Research on Autonomous Vehicle Technology Innovation Ecosystem in China Based on System Dynamics

Abstract

Based on the perspective of an innovation ecosystem, the system dynamics research method is used to construct a technological innovation ecosystem model of autonomous vehicles in China. Vensim PLE software was used for simulation to obtain the development trend of technological innovation from 2015 to 2030 and to explore the impact of various elements inside the system on the overall system. This research finds that the dynamic mechanism of China’s autonomous vehicle technology innovation ecosystem mainly includes the innovation resource supply subsystem, the technology innovation diffusion subsystem, and the incentive and guarantee subsystem. Each subsystem interacts to jointly promote continuous innovation and iterative upgrading of technology. Education investment, infrastructure construction, innovation platform construction, and other factors all have a positive impact on the technological innovation level of autonomous vehicles, and the effect of multiple parameter changes is far more significant than that of single factor changes. The number of research and development patents, level of technological innovation, actual adopters of technological innovation, and benefits of technological innovation are all showing a good growth trend in the future. Accordingly, it is concluded that there should be optimization of scientific research investment strategies, acceleration of infrastructure layout, and expansion of application scenarios. These insights provide a theoretical basis and practical guidance for promoting the high-quality development of autonomous vehicle technology in China.

1. Introduction

In the context of accelerating global technological change, autonomous vehicles have become the focus of attention of governments, industries, and scientific research groups by virtue of their great potential to subvert the traditional mode of transportation. Autonomous driving technology not only significantly improves the safety and efficiency of transportation, but also provides solutions to urban problems such as traffic congestion and environmental pollution, and is therefore of great strategic significance and has a broad application prospect. Governments around the world are actively introducing relevant regulations and policies, aiming to seize this cutting-edge technology highland to strengthen the international competitiveness in the automotive industry and reshape the future pattern of the transportation industry. The U.S. has taken the lead, with the federal government and a number of state governments successively issuing laws and regulations to relax the testing restrictions on autonomous vehicles, encourage technology giants to innovate together with traditional car companies, and give generous subsidies and tax incentives to research and development projects, in an attempt to stabilize its leading position in global technological competition. European countries are also not willing to show weakness, upholding the consistent high pursuit of environmental protection and intelligent transportation. The legislative framework to protect data security, provide privacy protection and other key aspects, and promote the deep integration of autonomous driving technology and urban planning aims to create intelligent transportation demonstration areas. In China, the national level will be included in the strategic emerging industry planning, from macro guidelines to special subsidies, and local government provides a positive response for the construction of test sites and enterprise innovation incubation to provide good conditions, thereby creating a policy environment suitable for the development of technological innovation.

With the continuous development of autonomous vehicles, the importance of technological innovation is also becoming more and more prominent, and is the core driving force for the industry to maintain its competitive advantage. Autonomous driving technology breaks the technical boundaries of traditional automobile manufacturing, prompting the in-depth integration of machinery manufacturing, electronic information, software algorithms, and other fields, deriving a brand-new industrial chain and business model, and giving rise to emerging sub-sectors such as high-precision mapping and telematics operation, injecting new variables into the global industrial competition. However, the technological innovation of autonomous vehicles is a complex systematic project with the participation of multiple subjects, the synergy of multiple elements, and the operation of multiple phases. Any emerging technology in the early stage of technological innovation has the characteristics of complexity, dynamics, and uncertainty, and the risk of technological innovation is relatively high. There are also problems such as the high cost of technological innovation, weak power of innovation subjects, imperfect innovation environment, and insufficient innovation concepts in the technological innovation process. Some key core technology areas, such as high-performance chips and advanced sensors, are still dependent on imports, which has become a bottleneck for the development of autonomous vehicles in China.

As a brand-new industrial form, it is difficult to adapt the traditional industrial development path to the development of autonomous vehicles. Innovation ecosystem theory is a new innovation paradigm, which emphasizes the ecological characteristics such as openness, synergy, symbiosis, and evolution. It mainly focuses on the interaction between the innovation subject and the environment, and has been widely used in the field of technological innovation research [1], which provides a solid theoretical support for the study of technological innovation of autonomous vehicles. Within this, the dynamic evolution characteristic is the essential difference between the innovation ecosystem and general system. Unlike the relatively static and solidified general system, the innovation ecosystem can keep the difference from different systems to maintain its own competitive advantage [2]. In the face of changes in internal and external environmental factors, it is able to maintain the overall structure and interaction of the system in a state of dynamic balance at all times through self-adjustment and optimization, thus achieving optimization of the internal innovation efficiency of the system. This enables the automated driving technological innovation to better cope with the market changes and technological challenges and achieve sustainable development.

This article takes China’s autonomous vehicle as the research object, regards autonomous vehicle technology innovation as a dynamic system, and uses the system dynamics method to simulate and examine the system from the perspective of an innovation ecosystem. By exploring the collaborative innovation mode among the innovation subjects in the system and the dynamic relationship among the innovation elements, this article aims to provide a new way for research in the field of technological innovation of autonomous vehicles. The research conclusions can be used as a reference by autonomous vehicle-related enterprises and institutions, so as to improve the ability of technological innovation and promote the sustainable development of the autonomous vehicle industry.

2. Literature Review

2.1. Research on Innovation Ecosystems

The concept of the innovation ecosystem originates from the theoretical fusion of “business ecosystem” and the value co-creation process [3], and aims to explain the structured and complex interaction system related to technological innovation, emphasizing that the open organic system is able to exchange resources and energy with the environment through self-organization, so as to promote the ecosystem to achieve the ultimate stable development [4]. In the context of science and technology innovation, knowledge or technology in the innovation ecosystem is dynamically generated through the exchange of resources or experience between subjects, and these innovation participants include enterprises, universities, research institutions, governments, and intermediary organizations, thus highlighting the diversity of subject members in the innovation ecosystem and the interaction between them [5].

