1. Executive Summary
Key Findings
Top Trends Driving the Development of AI for AD
Levels of Automation Defined With Regard to AI
Expanding Universe of AI in AD - Vital Pillars
Value Chain Development of AI in Universe of AD
Noteworthy Companies With AI Capabilities - By Region
Major Tech Companies' Approach - Overview
Adjoining Revenue Opportunities for Artificial Intelligence in AD
Major Challenges in Implementation of AI in AD
Key Trends

2. Research Scope and Segmentation
Research Scope
Key Questions This Study will Answer

3. Automated Driving Artificial Intelligence versus Traditional Approach
Traditional Approach Versus Deep Learning Approach
AI - Key Differentiators
Dependence of AI Development on Software
Progression of AI in Autonomous Vehicles
Disruption in the Automotive Industry with Developing AI
Role of Data Flow in AI in AD Cars

4. Deep Learning in AI
DNN to Drive Self-learning AI
Deep Neural Network - Training Cycle
Challenges for Deep Learning Adoption for AD
Machine Learning Approach - Case Study: Oxbotica
Deep Learning Approach - Case Study 1: Drive.ai
CNN - Case Study: AIMotive

5. Innovation Through Partnerships
NVIDIA - A Complete End-to-end AI Solution: Hardware
NVIDIA - A Complete End-to-end AI solution: DL Software
NVIDIA'S Activity - Highlighted Partnerships
Companies Ahead in the Business - Overview

6. Major OEM Activities
Major OEMs and AI - How They Rate Against Each Other?

7. Growth Opportunities and Companies to Action
Growth Opportunity - Investments and Partnerships from OEMs/TSPs
Strategic Imperatives for Success and Growth

8. Conclusions and Future Outlook
Conclusion and Future Outlook
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