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Proceedings of the American Society for Engineering Management 2019 International Annual ConferenceE. Schott, H. Keathley, and C. Krejci, eds.

STRATEGIC PLANNING FOR GOOGLE'S AUTONOMOUS VEHICLE TECHNOLOGY BASED ON PATENT ANALYSIS

Abstract ID: 108

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Abstract It would be reasonable to indicate that autonomous vehicle technology significantly affects the traditional automotive industry and other related industries. However, it is much more challenging to make a complete transformation of this effect than moving from the electric car. The study includes investigation of different perspectives with SWOT analysis for the autonomous driving impact. The research combines Patent analysis and Social Network Analysis (SNA) to devise an essential tool for strategic planning to reveal the implicit R&D partnerships and explicit strategies in the company level. The case study shows that the competitive and complementary interactions influence the formation of partnerships in the market. This study shows that the visualization in a sophisticated network setting provides abundant and unbiased analysis to high-quality decision-making. Based on the case study of Google’s self-driving company Waymo, this study finds that Waymo’s car should keep the best R&D for customer-oriented technology so that it can maintain continuous cooperation. Secondly, one should identify niche areas before the large-scale transformation, such as public transport; disable people usage; for campus or indoor-closed area use. Thirdly, Waymo shall provide users and collaborators self-driving car solution for sustaining ecosystem to expand to realize the economies of scale. The last but the most important is focusing on the R&D activities in critical technologies to achieve more core patents.

KeywordsSelf-driving technology, Patent analysis, SWOT analysis, Social Network Analysis, Waymo’s car

IntroductionNot every emerging technology may become an advanced and mature industry. Such technologies should be accepted, preferred, and adapted by people. Then, it is necessary to develop the product with the continuous innovation of the product that the technology converts and to create the supply capacity that this demand can meet to change something in a mature industry. There is a broad agreement in the car industry that autonomous vehicles are the future, as verified by significant research and development (R&D) investments by automotive players and tech giants alike how to examine it for success. Autonomous vehicle technology also seems to affect the traditional automotive industry and other related industries. However, it is much more challenging to make a complete transformation of this effect than moving from the electric car. The group intends to investigate the impact of autonomous driving in technology, market, legalization and infrastructure perspectives, as well as elucidate quantitative analysis as a vital tool for strategic planning in company level, provide the overview of the technologies, identify potential competitors along with partnership, and track the evolution of a landscape relative to possible strategy.

Literature ReviewDifferent perspective in Self-driving technologyOne should indicate that autonomous driving is recognized as the future of transportation systems. Companies like Google are leading the innovation to bring fully autonomous and commercially feasible vehicles to the market. Many automotive firms, including Ford and Tesla, are competing to be the first to bring viable and affordable self-driving cars. However, it is Waymo that has made significant milestones towards such an objective. In such a context, it could be considered that various factors have a substantial effect on technology. In fact, the study examines the SWOT analysis for the self-driving technology and the factors influencing the autonomous driving.

Importantly, the technology's strengths lie within the provision of safer transportation. The autonomous technology focuses on the elimination of human error in driving, which increases the ability to control traffic flow, thus promoting higher safety [1]. It seeks to reduce the fatalities associated with the current mode of transportation, which are attributable to negligence or distraction. Additionally, it offers convenience, smooth operation, and less stressful driving. In fact, autonomous technology is also about access to transportation for all, as well as provides

Copyright, American Society for Engineering Management, 2019


time for other activities. Finally, it enables the providers to create an engaging user experience, correspondingly increasing operation, and comfort.

