Product Code: 3199
Title:
Artificial Intelligence (AI) in Automotive Market Size, By Component (Software, Hardware, Service), By Technology (Computer Vision, Context Awareness, Deep Learning, Machine Learning, Natural language Processing (NLP)), By Process (Data Mining, Image/Signal Recognition), By Application (Semi-Autonomous Vehicles, Fully Autonomous Vehicles) Industry Analysis Report, Regional Outlook, Growth Potential, Competitive Market Share & Forecast, 2020 - 2026.
Artificial Intelligence (AI) in Automotive market is projected to surpass USD 12 billion by 2026. The market growth is attributed to the steadily growing uptake of driver assistance technologies for increasing driving comfort and ensuring safe driving experience. Consumers are increasingly exhibiting a positive attitude toward AI-powered vehicle driving systems, creating new avenues for market growth. Automotive manufacturers are capitalizing on the steadily growing industry by introducing new features in their vehicles including automated parking, lane assistance, driver behavior monitoring, and adaptive cruise control. For instance, in October 2019, Toyota announced the launch of level-4 driver assistance systems for enabling automated valet parking in its upcoming cars. The technology is developed in conjunction with Panasonic and is built with inexpensive sensors, offering affordable parking assistance solutions to Toyota's customers.
Some major findings of AI in automotive market report include:
With the dynamically changing technology landscape in the automotive sector, an increasing number of automobile manufacturers are focusing on integrating semi-autonomous and fully-autonomous technologies into their vehicles
Sophisticated onboard AI systems are providing real-time connectivity between vehicle & driver, enabling safe driving and reducing driver fatigue by suggesting resting periods & controlling car navigation during driver distraction
Machine learning solutions are witnessing a sustained rise in adoption, enabling AI systems to predict and decide driving patterns in dense traffic. With vastly improved neural network technologies, machine learning can achieve near human driving behavior without external assistance.
Technology providers including NVIDIA, Intel, and AMD are continuously upgrading their solutions and offering energy-efficient hardware, enabling AI technologies with low power consumption
The growing interest of government agencies in adopting autonomous mobility for reducing traffic accidents and improving traffic management is creating a positive outlook for the industry
Some of the leading market players are Alphabet Inc., Audi AG, BMW AG, Daimler AG, Didi Chuxing, Ford Motor Company, General Motors Company, Harman International Industries, Inc., Honda Motor Co., Ltd., IBM Corporation, Intel Corporation, Microsoft Corporation, NVIDIA Corporation, Qualcomm Inc., Tesla, Inc., Toyota Motor Corporation, Uber Technologies, Inc., Volvo Car Corporation, and Xilinx Inc.
AI platform providers are focusing on strategic collaboration and long-term contracts with automotive manufacturers to gain market share
The hardware segment held majority of the market with over 60% share in 2019 and is expected to continue its dominance over the forecast timespan. This is attributed to the increasing adoption of automotive AI components for implementation of AI solutions. Energy-efficient System-on-Chips (SoCs) and dedicated AI GPUs are assisting enterprises in deploying highly sophisticated onboard computers with robust computing power. In July 2019, Intel launched Pohoiki Beach, a new AI-enabled chip, which features 8 million neural networks and can reach up to 10,000 times faster computing speeds compared to traditional CPUs. Furthermore, the growing uptake of sensors including high-resolution cameras, LiDARs, and ultrasonic sensors for vehicle situational awareness is fueling the growth of AI hardware.
The context awareness segment is anticipated to register an impressive growth with a CAGR of over 35% from 2019 to 2026 due to the rapid proliferation of driver assistance solutions and semi-automated cruise control. Context awareness systems provide situational intelligence through multi-sensory input and enable onboard computers to detect & classify on-road entities including pedestrians, traffic, and road infrastructure. Customers are reaping the benefits of context-awareness systems by deploying effective navigation assistance, which enables safe driving even during driver distraction. Major technology companies are investing in innovative automotive technologies including context awareness. For instance, in November 2016, Intel announced an investment of USD 250 million in autonomous driving technology. This investment was focused on key technologies such as context awareness, deep learning, security, and connectivity.
