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Ethical and Social Aspects of Self-Driving Cars

Ethical and Social Aspects of Self-Driving Cars
Tobias Holstein
Mälardalen University
Västerås, Sweden
[email protected]
Gordana Dodig-Crnkovic, Patrizio Pelliccione
Chalmers University of Technology | University of
Gothenburg, Sweden
As an envisaged future of transportation, self-driving cars are being
discussed from various perspectives, including social, economical,
engineering, computer science, design, and ethics. On the one hand,
self-driving cars present new engineering problems that are being
gradually successfully solved. On the other hand, social and ethical
problems are typically being presented in the form of an idealized
unsolvable decision-making problem, the so-called trolley problem,
which is grossly misleading. We argue that an applied engineering
ethical approach for the development of new technology is what
is needed; the approach should be applied, meaning that it should
focus on the analysis of complex real-world engineering problems.
Software plays a crucial role for the control of self-driving cars;
therefore, software engineering solutions should seriously handle
ethical and social considerations. In this paper we take a closer
look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present
practical social and ethical challenges that have to be met, as well
as novel expectations for software engineering.
• Computer systems organization → Embedded systems; Redundancy; Robotics; • Networks → Network reliability;
Self-Driving Cars, Autonomous Cars, Trolley Problem, Decision
Making, Ethics, Social Aspects, Software Engineering, Challenges
Increasingly, prototypical self-driving vehicles are participating in
public traffic [48] and are planned to be sold starting in 2020 [56, 61].
Public awareness and media coverage contribute to a manifold of
discussions about self-driving vehicles. This is currently amplified
through recent accidents with autonomous vehicles [24, 58].
Software is playing a key role in modern vehicles and in selfdriving vehicles. Gigabytes of software run inside the Electronic
Control Units (ECUs), which are small computers embedded in
the vehicle. The number of ECUs has grown in the last 20 years
from 20 to more than 100. Software in cars is growing by a factor
of 10 every 5 to 7 years, and in some sense car manufacturers
are becoming software companies [47]. These novelties ask for a
change on how the software is engineered and produced and for a
disruptive renovation of the electrical and software architecture of
the car, as testified by the effort of Volvo Cars [47].
Moreover, self-driving vehicles will be connected with other vehicles, with the manufacturer cloud, e.g., for software upgrades,
with Intelligent Transport Systems (ITS), Smart Cities, and Internet of Things (IoT). Self-driving vehicles will combine data from
inside vehicle with external data coming from the environment
(other vehicles, the road, signs, and the cloud). In such a scenario,
different applications will be possible: smart traffic control, better
platooning coordination, and enhanced safety in general. However, the basic assumption is that future self-driving connected
cars must be socially sustainable. A typical discussion about ethical
aspects of self-driving cars starts with ethical thought experiment,
so called “trolley problem” described in [29] and [66], that has been
discussed in number of articles in IEEE [7, 9, 33], ACM [30, 40, 43],
Scientific American [16, 37, 41], Science [11, 36], other high-profile
journals [14, 32, 34], conference workshops [8, 50] and other sources
[2, 6, 44, 54]. Here is the general scenario being discussed:
A self-driving vehicle drives on a street with a high speed. In front
of the vehicle a group of people suddenly blocks the street. The vehicle
is too fast to stop before it reaches the group. If the vehicle does not react
immediately, the whole group will be killed. The car could however
evade the group by entering the pedestrian way and consequently
killing a previously not involved pedestrian. The following alternations
of the problem exist: (A) Replacing the pedestrian with a concrete wall,
which in consequence will kill the passenger of the self-driving car;
(B) Varying the personas of people in the group, the single pedestrian
or the passenger. The use of personas allows including an emotional
perspective [10], e.g., stating that the single pedestrian is a child, a
relative, a very old or a very sick human, or a brutal dictator, who
killed thousands of people.
Even though the scenarios are similar, the responses of humans,
when asked how they would decide, differ [11]. The problem is
that the question asked has limited number of possible answers,
which are all ethically questionable and perceived as bad or wrong.
Therefore, a typical approach to this problem is to analyze the
scenarios by following ethical theories, such as utilitarianism, other
forms of consequentialism or deontological ethics [42]. For example,
utilitarianism would aim to minimize casualties, even if it means to
kill the passenger, by following the principle: the moral action is the
one that maximizes utility (or in this case minimizes the damage).
Depending on the ethics framework, different arguments can be
used to justify the decision.