As to the opportunities presented by the technology, such seem limitless. Foremost, it offers a chance to obtain higher levels of road safety, fewer speed violations, and reduction in road congestion, factors facilitating the efficient flow of traffic reducing road accidents and deaths [1]. It promotes better use and shaping of scarce land in cities by reducing congested parking and eliminating parking spaces. The development of unused space supports the gradual but drastic growth of cities. The technology requires advertisements for linking the customer with the service and thus creating new mobility and business models[2]. The provision of better experiences enables providers like Google to obtain direct relationships with customers creating brand loyalty. Most importantly, it offers an opportunity for reducing carbon emissions, the factor adversely affecting the environment. Lastly, it provides a chance for ride-sharing, which possibly dominates transportation turning it into a pay-as-you-go-service.

Nonetheless, the technology presents weakness and threats. The weaknesses include concerns about the reliability of the technology. Specifically, relying on the process of driving entirely on automatic systems can be frightening for users, as well as it can raise several safety concerns[1]. First, the safety is contingent on the program's infrastructure and software, the systems inevitably including uncertainties of potential failure. Second, there are concerns regarding the system's ability to make complex decisions to uncertainties. The technology is yet to be modified to meet the criteria of mitigating uncertainties most effectively. From an economic standpoint, autonomous vehicles are expensive in terms of purchase and maintenance.

Similar to the opportunities, there are also threats that should be considered. The primary danger stems from the fact that in an unforeseeable and uncertain scenario, the autonomous system operates in a manner causing harm to people. In terms of security, the tampering of the technology is a significant threat due to possible access by unauthorized persons. When it comes to privacy, the related software and applications for linking with customers pose a risk of illegal access to personal data; hence, the concerns about the anonymity are inevitable. In such regard, it could be suggested that cybersecurity is the critical threat that elicits privacy concerns[2]. Besides, the efficient operation of the self-driving vehicles requires a single network for operation and communication making such a system susceptible to cyber-crime. Government intervention and regulation affect the potential market for self-driving cars in the effort to ensure the provision of high-level safety standards. Political factors and uncoordinated legislation can impede the technology as well[2].

Strategic planning based on Patent AnalysisIn the context of strategic planning, a patent serves as an useful document for competitive analysis and technical information[3][4][5]. Respectively, patent analysis is designed to understand IP for products and technologies relevant to the enterprise’s present and future, with many implications for R&D management, competitive intelligence, and businesses strategic planning[6]. Under this scenario, patent analysis has two major applications.

First, patent map analysis is used as a patent intelligence tool or system to discover the relationship between a patent assignee and inventor, as well as classification or text in various data analytics platforms, such as Derwent Innovation, PatSnap, Relecura etc [7]. Patent technology clustering usually had been helpful to monitor the competitive environment, innovation trends in macro-level to assist companies to gain a competitive advantage for R&D strategic planning[8][9]. Although the patent map is widely used for simplicity and ease of use based on patent platforms, the instruments are limited in technical clustering based on frequency and occurrence of words or text in a document, making it impossible to go further and be integrate into micro-level strategic planning.

Second, patent network analysis had been used to explore and identify R&D relationship in the open innovation to reveal internal business strategy and external challenges[10][11][12]. Cooperation networks between inventors and applicants are frequently helpful in the competitor analysis. Such explain behavior of actors in the marketplace, emphasizing R&D activity, technology trajectories and to what extent factors build on each other’s knowledge[13]. According to [14], the spatial and social proximity of actors increases the probability of knowledge flows. Therefore, the key premise is that both the R&D cooperation network and strategic partnership in business should be considered as factors that influence a company’s strategic planning. However, there is limited academic foundation and scholarly base linking the two as a whole.

To fill the gaps, the patent landscape and Social Network Analysis （SNA) was employed as a tool to analyze technology cluster, identify network and partnership in business, assess the competitor’ strength, weaknesses, and, most importantly, business strategy. In fact, the instrument was also utilized to track the evolution of a landscape relative to possible strategy.



MethodologyExhibit 1 illustrates the overall research process within several distinct steps.

