At what pace is the generative ai in autonomous vehicles market growing, and what is its estimated value?
The generative AI in autonomous vehicles market size has grown rapidly in recent years. It will grow from $2.66 $ billion in 2024 to $3.16 $ billion in 2025 at a compound annual growth rate (CAGR) of 18.9%. The growth in the historic period can be attributed to growth in big data, rise in AI research, increasing computational power, rise in connectivity, and growth in edge computing.
The generative AI in autonomous vehicles market size is expected to see rapid growth in the next few years. It will grow to $6.25 $ billion in 2029 at a compound annual growth rate (CAGR) of 18.6%. The growth in the forecast period can be attributed to the growing consumer demand for efficient transportation, growing data annotation services, increasing deployment of 5G, rising focus on personalized experiences, and increasing autonomous vehicle testing. Major trends in the forecast period include technological advancements, digital twins, natural language processing, integration of generative adversarial networks, and autonomous delivery services.
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What are the top drivers to the rising demand in the generative ai in autonomous vehicles market?
The increasing trend of connected cars is expected to propel the growth of generative AI in autonomous vehicle market going forward. Connected cars refer to vehicles integrated with technology that allows them to communicate with other vehicles, infrastructure, and the internet, enhancing features such as navigation, diagnostics, and updates. The trend toward connected cars is increasing due to advancements in technology that enable vehicles to interact with each other and their environment, providing enhanced safety, convenience, and functionality. generative AI in autonomous vehicles enhances connected cars by simulating and predicting complex driving scenarios, improving real-time decision-making and vehicle-to-everything (V2X) communication for better safety and efficiency. For instance, in October 2024, according to the report of Road Genius, a US-based transportation and logistics company, by 2023, there were more than 192 million connected cars operating worldwide, with 80% of new cars sold in the U.S. being equipped with this technology. The number of connected cars is projected to rise to 367 million by 2027, as connectivity increasingly becomes a standard feature in more vehicle models. Therefore, the increasing trend of connected cars is driving the growth of the generation of AI in autonomous vehicle market.
How is the generative ai in autonomous vehicles market segmented?
The generative AI in autonomous vehiclesmarket covered in this report is segmented –
1) By Component: Solution, Services
2) By Vehicle Type: Passenger Vehicles, Commercial Vehicles
3) By Application: Training And Data Augmentation, Simulation And Testing, Perception Systems Enhancement And Sensing, Localization And Mapping, Safety Verification And Testing, Behavior Prediction And Decision Making, Other Applications
4) By End Users: Automotive Manufacturers, Research And Development Organizations
Subsegments:
1) By Solution: Software Platforms, Simulation Tools, Data Management Systems, Computer Vision Systems, Natural Language Processing (NLP), Sensor Fusion Solutions
2) By Services: Consulting Services, Integration Services, Maintenance and Support Services, Training and Development Services, Data Annotation Services
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Who are the top competitors in the generative ai in autonomous vehicles market?
Major companies operating in the generative AI in autonomous vehicles market are Bayerische Motoren Werke AG, Honda Motor Co. Ltd., Tesla Inc., Nissan Motor Co. Ltd., Intel Corporation, International Business Machines Corporation, NVIDIA Corporation, Baidu Inc., Aptiv PLC, Ansys Inc., Faraday Future Intelligent Electric Inc., Cruise LLC, Waymo LLC, Samsara Inc., Argo AI LLC, Renovo Motors Inc., Aurora Innovation Inc., AImotive Inc., Nuro Inc., Zoox Inc., Applied Intuition Inc., DeepMap Inc., Cognata Ltd.
What significant trends should we anticipate in the generative ai in autonomous vehicles market over the forecast period?
Major companies operating in the generative AI in autonomous vehicles market are focusing on developing innovative advancements, such as deep teaching technology, to improve the learning abilities and decision-making processes of autonomous vehicles. Deep teaching technology refers to advanced machine learning methods that enable systems to learn complex tasks by mimicking human teaching techniques, improving their performance and adaptability over time. For instance, in July 2024, Helm.ai, a US-based technology company, launched VidGen-1, a generative video model for autonomous vehicles and robots. VidGen-1 produces realistic driving scene videos using deep learning and advanced neural networks. It cost-effectively trains on extensive driving footage, simulating diverse scenarios, weather conditions, and traffic dynamics. VidGen-1 enhances autonomous driving by accurately predicting real-world appearances, intent, and path planning, which are crucial for safe self-driving technology.
Which regional trends are influencing the generative ai in autonomous vehicles market, and which area dominates the industry?
Europe was the largest region in the generative AI in autonomous vehicles market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the generative AI in autonomous vehicles market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
What Does The Generative AI in Autonomous Vehicles Market Report 2025 Offer?
The generative ai in autonomous vehicles market research report from The Business Research Company offers global market size, growth rate, regional shares, competitor analysis, detailed segments, trends, and opportunities.
Generative artificial intelligence (AI) in autonomous vehicles refers to advanced algorithms that enable self-driving cars to simulate, predict, and respond to various driving scenarios, enhancing their learning, safety, and decision-making capabilities. By generating diverse driving scenarios, this technology improves real-time decision-making and system training, thereby boosting adaptability and overall vehicle performance.
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