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 52
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 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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
r r to
usiv to intro u Autom t v i s m ring n o vo n i u oin or s to s u utonomous riving r o istr t riv rs m ss ro u s riving tri it i t A ews Sep rs in in i ita rends Sep o
o r n ing n nt ui s utonomous riving v utonomous v i s Driving rs t m in i i on it ro o ired ct Financia imes o For es o
Am on ou om t t ig Driving rs n n r in Autonomous Driving it r in nor t nor no he Street Sep oom er Sep
r s s or rmission to r st rt s riving r t sts in itts urg ig t mont s mo i ts o n ustri i ing Driving t r its t st v i i n Ari on stri n s o ot i un rs ai Mai o For es Sep
utomotiv in ustr is r i moving or r n un rgoing m ssiv ng utomotiv om ni s t gi nts st rt u s n ot rs r or ing r on so utions
Positive developments Controversial developments
08 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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 will Our analysis concludes that change will lead to new offerings of passenger expe- happen unevenly around the world, with dif- riences at differentiated price points. The ferent types of customers requiring different earliest adopters seem likely to be urban modes of transportation. So all four future 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
utur st t s o mo i it
Autonomous
rson r n Autonomous Autonomous mi e1 mi e1
Low Asset efficiency Hi h i ontro
rson r n Driv r Driv n Driv r Driv n mi e1 mi e1
i o n rs i ri er
Persona Shared
1 cost estimate per mi e ased on S mar et e amp e A AS Ad anced ri er Assistance S stems Source e oitte FoM stud Statista IHS Automoti e
10 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
Currently, broad acceptance of autonomous vehicles seems much further away than a wide adoption of car and ridesharing. ADA s st m Sources of potential delay include the need n to address existing technological limitations, such as proper functioning of sensors in all weather conditions and comprehensive availability of high definition maps, as well as concerns over cyber security and liability. On the other hand, ridesharing services in particular have a strong economic incentive to accelerate the adoption of autonomous vehicles, since it could significantly reduce 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 i s rely on step-by-step driver-assist progres- sion as most industry forecasts predict Annua of Sa es in Mn (Figure 2: e.g. tripling of advanced driver assist system (ADAS) revenues between 2016 and 2025), but instead try to jump immediately to fully autonomous driving. Rather than following the historical pattern of technological innovation, autonomous driving could constitute a step-change in de- velopment. However, the majority of indus- try experts expect the inflection point for 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- rti on ition o Autom tion Driv r Assist n ig Autom tion u Autom tion ed driving is an evolution from the advanced Autom tion Autom tion driver assistance systems (ADAS) for active safety, which have been developed over v v v v v v recent decades and are still being contin- o s stem “Feet-off” “Hands-off” “Eyes-off” “Brain-off” o dri er 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 ri er needs to e ri er ess durin control by the vehicle in specific situations. ri er is in moni read to ta e o er as defined use cases ehic e in char e of torin mode at a However, the driver needs to monitor the ri er in char e of a ac up s stem atera and times car and traffic at all times and be ready to on itudina or on itudina contro ehic e in char e of take over vehicle control immediately. ri er comp ete in atera contro ehic e in char e of in a situations atera and char e atera and Accordin to use on itudina contro ehic e in char e of on itudina contro case no steerin in man situations atera and in man situations whee and or peda ehic e ta es o er Minimi ed ris for on itudina contro arns dri er in a re uired other tas s accidents in specific situations time manner
Source: Deloitte research 2018, SAE International 2014
12 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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 rti on ition o Autom tion Driv r Assist n ig Autom tion u Autom tion Autom tion Autom tion 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- v v v v v v latter case the driver is allowed to be out tance systems, where automation at level o s stem “Feet-off” “Hands-off” “Eyes-off” “Brain-off” o dri er 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-
