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Global Automotive Predictive Technology Market to reach USD 120.20 billion by the end of 2030.

Global Automotive Predictive Technology Market Size study & Forecast, by Vehicle Type( Passenger Vehicles, Commercial Vehicles) by End User (Fleet Owners, Insurers, Other End Users), by Hardware Type (ADAS, On-board Diagnosis, Other Hardware Types) and Regional Analysis, 2023-2030

Product Code: ALTST-72953154
Publish Date: 20-05-2023
Page: 200

Global Automotive Predictive Technology Market is valued at approximately USD 63.94 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 8.21% over the forecast period 2023-2030. Automotive Predictive Technology refers to the use of advanced analytics and predictive modeling techniques to anticipate and forecast various aspects of the automotive industry. It leverages data from various sources, such as vehicle sensors, historical records, market trends, and customer behavior, to make informed predictions about different aspects of the automotive ecosystem. The Automotive Predictive Technology market is expanding because of factors such as increasing installation of advanced driver-assistance systems (ADAS) and growing investment in research and development activities.

The automotive industry has been driving research and development in recent years to enhance ADAS systems. As a result, lane departure warning systems, cameras, RADAR, and other sensors are in high demand and are being integrated into vehicles at a rapid rate. This led to additional advancements in advanced driving aid systems. Companies, therefore, keep focusing on developing products utilising these technologies to increase demand in the industry which is driving the market growth. As an example, in the year 2021 Hyundai unveiled its upgraded Santafee model. Along with safety features, the car is loaded with numerous features. The Hyundai Santafee comes with SmartSense safety systems, which include a variety of cameras, radars, and motion detection technologies like Forward Collision-Avoidance Assist (FCA), meant to identify cars, pedestrians, or cyclists who are directly in front of the car. In April 2021, Toyota Motor Corp. unveiled updated models of the Toyota Mirai and Lexus LS in Japan. Both cars are equipped with Level 2 autonomous Advanced Drive, which helps with lane keeping, keeping a safe distance from other cars, lane changes, and advanced driver assistance. Additionally, Toyota has made known that it intends to pay USD 550 million to buy Lyft’s autonomous vehicle division. Such technological advancement is driving the market growth. In addition, rising investments by the companies to adopt advanced technologies such as IoT and AI and rising government support to the industry is creating lucrative growth to the market. However, the high cost of Automotive Predictive Technology stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Automotive Predictive Technology Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to presence of key market players, rising adoption of self-driving technology system, and rising technological advancement in the region. Asia Pacific is expected to grow significantly during the forecast period, owing to factors such as increasing production of electric vehicles, rising initiatives for implementation of predictive technology.

Major market player included in this report are:
Continental AG
Aptiv PLC
Garrett Motion Inc.
Harman International Industries Incorporated
Visteon Corporation
ZF Friedrichshafen AG
Valeo SA
Robert Bosch GmbH
Infineon Technologies AG

Recent Developments in the Market:
Ø In July 2021, ZF launched its brand-new ZF ProAI supercomputer. This system offers the most recent security measures against cyber threats by providing tailored computing power for cars of any level of automation. It is equipped with control units, software, sensors, and actuators for software-defined vehicles.
Ø In January 2021, HARMAN introduced HARMAN Turbo Connect (TBOT), a brand-new intelligent software agent that foresees and corrects on-the-road connectivity issues for vehicles. When combined with 5G-enabled technologies like HARMAN’s Smart Conformal Antenna and complete 5G or 5G-ready Telecommunications Control Units (TCU), the HARMAN TBOT meets the current demand for high-speed connectivity with low latency.

