AI in the automotive industry refers to the integration and application of Artificial Intelligence (AI) technologies and algorithms within vehicles and automotive systems. This involves leveraging machine learning, computer vision, natural language processing, and other AI-driven capabilities to enhance various aspects of the automotive sector. The application of AI in the automotive industry encompasses a wide range of functionalities, including Advanced Driver Assistance Systems (ADAS), autonomous driving, predictive maintenance, traffic management, navigation, voice-activated controls, and infotainment systems. The goal is to improve safety, efficiency, and the overall driving experience by enabling vehicles to perceive their environment, make intelligent decisions, and interact with drivers and passengers in a more intuitive and adaptive manner. The market for AI in the automotive industry includes technology development, integration into vehicles, and the provision of AI-driven solutions and services by manufacturers, suppliers, and technology companies operating within the automotive ecosystem.

Some of the benefits of designing a Artificial Intelligence in Automotive Industry Market include:

• Heightened Safety: Utilizing AI, ADAS functionalities, including lane-keeping assistance and collision avoidance, substantially elevate vehicle safety, mitigating the risk of accidents and enhancing overall road safety.
• Safety Amplification: AI plays a pivotal role in advancing autonomous vehicle technology, aiming to eradicate human error and heighten road safety. Autonomous driving has the potential to diminish accidents linked to factors like fatigue and distraction.
• Minimized Downtime: AI-driven predictive maintenance empowers vehicles to foresee and address potential issues proactively, curbing downtime, enhancing reliability, and prolonging the lifespan of automotive components.
• Route Optimization: AI-infused navigation systems assess real-time traffic data to deliver optimized routes, diminishing travel time, fuel consumption, and emissions. This fosters more streamlined traffic flow and improved fuel efficiency.
• Elevated User Experience: AI-driven voice-activated controls and infotainment systems present a hands-free and user-friendly experience, heightening driver focus, safety, and overall satisfaction.

Global Artificial Intelligence in Automotive Industry Market was valued at US $ 11.1 Billion in 2023 and is expected to reach US $ 487.9 Billion by 2032 growing at a CAGR of 52.25% during the forecast period 2024 – 2032.

COVID -19 Impact

The COVID-19 pandemic has significantly impacted the AI in the automotive industry, introducing challenges and opportunities. Disruptions in global supply chains led to shortages of AI-related hardware components, delaying the integration of AI technologies into vehicles. Research and development activities faced setbacks due to lockdowns, hindering the innovation of AI applications. Shifting consumer priorities towards essential features, economic uncertainties, and a decline in automotive sales influenced the adoption of AI. Automotive manufacturers prioritized safety and reliability features over high-end AI capabilities to meet immediate consumer needs. However, the pandemic also accelerated the digital transformation in the industry, emphasizing the importance of contactless services and connected technologies, renewing interest in AI applications. Companies demonstrated resilience, adapting to remote work challenges and exploring new business models. Government support and regulatory adjustments addressed safety concerns, highlighting the adaptability of the automotive industry amidst the ongoing global challenges. As the industry recovers, there is a growing emphasis on leveraging AI to address evolving consumer expectations and contribute to the continued evolution of the automotive sector.

Factors Driving the Market

Drivers

Increasing demand for safety and convenience features

The burgeoning market growth of AI in the automotive industry is significantly propelled by the escalating consumer demand for safety and convenience features. This paradigm shift is evident in the widespread adoption of Advanced Driver Assistance Systems (ADAS) that leverage AI to provide real-time assistance and enhance overall safety. Collision avoidance systems and object detection, powered by AI, contribute to accident prevention, addressing consumers' desire for vehicles equipped with proactive safety measures. The convenience aspect extends to AI-driven features such as parking assistance and automated parking, catering to the increasing preference for hassle-free parking experiences.

Moreover, AI plays a pivotal role in voice-activated controls and infotainment systems, enabling hands-free interactions and enhancing the overall driving experience. Predictive maintenance, facilitated by AI analysis of sensor data, adds a layer of convenience by anticipating and addressing potential vehicle issues before they occur. AI-based navigation systems optimize travel routes, reducing commute times and providing personalized recommendations. The personalization of the driving experience through AI, along with driver monitoring systems ensuring safety, aligns with consumer expectations for intelligent and tailored vehicle interactions. Furthermore, smart connectivity features, facilitated by AI integration, enhance convenience by seamlessly connecting vehicles to smartphones, smart homes, and IoT devices. The allure of autonomous driving, enabled by AI advancements, represents the epitome of convenience, promising a hands-free and stress-free commuting experience. As automotive manufacturers respond to this increasing demand, the integration of AI in vehicles not only addresses safety concerns but also elevates the overall convenience quotient, reshaping the automotive landscape to meet the evolving expectations of modern consumers.