all driving modes (eventually, these types of ing the driving environment, but at level 3 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
ri er needs to e ri er ess durin state in the case of unexpected events. ri er is in moni read to ta e o er as defined use cases ehic e in char e of torin mode at a ri er in char e of a ac up s stem atera and Figure 4 shows a roadmap towards 'real' times on itudina or on itudina contro autonomous driving (level 5) for passenger ehic e in char e of ri er comp ete in atera contro ehic e in char e of in a situations cars, expected to hit the road with wide- atera and char e atera and Accordin to use spread adoption not before 2030. On the on itudina contro ehic e in char e of on itudina contro case no steerin in man situations other hand, level 3 and level 4 market intro- atera and in man situations whee and or peda ehic e ta es o er Minimi ed ris for duction are already underway, with level 3 on itudina contro arns dri er in a re uired other tas s accidents being primarily focused on an automated in specific situations time manner highway pilot and level 4 on specified appli- cations such as automated valet parking or first robo taxi fleets in selected cities (e.g. 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”
Urban and suburban pilot
Highway autopilot including Level 4 highway convoy Automated valet parking
Parking garage pilot
GM 2019/ Traffic jam Level 3 Highway chauffeur Audi 2020/ chauffeur Bosch Daimler 2020/ AVP 2018 e.Go (ZF) BMW 2021 Mover iNext 2021 2019+/ Traffic jam Level 2 Conti CUbE assist, … Audi AI Traffic 2019+ Jam Pilot 2019
Tesla Autopilot ACC, Stop Level 1 v9 2018 & Go, …
Level 0
Source: ERTRAC 2017 “Automated Driving Roadmap”, VDA 2018 “Automatisiertes Fahren”, Testing and ramp-up phase The Guardian 2017 “Google sibling Waymo launches fully autonomous ride-hailing service”, Deloitte Research 2018 Series deployment
14 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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
n uv r
n uv r
n uv r Autonomous Driving v Machine Learnin
n uv r n no og r orm n v
u stitution o ru s o ing it tr ining o Artificial Intelligence algorithms
ssi ADA un tions ru s o ing
oda
Source: Deloitte Research 2018
16 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
Figure 5 – Quantum leap from classic rule-based coding to artificial intelligence
AI is crucial for AD level 3+, because classic coding is not sufficient to master and meet the necessary requirements
•• Traffic situations arehighly dynamic and complex •• Sensors collect tremendous amounts of data points, which »Artificial Intelligence is the need to be interpreted in real time (e.g. object detection, reduced visibility, natural behavior, map adjustments, …) new electricity.« •• 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 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
Figure 6 – Global automotive AI market forecast
Ser ice A Software
Automotiv A r t i i ion D Hardware
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
ig D t soning
apa e of processin A i it to reason deducti e massi e amounts of or inducti e and to draw structured and unstruc inferences ased on the tered data which ma situation onte t dri en chan e constant awareness of the s stem
rning ro m so ving
A i it to earn ased on apa e of ana in and historica patterns e pert so in comp e pro ems input and feed ac oops in specia purpose and enera purpose domains
Source: Deloitte 2018 “Artificial Intelligence”
20 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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 A i it to s ns r son vision-based object detection algorithms ng g n rn in autonomous driving systems. Such algo- oice reco nition omputer ision rithms have the potential to exceed human performance by orders of magnitude. P annin & atura an ua e General AI, on the other hand, refers to the optimi ation processin more human interpretation of intelligence in in rning the sense that such AI solutions are able to A i it to rn understand, interpret, reason, act and learn o otics & now ed e nsuper ised earnin from any given problem set. An AI system motion caputre typically combines machine learning and Super ised other types of data analytics methods to einforcement achieve AI capabilities (Figure 8). earin earnin t o s A i it to r son e ression decision trees etc
no ogi s Infrastructure si n m nt
P atform APIs sensors etc
Source: Deloitte Research 2018
21 Figure 9 – Autonomous vehicle disengagement report statistics1
rio D m r to ov m r
Ser ice
