1. Autonomy lessons learned from the aerospace industry pertinent
to the automotive industry
M.G. Spencer *, N.B. Durston †
*Director Engineering, Osprey Consulting Services Limited, UK, mike.spencer@ospreycsl.co.uk
† Senior Consultant, Osprey Consulting Services Limited, UK, nick.durston@ospreycsl.co.uk
Keywords: Autonomy, Automotive, Aerospace, System,
Safety.
Abstract
This paper explores some of the potential challenges
experienced by the aerospace industry following the
introduction of autonomous systems. The automotive industry
is currently transitioning to position itself to introduce
autonomous cars to the global market. This paper therefore
makes suggestions where the automotive industry can
potentially benefit from understanding lessons learned from
the aerospace industry to understand what is required with
regards to producing the safety assurance evidence
commensurate to the level of risk, in order to ultimately reduce
the cost and development timescales associated with the
introduction of safety critical autonomous systems.
1 Introduction - a compelling argument for the
introduction of autonomous cars onto UK roads
The United Kingdom Department for Transport (DfT) [4]
argue that autonomous cars will provide significant economic,
environmental and social benefits. The DfT [4] states driving
will be made easier through developments in vehicle
automation technology. Productivity will be increased since
the average English driver applies 235 hours to this task
annually and it is claimed that this time can be applied in more
useful ways. It is also argued that mobility will be improved
for those unable or disinclined to drive. The DfT [4] states that
31%, 14% and 46% of women, men and seventeen to thirty
year olds do not hold a full driving licence, respectively. It is
argued that greater mobility will increase social inclusion and
enhance quality of life for many.
The DfT [4] claims that autonomous cars, through the use of
connected vehicle technologies will communicate with their
environment, such as roadside infrastructure and other road
vehicles, enabling optimal use of road space, reducing
congestion and providing consistent journey times. However,
it will be interesting to see whether an autonomous car chooses
the perceived optimum route or routes chosen by driver
specific behaviours, such as routes chosen at random and the
use of rat runs. Congestion may actually result from multiple
autonomous cars travelling an autonomously perceived
optimal route. It is also argued that such improvements will
minimise fuel consumption and emissions.
It is argued that the introduction of autonomous cars and
developments in vehicle automation technology will
substantially reduce collisions, deaths and injuries. The DfT
[4] states that over90% of collisions are attributable to human
error and that the constant monitoring of an autonomous car’s
environment will not result in errors such as,driver distraction,
inappropriate speed or disobeyed signals or road markings.
Yeomans [19] states that incorporating autonomous cars into
extant legal and regulatory frameworks will present a
substantialchallenge. Additionally, there is significant public
concern regarding the responsible use of, security and safety of
autonomous cars. In order for drivers and pedestrians to accept
the notion of vehicles on the road without a human in control,
they must have confidence that the autonomous systems in
operation are safe and secure. To achieve this the automotive
industry must prove that there is no inherent danger to the
public through a rigorous development process. The aerospace
industry has many years of experiencing the use of such a
process, which has produced results acceptable to the public,
by means of organisations such as the International Civil
Aviation Organisation (ICAO) and other standard setting
bodies, both internationally and nationally, which work
together to harmonise the differing requirements of all
stakeholders.
2 Autonomy -“one who gives oneself one’s own
law”
The feasibility of autonomous aviation was first demonstrated
in Paris during the 1914 Concours de la Securité en Aéroplane
by US aviator Lawrence Sperry and his French mechanic, Emil
Cachin. They flew a single-engine Curtiss C-2 biplane with a
hydroplane fuselage which was equipped with a gyroscopic
stabilizer apparatus,designed to improve stability and control.
On their first demonstration, Sperry engaged his stabiliser
device, held his arms high out of the cockpit and revealed
straight and steady flight with the pilot not in control. On their
second demonstration, similarly with Sperry’s hands not at the
controls of the Curtiss, Cachin climbed out on the starboard
wing and moved about 7 feet away from the fuselage. The
aircraft momentarily banked due to the change in centre of
gravity, however the gyroscope-equipped stabiliser
immediately corrected the attitudinal change. On their third
demonstration both the mechanic and pilot left their seats and
took to the Curtiss’ wings, again revealing straight and steady
flight. Not surprisingly Sperry was awarded first prize in the
competition. In the succeeding century, the introduction of
many advancements in autonomous technologies have been
witnessed following this rudimentary example of autopilot
technology.

