Automotive Artificial Intelligence Market Size, Share & Trends Analysis Report By Component, By Level Of Autonomy, By Technology (Machine Learning, Natural Language Processing), By Vehicle Type, By Region, And Segment Forecasts, 2023 - 2030

Automotive Artificial Intelligence Market Size, Share & Trends Analysis Report By Component, By Level Of Autonomy, By Technology (Machine Learning, Natural Language Processing), By Vehicle Type, By Region, And Segment Forecasts, 2023 - 2030


Automotive Artificial Intelligence Market Growth & Trends


The global automotive artificial intelligence market size is expected to reach USD 14.92 billion by 2030, according to a new report by Grand View Research, Inc. The industry is anticipated to expand at a lucrative CAGR of 22.7% from 2023 to 2030. The artificial intelligence (AI) in the automotive industry is driven by factors such as government initiatives to incorporate autonomously and the growing demand for autonomous vehicles. Furthermore, the automotive industry's expansion will likely drive the artificial intelligence market. The automotive sector has benefitted from artificial intelligence and is one of the primary industries that use AI to augment and replicate human action. For instance, in March 2023, RoboSense announced the launch of the RS-Fusion-P6 (P6) automotive-grade solid-state LiDAR perception solution. The P6 LiDAR system is designed explicitly for level 4 autonomous driving and integrates cutting-edge software and hardware support, ensuring efficient and reliable perception capabilities for autonomous vehicles.