og rit mi s v u s in m Autonomous m riv n r is ng g m nt
Software
oo e M issan oo ri e ai aidu e ena Apti n idia Mercedes a eo osch en
Source: “Autonomous Vehicle Disengagement Reports 2017”, DMV, CA
1 Please note, that this figure is only indicative, given that it only shows the state of California. Some firms, including German OEMs, do not 22 test their vehicles in California and therefore do not appear in the data Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
•• Based on the 2017 “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 •• Nissan following in third place with a wider Please note that the DMV only registers safety requirements imposed by industry 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 not consid- and mid to low performers is accurate new central processing units and operating ered in this chart due to a lack of test data though. systems come new challenges regarding the 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 On-board hardware On-board software components
Camera/Optics System on a Chip (SOC) High Definition On-Board Maps Adaptive Cruise Control Emergency Braking o ect optica ima es to e inter Hi h performance ener Precise oca i ation information a out Pedestrian Detection preted ad anced AI & ana tics efficient computer hardware roads infrastructure and en ironment Collision Avoidance
r Localization & Mapping etermines speed and distance of Communication with vehicles & ata fusion for ehic e oca i ation and Traffic Sign Recognition Lane Departue Warning objects using electromagnetic waves infrastructure o er short ran e en ironment ana sis
iDA Actuators Perception & Object Analysis Hi h reso ution sensor usin rans ation of e ectronic si na s Algorithms etection and c assi Cross Traffic Alert i ht eams to estimate distance into mechanica actions fication of objects and obstacles from o stac es Park Assist Ultrasonic sensors r i tion Foresi ht of mo ements Surround View Surround View Short distance o ject Loca i ation of ehic e and actions ehic es pedestrians and reco nition e par in usin sate ite trian u ation other mo in o jects
Odometry Sensors Measure wheel speed to predict vehicle Decision-Making Blind Spot Detection tra e and comp ement oca i ation P annin of ehic e route maneu ers acce eration steerin and ra in Park Assist Rear Collision Warning Off-board hardware / software Vehicle Operating Systems D t nt r A ou r tion peratin s stem runnin Stora e and processin of Learnin adoptin and up a orithms in rea time hot and co d ehic e data datin H maps & a orithms
Park Assistance/Surround View Supervision platform
Source: Deloitte 2018 Ana tics to monitor the A s stem operation detectin & correctin fau ts 24 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
On-board hardware On-board software components
Camera/Optics System on a Chip (SOC) High Definition On-Board Maps o ect optica ima es to e inter Hi h performance ener Precise oca i ation information a out preted ad anced AI & ana tics efficient computer hardware roads infrastructure and en ironment
r Localization & Mapping etermines speed and distance of Communication with vehicles & ata fusion for ehic e oca i ation and objects using electromagnetic waves infrastructure o er short ran e en ironment ana sis
iDA Actuators Perception & Object Analysis Hi h reso ution sensor usin rans ation of e ectronic si na s Algorithms etection and c assi i ht eams to estimate distance into mechanica actions fication of objects and obstacles from o stac es
Ultrasonic sensors r i tion Foresi ht of mo ements Short distance o ject Loca i ation of ehic e and actions ehic es pedestrians and reco nition e par in usin sate ite trian u ation other mo in o jects
Odometry Sensors Measure wheel speed to predict vehicle Decision-Making tra e and comp ement oca i ation P annin of ehic e route maneu ers acce eration steerin and ra in
Off-board hardware / software Vehicle Operating Systems D t nt r A ou r tion peratin s stem runnin Stora e and processin of Learnin adoptin and up a orithms in rea time hot and co d ehic e data datin H maps & a orithms
Supervision platform Ana tics to monitor the A s stem operation detectin & correctin fau ts
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, com- ing 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 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
»We're entering a new world in which data may be more important than software.« 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” – in that, for car manufacturers, the car is becoming a rich source of data they can use to improve products and business operations.