Challenges

High cost of development and deployment

The growth of AI in the automotive industry faces a considerable impediment in the form of the high cost associated with development and deployment. Substantial investment in research and development is required to create sophisticated AI algorithms tailored for applications like autonomous driving and advanced driver assistance systems (ADAS). The integration of advanced hardware, including specialized processors and sensor systems, adds to the financial burden. Data collection, processing, and cybersecurity measures amplify costs, while extensive testing and validation processes contribute to the overall expenses of ensuring the safety and reliability of AI-driven automotive systems. Furthermore, addressing the shortage of skilled professionals and training initiatives incur additional labor costs. For consumers, the higher upfront costs of vehicles with advanced AI features can act as a deterrent, impacting the rate of adoption. Economic challenges for smaller players in the industry and concerns about the timeline for return on investment further underscore the complex financial landscape. Collaborative efforts, technological advancements, and economies of scale are imperative to address these cost challenges and foster the sustainable growth of AI in the automotive sector.

Trends

Growing autonomous cars

The burgeoning presence of autonomous cars is a catalyst for the remarkable growth of AI in the automotive industry. At the heart of this transformative relationship is the reliance on Advanced Driver Assistance Systems (ADAS) within autonomous vehicles. ADAS, a subset of AI, powers functionalities crucial for autonomous driving, such as collision avoidance and adaptive cruise control. The proliferation of autonomous cars amplifies the integration of AI-driven perception systems, utilizing a diverse array of sensors, including cameras, LiDAR, radar, and ultrasonic sensors. This expanding sensor integration, coupled with AI's role in complex decision-making processes, ensures the seamless adaptation of autonomous vehicles to real-time road conditions.

Furthermore, AI contributes significantly to enhancing safety and traffic efficiency by providing real-time risk assessment and proactive responses to dynamic environments. Autonomous cars also leverage AI for navigation and mapping, continuously learning and adapting to diverse driving scenarios. As vehicular communication becomes more sophisticated, AI interprets signals for coordinated responses between autonomous vehicles and infrastructure elements. The development of autonomous fleets, backed by regulatory and industry support, intensifies the demand for AI technologies. In essence, the trajectory toward autonomous mobility not only propels the advancement of AI in the automotive sector but also steers the industry toward safer, more efficient, and technologically advanced transportation systems.

Market Segmentation

By Vehicle Type

By Vehicle Type, the global Artificial Intelligence in Automotive Industry Market is divided into Passenger Vehicles, Commercial Vehicles.

In the automotive industry market, passenger vehicles currently take the lead in the integration of AI technologies with 79.42% CAGR, driven by robust consumer adoption and demand for advanced features enhancing safety and driving experience. Passenger vehicles have been pioneers in early AI implementation, fostering a mature landscape marked by diverse offerings and intense market competition.

Concurrently, commercial vehicles emerge as the fastest-growing segment in the AI adoption curve with 26.42% CAGR. The commercial sector prioritizes AI to optimize operational efficiency, achieve cost savings, and address specific industry challenges. From predictive maintenance to route optimization and compliance with safety regulations, AI applications contribute significantly to the cost-effectiveness and safety of commercial vehicle operations. The commercial vehicle segment is also actively exploring autonomous driving applications, promising increased efficiency and addressing labor cost concerns. The dynamic growth in the commercial vehicle sector reflects a strategic focus on industry-specific customization, emphasizing the transformative potential of AI in shaping the future of automotive operations and logistics.

By Offering

By Offering, the global Artificial Intelligence in Automotive Industry Market is divided into Hardware and Software.