Applying ethical doctrines to analyze a given dilemma and possible answers can presently only be done by humans. How would
self-driving cars solve such dilemmas? There are a number of publications that suggest to implement moral principles into algorithms
of self-driving cars [17, 18, 33]. We find that this does not solve the
problem, but it reassures that the solution is calculated based on
a given set of rules or other mechanisms, moving the problem to
engineering, where it is implemented.
arXiv:1802.04103v1 [cs.CY] 5 Feb 2018
ARXIV’18, January 2018, Gothenburg, Sweden Tobias Holstein and Gordana Dodig-Crnkovic, Patrizio Pelliccione
It is worth to notice that the engineering problem is substantially
different from the hypothetical ethical dilemma. While an ethical
dilemma is an idealized constructed state that has no good solution,
an engineering problem is always by construction such that it can
differentiate between better and worse solutions. A decision making process that has to be implemented in a self-driving car can be
summarized as follows. It starts with an awareness of the environment: Detecting obstacles, such as a group of humans, animals or
buildings, and also the current context/situation of the car using
external systems (GPS, maps, street signs, etc.) or locally available
information (speed, direction, etc.). Various sensors have to be used
to collect all required information. Gaining detailed information
about obstacles would be a necessary step before a decision can be
made that maximizes utility and/or minimizes damage. A computer
program calculates solutions and chooses the solution with the optimal outcome. The self-driving car executes the calculated action
and the process repeats itself.
The process itself can be used to identify concrete ethical challenges within the decision making by considering the current state
of the art of technology and its development. In a concrete car
both the parts of this complex system and the way in which it is
created have a critical impact on the decision-making. This includes
for instance the quality of sensors, code, and testing. We also see
ethical challenges in design decisions, such as whether a certain
technology is used because of its lower price, even though the quality of information for the decision making would be substantially
increased if more expensive technology (such as sensors) would be
Since building and engineering of self-driving vehicle involve
various stakeholders, such as software/hardware engineers, sales
people, management, etc., we can also pose the following questions:
does the actual self-driving car have a moral on its own or is it
the moral of its creators? And who is to blame for the decision
making of a self-driving car? In [22] the argument is put forward
that the systemic view must be used in case of socio-technological
systems. Thus the problems in the system can originate or be a combination coming from inadequate solutions in various steps from
requirements specification to implementation, testing, deployment
maintenance, safety regulation and other normative support etc.
Besides the self-driving vehicle itself, it is also important to
address yet another complex system: self-driving vehicles participating in public traffic among cars with human drivers. Therefore,
it is important to investigate how self-driving vehicles are actually
built, how ethical challenges are addressed in their design, production, and use and how certain decisions are justified. Discussing this
before self-driving vehicles are officially introduced into the market,
allows taking part in the setting and definition of ethical ground
rules. McBride states that “Issues concerning safety, ethical decision
making and the setting of boundaries cannot be addressed without
transparency” [43]. We think that transparency is only one factor,
as it is necessary to start further investigations and discussions.
In order to give a more detailed perspective on the complex decision making process, we propose to create a conceptual ethical
model that connects the different components, systems and stakeholders. It shows inter-dependencies and allows pinpointing ethical
challenges that will be presented in the concluding recommendations.
Focusing on important ethical challenges that should currently
be addressed and solved is an important step before ethical aspects
of self-driving cars can actually be meaningfully discussed from
the point of view of societal and individual stakeholders as well as
designers and producers. It is important to focus not on abstract
thought experiments but on concrete conditions that influence
the behavior of self-driving cars and their safety as well as our
expectations from them.
The paper is structured as follows. A short introduction to selfdriving cars and their current state of the art is provided in Section 2,
with the emphasis on the description of the decision making principles given in Section 2.1 and the role of software in Section 2.2.
Ethical and social challenges are addressed in Section 3 regarding
technical aspects, and Section 4 addressing social aspects. Section 5
describes the current state of norms and standards, while conclusions and final remarks are presented together with recommendations in Section 6.
The term “autonomous” could be ambiguous to some readers. It
can be used to describe certain autonomous features or functions,
such as advanced driver assistance systems, that for example assist
the driver in keeping the lane or adjust to the speed of vehicles
ahead. Those systems are designed to assist, but the driver is always
responsible and has to intervene if critical situations occur.
We use the term “self-driving” cars to avoid wrong interpretations of the terms “fully autonomous” or “driverless”. Self-driving
cars refer to cars that may operate self-driving without human help
or even without a presence of human being. This means that the unoccupied car can drive from place A to B to pick up someone. This
is the highest level of autonomy for cars and corresponds to the last
level of five as defined by the Society of Automotive Engineers [51]
and United States National Highway Traffic Safety Administration
(NHTSA), who, since September 2016, adopted SAE’s classification
with level 1 (no automation), level 2 (drive assistance), level 3 (partial automation), level 4 (conditional automation), and level 5 (full
automation) [45, p.9].
A concrete example is the self-driving Waymo car [65], former
known as the Google car [35], a fully autonomous and self-driving
2.1 Decision Making Process in Self-Driving
Developing self-driving cars that act without a driver means to
replace a human, who today is performing the complex tasks of
driving, with a computer system executing the same tasks. Figure 1
shows both variants and allows a comparison.