28 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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 Av r g utomotiv ro u t tim (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 rs semiconductor industry, development of E/E as well as software has increased exponen- tially over the past two decades. At the same rs time, the average product cycle time has halved over the same period (see Figure 11). rs
rs
rs
Av r g utomotiv ro u t tim
r Source: Diez (2015), Deloitte Research 2018
30 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
s r o tot v u in utomotiv
A S ectronic fue n oard H rid onnected Autonomous injection ia nostics cars cars cars
r 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 ro u t tru tur or tru tur o o s t ring o s st ring no ogi s established a very strong top-down chain Hardware s Software Share ro u t tru tur PI s Pro ress Indicator ooperation s Supp ier of command. This made sense when labor A i e e e opment r ani ation e ationship division between "thinkers" and "doers" was Processes strict. The engineer defines how the me- An og o it i os t r u i r t ring 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, Digit o it ross un tion Agi rogr ss n i tor rtn rs i 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 ultimately 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
Source: Deloitte 2018
32 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
ro u t tru tur or tru tur o o s t ring o s st ring no ogi s Hardware s Software Share ro u t tru tur PI s Pro ress Indicator ooperation s Supp ier A i e e e opment r ani ation e ationship Processes An og o it i os t r u i r t ring
Digit o it ross un tion Agi rogr ss n i tor rtn rs i
•• 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 and build up expertise in those areas. in measuring and steering the progress of hold great expertise and talent in software development. Instead, agile steering models development and artificial intelligence, but Area 2 describes the change that is should focus on continuously measuring lack the automotive know-how to build cars necessary on an organizational and work holistic progress and direction as compared themselves. Car manufacturers possess structure level. Structures and processes to performance at specific milestone dates. the necessary automotive expertise and that have proven successful for the devel- After all, agile development practices infrastructure, but lag behind with software opment of products with low shares of E/E support the ambition to cope with uncer- capabilities. For this reason, companies are and software are no longer ideal for the tainty by providing a new level of adaptivity. joining forces in development partnerships development of autonomous vehicles. Com- Upfront top-down planning, including metic- and increasingly via mergers and acquisi- panies are increasingly replacing classical ulous project timelines, run counter to the tions. waterfall structures with agile approaches. philosophy of agile development. However, 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 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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 r ition r r riv n i oso o t r riv n i oso
ne Function Man Functions n D vi n t orm
Source: Deloitte 2018 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
r r riv n o t r t s ou riv n o t r t s
A tor
mot o t r t
ront nning u t s t r rs t s or ing to s rint
Source: Deloitte 2018
36 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
»For whatever reason somebody can be convinced to buy a PC, it opens up a whole new market for all of us in the software business.« Kevin O’Leary (Co-founder SoftKey)
37 Figure 15 – Traditional waterfall vs. agile software development
Work Structure r ition so t r v o m nt Separated structure Agi so t r v o m nt Inte rated structure follows Product Structure As mentioned, traditional software devel- opment follows individual development Software component Software Software tur m ross un tion feature stages sequentially. From an architectural team inc component omponent omponent team with n to n standpoint, building blocks are divided into ead with o r s on r s onsi i it software components, which are taken si i it tur m care of by component teams (compare Software Software Figure 15). Since development times can be omponent omponent extensive for certain software components, tur m
ommunit ommunit ommunit ommunit m this approach requires meticulous upfront
planning: First, to enable teams to work v r st m in parallel with clearly defined interfaces, Software Software tur m n tasks and responsibilities; second, to reduce omponent omponent dependencies to a minimum, as queues have an exponential impact on cycle times. In complex systems, dependencies cannot omponent Feature ac o be avoided every time, which increases P an dri en eams ross tur eams dri en coordination efforts and complicates system D n n i s etween a i nment ia integration. Owing to this structure, water- components cause Function nd to nd ommand s org ni specific Ser ant e e opment fall development is characterized by local u u s which ha e an and ontro communities Leadership optimization of single software components e ponentia impact on Leadership due to silo development. Operational and c c e times Loca organizational structures are detached and o a ptimi require coordination teams. Management ptimi ation ation Inte rated Function follows a command-and-control style operationa & specific leadership, because technical decisions are or ani ationa e e opment t r Agi made top-down. Structure Iterati e Se uentia M P Approach eam Indi idua espon ross esponsi i it Si os si i it functiona
Source: Deloitte 2018 Minimum ia e Product Approach 38 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
r ition so t r v o m nt Separated structure Agi so t r v o m nt Inte rated structure
