AI in Automotive Market Size, Share, By Component (Hardware, Software, and Services), By Technology (Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, and Others), By Algorithm (Regression, Cluster, and Decision Matrix), By Application (Semi-autonomous Vehicles, and Fully-autonomous Vehicles), and By Region (North America, Europe, Asia Pacific, Middle East and Africa, and South America) - Trends, Analysis and Forecast till 2034

AI in Automotive Market, with a promising start at US$ 4 billion in 2024, is poised for significant growth. It is projected to surge at a CAGR of 31.6%, reaching a substantial US$ 48.9 billion by 2034.

AI in automotive industry has a noteworthy effect on the market. AI has revolutionized and transformed cars in the automotive industry. Various technologies, such as robotics, computer vision, and machine learning, have made innovative car technology possible. Thus, AI has simplified matters and provided automakers with more choices. Automakers are increasingly implementing state-of-the-art AI-based software development solutions to realize automated automobiles' objective. Autonomous, electric, and hybrid vehicles are, therefore, the way of the future for the automotive industry, which currently significantly depends on AI for both vehicle design and manufacture.

The automotive industry is increasingly leveraging artificial intelligence (AI) to boost overall vehicle performance and streamline processes. The integration of big data, IoT, AI, and ML has significantly transformed the industry, enhancing car design, production, and driving experiences. AI's potential applications in the automotive sector are vast, ranging from driverless cars to advanced safety systems.

Globally, there is a growing demand for smart technology such as voice and picture recognition, and connected cars are no different. Therefore, automation and artificial intelligence will continue to be essential to automobile design, production, and use. Large volumes of sensitive data are gathered in the automotive industry, making adopting additional security measures necessary. Because of this, the target market might be constrained during the anticipated term by the growing concerns about data breaches, which could result in a rise in cyber-attack events.

Key Drivers of Target Market:

The growing enthusiasm for autonomous vehicles:

• Autonomous vehicles can sense their environment and navigate independently without human intervention. Driving or even being in the automobile at all times is not necessary for a human passenger. These vehicles can perform every duty associated with a traditional car, and they can do it without the need for a human driver. This requires artificial intelligence to be possible. Due to the industry's transition brought about by using contemporary ultrasonic, radar, and Lidar sensors, machine learning systems, and video cameras in self-driving cars, the market for automotive artificial intelligence is growing.

Increasing Desire for a Better Drive Feeling:

• AI is not just revolutionizing the automotive industry, it's also enhancing the driving experience. Customized cars powered by AI technologies are gaining popularity because they make life easier for end users. The integration of AI in automotive systems is not just a trend, it's a necessity, thanks to the enhanced user experience that AI-enabled apps for autonomous operations offer. AI-powered entertainment systems are providing passengers and drivers with more intelligent, safe, and enjoyable rides. For instance, intelligent voice assistants in cars can understand and speak in the native language of their users, adding a personal touch to the driving experience.   

Restrains:

Issues with security and breaches of data:

• The increasing use of AI-based technology in cars raises concerns about cyber security and data privacy because these systems collect and send enormous amounts of personal data that could be stolen or exploited. Well-publicized data breaches that reveal driving habits, location tracking, and other sensitive vehicle data might undermine consumer trust in AI-powered auto technologies. This skepticism may lead to slower market expansion and poorer acceptance.  

Opportunities:

Expanding Beneficial Government Programmes in the Automobile Industry:

• The government's increasing support for developing environmentally friendly and publically safe solutions creates demanding growth opportunities for enterprises operating in the automotive artificial intelligence industry. Furthermore, cutting-edge AI technology is expected to be incorporated into cars since autopilot systems are predicted to function in them. Thus, AI integration becomes essential for the manufacturing of vehicles in the future. Government and authority bodies embracing sustainable technologies and pushing the same goal over automakers are driving demand for AI and machine learning solutions.

The market is segmented based on Component, Technology, Algorithm, Application, and Region.

Component Insight:

• Hardware: The hardware components enabling AI-based functionality in cars include CPUs, sensors, and other hardware components. The increasing advancements in AI technologies, such as machine learning and deep learning algorithms, have increased the demand for vehicle hardware. These systems require a lot of processing power to handle large volumes of data in real-time.
• Software: The software division is in charge of the component category. The software manages the increasing use of autonomous car services, including improved audio systems, self-driving, AC controls, and pairing help. Artificial intelligence (AI)--based software solutions, such as machine learning algorithms and neural networks, are used by autonomous cars to process information and make judgments.
• Services: Included in the services category are the advisory, integration, and upkeep services related to using AI technology in the automotive industry. The service segment is growing at the highest rate among its subsegments due to the service sector's formation, upgrading, and maintenance, which significantly expands its size.