There is an important difference in the feedback loop. While
humans continuously learn, for example from their mistakes or
misbehaviour, automotive software might be confined to slow updates. Approaches with self-adaptive software, such as machine
learning approaches, which learns and reacts immediately, aim
to overcome this constraint. Extraordinary road signs for example, which are new to the self-driving car’s software, present a
risk as they can pass unnoticed/uninterpreted, while they could
be understood by a human through context/interpretation. Also
Ethical and Social Aspects of Self-Driving Cars ARXIV’18, January 2018, Gothenburg, Sweden
Sense Think &
Decide Act
Sensor(s) &
other Inputs
Recognition &
Computation &
Decision Making
Learn from mistakes / misbehavior
Feedback to manufacturer
might change implementation, etc.
Figure 1: Comparison of human and computers sense, think
and act process (cf. [31]) which we extended by adding a feedback loop
unexpected and dangerous situations, like an attack or threat near
or even against the vehicle might not be correctly interpreted by a
self-driving car compared to a human.
Depending on the technology and the amount of sensors, the
type and quality of information that is gathered differ. However,
this extremely complex process might be difficult to imagine and in
order to give an idea of what self-driving cars “see” we refer to the
visualization depicted in Figure 2. It shows a rendered point cloud,
based on the data gathered by a laser radar (LIDAR) mounted on
the top of the vehicle.
Figure 2: Point cloud image of a vehicle approaching an intersection illustrates the complexity [25]
2.2 Complexity of Decision Making and the
Role of Software
The amount of sensors used to detect objects around the vehicle
and its surrounding environment differs among car manufacturers. Figure 3 shows an abstraction made to discuss the types of
information used and how they relate to each other.
Most of the functionality in the automotive domain is based on
software [12]. Software is written by software engineers and at
least for important components extensively tested to ensure their
Ultrasonic Sensor(s)
Orientation Sensor(s)
Laser Radar
Camera(s) Navigation Data
Vehicle to Vehicle
Vehicle to Infrastructure
Act & control the vehicle
Other External Services
Other External Devices
e.g., nearby phones
mobile networks
Figure 3: Abstract representation of decision making in autonomous vehicles composed from various sources (cf. [25,
59, 63, 64])
correct functioning. In self-driving cars software relies on different
disciplines, such as computer vision, machine learning, and parallel
computing, but also on various external services. It is a complex
process to calculate a decision, and it is also difficult to test those
against all possible real world scenarios [63].
One of the problems is that all calculations are based on an
abstraction of the real world. This abstraction is an approximate
representation of a real world situation and thus the decision making will create decisions for an imperfect world. This is a twofold
problem, because the more information is available the better the
decisions might be, but at the same time more interpretation and
filtering might have to be used to get the data that actually is useful
for the decision making.
Engineers have to decide what kind of data to use, how reliable or
trustworthy the data are and how to balance the different sources
of information in their algorithms. Also different sensors have
their specific limitations and to overcome those, a combination
of multiple sensors might be used. The overall problem is usually
referred to as sensor fusion. This problem is acerbated in the case of
connected vehicles since data will come not only from the sensors
of the car, but also from other vehicles, street infrastructure, etc.
In this case other factors should be taken into account since it is
not possible to have a perfect knowledge about the devices that are
used to sense information and about their status.
Imagine heavy weather conditions, the navigation reports a
street ahead, the radar is reporting a clear street, but the visual
camera reports an obstacle straight ahead. How will this “equation” be solved and what will be the result? The wrong decision
might lead to an accident, when important information of some
sensors is disregarded and other sensors do not detect the obstacle
or hazard in front of the vehicle [58]. Car manufacturers are constantly improving and testing the recognition capabilities of their
systems [59]. It is a multi-factor optimization task, which aims to
find an optimal solution under consideration of costs, quality, and
potential risk factors.
Some manufacturers are thinking to count miles covered without any accident, however this might be infeasible since a vehicle
should cover around 11 billion of miles to demonstrate with 95%
of confidence and 80% power that autonomous vehicle failure rate
is lower than the human driver failure rate [39]. Moreover, this
calculation holds if the software within the car does not change
ARXIV’18, January 2018, Gothenburg, Sweden Tobias Holstein and Gordana Dodig-Crnkovic, Patrizio Pelliccione
over time. Nowadays, manufacturer are increasingly interested in
continuous integration and deployment techniques that promise to
update the software even after the vehicle has been sold and is on
the street, like a common smart-phone. However, changing even a
single line of code might require to starting counting from 0 the
number of covered miles.
In the following, we will discuss ethical deliberations surrounding
the autonomous vehicle, including involved stakeholders, technologies, social environments, and costs vs. quality. The multifaceted
and complex nature of reality emphasizes again the importance to
look broader instead of focusing on single ethical dilemma like the
trolley problem.
3.1 Safety
Safety is the most fundamental requirement of autonomous cars.