The image/signal recognition segment held majority of the market with over 65% share in 2019 due to the growing importance of vehicle speed control for reducing on-road accidents. Image/signal recognition technologies can detect traffic signs & speed limit indicators and reduce the vehicle speed accordingly without human intervention. The technology is also expected to grow significantly as several government initiatives are promoting traffic sign recognition to ensure adherence to speed limits. In March 2019, the European Commission made it mandatory for all vehicles manufactured from 2022 to have built-in image/signal recognition capabilities. This is expected to reduce rash driving, over-speeding, and promote on-road safety.
The semi-autonomous vehicles segment will grow at an impressive CAGR of over 38% by 2026 due to the extensive demand for Advanced Driver Assistance Systems (ADAS) and facilitating driving during heavy traffic scenarios. Semi-autonomous technologies have already been commercialized and are expected to gain significant market proliferation over the forecast timespan. Major automotive manufacturers, such as Chrysler, Audi, and Ford, have started integrating semi-autopilot and drive cruise control technologies into their latest models. Driver behavior monitoring, road condition awareness, and lane tracking are a few of the innovative solutions that have been introduced through the implementation of AI technologies in semi-autonomous vehicles. Furthermore, supporting initiatives from various governments to incorporate semi-autonomous vehicle technologies by 2022 will positively impact industry growth.
Europe held majority of the market with over 35% share in 2019 due to the growing demand for autonomous technologies in the region. Presence of several industry leaders including BMW, Audi, Mercedes, Daimler, and Bentley accelerated the advancements in autonomous mobility including several successful trial runs of level-5 autonomous vehicles. The increasing focus of automotive manufacturers on AI technologies, especially in Germany and the UK is driving the adoption of AI across the Europe automotive sector. Supportive initiatives from the government to adopt AI for smart traffic control has propelled the development of automotive AI solutions. In 2017, the UK government invested more than USD 75 million for the development of AI solutions and improved mobility.
Companies operating in AI in automotive market are focusing on various business growth strategies including investments in autonomous mobility solutions, strengthening partner network, and expanding R&D activities. Through such strategic moves, companies are trying to gain a broader market share and maintain their leadership in the market. For instance, in September 2019, Daimler partnered with Torc Robotics, an automated mobility firm, to design and develop level-4 autonomous trucks. Under the partnership, the companies are jointly testing autonomous trucks in the U.S. and focusing on evolving automated driving for heavy-duty vehicles.
Table of Contents
Chapter 1. Methodology & Scope
- 1.1. Methodology
- 1.1.1. Initial data exploration
- 1.1.2. Statistical model and forecast
- 1.1.3. Industry insights and validation
- 1.1.4. Scope
- 1.1.5. Definitions
- 1.1.6. Methodology & forecast parameters
- 1.2. Data Sources
- 1.2.1. Secondary
- 1.2.1.1. Paid sources
- 1.2.1.2. Public sources
- 1.2.2. Primary
Chapter 2. Executive Summary
- 2.1. AI in automotive industry 360 degree synopsis, 2015 - 2026
- 2.2. Business trends
- 2.3. Regional trends
- 2.4. Component trends
- 2.5. Technology trends
- 2.6. Process trends
- 2.7. Application trends
Chapter 3. Artificial Intelligence (AI) in Automotive Industry Insights
- 3.1. Introduction
- 3.2. Industry segmentation
- 3.3. Industry landscape, 2015 - 2026
- 3.4. Evolution of AI in automotive
- 3.5. AI in automotive industry architecture
- 3.6. AI in automotive industry ecosystem analysis
- 3.7. Technology & innovation landscape
- 3.7.1. Machine learning and neural networks
- 3.7.2. Multiple sensor fusion technology
- 3.8. Regulatory landscape
- 3.8.1. Vienna Convention on Road Traffic (global)
- 3.8.2. North America
- 3.8.2.1. Algorithmic Accountability Act of 2019 (U.S.)
