Five key impediments to a successful self-driving car strategy

The auto industry increasingly recognizes the threats and opportunities associated with self-driving cars. Unfortunately several impediments stand in the way of formulating and implementing a strategy for dealing with self-driving car technology and its impacts:

1) Time: lack of urgency

Although the competition in autonomous car technology has heated up considerably over the last 2 years, most industry experts continue to expect a slow adoption curve which could easily span two to three decades. Unfortunately, adoption of self-driving car technology (level 4 and up) will be much faster than traditional adoption rates of new technologies in the auto industry. A key accelerator is the enormous net benefit of the technology not just in terms of safety but also as increase of available personal time, competitive position (for companies and countries) and a significant decrease of costs (labour, fuel, insurance, capital). As a consequence there is much less time to formulate a sound strategy for self-driving cars.

2) Shared auto industry perspective clouds impact analysis

Shared convictions and experiences make it much more difficult for the industry (including their consultants) to think through fundamental, deep, disruptive changes in the architecture of mobility. Whether it is the joy of driving, the importance of brand for the consumer, the assessment of the legislative and regulatory environment, the consumer’s propensity to use shared self-driving mobility services or the likely business models, industry insiders tend to reinforce a perspective on the impact of self-driving cars that remains much too close to the current model, experiences and structure.

3) Lack of understanding for self-driving car business models

For many years, the auto industry has recognized a trend towards shared mobility services. Automakers understand that self-driving fleets will accelerate this trend. But they seem to spend very little effort to think through the dynamics of this market (which differs fundamentally from the traditional car-sharing and mobility-brokering markets), the way that shared mobility services will operate and compete, the regulatory environment that will emerge around fleet oligopolies, the differences between urban and long distance shared self-driving mobility services or the cost structure, maintenance strategy and model mix for such services.

In addition, there are many other business models besides shared fleets which may provide opportunities related to self-driving car technology which established players need to carefully consider, evaluate and prioritize.

4) Relationship between electric vehicles and self-driving cars not understood

In parallel to the self-driving car phenomenon the auto industry is involved in the switch towards alternative propulsion modes. But the relationship between self-driving car technology and alternative fuels is widely overlooked: Because self-driving cars will change mobility patterns (increase of urban mobility services, changes in long-distance travel patterns) and self-driving fleet vehicles will be able to refuel autonomously (or nearly-autonomously), the context for the adoption of alternative fuels changes dramatically. Battery range will become much less important; rather than optimizing cars for maximum range they will be optimized for an optimal range with respect to the mobility pattern which they are used for. When fleets carry a larger share of traffic the dimensioning of an adequate charging infrastructure becomes much easier and much more economically viable. Thus autonomous vehicle technology will serve as an accelerator for the introduction of electric and alternative fuel vehicles.

5) Fear of cannibalization / resistance to change

Any organization that faces major change and must consider the effects of a disruption of its primary business model will encounter tremendous internal resistance. Those who see the writing on the wall will hesitate to become advocates of (painful) change because internal opposition is fierce, uncertainty abounds and – as a result – career risks are high. It is useful to seriously study other industries and companies which had to face disruptive change. One of many examples is Kodak, a company that had developed the first digital camera already in the Seventies and brought the first digital camera to the market in 1995. There may be some parallels to the auto industry, which has a multi-decade history of developing technologies for self-driving cars. But Kodak hesitated far too long to adapt and rethink its business models, fearing cannibalization of their very profitable film camera business. When their profits began dwindling, it was too late. The auto industry cannot afford to make the same mistake.

Learn more

For more on this topic please join us at the upcoming 1-day seminars on self-driving cars in Frankfurt (March 23) and Auburn Hills (May 16). The seminar will be run by Dr. Hars and will help to develop a better understanding and analysis of implications of self-driving cars. More info…

Workshop: Self-driving cars – strategic implications for the auto industry

Please join us for this 1-day workshop on March 23 in Frankfurt, Germany or on May 16 in Auburn Hills, USA. The workshop examines the disruptive implications of self-driving car technology and the strategic consequences for the auto industry, its suppliers and related industries. The workshop will be led by Dr. Alexander Hars.