The central question is then: how should a self-driving car be tested?
What guidelines should be fulfilled to ensure that it is safe to use?
There are several standards, such as the ISO 26262, that specify the
safety standard for road vehicles. For self-driving cars standards
are under development, based on experiences being made. Google
Car tests show one million kilometres without any accident, is
this a measurement to certify its software? As we discussed above,
this should not be a reasonable assurance for safety. Should a selfdriving car make a driver licence as suggested in [43]? How would
that work?
The source code of autonomous cars are typically commercial
and not publicly available. One possibility to assure code correctness
via independent control. Should there be an independent organization to check those? But could it actually be checked? Who else than
the developers at a car manufacturer or supplier will understand
such a complex system?
An alternative route seems to be preferred by legislators – instead
of control of the software which is in the domain of the producers,
legislation focus on behaviour that is being tested, based on the
“Proven in Use” Argument.
Testing of present-day cars should demonstrate the compliance
of their behaviour with legislative norms [20]. Disengagements,
accidents and reaction times based on data released in 2016 from
the California trials are discussed in [21].
In the case of the software of the car will evolve even when the
vehicle is already on the street, testing should account for this new
When it comes to hardware and hardware-software systems,
there have been discussions about the prices of laser radars compared to cameras or ultra-sonic sensors. Laser radars are very expensive, but deliver high quality data in diverse weather conditions.
Ultra-sonic sensors or cameras are less accurate and sensitive under
weather conditions like rain. Should a car manufacturer choose a
cheap over an expensive sensor, even if this raises the likelihood of
errors/faults/accidents? In advanced driving assistance systems, the
driver would take over, if a critical situation could not be handled
by the system. What happens in self-driving cars? Will the car
just stop and wait until the rain is over? Will passengers be able
and allowed to intervene? Under which conditions? Would it be required to have a driving licence for a self-driving car? Or would the
police have a possibility to intervene, and in what way, when a car
behaves inadequately or even dangerously? Also would the police
even have the possibility to stop a self-driving car that is behaving
correctly, with the sole purpose of checking the passengers?
The economic aspects might be seen as the highest priority.
Using cheap equipment might lead to wrong decision-making and
in a self-driving car, it would be impossible to interfere with the
decisions made. Assuming that wrong decision may lead to a loss of
human lives or property, having chosen a cheap component could
therefore be ethically unacceptable.
Learning from experience is the most important basis for improvement of safety in self-driving cars. This is for instance envisioned by the CEO of Tesla, Elon Musk, in the Tesla’s second 10
year master plan “part deux”, where the third element of the four
major elements is: develop a vehicle self-driving capability that is
10x safer than manual via massive fleet learning1
3.2 Security
For autonomous cars, security is of paramount importance, and
software security is a fundamental requirement. As an indication of
the development we mention that in August 2017 UK’s Department
for Transport, published the document “Key principles of vehicle
cyber security for connected and automated vehicles” [19]. It is
built on the following eight basic principles:
(1) Organizational security is owned, governed, and promoted
at board level;
(2) Security risks are assessed and managed appropriately and
proportionately, including those specific to the supply chain;
(3) Organizations need product aftercare and incident response
to ensure systems are secure over their lifetime;
(4) All organizations, including sub-contractors, suppliers, and
potential 3rd parties, work together to enhance the security
of the system;
(5) Systems are designed using a defence-in-depth approach;
(6) The security of the software is managed throughout its lifetime;
(7) The storage and transmission of data is secure and can be
(8) The system is designed to be resilient to attacks and respond
appropriately when its defences or sensors fail.
Similar documents are mentioned, such as Microsoft Security
Development Lifecycle (SDL), SAFE Code best practices, OWASP
Comprehensive, lightweight application security process (CLASP),
and HMG Security policy framework [19].
There have been a number of attacks at car systems and sensors (e.g., LIDAR and GPS) that were used to manipulate the cars
behaviour. Attacks might be inevitable, but should there be a minimum security threshold to allow a self-driving car to be used? This
leads to another question: How secure must the systems and the
connections be?
In aircrafts “black boxes” are used to determine what happened
after a crash. Should this be also a part of a self-driving car?
Ethical and Social Aspects of Self-Driving Cars ARXIV’18, January 2018, Gothenburg, Sweden
What about security issues and software updates? Should a selfdriving car be allowed to drive, when it does not have the latest
software version running? What about bugs in the new software?
Should the vehicle be connected or should the vehicle be completely disconnected? On one side, the most secure system is the
one that is disconnected from the network. On the side, it would
be unethical to do not deploy immediately new software or a new
version of the software on the car if there is evidence that the new
update will fix important problems that might endanger human
lives. In order to enable the massive fleet learning and to do the software update, connectivity is needed. Moreover, connected vehicles
might receive information from other systems that will enhance
the understanding of the reality thus opening new and promising
safety scenarios. Imagine, for instance, a pedestrian on a side of a
building, totally invisible to the instrumentations of the car, that is
approaching a cross and that will most probably have an impact
with the vehicle2
3.3 Privacy
The more information taken into consideration for the decision making, the more it might interfere with data and privacy protection.