 Technology Insight:

• Machine Learning: By offering services customized to meet each driver's individual needs, automotive machine learning software can increase customer satisfaction. To create a baseline of the driver's ideal setups, machine learning algorithms look through the options specified in the driver's user profile.
• Deep Learning: Deep learning algorithms are used to process sensor data from cameras, radar, and lidar so that the vehicle can recognise its surroundings with accuracy. This enables the car to function independently in terms of braking, acceleration, steering, and destination navigating.
• Natural Language Processing (NLP): Vehicles can understand and respond to human language thanks to the use of AI software development technologies for NLP in the automotive sector. This fosters better relationships and provides personalised services for drivers and their cars.
• Computer Vision: AI-powered computer vision systems are used to identify and recognise traffic signs and signals. Artificial intelligence (AI)-based computer vision is used to recognise and analyse lane markers, other road markings, and road boundaries.
• Others: From evolutionary algorithms to reinforcement learning, the "Others" section discusses a variety of other AI technologies that may be applied to specific automotive applications.

Algorithm Insight:

• Regression: Using past data as a base, regression algorithms project the outcomes of various situations. They alert drivers to impending obstacles on the road or when a pedestrian is about to cross in front of them. These algorithms acquire and process the data for every trip. It helps them become more adept at making decisions over time.
• Cluster: Cluster algorithms are used to handle and interpret massive data streams from many sources, such as cameras, sensors, and GPS devices. These algorithms enable the car to recognise and classify objects on the road and modify its path accordingly. They also help with route planning and navigation by constantly improving and adjusting routes to traffic situations as they arise.
• Decision Matrix: Algorithms that use decision matrix coding help with decision-making about preset rules and consequences. They may also consider the vehicle's current sensor data. The vehicle's next move, including where to go and when to turn, is decided by these algorithms. The effectiveness of these functions depends on the algorithm's ability to locate, recognise, and forecast an object's future motion.

Application Insights

• Semi-autonomous Vehicles: These kind of vehicles aren't able to drive themselves, but they can park and keep their lanes aligned. Generally speaking, drivers must have one hand on the wheel at all times. Semi-autonomous driving features can help limit the vehicle's speed to adhere to posted limits and the speed of the road while it is in a safe and acceptable lane. If drivers have specialised equipment monitoring them, especially on longer highway travels, they may be able to prevent errors attributable to human error.
• Fully-autonomous Vehicles: An autonomous vehicle is one that is equipped with sensors that enable it to sense its environment, including traffic, pedestrians, and potential hazards, and adjust its direction and speed without requiring human input. A widespread misconception is that "autonomous" means "self-driving cars." AI is used in self-driving automobiles for natural language processing, sensing, decision-making, and predictive modelling. They can now plan routes, predict conduct, recognise things, and communicate with passengers—all of which are necessary for safe driving.

Regional Insights

• North America: The United States and Canada, in particular, constitute a substantial market for artificial intelligence in the automotive industry because of the presence of major automakers and tech companies, as well as favourable legislation and regulations. The sales trends for electric vehicles, fully automated programmes, and autonomous cars indicate that the US leads the artificial intelligence (AI) auto industry in North America.
• Asia Pacific: The region, which includes countries like China, Japan, and South Korea, is seeing a sharp increase in the use of artificial intelligence (AI) in the automotive sector due to the growth of smart city initiatives and the rising usage of electric vehicles. China is a big player in the expanding market. The rapid use of AI and ML technologies by the electric car sector is expected to enhance the demand for AI software and hardware tools.
• Europe: The key drivers propelling the European market for artificial intelligence in automotive technology are the presence of well-known automakers and a focus on the development of advanced driver assistance systems (ADAS) and autonomous driving technologies.
• Latin America: While the Latin American automotive AI industry is smaller than other regions, it is expected to grow dramatically over the next few years as a result of the growing acceptance of electric vehicles and the development of the region's transportation infrastructure.
• Middle East and Africa: These regions are seeing an increase in demand for AI-based automotive technology, particularly in Saudi Arabia and the United Arab Emirates, which are two countries heavily investing in the construction of smart cities and transportation infrastructure.

AI in Automotive Market Report Scope:

Segments Covered in the Report:

This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends and opportunities in each of the sub-segments from 2024 to 2034. For the purpose of this study segmented the target market report based on Component, Technology, Algorithm, Application, and Region:

Segmentation:

By Component:

• Hardware
• Software
• Services

By Technology:

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Computer Vision
• Others

By Algorithm:

• Regression
• Cluster
• Decision Matrix

By Application:

• Semi-autonomous Vehicles
• Fully-autonomous Vehicles

By Region:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Russia
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Rest of Asia Pacific
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • GCC
  • Israel
  • South Africa
  • Rest of Middle East & Africa

The key players operating the AI in Automotive Market include Ford Motor Company, NVIDIA Corporation, Tencent, Microsoft, BMW AG, AUDI AG, Intel Corporation, Tesla Inc, Uber Technologies, Volvo Car Corporation, Honda Motors, IBM Corporation, and General Motors.

• In May 2021, Volvo Cars, a leader in automotive safety, and Didi Chuxing, the top mobility technology platform in the world, signed a strategic collaboration agreement on autonomous vehicles for DiDi's self-driving test fleet. Volvo Autos' DiDi Autonomous Driving will be combined with the additional hardware and software needed to outfit the XC90 cars for complete autonomy. The backup systems required for features like steering and braking will be installed in these cars.