Most scholars have studied the concept of innovation ecosystems from the perspective of concepts, characteristics, and research angles. From the perspective of innovation agents, within the innovation ecosystem, many innovation participants such as enterprises, research institutes, policy makers, and market demanders, driven by dynamically changing environments or demands, build up dependencies through cooperation or competition and other modes of interaction [6]. Granstrand and Holgersson balanced the emphasis on complementarity, collaboration, and innovation actors and proposed a comprehensive “3A Architecture” for innovation ecosystems: in an innovation ecosystem, innovation actors and knowledge elements construct social and knowledge networks through innovation behaviors [7]. In the research at national and regional levels, based on the framework of “subject resource environment”, Du and Jian analyzed the antecedent factors for enhancing the resilience of regional digital innovation ecosystems, and used the fuzzy set qualitative comparative analysis method to explore the configuration path and innovation element relationships for enhancing the resilience of regional digital innovation ecosystems [8]. In the research at the organizational and corporate levels, Zhang et al. explored the formation mechanism of the future industrial innovation ecosystem under the government structure, based on the theory of comprehensive advantages and taking China’s hydrogen energy industry as the object, using a longitudinal case study under the framework of “motivation process result” [9]. Dynamic evolution is the main feature of innovation ecosystems, and the process of innovation ecosystem evolution is a process in which the system continuously exchanges new material, energy, and information with the external environment of the system and makes the ecosystem continuously optimized [10]. Based on the core values of the technological evolution, the interdependent technological producers co-evolve in the innovation ecosystems, and the evolution of one ecosystem may lead to the evolution of other ecosystems [11].

Through this literature review, it can be found that the evolution of innovation ecosystems is the foundation of technological innovation in emerging industries, but the research on their mechanisms needs to be strengthened and expanded. At present, most research focuses on the evolution and upgrading of a single innovation ecosystem, the implementation path of technological innovation in emerging industries, etc. There is little research on how the evolution process of the innovation ecosystem promotes the upgrading of the technological innovation path in emerging industries and analyzes the mechanism from the perspective of the subsystems included in the innovation ecosystem.

2.2. Research on Autonomous Driving Technology

Autonomous vehicles are considered to be a forward-looking and disruptive technology with high efficiency, convenience, safety, and other characteristics. They have a positive impact on reducing traffic congestion, reducing pollution emissions, improving energy efficiency, and other aspects [12]. The emergence of autonomous vehicles has placed the transportation industry in a stage of rapid change and reshaped urban passenger and freight transportation [13].

At present, the academic research on autonomous vehicle technology mainly involves several aspects. One of these is research on the current situation of related technologies, including the development trend of autonomous driving technology and identification of technology hotspots, technological innovation, technical challenges, and technical cooperation. Parekh et al. believed that autonomous driving technology is designed to meet the demand for higher driving safety, expand infrastructure, and provide comfort in relying on machines to complete tasks such as driving, and the need to optimize resource and time management [14]. Research has been conducted on technical testing and security, including testing methods, safety standards, risk assessment, and accident liability determination. Rezaei and Caulfield, through the comments and opinions of 185 experienced industry professionals on the safety of autonomous vehicle, found that “autonomous vehicles’ wrong understanding of surrounding objects” may be the most important technical problem and may lead to accidents. In research on public acceptance and willingness to use technology [15], Hamadneh et al. studied the views of different participants on the acceptance of private sharing of autonomous vehicles. From the perspective of users, legislators, operators, and manufacturers, they used AHP to obtain the highest ranking of safety issues, and the lowest ranking of ease of use and cross-border interaction. In research on industry policies, laws and regulations, and ethical and moral standards related to technology, Poszler et al. focused on the ethical literature on autonomous driving, providing guidance on when, why, and how to apply specific ethical theories to autonomous driving decision-making [16]. In research on technology application and commercial operation, Qin et al. explored the scenario of mixed traffic with connected automated vehicles on minor roads at priority intersections and established a capacity model. The results showed that the model could analyze the impact of key factors such as CAV penetration rate, CV ratio, main road traffic volume, and vehicle spacing on mixed traffic capacity [17].

2.3. Research on Technological Innovation in Autonomous Vehicle

In recent years, some scholars began to apply ecological theories to the field of technological innovation to solve a technological innovation-related problems. The concept and method of innovation ecology were gradually introduced into the research related to the technological innovation of autonomous driving, and certain research results have been achieved. He et al. took Huawei Automobile as an example to explore the construction mechanism and path of the core enterprise’s innovation ecosystem for digitalization, and concluded that it should be constructed along the dynamic path of “environmental support → elemental empowerment → dynamic evolution → system formation” [18]. Chen studied the coupling power mechanism of the innovation ecosystem of the intelligent networked automobile industry from three aspects, namely, market demand, technological innovation, and factor conduction, and improved the level and efficiency of industrial innovation. The authors realized the innovative development of the industry by promoting the synergistic innovation of innovation clusters and the internal and external innovation environments, and by facilitating the sharing of test data, investment and financing, and scientific and technological resource information [19]. Wu et al. took Azalea New Energy Vehicle as an example to discuss the construction of an enterprise innovation ecosystem based on the “Internet+” environment, and proposed that the system construction efficiency can be improved from two functional dimensions: the cross-fertilization of Internet technologies and the collaboration of Internet platforms [20]. Ge constructed a system dynamics model for the innovation ecosystem of the intelligent networked automobile industry from the theory of the innovation ecosystem, and simulated the impact of different innovation development strategies on the development of the industry [21].

At present, as a strategic emerging industry, autonomous vehicles are at a critical stage of rapid development. Throughout the current academic research at home and abroad, most of the discussions on technological innovation of autonomous vehicles are still limited to the traditional analysis perspective, often focusing on a single technological breakthrough or the behavior of a single subject, and failing to take into account the complex ecological landscape and technological development routes of the industry at the present time. At the same time, most of the existing research focuses on the current policy and technology environment in China, but there is a lack of forward-looking prediction and analysis for the future development of autonomous driving technology. As national policies continue to lead the way, the impact of autonomous vehicles has far exceeded the scope of transportation, and has penetrated into many key areas such as the economy and society, and needs to be studied from the perspective of innovation. Based on this, building China’s autonomous vehicle technology innovation ecosystem from the perspective of an innovation ecosystem, exploring the interaction of various elements within the system on the basis of grasping the law of technology evolution, forecasting the future development trend of technology, and selecting an automated driving technology route suitable for China’s development have important theoretical guiding value and practical application significance for China’s autonomous vehicle technology to move to a higher level and achieve high-quality industrial development.

3. Methodology

The autonomous vehicle technology innovation ecosystem is composed of five technological innovation subjects, namely, government, autonomous vehicle enterprise, universities and research institutes, intermediary organizations, and market consumers, under the joint effect of the policy environment, market environment, transportation environment, and technological environment. At the early stage of technological innovation, the industrialization of autonomous vehicles is not high, but the related industrial chain is very complex, and the technological innovation ecosystem is affected by various elements, which usually shows a high degree of nonlinearity. System dynamics, as a structured simulation method, is an effective and applicable method to analyze such nonlinear complex economic systems [22].

