首頁 > 市場調查報告書 > 汽車工業

汽車電子人工智能

市場調查報告書

商品編碼

1446821

全球汽車人工智慧市場 - 2024-2031

Global Automotive Artificial Intelligence Market - 2024-2031

出版日期: 2024年02月13日 | 出版商:

DataM Intelligence | 英文 186 Pages | 商品交期: 約2個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

概述

全球汽車人工智慧市場將於2023年達到21億美元，預計2031年將達到86億美元，2024-2031年預測期間CAGR為24.1%。

汽車對人工智慧技術的需求是由增強安全性、提高效率和增加便利性的潛力所推動的。人工智慧演算法和系統即時分析來自感測器、攝影機和其他來源的大量資料，使車輛能夠做出智慧決策並適應不斷變化的路況。這些技術正在適應先進駕駛輔助系統和全自動駕駛汽車的發展方向。

人工智慧正在徹底改變汽車產業，特別是在自動駕駛汽車領域。自動駕駛汽車的概念與未來的技術願景相關，實際進展高於預期。然而，根據《富比士》報道，預計全球汽車人工智慧市場將大幅成長，到 2031 年價值將達到 600 億美元左右。

到2023年，北美預計將成為成長第二快的地區，約佔全球汽車人工智慧市場的25%。美國等國家隨著通貨膨脹削減法案的實施而不斷成長。例如，根據 IEA 的數據，2022 年 8 月至 2023 年 3 月期間，主要電動車和電池製造商宣佈在 IRA 後對北美電動車供應鏈累計投資 520 億美元，這將進一步擴大汽車人工智慧市場。

動力學

專注於人工智慧的永續發展

人工智慧在汽車行業，尤其是電動汽車行業的永續性因素是不可否認的。人工智慧有益於交通運輸的綠色和永續的未來，並為其做出貢獻。人工智慧在電動車領域的主要優勢是提高效率。人工智慧智慧地管理能源資源，最大限度地擴大電動車的續航里程並最大限度地減少能源浪費，使其更加高效和永續。

此外，例如，根據 IBM 的一項研究，50% 的消費者計劃在未來三年內採用電動車。人工智慧被用來最佳化充電基礎設施、預測能源需求並提高電網效率，以滿足不斷成長的電動車需求。消費者採用電動車的動機包括充電樁的使用、環保意識和充電便利性。

對人工智慧驅動的電動車的需求不斷成長

電動車的日益普及是全球汽車產業人工智慧市場的主要成長因素。人工智慧透過實現預測性維護、智慧能源管理和自動駕駛等功能，進一步增強電動車的功能。它創造了對人工智慧技術的需求，以最佳化電動車性能、增強用戶體驗和管理能源效率。

此外，例如豐田研究院推出了一種新的生成式人工智慧技術，透過改進汽車設計流程和最佳化車輛空氣動力學來增強電動車（EV）的續航里程。豐田的目標是最大限度地提高電動車的續航里程。這項創新與豐田計劃在 2026 年至 2028 年間推出下一代電動車電池的計劃相一致，承諾將其當前電動車型 bZ4X 的續航里程增加一倍。

與黑盒和人工智慧技能相關的挑戰

人工智慧的黑盒子問題是理解人工智慧模型如何做出決策的困難，這確實是汽車產業自主系統開發的重大挑戰。人工智慧模型缺乏透明度和可解釋性，限制了人們完全信任和驗證其決策過程的能力。

缺乏人工智慧專業知識是汽車產業和其他產業面臨的主要缺點。開發和部署具有專業技能和知識的人工智慧技術需要資料科學、機器學習和演算法開發的概念。人工智慧專業人員的短缺和這些技術的複雜性為充分發揮人工智慧在汽車產業的潛力帶來了挑戰。

The demand for AI technologies in cars is driven by the potential for enhanced safety, improved efficiency and increased convenience. AI algorithms and systems analyze vast amounts of data from sensors, cameras and other sources in real time which allows vehicles to make intelligent decisions and adapt to changing road conditions. The technologies are adapting the way for advanced driver assistance systems and fully autonomous vehicles.

Artificial intelligence is revolutionizing the automotive industry as particularly in the realm of autonomous cars. The concept of self-driving cars associated with futuristic visions of technology and the actual progress is higher than expected. However, according to Forbes, estimate suggest that the global market for AI in automobiles will experience substantial growth and reach the value of around US$ 60 billion by 2031.

In 2023, North America is expected to be the second-fastest growing region, holding about 25% of the global automotive artificial intelligence market. Countries like U.S. are growing with the implementation of the Inflation Reduction Act. For instance, according to IEA between August 2022 and March 2023, major electric vehicle and battery makers announced a cumulative post-IRA investment of US$ 52 billion in North American EV supply chains which is further going to increase the automotive AI market.

Dynamics

Focus on Sustainability with AI

The sustainability factor of AI in the automotive industry and particularly in the e-mobility sector is undeniable. AI benefits and contributes to a greener and sustainable future of transportation. Major advantage of AI in the e-mobility sector is increased efficiency. AI intelligently manages energy resources maximizing the range of electric vehicles and minimizing energy waste which makes them more efficient and sustainable.

