POV Autonomous-Driving Deloitte

Autonomous Driving
Moonshot Project with Quantum Leap

from Hardware to Software & AI Focus
02
Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
Summary 04
Autonomous Driving: Hype or Reality? 06
Deep dive: Artificial Intelligence 18
Impact on Today’s Automotive Industry 28
1. Product Structure 35
2. Work Structure follows Product Structure 38
3. New Steering Models 47
4. Mastering New Technologies 50
The Way Forward 54
Authors 56
Endnotes 57
03
Summary
Future autonomous (electric) vehicles are primarily
software-driven products compared to traditional cars.
The upcoming transformation in the automotive indus-
try from a “made of steel” business towards “software
is eating the world” will be no doubt a game changer –
for better or worse. Now that new players from the
tech sector have entered the stage in the automotive
industry, traditional manufacturers and suppliers try
hard to continuously shorten development cycles and to
catch up with the inevitable move into the new software
era. Collaborative agile working models predominantly
known from the software industry and more innovative
cooperation management approaches are paving the
way for tackling these challenges and turn them into
opportunities.
04
05
Autonomous Driving:
Hype or Reality?
In recent years, autonomous driving and so-called robo-
taxis have become one of the hottest topics in the auto-
motive industry - and beyond! Traditional car manufactur-
ers and established suppliers are not the only ones who
are trying hard to find the sweet spots in this new emerg-
ing mobility value chain.
Tech giants like Nvidia and Intel, leading increase in e-mobility, which the world is still
software and internet players like Google waiting for, and lately blockchain and Bitcoin,
(Waymo) and new mobility startups such which receive significant media attention.
as Aurora, Cruise and Uber are also on But where among all these trends and hypes
the verge of reaping the rewards of an can we place autonomous driving? The
entirely new future mobility era. Unlike the following paragraphs show some forecasts
stakeholders of today's automotive industry, to frame the general market potential for
they do not have vested stakes to protect. autonomous driving solutions and provide
However, on the other hand we are all aware a framework to align on common terms and
of several technological hype cycles, ranging wording when it comes to automated and
from the internet bubble at the turn of 'real' autonomous driving.
the millennium, the proclaimed significant
06
07
Voices on Autonomous Driving enthusiasm. However, there has also been to massively change the way we live and the
Autonomous driving is receiving significant some bad press, mostly because of fatalities significance it has owing to the fact that we
media attention, not least because traditional due to technological errors (see Figure 1). as humans give away control and thus put
car manufacturers and tech giants are invest- Some argue that these are individual our lives in the hands of an algorithm. It will
ing heavily in new technologies and prom- cases and should not distort the fact that need time and positive reinforcement for
ising start-ups or forging new partnerships, statistically speaking, autonomous driving the general public to ultimately accept and
but also because of significant technological is already safer than normal driving. At the trust this new technology –
advances. Overall, public perception is same time, autonomous driving is under the and its inventors.
positive and surrounded by an optimistic scrutiny of the public eye due to its potential
Figure 1 – Voices on autonomous driving
Where are we today?
»Exclusive: BMW to introduce »Automated vehicles may bring a new »Volvo and Baidu join forces to
‘safe’ fully autonomous driving breed of distracted drivers«2 mass produce self-driving electric
by 2021 with iNext«1 ABC News, 24 Sep. 2018 cars in China«3
Digital Trends, 28 Sep. 2018 CNBC, 1 Nov. 2018
»Have autonomous vehicles »Why People Keep Rear-Ending

»Tencent builds autonomous driving
hit a roadblock?«5 Self-Driving Cars«6
team in Silicon Valley«4
Wired, 18 Oct. 2018
Financial Times, 7 Nov. 2018 Forbes, 1 Nov. 2018
»Amazon Could Become the Next Big

»Self-Driving Cars Can Handle
Player in Autonomous Driving«7
Neither Rain nor Sleet nor Snow«8
The Street, 24 Sep. 2018
Bloomberg, 17 Sep. 2018
»Uber asks for permission to restart self-

driving car tests in Pittsburgh eight months »Waymo Shifts To 'Industrializing' Self-Driving
after its test vehicle killed an Arizona pedestrian«9 Tech as Robotaxi Launch Nears«10
Daily Mail, 2 Nov. 2018 Forbes, 6 Sep. 2018
The automotive industry is rapidly moving forward and undergoing massive change – automotive companies,
tech giants, start-ups and others are working hard on solutions
Positive developments Controversial developments
08
The Road Towards 'Real' Future state 3 embraces the driverless

Autonomous Driving revolution. Autonomous driving technology
Based on Deloitte's Future of Mobility study, proves to be viable, safe, convenient and
we envision four different personal mobility economical, yet private ownership continues
futures emerging from the intersection of to prevail.
two critical trends: Vehicle control (driver vs.
autonomous) and vehicle ownership (private Lastly, future state 4 envisions a new age of
vs. shared), as depicted in Figure 2. accessible autonomy. A convergence of both
autonomous and vehicle-sharing trends
Our analysis concludes that change will will likely lead to new offerings of passenger
happen unevenly around the world, with dif- experiences at differentiated price points.
ferent types of customers requiring different The earliest adopters seem likely to be ur-
modes of transportation. So all four future ban commuters, using fleets of autonomous
states of mobility may well exist simultane- shared vehicles combined with smart infra-
ously. Future state 1 describes the status structures to reduce travel time and costs.
quo in many markets, where traditional
personal car ownership and driver-driven
vehicles are the prevailing norm. While
incorporating driver-assist technologies,
this vision assumes that fully autonomous
driving will not become widely available
anytime soon.
Future state 2 anticipates continued

growth of car and ride sharing. In this state,
economic scale and increased competition
drive the expansion of shared vehicle servic-
es into new geographic territories and more
specialized customer segments. The costs
per mile decrease and certain customer
segments view car and ridesharing as more
economical and sustainable for getting
around, particularly for short point-to-point
movements.
09
Figure 2 – Autonomous driving is the main driver of future mobility
Future states of mobility

Autonomous
3 4
Personally Shared
Owned Autonomous Autonomous
Vehicle control
~$0.46/mileI ~$0.31/mileI
Low Asset efficiency High
1 2
Personally Shared
Owned Driver-Driven Driver-Driven
~$0.97/mileI ~$0.63/mileI
Driver
Vehicle ownership
Personal Shared
I
~$X.XX cost estimate per mile based on US market example ADAS: Advanced Driver Assistance Systems
II
Source: Deloitte FoM study 2018; Statista; IHS Automotive
10
Currently, broad acceptance of autonomous