3.1. Boundaries and Assumptions of the System Model

Determining the system boundary and assumptions is the basis for establishing the system dynamics model. The release of “Made in China 2025” in 2015 marked the starting point of the development of autonomous vehicles in China. Since then, the “Strategy for the Innovative Development of Intelligent Vehicles” and the “Technology Roadmap for Energy-saving and New Energy Vehicles” have specified 2025 and 2030 as the two key nodes of technological innovation.

Accordingly, this article focuses on China’s autonomous vehicle industry, with the time horizon set from 2015 to 2030, to explore the dynamics of technological innovation within this period. During the past decade, the government has continuously issued relevant policies to support and encourage the development of autonomous vehicles, creating a favorable policy environment for them. In the selection of innovation subjects, this paper mainly considers subjects that have significant influence on the system. Referring to the studies of Zhao et al. [23] and Xi [24], according to the research needs, the following basic assumptions are made for the model:

(1) In order to simplify the system dynamics model, the time delay problem is not considered in this article.

(2) During the operation of the model, the national policy guidelines remain stable with no significant changes in the general direction of these guidelines.

(3) The autonomous vehicle industry chain is relatively complex. In order to simplify the model, this article only considers the roles of five parties: enterprises, government, universities and research institutes, intermediary organizations, and market consumers, while the roles of other secondary subjects are ignored.

3.2. Causality Analysis

Through the dominant structure of system dynamics, it can be found that the system structure is composed of multiple feedback loops plus logic, material delay, information delay, and other links, organized according to causal logic relationships. In other words, the application of system dynamics can effectively combine the logical analysis of causal relationships with the control principles of information feedback. The technological innovation ecosystem of autonomous vehicles is a huge and complex system, and it is difficult to study it directly. In order to clarify the internal development law of the autonomous vehicle technology innovation ecosystem, this article, based on the perspective of the three driving forces of autonomous vehicle technology innovation, divides the entire system into three subsystems: the innovation resource supply subsystem, the technology innovation diffusion subsystem, and the incentive and guarantee subsystem. Each subsystem interacts with each other, and jointly affects the technology innovation of autonomous vehicles.

3.2.1. Innovation Resource Supply Subsystem

Innovation resources serve as the fundamental driving factors for enterprises to conduct technological innovation activities, encompassing human, material, financial, and other input elements. As shown in Figure 1, within the autonomous vehicle technological innovation ecosystem, the innovation resource supply subsystem primarily consists of financial capital, human capital, and technological capital. The efficient collaboration, timely updating, and supplementation of these innovation resources, along with the exploitation of their potential value, are crucial for enhancing the technological innovation capabilities of autonomous vehicles and ensuring the stability of the ecosystem. This, in turn, helps maintain the competitive advantage of the enterprise. Therefore, the high-quality supply of innovative resources is essential for the survival and sustained development of the autonomous vehicle technology innovation ecosystem.

Capital is the foundation and source of the development of the autonomous vehicle industry, and mainly comes from the enterprises’ own investment in the R&D and iteration of autonomous vehicle technology, the government’s financial investment to support the technological innovation of the autonomous vehicle, and loans from banks and risk institutions. Market demand, as a key driving force for economic development, also plays an unignorable role in the field of autonomous vehicle technology innovation. With the gradual growth in consumer demand for autonomous vehicles, enterprises are also bound to increase their investment in research and development, talent training, equipment acquisition, and other innovation resources in order to gain an advantage in market competition.

The supply of human capital can provide a talent guarantee for the high-quality development of the autonomous vehicle industry, and the R&D and service of high-tech innovative talents in technology and products are of great significance to the development of the autonomous vehicle industry. The conscious behavior of people can realize the organic collaboration of capital, manpower, technology, and other forms of capital in the technological innovation ecosystem of autonomous vehicles, and these can be transformed into the inexhaustible power for the development of the autonomous vehicle industry.

Competition in the autonomous vehicle industry ultimately boils down to the competition of key core technologies. For the emerging industry of autonomous vehicles, breakthroughs in the field of technology and innovation are the core elements of industrial development, and its development relies on a number of breakthroughs in cutting-edge technologies. A large number of well-known car companies, technology companies, and Internet companies are actively involved in the research and development and application of autonomous vehicle technology, and have demonstrated strong innovation capabilities in technology research and development, vehicle production, and intelligent travel services, which has further promoted the rapid development of the autonomous vehicle industry.

3.2.2. Technological Innovation Diffusion Subsystem

With the advancement of technological innovation activities among various innovation subjects within the autonomous vehicle technology innovation ecosystem, innovation resources begin to diffuse throughout the system. As shown in Figure 2, the diffusion of technological innovation commences when a technological innovation achievement is first commercially applied. This diffusion represents the spatial dissemination of technology, driven by the combined effects of technological overflow from developed regions and the demand for technology in less advanced regions. It primarily manifests in two forms: the first involves the transfer of innovative technology from suppliers to adopters, while the second encompasses the process of technology adopters utilizing, reusing, digesting, absorbing, and re-innovating the acquired technology [25]. Regardless of the form, the diffusion of technological innovation involves fundamental elements such as technology suppliers, technology adopters, and technology diffusion channels. Essentially, it is a process through which technology suppliers popularize the technology to potential adopters via specific dissemination channels, ultimately leading to the recognition and actual adoption of the technology. This process further accelerates the diffusion of new automated driving technologies, creating a virtuous cycle that enhances technological advancement and ecosystem stability.

The technology supplier is the source of new technology. In the autonomous vehicle technology innovation ecosystem, the supplier of technology innovation diffusion is usually the owner and diffuser of technological innovation, such as autonomous enterprises, universities, and research institutes, who produce new technology through independent innovation or cooperation and become the source of diffusion of new technology.

Technology adopters are individuals or collectives who act on the technology or technology products through practical activities in order to actualize a certain function of the technology to satisfy their own needs. They are the recipients and actual users of the results of technological innovations, and they are also important technology developers. On the one hand, they provide the real demand for the research and development, innovation, and diffusion of technology. On the other hand, when using the technology, they will also reinterpret the existing technology through their own innovative behavior, forming the re-innovation of the technology, and then promoting the innovative development of the technology. Innovations are more likely to be adopted quickly when they possess the following characteristics: (1) Advantage. They have a relative advantage compared to existing technologies. (2) Compatibility. They are compatible with existing values, past experiences, and current needs. (3) Complexity. This is inversely proportional to the adoption rate. Innovations are more likely to be adopted when they are less difficult to understand and apply.