In the dynamic landscape of AI in the automotive industry, hardware takes the lead as the foundational infrastructure with 72.41% market share, providing the essential backbone for AI applications. Dedicated processors, GPUs, and specialized components ensure the safety, reliability, and computational efficiency required for tasks ranging from autonomous driving to advanced driver-assistance systems. However, the fastest-growing segment lies within the realm of software with 29.41% CAGR. Rapid advancements in AI algorithms and continuous iterations drive the growth of software applications, enabling vehicles to adapt and improve over time. From sophisticated machine learning and computer vision algorithms to updates enhancing existing hardware capabilities, software plays a pivotal role in coordinating diverse AI functionalities. Moreover, software contributes significantly to user experience, connectivity, and the collaborative development of comprehensive AI ecosystems. The symbiotic relationship between hardware and software is integral to the transformative potential of AI in reshaping the future of automotive intelligence and connectivity.

By Level of Autonomy

By Level of Autonomy, the global AI in Automotive Industry market is divided into Semi-autonomous, Fully Autonomous.

Semi-autonomous technology currently dominates the landscape as the leading level of autonomy in the automotive industry with 79.43% market share, striking a balance between human control and automated assistance. This level, marked by features like adaptive cruise control and lane-keeping assistance, has seen widespread adoption due to its immediate benefits and integration into various vehicle models. Conversely, fully autonomous technology represents the fastest-growing segment with 28.53% CAGR, rapidly advancing toward a future where vehicles operate independently without human intervention. With ongoing developments in artificial intelligence, sensor technology, and machine learning, fully autonomous vehicles are experiencing accelerated growth. The promise of increased safety, improved traffic flow, and enhanced mobility positions fully autonomous technology as the forefront of the autonomous driving revolution, fostering innovation and reshaping the future of transportation.

By Technology

By Technology, the global Artificial Intelligence in Automotive Industry Market is divided into Machine Learning, Natural Language Processing, Computer Vision.

In the field of AI integration in the automotive industry, Computer Vision takes the lead with 46.32% market share, providing indispensable visual intelligence for vehicles to perceive and interpret their surroundings. Established and mature, Computer Vision plays a pivotal role in advanced driver assistance systems (ADAS), object recognition, and the development of autonomous driving, contributing significantly to safety and automation. Simultaneously, Natural Language Processing (NLP) emerges as the fastest-growing segment with 22.74% CAGR, fueled by a surge in demand for in-car voice assistants. NLP's role in developing sophisticated voice recognition systems enhances human-machine interaction within vehicles. This technology facilitates intuitive communication, allowing drivers to control various functions through natural language commands, contributing to a more seamless and user-friendly driving experience.

The growth of NLP is further propelled by its pivotal role in advancing infotainment systems, where interactive and context-aware voice-controlled interfaces are becoming integral components of modern vehicles. As automotive manufacturers focus on connectivity and smart vehicle solutions, NLP's integration into connected car platforms becomes increasingly crucial, enriching the overall driving experience. Beyond in-car applications, NLP is expanding its influence into innovative customer service solutions. Automotive companies are leveraging NLP to develop virtual assistants that enhance customer support, offering a more interactive and personalized experience for vehicle owners.

By Application

By Application the Artificial Intelligence in Automotive Industry Market is divided by Advanced Driver Assistance Systems (ADAS), Self-driving Cars, In-Vehicle Infotainment (IVI) Systems, Vehicle-to-Everything (V2X) Communication.

In the current landscape of the AI-driven automotive industry, Advanced Driver Assistance Systems (ADAS) stands as the leader with 54.6% market share, emphasizing incremental advancements in safety and comfort features integrated into modern vehicles. Widely adopted by automakers globally, ADAS technologies have become standard, with regulatory support and consumer acceptance contributing to their pervasive presence on the roads. Meanwhile, the fastest-growing segment in the industry is self-driving cars with 26.46% CAGR. Fueled by rapid technological advancements, substantial research investments, and a high market potential, autonomous vehicles leverage sophisticated AI technologies for navigation and decision-making. With numerous companies conducting pilot programs and extensive testing, coupled with the strategic collaborations between automotive and technology entities, self-driving cars are rapidly evolving to address urbanization challenges and meet the growing demand for innovative and autonomous mobility solutions. This dynamic interplay positions ADAS as the current leader, ensuring immediate safety benefits, while self-driving cars represent the rapidly advancing frontier, promising transformative changes in the future of automotive transportation.

By Region

By region, the global Artificial Intelligence in Automotive Industry Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.