vehicles seems much further away than
a wide adoption of car and ridesharing.
ADASII system
Sources of potential delay include the need
Bn. $ to address existing technological limitations,
30 such as proper functioning of sensors in
26 all weather conditions and comprehensive
availability of high definition maps, as well
20 18 as concerns over cyber security and liability.
13 On the other hand, ridesharing services in
10 8 particular have a strong economic incentive
to accelerate the adoption of autonomous
vehicles, since it could significantly reduce
0
2016 2019 2022 2025 one of the biggest operational cost in their
system: the driver! This is one of the main
reasons why tech players like Google do not
Autonomous Vehicle Sales rely on step-by-step driver-assist progres-
sion as most industry forecasts predict
Annual # of Sales in Mn. 33
(Figure 2: e.g. tripling of advanced driver
30 assist system (ADAS) revenues between
2016 and 2025), but instead try to jump
20 immediately to fully autonomous driving.
Rather than following the historical pattern
of technological innovation, autonomous
10 driving could constitute a step-change in de-
1 velopment. However, the majority of indus-
0 try experts expect the inflection point for
2021 2025 2040 widespread adoption of 'real' autonomous
vehicles not before 2030. The following
paragraph explains what we mean by 'real'
autonomous driving.
11
Classification of Autonomous Figure 3 – Vehicle automation levels
Driving Levels
In terms of enabling technologies, automat-
Partial Conditional
ed driving is an evolution from the advanced No Automation Driver Assistance Automation Automation
driver assistance systems (ADAS) for active
safety, which have been developed over Level 0 Level 1 Level 2 Level 3
recent decades and are still being contin- No system “Feet-off” “Hands-off” “Eyes-off”
uously improved. A classification system
based on six different levels, ranging from
fully manual to fully automated systems, was
published in 2014 by SAE International, an
automotive standardization body (compare
Figure 3). Level zero to level two requires
a human driver to monitor the driving
environment at all times. Level zero means
no driver assistance at all, while level one
provides simple support like speed control.
Level two combines lateral and longitudinal Driver needs to be
control by the vehicle in specific situations. Driver is in moni- ready to take over as
However, the driver needs to monitor the Driver in charge of toring mode at all a backup system
car and traffic at all times and be ready to longitudinal or times
take over vehicle control immediately. Driver completely in lateral control Vehicle in charge of
charge lateral and
Vehicle in charge of longitudinal control
lateral and in many situations.
Vehicle takes over longitudinal control Warns driver in a
other tasks in specific situations timely manner.
<2000 2000+ 2010+ 2018+
Source: Deloitte research 2018, SAE International 2014
12
The focus of current developments by car Roadmap to

manufacturers is in the range from level 2 'real' Autonomous Driving
to level 4. The most important transition is Automotive manufacturers are forging the
High Automation Full Automation between partial automation (level 2) and path to high and full automation based on
conditional automation (level 3), since in the previous experience regarding driver assis-
Level 4 Level 5 latter case the driver is allowed to be out tance systems, where automation at level
“Brain-off” No driver of the loop. The main difference between 2 has been realized successfully. However,
level 4 (high automation) and level 5 (full the quantum leap in system reliability is
automation) is the system's capability to between level 3 and level 4. At both levels,
handle specific restricted driving modes vs. the system is already in charge of monitor-
Z
Z all driving modes (eventually, these types of ing the driving environment, but at level 3
Z
vehicles will not have a steering wheel at all). (conditional automation), a human driver
still needs to be prepared to take control of
the vehicle within a couple of seconds. At
level 4, the system must be able to manage
specified traffic conditions without any driv-
er intervention and to reach a safety fallback
Driverless during state in the case of unexpected events.
defined use cases Vehicle in charge of
lateral and
Figure 4 shows a roadmap towards 'real'
longitudinal control autonomous driving (level 5) for passenger
Vehicle in charge of
in all situations. cars, expected to hit the road with wide-
lateral and
According to use spread adoption not before 2030. On the
longitudinal control
case, no steering other hand, level 3 and level 4 market intro-
in many situations.
wheel and/or pedal duction are already underway, with level 3
Minimized risk for
required.
accidents. being primarily focused on an automated
highway pilot and level 4 on specified appli-
cations such as automated valet parking or
first robo taxi fleets in selected cities (e.g.
2019+ 2021+ Phoenix, operated by Waymo).
13
Figure 4 – Roadmap towards 'real' autonomous driving
... 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 ...
“Google sibling Waymo

Autonomous private
Level 5 launches fully autonomous
vehicles on public roads
ride-hailing service”11
Urban and suburban pilot
Highway autopilot including

Level 4 highway convoy
Automated valet parking
Parking garage pilot
Traffic jam GM 201919/

Level 3 Highway chauffeur
chauffeur Bosch Audi 202020/
AVP 201815 Daimler 202021/
e.Go (ZF) BMW 202122
Mover
2019+16/
Traffic jam
Level 2 Audi AI Conti CUbE
assist, …
Traffic Jam Pilot 2019+17, 18
201913, 14
Tesla Autopilot
ACC, Stop
Level 1 v9 201812
& Go, …
Level 0
Testing and ramp-up phase

Source: ERTRAC 2017 “Automated Driving Roadmap”, VDA 2018 “Automatisiertes Fahren”, Deloitte Research 2018 Series deployment
14
The market introduction and adoption rate Key Challenges

of level 3 systems and above will differ by The progression from level 3 to level 4 is
market and region, because not only do cer- not a steady one. Classic rule-based ADAS
tain technological issues need an ultimate functions reach their limits with level 3
solution (e.g. how to cope with extreme requirements. Linear "if then" conditions
weather conditions like snow, heavy rain need to consider every possible use case
or fog etc.), but regulatory and customer or combination of use cases in any given
acceptance issues must also be addressed traffic situation, which is virtually impossible
sufficiently upfront. in urban environments (level 4 and 5). Apart
from confined spaces such as highways, traf-
fic situations are highly dynamic and com-
plex. For this reason, self-learning systems
based on artificial intelligence (AI) that mimic
human decision-making processes are
critical for meeting the demand for complex
scene interpretation, behavior prediction
and trajectory planning. AI is becoming a
key technology in all areas along the auto-
motive value chain and is paramount for
the success of level 4+ AD systems. Figure 5
illustrates the leap forward in technological
progression from traditional software devel-
opment to artificial intelligence.
However, AI talent is scarce and the market

is highly competitive, resulting in skills
shortages. This puts additional pressure
on automotive companies still needing to
build up expertise in those areas. But what
exactly is AI in the first place? We are going
to shed some light on this question in the
next chapter.
15
Figure 5 – Quantum leap from classic rule-based coding to artificial intelligence
Maneuver
1
Maneuver
2
Maneuver Autonomous
… Driving Level 3+
(Machine Learning)
Technology Performance Level
Maneuver
n
Substitution of rule-based
coding with training of
Artificial Intelligence algorithms
Classical ADAS
functions (rule-based coding)
2018 2025+
(Today)
Source: Deloitte Research 2018
16
AI is crucial for AD level 3+, because classic coding is

not sufficient to master and meet the necessary
requirements
•• Traffic situations are highly dynamic and complex
•• Sensors collect tremendous amounts of data points, which

need to be interpreted in real time (e.g. object detection,
»Artificial Intelligence is the
reduced visibility, natural behavior, map adjustments, …) new electricity.«23
•• The required processing speed for the amount of complex, Andrew Ng (AI Entrepreneur, Adjunct Professor Stanford University)
new data input can only realistically be mastered with
self-learning systems
•• Deep learning systems can be trained to mimic human

decision-making processes, which could ease human-
machine interaction on the road
•• Behavior prediction of other road users including vehicles,

