Automotive Artificial Intelligence Market Size, Share & Trends Analysis Report By Component, By Level Of Autonomy (Level 1, Level 2, Level 3, Level 4), By Technology, By Vehicle Type, By Region, And Segment Forecasts, 2022 - 2030

Automotive Artificial Intelligence Market Size, Share & Trends Analysis Report By Component, By Level Of Autonomy (Level 1, Level 2, Level 3, Level 4), By Technology, By Vehicle Type, By Region, And Segment Forecasts, 2022 - 2030

Automotive Artificial Intelligence Market Growth & Trends

The global automotive artificial intelligence market size is expected to reach USD 14.23 billion by 2030, advancing at 21.6% CAGR, according to a new report by Grand View Research, Inc. Artificial Intelligence (AI) in the automotive industry is driven by factors such as government initiatives to incorporate autonomous driving and the growing demand for autonomous vehicles. Furthermore, the automotive industry's expansion is likely to drive the artificial intelligence market’s growth. The automotive sector has benefitted from artificial intelligence and is one of the primary industries that use AI to augment and replicate human action.

The advent of standards such as Advanced Driver Assistance Systems (ADAS), blind-spot alert, Adaptive Cruise Control (ACC), and increased demand for convenience features continue to attract automotive providers to AI. AI mission-critical occurrences necessitate analysis, warnings, and directives. Automotive ADAS comprises various advanced sensors, such as LiDAR, Inertial Measurement Units (IMUs), radar, cameras, and pressure and temperature sensors for constant monitoring of surrounding conditions. The signal chain necessitates proper conditioning of sensor outputs, detection, and reliable low-latency communications within the vehicle and the surrounding infrastructure.

AI has enormous potential in the automobile industry when embedded within the industry's products, production, manufacturing processes, and value-added chains. AI deployment is expected to contribute significantly to a safer, cleaner, more efficient, and more reliable mobility ecosystem. For instance, AI applications in connected and automated vehicles improve driver safety, monitoring, situational awareness, comfort, and trajectory prediction. It can lead to significant gains in performance and efficiency, such as enhanced logistical flows, traffic fluidity, and reduced fuel or power consumption.

In recent years, businesses manufacturing Automated Driving Systems (ADS) technology have thoroughly live-tested autonomous vehicles operating in virtual environments to assure their dependability and safety. However, the COVID-19 pandemic, which began in March 2020, prevented, disrupted, and delayed the launch of these new product development test objectives due to its sudden beginning and continued resurgent impacts.

A study published by Adrian Chen Yang Tan on March 10, 2022, used data from the California Automated Vehicle Test Program to ascertain how the pandemic impacted testing trends, resumptions, and test conditions. The study emphasized how crucial it is for government measures to encourage and facilitate the development of autonomous vehicles in pandemic situations.