Step1: SWOT analysis as a part of strategic planning to specify the internal and external factors favorable and unfavorable to self-driving technology. Attempt to generate the environmental impact of Google’s self-driving technology. The different perspectives, such as market, technology, legal, social and infrastructure, etc., are identified with the strengths, weaknesses, opportunities, and threats (SWOT) of Google’s self-driving technology.

Step2: The case study focuses on the Google’s self-driving company Waymo, with the intent of discussing the potential strategy the business should pursue.

Step3: Patent map or landscape is used as a central instrument to analyze the technology, identify potential competitors and partnerships, and assess the competitors’ strengths, weaknesses and business strategy. In such sense, the key objective was to track the evolution of a landscape relative to potential strategy. In the study, themeScape in Derwent Innovation (DI) was employed as patent analysis tool, which shows data topographically, as well as identifies common themes text clustering groups’ records into related sets, to pinpoint into a apparent picture of company’s key technologies.

Step4: The three kinds of networks are built based on patent data, including the following: patentee collaboration network, transfer network, citation network, and partnership in business strategy. Social Network Analysis (SNA) is used for visualizing the various R&D relationships between companies. In fact, by analyzing the current partnerships and alliances in the press release while sorting out the competition and cooperation between companies, it deemed helpful to identify potential competitors and partners, to foresee the strategy for the development of Google driverless technology.

Step5: Considering the above conclusion, the internal and external strategies were developed comprehensively.

Exhibit 1. Overall process of the research

Factors that Impact Self-driving Technology Based on SWOTThe industry views the driverless technology as the next phase of transportation evolution, the one important to the reduce the number of road accidents and deaths. However, some people perceive it as a disruptive technology, which threatens the future of the current modes of transportation. Different factors influence autonomous driving, but the main issues still include safety, reliability, trust, legal, and performance expectations [15]. Safety is the primary factor influencing autonomous driving and forms the basis for the development of self-driving cars. The rising rates of accidents and deaths on the roads necessitated the conception of the idea of autonomous driving to help solve the problem. Autonomous vehicles present safety risks like serious crashes mainly during first deployment[16]. Safety is also a significant concern since the vehicles depend on programs or software, which can have possible glitches or suffer cyber-attacks. As such, the criteria for evaluating safety entails the safeguards placed in the vehicles to ensure safety, prevent, and respond to unforeseeable scenarios [17]. Other measures include the provider’s assurance of security and privacy as well as the government protection against the use of driverless cars in cases leading to liability as shown in Exhibit 2.



Exhibit 2. Different Factors Influence Autonomous Driving

Besides, it could be suggested that performance expectancy is another variable influencing autonomous driving in the matter of the adoption of the technology. One of the requirements of autonomous driving is the need for high performance, since failure in a single component of the driverless cars can result in fatalities. The criteria include the high performance over traditional vehicles that promote its adoption and usage. Further measures comprise driverless cars outperforming the safety records made by ordinary vehicles. There are non-autonomous cars with good safety records, and thus, driverless ones must perform better. The third criterion is the associated efficiency where the use of these vehicles should improve work and living efficiency through easier and better transportation. It also factors into reliability that autonomous cars must continuously demonstrate. Testing is one of the measures under reliability along with the cars ability to cope and effectively react to unlikely occurrences.

Another crucial factor, specifically trust is fundamental to the acceptance of autonomous driving technology. The development of trust between shareholders is essential for the success of driverless cars whose occurrence depends on the handling of concerns such as privacy, and security. One should associate Self-driving vehicles with security and privacy risks, such as hacking, remote access and control, and privacy invasion through the unauthorized acquisition of personal data [17]. As such, the criteria involve the likelihood of software error or failure and the guarantee of privacy and security. Further data protection and personal autonomy assurance are essential to its adoption and success. The other approach is the assurance of acceptance of liability by the manufactures together with government assurance. Finally, the certainty of the manufacturer's technology, reliability, and safety assurance will influence the acceptance of driverless driving. All these measures promote confidence in the technology and hence promote its usage.