pedestrians, cyclists
•• AI is becoming a key technology in all areas along the

automotive value chain
17
Deep dive
Artificial Intelligence
Artificial intelligence is at the top of its hype curve. With potential-
ly revolutionizing applications in almost every industry and do-
main, the market is experiencing explosive interest from estab-
lished companies, research institutions and startups alike. The
global automotive artificial intelligence market forecast shown in
Figure 6 reflects this interest with a CAGR of 48% between 2017
and 2025, culminating in a total volume of around 27 billion U.S.
Dollar in 2025.
18
Figure 6 – Global automotive AI market forecast
30
27
Automotive AI Market Size [billion USD]
25
21 Service
20
+48%
16
CAGR
15
12 Software
10
8
5
5
3
2 Hardware
1
0
2017 2018 2019 2020 2021 2022 2023 2024 2025
Historic data Forecast
Source: Tractica 2018, Deloitte Research 2018
At the same time, there could not be a model based on machine learning, which artificial intelligence realm. As Elon Musk
greater gap in experts' opinions on the has been around for years and would usual- put it: "The pace of progress in artificial
technological short-term potential of artifi- ly classify as data mining, is now rebranded intelligence (I’m not referring to narrow AI)
cial intelligence, which ranges from simple as 'AI'. Companies follow such strategies is incredibly fast. Unless you have direct
performance improvements in today's to tap into the hyped sales potential. One exposure to groups like Deepmind, you have
methods to artificial intelligence-powered of the prevailing reasons is that the term no idea how fast - it is growing at a pace
robots conquering and enslaving the human artificial intelligence is ill-defined. There is no close to exponential. The risk of something
race one day. single, agreed-upon definition that removes seriously dangerous happening is in the five-
all doubt; rather all definitions leave room year timeframe. 10 years at most.”
While there are promising advances across for interpretation and therefore room
the entire spectrum, we see an inflation of for deceptive product specifications. This
technologies branded as artificial intelli- should by no means diminish the impressive
gence. For example, a demand prediction advances and speed of development in the
19
In order to separate hype from reality, we ligence. Common to most definitions is that
will classify artificial intelligence in the broad- "intelligence" refers to the ability to sense
er context of science, which includes but is and build a perception of knowledge, to
not limited to computer science, psychology, plan, reason and learn and to communicate
linguistics and philosophy. Figure 7 shows in natural language. In this context, it also
the key characteristics of an AI system. The comprises the ability to process massive
common understanding of artificial intelli- amounts of data, either as a means of train-
gence is that it is used to get computers to ing AI algorithms or to make sense of hidden
do tasks that normally require human intel- information.
Figure 7 – Key characteristics of an AI system
Big Data Reasoning
Capable of processing Ability to reason (deductive

massive amounts of or inductive) and to draw
structured and unstruc- inferences based on the
tered data, which may situation. Context-driven
change constantly. awareness of the system.
Learning Problem-solving
Ability to learn based on Capable of analyzing and

historical patterns, expert solving complex problems
input and feedback loops. in special purpose and
general purpose domains.
Source: Deloitte 2018 “Artificial Intelligence”
20
We differentiate between narrow and gen- Figure 8 – 'Machine learning', 'Methods' and 'Technologies & Infrastructure'
eral artificial intelligence. Today's artificial in the context of AI
intelligence solutions are almost exclusively
narrow. In this context, narrow means that
an AI algorithm only works in the specific Artificial Intelligence
context it was designed for, e.g. computer Ability to sense, reason,
vision-based object detection algorithms engage and learn
in autonomous driving systems. Such algo-
Voice recognition Computer vision
rithms have the potential to exceed human
performance by orders of magnitude.
Planning & Natural language
General AI, on the other hand, refers to the
optimization processing
more human interpretation of intelligence in Machine Learning
the sense that such AI solutions are able to Ability to learn
understand, interpret, reason, act and learn Robotics & Knowledge
Unsupervised learning
from any given problem set. An AI system motion caputre
typically combines machine learning and
other types of data analytics methods to Reinforcement Supervised
achieve AI capabilities (Figure 8). learning learning
Methods
Ability to reason
Regression, decision trees etc.
Technologies &
Infrastructure
Physical enablement
Platform, UX, APIs,

sensors etc.
Source: Deloitte Research 2018
21
Figure 9 – Autonomous vehicle disengagement report statisticsI
20,000
Period: December 1, 2017 to November 30, 2018
Autonomous km driven per disengagement
16,000
[values in km]
12,000
8,000
4,000
0
Waymo GM Zoox Nuro Pony.AI Nissan Baidu AImotive AutoX WeRide
Cruise USA Technologies
Source: “Autonomous Vehicle Disengagement Reports 2018”, DMV, CA
I
Please note, that this figure is only indicative, given that it only shows the state of California. Some firms, including German OEMs,
22 do not test their vehicles in California and therefore do not appear in the data
•• Based on the 2018 “Autonomous Vehicle Artificial intelligence is one of the crucial Technological Hurdles
Disengagement Reports” published by the elements for level 4 and 5 autonomy. Recent On-board & Off-board
DMV, California, Waymo leads the race for autonomous vehicle disengagement reports Technological hurdles for level 3 automation
autonomous driving leadership by far issued by the Department of Motor Vehicles, and above are still manifold. We differentiate
California, illustrate the autonomous miles between on-board and off-board challeng-
•• GM’s investments in Cruise Automation driven before disengagement becomes nec- es, as shown in Figure 10. As far as on-board
helped them propel to second place in essary, in critical or non-critical situations issues are concerned, the key challenges
terms of autonomous kilometers driven (Figure 9). While many factors come into play revolve around sensors, computing hard-
per disengagement here, it is undeniable that the firms known ware, basic software and autonomous
to be strong in AI are leading the statistics. driving core software. In order to ensure the
•• Zoox following in third place with a Please note that the DMV only registers safety requirements imposed by industry
wider gap firms that perform test drives in the state and government, sensor quality still needs
of California. The graph therefore does not to be improved to cater for e.g. accuracy for
•• Noticeable advancements especially from represent the full picture, but rather serves speeds up to 130 km/h and in some cases,
startups and tech companies illustrative purposes. Based on our experi- especially with lidar, the price point is still
ence, the relation between top performers too high to be economically feasible. With
•• Audi, BMW, Volkswagen, Tesla, Ford not and mid to low performers is accurate new central processing units and operating
considered in this chart due to lack of test though. systems come new challenges regarding the
data tracked by the DMV vehicle's overall safety concept. ECUs need
to process large amounts of input data, but
also compute complex algorithms based
on artificial intelligence techniques, such
as convolutional neural networks (CNN) for
object detection, in real time. Considering
the industry-wide trend towards electric
drivetrains, the ECU's requirement for
computing power is counteracted by the de-
mand for low energy consumption. In terms
of software development, the challenge lies
in creating, training and securing (validation
and verification) safe algorithms.
23
Figure 10 – On-board and off-board challenges in autonomous driving
Adaptive Cruise Control Emergency Braking

Pedestrian Detection
Collision Avoidance
Traffic Sign Recognition Lane Departue Warning
Cross Traffic Alert
Park Assist
Surround View Surround View
Blind Spot Detection
Park Assist
Rear Collision Warning
Park Assistance/Surround View
Source: Deloitte 2018
24
On-board hardware On-board software components
Camera/Optics System on a Chip (SOC) High Definition On-Board Maps

Collect optical images to be inter- High performance energy- Precise localization information about
preted by advanced AI & analytics efficient computer hardware roads, infrastructure and environment
Radar V2V/V2I Localization & Mapping

Determines speed and distance of Communication with vehicles & Data fusion for vehicle localization and
objects using electromagnetic waves infrastructure over short range environment analysis
LiDAR Actuators Perception & Object Analysis

High resolution sensor using Translation of electronic signals Algorithms Detection and classi-
light beams to estimate distance into mechanical actions fication of objects and obstacles
from obstacles
Ultrasonic sensors GPS Prediction Foresight of movements

