Automotive Artificial Intelligence Market by Component (Hardware, Software, and Service), Technology (Machine Learning & Deep Learning, Computer Vision, and Natural Language Processing), and Application (Semi-Autonomous and Autonomous) - Global Opportunit
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Artificial intelligence has been increasingly adopted by the automotive industry, which is among the major industries using AI to mimic the action of humans. Applications that incorporate AI systems in automotive are semiautonomous and autonomous vehicles. In autonomous vehicles, AI collects, processes, and chooses the specific action based on the information gathered. Semiautonomous and autonomous vehicles use several advanced tools such as long-range radar, LIDAR, cameras, short/medium radar, and ultrasound to gather information and understand the world around them.
After collection of data through different tools, AI analyzes the situation through its technologies such as machine & deep learning, natural language processing, and computer vision. Artificial intelligence has several applications for these vehicles such as, incorporates speech recognition for advanced communication with passengers, directs vehicles towards refueling station or recharge station when it is running low on fuel and Analyzes less populated and congested roads among others.
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At present, North America dominates this market. In 2017, China is expected to register the highest growth in Asia-Pacific. Similarly, the UK is expected to lead the overall market in Europe in 2017. In the same year, the U.S. is estimated to dominate the North American market.
The growth of the automotive artificial intelligence market is driven by rise in demand for autonomous vehicles and increase in preference for enhanced user experience & convenience features. However, the threat of hackers and rise in cybercrime are expected to restrain the market growth. On the contrary, rise in demand for the premium vehicle segment is anticipated to provide lucrative growth opportunities for the market.
In 2017, based on component, the hardware segment is anticipated to dominate the global automotive artificial intelligence market, in terms of revenue. However, based on application, semiautonomous dominated the global market in the same year.
The report includes a micro-level study of different regions adopting automotive artificial intelligence for various application. In addition, it discusses the potential opportunities for market players to enter the market. Moreover, it provides an in-depth market analysis of automotive artificial intelligence, outlining current trends, key driving factors, and potential areas for product investments.
The global automotive AI market is analyzed based on four regions, North America, Europe, Asia-Pacific, and LAMEA. Furthermore, the report highlights the competitive landscape of the key market players to increase their shares and sustain the intense competition in the industry. NVIDIA Corporation, Waymo, Intel Corporation, IBM Corporation, Microsoft Corporation, Micron Technology, Otto Motors, BMW, Tesla Inc., and Toyota are some of the key players operating in the global automotive AI market.
Key Findings of the Automotive Artificial Intelligence Market:
•Based on component, the hardware segment is anticipated to dominate the global automotive artificial intelligence market in terms of revenue. However, based on application, semiautonomous dominated the market in 2017.
•In 2017, the machine learning & deep learning based on the technology segment accounted for the highest revenue.
•North America held the majority of market share in 2017.
•LAMEA is anticipated to exhibit the highest CAGR during the forecast period.
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We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.
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