2. The Autonomous Systems Technology Related Airborne
Evaluation and Assessment (ASTREA) II [2] project aims to
develop the Technology Readiness Levels (TRL) for a range
of systems to enable autonomous operation of Unmanned
Aerial Systems (UAS). The project is comprised of two main
sub projects:Separation Assurance and Controland Autonomy
and Decision Making. Common technological themes, such
as, sense and avoid; communications security and spectrum;
autonomy,decision making and contingency management; and
operations and human systems interaction exist with the design
and operation of UAS and autonomous cars. SAE
International’s J3016 [17] describes six levels of driving
automation ranging from no automation to full automation.
These levels indicate minimum rather than maximu m
capabilities. It is possible that a particular vehicle could
operate at different levels depending on the driving automation
features engaged. Within the narrative definitions of the SAE
International’s J3016 [17] driving autonomy levels, terms such
as dynamic driving task, driving mode and request to intervene
are used. The dynamic driving task includes the operational
(physical inputs of control) and tactical (response to driving
environment) aspects of the driving task but not the strategic
aspect (determining destination,waypoint and route). Driving
mode is a type of driving scenario with characteristic dynamic
driving task requirements, for example high speed motorway
cruising. The request to intervene is notification to a human
operator, by the autonomous driving system, prompting
initialisation or recommencement of the dynamic driving task.
These definitions can be equally applied to the aviation
domain. For example, the dynamic flying task includes the
operational (physical inputs of control) and tactical (response
to flying environment) aspects of the flying task but not the
strategic aspect (determining destination,waypoint and route).
Phase of flight is a type of flying scenario with characteristic
dynamic flying task requirements, for example initial climb.
The request to intervene is notification to a human operator, by
the autonomous flight system, prompting initialisation or
recommencement of the dynamic flying task.
With comparable dynamic tasks, scenarios, requests to
intervene and similar automated technologies being employed
in their respective products to reduce workloads for drivers and
pilots, it is proposed that the automotive and aerospace
industries collaborate far greater than that at present.
3 Collaboration between the automotive and
aerospace industries
Automated technology represents a significant area of interest
and investment in the global automotive and aerospace
industries. Both the automotive and aerospace industries
benefit from efficacious international collaboration,
demonstrated by ICAO and the World Forum for
Harmonization of Vehicle Regulations. However, a key
difference between the industries is that the automotive
industry produces higher volume units with a lower unit cost
than the aerospace industry.
Leahy [14] Airbus’ Chief Operating Officer stated Customers
in his global market forecast that the global passengeraircraft
fleet is comprised of over 17,300 passengeraircraft. The two
incumbent passenger aircraft original equipment
manufacturers (OEM) Airbus and Boeing delivered 629 and
723 orders in 2014, respectively. Leahy [14] estimates a
demand for 32,600 new passenger aircraft by 2034.
Conversely, the global car fleet is estimated to have exceeded
one billion vehicles in 2011. The Organisation for Economic
Co-operation and Development (OECD)’s International
Transport Forum (ITF) [12] claims over 20.7 million cars were
newly registered in its 34 member states in 2014 alone. The
OECD’s ITF [12] estimate that the global car fleet will exceed
2.5 billion by 2050.
The aerospace industry has typically been composed of less
OEMs than the automotive industry. Historically aerospace
manufacturers have originated from economically developed
countries and a large number of mergers and acquisitions have
taken place, resulting in only a few incumbents. Ascend [1]
expects Airbus and Boeing to remain the two largest
commercial aircraft OEMs, between them delivering an
estimated 86% by value of the world’s commercial jet aircraft
through to 2033. However, Bombardier, Comac, Embraer,
Irkut and Mitsubishi are all expected to capture increasingly
significant volumes of demand, accounting for $270 billion of
delivery value in the forecast period between them.
On the contrary, the automotive industry is largely composed
of national automobile OEMs, with many being subsidiary to
one of the international motor companies. The ten largest in
terms of units produced, being the Toyota Motor Company,
Volkswagen Group, Daimler, Ford Motor Company, BMW
Group, General Motors, Honda Motor Company, Hyundai
Motor Company, Nissan Motor Corporation and SAIC Motor
Corporation.
