Fatal Tesla accident exposes fundamental flaws in the levels of driving automation framework

Ill-conceived standards can kill. The Tesla accident in which Joshua D. Brown was killed in early May could not have happened if SAE (Society of Automative Engineers), NHTSA  and BAST had not provided a rationalization for placing cars with incomplete and inadequate driving software on the road.

Since their publication the frameworks for driving automation (by SAE 2014, NHTSA 2013, BAST 2010) have been criticized for ignoring established knowledge in human factors. All experts in the field agree that it is not possible to expect human drivers to continuously supervise driving automation software and correct its shortcomings and errors at split-second notice when problematic traffic situations occur. SAE Level 2 and level 3 are therefore inherently unsafe and these levels should not have appeared as a viable variant of driving automation software in any framework at all!

Frameworks are not arbitrary. Unfortunately, the driving automation frameworks were heavily influenced by the perceived needs of the auto industry which already had driver assistance systems on the road and favored a gradual evolution of their systems towards fully autonomous driving. It is understandable that the authors wanted a framework that simplifies the path towards fully autonomous driving not just from a technical but also from a legal and commercialization perspective where automation can occur in baby-steps, most of which would not involve fundamental changes and would not require legislators to take a hard look at the underlying technology.

This is how Tesla was able to put their vehicle with auto-pilot software on the market. It was presented as a small step from cruise control to full lateral and acceleration/deceleration control by the system. Nothing else should change, they argued: the human is still in full control and bears full responsibility (which means that the driver will always be the scapegoat if something goes wrong!); the vehicle does not have the ambition of performing all tasks by itself. The frameworks clearly provide support for this argument. But they overlook the key difference: the software now handles the driving task continuously, for longer stretches of time without the need for human action. There is a fundamental difference between continuous driving systems vs. ad-hoc, short-term operations of driver assistance systems (i.e. parking, emergency braking, lane warning etc.) which only take over driving functions for short periods of time. Any framework for automated driving should have included this distinction!

Software that assumes the driving task continuously changes everything! Human drivers can and will relax. Their minds will no longer be on the traffic around them at all times. It is well known that human drivers tend to trust autonomous driving algorithms too quickly and underestimate their deficiencies. And it takes a significant amount of time to get back into the loop when the car needs to return the control function back to the driver. Unfortunately the authors of the framework failed to think through the details and problems that follow on levels 2 and 3. They thought about strategies for handing back the control from the car to the human; but apparently they did not perform a risk analysis where they considered how potential crisis situations that require rapid reaction could be mastered. Such an analysis would have shown immediately that
a) there are many possible critical situations where a hand-off from the vehicle to the driver can not be carried out quickly enough to avoid catastrophic consequences and
b) there are many situations where a driver in supervision mode is not able to detect a lack of capability or misbehavior by the driving automation software fast enough.

The Tesla accident is a good example to illustrate these problems. Although the accident occurred on May 7th, only some details have been released. The accident occurred around 3:40 PM on a divided highway 500 near Williston, Florida (view the map). A tractor-trailer turned left, crossing the path of the Tesla. Without braking at all, the Tesla hit the trailer approximately in the middle, went under it, emerged on the other side and continued driving for several hundred feet before coming to a stop at a telephone pole. More info on the accident (including the police sketch). The weather was good, no rain, dry road, good visibility. The road runs straight for miles. At 3:40 PM the sun stood in the West, behind the Tesla. The speed limit on the road was 65mph (104km/h), which translates into a stopping distance of 64 meters. Stopping time would have been about 4 seconds (which would also have been enough time for the truck to clear the intersection). The size of the tractor-trailer has not been made public but it was probably between 65 and 73 feet (20 and 22 meters). Assuming a standard lane width of 12 feet (3.7m), and estimating the distance between both sections of the divided highway based on the Google earth image to be about 20m, the trailer had almost enough available space between both lanes to make the 90 degree turn and could then continue straight on crossing the two lanes of the highway. If we assume that the left turn (the part at the lowest average speed) takes at least 6 seconds (time estimated from a video showing trailer trucks making a left turn) and the truck then passes the intersection at an average speed of 10mph (16km/h), then the truck needs an additional 6 seconds to clear the intersection. As the trailer was hit in the middle by the Tesla driving in the outer lane, the truck must have been about 30 feet (10m) short of clearing the intersection. Thus the tractor-trailer would have cleared the intersection about 2 seconds later.