In terms of the acceptance of technology and its integration, legal factors have a significant effect. The reality of driverless vehicles and their feasibility for commercial use raises red flags for government intervention, forcing the industry ensure high safety standards. Government regulations influence the making and adoption of innovations for the sake of public protection, hence affecting the adoption of the autonomous technology per se. In fact, regulations can either promote or cripple further developments by allowing or impede testing and deployment of the driverless technology [18][19]. Legal standards impose a high level of safety influencing the costs manufacturers face in terms of improvements designed to meet the standards. Evidently, such process translates into high prices on vehicles, which, in turn, affect the number of purchases and utilization. As such, legal factors can present challenges such as regulatory restrictions and political blowback since self-driving technology depends mostly on political and legal factors. Others factors that can affect autonomous driving include consumer behaviors due to fear of the unknown and change, road infrastructure, liability issues, and political problems like lobbying groups opposing the technology.

Concisely, there has been a massive and aggressive investment into self-driving technologies. Many companies are racing to create the technology that will lead to the inevitable transformative change and paradigmatic shift. The SWOT analysis of the technology shows enormous potential, mainly in the provision of road safety, convenience, and the elimination of human error. However, there are threats in terms of safety and privacy. Nevertheless, autonomous driving technology will undoubtedly dominate the roads despite the factors influencing its adoption, the ones stemming from legal and political realms.

The Case Study of Google's Autonomous Vehicle TechnologyData Collection



Importantly, in the recent years, the patent race of autonomous vehicles has become more and more competitive among both automakers and tech companies. The R&D budget rose 20% for tech players and 5% for a car. According to research conducted by Oliver Wyman and WIPO (2017), between 2012 and 2016, there were slightly more than 5000 mobility patents filed by 12 leading automakers and global tech companies[20]. The six giant car companies — Audi, Daimler, General Motors, Volkswagen, BMW, and Tesla, are more focusing on the hardware for mobility services, related to green car technology, including electric cars, batteries, fuel cells, and alternative fuel. Tech companies like Google, Facebook, and Apple are pouring enormous resources into software, with the number increased over the five years by 50%, while the six automakers decreased[21]. Therefore, the industry requires alliances and concentration of R&D resources. For auto manufacturers, businesses should pay a greater focus on patent strategy. There is an excellent potential partnership with tech companies in traffic monitoring and navigation services. For tech companies, it is crucial to select areas with a high level of customer interaction and engagement, which establishes a new customer interface.

The case study is focused on tech company Google's autonomous vehicle technology for its earliest move, to begin with, self-driving project. Google had begun the self-riving car project in 2009. Waymo is an autonomous car development company and subsidiary of Google's parent company. Waymo is a self-driving technology company with the mission to make it safe and secure for everyone to get around—without the need for anyone in the driver's seat. Although Google had begun a self-driving project in 2009 and is racking up more patents than most automakers on connected and self-driving cars, it still lacks experience in building, maintaining, and engineering of cars. The purpose of the case study is to discuss the future of the autonomous automotive industry in detail, and what kind of strategy Google should pursue, which has significant investments in this technology.

In the case study, the patent landscape was employed as a vital tool to analyze the technologies, identify potential competitors and partnership, assess the competitors' strength, weaknesses, business strategy, and track the evolution of a landscape relative to possible strategy. We retrieved patent data in the United States from 2013 to 2017 on Derwent Innovation (DI) on May 8th in 2019. In line with patent analysis, the technology strategy is made for Google's related investments. The news data of Waymo were retrieved from the website Autoblog on February 26th in 2018 (https://www.autoblog.com/tag/waymo/).