North America currently holds the lead with 36.43% market share in the AI in automotive market, largely attributed to its well-established technological ecosystem and the strong presence of major tech giants actively investing in AI research and development. The region has been an early adopter of autonomous vehicle technology, with a robust automotive industry that readily integrates AI for safety and efficiency enhancements. Regulatory support and frameworks further contribute to North America's prominence, providing a conducive environment for testing and deploying AI solutions in the automotive sector.

Conversely, the Asia-Pacific region emerges as the fastest-growing market with 22.4% CAGR in AI for automotive applications. This rapid growth is fueled by the region's emerging markets, particularly in countries like China and India, where the automotive industry is experiencing significant expansion. Government initiatives and substantial investments in AI development, coupled with the rising challenges of urbanization and traffic congestion, drive the adoption of AI technologies in vehicles. Collaborations between technology companies and automakers are playing a pivotal role in fostering innovation and integrating AI capabilities to meet the dynamic demands of the Asia-Pacific automotive market.

Competitive Landscape

The global Artificial Intelligence in Automotive Industry Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified AI technologies for automotive industries.

• Alphabet (Google)

Alphabet Inc., an American multinational technology conglomerate, specializes in Internet-related services and products, standing among the prominent Big Five information technology companies alongside Amazon, Apple, Meta, and Microsoft. Established on October 2, 2015, Alphabet resulted from the restructuring of Google. The decision to create Alphabet stemmed from the challenges posed by Google's vast size and diversity, aiming to enhance management effectiveness. This restructuring enabled Google to concentrate on its core businesses, while entities under the Alphabet umbrella gained greater autonomy to foster innovation and expansion.

• Amazon

Amazon.com, Inc., commonly known as Amazon, is a prominent American multinational technology company specializing in e-commerce, cloud computing, digital streaming, and artificial intelligence. Recognized as one of the most influential economic and cultural forces globally, Amazon is among the world's most valuable brands. Founded by Jeff Bezos in his Bellevue, Washington garage on July 5, 1994, Amazon initially focused on online book retailing but has since diversified into various product categories, earning it the nickname "The Everything Store." In addition to its e-commerce platform, Amazon is a leading developer of consumer electronics, including Kindle e-readers, Fire tablets, Fire TV, and Echo devices. Moreover, the company holds the distinction of being the largest provider of cloud computing services globally through its subsidiary, Amazon Web Services (AWS).

• Apple

Apple Inc., an American multinational technology company, specializes in consumer electronics, computer software, and online services. As one of the Big Five American information technology firms, alongside Amazon, Alphabet (Google), Meta (Facebook), and Microsoft, Apple was established on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne. Originally focused on personal computers, Apple swiftly became a prominent PC manufacturer globally. In the 1990s, the company diversified its product line, introducing successful innovations such as the iMac, iPod, and iPhone, ultimately propelling Apple to its status as one of the world's most valuable companies.

• Baidu
• Bosch
• Continental
• Daimler
• Ford
• General Motors
• HERE Technologies
• Huawei
• Intel
• Nvidia
• Qualcomm
• Samsung
• Sony
• Tesla
• Toyota
• Volkswagen
• Waymo
• ZF Friedrichshafe

Recent Developments

• In May 2021, Didi Chuxing forged a strategic alliance with Volvo Cars to integrate didi Gemini, a new self-driving hardware platform, equipped with NVIDIA DRIVE AGX Pegasus, into Volvo's autonomous drive-ready XC90 cars. These vehicles, part of didi's self-driving test fleet, are slated for eventual deployment in robotaxi services.
• In March 2021, BMW introduced its advanced idrive 8 infotainment system, designed to serve as a digital, intelligent partner for drivers. Fueled by machine learning, natural language processing, AI cloud, and 5G, idrive 8 will debut in the upcoming BMW ix and i4.
• In February 2021, Volkswagen and Microsoft collaborated to develop self-driving car software, aiming to streamline development processes, facilitate faster integration into the vehicle fleet, and simplify software updates. Mercedes-Benz and NVIDIA entered a cooperation in June 2020 to create an innovative in-vehicle computing system and AI infrastructure. Set for implementation in the 2024 fleet of next-generation Mercedes-Benz vehicles, this collaboration enables upgradable automated driving functions.
• In March 2020, Waymo harnessed AI to generate camera images for simulation using sensor data from its self-driving vehicles. By leveraging real-world lidar sensors and cameras, the AI preserves comprehensive information about the 3D geometry, semantics, and appearance of objects within the scene.