At the moment, much of the discussion about the accident centers around the driver’s attention. We will never know whether or when the driver saw the truck. There are several possible scenarios: If we take the time horizon of 10 seconds (=6+6-2) before the accident when the trailer-truck initiated the turn, then the Tesla had a distance of about 280 meters to the intersection. At this distance, the large trailer-truck moving into the intersection would have been clearly visible. A driver engaged in the driving task (not on auto-pilot) could not have failed to see the truck and – given the lack of other nearby traffic or visual distractions – would have noticed with enough lead time that the truck is continuing onto the intersection. A step on the brake would have defused the situation and avoided the accident.

The scenario looks very different with auto-pilot. The driver knew that the road went straight for miles, with optimal visibility which translates into a low overall driving risk. The driver may have paid attention, but not as much attention as when driving without auto pilot. When a car drives by itself for many miles a driver won’t be as alert as when he performs the driving function himself. The attention will wane, the truck on the left side may have received a short glance by the driver. The truck’s intent to make a left turn would have been obvious;  but the truck slowed down when he entered the turn about 10 seconds before impact and the driver would certainly have expected that the truck will come to a stop and that the auto-pilot is also aware of the large truck. Thus even if the driver saw the truck initiate the turn, he would probably not have been concerned or inclined to pay special attention to the truck. This was just another one of probably thousands of intersections that Joshua Brown, who used the auto-pilot frequently and blogged about it, had passed. His confidence in the Tesla for handling intersections may have been high. Although he knew that the auto-pilot is not perfect, he probably did not expect that a large truck would be overlooked. In addition, he was probably aware of a Youtube video entitled “Tesla saves the day” which had circulated widely a few months ago. It showed how a Tesla had auto-braked just in time for a car crossing the path from the left.

The critical time window for recognizing the gravity of the situation and acting to prevent the accident was less then 10 seconds; and only 6 seconds before impact was it unmistakably clear that the truck is moving into the intersection instead of coming to a stop. If the driver was not fully focused on the road all the time but was alert in the 3 seconds between 6 and 3 seconds prior to impact he could have prevented the accident. But it is unrealistic to expect that a non-active driver will become fully focused on the traffic at each and every intersection that a car on auto-pilot passes and that he will always be alert for hard to anticipate, extremely rare but very critical short-term situations.

Even if the driver saw the truck and recognized that it was moving into the intersection 3 to 6 seconds before impact, then other problems arise: he has to jump into action and take over from the car. This needs time – both for the decision to revoke control from the car and for physically assuming control of the vehicle. Part of the driver’s brain has to work through the expected behavior of the car: If the car has not yet decelerated does this mean that it has not seen the large truck at all or does it mean that it is not necessary to brake (the car may have come to the conclusion that the trailer-truck will clear the intersection in time). Could it really be that the car does not see this blatantly obvious trailer-truck….? Have I completely overestimated the capability of this car? The shorter the remaining reaction time when the driver realizes the impending crisis, the more dangerous and potentially paralyzing this additional mental load may become.

Developers of driver assistance systems can not expect that drivers are fully alert all the time and ready to takeover in a split second. Moreover, they can not expect that drivers understand and can immediately recognize deficiencies or inadequacies of the software. Who would have expected that Tesla’s auto pilot does not recognize a tractor trailer in the middle of an intersection?

But the key problem is not a software issue. It is the mindset which offloads the responsibility from the driving software to the driver. Developers will be much more inclined to release imperfect software if they can expect the driver to fill any gap. That Tesla uses a non-redundant mono camera is another illustration of the problem. What if the camera suddenly malfunctions or dies on a winding road with the auto-pilot engaged and the driver does not pay enough attention to take over in a split-second? How is it possible to release such a system fully knowing that drivers using these systems will not always be paying full attention. This is only possible because we have standards that let developers offload the responsibility to the driver.

The often-raised counter argument that the level 2 auto pilot has already saved lives is not valid: it confuses two different kinds of driver assistance systems: those – such as emergency braking systems – which only take over the driving function for short periods of time when they are really needed and those that assume continuous control of the driving function for longer stretches of time and thus lead human drivers to take their minds off the road at least part of the time. Short term functions such as emergency braking are not controversial. They do not depend on the auto-pilot and it is them, not the auto-pilot, which is saving the lives.