For example, a sensor that detects obstacles, such as human beings
in front of the car is based on visual information. Even the use of a
single sensor could invade privacy, if the data is recorded/reported
and/or distributed without the consent of the involved people. The
general question is: How much data is the car supposed to collect
for the decision making? Who will access those data? When will
these data be destroyed?
What about using active signals by devices people carry around
to detect moving obstacles in front or near the car? What about
people who do not carry such devices? Would they more likely be
hit by the self-driving car, because they were not “present enough”
in the data?
And how much data is actually used for evaluation? Is it anonymous? Does it contain more data than “just” the position of a human? Can it be connected to other types of data like the phone
number, the bank account, the credit cards, personal details, or
health data?
Those and similar questions are met by legislation such as Regulation (EU) 2016/679 of the European Parliament and of the Council
(the General Data Protection Regulation) setting a legal framework
to protect personal data [28], and discussed in [62].
3.4 Trust
Trust is an issue that appears in various forms in autonomous cars
e.g. in production (when assembled, trust is the requirement for
both hardware and software components) as well as in use of the
car. A human might define where the car has to go, but the selfdriving car will make the decisions how to get there, following the
given rules and laws. However the self-driving car might already
distribute data like the target location to a number of external
services, such as traffic information or navigation data, which are
used in the calculation of the route. But how trustworthy are those
data sources (e.g., GPS, map data, external devices, other vehicles)?
In regard of the used sensors and hardware, the question is, how
trustworthy are those? How can trust be implemented, when so
many different systems are involved?
3.5 Transparency
The transparency is of central importance for many of the previously introduced challenges. Without transparency none of them
could be analyzed, because the important information would be
missing. “Transparency is a prerequisite for ethical engagement in
the development of autonomous cars. There can be nothing hidden,
no cover-ups, no withholding of information” [43]. It is a multidisciplinary challenge to ensure transparency, while respecting e.g.,
copyright, corporate secrets, security concerns and many other
related topics.
How much should be disclosed, and disclosed to whom? The car
development ecosystem includes many other companies acting as
suppliers that produce both software and hardware components.
Should the entire ecosystem be transparent? Also to whom should
it be transparent? How to manage the intellectual property rights?
Some initial formulations are already present in the current policy
documents and initial legislative that will be discussed later on.
Declaration of Amsterdam [4] lists among the objectives “to
adopt a ‘learning by experience’ approach, including, where possible, cross-border cooperation, sharing and expanding knowledge
on connected and automated driving and to develop practical guidelines to ensure interoperability of systems and services”.
Goodman and Flaxman in [34] present EU regulations on algorithmic decision-making and a “right to explanation” that is the
right for user to ask for an explanation of an algorithmic (machine)
decision that was made about them. The Department of Motor
Vehicles provides the law requirements [20] “Under the testing
regulations, manufacturers are required to provide DMV with a
Report of Traffic Accident Involving an Autonomous Vehicle (form
OL 316) within 10 business days of the incident”. The list of all
incidents can be found in [5].
3.6 Reliability
One of the basic questions is: How reliable is the cell network?
What if there is no mobile network available? What if sensor(s) fail?
Should there be redundancy for everything? Is there a threshold
that determines when the car is reliable, e.g., when two out of four
sensors fail?
In connected vehicles there are different levels that should be
considered for reliability purposes. First the diagnostic of the vehicle
that might be subject to failures. Then, the vehicle sensors that
enable the vehicle to sense the surrounding environment of the
vehicle. Finally, the data coming from external entities, like other
vehicles and road infrastructures. Reliability approaches should
consider all these levels.
3.7 Responsibility and Accountability
In the case of autonomous cars responsibility will obviously be
redefined. The question is how will responsibility be defined in case
of incidents and accidents. Regarding ethical aspects of responsibility, a lot can be learned from the existing Roboethics and the debate
about responsibility in autonomous robots, e.g., [23]. This is still
ARXIV’18, January 2018, Gothenburg, Sweden Tobias Holstein and Gordana Dodig-Crnkovic, Patrizio Pelliccione
an open problem even though important steps forward are being
made by legislators, such as mentioned “Key principles of vehicle
cyber security for connected and automated vehicles” [19].
3.8 Quality Assurance Process
Detailed Quality assurance programs covering all relevant steps
must be developed in order to ensure high quality components. The
question is also how is the decision making to be implemented. How
to ensure overall quality of the product? What about the lifetime
of components? How will maintenance be organized and quality
assured? When car manufacturers follow a non-transparent process
of software engineering, how could anyone make sure that the car
follows a certain ethical guideline? Whose responsibility will it be
that car software follows ethical principles?