Methods of co-operation and collaboration, such as,
memoranda of understanding and working groups have
enabled aviation industry stakeholders,principally OEMs and
their suppliers to reach agreement on how safety critical
systems shallbe designed and implemented in accordance with
statutory and regulatory requirements. One such collaboration
success is a framework agreement between passenger aircraft
OEMs to design acoustic,visual and haptic pilot warnings in a
consistent manner. This has reduced the potential to mislead a
pilot with multiple type ratings, by presenting unfamiliar and
confusing warnings during periods of higher workload, where
greater situational awareness is typically required.
An example of where the automotive industry could have
collaborated better was the failure to agree a single global
charging solution for electric vehicles. Currently there are
three types ofcharging connectorin the UK market all derived
from different geographical regions and their respective
OEMs. These differing connectors combined with the
leisurely introduction of charging infrastructure most certainly
contributed to increasing range anxiety in the UK, the fear that
an electric vehicle has insufficient range to reach its
destination, thus stranding the vehicle's occupants. It is
therefore proposed that lessons in automotive collaboration can
be learnt from the aerospace industry.

3. 4 Case Study 1: – Boeing 787 Dreamliner
Autonomous and automated technologies are fully integrated
into modern passengeraircraft, one such being the Boeing 787
Dreamliner. The Boeing 787 Dreamliner fuselage is composed
of 50% composites and 20% aluminium. This is almost a
reversal of the manufacture of the 777 fuselage, which
composes 12% composites and 50% aluminium. Boeing [3]
has described the 787 Dreamliner as visionary in design with
advancements in flight deck, electric architecture, smooth
wing, advanced fly-by-wire, laminar flow nacelle, one-piece
barrel construction and advanced composite technologies. It
has been claimed that these advancements will yield
unparalleled fuel efficiency and range flexibility enabling
operators ofthe 787 to open profitable new routes and optimise
their fleet and network performance whilst enhancing
passengerexperience. These claims are currently supported by
the airline industry with net orders for the aircraft of 1060 for
the last decade, as detailed in Table 1, made by 59 customers.
Year Net
Order
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
52
197
99
269
59
24
29
45
38
183
65
Table 1: Boeing 787 Dreamliner Net Orders by Year
Gates [6] stated that the total cost for development and
manufacturing of the 787 exceeded $32 billion, in his article
published on 24th September 2011, the same week as the first
delivery to launch customer All Nippon Airways. Table 2
details the list prices for the 787 family. Boeing [3] states the
global fleet of Dreamliners currently stands at 282.
Variant Price ($ M)
787-8
787-9
787-10
218.3
257.1
297.5
Table 2: Boeing 787 Dreamliner Average List Price
1 This is a harmonised accident rate developed through the
Global Safety Information Exchange (GSIE) and facilitated
by the alignment of the International Civil Aviation
Organisation (ICAO) and IATA accident definitions, criteria
and analysis methods. This joint analysis includes accidents
meeting the ICAO Annex 13 criteria. A total of 122 accidents
were considered for 2014, including scheduled and non-
5 Case Study 2: – Mercedes-AMG S65 Coupé
Autonomous and automated technologies are emerging into
modern car design. The Daimler Mercedes-Benz S Class
product has for a long time been seen as a class leader with the
introduction of automotive technologies. The current
Mercedes-AMG S65 Coupé is priced in the UK at £181,305
and comes with adaptive cruise control, park assist,blind spot
assist, lane assist, electronic stability protection, automatic
emergency breaking, traffic sign recognition, night vision and
adaptive lighting as standard [15].
The Mercedes-AMGS65 Coupé would be described as level 2
– Partial Automation by the criteria detailed in SAE
International’s J3016 [17]. This is concurrent with the
emerging market claim made by Edwards [5]. However, the
current cost of such automotive technologies presents an
affordability challenge to the majority of the driving
population. Incremental decreases in cost are projected based
on the adoption of such technologies.
6 Commitment to safety
Both the aerospace and automotive industries are thoroughly
dedicated to improving the safety of the systems they produce.