There is only one variant in which software that assumes the driving task continually, for longer stretches of time can be developed and released to the market: the autonomous driving system must take full responsibility for the driving task and it may not require human supervision when engaged. Thus Levels 4 and up are viable approaches. The Tesla accident does not only show a software problem; it illustrates the dangers of levels 2 and levels 3. Theses levels must be scrapped from the framework!

The left turn problem for self-driving cars has surprising implications

Self-driving car technology advances rapidly, but critics frequently point out that some hard problems remain. John Leonard, who headed MIT’s self-driving cars project at the DARPA 2007 urban challenge, eloquently describes various challenging situations including hand-waving police officers and left turns in heavy traffic.

The hand-waving police officer problem can be solved easily with a simple workaround: The car just detects the hand waving situation. It then dispatches a camera feed to a remote control center and asks a remote human operator for guidance (similar to Google’s patent 8996224).

The left turn problem is more interesting. Such situations occur more frequently and they do present significant challenges. Self-driving car prototypes have been known to wait for long intervals at intersections before they finally made the left turn – heavily testing the patience of human drivers stuck behind them. The video by John Leonard clearly shows how hard it can be to make a left turn when traffic is heavy  in all directions and slots between cars coming from the left and the right are small and rare.

How do human drivers handle such situations? First they wait and observe the traffic patterns. If opportunities for left turns are rare they adjust their driving strategy. They may accelerate faster and will try to inch into a smaller slot than usual. Sometimes they will move slightly into the lane of cars coming from the left to signal that they are intent on making a turn and expect other cars to make room or they will try to find an intermediate spot between the various lanes and break the left turn down into one move towards this spot, come to a stop there and then into a second move from the the intermediate position into the the target lane. Leonard is right that programming such maneuvers into self-driving cars presents a major challenge.

But the problem is more fundamental. When we develop self-driving cars, we gain insights about the domain of driving and extend our knowledge not only about algorithms but also about human driving. To make a left turn, self driving cars have to analyze the traffic situation at the intersection. They are much better than humans at simultaneously identifying the traffic participants in all directions, to detect their speed, and they are quite good at anticipating their trajectories. Current driverless car prototypes also have no problems to decide on an appropriate path for the left turn. When a self-driving car hesitates at an intersection, the reason is not a problem with the algorithm but rather that the self-driving car finds that the safety margins for executing the turn are too small in the current situation: the risk is too high. Unfortunately, this problem can not be solved through better algorithms but only by increasing the level of acceptable risk! The risk of a left turn at an intersection is determined by the layout of the intersection, physics and the range of potential behavior of the other traffic participants, none of which can be changed by the self-driving car.

Left turns are indeed known to be risky. We may not think about it when we make a left turn, but accident statistics paint a very clear picture. An NHTSA study that analyzed crossing path crashes found that police-reported crashes involving left turns (913,000) are almost 10 times as frequent than police-reported crashes of right turns (99,000). If we consider that right and left turns are not equally distributed in normal driving (right turns occur more frequently but exact data are not available)  then the risk of a left turn may be between 10 and 20 times larger than the risk of a right turn. In 2013 crashes between a motorcycle and another vehicle making a left turn cost 922 lives; this amounted to nearly half (42%) of all fatalities due to crashes involving a motorcycle and another vehicle. Arbella insurance reported that in 2013 31% of its severe accident claims involved left turns. Thus human drivers have little reason to be proud of their left-turn capabilities.

As a consequence, UPS has largely eliminated left turns many years ago. Recently the route planning app Waze has rolled out a new feature that allows users to plan routes without left turns. These two examples show that self-driving cars do not even need the capability of making left turns in heavy traffic. It is possible to get along without such turns.

Thus the left turn problem for self-driving cars leads to the following three insights:

1) The left turn problem is not so much a problem of self-driving cars, it really is a problem of human drivers who take too many risks at left turns as we can see from the large number of left-turn accidents and from the risk analysis which self-driving cars carefully perform when making a left turn. Autonomous cars should never make left turns as readily and rapidly as human drivers. As human drivers we need to be more self-critical about our own capabilities and more ready to question our assumptions about driving instead of using our driving behavior as our implicit standard for self-driving cars.

