can be used for emergency braking and to some degree for emergency steering). They are also much more durable (an electric motor easily lasts 1 million kilometers), less expensive and less complex than conventional engines. In addition self-driving taxis that operate in local traffic will not need huge battery packs when average trip sizes rarely exceed 15 kilometers and when they can drive themselves to the next high efficiency charging station as needed. Their batteries won’t be sized to last a whole day; they will need to be just large enough to service a little more than the trips of the morning peak – after which they can recharge.

There can be no doubt that self-driving taxis and buses will change the nature of urban mobility. Much more short-distance travel than today will occur in small, lightweight, extremely energy efficient self-driving taxis. Although this may lead to a certain increase in total miles traveled, the following effects combine to reduce greenhouse gas emissions:

  1. Self-driving taxis will be mostly electric which reduces carbon emissions (approximately 25% less emissions compared to internal combustion engine)
  2. Self-driving urban taxis will be smaller and much lighter than the average car which further reduces energy consumption per kilometer
  3. Self-driving taxis reduce demand for private cars and therefore reduce the sizable greenhouse gas emissions during vehicle manufacturing which are typically more than 10% of total life-cycle emissions of a car. According to some estimates, a self-driving car-sharing vehicle or taxi can eliminate 7 to 10 private cars. What a potential for greenhouse gas reduction in auto manufacturing!
  4. Self-driving taxis facilitate multi-modal travel (taking an autonomous taxi to the train or bus station, continuing with bus or train, using an autonomous taxi for local transport at the destination)
  5. Self-driving taxis facilitate ride sharing especially during peak hours and on certain routes.

On the other hand, the effect of self-driving taxis on public transport is not yet clear. There is both the risk that some local trips which are taken by public bus today will migrate to self-driving taxis and the opportunity to capture a much larger share of the mobility demands with self-driving scheduled and on-demand buses and mini-buses – potentially in multi modal combinations. The potential benefits are large and there will certainly be a place for efficient self-driving mobility services using self-driving buses and mini-buses. Concerns that new mobility solutions centered around self-driving taxis and mini-buses will be less environmentally efficient than current scheduled buses are not warranted because today’s scheduled buses are not very good for the environment during off-peak hours when they travel near-empty.

The currently most overlooked aspect of self-driving vehicles is their effect on medium and long-distance travel in areas with sufficient population densities. Whereas today many people choose their own vehicle for distances between 100km and 500km self-driving taxis and self-driving buses make it much easier to provide excellent, extremely cost efficient long distance mobility services. When urban taxis at both origin and destination guarantee painless individual personal mobility and when small or medium-size autonomous buses provide long distance travel at extremely low rates which are much lower than the cost of traveling in a private car, then greenhouse gas emissions can be reduced very significantly. Although only a small percentage of all trips are more than 100km in length, these trips represent a large share of the total distance traveled in private cars and therefore have a large and easily overlooked potential for reducing greenhouse gas emissions.

The big advantage of self-driving car technology is that it can accomplish several benefits at the same time: It increases the options for individual mobility and lowers the cost of individual mobility because of new driverless mobility services which through increased sharing, more efficient use and quicker adoption of alternative fuels reduces greenhouse gas emissions. Nobody will have to abandon their cherished car but the joint actions of the large group of less or only moderately affluent consumers who value the flexibility and cost-saving associated with self-driving mobility services will inexorably lead to a reduction of greenhouse car emissions. It is time for the political leaders searching for solutions to combat climate change to take notice!

US Secretary of Transportation: driverless cars all over the world by 2025

Anthony Foxx, Secretary of Transportation visited the Frankfurt Auto Show together with his colleagues from the G7 and German Chancellor Merkel. In an interview with German newspaper Frankfurter Allgemeine Zeitung, he stated that he is very optimistic with respect to driverless cars and expects to see them in use everywhere in the world within 10 years. He wants to accelerate the process for the introduction of new technologies such as self-driving cars and avoid the current legislative delays of five or six years. Of, course safety must always be assured.

The Frankfurt Auto Show clearly demonstrates how much more seriously politicians and the auto industry are taking autonomous car technology and the changes that they will bring.

Source: Frankfurter Allgemeine Zeitung, 2015-09-19

Chinese company unveils prototype of self-driving bus

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).


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.