As the path of technology dissemination in the autonomous vehicle technology innovation ecosystem, the technology diffusion channel is a bridge connecting technology suppliers and technology adopters, which is important for whether the adopters can obtain comprehensive, accurate, and timely access to innovation-related information so as to ensure their willingness to accept and behavior, and ultimately become the actual adopters of the technology. This is an indispensable factor for the diffusion of technology innovation in the autonomous vehicle, and common channels can be categorized into mass communication and interpersonal communication.

3.2.3. Incentives and Guarantees Subsystem

As an emerging industry, autonomous vehicles in the early stages of technological research and development are characterized by high innovation complexity, significant technical challenges, and considerable uncertainty regarding future prospects. The process of technological innovation often necessitates access to knowledge and resources that exceed the enterprise’s existing capabilities and conditions. This limitation makes it difficult for enterprises to possess all the necessary resources and knowledge required for exploratory and applied research. Consequently, the initial investment in technological development and the anticipated returns from innovation often fall short of expectations, thereby diminishing enterprises’ confidence in pursuing technological innovation for autonomous vehicles.

Given these challenges, the technological innovation of autonomous vehicles requires comprehensive support from various sectors of society. Effective incentives and safeguards can significantly enhance the synergistic innovation capabilities within the technological innovation ecosystem of autonomous vehicles, thereby further advancing the overall level of technological innovation. As shown in Figure 3, the incentives and safeguards subsystem of the autonomous vehicle technology innovation ecosystem primarily encompasses four elements: policy incentives, enterprise incentives, resource safeguards, and environmental safeguards.

Policy incentives are primarily reflected in the government’s support and guidance for autonomous vehicle technology as an emerging field in industrial development, encompassing financial subsidies, infrastructure construction, and other supportive policies. In the early stages of technological innovation, autonomous vehicles rely on the government to provide a clear development path and goals through a combination of top-level design and local implementation. Additionally, the government employs a series of measures to motivate the main actors involved in technological innovation, encouraging them to engage in innovative activities and stimulating their innovative vitality.

Enterprise incentives are mainly reflected in the need to strengthen the role of enterprises as the primary drivers of technological innovation. Within the autonomous vehicle technology innovation ecosystem, enterprises are the main organizers and participants of technological innovation activities. They are also the key decision-makers in technological innovation, and are responsible for R&D investment, scientific research organization, and the transformation of innovation outcomes. Promoting the aggregation of various innovation factors within enterprises and enhancing their role as the main body of technological innovation are crucial for driving progress.

Resource guarantees are primarily embodied in the provision of essential resources and elements, such as knowledge, talent, technology, capital, information, and infrastructure, to ensure the smooth execution and successful operation of technological innovation activities within the autonomous vehicle technology innovation ecosystem. The aggregation, collaboration, and rational distribution of these innovation resources and elements will inevitably provide strong support and momentum for autonomous vehicle technological innovation.

Environmental guarantees are mainly reflected in the creation of a fair and competitive market environment within the autonomous vehicle technology innovation ecosystem. This environment serves as the foundation and key to technological innovation in autonomous vehicles. Strengthening the standardization and governance of market order, as well as protecting the outcomes of technological innovation, are essential for fostering a sustainable and innovative ecosystem.

3.2.4. Systematic Causality Diagram

Based on the comprehensive analysis of the three subsystems and the influencing factors within China’s autonomous vehicle technology innovation ecosystem, the elements within the system boundary have been clearly defined. By integrating the interactions among these subsystems and utilizing Vensim PLE software to eliminate redundant causal relationship chains, the overall causal relationship diagram of China’s autonomous vehicle technology innovation ecosystem has been constructed. This diagram provides a structured representation of the dynamic interactions and feedback loops that drive the innovation process within the ecosystem.

As shown in Figure 4, the innovation resource supply subsystem serves as the foundational condition for ensuring the smooth progression of technological innovation activities and the sustainable development of the technological innovation ecosystem. This subsystem facilitates the efficient supply and operation of essential resources such as capital, human resources, and technological capital within the system, thereby enabling the generation of new technologies. The technological innovation diffusion subsystem represents the core of the healthy growth of the technological innovation ecosystem. It achieves the adoption and application of new technologies by disseminating these innovations and introducing them to the market. This stage is crucial for evaluating whether the new technology can yield economic benefits and be effectively transformed into productive forces [26]. The incentive and guarantee subsystem provides the necessary support and safeguards for the development of the technological innovation ecosystem. These subsystems mutually reinforce each other, creating a virtuous cycle that collectively drives the advancement of the autonomous vehicle technology innovation ecosystem in China.

Through the feedback loop, it can be seen that the strength of government support and the degree of market demand are the key elements to promote the development of autonomous vehicle technology innovation. The government’s support is mainly reflected in the government’s R&D investment, construction of innovation platforms, promulgation of strategic plans, industrial policies, laws and regulations, and safety standards related to autonomous vehicles, so that enterprises, colleges and universities, scientific research institutes, and other industry-academia-research organizations have joined in the promotion of technological innovation and industrial development of autonomous vehicles. With more and more innovation bodies joining in and the efficient and rational use of innovation factors, the level of technological innovation has been continuously improved, which has led to the continued advancement of the autonomous vehicle technological innovation ecosystem, and further promoted the development of autonomous vehicle technology from R&D stage to road testing and the commercial operation stage. At the same time, the progress of technology has also improved the trust and willingness of consumers to use autonomous vehicles, and more consumers are willing to accept and adopt the emerging technology of autonomous vehicles, which has also led to the gradual proliferation of autonomous technology, increased the market demand for autonomous vehicles, and realized the benefits of technological innovation. The technological innovation gain will make the autonomous vehicle-related enterprises gain profits, and then continue to increase R&D investment for the re-upgrading and innovation of autonomous technology, etc. Additionally, government departments will continue to increase the support for the strategic emerging industry of autonomous vehicles, in order to maintain and strengthen China’s innovation ability and international competitiveness in the field of autonomous vehicles.

3.3. Construction of Stock–Flow Diagram

The causality diagram is a qualitative description of the technological innovation ecosystem, which is unable to determine the attributes of the variables in the system and the mechanism of the interaction between the variables. Therefore, a stock–flow diagram is constructed on the basis of the causality diagram, and the main stocks, flows, auxiliary variables, and constants are further added, so that functional equations are used to show the quantitative and logical relationships between the variables in the system, and the whole system is analyzed quantitatively. Based on the consideration of data availability and realism, the causal diagram is adjusted to a certain extent by using Vensim PLE, and the stock–flow diagram of China’s autonomous vehicle technology innovation ecosystem is constructed, as shown in Figure 5.