One part of the Quality Assurance (QA) process regards assembling of components. All parts of a vehicle are designed, fabricated
and then assembled to the overall car. A standard non-autonomous
premium vehicle today has more than 100 electronic control units
that are responsible for the control of e.g., the engine, the wipers,
the navigation system or the dashboard [47]. We assume that for
self-driving the number will be increased. Parts are usually built
by not one, but a multitude of suppliers. This requires an extensive
design and development process, which again involves various disciplines, such as requirements engineering, software engineering
or project management. It is an overall extensive process which
holds ethical questions and challenges. Thus, it is necessary to
include ethical deliberations in the overall process but also in all
sub processes. As it is stated in [52]: “value-based ethical aspects,
which today are implicit, should be made visible in the course of
design and development of technical systems, and thus a subject of
Including ethics-aware decision making in all processes will
help to make ethically justified decisions. This is important when it
comes to questions: Which parts/components are used for a vehicle?
Can we choose a cheaper component with less accuracy instead?
Is the reliability of this part high enough for a self-driving car?
Self-driving cars will influence job markets, as for example for taxi
drivers, chauffeurs or truck drivers. The perception of cars will
change and cars might be seen as a service that is used for transportation. The idea of having a vehicle that is specialized for the
specific use, e.g. off road, city road, long travels might become attractive. This might impact the business model of car manufacturer
and their market.
This in itself poses ethical problems: what strategy should be
applied for people loosing jobs because of the transition to selfdriving cars? It is expected that the accident frequency will decrease
rapidly, so car insurances may become less important. This may
affect insurance companies in terms of jobs and the business. There
is a historical parallel with process of industrialization and automatization, and there are experiences that may help anticipate and
better plan for the process of transition.
4.1 Stakeholders – General Public Interests
Humans concerns must be taken into account in the decision making of self-driving cars. Should there be an emergency button to
allow the human to interfere with the decision making of the selfdriving car? Putting the human back in the loop of decision making
also inflicts with the autonomy of the system. Is it then truly selfdriving? Giving passengers a choice to interfere with the decisions
of the self-driving car puts the passenger back in charge, who would
be responsible to press or not press the button in all situations. In
the context of the self-driving car the computer decision might be
better, but it might also be worse than human, because of possible
errors [26].
Another perspective on the human interest is the granularity of
the settings or configurations given to the user. How for example
will a route be planned?
In an extreme scenario self-driving cars might even avoid or
reject to drive to a certain region or position. Would that be an interference with the freedom of choice, will passengers be informed
about the reasons for such decisions? It is important to determine
how much control the human should have, that will be taken into
account when making design choices for a self-driving car.
4.2 Possible New Selling Points
The automotive industry has a highly competitive market. What
will be the difference between buying a self-driving car of brand A
compared to brand B?
Taking away the primary and secondary tasks of driving, i.e.
the driving controls, safety features, assistance etc., leaves only
entertainment and comfort functions in control of the passengers,
the former drivers. The interior becomes more important and factors that cannot be controlled less in the focus of the user. What
will be the main buying criteria? Will it be the interior/exterior,
speed (as often with traditional cars) or other new services? Will
it be possible for the users of the car to choose the priorities in its
decision-making? The latter is difficult, since decision choices [57]
supporting the survival of passengers over other traffic participants
by car manufacturers would have legal implications in most countries [15]. The question is also who will own the cars. Will they
become a service for individual users, and owned by companies?
Buying criteria will be different depending on the ownership.
Surveys based on hypothetical trolley problem scenarios show
that people feel less attracted to buy a car that would sacrifice the
passengers in order to save more human lives [11]. Would that
decision be left to car manufacturers? Existing policy documents
do not seem to leave possibilities open for anti-social cars to be
developed [3, 13, 27, 46, 49].
Table 1 and 2 present the summary of ethical and social challenges with recommendations (action points) grouped by requirement to be taken into account in policy-making as well as software
design and development for self-driving cars.
Present-day regulatory instruments for transportation systems are
based on the assumption of human-driven vehicles. As the development and introduction of increasingly automated and connected
Ethical and Social Aspects of Self-Driving Cars ARXIV’18, January 2018, Gothenburg, Sweden
cars proceed, from level 1 towards level 5 of automation, legislation
needs constant updates [3, 27, 46, 49]. It has been recognized that
present state regulatory instruments for human-controlled vehicles
will not be adequate for self-driving cars: “existing NHTSA authority is likely insufficient to meet the needs of the time and reap
the full safety benefits of automation technology. Through these
processes, NHTSA will determine whether its authorities need
to be updated to recognize the challenges autonomous vehicles
pose” [46].