- 3.8.2.2. Directive on Automated Decision-Making (Canada)
- 3.8.3. Europe
- 3.8.3.1. EU guidelines on ethics in artificial intelligence (EU)
- 3.8.3.2. General standards for algorithmic systems - The Data Ethics Commission (Germany
- 3.8.4. APAC
- 3.8.4.1. National Strategy for Artificial Intelligence (India)
- 3.8.4.2. Contract Guidance on Utilization of AI and Data (Japan)
- 3.8.5. Latin America
- 3.8.5.1. The General Data Protection Law (Brazil)
- 3.8.6. MEA
- 3.8.6.1. UAE Strategy for Artificial Intelligence (UAE)
- 3.9. Industry impact forces
- 3.9.1. Growth drivers
- 3.9.1.1. Growing need for autonomous vehicles
- 3.9.1.2. Increasing adoption of IoT in automotive industry
- 3.9.1.3. Growing trend of Advance Driver Assist System (ADAS) technology
- 3.9.1.4. Rising demand for enhanced driver convenience
- 3.9.2. Industry pitfalls & challenges
- 3.9.2.1. High cost of AI enabled vehicles
- 3.9.2.2. Threat to cyber security
- 3.9.2.3. Issues related to accidents of driverless cars
- 3.10. Growth potential analysis
- 3.11. Porter's analysis
- 3.12. PESTEL analysis
Chapter 4. Competitive Landscape
- 4.1. Introduction
- 4.2. Competive analysis of major service providers, 2019
- 4.2.1. Alphabet Inc.
- 4.2.2. Intel Corporation
- 4.2.3. IBM Corporation
- 4.2.4. NVIDIA Corporation
- 4.2.5. Microsoft Corporation
- 4.3. Competive analysis of major automotive manufacturers, 2019
- 4.3.1. Audi AG
- 4.3.2. Ford Motor Company
- 4.3.3. General Motors Company
- 4.3.4. Tesla, Inc.
- 4.3.5. Toyota Motor Corporation
Chapter 5. Artificial Intelligence (AI) in Automotive Market, By Component
- 5.1. Key trends, by component
- 5.2. Hardware
- 5.2.1. Market estimates and forecast, 2015 - 2026
- 5.3. Software
- 5.3.1. Market estimates and forecast, 2015 - 2026
- 5.4. Service
- 5.4.1. Market estimates and forecast, 2015 - 2026
Chapter 6. Artificial Intelligence (AI) in Automotive Market, By Technology
- 6.1. Key trends, by technology
- 6.2. Computer Vision
- 6.2.1. Market estimates and forecast, 2015 - 2026
- 6.3. Context Awareness
- 6.3.1. Market estimates and forecast, 2015 - 2026
- 6.4. Deep Learning
- 6.4.1. Market estimates and forecast, 2015 - 2026
- 6.5. Machine Learning
- 6.5.1. Market estimates and forecast, 2015 - 2026
- 6.6. Natural Language Processing (NLP)
- 6.6.1. Market estimates and forecast, 2015 - 2026
Chapter 7. Artificial Intelligence (AI) in Automotive Market, By Process
- 7.1. Key trends, by process
- 7.2. Data mining
- 7.2.1. Market estimates and forecast, 2015 - 2026
- 7.3. Image/signal recognition
- 7.3.1. Market estimates and forecast, 2015 - 2026
Chapter 8. AI in automotive Market, By Application
- 8.1. Key trends, by application
- 8.2. Semi-autonomous vehicles
- 8.2.1. Market estimates and forecast, 2015 - 2026
- 8.3. Fully autonomous vehicles
- 8.3.1. Market estimates and forecast, 2015 - 2026
Chapter 9. AI in automotive Market, By Region
- 9.1. Key trends, by region
- 9.2. North America
- 9.2.1. Market estimates and forecast, 2015 - 2026
- 9.2.2. Market estimates and forecast, by component, 2015 - 2026
- 9.2.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.2.4. Market estimates and forecast, by process, 2015 - 2026
- 9.2.5. Market estimates and forecast, by application, 2015 - 2026
- 9.2.6. U.S.