Furthermore, for instance, according to an IBM study, 50% of consumers plan to adopt EVs in the next three years. AI is being used to optimize charging infrastructure, predict energy demand and improve grid efficiency to meet rising EV demands. Consumer motivations for adopting EVs include access to charge points, environmental awareness and charging convenience.

Rising Demand for AI-Powered EVs

The increasing adoption of electric vehicles is a major growth factor for the global market for AI in the automotive industry. AI further enhances the capabilities of EVs by enabling features like predictive maintenance, intelligent energy management and autonomous driving. It created a demand for AI technologies to optimize EV performance, enhance user experience and manage energy efficiency.

Furthermore, for instance Toyota's Research Institute unveiled a new generative AI technique to enhance electric vehicle (EV) range by improving the car design process and optimizing vehicle aerodynamic. Toyota aims to maximize EV range. The innovation aligns with Toyota's plans to introduce next-gen EV batteries between 2026 and 2028, promising double the range of their current electric model, the bZ4X.

Challenges Related to Black-box and AI Skills

The black-box problem of AI which is a difficulty in understanding how AI models make decisions, is indeed a significant challenge in the development of autonomous systems for the automotive industry. The lack of transparency and interpretability in AI models limiting people ability to fully trust and validate their decision-making processes.

The lack of AI expertise is a major drawback faced by the automotive industry and other sectors. Developing and deploying AI technologies with specialized skills and knowledge requires the concepts of data science, machine learning and algorithm development. The shortage of AI professionals and the complexity of these technologies make challenges for fully harnessing the potential of AI in the automotive industry.

Segment Analysis

The global automotive artificial intelligence market is segmented based on technology, application and region.

Rising Demand for Driving Assistance Drives the Segment Growth

AI driving features is expected to be the fastest growing segment with 1/3rd of the market during the forecast period 2024-2031. Self-driving vehicles depends on five essential components to navigate and operate on roads. The initial step in this process is computer vision which differs from how humans rely on their eyes and brain to drive. Driverless cars utilize computer images to identify lane lines and track other vehicles.

To effectively monitor their surroundings vehicles also incorporate multiple cameras. Tesla equips its cars with eight surround cameras which enable a 360-degree view of the area within approximately 500 feet of the vehicle. The cameras facilitate various tasks like lane detection, estimating road curvature, detecting obstacles, classifying stop signs, identifying traffic lights and many more.

Geographical Penetration

Rising AI Implementation in Automotive in Asia-Pacific

Asia-Pacific is the dominant region in the global automotive artificial intelligence market covering about 30% of the market. The region is growing in AI-based automotive market driven by factors like technological advancements, strong manufacturing base, government support, rising demand for smart and connected vehicles and collaborations between automotive companies and technology partners. Japan and China made a notable strides in AI and automotive technologies with companies like Toyota, Hyundai and Honda investing in AI to enhance vehicle capabilities.

The latest data from China indicates a significant increase in shipments and retail sales of new-EVs in 2022. Shipments of EVs to dealerships surged by about 95% to reach around 6.5 million units and in line with the forecast of about 6.5 million made by the Passenger Car Association. Moreover, nationwide retail sales of NEVs including pure electric cars and hybrids have experienced a notable growth of 90% to reach about 5.7 million units. In December 2022 NEV retail sales rose by 6.5% compared to November, reaching around 641,000 units.

Competitive Landscape

The major global players in the market include Carvi, German Autolabs, Raven, Argo AI, Deepscale, Cisco, Waymo, Microsoft Azure, Nvidia and Tesla.

COVID-19 Impact

The COVID-19 pandemic shows both positive and negative impact on the integration of AI in the automotive industry. It caused delays in development and testing of AI-based automotive technologies, but also accelerated the adoption of digital tools and remote collaboration platforms. The focus on safety and hygiene led to increased interest in AI-powered features such as touchless interfaces and improved air filtration systems.

COVID-19 caused disruptions in R&D activities due to travel restrictions and facility closures which results in delays in projects and testing. The pandemic also prompted increased investment in digital tools and virtual collaboration platforms to continue R&D efforts remotely. The global supply chains of automotive companies were severely affected by delayed production and shortages of key components due to lockdown measures and transportation restriction.

Russia-Ukraine War Impact

The conflict between Russia and Ukraine has potentially impacted the AI automotive market in several ways. The disruption of supply chains and trade routes between the two countries could affect the sourcing of components including AI-related technologies for the automotive industry. The conflict results in damage to infrastructure including transportation networks and supply chains, further complicating business operations and hindering the smooth flow of goods and services.

Geopolitical uncertainties resulting from the conflict may lead to cautious investment decisions and business operations, potentially slowing down collaborations and expansions related to AI integration in the automotive sectors. The conflict's focus on military and security technologies could divert attention and resources away from commercial developments which potentially impact the pace of AI innovation within the automotive industry.

