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New Firms in the Autonomous Vehicle Landscape: An Exploratory Analysis of Their Technology Positions Using Global Patent Data. This study identifies the key technological components of the autonomous vehicle domain and evaluates the innovations of the participants and their competitive positions. The results of this paper contribute to the application of these measures to examine the dynamics of technology components and their combinations, as well as the competitive positions of the new companies based on their inventions.

This paper explores the technology landscape in the autonomous vehicle industry, with particular attention to emerging technology-based firms and their patent records.

Transformation of Automotive Industry

The primary purpose of this paper is to provide a comprehensive and up-to-date picture of technology trends and emerging innovators in the autonomous vehicle industry. Two major trends of decarbonization and digitization have challenged the automotive industry to establish a new dominant design of a car, 'autonomous vehicle'. They announced that they intend to launch autonomous vehicle in Texas, Miami and Washington DC in 2021.

Compared to existing automakers, entrants in the autonomous vehicle industry have pursued relatively aggressive strategies to implement level 3 or higher levels immediately based on AI and software technologies.

Emergence of Autonomous Vehicle

Between 2000 and 2015, a total of 24,311 inventions related to autonomous vehicle technologies were subject to one or more patent applications in Europe. The annual number of these autonomous vehicle inventions increased six and a half times between 2000 and 2015. This suggests that the growth of autonomous vehicle technologies is mainly driven by the integration of ICT with established automotive technologies (European Patent Office, 2018).

Now, companies from various industries, including ICT, electronic components and car-sharing services, are focusing on the development of autonomous driving software, computing platforms and system operation to take the lead and integrate the existing value chain in the autonomous vehicle industry.

Literature Review on Technological Capabilities and Technology Landscape

In the traditional automotive industry, the added value of the parts industry remained low because parts suppliers had to go through many steps to deliver their products to finished car manufacturers, but in the autonomous vehicle industry, the importance of core infrastructure technologies has increased significantly. The measures also assess the technological overlap, similarity and proximity of the technological footprint of two or more companies. At the level of the macrotechnology landscape, the measures can be used to investigate dynamics such as technology agglomeration, knowledge spillovers and evolution of the technology landscape.

The technological positions represent the graphical and quantitative assessments of the extent to which firm's search behavior is locally bounded. Yayavaram and Ahuja (2008) use patent data from the global semiconductor industry from 1984 to 1994 to study the effect of the structure of organizational knowledge bases, or the patterns of linkage between their elements of technical knowledge, on the utility of inventions and knowledge. base malleability. Results show that a nearly decomposable knowledge base increases the utility of the inventions generated from it, as measured by patent citations, and also the knowledge base's malleability or capacity for change.

The authors also examine how perceived domain complexity, an indicator of the inherent interdependence between knowledge domains, moderates the effects of changes in a firm's knowledge linkages on innovation performance. For knowledge expansion, they are more likely to form alliances with companies that have expertise in the same technological areas, but have different recipes for combining knowledge from those areas. The findings in the global robotics industry suggest that companies' search efforts actually vary along two different dimensions: search depth, or how often the company reuses its existing knowledge, and search scope, or how broadly the company explores new knowledge.

The author conceptualized company search types with two distinct dimensions, search goal and search limit, and suggests contrasting effects of the search limit, where companies seek prior original knowledge about the companies' propensity to create ground-breaking innovations and breakthroughs with high impact. The results highlight the importance of searching for original knowledge and the advantage of local search in the creation of breakthrough inventions, thereby suggesting a refinement of the conventional knowledge search framework.

Empirical Analysis

  • Sample
  • Patent Data
  • Cross-firm Overlap in Technological Characteristics
  • Exploratory Analysis
    • Descriptive Analysis on Technology Components and Positions
    • Descriptive Analysis on Interfirm Overlap in Technological Characteristics

In addition, capital investment is one of the key factors for assessing the potential and value of a nascent company. The share of the total amount of equity financing of these 20 companies represents approximately 81% of the share of all 154 companies. Owners can also sell their invention rights to someone else, who then becomes the new owner of the patent.

Cell 𝑖𝑗 in the matrix is ​​the total number of common predecessors of invention 𝑖 and 𝑗 at time 𝑡𝑚 divided by the invention number of 𝑖 occupied by its technological predecessor. It determines how many of the paired firms' patent subclasses overlap and how distant the firms are based on the technological features of their inventions. Since patent data from Google Patent was collected in July 2020, most patent applications filed after the beginning of 2019 are not included in the data set.