Short distance object Localization of vehicle and actions by vehicles, pedestrians and
recognition (e.g. parking) using satellite triangulation other moving objects
Odometry Sensors
Measure wheel speed to predict vehicle Decision-Making
travel and complement localization Planning of vehicle route, maneuvers,
acceleration, steering and braking
Off-board hardware / software

Vehicle Operating Systems
Data Center AV Cloud Operation
Operating system running
Storage and processing of Learning, adopting and up-
algorithms in real time
hot and cold vehicle data dating HD maps & algorithms
Supervision platform
Analytics to monitor the AV system
operation, detecting & correcting faults
25
While some companies see level 3 func- be a swift approach to achieve this amount
tionalities as an evolution of classic ADAS of mileage in real world testing. Simulations
functions, which can be mastered with effectively contribute to this requirement,
rule-based coding, level 4 and 5 autonomy covering more than 95% of the mileage
require artificial intelligence to cope with demand. However, it remains a challenge to
the complexity of traffic situations. The set up the proper test concept and collect or
latter typically demand large data sets (e.g. create sufficient data for validation.
raw sensor data) for training, testing and
validation of (deep learning) algorithms. In Overall, the challenges for companies work-
order to store and process these data, coming on autonomous driving technology are
panies make use of data centers or cloud significant and vastly affect the dynamics of
solutions. The data are labelled, clustered the automotive industry. The following par-
and ultimately used to optimize and update agraph discusses our view on some of the
algorithms. It remains an open challenge to- key implications confronting the automotive
day to efficiently validate artificial intelligence industry.
algorithms such as CNNs. AI algorithms
operate like a black box in the sense that it is
not trivial to determine what triggers certain
decisions. Validating correct functionality is
cumbersome and today only feasible statis-
tically via numerous test cases.
Besides, data centers constitute the

basis for simulation purposes. Theoretical
estimates show that in order for level 3+
autonomous vehicles to achieve approval
for commercial use, the system needs to
undergo billions of kilometers of testing. It
is neither economical nor does it prove to
26
»We're entering a new world in which data may be

more important than software.«24
Tim O’Reilly (Founder O'Reilly Media)
27
Impact on Today’s
Automotive Industry
Cars are no longer merely the means of getting from point A to point
B, nor are they simply status symbols; instead, they have become
functional assets. Particularly in recent years, car manufacturers have
discovered growing customer demand for digital infotainment solu-
tions and other in-car services. In the telecommunications industry,
smartphones replaced the traditional mobile phone, with telephony
being just one of many features and oftentimes not even the most
important one.
Car manufacturers face a similar trend

nowadays, namely that the car is turning
into a platform to serve a variety of
functions. As Nitesh Bansal, Senior Vice
President and Head of Manufacturing
Practice Americas and Europe at Infosys,
put it: “The modern car is a supercomputer
on wheels, and its sensors and cameras
generate a wealth of data that someday
might be worth more than the automobile
itself”25 – in that, for car manufacturers, the
car is becoming a rich source of data they
can use to improve products and business
operations.
28
29
The same goes for development: 50 years Figure 11 – From hardware to software focus
ago, the distribution of a car's added value
between hardware and electrics, electronics Average automotive product cycle time
(E/E) and software was approximately 95%
compared to 5% respectively. In an average
car today, the distribution is closer to 50%
hardware and 50% E/E and software. Along
with the technological progression of the
8 years
semiconductor industry, development of E/E
as well as software has increased exponen-
tially over the past two decades. At the same
Average automotive product cycle time 7 years
time, the average product cycle time has
halved over the same period (see Figure 11). 6 years
5 years
4 years
2000 2005 2010 2015 2020 2025

Year
Source: Diez (2015), Deloitte Research 2018
30
E/E & SW share of total value add in automotive
ABS Electronic fuel On-board Hybrid Connected Autonomous

injection Diagnostics cars cars cars
50%
30%
20%
15%
10%
5%
1970 1980 1990 2000 2010 2020

Year
Source: Statista 2018, GTAI 2016, Brandt 2016, freescale semiconductor 2010,
Wallentowitz et al. 2009, Deloitte Research 2018
That said, OEMs increasingly deviate from a around 60 control units to manage the signals and the bandwidth of bus connec-
"One Product, One Function" strategy and multitude of functionalities in the vehicle, tions. Car manufacturers have started to
instead approach a "One Product, Many the trend is going clearly towards a central accept the challenge and subject themselves
Functions" philosophy, similar to what we processing unit that controls all functions of to - in some cases drastic - transformation
have seen in the telecommunications indus- the vehicle in unison. One of the crucial chal- programs, which we will dive into in the
try with the introduction of smartphones. lenges associated with a central processing following paragraph.
While an average car today still features unit lies in managing the criticality of control
31
Paradigm Shift in OEM Product Figure 12 – Four major areas of organizational change
Development Organizations
Historically, car manufacturers have Product Structure: Work Structure follows
established a very strong top-down chain Hardware vs. Software Share Product Structure:
of command. This made sense when labor Agile Development Organization
division between "thinkers" and "doers" was Processes
strict. The engineer defines how the me- Analog Cockpit Silos & Waterfall
chanic needs to assemble the car, the senior
engineer instructs the junior engineer, etc.
In today's VUCA world (volatile, uncertain,
complex, ambiguous) these rules do not ap-
ply anymore. The environment has changed,
new competitors have entered the market
and are constantly challenging and changing
the rules and dynamics of the game. Core
competencies, skills and know-how that
have been perfected for decades to build
great quality cars fade into the background, Digital Cockpit Cross-Functional & Agile
while the focus is placed on innovation, agil-
ity and software, including but not limited to
autonomous driving, artificial intelligence,
agile working, electric vehicles and new
business models.
Based on our experience, Deloitte sees four

major areas of change that will likely change
the dynamics of the automotive industry for
good (Figure 12). •• Value add of software in the vehicle con- •• Better consideration of 'real' customer
tinues to increase needs & requirements by applying mini-
mal viable product (MVP) approach
•• Trend is supported by surging importance
of artificial intelligence (AI) •• Reduced development time, shorter time-
to-market and lower development cost
•• Shift away from “one function, one device”
philosophy towards a “many functions, •• Ability to cope with uncertainty and com-
one software platform” approach plexity
32
New Steering Models: Mastering New Technologies:

KPI vs. Progress Indicator Cooperation vs. Supplier
Relationship
KPI Supplier Steering
Progress Indicator Partnership
•• The nature of agile working environments •• Autonomous driving brings a new level
requires new steering models of development complexity that can no
longer be managed by individual players
•• Progress indicators, such as OKRs (Ob- alone
jectives and Key Results), should be used
more as a compass to ensure movement •• Both technology companies and car man-
in the right direction rather than a numeri- ufacturers benefit from complementing
cal control on detail level each others’ skill sets and sharing develop-
ment efforts
33
Area 1 refers to the product structure. The dictable in nature, such as the development clear interfaces. In the case of autonomous
share of value added to the vehicle between of autonomous vehicles, require different driving, there are many unknowns for what
hardware versus E/E and software is shifting approaches to success evaluation. Where constitutes the optimum technical solution.
in favor of the second. Consequently, car the technology is new, the outcome uncer- Additionally, no single player in the industry
manufacturers undergo major transforma- tain and the timeline unpredictable, classical possesses all the skills necessary to develop
tions to refocus their core competencies KPIs often do not provide sufficient benefit the perfect solution. Google and Apple hold
and build up expertise in those areas. in measuring and steering the progress of great expertise and talent in software devel-
development. Instead, agile steering models opment and artificial intelligence, but do not
Area 2 describes the change that is should focus on continuously measuring quite match the production infrastructure
necessary on an organizational and work holistic progress and direction as compared and century old automotive engineering
structure level. Structures and processes to performance at specific milestone dates. prowess of leading car manufacturers yet.
that have proven successful for the devel- After all, agile development practices Car manufacturers possess the necessary
opment of products with low shares of E/E support the ambition to cope with uncer- automotive expertise and infrastructure,
and software are no longer ideal for the tainty by providing a new level of adaptivity. but lag behind with software capabilities.
development of autonomous vehicles. Com- Upfront top-down planning, including metic- For this reason, companies are joining forces
panies are increasingly replacing classical ulous project timelines, run counter to the in development partnerships and increas-
waterfall structures with agile approaches. philosophy of agile development. However, ingly via mergers and acquisitions.
The goal is to move away from long devel- a smart alignment between major top-down
opment cycles, inflexibility and hierarchical project milestones (e.g. on a quarterly basis)
command-and-control style management and bottom-up progress indicators, which
practices and replace them with shorter de- show operational work advancements (e.g.
velopment cycles, adaptivity, flat hierarchies on a bi-weekly basis), provide good orienta-
and team empowerment. tion regarding the overall project status and
direction.
New work structures and development
processes require new steering models, Finally, Area 4 addresses new forms of work-
which is the focus of Area 3. Key perfor- ing with suppliers and partners. Traditional-
mance indicators (KPIs) are an effective ly, car manufacturers have outsourced large
tool to evaluate an organization's success shares of development work to suppliers
at reaching targets. This applies mostly to in classic contract relationships. This is a
situations that are predictable and linear. viable option when confronted with known
Environments that are complex and unpre- technologies that can be partitioned with
34
architectural philosophy often still allocates software oriented "automotive technology

specific functions to dedicated controller companies" with shorter release cycles for
Product Structure: units). Simply put, this remains the industry software applications. The underlying change
From Hardware to Software Focus standard today: One device, one function. To in product structure goes away from the "one
For decades, traditional car manufacturers the same degree, it still holds true that tradi- function, one device" philosophy, towards
have perfected their craft to build great qual- tional car manufacturers are cost optimizers a "many functions, one software platform"
ity cars. Over the last 30 years, automotive more than innovative game changers. The approach, as shown in Figure 13. The
E/E as well as software applications have current business model is to make money computing power required to process the
gained great traction, along with the rise of by selling cars. New entrants follow a more huge amounts of sensor data, primarily from
enabling technologies including computer radical strategy. Technology companies have cameras, radars and lidars in autonomous
chips, the internet, etc. Initially, speed of inno- identified the value of data for their business vehicles, makes those cars supercomputers
vation was slow in this domain compared to and leverage business models that support on wheels, as stated earlier. Thus, it makes
modern standards. This is why it made sense this notion, such as Waymo's robo-taxi pilot sense to take advantage of this technology
that a single control unit had the sole pur- program in Phoenix, Arizona. potential and use it as a central source for
pose of powering a single function (of course, data processing.
this is a simplified explanation because Traditional car manufacturers have identified
several E/E functions are commonly execut- their need for change and are drastically
ed across several ECUs. However, the E/E investing in becoming more agile, more
Figure 13 – Shift from hardware to software focus

Traditional Hardware-driven Philosophy New Software-driven Philosophy
One Function Many Functions

» One Device » One SW-Platform
35
The aforementioned changes do not only approaches. The main difference lies in the continue to become more connected and
apply to hardware, but also to the way soft- software development sequence: While autonomous. The most prominent example
ware is applied (Figure 14). Up until recently, waterfall follows a sequential path from of a company that is already using RSUs
there was no demand for regular software conception to deployment, agile has an successfully is Tesla, which regularly releases
updates. Control units were flashed during iterative approach where potentially ship- updates to its fleet to improve the autopilot
production and in most cases never saw an pable software increments are developed function, battery range, or others. Other
update until the end of the car's product according to a minimum viable product prominent players, including traditional car
lifecycle. Software was designed under the approach. This is relatable to the way smart- manufacturers such as BMW and Daimler,
premise of avoiding errors at all costs, and phone apps are created today. The updates have gained substantial experience with
therefore required substantial lead and regularly released to app stores are product RSUs, not least because of their car-sharing
development times. increments resulting from a sprint (in Scrum, fleets.
a time-box of 4 weeks or less during which
In agile software development, software a potentially shippable product increment is
quality is of paramount importance as well, developed). Remote software updates (RSU)
oftentimes even more so than in waterfall in automotive gain in importance as vehicles
Figure 14 – Shift from hardware-driven to cloud-driven software updates
Hardware-driven Software Updates Cloud-driven Software Updates
App
Store
Remote
Software
Update
Upfront planning, updates after years Updates according to sprint cycle
36
»Companies in every industry need to assume that a

software revolution is coming.«26
Marc Andreessen (Cofounder and General Partner, Andreessen Horowitz)
37
Figure 15 – Traditional waterfall vs. agile software development
Work Structure Traditional software development: Separated structure

follows Product Structure
As mentioned, traditional software devel-
opment follows individual development Software component
Software Software
stages sequentially. From an architectural team incl. component
Component Component
standpoint, building blocks are divided into lead with local respon-
1 4
software components, which are taken sibility
Overall System
care of by component teams (compare
Software Software
Figure 15). Since development times can be
Component Component
extensive for certain software components,
2 …
this approach requires meticulous upfront
planning: First, to enable teams to work
in parallel with clearly defined interfaces, Software Software
tasks and responsibilities; second, to reduce Component Component
dependencies to a minimum, as queues 3 n
have an exponential impact on cycle times.
In complex systems, dependencies cannot
Component
be avoided every time, which increases Plan-driven Teams
coordination efforts and complicates system Dependencies between
integration. Owing to this structure, water- components cause Function-
Command-
fall development is characterized by local queues, which have an specific
and-Control
optimization of single software components exponential impact on Leadership
due to silo development. Operational and cycle times
organizational structures are detached and Local
Optimi-
require coordination teams. Management
zation
follows a command-and-control style Function-
specific
leadership, because technical decisions are
Development
Waterfall
made top-down.
Sequential
Individual
Responsibility Silos
38
Agile software development: Integrated structure
Feature Team 1 Cross-functional feature