Software component Software Software tur m ross un tion feature team inc component omponent omponent team with n to n ead with o r s on r s onsi i it si i it tur m Software Software omponent omponent tur m
ommunit ommunit ommunit ommunit m v r st m Software Software tur m n omponent omponent
omponent Feature ac o P an dri en eams ross tur eams dri en D n n i s etween a i nment ia components cause Function nd to nd ommand s org ni specific Ser ant e e opment u u s which ha e an and ontro communities Leadership e ponentia impact on Leadership c c e times Loca o a ptimi ptimi ation ation Inte rated Function operationa & specific or ani ationa e e opment t r Agi Structure Iterati e Se uentia M P Approach eam Indi idua espon ross esponsi i it Si os si i it functiona
Minimum ia e Product Approach 39 This is in stark contrast to the characteristics are fundamentally different from the ones embodied by agile software development, in the automotive industry. The car industry which in its core embraces self-organization is highly sophisticated and consists of an on team level, pushes decision-making advanced network of manufacturer, supplier down to the lowest possible hierarchy level and partner relationships. OEMs outsource and fosters servant leadership. Teams large portions of development work to sup- are cross-functional and assume end-to- pliers, which creates dependencies and the end responsibility for a product feature need to define and manage clear interfaces. (i.e. minimum viable product increment). In addition, we are talking about embedded In analogy to the most common agile software development with significant framework, Scrum, teams are by definition hardware shares. These circumstances pose feature teams; development is consequently new challenges to agile working models, oriented toward the highest customer value which have their origins in pure software de- and prioritized via a backlog of development velopment. In order to cope with such high items. Consequently, the organization levels of dependency and hardware shares, continuously strives to achieve a global op- companies have to be willing to continuously timum. A perfect feature team is able to do challenge the status quo and adapt where all the work necessary to complete a feature necessary. When considering introducing (backlog item) end-to-end. Cross-feature an agile working model, you should ensure alignment is self-organized and all feature that not only is it compatible with your over- teams work on a common software re- arching product development process, but pository. Operational and organizational also meets the demands posed by supplier structures are integrated and streamlined relationships, hardware development and to focus on value-adding activities while computing power constraints. Bill Gates eliminating overhead. famously coined the phrase: "Intellectual property has the shelf life of a banana." If Many companies view agile as the holy grail you want to create the future, you have to for securing innovation leadership, which innovate as fast as your competition, at the leads to massive transformation programs, very least. Becoming agile and adaptive sometimes without taking the necessary can help achieve that goal, but you need to time to analyze and evaluate the full spec- be smart about it. Agile transformations in trum of implications. Agile (software) de- complex environments such as the automo- velopment brings many benefits, but it has tive industry constitute a fine line between to suit the purpose and environment. The significant performance improvements and dynamics and challenges in pure software the complete inability to act. development environments such as banks, insurance companies or in app development
40 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
»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.« Nolan Bushnell (Co-founder Atari, Inc.)
41 You also need to use the right scaling strat- quickly enough and therefore cannot sus- egy. Autonomous driving development divi- tain the momentum, support and positive sions typically employ several hundred em- energy required to drive the undertaking ployees for the software content alone. It is towards success. Top management as well a tremendous challenge for any organization as employees will soon lose trust in the to change everything from the ground up. A change if it does not yield positive results. real agile transformation not only changes Therefore, we recommend starting with a the way developers work with one another; pilot, a small agile nucleus, where a group of it fundamentally changes how the organ- highly motivated people come together to ization operates, from the organization function as spark for the change and drive structure, via the operating model, product its initiation phase. Word about the pilot's architecture, verification and validation pro- first successes will soon spread into other cedures, to the culture, to name but a few. groups or departments, who will then be For example, hierarchical barriers are bro- eager to "go agile". Figure 16 shows an ex- ken down, technical experts become tech- ample of a structured agile transformation nical leaders empowered to make technical roadmap, including some of the most crucial decisions without the alignment obligation steps to consider in an agile transformation with their superiors, silos are eliminated and (Deloitte's structured enterprise agile trans- replaced with strong cross-functional team formation 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 or- ganization is not able to change everything
42 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
»Agile is more a 'direction', than an 'end'. Transforming to Agile culture means the business knows the direction they want to go on.« 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 ...