The advent of standards such as Advanced Driver Assistance Systems (ADAS), blind-spot alert, Adaptive Cruise Control (ACC), and increased demand for convenience features are attracting 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, and cameras, as well as data connectivity and pressure and temperature sensors for constant uploads and downloads of surrounding conditions. The signal chain necessitates proper conditioning of sensor outputs and 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 and 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 substantially invested in live testing 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 achievement 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 software segment is anticipated to grow significantly in the coming years. ADAS or autonomous driving runtime software and the employment of specialized processing element types speed up specific algorithmic steps. The ongoing trend of AI adoption in the automotive industry will drive market growth in the coming years
  • The level 2 segment led the market in 2022, accounting for over 67% share of the global revenue. Level-2 self-driving technology refers to a system that can handle certain driving tasks, such as acceleration, braking, and steering, but still requires the driver to be alert and ready to take control when needed
  • The APAC region is anticipated to witness the highest CAGR during the forecast period. The region's increasing sales of premium passenger automobiles equipped with advanced AI features have attracted consumers seeking enhanced driving experiences. Consumers’ rise in disposable incomes to purchase technologically advanced vehicles is fueling the demand for AI-driven automotive solutions
  • Automotive manufacturers investments in AI and LiDAR have responded to the growing demand for intelligent vehicles, which has driven growth across the entire industry
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Chapter 1. Methodology And Scope
1.1. Research Methodology
1.2. Research Scope And Assumptions
1.3. Information Procurement
1.3.1. Purchased Database
1.3.2. Gvr’s Internal Database
1.3.3. Secondary Sources & Third-Party Perspectives
1.3.4. Primary Research
1.4. Information Analysis
1.4.1. Data Analysis Models
1.5. Market Formulation & Data Visualization
1.6. Data Validation & Publishing
Chapter 2. Executive Summary
2.1. Market Outlook
2.2. Segmental Outlook
2.3. Competitive Insights
Chapter 3. Market Variables, Trends, And Scope
3.1. Market Lineage
3.2. Industry Value Chain Analysis
3.3. Automotive Ai Market - Market Dynamics
3.3.1. Market Driver Analysis
3.3.1.1. Increasing Government Vehicle Safety Regulations
3.3.1.2. Increased Demand For Enhanced User Experience And Convenience Features
3.3.1.3. Increasing Popularity Of Autonomous Vehicles
3.3.2. Market Restraint Analysis
3.3.2.1. Increase In Cost Of Vehicles
3.3.2.2. Threat To Vehicles Cybersecurity
3.3.3. Industry Challenges
3.3.4. Industry Opportunities
3.4. Business Environmental Tools Analysis: Automotive Ai Market
3.4.1. Porter’s Five Forces Analysis
3.4.1.1. Bargaining Power Of Suppliers
3.4.1.2. Bargaining Power Of Buyers
3.4.1.3. Threat Of Substitution
3.4.1.4. Threat Of New Entrants
3.4.1.5. Competitive Rivalry
3.4.2. Pestle Analysis
3.4.2.1. Political Landscape
3.4.2.2. Economic Landscape
3.4.2.3. Social Landscape
3.4.2.4. Technology Landscape
3.4.2.5. Environmental Landscape
3.4.2.6. Legal Landscape
3.5. Economic Mega Trend Analysis
Chapter 4. Automotive Ai Market: Component Estimates & Trend Analysis
4.1. Automotive Ai Market, By Component: Key Takeaways
4.2. Automotive Ai Market: Component Movement Analysis, 2022 & 2030
4.3. Hardware
4.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
4.4. Software
4.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
Chapter 5. Automotive Ai Market: Level Of Autonomy Estimates & Trend Analysis
5.1. Automotive Ai Market, By Level Of Autonomy: Key Takeaways
5.2. Automotive Ai Market: Level Of Autonomy Movement Analysis, 2022 & 2030
5.3. Level 1
5.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
5.4. Level 2
5.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
5.5. Level 3
5.5.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
5.6. Level 4
5.8.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
Chapter 6. Automotive Ai Market: Technology Estimates & Trend Analysis
6.1. Automotive Ai Market, By Technology: Key Takeaways
6.2. Automotive Ai Market: Technology Movement Analysis, 2022 & 2030
6.3. Machine Learning
6.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
6.4. Natural Language Processing
6.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
6.5. Computer Vision
6.5.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
6.6. Context-Aware Computing
6.6.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
6.7. Others
6.7.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
Chapter 7. Automotive Ai Market: Vehicle Type Estimates & Trend Analysis
7.1. Automotive Ai Market, By Vehicle Type: Key Takeaways
7.2. Automotive Ai Market: Vehicle Type Movement Analysis, 2022 & 2030
7.3. Passenger Vehicles
7.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
7.4. Commercial Vehicles
7.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
Chapter 8. Automotive Ai Market: Regional Estimates & Trend Analysis
8.1. Automotive Ai Market: Regional Movement Analysis, 2022 & 2030
8.2. North America
8.2.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.2.2. U.S.
8.2.2.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.2.3. Canada
8.2.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.2.4. Mexico
8.2.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.3. Europe
8.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.3.2. Germany
8.3.2.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.3.3. France
8.3.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.3.4. Uk
8.3.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.4. Asia Pacific
8.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.4.2. China
8.4.2.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.4.3. Japan
8.4.3.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.4.4. India
8.4.4.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.5. Central & South America
8.5.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.5.2. Brazil
8.5.2.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
8.6. Middle East & Africa
8.6.1. Market Estimates And Forecasts, 2017 - 2030 (USD Million)
Chapter 9. Competitive Landscape
9.1. Company Categorization
9.2. Company Market Positioning
9.3. Company Heat Map Analysis
9.4. Strategy Mapping
9.4.1. Expansion
9.4.2. Mergers & Acquisition
9.4.3. Partenerships & Collaborations
9.4.4. New Product Launches
9.4.5. Research & Development
9.5. Company Profiles/Listing
9.5.1. Alphabet Inc.
9.5.1.1. Overview
9.5.1.2. Financial Performance
9.5.1.3. Product Benchmarking
9.5.2. Intel Corporation
9.5.2.1. Overview
9.5.2.2. Financial Performance
9.5.2.3. Product Benchmarking
9.5.3. Microsoft
9.5.3.1. Overview
9.5.3.2. Financial Performance
9.5.3.3. Product Benchmarking
9.5.4. Ibm Corporation
9.5.4.1. Overview
9.5.4.2. Financial Performance
9.5.4.3. Product Benchmarking
9.5.5. Nvidia Corporation
9.5.5.1. Overview
9.5.5.2. Financial Performance
9.5.5.3. Product Benchmarking
9.5.6. Qualcomm Technologies, Inc
9.5.6.1. Overview
9.5.6.2. Financial Performance
9.5.6.3. Product Benchmarking
9.5.7. Tesla
9.5.7.1. Overview
9.5.7.2. Financial Performance
9.5.7.3. Product Benchmarking
9.5.8. Ab Volvo
9.5.8.1. Overview
9.5.8.2. Financial Performance
9.5.8.3. Product Benchmarking
9.5.9. Bmw Ag
9.5.9.1. Overview
9.5.9.2. Financial Performance
9.5.9.3. Product Benchmarking
9.5.10. Audi Ag
9.5.10.1. Overview
9.5.10.2. Financial Performance
9.5.10.3. Product Benchmarking

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