The Artificial Intelligence (AI) in automotive market is anticipated to record a robust CAGR of 55% between 2023 and 2033. The market is anticipated to cross a market share of US$ 9.3 billion in 2023, while it is expected to be valued at US$ 744.39 billion by 2033.

  • Advanced vehicles with self-driving and fully autonomous transmissions are in high demand. Hence, the AI-integration vendors are working on personalized solutions, fueling the demand for Artificial Intelligence (AI) in automotive.
  • The rising demand for EVs and hybrid vehicles is followed by the integration of AI units, as they enhance the operational capability, deliver ease, and protect the vehicle through systems like parks-guided systems.
  • The Advanced Driver Assistance Systems (ADAS), level 2, is getting popular among automotive enthusiasts, which further extends the research and development of ADAS integration.
  • The new car builders are delivering OEM-based AI chips that work on different functions of a vehicle. From AC, lights, and cruise control to guided parking and autonomous driving systems, AI supports each component.
  • The restoration of the automotive industry, coupled with the high-end AI and machine learning set-up, is fueling the demand for Artificial Intelligence (AI) in automotive. Advanced automatic emergency braking is regulated through AI chips.
  • The passive and active safety systems are specially designed to prevent human error while operating vehicles.

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Key Points

  1. The United States market is another significant market. The market growth is attributed to the presence of EV giants like Tesla, which delivers advanced AI and ML integration. The increased acceptance of autonomous and self-driving vehicles is fueling the sales of AI in the automotive
  2. Artificial intelligence (AI) in the automotive market in China is another significant market. The growth is attributed to the expanded manufacturing units, high-end research, and advanced experimentation.
  3. Europe with its environmental compliance adopts EVs and Hybrid Vehicles, which further fuels the demand for AI-integration prospects.
  4. The software segment is likely to thrive in the component category due to the common application and automotive implementation. It is expected to hold a value of US$ 200 billion by 2033.
  5. The fully autonomous segment tops the application category with an anticipated value of US$ 30 billion by 2033. The growth is attributed to the advent of self-driving assistance systems.

Competitive Landscape

The key competitors focus on inclusivity, while adding sensors and smart software to enhance the guided systems. Companies follow the artificial intelligence innovation with the key competitors and merge, acquire, and partner with other companies to increase the network range, connectivity, supply chain, and distribution channel. The key players in the market are: BMW AG, AUDI AG, Intel Corporation, Tesla Inc, Uber Technologies, Volvo Car Corporation, Honda Motors, Ford Motor Company, NVIDIA Corporation, and Tencent

Recent Market Developments

  • Ford Motor Co is the leading patent filer when it comes to the neural network for radar-LiDAR. The company operates with Baidu in China to implement AI technology in automotive.
  • A technology company, Aptiv is promoting enhanced automated vehicles that are loaded with AI-based sensors, cameras, and other components.

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Key Segments

By Component:

  • Hardware
  • Software
  • Services

By Technology:

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

By Process:

  • Data Mining
  • Image/signal Recognition

By Application:

  • Semi-autonomous vehicles
  • Fully-autonomous Vehicles

By Region:

  • North America
  • Latin America
  • Europe
  • Asia Pacific (APAC)
  • The Middle East & Africa (MEA)

Author

Nikhil Kaitwade (Associate Vice President at Future Market Insights, Inc.) has over a decade of experience in market research and business consulting. He has successfully delivered 1500+ client assignments, predominantly in Automotive, Chemicals, Industrial Equipment, Oil & Gas, and Service industries.

His core competency circles around developing research methodology, creating a unique analysis framework, statistical data models for pricing analysis, competition mapping, and market feasibility analysis. His expertise also extends wide and beyond analysis, advising clients on identifying growth potential in established and niche market segments, investment/divestment decisions, and market entry decision-making.

Nikhil holds an MBA degree in Marketing and IT and a Graduate in Mechanical Engineering. Nikhil has authored several publications and quoted in journals like EMS Now, EPR Magazine, and EE Times.

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