3.4. Data Sources

By grasping the relationship of the main variables in the stock–flow diagram, the years 2015–2030 are selected as the test years of the model in the assignment. The data sources mainly include statistical yearbooks such as China Statistical Yearbook, China Automotive Industry Yearbook, China Automotive Market Yearbook, China Smart City Yearbook, China Fixed Asset Investment Statistical Yearbook, and China Intelligent Networked Vehicle Industry Development Report edited by China Society of Automotive Engineering. Interpolation was used to supplement individual data that may be missing from the yearbook. Some directly obtainable parameters, such as policy support intensity and financial institution loans, can be extracted directly from relevant policy documents and publicly available data of financial institutions. For parameters that cannot be directly obtained, such as market demand and willingness to adopt technology, a combination of literature research, expert consultation, and statistical analysis was adopted. By reviewing a large quantity of relevant research literature, both domestically and internationally, parameter value ranges were obtained for similar studies. Senior experts in the industry were invited to subjectively judge and revise parameters based on their rich practical experience. At the same time, members of the research team also traveled to Beijing, Chongqing, Shenzhen, Wuhan, Chengdu, and other cities to conduct on-site research and interviews with a number of autonomous vehicle companies and consumers in the market to obtain the data needed for this article.

In the diffusion of technology innovation subsystem, referring to the research of Xu and Hu, since the adoption rate of autonomous vehicle technology is predicted, the number of autonomous vehicle enterprises has no influence on the research results, so it is assumed that the total number of autonomous vehicle enterprises is 1000 [27,28]. According to the theory of diffusion of innovation, the proportion of innovators is 2.5%, that is, the initial value of the actual adopters of technological innovation is 25, and the initial value of the potential adopters of technological innovation is 25, which is the same as that of the potential adopters of technological innovation. The initial value of potential technology innovation adopters is 975. The main equations constructed are shown in Appendix A.

4. Model Testing and Simulation

The system dynamics model is a complex system for simulating the real data, and needs to be tested for certain validity in order to be used as a basis for theoretical research. In order to ensure that the model system changes can be close to the real situation, and can reasonably describe the pattern of change of the real-world system, any model needs to be validity tested. This paper is based on China’s self-driving car technology innovation ecosystem model using Vensim PLE, from the historicity test and behavioral pattern test for its validity test.

4.1. Historical Testing

Due to the complex internal structure of China’s autonomous vehicle technology innovation ecosystem, the parameters of some of the variables are calculated based on references or relevant data, but there is still subjectivity in the parameter setting of some of the variable relationships. On this basis, the model takes historical testing, aiming to verify whether the output value of the system is basically consistent with the actual value of the variable, so as to determine whether the model assumptions, interrelationships among variables, etc., are reasonable, and to ensure the scientific, accuracy, and credibility of the simulation results [29].

Taking 2015–2023 as the time range of the model’s historical testing, the two state variables of government support and technological innovation level of autonomous vehicles are selected for the historical test, and the data obtained after simulation are compared with the actual data published by the China Statistical Yearbook, the China Automotive Industry Yearbook, and the China Society of Automotive Engineering. The results of the historical testing of the model are shown in

Table 1. Results of historical testing.

Year	Government Support			Level of Technological Innovation
	Simulated Value	Actual Value	Error Rates	Simulated Value	Actual Value	Error Rates
2015	2488.45	2630.24	5.39%	346	378	8.47%
2016	2557.34	2695.17	5.11%	829	811	−2.22%
2017	2640.62	2744.83	3.80%	1350	1491	9.46%
2018	2736.82	2902.60	5.71%	2273	2454	7.38%
2019	2826.41	2986.35	5.36%	3528	3741	5.69%
2020	2910.77	3072.96	5.28%	4695	4911	4.40%
2021	2884.05	2913.07	1.00%	5806	5784	−0.38%
2022	3094.13	3058.28	−1.17%	7042	7513	6.27%
2023	3159.35	3147.63	−0.37%	7834	7648	−2.43%

, from which it can be seen that the absolute value of the error between the simulated value and the actual value of the two variables, namely, government support and technological innovation level, is within 10% in the period of 2015–2023. Furthermore, even though the government’s attention to autonomous vehicles is weakened during the period of 2020–2022 due to the impact of the COVID-19, with the effective control of the epidemic, the level of technological innovation of autonomous vehicles once again ushers in a new era of development of automotive technology. According to the research of Li and Wu [30], the results show that the constructed system dynamics model has passed the historical testing, and its running condition has a high degree of fit with the actual condition, and thus can accurately simulate the actual situation of the technological innovation of autonomous vehicles.

4.2. Behavioral Pattern Testing

In the technological innovation diffusion subsystem of autonomous vehicles, since there is not yet enough data to validate it, this article combines the innovation diffusion theory, industry analysis reports, and relevant industrial policies to test the behavioral pattern of the technology innovation diffusion model of autonomous vehicles, in order to verify whether the behavioral pattern of the model is consistent with the real system. According to the research on the diffusion rate and adoption rate in the innovation diffusion theory, the diffusion rate should show an inverted U-shaped curve, and the adoption rate shows an S-shaped curve. From Figure 6, it can be seen that the diffusion rate of autonomous vehicle technology has been rising during the 15 years of simulation. Although this is not fully consistent with the inverted U-shaped curve in the theory of diffusion of innovations, as shown in Figure 7 and Figure 8, with the continuous progress of the autonomous technology and the accelerated rate of diffusion, the willingness to adopt technological innovations is also getting stronger and stronger, and the rate of adoption is gradually increasing, which indicates that the 15 years of simulation in this article coincide with the development period of autonomous driving technology. As a result, it also conforms to the general pattern of the inverted U-curve and the early stage of the development of the S-curve of the diffusion of innovations theory, i.e., it shows a trend of gradual growth.

In addition, in the Hype Cycle for Emerging Technologies, 2019, published by Gartner, “Autonomous Driving Levels 4 and 5” are predicted to be more than 10 years away from reaching technological maturity; After 5 years of development, the Hype Cycle for Artificial Intelligence, 2024, published by Gartner, shows that “Autonomous Vehicles” are still 5–10 years away. The simulation results of the study show that by 2030, the adoption rate of autonomous driving technology will be about 82%, which is basically in line with the trend of Gartner’s prediction. Therefore, it can be judged that the diffusion subsystem model of autonomous vehicle technology innovation proposed in this article passes the behavioral pattern testing.

