Global Automotive Artificial Intelligence Market Size, Share & Trends Analysis Report By Offering( Hardware, Software), By Technology ( Deep Learning, Machine Learning, Context- aware Computing, Computer Vision, Natural Language Processing), By Process( Signal Recognition, Image Recognition, Data Mining ), By Application ( Human–Machine Interface, Semi-autonomous Driving, Autonomous Driving, Identity Authentication, Driver Monitoring, Autonomous Driving Processor Chips), By Component ( Graphics processing unit (GPU), Microprocessors (Incl. ASIC), Field Programmable Gate Array (FPGA), Memory and Storage systems, Image Sensors, Biometric Scanners, Others) and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America) - Forecast (2024-2032)

The report offers the value (in USD Billion) for the above segments.

Region: Global | Format: Word, PPT, Excel | Report Status: Published

Global Automotive Artificial Intelligence Market

 

Market Overview

Global Automotive Artificial Intelligence Market size was valued at USD 2.85 Billion in 2023 and is poised to grow from USD 3.54 Billion in 2024 to USD 19.93 Billion by 2032, growing at a CAGR of 21% in the forecast period (2024-2032).

The Global Automotive Artificial Intelligence (AI) market is evolving as AI technologies are reshaping the entire realm of the automotive industry. From designing and manufacturing to operating vehicles, AI has had a phenomenal and significant impact on improving performance, safety, and driving experience for vehicles. These systems are being installed within vehicles - autonomous driving and driver assistance systems, predictive maintenance, and intelligent infotainment are some examples.

Another key driver of demand for autonomous vehicles within the market. AI greatly empowers self-driving cars to make real-time decisions based on data-sensor, camera, and radar-and to minimize human intervention and increase road safety significantly. Another fastest-growing segment of the ADAS powered by AI technologies is adaptive cruise control, lane-keeping assist, and collision detection features in most new models of vehicles. AI is also integrated into preventive maintenance applications; machine-learning algorithms analyze the data generated from vehicle sensors to determine possible issues before they become events that require urgent attention, thus decreasing downtime and repair costs.

Manufacturing now entails vehicle AI, which would automate robotics and smart manufacturing processes, increasing precision and productivity as well as cost benefits. AI is also gaining interest with the high center of gravity brought to bear by electric vehicles (EVs) and their optimization by AI for battery performance, charging infrastructure, and energy management systems.

Personalized, efficient, and safe, driving experiences are what consumers demand AI continues to change in automotive technology. With the advancement of AI techniques in machine learning, deep learning.

Key Findings:

  • In February 2024, Dunmore introduced DUN-GREENTM, a new eco-friendly and sustainable polyester (PET) film with a printable topcoat that contains 75% post-consumer recycled (PCR) material.

 

Market Dynamics - Market Drivers

OEMs' Increasing Use of Adas Technology

  • Currently, most Advanced Driver Assistance Systems by Original Equipment Manufacturers (OEM) also increases in the global automotive market. With the rising expectations of consumers for safer and more comfortable vehicles and automation, OEM has recently stopped installing ADAS into their vehicles. ADAS utilizes artificial intelligence (AI) and machine learning algorithms for many applications such as adaptive cruise control, lane departure warnings, automatic emergency braking, parking assistance, and collision detection in order to realize the main purpose: improving driver safety through accident prevention.
  • The safety of the vehicle is the prime reason why OEMs are seeking and adopting ADAS. Around the globe, such stringent safety regulations are introduced by governments and other regulatory bodies from time to time that even the newer-breed automakers are finding it extremely difficult to have their rated vehicles with advanced safety features. As a by-product, OEMs are providing ADAS solutions in brand-new models to meet such regulations and make their vehicles compliant for quality rating as well. Some countries have gone out of their way to make ADAS compulsory in vehicles, thus putting pressure on OEMs to adopt the technology.
  • Rising consumer needs also add another significant addiction to the list - that of owning or using a vehicle with semi-autonomous or fully autonomous capabilities. ADAS is a basic technology for self-driving; hence, while most automakers will continuously develop and test their autonomous driving systems, the fundamental interface of ADAS integration would remain vital. For OEMs, it would rather be too concerned about trying to cater now to the consumer that wants an experience with a more increasingly hands-free driving experience. Thus, ADAS becomes an important prior component for the future of automotive design.