The aviation industry’s commitment to improving safety has
yielded great success. 2014 was an extraordinarily challenging
year, with unique events experienced, such as the
disappearance of Malaysia Airlines MH370. However, the
International Air Transport Association (IATA) [9] state that
2014’s overall accident rate1 was of 1.92 accidents per million
flights. This rate was 14% lower than 2013’s overall accident
rate and 23% lower than the previous five year average of 2.24
and 2.48 accidents per million flights, respectively.
Globally there were 641 fatalities from commercial aviation
accidents in 2014. This total increased from 210 in 2013 and
517 of the five year average. There were 12 fatal accidents
versus 16 and 19 in 2013 and the five year average,
respectively. Conversely, Yeomans [19] states that 1.3 million
fatalities and 50 million injuries result globally and annually
from the 93% of road traffic accidents caused by human error.
By expanding on human error, particularly focusing on the DfT
[4] claim that over 90% of accidents in cars are caused by
human error. Almost all of these accidents will result in a
collision with another vehicle, object or person. Conversely,
Johnson and Holloway [13] claim the aerospace domain has a
lower human error rate of 80% to 90%. Human error within
aviation has been the focus of a vast amount of research.
Wiegmann and Shappell [20] also argue human error can be
subdivided into categories, such as, organisational and
regulation human error (39%) and pure human error (27%).
This lower human error rate results from the contribution of
scheduled commercial operations,including ferry flights, for
aircraft with a maximum certified take-off weight above 5700
Kg. The shooting down of Malaysia Airlines MH17 by a Buk
surface-to-air missile was not consideras an accident since it
was an unlawful act and classified as an aviation security
incident.

4. highly trained operators and maintainers and the variety of
systems which aid pilots.
One major difference between the aerospace and automotive
industries is how accidents are managed. Aircraft accidents are
widely reported in the media and generally there is a great deal
of social interest, largely propagated by the large number of
deaths and injuries associated with an accident. Conversely,
most road traffic deaths and injuries remain invisible to society
at large. Aircraft accident investigation is mandated through
Annex 13 to the Convention on International Civil Aviation
[10]. Typically, the investigation of a passenger aircraft
accident is a long and expensive process, but is considered
extremely worthwhile, since the findings of the investigation
may yield evidence of functional failures and resulting hazards
not identified prior to the accident. Important safety lessons
are generally learned in retrospect and through the application
of design and procedural changes,the risk of a similar aircraft
accident occurring is largely mitigated.
Whereas the investigation that follows a road traffic accident
is principally performed by the police on arrival at the accident
scene following management of the immediate aftermath .
Road traffic accident investigations are certainly not as
complex as aircraft accident investigations, simply because
they are not afforded the time or funds to meticulous
investigate details, such as component failures. Firstly police
will manage casualties, investigate and then relieve any
congestion that may result from an accident. Road traffic
accident investigations also look to attribute culpability,
whereas aircraft accident investigations are solely concerned
with identifying the cause of the accident and not apportioning
blame.
7 Software safety assurance
Increasing the complexity of automated systems inherently
increases the dependency upon electronic and software
systems. The autonomous technological themes common to
aircraft and automobiles present shared challenges. These
shared challenges include safety critical elements, such as
maintaining safe separation, addressing adaptive re-routing
and collision avoidance; the availability of secure frequencies
for command and control and data transfer; decision making in
routine and emergency situations; situational awareness and
prognostics and health management.
The prevalence of increased software use in airborne systems
and equipment design during the early 1980s ensued a
prerequisite for industry-accepted guidance for satisfying
airworthiness requirements. Consequently, this resulted in a
significant amount of collaboration, discussion and agreement
within the industry, which ultimately produced RTCA DO-
178. RTCA DO-178C [16], in its current issue, provides the
aviation industry with guidance for determining, in a consistent
manner and with an acceptable level of confidence that
software aspects of airborne systems and equipment comply
with airworthiness requirements.
The automotive industry has respondedin a similar manner and
agreed guidance in the ISO26262 [11] Automotive Safety
Integrity Level (ASIL) scheme. ASILs specify a system’s
necessary safety requirements for achieving an acceptable
residual risk. ISO26262 [11] provides guidance for producing
the safety assurance evidence required for satisfying
roadworthiness requirements, commensurate to the safety risk
posed by a functional failure resulting from, or in combination
with a software fault and otherindependent failures. Similarly
to RTCA DO-178C [16], the automotive industry-accepted
guidance is reviewed and revised accordingly, as software use,
complexity and experience of application of the guidance
increase. However, the automotive industry has relatively little
experience of developing autonomous vehicles using
ISO26262 [11].