- 9.2.6.1. Market estimates and forecast, 2015 - 2026
- 9.2.6.2. Market estimates and forecast, by component, 2015 - 2026
- 9.2.6.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.2.6.4. Market estimates and forecast, by process, 2015 - 2026
- 9.2.6.5. Market estimates and forecast, by application, 2015 - 2026
- 9.2.7. Canada
- 9.2.7.1. Market estimates and forecast, 2015 - 2026
- 9.2.7.2. Market estimates and forecast, by component, 2015 - 2026
- 9.2.7.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.2.7.4. Market estimates and forecast, by process, 2015 - 2026
- 9.2.7.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3. Europe
- 9.3.1. Market estimates and forecast, 2015 - 2026
- 9.3.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.6. UK
- 9.3.6.1. Market estimates and forecast, 2015 - 2026
- 9.3.6.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.6.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.6.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.6.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.7. Germany
- 9.3.7.1. Market estimates and forecast, 2015 - 2026
- 9.3.7.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.7.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.7.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.7.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.8. France
- 9.3.8.1. Market estimates and forecast, 2015 - 2026
- 9.3.8.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.8.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.8.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.8.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.9. Italy
- 9.3.9.1. Market estimates and forecast, 2015 - 2026
- 9.3.9.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.9.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.9.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.9.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.10. Spain
- 9.3.10.1. Market estimates and forecast, 2015 - 2026
- 9.3.10.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.10.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.10.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.10.5. Market estimates and forecast, by application, 2015 - 2026
- 9.3.11. Russia
- 9.3.11.1. Market estimates and forecast, 2015 - 2026
- 9.3.11.2. Market estimates and forecast, by component, 2015 - 2026
- 9.3.11.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.3.11.4. Market estimates and forecast, by process, 2015 - 2026
- 9.3.11.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4. Asia Pacific
- 9.4.1. Market estimates and forecast, 2015 - 2026
- 9.4.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4.6. China
- 9.4.6.1. Market estimates and forecast, 2015 - 2026
- 9.4.6.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.6.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.6.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.6.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4.7. India
- 9.4.7.1. Market estimates and forecast, 2015 - 2026
- 9.4.7.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.7.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.7.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.7.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4.8. Japan
- 9.4.8.1. Market estimates and forecast, 2015 - 2026
- 9.4.8.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.8.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.8.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.8.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4.9. Australia
- 9.4.9.1. Market estimates and forecast, 2015 - 2026
- 9.4.9.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.9.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.9.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.9.5. Market estimates and forecast, by application, 2015 - 2026
- 9.4.10. South Korea
- 9.4.10.1. Market estimates and forecast, 2015 - 2026
- 9.4.10.2. Market estimates and forecast, by component, 2015 - 2026
- 9.4.10.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.4.10.4. Market estimates and forecast, by process, 2015 - 2026
- 9.4.10.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5. LAMEA
- 9.5.1. Market estimates and forecast, 2015 - 2026
- 9.5.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5.6. Brazil
- 9.5.6.1. Market estimates and forecast, 2015 - 2026
- 9.5.6.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.6.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.6.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.6.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5.7. Mexico
