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

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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

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. Self Learning—Forecasting and Market Sizing
Forecast (Level 1 & Level 2)
Forecast (Level 3 & Level 4)
8. 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
9. Appendix
Market Engineering Methodology
Abbreviations and Acronyms Used


List of Figures & Charts

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



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Release Date : 7-Aug-2015

Region : North America

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