4.3. Simulation

The year 2015 is the “first year” of China’s autonomous vehicle development, and the development goal of autonomous vehicles is clearly put forward in the “Intelligent Vehicle Innovation and Development Strategy”: strive to realize the mass production of L5-level autonomous vehicles in 2030. Therefore, the system constructed in this article takes China’s autonomous vehicles as the research object, and selects 2015–2030 as the time boundary of the model to explore the technological innovation process of the industry.

The basic parameters in the simulation are set as INITLAL TIME = 2015, FINAL TIME = 2030, TIME STEP = 1, and Unites for Time = year, and the simulation is carried out for the level of technological innovation, the actual technological innovation adopters, and the technological innovation benefits.

As shown in Figure 9, the technological innovation level of automatic driving shows a gradual upward trend, and the results are in line with the trend of the development of China’s autonomous driving technology. The number of R&D patents is selected as an important standard to measure the level of technological innovation of automatic driving [31], and this number will directly reflect the level of technological innovation.

In 2015, the State Council released “Made in China 2025”, which elevated the development of autonomous vehicles to the height of national strategy and listed it as a strategic emerging industry in China. In the same year, Baidu announced that its driverless car had realized fully automatic driving under mixed road conditions in cities, ring roads, and high-speed roads for the first time in China, marking the gradual entry of autonomous vehicles as a new type of transportation into the public’s view. Although the autonomous vehicle industry has not yet fully reached a scale before the introduction of policies to support the development of the industry, after a period of development, the relevant technology has been gradually accumulated, and the number of patents has been realized from scratch and gained sustained growth. During 2015–2019, driven by national policies, high-tech companies, OEMs and parts suppliers, chip companies, startups, colleges and universities, research institutes, etc., have entered the track of autonomous driving technology innovation and development. All kinds of enterprises and scientific research institutions have joined the autonomous driving technology patent applications to obtain rapid growth and completed a large quantity of technical accumulation, and some automotive products equipped with L2 level autonomous driving technology have begun to enter the market. From 2020 onwards, due to the impact of COVID-19, the autonomous driving technology innovation activities have been subjected to a short-lived impact, but with its prior technical accumulation, the automotive industry is still in the process of development. However, with its previous technical accumulation, breakthroughs in artificial intelligence technology, and the increasing maturity of the industrial environment, the level of autonomous driving technology innovation has made a qualitative leap. In recent years, adaptive cruise control system, assisted driving, assisted parking, blind spot monitoring, and other L1/L2 level autonomous driving technology has been very mature, covering the three major scenarios of high-speed expressway, urban roads, and low-speed closed environment parking lots. Thus, automatic driving functions began to be carried in mass production vehicles.

With a variety of key technology breakthroughs, the level of innovation in autonomous driving technology has been continuously improved, and technology updates and iterations have become more frequent. The mass production of L3 and L4 high-level autonomous driving vehicle technology has been realized, and the document “Notice on the Pilot Work of Intelligent Connected Vehicles’ Access and Road Passage”, which was introduced in 2023, clearly specifies the access norms for L3 and L4 level autonomous driving vehicles. The implementation of this policy will greatly accelerate the promotion of autonomous technology, further promote autonomous vehicles to a higher level of L5, and ultimately realize commercial application.

Figure 10 shows the simulation results of the actual technology innovation adopters, and it can be seen that with the continuous progress of autonomous technology innovation, the public has very optimistic expectations for the commercialization of autonomous technology and the development of the autonomous vehicle industry. To be accepted by users, emerging technologies to the market follow the “law of technology adoption life cycle”. Autonomous driving technology is no exception, and in order to be ultimately adopted by the mass market and to realize the commercialization of the operation of the landing, it must cross the market in the “chasm”.

As shown in Figure 10, when autonomous driving technology entered the market in 2015, there were very few adopters practicing technological innovation, and these groups mainly included (1) innovators, a small number of individuals with a strong curiosity about new technologies, whose exploration and experimentation with new technologies provided impetus for the early development of autonomous driving technology; (2) high-tech enterprises with an eye on the future, who believe that new technologies are the way to gain a competitive advantage and greater benefits; (3) future-focused high-tech companies that see new technologies as a way to gain a competitive advantage and greater benefits, and thus actively adopt new technologies to drive business development. Although early adopters play an important role in driving technology development, because this group accounts for a relatively small portion of the market, they are unable to serve as the mainstay of the diffusion of technological innovations, and widespread adoption of the technology will require more businesses and consumers to join the ranks of adopters.

The vigorous promotion of national policies and the continuous maturation of autonomous driving technology have created favorable conditions for the wide application of the technology. At the same time, the gradual enrichment of application scenarios makes the autonomous driving technology better able to adapt to diversified market demands. For the general public, technology trust is always the main factor affecting the acceptance of autonomous driving technology [32]; for enterprises, technical advantages and the ability to create commercial value are the key to the adoption of autonomous driving technology. During the period of 2016–2023, a total of 17 national-level intelligent networked vehicle test zones, 7 vehicle networking pilot zones, and 16 “dual intelligence” pilot cities were built across the country, with more than 32,000 km of open test roads, more than 7700 test licenses issued, and more than 120 million kilometers of test mileage; this provided an adequate environment for the validation of automated driving technology. In these test areas and open roads, the autonomous driving technology has been repeatedly verified and optimized to ensure its reliability, stability, and safety, laying a solid foundation for the commercial application of the technology.

Currently, L2 assisted driving has entered the mass market due to its lower cost and reliable functions, while higher-order automated driving is still in the early market and still needs to cross the chasm to enter the mainstream market. With the continuous validation of the technology and the improvement of public trust, the market’s acceptance of autonomous driving technology is gradually increasing. According to the “China Intelligent Networked Vehicle Social Experiment Report 2022”, 86.82% of the respondents believe that the current stage of autonomous driving technology is expected to reach, or has already reached or even surpassed, the driving level of the majority of drivers, and 57.00% of the respondents who have had the experience of riding in an autonomous driving vehicle believe that autonomous driving technology will be widely promoted within five years. In the future, with the extension of open roads, the increase in coverage areas, and the expansion of more application scenarios, more enterprises and users will begin to use autonomous driving technology. For example, autonomous driving technology will be applied in more fields such as urban public transportation, logistics, and transportation, and as park feeders, thus attracting more enterprises and consumers to participate in it. At the enterprise level, the advantages of autonomous driving technology lie in its ability to improve transportation efficiency, reduce operating costs, and enhance safety and comfort, which will create greater business value for enterprises, thus further promoting the widespread application of the technology and the increase in the number of users.

