Executive Analysis of Self-learning Artificial Intelligence in Cars, Forecast to 2025
Executive Analysis of Self-learning Artificial Intelligence in Cars, Forecast to 2025
Investments Worth $7.1 Billion to Develop 12 Use Cases across 3 Broad Applications by 2025
19-Sep-2016
North America
$4,950.00
Special Price $3,712.50 save 25 %
Description
Self-learning artificial intelligence (AI) in car is anticipated to be one of the biggest disruptions in the automotive industry. The technology offers original equipment manufacturers (OEMs) fresh revenue streams through licensing, partnerships and new mobility services. Self-learning AI in cars is the key to unlocking fully autonomous cars and enhancing value to the end users through virtual assistance. The technology will enable OEMs to diversify from product focus to service focus. This has resulted in 13 key OEMs to invest over $7.1 billion in the development of self learning AI for cars. Frost & Sullivan has recognized 4 levels of evolution between 2015 – 2025, which would lead to 12 use case scenarios for industry participants to capitalize. A total market potential of $78.6 billion by 2025 has lead government bodies, technology companies, internet of things (IoT) companies, new mobility service providers and investors to accelerate the technology development.
Frost & Sullivan has conducted research on the market potential, competitive landscape, key challenges, business models and use case scenarios to understand the impact of self learning AI in automotive industry.
Table of Contents
Key Takeaways, Global, 2015–2025
Four Levels of Self Learning
- Self-learning Cars: Key Takeaways, Global, 2015–2025
Self-learning Cars Evolution
- Self-learning Cars: Level 1 Evolution and Key OEMs Initiatives, Global, 2017
- Self-learning Cars: Level 2 Evolution and Key OEMs Initiatives, Global, 2018
- Self-learning Cars: Level 3 Evolution and Key OEMs Initiatives, Global, 2022
- Self-learning Cars: Level 4 Evolution and Key OEMs Initiatives, Global, 2025
Key OEMs Strategy on Self Learning Technology
- Self-learning Cars: Technology Transforming Business—Toyota, Global, 2016–2025
- Self-learning Cars: Technology Transforming Business—Ford, Global, 2016–2025
- Self-learning Cars: Technology Transforming Business—Volkswagen, Global, 2016–2025
Comparative Analysis of OEMs, , Global, 2015–2025
Self-Learning Revenue Opportunities, Overview, Global, 2016–2025
Self-learning Cars: Drivers, Global, 2016–2025
- Processing capability
- Development of Algorithms
- Data collection
Self-learning Cars: Restraints, Global, 2016–2025
- Legal ambiguity
- Software training and validation
- Security Risks
Regional Analysis and Adoption/Rollout Roadmap. Global, 2015–2025
Key Findings and Future Outlook, 2015 and 2025
Research Scope
Research Aims and Objectives
Research Methodology
Research Background
Key OEM Groups Analysed in this Study
Defining a Self-learning Car, Global, 2015–2025
Three Levels of AI to Disrupt the Automotive Industry
Deep Neural Networks to Drive Self-learning AI
Evolution of Self-learning Cars in 4 Levels
Self-Learning is not Autonomous—It is Beyond
Key findings, Global, 2015–2025
Need for Self Learning Technology in Cars
Advantages and Limitations, Global, 2016
Self-learning Cars will Scale with Data
Applications of Self Learning Technology in Cars, Global, 2016–2025
Technology Requirements, Global, 2016–2025
Working principal of Self-learning Cars, Global, 2016–2025
Three Big Challenges, Global, 2016–2025
Key findings, Global, 2016–2025
Technology Companies in the Value Chain, Global, 2016–2025
OEM Groups are Partnering with Tech Companies, Global, 2015–2025
13 OEMs Focus on Self Learning Technology
Strategy on Self Learning Technology
Comparative Analysis of OEMs
Electronic Companies Strategy, Global, 2015–2025
Self-learning Cars: Electronic Companies Strategy, Global, 2015–2025
NVIDIA Strategy on Self Learning Technology
Technology Companies Strategy, Global, 2015–2025
Cloud made Strategy on Self Learning Technology, Global, 2016
Business Models, Global, 2015–2025
Key Findings, Global, 2015
User Preferences (Level 1 Self Learning), Global, 2016–2025
Near Field Vision (Level 2 Self Learning), Global, 2016–2025
Highly Autonomous Maps (Level 3 Self Learning), Global, 2016–2025
New Mobility Services (Level 4 Self Learning), 2025
Forecast (Level 1 & Level 2)
Forecast (Level 3 & Level 4)
Technology Outlook
Conclusions and Future Outlook—So-what Analysis
Current and Future Outlook—Key Findings, 2015 and 2025
5 Growth Opportunities
Key Conclusions, Global, 2015
The Last Word—3 Big Predictions
Legal Disclaimer
Market Engineering Methodology
Abbreviations and Acronyms Used
- 1. Self-learning Cars: Investments in Technology, Global, 2016–2022
- 2. Self-learning Cars: Self Learning Technology Cost Estimation, Global, 2016–2025
- 3. Self-learning Cars: Self Learning Technology Cost Estimation, 2016–2025
Related Research
Strategic Analysis of WBG Semiconductors in Power Electronics Applications for EVs, Forecast to 2030
Popular Topics
No Index | No |
---|---|
Podcast | No |