ui d
Work streams
Pi ot Pursue Establish agile vision and Define agile change and Stand-up agile change and Establish enterprise success criteria ops team ops team wide agile COE4 Operating model, alignment and organization design Measure Learn Define base operating Stand-up organization and Integrate and extend Refine and extend model and road map team structure across the enterprise
sur m nt r m or Assess architecture and Componentization and Refactor architecture and Mature architecture Architecture and DevOps Operating DevOps capability DevOps strategy stand-up DevOps and DevOps model and alignment
Organization Architecture Define core agile SDLC1 Refine SDLC method Apply and refine agile SDLC method design and DevOps method for pilots Agile Team Process and Practices transfor- Stand-up tooling Stand-up tooling Define agile tooling stack Setup agile tooling mation enablement team enablement team program Define training and Execute training Program Training Coaching Train pilot groups coachinng program (executive, management, PO2, scrum master, RTE3, team)
Team Training and Coaching process and Coach pilot groups Transition portfolio, programs, and teams by waves practices
Source: Deloitte 2018
44 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
... 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 COE4
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 SDLC1 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, PO2, scrum master, RTE3, team)
Coach pilot groups Transition portfolio, programs, and teams by waves
1 SDLC: System Development Life Cycle 3 RTE: Release Train Engineer 2 PO: Product Owner 4 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- i rri rs o utions dency to resist any kind of change in culture, structure and roles within a company. You reate a mutua understandin of Human resistance to can counteract this resistance by creating a the chan e add new competences chan e in cu ture structure and ro es mutual understanding of the change, adding and tr it out in a pi ot new competences and trying it out in a pilot. Next, there is a lack of open communication. Instead of forcing a form of communication reate transparenc throu h sharin onto employees, create transparency, e.g. Lac of open communication information and wor in in pairs through sharing information, avoiding information access restrictions or working in pairs. Especially large, established corpo- Fai fast earn fast rations are often too risk-averse. In order to ein too ris a erse Fai ure is success if we earn become agile, you need to adopt a fail fast, from it Ma co m For es learn fast mentality, because "failure is suc- cess if we learn from it" - Malcolm Forbes. Another crucial aspect is the often seen lack Pro ide a so id mandate to of leadership buy-in. If there are no senior Lac of eadership u in leaders backing the agile transformation, it mana ers and stron eadership is doomed to fail. You have to provide a solid mandate to managers and strong leadership support if you want the transformation to An gi tr ns orm tion re uires a ot sis riv n ori nt focus and imp ementation 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 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
New Steering Model i rri rs o utions Classical KPI-driven reporting systems reate a mutua understandin of often follow an underlying traffic light logic. Human resistance to the chan e add new competences Holistic top-down defined project plans with chan e in cu ture structure and ro es and tr it out in a pi ot 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 reate transparenc throu h sharin Lac of open communication implementing a steering model, the mile- information and wor in in pairs stone-related progress tracking with classic KPIs usually indicates a (negative) deviation from the originally projected timeline and Fai fast earn fast consequently leads to red traffic lights at ein too ris a erse Fai ure is success if we earn aggregated project tracking overviews. Of from it Ma co m For es course, the underlying reasons range from bad planning accuracy and over-optimistic assumptions to execution problems. The Pro ide a so id mandate to Lac of eadership u in real problem here comes into play because mana ers and stron eadership of natural human behavior: Every single red traffic light seems to indicate a need for action. As a consequence, countermeasures An gi tr ns orm tion re uires a ot sis riv n ori nt focus and imp ementation 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
r ition riv n st ring mo t ring in n gi or ing mo
If KPI shows a red traffic Product wner P prioriti es the product ac o ased on a light, specific counter comprehensi e picture ena ed a ho istic set of pro ress measure has to e indicators and feed ac s defined and triggered
If PI tar et a ue is achie ed no counter measure is needed
ottom op down up Se f Steerin or ani ed rans oncea ment parenc
ounter Fai Learn measure orrect
ross Si o functiona rogr ss Menta it Mindset ar et n i tor Indicator for a ues Prioriti ation
Sp it Shared Ho istic esponsi i i is of Loca esponsi i i Interpre ties Indi idua ptimi ation ties eam tation
Source: Deloitte 2018
48 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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.