Key Findings:

  • In 2021, By launching its Drive Pilot system, one of the first Level 3 autonomous driving systems to be made publicly accessible, Mercedes-Benz achieved major advancements. An important turning point in the automobile industry's drive for greater automation was reached when the company's technology was approved by German regulators.

Growing Need for More Convenient Features and Improved User Experience

  • The Global Automotive Market is driven mainly by the increasing demand for more convenient features and an enhanced user experience. One major area concerned is that of advanced technology such as AI and connected cars. In today's world, a consumer expects his vehicle to be more than just a means of transport, but rather a complete system that provides an easy and intuitive driving experience that enhances comfort, convenience, and entertainment. Increased AI-integration thus drives an increase in market growth by improving profit margins for automakers.
  • Personalized and human-centered interfaces inside a vehicle are two reasons for the increasing demand. AI makes voice controls, smart navigation systems, and intuitive climate prediction a possible reality for drivers and passengers and allows everybody to relate to the automobile in more intuitive, efficient, and individualistic ways. Driving then becomes enjoyable and less distracting for drivers on the road.
  • On top of this, customers are now increasingly looking for more connectivity features in their vehicles. These include any feature that can bring smartphones closer into the car and even such basics as traffic updates in real time, remote vehicle monitoring, and over-the-air software updates. All of these ensure good convenience and keep the vehicle up-to-date with modern technological advancements, hence, increasing the overall satisfaction of a user.

 

Market Opportunities

Growing Need for Sensor Fusion

  • Flexibility within the automotive domain has a growing requirement for sensor fusion in the automotive industry. The most substantial advantages are expected from it in the areas of advanced driver-assistance systems (ADAS) or self-driving cars. Sensor fusion is the final holistic view of data received from a variety of sensors including cameras, radar, LiDAR, and ultrasonic sensors. It takes noise-free and definite information, appraises accuracy and reliability in the vehicle's perception systems, thus improving safety, performance, and overall experience.
  • Sensor fusion will provide an opportunity to make more precise and stronger vehicle perception. To combine data from various kinds of sensors enables alleviating limitations of the individual types of sensing devices, for example, poor visibility of the objects in low light or reduced detection from a longer distance. This means that better-informed decisions could be made within vehicles, which is of utmost importance for the safe operation of autonomous vehicles. On an aggressive note, the demand for effective sensor fusion systems is also going to increase with the move toward entirely autonomous driving.
  • Automatic emergency braking, lane-keeping assistance, and adaptive cruise control comprise more areas in which sensor fusion is used to improve ADAS applications, which require the accurate perception of the vehicle environment. By better inputs of more reliable data, these systems could perform better, thereby contributing to better safety outcomes with fewer accidents and improving consumer trust in autonomous technologies.

Key Findings:

  • In 2021, A major advancement in sensor fusion was achieved by Waymo, a division of Alphabet, the parent company of Google, which improved its self-driving technology by integrating many sensor types. In order to give a 360-degree picture of the surroundings and enable safe and dependable autonomous navigation in intricate urban settings, the business combines LiDAR, radar, and cameras.

 

Market Restraining Factors

An Increase in the Total Cost of Automobiles

  • Increased total cost puts serious brakes on the automotive industry, especially for adopting the latest technologies such as artificial intelligence (AI), sensor fusion, and autonomous-driving systems. These technologies bring along a high integration cost in vehicles mostly in their production, thus making cars expensive for consumers. The increase in the total cost can also discourage likely buyers, especially in a price-sensitive market, and will slow the adoption of the new technologies.
  • Advanced hardware components, for example, high-resolution cameras, enhanced radar, and LiDAR sensors, powerful onboard computing systems are some major contributors to this increase as they have a supplementary added cost. These hardware components will now support autonomous driving, advanced driver-assistance systems (ADAS), and better infotainment systems. Their costs then affect the final price of the vehicle directly. This means that the manufacturers may be pressed for a balance between innovation and cost-efficiency, defining the competitiveness of the products while still catering to the requirements for consumer affordabilities.
  • They contribute significantly to the general cost model with software supported by frequent updates and the development of intricate algorithms necessitated by such technologies. Costs charged into research and developments (R&D) will be immense to assure the feasibility and safety of these systems, and this contributes to a heavier financials load. Such costs will raise vehicle prices and hence limit affordability from the typical consumer for advanced features.

