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

RELEASE DATE
19-Sep-2016
REGION
North America
Research Code: K053-01-00-00-00
SKU: AU01364-NA-MR_19001
AvailableYesPDF Download

$4,950.00

Special Price $3,712.50 save 25 %

In stock
SKU
AU01364-NA-MR_19001

$4,950.00

$3,712.50save 25 %

DownloadLink
ENQUIRE NOW

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

List of 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
Related Research
Scope of the report The research report includes the following segments: Product scope: Self-learning AI in cars - Autonomous Cars, Virtual Assistance in Cars, new revenue streams through data analytics and licensing, and HAD mapping Geographic scope: North America, Europe, China, and Japan End-user scope: Automotive Industry Participants Drivers and restraints, a detailed discussion of the four levels of evolution of self-learning cars, discussion of technology trends of key OEMs, and use case scenarios have also been provided for self-learning cars market. What makes our reports unique? We provide one of the longest market segmentation chains in this industry. We conduct detailed market positioning, product positioning, and competitive positioning. Entry strategies, gaps, and opportunities are identified for all the stakeholders. Comprehensive market analysis for the following sectors: Pharmaceuticals, medical devices, biotechnology, semiconductor and electronics, energy and power supplies, food and beverages, chemicals, advanced materials, industrial automation, and telecom, and IT. We also analyze retailers and super-retailers, technology partners, and research and development (R&D) companies. Key Questions Answered What are self-learning cars? Who are the key industry participants applying this technology? Where are the growth opportunities in the value chain? What are the roadmaps to reach a self-learning car? What is the strategy of various industry participants, and what are the use case scenarios? Is self-learning car the best route to a self-driving car? What are the new business models evolving around self-learning cars? Who are the key industry participants that will benefit from Self Learning Technology adaption? How are new partnerships evolving and disrupting the traditional supply chain in the automotive industry?
More Information
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