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