team with end-to-end
responsibility
Community m
Community …
Community 1
Community 2
Feature Team 2
Feature Team ...
Feature Team n
Feature
Cross-feature Backlog-
Teams
driven
alignment via
self-organized End-to-End
Servant Development
communities
Leadership
Global
Optimization
Integrated
operational &
organizational Agile
Structure Iterative
(MVPI
Approach)
Team
Respon- Cross-
sibility functional
I
Minimum Viable Product Approach
39
This is in stark contrast to the characteristics velopment are fundamentally different from
embodied by agile software development, the ones in the automotive industry. The car
which in its core embraces self-organization industry is highly sophisticated and consists
on team level, pushes decision-making of an advanced network of manufacturer,
down to the lowest possible hierarchy level supplier and partner relationships. OEMs
and fosters servant leadership. Teams outsource large portions of development
are cross-functional and assume end-to- work to suppliers, which creates depend-
end responsibility for a product feature encies and the need to define and manage
(i.e. minimum viable product increment). clear interfaces. In addition, we are talking
In analogy to the most common agile about embedded software development
framework, Scrum, teams are by definition with significant hardware shares. These
feature teams; development is consequently circumstances pose new challenges to agile
oriented toward the highest customer value working models, which have their origins
and prioritized via a backlog of development in pure software development. In order to
items. Consequently, the organization cope with such high levels of dependency
continuously strives to achieve a global op- and hardware shares, companies have to be
timum. A perfect feature team is able to do willing to continuously challenge the status
all the work necessary to complete a feature quo and adapt where necessary. When
(backlog item) end-to-end. Cross-feature considering introducing an agile working
alignment is self-organized and all feature model, you should ensure that not only is it
teams work on a common software re- compatible with your overarching product
pository. Operational and organizational development process, but also meets the
structures are integrated and streamlined demands posed by supplier relationships,
to focus on value-adding activities while hardware development and computing
eliminating overhead. power constraints. Bill Gates famously
coined the phrase: "Intellectual property
Many companies view agile as the holy has the shelf life of a banana."27 If you want
grail for securing innovation leadership, to create the future, you have to innovate
which can lead to massive transformation as fast as your competition, at the very
programs, sometimes without taking the least. Becoming agile and adaptive can help
necessary time to analyze and evaluate the achieve that goal, but you need to be smart
full spectrum of implications. Agile (soft- about it. Agile transformations in complex
ware) development brings many benefits, environments such as the automotive
but it has to suit the purpose and environ- industry constitute a fine line between sig-
ment. The dynamics and challenges in pure nificant performance improvements and the
software development environments such complete inability to act.
as banks, insurance companies or in app de-
40
»Today, companies have to radically revolutionize

themselves every few years just to stay relevant.
That's because technology and the Internet have
transformed the business landscape forever.
The fast-paced digital age has accelerated the need
for companies to become agile.«28
Nolan Bushnell (Co-founder Atari, Inc.)
41
You also need to use the right scaling strat- enough and therefore cannot sustain the
egy. Autonomous driving development divi- momentum, support and positive energy
sions typically employ several hundred em- required to drive the undertaking towards
ployees for the software content alone. It is success. Top management as well as em-
a tremendous challenge for any organization ployees may soon lose trust in the change if
to change everything from the ground up. A it does not yield positive results. Therefore,
real agile transformation not only changes we suggest starting with a pilot, a small agile
the way developers work with one another; nucleus, where a group of highly motivated
it fundamentally changes how the organ- people come together to function as spark
ization operates, from the organization for the change and drive its initiation phase.
structure, via the operating model, product Word about the pilot's first successes will
architecture, verification and validation pro- soon spread into other groups or depart-
cedures, to the culture, to name but a few. ments, who will then be eager to "go agile".
For example, hierarchical barriers are bro- Figure 16 shows an example of a structured
ken down, technical experts become tech- agile transformation roadmap, including
nical leaders empowered to make technical some of the most crucial steps to consider
decisions without the alignment obligation in an agile transformation (Deloitte's
with their superiors, silos are eliminated and structured enterprise agile transformation
replaced with strong cross-functional team playbook).
setups - in short, interaction mechanisms,
processes, structures and skill requirements
change and need to be re-trained. For tradi-
tional car manufacturers, this is a particular
challenge owing to long-established and
perfected processes, legacy systems and
complex dependencies across departments,
cooperation partners and suppliers.
Our experience with hundreds of agile

transformation programs across the globe
has shown that large enterprises are suc-
cessful when they follow a structured agile
transformation playbook. Most often, a "big
bang" implementation, where the entire
organization is flipped at once, culminates in
a "big failure", simply because the organiza-
tion is not able to change everything quickly
42
»Agile is more a 'direction', than an 'end'.

Transforming to Agile culture means the business
knows the direction they want to go on.«29
Pearl Zhu (Author of "Digital Master"book series)
43
Figure 16 – Deloitte's structured enterprise agile transformation playbook
Lean change: An agile approach to change Structured ...
Build
Work streams
Pivot Pursue
MVC
Operating model, alignment

and organization design
Measure Learn
Measurement Framework
Architecture and DevOps

Operating
model and
alignment
Organization Architecture
design and DevOps
Agile Team Process and Practices
transfor-
mation
program
Training Coaching
Team Training and Coaching

process and
practices
44
... enterprise agile transformation playbook
Scale
Pilot Refine Adopt
Establish agile vision and Define agile change and Stand-up agile change and Establish enterprise
success criteria ops team ops team wide agile COEIV
Define base operating Stand-up organization and Integrate and extend

Refine and extend
model and road map team structure across the enterprise
Assess architecture and Componentization and Refactor architecture and Mature architecture
DevOps capability DevOps strategy stand-up DevOps and DevOps
Define core agile SDLCI

Refine SDLC method Apply and refine agile SDLC method
method for pilots
Stand-up tooling Stand-up tooling

Define agile tooling stack Setup agile tooling
enablement team enablement team
Define training and Execute training Program

Train pilot groups
coachinng program (executive, management, POII, scrum master, RTEIII, team)
Coach pilot groups Transition portfolio, programs, and teams by waves
I
SDLC: System Development Life Cycle III
RTE: Release Train Engineer
II
PO: Product Owner IV
COE: Center of Excellence
45
Typically, you need to overcome a number Figure 17 – Barriers and solutions in the agile transformation
of barriers during the agile transformation
(see Figure 17). First, humans have the ten-
Typical Barriers
dency to resist any kind of change in culture,
structure and roles within a company. You
can counteract this resistance by creating a Human resistance to
mutual understanding of the change, adding change in culture, structure and roles
new competences and trying it out in a pilot.
Next, there is a lack of open communication.
Instead of forcing a form of communication
onto employees, create transparency, e.g. Lack of open communication
through sharing information, avoiding
information access restrictions or working
in pairs. Especially large, established corpo-
rations are often too risk-averse. In order to
Being too risk-averse
become agile, you need to adopt a fail fast,
learn fast mentality, because "failure is suc-
cess if we learn from it"30 - Malcolm Forbes.
Another crucial aspect is the often seen lack
of leadership buy-in. If there are no senior Lack of leadership buy-in
leaders backing the agile transformation,
this could lead to failure. You have to provide
a solid mandate to managers and strong
leadership support if you want the transfor- An agile transformation requires a hypothesis-driven,
mation to be sustainable.
Source: Deloitte 2018 “Agile 101: Discover the agile ways of working”
Naturally, agile working models do not
respond to the same steering mechanisms
as waterfall approaches. The following
paragraph discusses the differences in more
detail.
46
New Steering Model