This inexperience coupled with increased system complexity
due to the exponential increase of software and electronics that
enable automotive autonomous functionality, may lead to
unfavourable conditions, increasing cost and development
timescales. The industry will undeniably experience disparate
autonomous systemand software development tools and data,
resulting in a deficiency of global software development
artefacts, reducing the potential for re-use and reductions in
cost and development timescales.
Industry collaboration for intensive software development
programmes will undoubtedly be beneficial for the provision
of safety assurance. The major challenge faced by the
automotive industry is how to set software integrity levels for
systems which provide greaterauthority over a driver, that may
fail and result in a catastrophic accident without significantly
increasing the development cost. Commercial aircraft OEM
and their suppliers have managed development costs by
integrating similar systems across multiple platforms.
Airborne systems and software are developed in accordance
with stringent processes concerning requirements
management, design, coding, integration, verification ,
configuration management and quality assurance. Such
processes have facilitated the development of safety critical
software components which can be selected as commercial off
the shelf products. The provision of passenger aircraft fleet
commonality has been supported by this development process.
The car manufacturing giant Fiat Chrysler is underscrutiny for
its management of 23 recalls involving about 11 million
vehicles [18] and is likely to be liable to punitive damages and
other penalties. These recalls have been due to faults with
airbags, braking systems and fuel tank fires. As complexity
increases, the safety performance of car manufacturers will
undeniably attract further examination.
8 Resilience to environment
The introduction of autonomous cars will reduce the human
error rate if dependable and resilient systems are developed.
Aircraft operate in an environment where the airspace is
heavily controlled and regulated and are supported by systems
such as Traffic Collision and Avoidance Systems (TCAS),
Primary Surveillance Radars (PSR), Secondary Surveillance
Radars (SSR) and various transponder systems. However
vehicles operating on roads, generally do not have such
external control and support systems. Such vehicles are
heavily reliant on the ‘see and avoid’ principle undertaken by
the driver with no supporting external systems.

5. The aerospace industry designs and develops their products
within a strict regulatory framework that ensures
interoperability. For example, spectrumcontrol for radios and
data links. Autonomous cars will initially need to operate in a
mixed environment. Aerospace flight control systems are
developed around the flight envelope of the aircraft and the
rules of the air. Autonomous cars will need to understand the
rules of the road, for example, the Highway Code and the legal
framework, the Road Traffic Act 1988, understanding signage,
road conditions, weather conditions and interaction with
vehicles both driver operated and autonomous. It is argued that
whilst aircraft are significantly more complex than cars, they
operate in a far simpler environment. The complexity of an
autonomous car’s operating environment should not be
underestimated and therefore there are a number of key areas
and strategies that will need to be addressed. Haddon [7]
famously described road transport as “an ill designed ‘man-
machine’ system needing comprehensive systematic
treatment”. A strategy for the deployment of autonomous cars
needs to be developed in accordance with the levels of
autonomy detailed in SAE International’s J3016 [17].
Autonomous cars will need to be sufficiently sophisticated that
they can operate totally independently and require no reliance
or support from roadside infrastructure. Such autonomous cars
will require sophisticated decision making capability.
Alternatively, autonomous cars will require a complete support
network from roadside infrastructure deploying a vast array of
sensors and situational awareness systems.
The environment, in which cars operate, as has been stated,is
complex and the actions required by a driver need to be
replicated through the use of sensors, software and decision
making algorithms and or artificial intelligence. The
autonomous systems will need to be able to monitor the
environment including conducting dynamic risk assessments.
To be able to manage a hazards process such as being able to
distinguish between an object that could be a hazard to
something benign as a plastic bag floating across the road. Will
the systems be sufficiently sophisticated to differentiate
between these hazards? Depending on the sensors deployed,
the differences between day and night may be eliminated,
however other weather conditions such as rain, snow, ice and
fog will require careful consideration and not just from the
subject vehicle but other road users. We also need to
understand that the road infrastructure is dynamic with changes
to road layout, the building or improvement of roads,
temporary layouts and road works as well as the growth of trees
and hedges that can obscure signage or road markings.