Data Analysis and ResultsPatent Race in Self-Driving Technology. According to patent analysis for automated driving vehicles Exhibit 3, Top 20 companies were investigated for assessment of strategy. Toyota, Ford, Hyundai, GM, and Google are the aggressive leaders in this field. Through the analysis of self-driving patent map as in Exhibit 4 and 5, self-driving car industry chain should be divided into three main participators: the yellow area is producers and suppliers of auto parts, accessories, and coating, such as Bosch, Dense, ZF Friedrichshafen, and Aisin et al.; The red area is automotive manufacturers and suppliers, such as Ford, GM, Toyota, and Honda et. al; The green area is emerging high-tech enterprises (including Internet companies and IT companies), such as Google, IBM, and Uber. Both carmakers and high technology companies are involved with the automated driving system.

For high-technology company Google, it is more likely a self-driving technology companies than becoming a manufacturing car company. Google enjoys a strong position to leverage its patents in self-driving.

Exhibit 3. Top20 Patent Assignee/ Applicants in self-driving technology


https://www.autoblog.com/tag/waymo/


Following Exhibit 4 and 5 patent maps for Google’s self-driving technology, it is already entering Level 4 of the autonomous car industry, which is no human behind the wheel. The patents of Google demonstrated three features, which is safety-critical driving functions, monitoring roadway conditions, and also the option for human driving. The patent strategies of Google are as follows: Firstly, it plays a full layout on the software-based technology for self-driving systems, such as vision system, light detection, LiDAR(ranging radar), sensors and objects detection. Secondly, it is developing critical components of SDVs, such as camera, radars, a sensor device, safety mechanism, windowsill, puller shaft, and rotary joint devices. Thirdly, it is working on the different roadway conditions, for instance, different weather condition, stop sign, signal light, height dimension, map data, and fields of view. Fourthly, there is a trend for car-sharing services, such as predetermined location, mobility services, navigation, pickup destination, and suggested a location. Recently, two kinds of direction created for minimizing injuries and delivering freight. There are unique, attractive patent filing technologies about softer cars, autonomous truck, and minivans.

Exhibit 4. Patent map for self-driving technology

Exhibit 5. Google’s patent map

Google’s Network and Partnership in Self-Driving Technology. In terms of a patent document with its legal, technical, economic value, the patent has become the primary source of measurement of R&D development and knowledge flow. Patent analysis and Social Network Analysis (SNA) are used to build Google's various networks and partnerships, including patentee cooperation network, patentee transfer network, patentee citation network and



partnership built from 2013 to 2017. Therefore, the comparison among networks provides essential pieces of information for us to comprehensively understand Google's strategies in R&D development, technology influence, and patent transaction, to help better planning of the company's future development direction and strategy.

Respectively, Exhibits 6 and 7 show Google's patentee cooperation network and transfer network. Google has very close relationships with Waymo in self-driving technology. Waymo is an autonomous car development company and subsidiary of Google's parent company. Thus, Google made a great deal of patent transfer with Waymo, to help Waymo improve its patent layout.

Exhibit 6. Patentee collaboration network for Google（2013-2017） Exhibit 7. Patentee transfer network for

Google（2013-2017）Exhibit 8 illustrates Google's citation network based on VOSviewer. Compared with other networks, it's the

largest one with a relative abundance of information, which means Google driverless technology has a significant impact on other companies in the industry. According to visualization Exhibit 8, IBM, Toyota, Ford, Uber had been citing Google's patent extensively. In other words, Google's patented technology has received the continuous attention of patent applicants in this field and may contain groundbreaking technical contributions. Second, automobile companies take the initiative to layout patents. By focusing on Google related technologies, such lay out a large number of peripheral patents with similar technologies, to curb the expansion of Google driverless technology and obtain market and technical advantages for themselves. Besides, Google has a high rate of self-citation, which means that Google is also developing its technology and targeting in patent protection. In fact, the company has put forward a large number of follow-up patent applications for improvement, to achieve technology continuity and improve the layout.