Solutions
Classical KPI-driven reporting systems
Create a mutual understanding of often follow an underlying traffic light logic.
the change, add new competences Holistic top-down defined project plans with
and try it out in a pilot detailed milestones and deliverables for
each single work package (or even on the
task level) are still considered to be the holy
grail. On the other hand, a few months after
Create transparency through sharing
implementing a steering model, the mile-
information and working in pairs
stone-related progress tracking with classic
KPIs usually indicates a (negative) deviation
from the originally projected timeline and
Fail fast, learn fast.
consequently leads to red traffic lights at
„Failure is success if we learn
aggregated project tracking overviews. Of
from it.“30 – Malcolm Forbes
course, the underlying reasons range from
bad planning accuracy and over-optimistic
assumptions to execution problems. The
Provide a solid mandate to real problem here comes into play because
managers and strong leadership of natural human behavior: Every single
red traffic light seems to indicate a need for
action. As a consequence, countermeasures
feedback-oriented focus and implementation are often initiated to fight red traffic lights
individually without keeping an eye on the
big picture, see Figure 18.
47
Figure 18 – From KPIs to progress Indicator
Traditional KPI-driven steering model Steering in an agile working model
If KPI shows a red traffic Product Owner (PO) prioritizes the product backlog based on a
light, specific counter- comprehensive picture enabled by a holistic set of progress
measure has to be indicators and feedbacks
defined and triggered
1.
If KPI target value is
2.
achieved, no counter 3.
measure is needed
Bottom-
Top-down
up Self-
Steering
organized
Trans-
Concealment
parency
Counter- Fail, Learn,

measure Correct
Cross-
Silo
functional Progress
Mentality
KPI Mindset
Indicator
Target Indicator for
Values Prioritization
Split Shared
Holistic
Responsibili- Risk of Local Responsibili-
Interpre-
ties (Individual) Optimization ties (Team)
tation
48
Agile working environments require (objectives and key results) which combine
progress indicators more as a compass to top-down planning (approx. 30%) with
ensure movement in the right direction bottom-up defined OKRs (approx. 70%).
rather than a numerical control on detail It is more important to spend some time
level. The old mantra of "the more, the defining the right OKRs rather than having
merrier" in terms of numbers of KPIs does too many, and they should follow some
not hold true anymore - and in fact it never simple rules: Define SMART goals (specific,
really did. Likewise, agile working principles measurable, actionable, relevant and time-
should not be used as an excuse to avoid ly). Furthermore, make sure that there is
any type of top-down milestone planning. one responsible individual for each OKR or
At the end of the day, project success in progress indicator. The tricky part is being
agile environments is similar to sailing: If smart in aligning the big picture milestone
you do not plan any course or direction plan with short- and midterm agile pro-
before you set sail, you will not know where gress indicators and deriving reasonable
you end up. Even Silicon Valley tech players holistic countermeasures in case of major
from Intel to Google use so-called OKRs deviations.
»Short for Objectives and Key Results. It

is a collaborative goal-setting protocol for
companies, teams, and individuals. Now,
OKRs are not a silver bullet. They cannot
substitute for sound judgment, strong
leadership, or a creative workplace culture.
But if those fundamentals are in place,
OKRs can guide you to the mountaintop.«31
John Doerr (Venture Capitalist and Chairman of Kleiner Perkins)
49
2.1.4 Mastering New Technologies Cross-industry partnerships are an inevita-
As mentioned above, autonomous driving ble prerequisite to mitigate these complex
is one of the most complex development challenges and to close own technology
challenges in the automotive industry. The blind spots. All major stakeholders engaged
broad range of required skills and capabili- in the development of autonomous driving
ties barely exists in-house at any traditional solutions have established or joined specif-
OEM, supplier or tech player. The latter are ic cooperations or partnerships (Figure 19,
well positioned when it comes to software exemplary selection only, this overview is
development and agile working principles not intended to reflect the full extent of AD
to achieve shorter development cycles and partnerships and investments). In addition
time to market, but often lack the experi- to the lack of technological or process
ence with industrialization and scaling a expertise, there are several other reasons
real hardware business like building cars. to join forces. Reduced development costs
On the other side, OEMs and traditional and risk sharing between partners are
automotive suppliers often struggle with further important drivers for the emer-
the transformation towards a new agile gence of those cooperations. Lastly, from
product and software development system a topline perspective, a larger addressable
with significantly shorter cycle times for E/E customer base and associated revenue
and software-related functions. potentials have to be mentioned.
50
Figure 19 – Development partnerships and strategic investments (exemplary selection, non-exhaustive)
Daimler32
Car2Go49 Jaguar
FCA33, 34 BMW44
Landrover53
Continental35, 36 Lyft58
DriveNow43 Bosch45 mytaxi50, 51
FCA 54
Aptiv37, 38 BMW DAIMLER Uber52

WAYMO
Magna39 Magna55, 56
Intel40
HERE46
HERE41 Intel57
Mobileye42 NVIDIA47, 48
VW76, 77, III

Harman59, 60, I Baidu65
Honda66, 67 Lyft74, 75 Velodyne78, 79 Lyft86
HYUNDAI GM FORD
KIA Magna68 Magna80
Cruise69 Saips81
Aurora 61
Mobileye70, 71
Argo AI82, 83
Telenav72
Samsung62, 63, 64, II
Strobe73 Civil Maps84, 85
Ford76, 77, III

Autoliv98
Audi87 MOIA97 Uber104 Denso105 Uber111
Geely99
Aptiv88, 89 VW VOLVO TOYOTA
NVIDIA 90
Microsoft100 Maxar106
HERE91, 92 Aurora93, IV
Zenuity101 NTT DATA107, 108
Microsoft94 AID95, V
WirelessCar96 NVIDIA 102, 103
NVIDIA109, 110
I
As part of Samsung's collaborative activities with Hyundai/Kia Automotive OEMs Mobility
II
Partnering in specific projects & Suppliers Provider
III
Global collaboration on electric vehicles and autonomous driving technology (incl. ArgoAI and AID)112
IV
Partnership discontinued as of June 2019113
V
AID: Autonomous Intelligent Driving GmbH Technology Firms 51
Why Partnerships? Specific collaboration setups range from
classic development contracts to joint ven-
Gain access to necessary capabilities tures and mergers and acquisitions. While
European automotive OEMs tend to prefer
Partner up with others to combine complementary ca-
contractual development agreements to
pabilities in order to create a superior product or service
coordinate collaboration efforts, especially
and cover own capability blind spots and/ or resource
US companies are more open to buying
bottlenecks
stakes in startups, like General Motors did
for example with Lyft and Cruise Automa-
Foster sales & market penetration
tion. Either way, the key success factor for all
Gain access to foreign markets and customers by collab- types of cooperations is to align and stream-
orating with local partners in unexploited geographic re- line the interests of all stakeholders towards
gions and leverage regional know-how and relationships a common goal. This sounds trivial, but has
often been a major obstacle for sustainable
Share risks results and success in former cooperation
initiatives.
Share commercial (investment, deployment) and techni-
cal risks (feasibility, operability) among several partners
as well as potential risks resulting from liability and
warranty claims
Reduce costs
Reduce own investment costs (manpower, equipment,