Regional differences of road signage and travelling abroad are
otherfactors that the software and systems will need to address.
All these factors will, as a minimum, require the setting of
system and software assurance levels as well as rigorous
testing as undertaken within the aerospace sector with
associated costs. Audi claim [8] that they will have a fully
autonomous A8 model available in 2017 equipped with
cameras and Light Detection And Ranging (LIDAR) however
its full functionality is unlikely to be realised due to legal and
regulatory barriers. They claim its system drives better than
any human. Conflictingly, Volkswagen Group’s CEO Martin
Winterkorn remains sceptical about fully autonomous
technology, even though Audi is owned by Volkswagen
Group.
9 Systems Dependability
If we make the assumption that the enabling technology for
autonomous vehicles will be reliant on detection and
monitoring systems, supported by software and a degree of
artificial intelligence then there is a question about the updating
of software systems and the protection of that software from
cyber security threats. Integrity of software and in particular
updating operating systems or deploying patches is a risk area.
The cyber security threat is also significant either from denial
of service or taking control of the vehicle for criminal or
terrorism activities. One example might be where terrorists or
activists take control of a large number of vehicles in a major
city centre causing gridlock and economic impacts that may
result. Yeomans [19] states increasingly companies which
develop software, for example, Google have recently joined
forces with a major car manufacturer to develop an android
operating system. Such development centres have little
experience of developing safety related or safety critical
software and have a culture of 80% development with 20%
improvements after release, similar to such criticisms
associated with Microsoft products. However, the risk
associated with a failed software update to a word processing
application will have little consequence. Nevertheless a
software failure that causes a vehicle to fail to negotiate a bend
in the road is a far more critical failure.
10 Culture
Culture is an influencing factor for software development.
There are now extremes in the different cultures that exist in
the high end Aerospace and Nuclear industries compared to the
high volume, lower risk software houses that have recently
emerged due to the explosion of software dependent cars and
equipment such as phones, tablets and their associated
applications.
This has seen a paradigm shift away from traditional software
development which focused on scalability, efficiency, safety
and accuracy to an agile, yet more relaxed and vibrant
environment with flexible and softer attitudes to work. This
evolving culture is certainly not ideal for the development of
software that is either safety related or safety critical due to the
requirement of getting it right (and being accountable) at the
point of issue to the wider public.
Culture will inherently influence the success and introduction
of autonomous cars. The aerospace sector has been dealing
with cultural issues for a considerable amount of time and it is
claimed that as many as 1 in 4 people are apprehensive about
flying. This is directly related to the fear of lack of control.
Will this anxiety manifest itself following the introduction of
autonomous cars? At the introduction ofthe elevatorthe public
was hesitant to use elevators. For some time after, and even
remaining to this day,elevator attendants have been present as
a symbol of someone in control. Today the public do not think
twice before using an elevator, and the use of autonomy has
made the attendant operationally redundant. Cultural barriers

6. to the use of autonomous cars will be significant, as will some
of the technology and legal barriers, which still require
solutions. The early adopter sector of the market may need
forms of financial encouragement. Early adopters are likely to
be the young who are at the lower end ofthe disposable income
scale and have a greater tolerance of perceived risk.
11 Conclusions
A key difference between the automotive and aerospace
industries is that the automotive industry produces higher
volume units with a lower unit cost than the aerospace industry,
therefore demanding a lower cost and shorter development
timescales associated with comparable autonomous and
automated technologies. Given the significant differences
within the way the aerospace domain operates,its environment
and the way it is regulated raises the question, are the two
industries actually that comparable? Can lessons learned from
the development of autonomous and automated technologies
for aircraft be applied to the automotive industry? It is argued
that the driving environment contains far more uncontrolled
variables and components which will undoubtedly increase the
complexity of required autonomous systems, than the flying
environment. The sheer number of cars in operation globally
dwarfs the global fleet of passenger aircraft. It is suggested
that there is in fact only read across between the two industries
in the sharing of development of some technologies such as
sensors and software assurance including decision making.
If the automotive industry can reduce disparate autonomous
system and software development practices, and not be
deficient in the production of global software development
artefacts, increasing the potential for their re-use, then
reductions in cost and development timescales are more likely
to be realised. This ultimately will provide higher quality
software assurance evidence, which undoubtedly will increase
confidence that the autonomous systems in operation are safe
and secure.