Exhibit 9 portrays us the partnerships of Google's self-driving company Waymo in Autoblog from 2013-2017. Both car manufacturers and high-tech companies are working together now. Waymo has accumulated important allies with Lyft, Chrysler, Avis, and Intel. From Waymo's perspective, it works with automotive partners to provide service support, such as Chrysler and Honda. Waymo, in turn, has been using Intel-produced technology from connectivity to sensor-data processing since 2009. Under this circumstance, the Intel-Waymo collaboration can help each other in making self-driving vehicles valid during the design stage[22]. At the same time, Intel is continuing to position itself at the forefront of autonomy and self-driving by teaming up with other companies. Besides, Waymo still announced a partnership with Trov, the world's leading on-demand insurance technology company, and a car rental giant Avis in 2017[23]. Another powerful GPUs company, NVIDIA still has partnerships with Audi, Tesla, Honda, BMW and Bosch and makes it more formidable in the market.

Exhibit 8. Patentee Citation Network(2013-2017)



Through these partnerships, collaborations, and patent analysis, it could be suggested that Toyota and GM might be Waymo's most significant competitors. Intel, as a new entrant, could be the potential competitors and also the most profitable company in the self-driving technology ecosystem. By working with intensive collaboration, Intel can provide car chips and sensor-data processing services to both carmakers and high-tech companies. It is said by Krzanich[24], “ensures Intel continues its leading role in helping realize the promise of autonomous driving and a safer, collision-free future,” which implies Intel’s grand ambitions in the self-driving systems. Secondly, Tesla is embarking on level 5 to produce fully autonomous cars alone and continues to grow very well. Recently, Tesla’s autopilot is semi-autonomous and states that it had tested tech well beforehand. The new partnership for Waymo could be expanding to Ford. According to Morgan Stanley forecasting[25], a partnership between Ford and Waymo could shake up the auto sectors, for the deal speculation was that Waymo’s CEO spent 14 years in Ford for various roles.

Exhibit 9. Partnerships in self-driving technology

Waymo’s Possible Strategy. Considering the current circumstances, Waymo is aiming at the market leader in integrating self-driving technology into carmakers' vehicles. From the company level, Waymo is not only already well advanced developing AVs, but it's also not a mass car manufacturer, so it's dependent on others to partner, acquire or license its technology. Therefore, Waymo's potential internal and external strategies needed as follows:

First, internal strategies based on building capabilities for the development of customer-oriented technology. Four kinds of direction considered as the critical business, such as the following: autonomous mobility



services ("robot-taxi"), technology for monitoring systems to reduce congestion, delivery, and logistics services, as well as the autonomous trucks along with softer cars.

Second, external strategies based on the business model need to be explored and adjusted. 2 kinds of direction should be possible and have potentials, such as focusing on shared mobility or driverless services. While Waymo is expanding for partnership, the self-driving ecosystem will be attached great importance in the future. At the same, Waymo should try best to link hardware with software and integrate into a car through partnerships with automakers.

Key Findings. Evidently, electric car technology has massively changed traditional car technology. Fuel changes still are the big concern of car companies. Nevertheless, electric cars could not transform the entire market. The technology is still expensive, there is production at a remote location to meet the demands of the car, and more importantly, the change is towards the energy foot of the transportation. Autonomous vehicle technology also seems to affect the traditional automotive industry and other related industries. However, it is much more challenging to make a complete transformation of this effect than moving from the electric car. Besides, the conversion is not on the energy side, but on the data (IoT, Big Data) part.

On the other hand, in the choice of electric vehicles, people still prefer the design of the vehicle and do price analysis according to the fashion of it. In this context, high-tech companies in self-driving cannot ignore the necessity to keep the design perception, which has human beings inside without function, which is a severe handicap. Nowadays, it is perilous for the people to completely abandon the will to govern in a period when technology-personal interaction in the person-specific configuration is rapidly developed.