2) We need to carefully consider the acceptable risk profiles for self driving vehicles. Risk profiles are not black and white; there are more alternatives than the high levels of risk that we take every day as human drivers without much thinking and the minimal risk strategies adopted by all current self-driving car prototypes. It would be unacceptable to let self-driving cars barge into dense traffic in the same way as we sometimes consider viable and mostly get away with. But it would be possible to reduce the time that a driverless car has to wait when turning or merging by allowing the car to increase the acceptable risk by a small amount if clearly defined conditions are met.In this area, much work and thinking is required. Expecting a self-driving car to minimize all conceivable risks and then operate as quickly as human drivers is a contradiction in itself. Instead of minimizing all risks, we need to seriously discuss what kind of small risks should be allowed in well defined situations.

3) We should not underestimate the creativity of the market to deal with the remaining problems of self-driving cars. Many of the frequently-cited problems of self-driving cars have practical workarounds that don’t require that much intelligence (triple right-turns instead of a left turn, remote assistance to deal with the hand gesture problem, limit the first self-driving taxis to snow-free regions for the snow problem etc.).

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.

Shared autonomous vehicles could increase urban space by 15 percent

A recent UK study has looked at the transformative implications of self-driving vehicles on cities. The authors found that shared autonomous vehicles could increase available urban space by 15 to 20 percent, largely through the elimination of parking spaces. Today central London has about 6.8 million parking spaces and a parking coverage of around 16%! Many large cities have even larger coverage ratios for parking space of up to 30%. Freeing up this space would make our cities greener, increase quality of life and also create the potential for additional housing.

Autonomous vehicles will also make the rural communities more attractive because shared travel to nearby cities becomes widely available, affordable and does not lead to loss of productive time.

The authors also consider autonomous vehicle only development areas and highways that are limited to autonomous vehicles. This could reduce costs as lane markings and signage would no longer be needed, the lanes could be narrower and throughput per lane would be higher.

Overall the authors from a cooperation between professional services firm WSP Parsons Brinckerhoff and architect planners Farrells conclude that autonomous vehicles will be transformational:  Future mobility may be headed to a shared pay-as-you-go transport system. The study provides many key points which infrastructure planners and legislators need to consider!

Source: “Making better places: Autonomous vehicles and future opportunities“, 2016 by WSP | Parsons Brinckerhoff, Farrells

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, ibamag.com, Kargas

Google prepares for manufacturing of driverless car

Google continues to push for the introduction of their self-driving cars on public roads. After positive statements by NHTSA and overtures from the United Kingdom and Isle of Man to test their cars there, job postings show that Google aims to significantly grow their self-driving car team. The 36 job descriptions below show that Google expands activities on all aspects of their self-driving car, including manufacturing, global sourcing, automotive noise and vibration, electrical engineering etc. It remains unlikely that Google intends to manufacture their cars themselves but the job postings complete the picture that Google wants to build a manufacturing-ready reference design of a fully self-driving car which they can either use for having their cars manufactured by a supplier or which can inform licensing and cooperation discussion with OEMs from the auto industry.

The job postings below were obtained from the Google job search engine on 2016-02-13 with a reusable query. All 36 jobs are for the Self-Driving Car team at Google-X:

  1. Mechanical Global Supply Chain Manager
  2. Mechanical Manufacturing Development Engineer
  3. Manufacturing Process Engineer
  4. Manufacturing Supplier Quality Engineer
  5. PCBA and Final Assembly Global Supply Manager
  6. Automotive NVH (Noise, Vibration, Harshnees), Lead
  7. Manufacturing Test Engineer
  8. Reliability Engineer, Vehicle Test Lead
  9. Reliability Engineer
  10. Product Manager, Vehicle 
  11. Global Commmodity Manager
  12. Industrial Designer
  13. Marketing Manager
  14. Technical Program Manager, Vehicle Safety
  15. Operations Program Manager
  16. Policy Analyst
  17. Head of Real Estate and Workplace Services
  18. Product Manager, Robotics
  19. User Experience Researcher
  20. Mechatronics Engineer
  21. Electrical Engineer
  22. Mechanical Engineer, Lead
  23. Systems Engineer, Motion Control
  24. Systems Engineer, Compute and Display
  25. Reliability Engineer, Lead
  26. Vehicle Systems Engineer
  27. Perception Sensing Systems Engineer
  28. Embedded Software Engineer
  29. Electrical Validation Engineer
  30. Systems Engineer
  31. Radio-Frequency Test Engineer
  32. Researcher/ Robotics Software Engineer
  33. Radio Frequency/High Speed Digital Hardward Design Engineer
  34. Camera Hardware Engineer
  35. Mechanical Engineer, Laser
  36. HMI Displays Hardware Engineering Lead

 

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!