Figure 11 shows the simulation results of the technological innovation revenue, from which it can be seen that the technological innovation revenue of autonomous vehicles presents a trend of slow growth at the initial stage, followed by a gradual acceleration of the growth rate. As an emerging technology, autonomous vehicles face challenges such as high technical difficulty, high investment capital, and long return cycle in the early stage of development.

As shown in the figure, in the early stage of autonomous vehicle technology innovation in 2015–2019, the innovation revenue mainly came from the sale of cars by automobile manufacturers. With the completion of the construction of autonomous vehicle test sites and demonstration areas, more and more autonomous vehicles are entering the open road testing and trial operation stage, laying the foundation for commercialized operation afterwards. The emergence of Robotaxi has not only changed the way people travel, but also shifted the acquisition of technological innovation revenue from consumers’ purchase of automotive products to the purchase of the travel service experience. The rapid spread of intelligent driver assistance systems in the end market provides unprecedented opportunities for the commercialization of autonomous driving technology, which not only implies the widespread application of the technology, but also indicates that the startups in the field of autonomous driving are gradually demonstrating their ability to make profits. The profitability of Robotaxi’s commercial operation includes the elimination of the safety officer and the decline in the cost of core components, as well as the membership model fees for providing customers with a unique travel experience, the hardware payment fees, and the hardware payment fees. Led by this new business model, the importance of technology has been further amplified, with technology companies including Baidu, Tencent, and Huawei, as well as automakers such as GM and SAIC, focusing more on breakthroughs in key core technologies of autonomous vehicles, upgrading of software algorithms, and optimization of products, as well as applying autonomous technology to a wider range of scenarios. By comparison, some smart driving technology providers rely on their highly market competitive technologies, and superior autonomous driving solutions and products, and gained huge revenue in areas such as Smart Driving Domain Control, smart chips, and LiDAR. The increase in revenue from technological innovation signals that the automated driving industry is gradually moving from the stage of technological research and development to the stage of commercialization and operation. In the future, as technology continues to progress and market demand continues to grow, more innovations and breakthroughs will emerge in the field of automated driving vehicles, and the value of those companies that have long been committed to the research, development, and application of automated driving technology will gradually be recognized and reflected by the market.

4.4. Sensitivity Analysis

China’s autonomous vehicle technology innovation ecosystem is an extremely complex system, and there is a causal relationship between the elements within the system; changing a certain parameter can cause changes in the output results of the system, reflecting which parameters have a greater impact on the system. Sensitivity analysis is precisely a method used to study the degree of sensitivity of the state or output changes of a system or a model to the changes in the parameters of the system or the surrounding conditions.

In the innovation resource supply subsystem, education input and infrastructure construction input are important factors that affect the level of technological innovation of autonomous vehicles. Therefore, on the basis of keeping other parameters unchanged, the changes in the technological innovation level of autonomous vehicles are explored by adjusting the values of the education input coefficient and infrastructure construction factor. By varying the education input coefficient and infrastructure construction factor by 30% up and down, respectively, the technological innovation levels under different scenarios are obtained as shown in Figure 12 and Figure 13.

The simulation results show that the education input factor and the infrastructure construction factor have a significant effect on the technological innovation level of autonomous vehicles. Among them, the change in the infrastructure construction factor has a more significant impact on the level of technological innovation. The reason for this is that the goal of autonomous vehicle technological innovation is to ultimately realize commercial operation, and the construction of infrastructure such as the test site, demonstration area, vehicle, road and cloud collaboration, and intelligent network cloud control platform is crucial to the commercialization landing. Perfect infrastructure construction can help improve the information collection ability, perception decision-making ability, data analysis ability, and service ability of autonomous vehicles, and provide different road conditions, climate conditions, and other test environments and application scenarios for technological innovation, thus accelerating the commercialization landing process. At the same time, as an emerging industry, the key role of education should also be given full play in the process of autonomous vehicle technology innovation, attention should be paid to the input of scientific researchers, R&D investment should be transformed into a core technology of autonomous vehicles, and the transformation and upgrading of the automobile industry should be promoted towards intelligence, network connectivity, and automation.

In the diffusion of the technological innovation subsystem, technological innovativeness is an important factor influencing the actual technological innovation adopters, while relative advantage, technological compatibility, and technological complexity are three indicators of technological innovativeness. Therefore, on the basis of keeping the other parameters unchanged, the changes in actual technological innovation adopters are explored by adjusting the values of relative advantage, technological compatibility, and technological complexity. Among them, relative advantage and technological compatibility are positively related to technological innovativeness, and technological complexity is negatively related to technological innovativeness. In the enhancement scenario (Test 1), the positive variable is increased by 20% while the negative variable is decreased by 20%; in the reduction scenario (Test 2), the positive variable is decreased by 20% while the negative variable is increased by 20%. The obtained scenarios are shown in

Table 2. Innovativeness parameter setting.

Variables	Current	Test 1	Test 2
Complexity	0.254	0.203	0.305
Compatibility	0.368	0.442	0.294
Advantage	0.421	0.505	0.337
Innovativeness	0.535	0.744	0.326

.

The simulation of these three scenarios is simulated and the results obtained are shown in Figure 14. As can be seen from the figure, the higher the technological innovativeness, the greater the number of actual technological innovation adopters. Compared with traditional driving methods, autonomous driving technology can significantly reduce the risk of traffic accidents caused by human errors and improve driving safety through advanced sensors, algorithms, and control systems; at the same time, autonomous driving technology can also optimize driving routes and reduce congestion, thus improving overall travel efficiency. For logistics companies, cab companies, and other enterprises, autonomous driving technology can reduce transportation costs, improve transportation efficiency, and bring significant economic benefits. All of these advantages are driving more users and businesses to adopt autonomous driving technology. In addition, with the advancement of core technologies such as sensors, radar, and high-precision maps, autonomous must have a high level of compatibility with existing roads, traffic signals, traffic signs, and other infrastructures, as well as comply with existing traffic rules, which reduces adoption hindrances caused by compatibility issues. However, as autonomous technology involves some complex technologies, higher technological complexity may increase the learning cost and psychological burden of consumers and reduce their willingness to adopt. With the continuous optimization and popularization of the technology, consumers’ understanding and adaptability to autonomous driving technology will gradually improve, reducing to a certain extent the negative impact of complexity on the adoption of autonomous driving technology innovations.