On 14 April 2016 EU member states endorsed the Declaration of
Amsterdam [4] that addresses legislation frameworks, use of data,
liability, exchange of knowledge and cross-border testing for the
emerging technology. It prepares a European framework for the
implementation of interoperable connected and automated vehicles
by 2019 [27]. It also considers roles of stakeholders:
Agreement by all stakeholders on the desired deployment of the new technologies will provide developers
with the certainty they need for investments. For an
effective communication between the technological and
political spheres, categorization and terminology are
being developed which define different levels of vehicle
automation. [49]
The question is thus how to ensure that self-driving cars will be
built upon ethical guidelines, which will be adopted by society. The
strategy is to rely on rigorously monitoring the behaviour of cars,
while the details of implementation are within the responsibility of
producers. That means among others that design and implementation of software should follow ethical guidelines. An example of
ethical guidelines trying to think one step further is described in
Sarah Spiekermann’s book Ethical IT innovation [55].
The approach based on “learning by experience” and “Proven in
use” argument [1, 4, 53] presupposes a functioning socio-technological
assurance system that has strong coupling among legislation, guidelines, standards and use, and promptly adapts to lessons learned.
Ethical analysis in [22, 38, 60] addresses this problem of establishing and maintaining a functioning learning socio-technological
system, while [38] discusses why functional safety standards are
not enough.
Self-driving vehicles have been recognized as the future of transportation systems and will be successively introduced into the transport systems globally [3, 46, 49]. It is now the right time to start an
investigation into the manifold of ethical challenges surrounding
self-driving and connected vehicles [27]. As this new technology is
being tested and gradually allowed on the roads under controlled
conditions, the focus should be on the practical technological solutions and their social consequences, rather than on idealized
unsolvable problems such as much discussed trolley problem. Conclusions reached from idealized problem discussions would be that
it has no general solution under all circumstances. We can compare
this situation with the development and introduction of first cars.
If the developers of traditional driver-controlled cars asked about
general responsibility of a human driver for traffic accidents before
allowing them to enter traffic, they would never be accepted, as
safety in general and under all circumstances cannot be guaranteed
and indeed human factor is the major safety concern. This does not
mean that we should not take care of the basic requirements like
security, safety, privacy, trust etc. and social challenges in general
including legislation and stakeholders interests. On the contrary,
those real-world techno-social problems must be taken seriously.
Focusing on unsolvable idealized ethical dilemmas such as the
trolley problem obfuscates true ethical challenges, starting with
characteristics of the whole techno-social system supporting new
technology, with the emphasis on maximizing learning, on machine-
, individual-, and social-level [13, 22]. The decision-making process
and its implementation, which is central for the behaviour of a car,
might internally use unreliable or insecure technology. Emerging
technology of self-driving cars should follow ethical guidelines
that stakeholders agree upon and should not be an autonomous
black box with unknown performance. This poses new expectations,
which affect software engineering that is involved in all its stages –
from its regulatory infrastructure, to the requirements engineering,
development, implementation, testing and verification [2, 7, 13, 36,
44]. As software is integral part of a complex software-hardwarehuman-society system, we presented different types of issues that
we anticipate will affect software engineering in the near future.
It is also the right time to discuss the border between what is
technically possible in relation to what is ethically justifiable. Even
if this might limit the possibilities, it will set the necessary ground
for further developments. The discussion should cover different
dimensions, namely business, technical, process, and organization.
First of all, there is the need to open a serious trade off analysis
between business needs and ethics. As discussed above we should
certainly avoid to compromise safety because of business priorities, e.g. equipping the car with cheaper but unreliable sensors. For
what concerns technical aspects, it is of key importance to include
ethical thinking and reasoning into the design and development
process of autonomous and self-driving vehicles. Ethical aspects
should be considered in every phase of a software development
process, from requirements, till testing, maintenance, and evolution. Architectural and design decisions should be taken through
a process that includes ethics as first-class actor and by involving
stakeholders that are relevant to this concern. These architectural
and design decisions should then be embedded into the code that
will run the self-driving vehicles and ensure its ethical aspects are
taken care of. It is also necessary to enforce the transparency on
those processes, so that independent evaluations become possible.
Proper development processes, supported by suitable organization
structure should promote and enable a serious discussion of ethics,
and should emphasize the human interests, to make sure that the
freedom of choice does not disappear in the new era of fully autonomous and self-driving vehicles.
