According to Market Research Future (MRFR)’s recently published report, the global AI in transportation market is expected to expand at 18.03% CAGR during the forecast period 2017 to 2023. It has been estimated that the valuation of the market will grow from USD 1.2 Bn in 2017 to USD 3.07 Bn by the end of 2023. The strong value maintained by the transportation industry has paved the way for the swift adoption of AI. The rising need for vehicle and driver safety due to increasing road accidents have catalyzed the demand for AI in transportation. Additionally, the developed country-level markets have already started the production of autonomous cars, which is projected to have a positive influence on the market growth.
The ongoing industry trend of truck platooning coupled with the growing applications of AI in railway cargo transportation is predicted to accelerate the revenue creation for the market participants over the next couple of years. However, security concerns associated with the handling of transportation data and the lack of supportive infrastructure for AI enabled vehicles remains a challenge to the market proliferation.
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Major Key players
- Continental AG
- Robert Bosch GmbH
- NVIDIA Corporation
- Microsoft Corporation
- Volvo Group
- Daimler AG
- Scania Group
- MAN SEPACCAR Inc.
- ZF Friedrichshafen AG
- Valeo SA
This MRFR’s report offers a detailed segmental analysis of the global AI in transportation market based on offerings, IoT communication, technologies, machine learning technology, and applications. By offerings, the market has been segmented into hardware and software. The hardware segment is further sub-segmented into sensors, CPUs, GPUs, and others. The software segment has been sub-segmented into AI platforms, and AI solutions. The AI solutions market is further sub-segmented into autonomous driving solutions, and intelligent repair solutions.
By IoT communication technologies, the market has been segmented into LTE, 5G, and LPWAN. The LTE segment has successfully penetrated the market and is presently leading it. Meanwhile, the LPWAN segment is estimated to expand at a higher CAGR over the forecast period.
By machine learning technology, the market is segmented into deep learning, computer vision, natural language processing, and context awareness. The deep learning segment is forecasted to proliferate substantially at the highest CAGR owing to its extensive use in the production of autonomous cars.
By applications, the market has been segmented into autonomous truck, semi-autonomous trucks, truck platooning, human-machine interface (HMI), predictive maintenance, precision and mapping, and others (driverless buses, smart traffic management). Among these, the human-machine interface (HMI) segment accounts for the largest market share presently. Meanwhile, the semi-autonomous truck segment is expected to mark a relatively higher CAGR due to the rising demand for semi-autonomous trucks across the world.
The global market for AI in transportation, by region, has been segmented into North America, Europe, Asia Pacific, and the Rest of the World. North America is currently leading the global market and is projected to retain its prominence over the next couple of years. The presence of prominent country-level markets and key players are supposed to have a favorable impact on the growth of the market.
Additionally, the region is a pioneer in technological advancements and is well-equipped for the adoption of AI technology in transportation. All these factors are projected to combinedly boost the expansion of the regional market over the assessment period. Meanwhile, the market in Europe exhibits immense potential for growth and development. It is expected to register the highest CAGR during the forecast period. The adoption of autonomous cars, high economic growth rate, thriving automotive sector, etc. are projected to catapult the market on an upward trajectory.
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TABLE OF CONTENTS
1 Executive Summary
2 Scope Of The Report
2.1 Market Definition
2.2 Scope Of The Study
2.2.1 Research Objectives
2.2.2 Assumptions & Limitations
2.3 Markets Structure
3 Market Research Methodology
3.1 Research Process
3.2 Secondary Research
3.3 Primary Research
3.4 Forecast Model
4 Market Landscape
4.1 Porter’s Five Forces Analysis
4.1.1 Threat Of New Entrants
4.1.2 Bargaining Power Of Buyers
4.1.3 Threat Of Substitutes
4.1.4 Segment Rivalry
4.2 Value Chain/Supply Chain Of Global AI In Transportation Market
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