Governments, Regulatory bodies and the automotive industry
need to develop a robust strategy and regulatory framework for
the introduction of autonomous cars.
Both industries are committed to increasing the safety of the
products they produce. However, differences in the way
accidents are investigated may propagate conditions in which
hazards and failure modes are not properly identified and risk
mitigation is omitted and or not correctly implemented.
Initially autonomous cars will need to operate in limited
controlled environments and will require full driver
intervention until such times that system maturity has been
achieved, deployment strategies have been agreed and cultural
barriers have been removed.
12 References
[1] Ascend. “Flightglobal Fleet Forecast 2014-2033 –
Independent outlook of the global commercial passenger
and freighter aircraft market”, Flightglobal Consultancy,
(2014).
[2] Autonomous Systems Technology Related Airborne
Evaluation & Assessment.“Current Projects”, ASTREA,
http://astraea.aero/current-projects-2, (2015)
[3] Boeing. “About Boeing Commercial Aeroplanes”
http://www.boeing.com/company/about-bca/ (2015).
[4] Department for Transport. “The Pathway to Driverless
Cars: A detailed review of regulations for automated
vehicle technologies”, pp. 15-17, ISBN 978-1-84864-
152-5, (2015).
[5] T Edwards. “Connected and automated vehicles:
Concepts of V2X communications and cooperative
driving”, Autonomous Passenger Vehicles Essential
Strategies in bringing driverless cars to the market, The
Institution of Engineering and Technology, (2015).
[6] D. Gates. “Boeing celebrates 787 delivery as program’s
costs top $32 billion”, The Seattle Times, (2011).
[7] W Haddon. “The Changing Approach to the
Epidemiology, Prevention and Amelioration of Trauma:
The Transition to Approaches Etiologically Rather than
Descriptively Based”, Am J Public Health, vol 58, pp.
143--1438 (1968).
[8] D Hars. “First fully autonomous Audiexpected by 2017”,
Driverless car market watch, (2014).
[9] International Air Transport Association. “Safety Report
2014”, pp.4-130, ISBN 978-92-9252-582-8, (2015).
[10] International Civil Aviation Organisation. “Annex 13 To
the Convention on International Civil Aviation Aircraft
Accident and Incident Investigation – 9th Edition, ICAO
International Standards And Recommended Practices,
(2001).
[11] International Standards Organisation. “ISO 26262 Road
vehicles — Functional safety - Part 9: Automotive Safety
Integrity Level (ASIL)-oriented and safety-oriented
analyses”, ISO, (2011).
[12] International Transport Forum. “Key Transport Statistics
2014”, Organisation for Economic Co-operation and
Development, (2015).
[13] C.W. Johnson & C.M. Holloway. “On the Over-
Emphasis of Human ‘Error’ As A Cause of Aviation
Accidents”, (2003).
[14] J Leahy. “Global Market Forecast 2015-2034”, Airbus,
(2015).
[15] Mercedes Benz. “The new S-class Coupé”,
http://www2.mercedes-
benz.co.uk/content/unitedkingdom/mpc/mpc_unitedking
dom_website/en/home_mpc/passengercars/home/new_c
ars/models/s-class/c217.flash.html, (2015).
[16] Radio Technical Commission for Aeronautics. “DO-
178C Software Consideration in Airborne Systems and
Equipment Certification”, RTCA, Inc. (2011).
[17] Society of Automotive Engineers (SAE) International
“Taxonomy and Definitions for Terms Related to On-
Road Motor Vehicle Automated Driving Systems”, pp.
15-17, (2015).
[18] T Spangler & N Bomey. “NHTSA head: Fiat Chrysler
faces punishment over recalls”, Detroit Free Press,
http://www.freep.com/story/news/local/%20michigan/20
15/07/02/nhtsa-chrysler-hearing/29586435/, (2015).

7. [19] G. Yeomans. “Autonomous Vehicles Handling Over
Control: Opportunities and Risks for Insurance”,Lloyd’s,
pp. 4-23, (2014).
[20] D.A. Wiegmann & S.A. Shappell. “A Human Error
Analysis of Commercial Aviation Accidents Using
Human Factors Analysis and Classification System
(HFACS)”, DOT/FAA/AM-01/3 (2001).