Table of Contents | | Executive Summary~ || Key Takeaways, Global, 2015–2025~ || Four Levels of Self Learning~ ||| Self-learning Cars: Key Takeaways, Global, 2015–2025~ || Self-learning Cars Evolution~ ||| Self-learning Cars: Level 1 Evolution and Key OEMs Initiatives, Global, 2017~ ||| Self-learning Cars: Level 2 Evolution and Key OEMs Initiatives, Global, 2018~ ||| Self-learning Cars: Level 3 Evolution and Key OEMs Initiatives, Global, 2022~ ||| Self-learning Cars: Level 4 Evolution and Key OEMs Initiatives, Global, 2025~ || Key OEMs Strategy on Self Learning Technology~ ||| Self-learning Cars: Technology Transforming Business—Toyota, Global, 2016–2025~ ||| Self-learning Cars: Technology Transforming Business—Ford, Global, 2016–2025~ ||| Self-learning Cars: Technology Transforming Business—Volkswagen, Global, 2016–2025~ || Comparative Analysis of OEMs, , Global, 2015–2025~ || Self-Learning Revenue Opportunities, Overview, Global, 2016–2025~ || Self-learning Cars: Drivers, Global, 2016–2025~ ||| Processing capability~ ||| Development of Algorithms~ ||| Data collection~ || Self-learning Cars: Restraints, Global, 2016–2025~ ||| Legal ambiguity~ ||| Software training and validation~ ||| Security Risks~ || Regional Analysis and Adoption/Rollout Roadmap. Global, 2015–2025~ || Key Findings and Future Outlook, 2015 and 2025~ | Research Scope, Objectives, Background, and Methodology~ || Research Scope~ ||| Base Year~ ||| Study Period~ ||| Forecast Period~ ||| Vehicle Type~ ||| Geographical Scope~ || Research Aims and Objectives~ || Research Methodology~ ||| Self-learning Cars: Key Participants, Global, 2015~ || Research Background~ || Key OEM Groups Analysed in this Study~ ||| Self-learning Cars: OEMs, Global, 2015~ | Definitions~ || Defining a Self-learning Car, Global, 2015–2025~ || Three Levels of AI to Disrupt the Automotive Industry~ ||| Self-learning Cars: Levels of artificial intelligence, Global, 2015– >2025~ || Deep Neural Networks to Drive Self-learning AI~ ||| Visual Depiction of Deep Neural Network~ ||| Self-learning Cars: Popular Forms of Machine Learning Algorithms, Global, 2016~ || Evolution of Self-learning Cars in 4 Levels~ ||| Self-learning Cars: Levels of Self-learning Cars, Global, 2016–2025~ || Self-Learning is not Autonomous—It is Beyond~ ||| Self-learning Cars: Self-learning Cars Capability, Global, 2015–2025~ | Self-learning Cars—Overview~ || Key findings, Global, 2015–2025~ || Need for Self Learning Technology in Cars~ ||| Self-learning Cars: AI transformation in automotive industry, Global, 2012–2025~ || Advantages and Limitations, Global, 2016~ || Self-learning Cars will Scale with Data~ ||| Self-learning Cars: Evolution of Self-learning Cars, Global, 2012–2025~ || Applications of Self Learning Technology in Cars, Global, 2016–2025~ || Technology Requirements, Global, 2016–2025~ || Working principal of Self-learning Cars, Global, 2016–2025~ || Three Big Challenges, Global, 2016–2025~ | Key Participants Technology Strategies~ || Key findings, Global, 2016–2025~ || Technology Companies in the Value Chain, Global, 2016–2025~ || OEM Groups are Partnering with Tech Companies, Global, 2015–2025~ || 13 OEMs Focus on Self Learning Technology~ || Strategy on Self Learning Technology~ ||| Self-learning Cars: Technology Transforming Business—Toyota, Global, 2016-2025~ ||| Self-learning Cars: Technology Transforming Business—Ford, Global, 2016–2025~ ||| Self-learning Cars: Technology Transforming Business—Volkswagen, Global, 2016–2025~ || Comparative Analysis of OEMs~ ||| Self-learning Cars: Comparative Analysis, Global, 2016–2025~ || Electronic Companies Strategy, Global, 2015–2025~ || Self-learning Cars: Electronic Companies Strategy, Global, 2015–2025~ || NVIDIA Strategy on Self Learning Technology~ ||| Self-learning Cars: NVIDIA Strategy, Global, 2015~ ||| Self-learning Cars: NVIDIA Strategy, Global, 2016~ || Technology Companies Strategy, Global, 2015–2025~ ||| Self-learning Cars: Competitive analysis of technology companies, Global 2016–2025~ || Cloud made Strategy on Self Learning Technology, Global, 2016~ || Business Models, Global, 2015–2025~ | Use Case Scenarios~ || Key Findings, Global, 2015~ || User Preferences (Level 1 Self Learning), Global, 2016–2025~ || Near Field Vision (Level 2 Self Learning), Global, 2016–2025~ || Highly Autonomous Maps (Level 3 Self Learning), Global, 2016–2025~ || New Mobility Services (Level 4 Self Learning), 2025~ | Self Learning—Forecasting and Market Sizing~ || Forecast (Level 1 & Level 2)~ || Forecast (Level 3 & Level 4)~ | Conclusions and Future Outlook~ || Technology Outlook~ || Conclusions and Future Outlook—So-what Analysis~ || Current and Future Outlook—Key Findings, 2015 and 2025~ || 5 Growth Opportunities~ || Key Conclusions, Global, 2015~ || The Last Word—3 Big Predictions~ || Legal Disclaimer~ | Appendix~ || Market Engineering Methodology~ || Abbreviations and Acronyms Used~ |
List of Charts and Figures | 1. Self-learning Cars: Investments in Technology, Global, 2016–2022~ 2. Self-learning Cars: Self Learning Technology Cost Estimation, Global, 2016–2025~ 3. Self-learning Cars: Self Learning Technology Cost Estimation, 2016–2025~ |
Author | Sistla Raghuvamsi |
Industries | Automotive |
WIP Number | K053-01-00-00-00 |
Is Prebook | No |