R&D) and create further synergies through joint
activities, e.g. industrialization
52
»Some companies will seek to minimize risks by

partnering with others, which is one reason why
I expect to see more mergers, collaborations,
partnerships and consolidations in the autonomous
vehicle industry. [...] At some point, the technology
stack options will narrow and clear choices will
be made. Similarly, the choice between creating a
highly integrated, in-house solution will be weighed
against the more traditional strategy of integrating
multiple tier one suppliers. [...] Expect to see more
collaborations and consolidations in 2019.«114
Michelle Avary (Project Head, Autonomous and Urban Mobility, World Economic Forum)
53
The Way Forward
The trend towards a continued substantial increase in the importance
of software development and the application of artificial intelligence and
machine learning techniques in the automotive industry is irreversible,
or in the words of Marc Andreessen: "Software is eating the [automotive]
world".26
OEMs and suppliers are already aware of the Figure 20 – Lines of code in millions
situation, but sometimes still struggle to em-
brace these inevitable changes. The trend
500+
from hardware to software in the automo-
tive industry requires new thinking, starting
with innovative product architectures (i.e.
onboard vs. off-board service architecture)
up to new target costing approaches and
entire vehicle business cases. Independent
from the vehicle ownership question, future
revenues and especially profits will gradually 100
shift towards the aftersales phase. Frequent
0,4 14
remote software updates and the provision
of new (software-enabled) functions over
Space Boeing 787 Modern Fully
the entire vehicle lifecycle will change the ex- Shuttle Dreamliner car autonomous
isting profit generation pattern in the auto- 90 vehicle
motive industry. It is not clear right now who
the leaders of tomorrow's mobility world will
be, but if OEMs consistently work on their
ability to quickly adapt to these changes and
become digitally fluent, they are in a strong
position to capture a significant share of the
future automotive and mobility value chain. Source: Deloitte research 2018, FEV 2018, Wired 2018, NXP 2017, MIT 2016
54
55
Authors
Dr. Harald Proff Thomas Pottebaum

Partner Director
Operations | Deloitte Germany Strategy | Deloitte Germany
Tel: +49 (0)151 5800 2696 Tel: +49 (0)151 5800 4516
hproff@deloitte.de tpottebaum@deloitte.de
Philipp Wolf
Senior Consultant
Strategy & Operations | Deloitte Germany
Tel: +49 (0)151 5807 0480
phwolf@deloitte.de
56
Endnotes
Chris Chin, "Exclusive: BMW to introduce ‘safe’ fully autonomous
1 9
A ssociated Press, “Uber asks for permission to restart selfdriving
driving by 2021 with iNext," Digital Trends, September 28, 2018, car tests in Pittsburgh eight months after its test vehicle killed an
https://www.digitaltrends.com/cars/exclusive-production-bmw-in- Arizona pedestrian,” Daily Mail, November 2, 2018, https://www.
ext-will-have-fully-autonomous-tech-by-2021/. dailymail.co.uk/sciencetech/article-6346925/Uber-wants-resume-
self-driving-car-tests-public-roads.html.
Mitchell Cunningham and Michael Regan, "Automated vehicles may
2
bring a new breed of distracted drivers," ABC News, September 24, 10

Alan Ohnsman, “Waymo Shifts To 'Industrializing' Self-Driving
2018, https://www.abc.net.au/news/2018-09-25/automated-vehi- Tech as Robotaxi Launch Nears,” Forbes, September 6, 2018,
cles-may-bring-a-new-breed-distracted-drivers/10299952. https://www.forbes.com/sites/alanohnsman/2018/09/06/way-
mo-shifts-to-industrializing-self-driving-tech-as-robotaxi-launch-n
R yan Browne, “Volvo and Baidu join forces to mass produce
3
ears/#51aa0a3a4dc1.
self-driving electric cars in China,” CNBC, November 1, 2018, https://
www.cnbc.com/2018/11/01/volvo-baidu-to-mass-produce-self- 11
Samuel Gibbs, “Google sibling Waymo launches fully autonomous
driving-electric-cars-in-china.html. ride-hailing service,” The Guardian, November 7, 2017, https://www.
theguardian.com/technology/2017/nov/07/google-waymo-an-
Tom Hancock, “Tencent builds autonomous driving team in Silicon
4
nounces-fully-autonomous-ride-hailing-service-uber-alphabet.
Valley,” Financial Times, November 7, 2018, https://www.ft.com/
content/e01d0e04-e237-11e8-a6e5-792428919cee. 12
Mike Brown, “Tesla Autopilot V9 Takes a Step to Full Self-Driving
in These Screenshots,” Inverse, September 27, 2018: https://www.
Neil Howe, “Have autonomous vehicles hit a roadblock?,”
5
inverse.com/article/49359-tesla-autopilot-v9-takes-a-step-to-full-
Forbes, November 1, 2018, https://www.forbes.com/sites/
self-driving-in-these-screenshots.
neilhowe/2018/11/01/have-autonomous-vehicles-hit-a-road-
block/#60c6c93244a3. 13
Dirk Gulde, “Autonom fahren im Stau, so läuft´s,“ auto motor und
sport, August 31, 2017, https://www.auto-motor-und-sport.de/
Jack Stewart, “Why People Keep Rear-Ending Self-Driving Cars,”
6
news/ai-staupilot-im-audi-a8-autonom-fahren-im-stau/.
Wired, October 18, 2018, https://www.wired.com/story/self-driving-
car-crashes-rear-endings-why-charts-statistics/. 14
Audi, “Audi A8 - Audi AI Staupilot,” July 2017, https://www.audi-tech-
nology-portal.de/de/elektrik-elektronik/fahrerassistenzsysteme/
Bret Kenwell, “Amazon Could Become the Next Big Player in Au-
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Autonomous Driving

Moonshot Project with Quantum Leap

Figure 1 – Voices on autonomous driving

Where are we today?

»Have autonomous vehicles »Why People Keep Rear-Ending

»Amazon Could Become the Next Big

»Uber asks for permission to restart self-

Positive developments Controversial developments

The Road Towards 'Real' Future state 3 embraces the driverless

Future state 2 anticipates continued

Future states of mobility

Source: Deloitte FoM study 2018; Statista; IHS Automotive

Currently, broad acceptance of autonomous

<2000 2000+ 2010+ 2018+

Source: Deloitte research 2018, SAE International 2014

The focus of current developments by car Roadmap to

“Google sibling Waymo

Urban and suburban pilot

Highway autopilot including

Parking garage pilot

Traffic jam GM 201919/

Testing and ramp-up phase

The market introduction and adoption rate Key Challenges

However, AI talent is scarce and the market

Source: Deloitte Research 2018

AI is crucial for AD level 3+, because classic coding is

•• Traffic situations are highly dynamic and complex

•• Sensors collect tremendous amounts of data points, which

•• Deep learning systems can be trained to mimic human

•• Behavior prediction of other road users including vehicles,

•• AI is becoming a key technology in all areas along the

Figure 6 – Global automotive AI market forecast

Historic data Forecast

Source: Tractica 2018, Deloitte Research 2018

Figure 7 – Key characteristics of an AI system

Big Data Reasoning

Capable of processing Ability to reason (deductive

Ability to learn based on Capable of analyzing and

Source: Deloitte 2018 “Artificial Intelligence”

Platform, UX, APIs,

Source: Deloitte Research 2018

Source: “Autonomous Vehicle Disengagement Reports 2018”, DMV, CA

Adaptive Cruise Control Emergency Braking

Traffic Sign Recognition Lane Departue Warning

Cross Traffic Alert

Surround View Surround View

Blind Spot Detection

Park Assistance/Surround View

Source: Deloitte 2018

On-board hardware On-board software components

Camera/Optics System on a Chip (SOC) High Deﬁnition On-Board Maps

Radar V2V/V2I Localization & Mapping

LiDAR Actuators Perception & Object Analysis

Ultrasonic sensors GPS Prediction Foresight of movements

Oﬀ-board hardware / software

Besides, data centers constitute the

»We're entering a new world in which data may be

Car manufacturers face a similar trend

2000 2005 2010 2015 2020 2025

E/E & SW share of total value add in automotive

Federico Guerrini, “BMW, Mercedes And Audi Cars To Share Real

NVIDIA, “Volvo Cars and Autoliv Select NVIDIA DRIVE PX Platform