»Measuring programming progress by lines of code is like measuring aircraft building progress by weight.« Bill Gates (Founder Microsoft)
49 Figure 19 – Cooperations and partnerships
2.1.4 Mastering New Technologies F A As mentioned above, autonomous driving ar o is one of the most complex development ontinenta osch challenges in the automotive industry. Apti ri e ow m ta i The broad range of required skills and Ma na D capabilities barely exists in-house at any er traditional OEM, supplier or tech player. The Inte H latter are well positioned when it comes to H a uar software development and agile working Mo i e e I IA principles to achieve shorter development Landro er L ft cycles and time to market, but often lack the F A A experience with industrialization and scaling a real hardware business like building cars. On the other side, OEMs and traditional Inte automotive suppliers often struggle with the Harman aidu transformation towards a new agile product Honda L ft and software development system with DA significantly shorter cycle times for E/E and A software-related functions. ruise Aurora Mo i e e Cross-industry partnerships are an inevita- Samsun e ena ble prerequisite to mitigate these complex e od ne L ft challenges and to close own technology blind spots. All major stakeholders engaged D in the development of autonomous driving Saips solutions have established or joined specific Ford Ar o AI cooperations or partnerships (Figure 19). In ett Audi i i Maps Auto i addition to the lack of technological or pro- er ee cess expertise, there are several other rea- e phi M IA sons to join forces. Reduced development costs and risk sharing between partners are I IA Microsoft further important drivers for the emergence H enuit of those cooperations. Lastly, from a topline perspective, a larger addressable customer Aurora I IA base and associated revenue potentials have to be mentioned.
50 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
Automotive Specific collaboration setups range from Mobility OEMs classic development contracts to joint ven- Provider & Suppliers tures and mergers and acquisitions. While European automotive OEMs tend to prefer contractual development agreements to Technology Firms coordinate collaboration efforts, especially US companies are more open to buying stakes in startups, like General Motors did for example with Lyft and Cruise Automa- tion. Either way, the key success factor for all types of cooperations is to align and stream- Why Partnerships? line the interests of all stakeholders towards a common goal. This sounds trivial, but has Gain access to necessary capabilities often been a major obstacle for sustainable results and success in former cooperation Partner up with others to combine complementary ca- initiatives. pabilities in order to create a superior product or service and cover own capability blind spots and/ or resource bottlenecks
Foster sales & market penetration
Gain access to foreign markets and customers by collab- orating with local partners in unexploited geographic re- gions and leverage regional know-how and relationships
Share risks
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 activi- ties, e.g. industrialization
51 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".
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 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 shift towards the aftersales phase. Frequent remote software updates and the provision of new (software-enabled) functions over Space oein Modern Fu the entire vehicle lifecycle will change the ex- Shutt e ream iner car autonomous isting profit generation pattern in the auto- ehic e 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
52 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
53 Authors
Dr. Harald Proff Thomas Pottebaum Lead Partner Director Monitor Deloitte Strategy Operations Germany Automotive Tel: +49 (0)151 5800 2696 Tel: +49 (0)151 5800 4516 [email protected] [email protected]
Philipp Wolf Senior Consultant Strategy & Operations Tel: +49 (0)151 5807 0480 [email protected]
54 Autonomous Driving | Moonshot Project with Quantum Leap from Hardware to Software & AI Focus
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Issue 01/2019