 

Segmentation Analysis

The market scope is segmented because of by Offering, by Technology, by Application.

By Offering

Based on the Offering of the market is segmented into Hardware, Software

Hardware segment would include everything from the physical components necessary for functioning AI systems, such as sensors (LiDAR, radar, cameras, ultrasonic sensors), processors and in-vehicle computing systems. These hardware components serve the purpose of collecting and processing real-time information from the surrounding space of the vehicle, which assists it in taking intelligent decisions such as the ones availing sensors. An example showing the need of sensors under this definition is for perceiving the environment, obstacles detection, and therefore ensuring safety for the vehicle.

The software segment would thus comprise all those algorithms, machine learning models and artificial intelligence frameworks that give functionality to this hardware. AI programs would thus give features including real-time decision-making, predictive maintenance, intelligent infotainment systems, and improved safety protocols.

By Technology

Based on the Technology of the market is segmented into Deep Learning, Machine Learning, Context- aware Computing, Computer Vision, Natural Language Processing.

Deep learning is that branch of machine learning that uses neural networks to manipulate and analyze large sets of data. It is useful in the autonomous drive systems where vehicles use deep learning algorithms to develop the capability to learn and read the environment, take decisions, and drive. Learning: machine learning refers to those algorithms which cause a machine to learn from data and enhance that learning. Therefore, ML is most commonly accepted as an adjective in a car, where particular applications mostly have predictive maintenance, used in the digital domain to describe an ML application where algorithms analyze vehicle data to predict and prevent breakdowns before they happen. One of them is Context-aware computing; it enables vehicles to perceive and act on their environment in real-time. What this technology does is help vehicles alter their actions depending on other pieces of contextual information, including traffic conditions, weather, or even what the driver prefers.

With such input, computer vision makes it possible for cars to interpret and understand visual information emanating from their environment, thus enabling such features as collision avoidance, pedestrian detection, and lane-keeping assistance.

Natural language processing (NLP) is evolving the experience of using the car by voice operations, enabling users to access the system for navigation and infotainment interacts with driving.

Regional Snapshots

The regional markets such as North America, Europe, Asia-Pacific, Latin America, and MEA, North America with major shares in the global Automotive Artificial Intelligence (ADC) market as it relates to an advanced healthcare infrastructure, substantial investments in research and development, and large requirement for innovative cancer therapies. India ranks first in the adoption of cutting-edge medical technologies, including ADCs. The existence of various major pharmaceutical and biotechnology companies in North America accelerates the development and launch of ADCs and is well known for its developed system-based healthcare reimbursement that enables the application of these high-cost therapies. As these criteria match, increased cancer prevalence and a growing focus on personalized medicine have contributed even better to ADC leadership in North America. Strong regulatory and clinical infrastructure, in addition to a high incidence of malignant diagnoses, makes North America the leader in market share for ADCs.

 

List of Companies Profiled
  • Nvidia Corporation (US),
  • Alphabet Inc. (US),
  • Intel Corporation (US),
  • Microsoft Corporation (US),
  • IBM Corporation (US),
  • Qualcomm Inc. (US),
  • Tesla Inc. (US),
  • BMW AG (Germany),
  • Micron Technology (US), and
  • Xilinx Inc. (US).

 

Key Industry Developments
  • In April 2024, In addition to offering its clients and partners its Torayfan® polypropylene film created with certified-circular resins, Toray Plastics (America), Inc., has announced that it is utilizing ExxonMobil's ExxtendTM technology for advanced recycling.
  • In September 2023, Grupo Agusa de México is acquired by Oben Group, along with the KristaFilms trademark and its line of BOPP, CPP, metalized, and PE films.

 

Report Coverage

The report will cover the qualitative and quantitative data on the Global Automotive Artificial Intelligence Market. The qualitative data includes latest trends, market players analysis, market drivers, market opportunity, and many others. Also, the report quantitative data includes market size for every region, country, and segments according to your requirements. We can also provide customize report in every industry vertical.

