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ARTIFICIAL INTELLIGENCE IN AUTOMOTIVE MARKET ANALYSIS

Artificial Intelligence in Automotive Market, By Offering (Hardware and Software), By Technology (Deep Learning, Machine Learning, Infotainment Systems, and IOT (Internet of Things), Cognitive Capabilities), By Application (Semi-autonomous and Fully-autonomous), and By Geography - Global Industry Insights, Trends, Outlook, and Opportunity Analysis, 2022-2028

Artificial Intelligence is increasingly being adopted in the automotive sector, in order to reduce human errors and human intervention. Artificial intelligence autonomously enhances visual perception, decision making, and speech recognition.  The increasing government regulations for vehicle safety become one of the major driver for the growth of the market.

Increasing demand for autonomous vehicles is a major factor driving growth of the artificial intelligence in automotive market

Increasing demand for autonomous vehicles is expected to support growth of the artificial intelligence in automotive market. Increasing number of road accidents and fatal injuries, due to human errors has led to increasing demand for autonomous vehicles. For instance, according to the World Health Organization (WHO), 2015, fatalities of 1.25 million people were reported and 50 million people were injured, due to road accidents globally. Therefore, using autonomous vehicles could reduce accidents due to human errors. Key automakers are focusing on developing autonomous vehicles, which is expected to aid in growth of the market. For instance, Volkswagen is expected to launch self-driving cars by 2019 and BMW is expected to launch autonomous iNext by 2021. Moreover, fleet operators are also focusing on deploying self-driving cars, in order to enhance their market share. For instance, 2016, NuTonomy—a U.S.-based company, announced its plans to deploy self-driving taxi services in Singapore by 2018, and plans to be operational with fleets of self-driving taxies in 10 cities of the world by 2020.

Deep learning segment is expected to hold the largest share in the market over the forecast period

Deep learning segment held the largest share in 2016 and is projected to retain its dominance throughout the forecast period. Increasing use of deep learning by automotive manufacturers such as Audi, Delphi, Ford, and Volkswagen for various applications such as data analysis, speech recognition, image processing has led to growth of this segment.

Artificial Intelligence in Automotive Market Taxonomy

On basis of offering, the artificial intelligence in automotive market is segmented into

  • Hardware
  • Software

On basis of technology, the artificial intelligence in automotive market is segmented into

  • Deep learning
  • Machine learning
  • Infotainment Systems
  • IOT (Internet Of Things)
  • Cognitive Capabilities

On basis of application, the artificial intelligence in automotive market is segmented into

  • Semi-autonomous applications
  • Fully-autonomous applications

Artificial intelligence in automotive market- Regional Insights

The global artificial intelligence in automotive market is segmented on the basis of regions into North America, Europe, Asia Pacific, Latin America, and Middle East, and Africa. North America is expected to account for the largest market share in the artificial intelligence in automotive market over the forecast period. This is due to existence of government initiatives regarding the enhancement of the automotive vehicles.  For instance, in 2016, the US government has invested US$ 4 billion for autonomous vehicles. Also, high presence of market leaders such as Qualcomm Inc., Ford Motor Company, and General Motors Company is another factor for growth of this market. U.S. and Canada are the growth engines in North America.

Artificial intelligence in automotive market- Competitive Background

Major players of global artificial intelligence in automotive market are Qualcomm Inc. (U.S.), Tesla Inc., Volvo Car Corporation, BMW AG, Audi AG (Germany), General Motors Company (U.S.), Ford Motor Company (U.S.), Toyota Motor Corporation (Japan), Hyundai Motor Corporation (South Korea), Uber Technologies Inc. (U.S.), and Apple Inc.

Key Developments:

  • In July 2019, Intel Corporation launched its deep-learning chip system “Pohoiki Beach” which is capable of crunching complex AI algorithms 10,000 times more efficiently up to 1,000 times faster than regular CPUs. This system is made up of 64 Loihi 'neuromorphic' deep-learning chips which can be used in autonomous vehicles.
  • In January 2019, Qualcomm Technologies, Inc. has launched its 3rd Generation Qualcomm Snapdragon Automotive Cockpit Platforms. These platforms are based on artificial intelligence (AI) which will enhance in-vehicle experience by supporting the higher levels of computing required for semi-autonomous and autonomous vehicles.
  • In September 2019, Israeli high-tech firm Cortica enters into partnership with Toyota and BMW. The companies will share their expertise in the field of artificial intelligence (AI) for autonomous driving.
  • In January 2019, Microsoft Corporation enters into partnership with LG Electronics. Microsoft will provide its Azure Cloud and artificial intelligence technologies to enhance LG’s Advanced Driver Assistance Systems (ADAS), Driver-Status Monitoring Camera (DSM) and Multi-Purpose Front Camera products.
  • In September 2019, Volkswagen and Ford announced the expansion of their alliance in the field of artificial intelligence and autonomous vehicle. The companies will be investing in Argo AI the autonomous vehicle technology platform company. Volkswagen and Ford are planning to commercially deploy in the U.S. and Europe.

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About Author

Ameya Thakkar is a seasoned management consultant with 9+ years of experience optimizing operations and driving growth for companies in the automotive and transportation sector. As a senior consultant at CMI, Ameya has led strategic initiatives that have delivered over $50M in cost savings and revenue gains for clients. Ameya specializes in supply chain optimization, process re-engineering, and identification of deep revenue pockets. He has deep expertise in the automotive industry, having worked with major OEMs and suppliers on complex challenges such as supplier analysis, demand analysis, competitive analysis, and Industry 4.0 implementation.

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