Automotive Artificial Intelligence Market Report Highlights

  • The features of autonomous vehicles powered by AI, such as self-driving, autopilot, and automatic parking are widely adopted due to their low human intervention requirements
  • Market growth is positively impacted by the introduction of vehicle tracking software allowing for real-time tracking of vehicles at different locations
  • Automotive manufacturers' investments in AI and LiDAR have responded to the growing demand for intelligent vehicles, which has driven growth across the entire industry
  • Asia Pacific is anticipated to witness the highest CAGR during the forecast period. Growing economies along with massive population will drive the growth
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Chapter 1 Report Scope and Objectives
1.1 Market Segmentation
1.2 Regional Scope
1.3 Estimates and Forecast Timeline
1.4 Research Objectives
1.4.1 Objective - 1
1.4.2 Objective - 2
1.4.3 Objective - 3
Chapter 2 Methodology
2.1 Research Methodology
2.2 Information Procurement
2.2.1 Purchased Database
2.2.2 GVR’s Internal Database
2.2.3 Secondary Sources
2.2.4 Primary Research
2.2.5 Details of Primary Research
2.3 Information or Data Analysis
2.3.1 Data Analysis Models
2.4 Market Formulation & Validation
2.5 Model Details
2.5.1 Commodity Flow Analysis
2.6 List of Secondary Sources
2.7 List of Abbreviations
Chapter 3 Executive Summary
3.1 Market Outlook
3.2 Segment Outlook
3.2.1 Business Model
3.2.2 End - use
3.3Competitive Insights
Chapter 4 Market Variables, Trends & Scope
4.1 Market Lineage outlook
4.1.1 Parent market outlook
4.1.2 Ancillary/related market outlook
4.2 Market Dynamics
4.2.1 Market driver analysis
4.2.1.1 Vertical integration of key market players
4.2.1.2 Introduction of improved health benefit plans
4.2.1.3 Growing prevalence of chronic diseases in the U.S.
4.2.2 Market restraint analysis
4.2.2.1 Stringent regulations to cut back rebates
4.3 Penetration & Growth Prospect Mapping
4.4 Business Environment Analysis Tools
4.4.1 Porter’s Five Forces analysis
4.4.1.1 Competitive rivalry
4.4.1.2 Threat of new entrants
4.4.1.3 Bargaining power of buyers
4.4.1.4 Bargaining power of suppliers
4.4.1.5 Threat of substitutes
4.4.2 PESTEL Analysis
4.4.2.1 Political landscape
4.4.2.2 Economic and social landscape
4.4.2.3 Technological landscape
4.3 Impact of COVID - 19: Qualitative Analysis
Chapter 5 U.S. Pharmacy Benefit Management Market: Business Model Analysis
5.1 U.S. Pharmacy Benefit Management Business Model Market Share Analysis, 2022 & 2030
5.2 U.S. Pharmacy Benefit Management Business Model Market: Segment Dashboard
5.3 Market Size & Forecasts and Trend Analyses, 2017 to 2030 for the Business Model Segment
5.3.1 Standalone PBM
5.3.1.1 Standalone PBM market, 2017 - 2030 (USD Billion)
5.3.2 Health Insurance Providers
5.3.2.1 Health insurance providers market, 2017 - 2030 (USD Billion)
5.3.3 Retail Pharmacy
5.3.3.1 Retail pharmacy market, 2017 - 2030 (USD Billion)
Chapter 6 U.S. Pharmacy Benefit Management Market: End - use Analysis
6.1 U.S. Pharmacy Benefit Management End - use Market Share Analysis, 2022 & 2030
6.2 U.S. Pharmacy Benefit Management End - use Market: Segment Dashboard
6.3 Market Size & Forecasts and Trend Analyses, 2017 to 2030 for the End - use Segment
6.3.1 Commercial
6.3.1.1 Commercial market, 2017 - 2030 (USD Billion)
6.3.2 Federal
6.3.2.1 Federal market, 2017 - 2030 (USD Billion)
Chapter 7 Competitive Analysis
7.1 Recent Developments & Impact Analysis, by Key Market Participants
7.2 Competition Categorization (Key innovators, Market leaders, Emerging Players
7.3 Company Market Position Analysis
7.4 Company Profiles
7.4.1 CVS HEALTH
7.4.1.1 Company overview
7.4.1.2 Financial performance
7.4.1.3 Service benchmarking
7.4.1.4 Strategic initiatives
7.4.2 Elixir Rx Solutions LLC
7.4.2.1 Company overview
7.4.2.2 Service benchmarking
7.4.2.3 Strategic initiatives
7.4.3 Optum, Inc
7.4.3.1 Company overview
7.4.3.2 Financial performance
7.4.3.2 Service benchmarking
7.4.3.3 Strategic initiatives
7.4.4 Cigna
7.4.4.1 Company overview
7.4.4.2 Financial performance
7.4.4.2 Service benchmarking
7.4.4.3 Strategic initiatives
7.4.5 Change Healthcare
7.4.5.1 Company overview
7.4.5.2 Service benchmarking
7.4.5.3 Strategic initiatives
7.4.6 MedImpact
7.4.6.1 Company overview
7.4.6.2 Financial performance
7.4.6.3 Service benchmarking
7.4.6.4 Strategic initiatives
7.4.7 Prime Therapeutics LLC
7.4.7.1 Company overview
7.4.7.2 Service benchmarking
7.4.7.3 Strategic initiatives
7.4.8 HUB International Limited.
7.4.8.1 Company overview
7.4.8.2 Service benchmarking
7.4.8.3 Strategic initiatives
7.4.9 Anthem
7.4.9.1 Company overview
7.4.9.2 Financial performance
7.4.9.3 Service benchmarking
7.4.9.4 Strategic initiatives  

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