Global Automotive Predictive Technology Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Vehicle Type, End User, Hardware Type, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and Vehicle Type offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Vehicle Type:
Passenger Vehicles
Commercial Vehicles

By End User:
Fleet Owners
Other End Users

By Hardware Type:
On-board Diagnosis
Other Hardware Types

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Automotive Predictive Technology Market, by Region, 2020-2030 (USD Billion)
1.2.2. Automotive Predictive Technology Market, by Vehicle Type, 2020-2030 (USD Billion)
1.2.3. Automotive Predictive Technology Market, by End User, 2020-2030 (USD Billion)
1.2.4. Automotive Predictive Technology Market, by Hardware Type, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Automotive Predictive Technology Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Automotive Predictive Technology Market Dynamics
3.1. Automotive Predictive Technology Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Increasing installation of advanced driver-assistance systems (ADAS) Growing investment in research and development activities
3.1.2. Market Challenges High Cost of Automotive Predictive Technology
3.1.3. Market Opportunities Rising investments by the companies to adopt advanced technologies such as IoT and AI Rising government support to the industry
Chapter 4. Global Automotive Predictive Technology Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Automotive Predictive Technology Market, by Vehicle Type
5.1. Market Snapshot
5.2. Global Automotive Predictive Technology Market by Vehicle Type, Performance – Potential Analysis
5.3. Global Automotive Predictive Technology Market Estimates & Forecasts by Vehicle Type 2020-2030 (USD Billion)
5.4. Automotive Predictive Technology Market, Sub Segment Analysis
5.4.1. Passenger Vehicles
5.4.2. Commercial Vehicles
Chapter 6. Global Automotive Predictive Technology Market, by End User
6.1. Market Snapshot
6.2. Global Automotive Predictive Technology Market by End User, Performance – Potential Analysis
6.3. Global Automotive Predictive Technology Market Estimates & Forecasts by End User 2020-2030 (USD Billion)
6.4. Automotive Predictive Technology Market, Sub Segment Analysis
6.4.1. Fleet Owners
6.4.2. Insurers
6.4.3. Other End Users
Chapter 7. Global Automotive Predictive Technology Market, by Hardware Type
7.1. Market Snapshot
7.2. Global Automotive Predictive Technology Market by Hardware Type, Performance – Potential Analysis
7.3. Global Automotive Predictive Technology Market Estimates & Forecasts by Hardware Type 2020-2030 (USD Billion)
7.4. Automotive Predictive Technology Market, Sub Segment Analysis
7.4.1. ADAS
7.4.2. On-board Diagnosis
7.4.3. Other Hardware Types
Chapter 8. Global Automotive Predictive Technology Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Automotive Predictive Technology Market, Regional Market Snapshot
8.4. North America Automotive Predictive Technology Market
8.4.1. U.S. Automotive Predictive Technology Market Vehicle Type breakdown estimates & forecasts, 2020-2030 End User breakdown estimates & forecasts, 2020-2030 Hardware Type breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Automotive Predictive Technology Market
8.5. Europe Automotive Predictive Technology Market Snapshot
8.5.1. U.K. Automotive Predictive Technology Market
8.5.2. Germany Automotive Predictive Technology Market
8.5.3. France Automotive Predictive Technology Market
8.5.4. Spain Automotive Predictive Technology Market
8.5.5. Italy Automotive Predictive Technology Market
8.5.6. Rest of Europe Automotive Predictive Technology Market
8.6. Asia-Pacific Automotive Predictive Technology Market Snapshot
8.6.1. China Automotive Predictive Technology Market
8.6.2. India Automotive Predictive Technology Market
8.6.3. Japan Automotive Predictive Technology Market
8.6.4. Australia Automotive Predictive Technology Market
8.6.5. South Korea Automotive Predictive Technology Market
8.6.6. Rest of Asia Pacific Automotive Predictive Technology Market
8.7. Latin America Automotive Predictive Technology Market Snapshot
8.7.1. Brazil Automotive Predictive Technology Market
8.7.2. Mexico Automotive Predictive Technology Market
8.8. Middle East & Africa Automotive Predictive Technology Market
8.8.1. Saudi Arabia Automotive Predictive Technology Market
8.8.2. South Africa Automotive Predictive Technology Market
8.8.3. Rest of Middle East & Africa Automotive Predictive Technology Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. Continental AG Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.3.2. Aptiv PLC
9.3.3. Garrett Motion Inc.
9.3.4. Harman International Industries Incorporated
9.3.5. Visteon Corporation
9.3.6. ZF Friedrichshafen AG
9.3.7. Valeo SA
9.3.8. Robert Bosch GmbH
9.3.9. Verizon
9.3.10. Infineon Technologies AG
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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