Volvo’s liability promise for autonomous mode may cut out insurance companies and independent repair shops

Volvo has recently stated that they will accept full liability for accidents that happen while the car drives in fully autonomous mode. This takes the heat away from the discussion about liability issues for self-driving cars. But it also has side effects that strengthen the business model of the auto maker: By accepting full liability the auto maker in effect shoulders the liability not only for all defects of the software (which no auto maker can evade anyhow) but also for all other accidents that may occur in autonomous mode. Some accidents can not be prevented: Obstacles may suddenly appear on the way (animals, pedestrians, other objects) and make an accident unavoidable. Defects of the roadway, certain weather conditions, and certain questionable behaviors of other traffic participants may lead to accidents that even the best software can not prevent.

Therefore the acceptance of full liability contains both a promise regarding the quality of the software and an insurance element: Volvo must either add the total, non-zero, lifetime risk of driving in autonomous mode to the purchase price of their self-driving cars. This could have the disadvantage of making their cars more expensive. Or they could duplicate the insurance industry’s business model and request that their customers subscribe to a (low) supplementary insurance policy. The latter has the advantage that risk profiles – total number of miles driven per year and the area where the cars are driven (urban, country, highway) can be taken into account. But the insurance industry would surely mobilize against the latter approach and decry it as anti-competitive.

In the following we therefore examine the first case where Volvo decides to include the cost of insurance as a hidden element in the purchase price in more detail: It is hard to provide a good estimate of the risks but there are some numbers we can build from: In 2012 US insurance expenditures for a car had an average value of $815 per year. If we take this as a proxy for the risk of human driving, then factoring in the risk of human driving for a 12 year life expectancy of a car would increase the purchase price by $9780. How much lower will the risk of autonomous mode driving be? A representative study of more than 5000 severe accidents in the United States published by the NHTSA which was carried out between 2005 and 2007 provides some clues: The study found that human errors were the most critical factor in more than 93% of the accidents. In less severe accidents human error probably plays an even bigger, but certainly not smaller role. Other factors were: Technical failures: 2.0%, road conditions: 1.8%, atmospheric conditions (including glare): 0.6%. If we assume that autonomous vehicles do not add significant additional modes of error, then they should be able to reduce the number of accidents by at least a factor of 10 ( 1/(1-0.93) = 14.2). Because the vehicles drive more defensively, break earlier in critical situations, are much more consistent in their behavior in critical situations than humans (some of whom will not react at all in a critical situation, not even step on the brakes) the average damage per accident is likely to be significantly smaller than the average current damage. Therefore the costs of vehicle accidents are likely to fall even further; we estimate that autonomous vehicles have the potential of reducing accident costs by a factor between 15 and 50. This assumes that autonomous vehicles do not create major additional risks and don’t somehow cause rare but unusually enormous accidents. Under these assumptions, Volvo’s liability promise can be added into the purchase price: If we assume a reduction of damages by a factor of 15, the life-span risk (12 years) translates into 652$ of additional costs for each fully autonomous car which Volvo sells.

Accepting full liability for all accidents in autonomous mode may therefore indeed be a viable strategy for Volvo and other makers of fully autonomous vehicles. This move cuts out the insurance industry and – if copied by other auto makers – should not be a competitive disadvantage, because the risks are unlikely to differ greatly from auto maker to auto maker. In addition, auto makers might use this approach to open additional revenue streams for more risky use of vehicles where they might request additional fees – for example for heavily used fleet vehicles.

There is another side-effect of assuming liability for accidents in autonomous mode. Accidents are more likely if the cars are not maintained properly. Therefore auto makers may place more stringent requirements on maintenance, shorten maintenance intervals and require that the cars be maintained in certified repair shops only – which eliminates the business of independent repair shops. By increasing maintenance revenues, auto makers may be able to offset the costs of assuming liability for accidents.

In summary, Volvo’s shrewd move to assume liability may extend their revenue streams while cutting out insurance companies and independent repair shops.