One of the most important technological issues in the autonomous vehicle industry is to improve the quality of on-board batteries. All of these subclasses are recombinations of the aforementioned subclasses relevant to the processing and transmission of digital data. Positions related to data processing, transmission of digital information, and recognition and presentation of data take approximately 75% of the total frequency of 10 positions (741 times).

The frequency of these two positions takes about 56% of the total frequency of the 10 positions. The value of the competition coefficients ranges from 1 to 0, and each row and column shows the degree of overlap of two firms' niche based on their patents' main subclass information. The ride sharing service providers are all located in the upper left side of the MDS map: they are Uber, Waymo, Zoox and DiDi.

Interestingly, Xiaopeng Motors is the only EV manufacturer that ranks closer to these firms in the upper left side of the MDS map.

Discussion and Conclusion

Directions for Further Research

The objective of this paper is to measure and represent the technological landscape of autonomous vehicles based on patent data. The empirical analysis in this paper is focused on the patent data of only 20 emerging firms in the autonomous vehicle industry, so the sample data should be expanded to a larger scale to fully investigate the characteristics of the rest of the emerging companies. . For example, patent-level and firm-level analysis can be done on 50 emerging firms in the autonomous vehicle industry.

For example, you can compare the patent data of twenty emerging companies with that of twenty established car manufacturers. The incumbents also play an important role in technological innovation, so the interaction and cooperation between the incumbents and the new entrants should be studied and will provide insights relevant for exploration. Mapping the technological landscape: measuring technological distance, technological footprints and technological evolution', Research Policy, 45, pp.

Something old, something new: a longitudinal study of search behavior and new product introduction', Academy of Management Journal, 45(6), pp.自律주행차 국내외 개발현황 [Status quo of domestic and foreign R&D on autonomous vehicles]. Overview of the development of multidimensional scaling methods', Journal of the Royal Statistical Society, 41, pp.

Changed in firm knowledge linkages and firm innovation performance: The moderating role of technological complexity', Strategic Management Journal, 36(3), pp. Role of domain knowledge search and architectural knowledge in alliance partner selection', Strategic Management Journal, 39, pp.

Appendix

Joint control of vehicle sub-units of different types or different functions; Control systems specially adapted for hybrid vehicles; Road vehicle steering control systems for purposes not related to the control of a specific sub-unit. Data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or predictive purposes; Systems of methods specially adapted for administrative, commercial, financial, managerial, supervisory or predictive purposes, not otherwise provided for. Systems for controlling or regulating non-electrical variables (for continuous metal casting B22D 11/16; . valves per se F16K; non-electrical variable sensors, see the relevant subclasses of G01; for regulating electric or magnetic variables G05F).

Radio direction finding; Radio navigation; Determination of speed distance using radio waves; Location or presence detection using reflection or re-radiation of radio waves; Analog adjustments using other waves. Photogrammetry or videogrammetry. liquid level measurement G01F; radio navigation, determining distance or speed using propagation effects, e.g. Doppler effects, propagation time, of radio waves, analog adjustments using other waves G01S). B41J typewriter; order telegraphs, fire or police telegraphs G08B; visual telegraphy G08B, G08C; .. teleautographic systems G08C; encryption or decryption apparatus per se G09C; encoding, decoding or code conversion, generally H03M; joint agreements for telegraphic and telephone communication H04M;. Data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or predictive purposes;.

Systems or methods specially adapted for administrative, commercial, financial, management, supervisory or forecasting purposes, not otherwise provided for. computer systems based on specific computational models G06N). Measure distances, levels or bearing; Survey;. measure liquid level G01F; radio navigation, determination of distance or speed using propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements with other waves G01S). Determination of distance from velocity using radio waves; Detection or presence detection using the reflection or reradiation of radio waves; Analog arrangements using other waves.

Data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or predictive purposes;. Systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes, not otherwise provided for.

Figure 2. Operational process of human-driven vehicle and autonomous vehicle
Figure 2. Operational process of human-driven vehicle and autonomous vehicle

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Figure 2. Operational process of human-driven vehicle and autonomous vehicle
Figure 3. Industries of top 20 Companies - Frequency
Figure 4. Number of companies by country
Figure 5. CPC Code hierarchy – example code A01B 33/08
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The Cell State of the LSTM means an area to decide what data to memorize, by comparing previous output with current input and the Hidden State means an area