ARXIV’18, January 2018, Gothenburg, Sweden Tobias Holstein and Gordana Dodig-Crnkovic, Patrizio Pelliccione
Table 1: Summary of the technical challenges and recommendations grouped by requirement
Requirements Challenges Recommendations
Safety • Trade-off between safety and other aspects like economic
• Boundaries of autonomy of self-driving cars and human
(passenger) interactions
• Police control and possibility of intervention with
self-driving cars
• Systemic solutions to guarantee safety in organizations
(regulations, authorities, safety culture)
• Assure means to guarantee that safety is not sacrificed because
of other aspects (this is not so different from today. This can
happen also for the braking system of non-autonomous cars)
• It should be specified how a self-driving car will behave in cases
that the car is not able to deal autonomously. In the future
passengers will be able to drive a car
• There is the need of clarifying the relationship of police and
self-driving cars
• New techniques and standards are necessary to guarantee safety
in self-driving cars that will continuously update their software
Security • Identification and declaration of minimal necessary security
requirements, that work as a threshold for deployment of
self-driving cars
• Security in systems and connections
• Deployment of software updates
• Storing and using received and generated data in a secure
• Provide technical solutions that will guarantee minimum
security under all foreseeable circumstances
• Anticipate and prevent worst case scenarios regarding security
• Continuous learning process must be in place to provide active
• Assure accessibility of the data even in the case of accidents so
that it could be analyzed and lessons learned.
Privacy • Trade-off between privacy and data collection/recording
• Use of technology that detects humans near/around the car,
even if those humans do not carry any kind of electronics
• Following/applying legal frameworks to protect personal data,
such as Regulation (EU) 2016/679 of the European Parliament [28]
(discussed in [62])
• Justify the use of collected data through a transparent decision
making process
Trust • How trust between both software and hardware components
of complex systems can be implemented is not clear
• Further research on how to implement trust across multiple
• Provide trusted connections between components as well as
external services
Transparency • Information disclosure, what and to whom
• Transparency of the ecosystem
• Management of intellectual property rights
• Ensure transparency and provide insight into decision making
• Actively share knowledge, gained by “learning through
experience”, to ensure the interoperability of systems and services.
• Transparency is a prerequisite for the herein introduced
challenges, since it is the key to potentially undisclosed
background information
Reliability • Reliability of required networks and solution for the case
when the network is unavailable
• Reliability of sensors, and need for redundancy
• Way to determine when a car is not reliable anymore
• Define different levels for reliability (diagnostics, vehicle input
sensors, external services)
• Determine reliability for components and the overall car
• Responsibility and accountability in case of incidents and
• Responsibility that car software follows ethical principles
• Consider research and learn from robotics, i.e. Roboethics [23]
• Support development of solutions, e.g. by contributing to
existing approaches [19]
Assurance (QA)
• Quality of components
• Quality of decision making
• Lifetime and maintenance
• Trade-offs between non-transparent processes and external
QA control of adherence of ethical principles/guidelines
• Ethical deliberations must be included in the process of design
and development of self-driving cars
• Ethics-aware decision making must be part of the process to
ensure ethically justified decisions
Ethical and Social Aspects of Self-Driving Cars ARXIV’18, January 2018, Gothenburg, Sweden
Table 2: Summary of social challenges and recommendations grouped by requirement
Requirements Challenges Recommendations
challenges of
• Handling job losses (e.g., taxi/truck drivers, traditional
mechanics, insurance agents, etc.)
• Change of related markets and business models (e.g., car
insurances, car manufacturers, etc.)
• Prepare strategic solutions for people loosing jobs
• Take advice/learn from historic parallels to industrialization
and automatization
Stakeholders –
general public
• Human concerns in the decision making of self-driving cars,
e.g. possibility of interference with the decision making
• Freedom of choice hindered by the system (e.g. it may not
allow to drive into a certain area)
• Values and priorities: Ensure that general public values will be
embodied in the technology, with interests of minorities taken
into account
• Determine and communicate the amount of control a human
has in context of the self-driving car
• The freedom of choice determined by regulations
• Active involvement of stakeholders in the process of design
and requirements specification
Selling points • Self-driving cars will have different buying criteria, depending
on who will own the cars, big companies, social institutions such
as municipalities or individual users, as they all have different
preferences. Among those preferences environmental and
sustainability criteria can be expected to play central role
• Existing policy documents don’t seem to leave possibilities
open for anti-social self-driving cars to be
developed [13, 27, 46, 49].
• Priorities and choices for the self-driving cars will result from
the dialog between producers and future users
• Ensure that existing and future policies and standards prevent
the possibility of developing “anti-social” self-driving cars.
norms, policies
and standards
• Keeping legislation up-to-date with current level of automated
driving, and emergence of self-driving cars
• Creating and defining global legislation frameworks for the
implementation of interoperable and development of
increasingly automated vehicles
• Defining the guidelines that will be adopted by society for
building self-driving cars.
• Including ethical guidelines in design and development
• Car producers supporting and collaborating with legislators in
their task to keep up-to-date with the current level of automated
• Legislative support and contribution to global frameworks to
ensure a smooth enrollment of the emerging technology
• Include ethics in the overall process of design, development
and implementation of self-driving cars. Ensure Ethics training
for involved engineers [52, 55]
• Establish and maintain a functioning socio-technological
system in addition to functional safety standards.
ARXIV’18, January 2018, Gothenburg, Sweden Tobias Holstein and Gordana Dodig-Crnkovic, Patrizio Pelliccione
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