• Ford Motor Company *
  • Company Overview
  • Product Portfolio
  • Key Highlights
  • Financial Performance
  • Business Strategies
• NVIDIA Corporation
• Tencent
• Microsoft
• BMW AG
• AUDI AG
• Intel Corporation
• Tesla Inc
• Uber Technologies
• Volvo Car Corporation
• Honda Motors
• IBM Corporation
• General Motors

“*” marked represents similar segmentation in other categories in the respective section.

1. Research Objective and Assumption
   • Research Objectives
   • Assumptions
   • Abbreviations
2. Market Preview
   • Report Description
     • Market Definition and Scope
   • Executive Summary
     • Market Snippet, By Component
     • Market Snippet, By Technology
     • Market Snippet, By Algorithm
     • Market Snippet, By Application
     • Market Snippet, By Region
   • Opportunity Map Analysis
3. Market Dynamics, Regulations, and Trends Analysis
   • Market Dynamics
     • Drivers
     • Restraints
     • Market Opportunities
   • Market Trends
   • Product Launch
   • Merger and Acquisitions
   • Impact Analysis
   • PEST Analysis
   • Porter’s Analysis

Market Segmentation, Component, Forecast Period up to 10 Years, (US$ Bn)

• Overview
  • Market Value and Forecast (US$ Bn), and Share Analysis (%), Forecast Period up to 10 Years
  • Y-o-Y Growth Analysis (%), Forecast Period up to 10 Years
  • Segment Trends
• Hardware
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
• Software
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends
• Services
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends

Market Segmentation, Technology, Forecast Period up to 10 Years, (US$ Bn)

• Overview
  • Market Value and Forecast (US$ Bn), and Share Analysis (%), Forecast Period up to 10 Years
  • Y-o-Y Growth Analysis (%), Forecast Period up to 10 Years
  • Segment Trends
• Machine Learning
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
• Deep Learning
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends
• Natural Language Processing (NLP)
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends

• Computer Vision
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends
• Others
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends

Market Segmentation, Algorithm, Forecast Period up to 10 Years, (US$ Bn)

• Overview
  • Market Value and Forecast (US$ Bn), and Share Analysis (%), Forecast Period up to 10 Years
  • Y-o-Y Growth Analysis (%), Forecast Period up to 10 Years
  • Segment Trends
• Regression
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
• Cluster
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends
• Decision Matrix
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
  • Segment Trends

Market Segmentation, Application, Forecast Period up to 10 Years, (US$ Bn)

• Overview
  • Market Value and Forecast (US$ Bn), and Share Analysis (%), Forecast Period up to 10 Years
  • Y-o-Y Growth Analysis (%), Forecast Period up to 10 Years
  • Segment Trends
• Semi-autonomous Vehicles
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years
• Fully-autonomous Vehicles
  • Overview
  • Market Size and Forecast (US$ Bn), and Y-o-Y Growth (%), Forecast Period up to 10 Years

Market Segmentation, By Region, Forecast Period up to 10 Years, (US$ Bn)

• Overview
  • Market Value and Forecast (US$ Bn), and Share Analysis (%), Forecast Period up to 10 Years
  • Y-o-Y Growth Analysis (%), Forecast Period up to 10 Years
  • Regional Trends
• North America
  • Market Size and Forecast (US$ Bn), By Component, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Technology, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Algorithm, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Application, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Country, Forecast Period up to 10 Years
    • U.S
    • Canada
• Asia Pacific
  • Market Size and Forecast (US$ Bn), By Component, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Technology, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Algorithm, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Application, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Country, Forecast Period up to 10 Years
    • India
    • Japan
    • South Korea
    • China
    • Rest of Asia Pacific
• Europe
  • Market Size and Forecast (US$ Bn), By Component, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Technology, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Algorithm, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Application, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Country, Forecast Period up to 10 Years
    • UK
    • Germany
    • France
    • Russia
    • Italy
    • Rest of Europe
• Latin America
  • Market Size and Forecast (US$ Bn), By Component, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Technology, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Algorithm, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Application, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Country, Forecast Period up to 10 Years
    • Brazil
    • Mexico
    • Rest of Latin America
• Middle East and Africa
  • Market Size and Forecast (US$ Bn), By Component, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Technology, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Algorithm, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Application, Forecast Period up to 10 Years
  • Market Size and Forecast (US$ Bn), By Country, Forecast Period up to 10 Years
    • GCC
    • Israel
    • South Africa
    • Rest of Middle East and Africa

Competitive Landscape

• Heat Map Analysis
• Company Profiles
  • Ford Motor Company
  • NVIDIA Corporation
  • Tencent
  • Microsoft
  • BMW AG
  • AUDI AG
  • Intel Corporation
  • Tesla Inc
  • Uber Technologies
  • Volvo Car Corporation
  • Honda Motors
  • IBM Corporation
  • General Motors

The Last Word

• Future Impact
• About Us
• Contact