Enjoy the new simulator called City Car Driving , developed by Multisoft. The purpose of this software is to help users drive their cars better , feel it in the big city or in a country , encountering different weather conditions or just enjoy a free , relaxing ride.
The two recent fatal accidents with self-driving cars by Uber and Tesla have not led to the major backlash which many people had predicted. While this does not come as a surprise (the predictions ignored the long history of technical innovations, where accidents have rarely slowed or even halted the advance of a technology), nevertheless, the two harrowing accidents increase the concern of the public and of regulators about the safety of self-driving cars.
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Therefore this is the right time to perform a more careful analysis of the risk profile of this technology. As we will show in the following, the specific forms of risk, accident scenarios, and risk mitigation strategies for self-driving cars differ very significantly from other technologies that have been developed over the last centuries. To illustrate the differences, we will examine three key aspects of the risk profile of self-driving car technologies and contrast them with established technologies:
In the health sector, this dilemma is a well-known problem for the approval of medical treatments. And the US Food and Drug Administration (FDA) has worked hard to balance both sides of the distribution (both by speeding up the approval process and by enabling critically ill patients to get access to experimental treatments in certain cases). But self-driving cars differ from medical treatments in a very positive way: Whereas the expected positive effects of a treatment often do not materialize (uncertainty on the positive part of the distribution), there is much more certainty about the positive safety outcomes of self-driving cars (accident prevention) and we already have statistical data for the safety benefits of some driver assistance systems.
3) Efficiency of recall process for defective productsSelf-driving cars are almost unique in another, third dimension of risk: For most technologies it is difficult to prevent harm once a defective model is released to the public (and this has important implications for regulation). Once an Espresso machine, a drug or another product reaches the hands of thousands or millions of users it is very difficult to ensure that a defective product model will not lead repeatedly to harm somewhere. Recalls take time and rarely reach all owners. Again, the situation is very different for self-driving cars. They incorporate wireless communication and update mechanisms that allow the near-instant grounding of defective vehicles models. A worst-case scenario where a flaw is discovered after tens of thousands of vehicles have been released to public roads is not realistic: when accidents point to the flaw, the other cars on the road will quickly be grounded and thus further accidents will be prevented from happening. Of course this does not mean that standards for approving self-driving cars should be lax but rather that we should keep the likely risk scenarios in perspective, when we consider regulations for self-driving cars.
P.S. For more on self-driving car safety and how (not) to determine statistically whether self-driving cars are safe, see my earlier post on Misconceptions of Self-Driving cars: Misconception 7: To convince us that they are safe, self-driving cars must drive hundreds of millions of miles
Across the world billions of dollars are committed every year for new public transport and road infrastructure projects: commuter rail, subways, new roads, bypasses, tunnels, bridges, etc. Committees spend years planning these projects; it often takes more than a decade until a project is implemented. Once completed, we expect the projects to yield their benefits over many, many decades. Over the last century planning and estimation processes have been refined; they work reasonably well. Unfortunately, current processes can not and do not take self-driving vehicles into account. But it is now clear that self-driving cars will fundamentally change our traffic patterns. This greatly increases the risk that public transport projects will already be obsolete at the time they are completed. In the following we will show that the most adequate action for cities and states is a temporary moratorium on new public transport projects (i.e. by systematically delaying the start of the planning phase):
At the current point in time self-driving car technology is not yet ready for widespread adoption but there can no longer by any doubt about its viability. Many companies are racing for implementation. Millions of kilometers are now routinely test-driven in self-driving vehicles; GM and Jaguar have started producing self-driving car models; Waymo is now operating self-driving cars without test-drivers inside the car. Anyone who performs an extensive analysis about the size of the self-driving car problem, the economic incentives for participants in the self-driving car space and the state of the industry must come to the conclusion that we are very likely to have large numbers of self-driving cars, buses, trucks and machines in our cities within the next decade (see the postscript of this article for a brief outline of key elements of such an analysis).