 

Report Scope and Segmentations

Study Period

2024-32

Base Year

2023

Estimated Forecast Year

2024-32

Growth Rate

CAGR of 21% from 2024 to 2032

Segmentation

By Offering, By Technology, By Process, By Application, By Component, By Region

Unit

USD Billion

By Offering

  • Hardware
  • Software

By Technology

  • Deep Learning
  • Machine Learning
  • Context- aware Computing
  • Computer Vision
  • Natural Language Processing

By Process

  • Signal Recognition
  • Image Recognition
  • Data Mining

By Application

  • Human–Machine Interface
  • Semi-autonomous Driving
  • Autonomous Driving
  • Identity Authentication
  • Driver Monitoring
  • Autonomous Driving Processor Chips

By Component

  • Graphics processing unit (GPU)
  • Microprocessors (Incl. ASIC)
  • Field Programmable Gate Array (FPGA)
  • Memory and Storage systems
  • Image Sensors
  • Biometric Scanners
  • Others

By Region

  • North America (U.S., Canada)
  • Europe (Germany, France, UK, Italy, Spain, Russia, Rest of Europe)
  • Asia-Pacific (China, India, Japan, ASEAN, Rest of Asia-Pacific)
  • Latin America (Brazil, Mexico, Rest of Latin America)
  • MEA (Saudi Arabia, South Africa, UAE, Rest Of MEA)

 

 

Global Automotive Artificial Intelligence Market Regional Analysis

North America accounted for the highest xx% market share in terms of revenue in the Automotive Artificial Intelligence market and is expected to expand at a CAGR of xx% during the forecast period. This growth can be attributed to the growing adoption of Automotive Artificial Intelligence. The market in APAC is expected to witness significant growth and is expected to register a CAGR of xx% over upcoming years, because of the presence of key Automotive Artificial Intelligence companies in economies such as Japan and China.

The objective of the report is to present comprehensive analysis of Global Automotive Artificial Intelligence Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language.

Automotive Artificial Intelligence Market Report is also available for below Regions and Country Please Ask for that

North America

  • U.S.
  • Canada

Europe

  • Switzerland
  • Belgium
  • Germany
  • France
  • U.K.
  • Italy
  • Spain
  • Sweden
  • Netherland
  • Turkey
  • Rest of Europe

Asia-Pacific

  • India
  • Australia
  • Philippines
  • Singapore
  • South Korea
  • Japan
  • China
  • Malaysia
  • Thailand
  • Indonesia
  • Rest Of APAC

Latin America

  • Mexico
  • Argentina
  • Peru
  • Colombia
  • Brazil
  • Rest of South America

Middle East and Africa

  • Saudi Arabia
  • UAE
  • Egypt
  • South Africa
  • Rest Of MEA

 

Points Covered in the Report
  • The points that are discussed within the report are the major market players that are involved in the market such as market players, raw material suppliers, equipment suppliers, end users, traders, distributors and etc.
  • The complete profile of the companies is mentioned. And the capacity, production, price, revenue, cost, gross, gross margin, sales volume, sales revenue, consumption, growth rate, import, export, supply, future strategies, and the technological developments that they are making are also included within the report. This report analysed 12 years data history and forecast.
  • The growth factors of the market are discussed in detail wherein the different end users of the market are explained in detail.
  • Data and information by market player, by region, by type, by application and etc., and custom research can be added according to specific requirements.
  • The report contains the SWOT analysis of the market. Finally, the report contains the conclusion part where the opinions of the industrial experts are included.
 
Key Reasons to Purchase
  • To gain insightful analyses of the Automotive Artificial Intelligence market and have comprehensive understanding of the global market and its commercial landscape.
  • Assess the production processes, major issues, and solutions to mitigate the development risk.
  • To understand the most affecting driving and restraining forces in the market and its impact in the global market.
  • Learn about the Automotive Artificial Intelligence market strategies that are being adopted by leading respective organizations.
  • To understand the future outlook and prospects for the Automotive Artificial Intelligence market. Besides the standard structure reports, we also provide custom research according to specific requirements.

 

Research Scope of Automotive Artificial Intelligence Market
  • Historic year: 2019-2022
  • Base year: 2023
  • Forecast: 2024 to 2032
  • Representation of Market revenue in USD Million


Automotive Artificial Intelligence Market Trends: Market key trends which include Increased Competition and Continuous Innovations Trends:

  • PUBLISHED ON : July, 2022
  • BASE YEAR : 2023
  • STUDY PERIOD : 2020-2032
  • COMPANIES COVERED : 20
  • COUNTRIES COVERED : 25
  • NO OF PAGES : 380

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