- 9.5.7.1. Market estimates and forecast, 2015 - 2026
- 9.5.7.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.7.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.7.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.7.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5.8. Saudi Arabia
- 9.5.8.1. Market estimates and forecast, 2015 - 2026
- 9.5.8.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.8.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.8.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.8.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5.9. UAE
- 9.5.9.1. Market estimates and forecast, 2015 - 2026
- 9.5.9.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.9.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.9.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.9.5. Market estimates and forecast, by application, 2015 - 2026
- 9.5.10. South Africa
- 9.5.10.1. Market estimates and forecast, 2015 - 2026
- 9.5.10.2. Market estimates and forecast, by component, 2015 - 2026
- 9.5.10.3. Market estimates and forecast, by technology, 2015 - 2026
- 9.5.10.4. Market estimates and forecast, by process, 2015 - 2026
- 9.5.10.5. Market estimates and forecast, by application, 2015 - 2026
Chapter 10. Company Profiles
- 10.1. Alphabet Inc
- 10.1.1. Business Overview
- 10.1.2. Financial Data
- 10.1.3. Product Landscape
- 10.1.4. Strategic Outlook
- 10.1.5. SWOT Analysis
- 10.2. Audi AG
- 10.2.1. Business Overview
- 10.2.2. Financial Data
- 10.2.3. Product Landscape
- 10.2.4. Strategic Outlook
- 10.2.5. SWOT Analysis
- 10.3. BMW AG
- 10.3.1. Business Overview
- 10.3.2. Financial Data
- 10.3.3. Product Landscape
- 10.3.4. Strategic Outlook
- 10.3.5. SWOT Analysis
- 10.4. Daimler AG
- 10.4.1. Business Overview
- 10.4.2. Financial Data
- 10.4.3. Product Landscape
- 10.4.4. Strategic Outlook
- 10.4.5. SWOT Analysis
- 10.5. Didi Chuxing
- 10.5.1. Business Overview
- 10.5.2. Financial Data
- 10.5.3. Product Landscape
- 10.5.4. Strategic Outlook
- 10.5.5. SWOT Analysis
- 10.6. Ford Motor Company
- 10.6.1. Business Overview
- 10.6.2. Financial Data
- 10.6.3. Product Landscape
- 10.6.4. Strategic Outlook
- 10.6.5. SWOT Analysis
- 10.7. General Motors Company
- 10.7.1. Business Overview
- 10.7.2. Financial Data
- 10.7.3. Product Landscape
- 10.7.4. Strategic Outlook
- 10.7.5. SWOT Analysis
- 10.8. Harman International Industries, Inc
- 10.8.1. Business Overview
- 10.8.2. Financial Data
- 10.8.3. Product Landscape
- 10.8.4. Strategic Outlook
- 10.8.5. SWOT Analysis
- 10.9. Honda Motor Co. Ltd
- 10.9.1. Business Overview
- 10.9.2. Financial Data
- 10.9.3. Product Landscape
- 10.9.4. Strategic Outlook
- 10.9.5. SWOT Analysis
- 10.10. IBM Corporation
- 10.10.1. Business Overview
- 10.10.2. Financial Data
- 10.10.3. Product Landscape
- 10.10.4. Strategic Outlook
- 10.10.5. SWOT Analysis
- 10.11. Intel Corporation
- 10.11.1. Business Overview
- 10.11.2. Financial Data
- 10.11.3. Product Landscape
- 10.11.4. Strategic Outlook
- 10.11.5. SWOT Analysis
- 10.12. Microsoft Corporation
- 10.12.1. Business Overview
- 10.12.2. Financial Data
- 10.12.3. Product Landscape
- 10.12.4. Strategic Outlook
- 10.12.5. SWOT Analysis
- 10.13. NVIDIA Corporation
- 10.13.1. Business Overview
- 10.13.2. Financial Data
- 10.13.3. Product Landscape
- 10.13.4. Strategic Outlook
- 10.13.5. SWOT Analysis
- 10.14. Qualcomm Inc
- 10.14.1. Business Overview
- 10.14.2. Financial Data
- 10.14.3. Product Landscape
- 10.14.4. Strategic Outlook
- 10.14.5. SWOT Analysis
- 10.15. Tesla, Inc.
- 10.15.1. Business Overview
- 10.15.2. Financial Data
- 10.15.3. Product Landscape
- 10.15.4. Strategic Outlook
- 10.15.5. SWOT Analysis
- 10.16. Toyota Motor Corporation
- 10.16.1. Business Overview
- 10.16.2. Financial Data
- 10.16.3. Product Landscape
- 10.16.4. Strategic Outlook
- 10.16.5. SWOT Analysis
- 10.17. Uber Technologies, Inc
- 10.17.1. Business Overview
- 10.17.2. Financial Data
- 10.17.3. Product Landscape
- 10.17.4. Strategic Outlook
- 10.17.5. SWOT Analysis
- 10.18. Volvo Car Corporation
- 10.18.1. Business Overview
- 10.18.2. Financial Data
- 10.18.3. Product Landscape
- 10.18.4. Strategic Outlook
- 10.18.5. SWOT Analysis
- 10.19. Xilinx Inc.
- 10.19.1. Business Overview
- 10.19.2. Financial Data
- 10.19.3. Product Landscape
- 10.19.4. Strategic Outlook
- 10.19.5. SWOT Analysis
自動車用AI (人工知能) の世界市場の分析と予測
自動車用途の人工知能 (AI) 市場の分析・予測：自動運転・パーソナライズドサービス・予知保全・ローカライゼーション & マッピング・センサーデータ融合向けソフトウェア・ハードウェア・サービス
世界の自動車向け人工知能（AI）市場：市場機会の分析と産業予測
自動車向け人工知能（AI）の世界市場：2018年～2022年
自動車向け人工知能 (AI) ：世界市場の見通し
自動車向けAIの世界市場 ～2025年：ディープラーニング・マシンラーニング・コンピュータビジョン・コンテキストアウェアネス・自然言語処理
人工知能搭載車・小型トラックの世界市場：シェア・戦略・予測
車載自己学習型人工知能：分析・予測
