Executive Analysis of Self-learning Artificial Intelligence in Cars, Forecast to 2025

Executive Analysis of Self-learning Artificial Intelligence in Cars, Forecast to 2025

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?

  • Executive Summary
    • Executive Summary-Key Findings
    • Four Levels of Self Learning
    • Self-learning Cars Evolution
    • Key OEMs Strategy on Self Learning Technology
    • Comparative Analysis of OEMs
    • Self Learning Revenue Opportunities
    • Drivers
    • Restraints
    • Regional Analysis and Adoption/Rollout Roadmap
    • Executive Summary-Key Findings and Future Outlook
  • Research Scope, Objectives, Background, and Methodology
    • Research Scope
    • Research Aims and Objectives
    • Research Methodology
    • Key Questions This Study Will Answer
    • Research Background
    • Key OEM Groups Analyzed in this Study
  • Definitions
    • Defining a Self-learning Car
    • 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
  • Self-learning Cars-Overview
    • Overview of Self Learning-Key Findings
    • Need for Self Learning Technology in Cars
    • Self-learning Cars-Advantages and Limitations
    • Self-learning Cars will Scale with Data
    • Applications of Self Learning Technology in Cars
    • Technology Requisites
    • Working Principle of Self-learning Cars
    • Three Big Challenges
  • Key Participants Technology Strategies
    • Technology Strategies-Key Findings
    • Technology Companies in the Value Chain
    • OEM Groups are Partnering with Tech Companies
    • 13 OEMs Focus on Self Learning Technology
    • Toyota Strategy on Self Learning Technology
    • Ford Strategy on Self Learning Technology
    • Volkswagen Strategy on Self Learning Technology
    • Comparative Analysis of OEMs
    • Electronic Companies Technology Strategy-Overview
    • Electronic Companies Strategy-Comparison
    • NVIDIA Strategy on Self Learning Technology
    • Technology Companies Strategy-Overview
    • Technology Companies Strategy-Comparison
    • Cloudmade Strategy on Self Learning Technology
    • Business Models
  • Use Case Scenarios
    • Use Case Scenario-Key Findings
    • Use Case Scenarios-User Preferences (Level 1 Self Learning)
    • Use Case Scenarios-Near Field Vision (Level 2 Self Learning)
    • Use Case Scenarios-Highly Autonomous Maps (Level 3 Self Learning)
    • Use Case Scenarios-New Mobility Services (Level 4 Self Learning)
  • Self Learning-Forecasting and Market Sizing
    • Self-learning Cars-Forecast (Level 1 & Level 2)
    • Self-learning Cars-Forecast (Level 3 & Level 4)
  • Conclusions and Future Outlook
    • Technology Outlook
    • Conclusions and Future Outlook-So-what Analysis
    • Key Findings and Future Outlook
    • 5 Growth Opportunities
    • Key Conclusions
    • The Last Word-3 Big Predictions
  • Appendix
    • Market Engineering Methodology
    • Abbreviations and Acronyms Used

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