In the incentive and guarantee subsystem, innovation platform construction is an important factor affecting the technological innovation level of autonomous vehicles. Therefore, on the basis of keeping other parameters unchanged, the changes in the technological innovation level of autonomous vehicles are explored by adjusting the value of the innovation platform construction factor. The innovation platform construction factor is varied by 30% up and down, and the adjusted values are 0.264 and 0.142, respectively, which results in the technological innovation level, under three different innovation platform construction factors, shown in Figure 15.

The simulation results show that the innovation platform construction factor has a significant impact on the technological innovation level of autonomous vehicles, and the increase in the innovation platform construction factor will effectively improve the technological innovation level. The construction of the innovation platform not only builds a bridge connecting enterprises, universities, and research institutions, and breaks the information barriers; it also builds a good ecology of resource sharing, complementary advantages, and synergistic development for the development of autonomous technology innovation, and provides strong support for the in-depth cooperation between industry, academia, and research. On the one hand, colleges, universities, and scientific research institutions can use this platform to display the latest academic achievements and technology patents, which improves the conversion rate of scientific research results and reduces the cost of conversion; it also adjusts the research direction according to the market demand and feedback, develops technological innovations with application prospects and market value, and pushes the iterative upgrading of autonomous driving technology. On the other hand, through the innovation platform, autonomous driving-related enterprises can understand and master the cutting-edge technology dynamics and development trends in the industry for the first time, so as to make more accurate judgments on the direction and focus of technology research and development, and improve the efficiency and success rate of research and development. In addition, in cooperation with universities and scientific research organizations, enterprises can develop more innovative and differentiated products to meet the diversified needs of the market and consumers, thus enhancing their competitiveness. The construction of the innovation platform greatly shortens the time for autonomous driving technology to move from the laboratory to market application, accelerates the deep collaboration of cutting-edge autonomous driving technology and industry, and is an important engine for realizing commercial operation landing.

5. Conclusions and Policy Suggestions

5.1. Conclusions

Based on the system dynamics method, this article constructs a model of China’s autonomous vehicle technology innovation ecosystem, obtains a development trend map for 2015–2030 through dynamic simulation of the model, and explores the effect of different elements in the autonomous technology innovation ecosystem on the industry’s future technological innovation results output.

The main results of this study can be summarized as follows:

This article identified the components and interactions within the autonomous vehicle technology innovation ecosystem. The model confirmed that the synergistic effects of multiple innovation main bodies, the environment, and subsystems are crucial for driving the iterative innovation and upgrading of autonomous driving technology.

The simulation results of the model clearly show the continuous growth trend of relevant innovation indicators during the period of 2024–2030. This is consistent with the 2030 milestones in the Intelligent Networked Vehicle Technology Roadmap 2.0, indicating that China’s autonomous vehicle industry is on track for large-scale commercial operation and industry transformation.

This article demonstrated the significant positive impacts of infrastructure construction, innovation platform construction, educational investment, and other elements on technological innovation levels. The analysis highlighted that considering multiple parameter changes provides a more accurate prediction of technological and market trends, offering a robust basis for policy formulation and enterprise decision-making.

5.2. Policy Suggestions

Based on the research results, in order to better enhance the technological innovation capability of autonomous vehicles, this study puts forward suggestions from the perspectives of the main stakeholders of technological innovation.

In the context of increasingly fierce competition in global autonomous driving technology, the government can establish a research group on autonomous driving technology to closely monitor the policy trends of foreign governments in the field of autonomous driving, in order to grasp cutting-edge trends and development models. In terms of financial support, the government can allocate special funds from the fiscal budget to provide direct funding support for innovative and promising autonomous driving technology research and development projects. At the same time, the government can introduce preferential policies for talent introduction to attract outstanding talents from home and abroad to participate in the innovation and development of autonomous driving technology.

Autonomous driving companies actively participate in the construction of new infrastructure based on their technological advantages, promoting the technology from the validation stage to large-scale marketization. On the one hand, this can occur via cooperation with the government to accelerate the coverage of 5G networks across the region to ensure real-time data collection and transmission for autonomous vehicles. On the other hand, by integrating IoT and big data resources, it has improved the intelligence level of transportation infrastructure and constructed a municipal intelligent transportation sensing platform.

Universities and research institutes can attract domestic and foreign peers to participate and expand the influence of technology innovation exchange platforms by holding annual international forums on autonomous driving technology and regularly organizing online academic exchange seminars. In deepening industry university research cooperation, cooperation agreements are signed with universities and enterprises to establish demonstration bases for the application of research and development achievements within enterprises. The research achievements of universities and research institutes are tested and optimized in actual production and operation scenarios to promote the transformation of scientific research achievements.

As the end-users of technology implementation, consumer demand perception and feedback are crucial for technological iteration. The government and enterprises can establish a consumer demand feedback mechanism by conducting regular market research, establishing user experience testing platforms, setting up consumer feedback hotlines and email addresses, etc., to obtain consumers’ needs and suggestions for autonomous driving technology in a timely manner. At the same time, consumers also need to actively participate in technology testing and demonstration applications, provide real scenario data for technology optimization, promote technology to be closer to market demand, and form a virtuous cycle of innovative market innovation.

Financial institutions can explore diversified financing models by establishing special venture capital funds, conducting intellectual property pledge financing, issuing special bonds, and other means to support the research and commercialization of autonomous driving technology. They can provide financial support to startups through equity financing and bond issuance. In addition, a dedicated venture capital fund can be established to focus on supporting technology projects with innovation and market potential.

5.3. Limitations and Future Research

Although this research has made some achievements in exploring the evolution of technological innovation of autonomous vehicles, it also has the following limitations. The current model simplifies reality by excluding time delay factors, which are present in technology development, policy implementation, and market response. Incorporating time delays in future work will enhance the realism of the model. This study assumes stable national policies during the research period, yet policies in this field change constantly due to technological progress, social needs, and international trends. Future research should explore how dynamic policy adjustments, like subsidy or regulation changes, affect the system. The model only accounts for five main stakeholders, while the industry’s complex supply chain includes many secondary stakeholders. Future models should incorporate these to better represent industry relationships. Data collection is limited to the domestic market, overlooking international factors influencing autonomous vehicle technology innovation, like different regional tech requirements, consumer preferences, and regulations. Expanding data sources to include international data will lead to a more comprehensive analysis.