Program highlights

  • The workshop begins with a review of the current state of the global, distributed innovation process related to self-driving cars, and examines the underlying technical, economic, legal and geopolitical factors upon which it depends.
  • Key implications for the mobility space will be discussed through an in-depth analysis of the many facets of the economics of self-driving mobility services.
  • We will examine how fully self-driving cars will affect different aspects of personal mobility – the propensity to use self-driving mobility services for local or long distance travel, the decision to purchase a car, buyer preferences for specific car models and features as well as the transition towards electric vehicles.
  • We will then focus on the various players in the SDC field, including leading OEMs, new entrants such as Google, Uber, key suppliers, including sensor and hardware providers as well as various governments, including the US, UK, Singapore, Japan and China.
  • We explore four potential strategic responses for the auto industry and discuss business models associated with self-driving vehicles and their suitability for the various players.
  • We review key implications for model mix, volume, as well as sales and design processes.

Who should attend?
This workshop is intended for executives who need to think through the consequences of self-driving cars on the automotive sector. It offers frameworks and insights to help them develop their understanding and analysis of the threats and opportunities of SDCs for the industry.  It will help them to understand the implications of SDCs and to formulate appropriate strategies for their business.

More information, event agenda and registration
This event is organized by Autelligence. Further details are available on Autelligence site.

 

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.

Misconception 7: To convince us that they are safe, self-driving cars must drive hundreds of millions of miles

Top-misconceptions-of-self-driving-cars-arrow
One of the most difficult questions for self-driving cars
concerns their safety: How can we determine whether a particular self-driving car model is safe? The most popular an­swer to this question is based on a straightforward application of statis­tics and leads to conclusions such as that “…fully autonomous vehicles would have to be driven hundreds of millions of miles and sometimes hun­dreds of billions of miles to demon­strate their reliability…”. This state­ment comes from a recent RAND re­port by Nidri Kalra and Susan Pad­dock on the topic. Unfortunately, these statements are untenable in this form because the statistical argument contains major oversights and mis­takes, which we will point out in the following.

7.1 Failure rate estimation

The argument is usually presented as a problem of failure rate estimation where observed failures (accidents involving self-driving cars) are com­pared against a known failure rate (accident rates of human drivers). Accidents are modeled as discrete, independent and random events that are determined by a (statistically con­stant) failure rate. The failure rate for fatal accidents can be calculated by dividing the number of accidents with fatalities by the number of vehi­cle miles traveled. If we consider the 32,166 crashes with fatalities in traf­fic in the US in 2015 and relate them to the 3.113 billion miles which mo­tor vehicles traveled, then the failure rate is 32,166 / 3.113 billion = 1.03 fatalities per 100 million miles. The probability that a crash with fatality occurs on a stretch of 1 mile is ex­tremely low (0,0000010273%) and the opposite, the success rate, the probability that no accident with fa­tality occurs on a stretch of 1 vehicle-mile-traveled (VMT) is very high (99,999998972%). By observing cars driving themselves, we can obtain es­timates of their failure rate. The con­fidence that such estimates reflect the true failure rate increases with the number of vehicle miles traveled. Simple formulas for binomial proba­bility distributions can be used to cal­culate the number of miles which need to be driven without failure to reach a certain confidence level: 291 million miles need to be driven by a self-driving car without fatality to be able to claim with a 95% confidence level that self-driving cars are as reli­able as human drivers. This is nearly three times the distance between fa­talities that occur during human driv­ing. If we relax the required confi­dence level to 50%, then at least 67 million miles need to be driven with­out fatality before we can be confi­dent that self-driving cars are safe. Although this calculation is simple most authors – including the authors of the RAND report – use the wrong measures. Instead of dividing the number of crashes involving fatalities (32,166) by VMT, they divide the number of fatalities (35,091) by VMT. This overstates the failure rate of human drivers because a single ac­cident may lead to multiple fatalities and the number of fatalities per fatal accident may depend on many fac­tors other than the reliability of the driver.

Continue reading the full text of this misconception or go to the list of misconceptions

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…

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.