Artificial Intelligence in Automotive Industry Market Scope

Table of Contents

1 INTRODUCTION OF GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

4 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET OUTLOOK

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.3.1. Bargaining Power of Suppliers

4.3.2. Threat of New Entrants

4.3.3. Threat of Substitutes

4.3.4. Competitive Rivalry

4.3.5. Bargaining Power among Buyers

4.4 Value Chain Analysis

5 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, BY OFFERING

5.1 Overview

5.2 Hardware

5.3 Software

6 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, BY VEHICLE TYPE

6.1 Overview

6.2 Passenger Vehicles

6.3 Commercial Vehicles

7 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, BY LEVEL OF AUTONOMY

7.1 Overview

7.2 Semi-autonomous

7.3 Fully Autonomous

8 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, BY TECHNOLOGY

8.1 Overview

8.2 Machine Learning

8.3 Natural Language Processing

8.4 Computer Vision

9 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, BY APPLICATION

9.1 Overview

9.2 Advanced Driver Assistance Systems (ADAS)

9.3 Self-driving Cars

9.4 In-Vehicle Infotainment (IVI) Systems

9.5 Vehicle-to-Everything (V2X) Communication

9 GLOBAL ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE INDUSTRIES MARKET, By REGION

9.1 North America

9.1.1 U.S.

9.1.2 Canada

9.2 Europe

9.2.1 Germany

9.2.3 U.K.

9.2.4 France

9.2.5 Rest of Europe

9.3 Asia Pacific

9.3.1 China

9.3.2 Japan

9.3.3 India

9.3.4 South Korea

9.3.5 Singapore

9.3.6 Malaysia

9.3.7 Australia

9.3.8 Thailand

9.3.9 Indonesia

9.3.10 Philippines

9.3.11 Rest of Asia Pacific

9.4 Others

9.4.1 Saudi Arabia

9.4.2 U.A.E.

9.4.3 South Africa

9.4.4 Egypt

9.4.5 Israel

9.4.6 Rest of Middle East and Africa (MEA)

9.4.7 Brazil

9.4.8 Argentina

9.4.9 Mexico

9.4.10 Rest of South America

10 COMPANY PROFILES

10.1 Alphabet (Google)

10.1.1. Company Overview

10.1.2. Key Executives

10.1.3. Operating Business Segments

10.1.4. Product Portfolio

10.1.5. Financial Performance (As per availability)

10.1.6 Key News

10.2 Amazon

10.2.1. Company Overview

10.2.2. Key Executives

10.2.3. Operating Business Segments

10.2.4. Product Portfolio

10.2.5. Financial Performance (As per availability)

10.2.6. Key News

10.3 Apple

10.3.1. Company Overview

10.3.2. Key Executives

10.3.3. Operating Business Segments

10.3.4. Product Portfolio

10.3.5. Financial Performance (As per availability)

10.3.6. Key News

10.4  Baidu

10.4.1. Company Overview

10.4.2. Key Executives

10.4.3. Operating Business Segments

10.4.4. Product Portfolio

10.4.5. Financial Performance (As per availability)

10.4.6. Key News

10.5 Bosch

10.5.1. Company Overview

10.5.2. Key Executives

10.5.3. Operating Business Segments

10.5.4. Product Portfolio

10.5.5. Financial Performance (As per availability)

10.5.6. Key News

10.6 Continental

10.6.1. Company Overview

10.6.2. Key Executives

10.6.3. Operating Business Segments

10.6.4. Product Portfolio

10.6.5. Financial Performance (As per availability)

10.6.6. Key News

10.7 Daimler

10.7.1. Company Overview

10.7.2. Key Executives

10.7.3. Operating Business Segments

10.7.4. Product Portfolio

10.7.5. Financial Performance (As per availability)

10.7.6. Key News

10.8 Ford

10.8.1. Company Overview

10.8.2. Key Executives

10.8.3. Operating Business Segments

10.8.4. Product Portfolio

10.8.5. Financial Performance (As per availability)

10.8.6. Key News

10.9 General Motors

10.9.1. Company Overview

10.9.2. Key Executives

10.9.3. Operating Business Segments

10.9.4. Product Portfolio

10.9.5. Financial Performance (As per availability)

10.9.6. Key News

10.10 HERE Technologies

10.10.1. Company Overview

10.10.2. Key Executives

10.10.3. Operating Business Segments