Moving from these realities, the change in this auto sector can be achieved as the result that the "low hanging fruit" going through. It can be predicted that technology tends to be in areas that are easily accessible and yield quick results. In this context, Waymo and Google should determine areas that can be transformed without reaching this easily accessible and critical mass. One is for public transport, one for other disable people usage, and another for campus or indoor-closed area use. These uses lead the industry if there is a niche in the market. Moreover, there must be a strategy in which the design perception and price for this purpose is balanced. Such should be the strategy until reaching the threshold of critical mass that would provide the technology with an overall transformation.

Specifically, the critical mass access strategy should be on the development of sufficient capacity to meet production priority and demand. Waymo is not a car manufacturer, so creating a serious production capacity from scratch and achieving economies of scale requires serious resources. No matter how much hope the industry has given, it is not as likely that Google has many production sectors funding, so production capacity at this scale is in the case of electric car company Tesla. Strategic cooperation in production at this point is essential. However, traditional car manufacturers like to be in electric car technology, like to partner with the company, to buy or to benefit the most with limited cooperation. In this context, Waymo skates and the process of converting any car into an automatic car is critical.

ConclusionConsidering the above, the strategic planning of patent analysis and company’s networks, based on both patent data and news release, play a crucial role in revealing the implicit R&D partnerships and explicit strategies in the company level. The case study shows that the competitive and complementary interactions influence the formation of partnerships in the market. The authors of the study believe that the visualization in a sophisticated network setting provides abundant and unbiased analysis to high-quality decision-making..

In the nearest future it is expected that Waymo will be one of the most innovative companies. Technology adoption varies since it is a new product and a new way of driving. Hence, Waymo’s car should keep the best R&D for customer-oriented technology so that it can maintain continuous cooperation. In this context, Waymo is already well advanced developing AVs, rather than a mass car manufacturing. The advanced technology should be attractive for others to partner, acquire, or license Waymo’s technology. Therefore, partnerships assist Waymo to expand its demands intensively in the automobile industry. However, it is necessary to make a differentiation strategy in this emerging area and develop many different business models to share the same technology, which reduces R&D costs and enjoys economies of scale. The connected ecosystem of both hard and software in self-driving with users along with partners is an ideal network for Waymo, the one providing the firm with a unique position..

Succinctly, the complete transformation of the automotive technology into the entire sector is a long and difficult road, because consumers are not willing to pay much money for self-driving or have different calculation about time-consuming or driving experience. However, the technology is still in development and is creating its



market, which makes a massive impact on future transportation. The important thing is to construct the strategy in two stages. The first stage is to focus on pre-conversion, niche areas are needed to be identified before the large-scale transformation. The second stage is to provide self-driving car solution for sustaining the ecosystem of users and collaborators, such as building (not construct) permanent capacity of car manufacturers permanently such as long-term company marriage or joint venture, to expand to realize the economies of scale. The last but the most important is focusing on the R&D activities in critical technologies to achieve more core patents.

The study has some limitations, the ones that can be surpassed in future researches. First, from the technology adaption perspective, five essential perspectives of self-driving technological impact were explored and analyzed. Unfortunately, the research did not reveal the potentials that self-driving would be a profound and lasting reshaping of the future transportation system. Also, the key factors that could improve the user’s adoption of autonomous vehicles need to be identified, to help form policy and regulation governing self-driving technology. Second, from the perspective of business and market level, the business model for generating profits based on self-driving car technology is not transparent, lack of focused commercialization and value chain. Thus the company’s strategies should be taken into account for the interactive relationship between technology, business model, and market. Thirdly, from technology convergence point of view, the available technologies, such as sensor-based solution and commutation application, don’t enable completely self-driving and also not robust enough to achieve Level 5.

Acknowledgment The authors would like to thank Mert Tonkal, Ali Al Khafaji and Lily Fitz for the helpful suggestions. The research is supported by the Knowledge Innovation Program of the Chinese Academy of Sciences.

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Introduction€¦ · Web viewThe study includes investigation of different perspectives with SWOT...

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