Once self-driving cars operate in cities by the thousands, we will see fundamental changes: the number of privately owned cars will fall. The higher urban density, the quicker car ownership will recede and with it parking lots. Traditional public transport will be challenged by self-driving taxis and ridesharing services. Rail-based transport solutions will suffer from their inflexibility compared to buses. The biggest problems will occur on the feeder lines; not so much on the high-capacity, high-frequency core lines. Urban and highway traffic will flow better as self-driving cars become life traffic sensors and city-wide traffic routing algorithms are applied (no, this is not science fiction, this will be a core, immanent concern of any provider of self-driving mobility services and has the benefit of being a win-win situation (identical goals) with city traffic management). We will see the distribution of traffic change significantly as trucks begin to operate 24/7, self-driving fleet vehicles are applied for delivery at night and ridesharing services increase the average occupancy per vehicle on certain routes (more likely on long distance trips as well as long commutes, less relevant for inner cities). As a consequence our road-based mobility system will change fundamentally. Of course, this will not occur over night, but the changes will greatly affect any new road infrastructure project being planned today.
The conditions under which alternative 1 is rational are very narrow: This only makes sense for projects which are unlikely to be challenged significantly by self-driving cars. New rail-based projects certainly do not fall int his category. But bypasses, highway extensions (or new highways) and most other projects also critically depend on estimates of traffic distributions which we can no longer extrapolate from today. Therefore we must balance the disadvantages of delaying the start of such a project for a few years against the advantages of fewer expenses in the near- and medium term and possibly a better system in the long term. Because we are likely to have much better ways of managing traffic flow in 20 years it is unlikely that the congestion problems which we may fear as a result of delaying a project today will actually materialize. If we do business as usual, we may find in 20 years time that a significant share of the projects we are starting today are no longer necessary and billions of dollars have been wasted.
Thus at this point, the most rational approach for new public transport and road infrastructure projects is to put the initiative on hold! This is an action for which a consensus can be found much more easily among the various stakeholders than finding consensus to plan directly for an unknown future with self-driving cars. It also has the side-benefit of increasing the pressure on the planners to seriously consider the effects of self-driving cars. We will all be better off if we place a moratorium on new public transport and road infrastructure projects today!
1.2. Self-driving cars will reach a state where they are much safer than the average human driver1.2.1. Much better attention than human drivers1.2.2. Larger field of view than human drivers (exception: highways)1.2.3. Fast, continous learning and refinement of algorithms.1.2.4. Human drivers make many preventable accidents.1.2.5. Human is better at interpreting certain rare scenes1.2.6. Self-driving cars are better at detecting common situations early1.2.7. Vehicles have sufficient processing power and sensor mix for self-driving1.2.8. Economic usefulness of SDC technology does not require ability to operate everywhere (-> technology can start early)
1.3. Rapid evolution of technology1.3.1. Innovation process is spread across the world; involves many companies in hardware, sensors, software, mobility, etc.1.3.2. Enormous progress in AI algorithms1.3.3. Sensor mix is maturing; still rapid innovation in sensor technology and rapid fall of sensor and hardware prices1.3.4. Number of companies working on self-driving cars still increasing1.3.5. Production of first self-driving car models has already started (GM/Jaguar/not quite there yet: Tesla)
2.2. SDCs will lead to increased use of mobility as a service2.2.1. Car ownership must fall (a detailed analysis of cost/benefit/comfort associated with owning a car / calling a self-driving taxi)2.2.2. Self-driving taxis will slash costs for individual motorized mobility (but costs for privately owned SDCs will rise compared to current cars)2.2.3. Vehicle stock in developed nations will fall significantly2.2.4. Mobility as a service market exhibits network effects -> first mover advantage means winner gets all -> extreme race for being first2.2.5. Regulation of SDC fleets by cities or countries is very likely2.2.6. Public transport will face significant challenges from providers of self-driving mobility services2.2.7. Rail-based networks are at a disadvantage because of their low flexibility. Only high-capacity lines can remain profitable.2.2.8. SDCs will increase throughput in cities; increased congestion very unlikely (this is contrary to many intuitions) 2ff7e9595c
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