1010.4. Product Portfolio

10.10.5. Financial Performance (As per availability)

10.10.6. Key News

10.11 Huawei

10.11.1. Company Overview

10.11.2. Key Executives

10.11.3. Operating Business Segments

10.11.4. Product Portfolio

10.11.5. Financial Performance (As per availability)

10.11.6. Key News

10.12 Intel

10.12.1. Company Overview

10.12.2. Key Executives

10.12.3. Operating Business Segments

10.12.4. Product Portfolio

10.12.5. Financial Performance (As per availability)

10.12.6. Key News

10.13 Nvidia

10.13.1. Company Overview

10.13.2. Key Executives

10.13.3. Operating Business Segments

10.13.4. Product Portfolio

10.13.5. Financial Performance (As per availability)

10.13.6. Key News

10.14  Qualcomm

10.14.1. Company Overview

10.14.2. Key Executives

10.14.3. Operating Business Segments

10.14.4. Product Portfolio

10.14.5. Financial Performance (As per availability)

10.14.6. Key News

10.15 Samsung

10.15.1. Company Overview

10.15.2. Key Executives

10.15.3. Operating Business Segments

10.15.4. Product Portfolio

10.15.5. Financial Performance (As per availability)

10.15.6. Key News

10.16 Sony

10.16.1. Company Overview

10.16.2. Key Executives

10.16.3. Operating Business Segments

10.16.4. Product Portfolio

10.16.5. Financial Performance (As per availability)

10.16.6. Key News

10.17 Tesla

10.17.1. Company Overview

10.17.2. Key Executives

10.17.3. Operating Business Segments

10.17.4. Product Portfolio

10.17.5. Financial Performance (As per availability)

10.17.6. Key News

10.18 Toyota

10.18.1. Company Overview

10.18.2. Key Executives

10.18.3. Operating Business Segments

10.18.4. Product Portfolio

10.18.5. Financial Performance (As per availability)

10.18.6. Key News

10.19 Volkswagen

10.19.1. Company Overview

10.19.2. Key Executives

10.19.3. Operating Business Segments

10.19.4. Product Portfolio

10.19.5. Financial Performance (As per availability)

10.19.6. Key News

10.20 Waymo

10.20.1. Company Overview

10.20.2. Key Executives

10.20.3. Operating Business Segments

10.20.4. Product Portfolio

10.20.5. Financial Performance (As per availability)

10.20.6. Key News

10.21 ZF Friedrichshafen

10.21.1. Company Overview

10.21.2. Key Executives

10.21.3. Operating Business Segments

10.21.4. Product Portfolio

10.21.5. Financial Performance (As per availability)

10.21.6. Key News

Global Artificial Intelligence in Automotive Market Segmentation

Artificial Intelligence in Automotive by Vehicle Type: Market Size & Forecast 2023-2032

• Passenger Vehicles
• Commercial Vehicles

Artificial Intelligence in Automotive by Offering: Market Size & Forecast 2023-2032

• Hardware
• Software

Artificial Intelligence in Automotive by Level of Autonomy: Market Size & Forecast 2023-2032

• Semi-autonomous
• Fully Autonomous

Artificial Intelligence in Automotive by Technology: Market Size & Forecast 2023-2032

• Machine Learning
• Natural Language Processing
• Computer Vision

Artificial Intelligence in Automotive by Application: Market Size & Forecast 2023-2032

• Advanced Driver Assistance Systems (ADAS)
• Self-driving Cars
• In-Vehicle Infotainment (IVI) Systems
• Vehicle-to-Everything (V2X) Communication

Artificial Intelligence in Automotive by Geography: Market Size & Forecast 2023-2032

• North America (USA, Canada, Mexico)
• Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
• Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
• South America (Brazil, Argentina, Columbia, Rest of South America)
• Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

Major Players:

• Alphabet (Google)
• Amazon
• Apple
• Baidu
• Bosch
• Continental
• Daimler
• Ford
• General Motors
• HERE Technologies
• Huawei
• Intel
• Nvidia
• Qualcomm
• Samsung
• Sony
• Tesla
• Toyota
• Volkswagen
• Waymo
• ZF Friedrichshafe