The conflict puts a negative impact on consumer confidence. The uncertainties surrounding the conflict and its potential consequences led to a decrease in consumer confidence which results in reduced spending and a decline in demand for products and services, including automobiles and AI-powered vehicles. The factors collectively highlight the adverse effects of the conflict on business operations, infrastructure and consumer sentiment in the region.

AI Impact

The integration of artificial intelligence had a transformative impact on the automotive industry. AI technologies played a crucial role in enhancing vehicle intelligence, safety and autonomous capabilities. In the new world of advanced driver-assistance systems AI algorithms supports features like adaptive cruise control, lane-keeping assistance and automatic emergency braking and enhancing the overall safety of vehicles. Also, AI powers computer vision systems recognizes and interpret road signs, pedestrians and other objects which provide valuable information to the driver and supporting decision-making.

Generative AI models like Jasper and DALL-E 2, are indeed revolutionizing customer engagement in marketing and advertising which includes within the automotive industry. The powerful tools leverage the capabilities of generative models like GPT-3 to automatically generate customer-centric marketing content across various channels.

By Technology

Machine Learning & Deep Learning
Computer Vision
Natural Language Processing

By Application

AI Driving Features
AI Cloud Services
AI Automotive Insurance
AI in Car Manufacturing

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Russia
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

In May 2021, Didi Chuxing announced a strategic agreement with Volvo Cars to develop autonomous vehicles for DiDi's self-driving test fleet. Volvo Cars' autonomous drive-ready XC90 vehicles will be the first to feature DiDi Gemini, a new self-driving hardware platform powered by NVIDIA DRIVE AGX Pegasus. The vehicles, outfitted with DiDi's Gemini self-driving hardware platform, will eventually be used for robotaxi services.
In March 2021, BMW announced its next-generation infotainment system, iDrive 8, intended to operate as a digital, intelligent and active partner for drivers. The technology driven by machine learning, natural language processing, AI cloud and 5G will have its debut with the next BMW iX and i4.
In February 2021, Volkswagen and Microsoft collaborated to make self-driving car software. VW's new software division will establish a cloud-based platform with Microsoft to assist streamline development processes, allow for speedier integration into its vehicle fleet and make it much easier to send software upgrades to add new features to cars.

Why Purchase the Report?

To visualize the global automotive artificial intelligence market segmentation based on technology, application and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of automotive artificial intelligence market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global Automotive Artificial Intelligence market report would provide approximately 54 tables, 43 figures and 186 pages.

Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Technology
3.2. Snippet by Application
3.3. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Focus on Sustainability with AI
  - 4.1.1.2. Rising Demand for AI-Powered EVs
- 4.1.2. Restraints
  - 4.1.2.1. Challenges Related to Black-box and AI Skills
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Consumer Electronics Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Technology

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 7.1.2. Market Attractiveness Index, By Technology
7.2. Machine Learning & Deep Learning*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Computer Vision
7.4. Natural Language Processing

8. By Application

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 8.1.2. Market Attractiveness Index, By Application
8.2. AI Driving Features*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. AI Cloud Services
8.4. AI Automotive Insurance
8.5. AI in Car Manufacturing

9. By Region

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 9.1.2. Market Attractiveness Index, By Region
9.2. North America
- 9.2.1. Introduction
- 9.2.2. Key Region-Specific Dynamics
- 9.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 9.2.5.1. U.S.
  - 9.2.5.2. Canada
  - 9.2.5.3. Mexico
9.3. Europe
- 9.3.1. Introduction
- 9.3.2. Key Region-Specific Dynamics
- 9.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 9.3.5.1. Germany
  - 9.3.5.2. UK
  - 9.3.5.3. France
  - 9.3.5.4. Italy
  - 9.3.5.5. Russia
  - 9.3.5.6. Rest of Europe
9.4. South America
- 9.4.1. Introduction
- 9.4.2. Key Region-Specific Dynamics
- 9.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 9.4.5.1. Brazil
  - 9.4.5.2. Argentina
  - 9.4.5.3. Rest of South America
9.5. Asia-Pacific
- 9.5.1. Introduction
- 9.5.2. Key Region-Specific Dynamics
- 9.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 9.5.5.1. China
  - 9.5.5.2. India
  - 9.5.5.3. Japan
  - 9.5.5.4. Australia
  - 9.5.5.5. Rest of Asia-Pacific
9.6. Middle East and Africa
- 9.6.1. Introduction
- 9.6.2. Key Region-Specific Dynamics
- 9.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 9.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

10. Competitive Landscape

10.1. Competitive Scenario
10.2. Market Positioning/Share Analysis
10.3. Mergers and Acquisitions Analysis

11. Company Profiles

11.1. Carvi*
- 11.1.1. Company Overview
- 11.1.2. Product Portfolio and Description
- 11.1.3. Financial Overview
- 11.1.4. Key Developments
11.2. German Autolabs
11.3. Raven
11.4. Argo AI
11.5. Deepscale
11.6. Cisco
11.7. Waymo
11.8. Microsoft Azure
11.9. Nvidia
11.10. Tesla

LIST NOT EXHAUSTIVE