PUBLISHER: TechSci Research | PRODUCT CODE: 1218299
PUBLISHER: TechSci Research | PRODUCT CODE: 1218299
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United Kingdom AI in the transportation market is anticipated to register growth with an impressive CAGR in the forecast period, 2023-2027. The market growth can be attributed to increasing demand for traffic regulations. A surge in the adoption of artificial intelligence and the internet of things in the automotive and related services further drives the growth of the United Kingdom AI in the transportation market in the upcoming five years. Increasing advancement in the technology incorporated in the automotive, surging investment in the development of these technologies further supports the growth of the United Kingdom AI in the transportation market in the next five years.
Artificial intelligence has penetrated almost all sorts of technological products and increasing dependency on technical prospects enhances product value as well as making consumers' issues sorted. The use of AI and IoT (Internet of Things) in automobiles has revolutionized the automotive industry. AI has the potential to make traffic more efficient, ease traffic congestion, free driver's time, make parking easier, and encourage car- and ridesharing.
The emergence of advanced technology in the automotive fuel the growth of the United Kingdom AI in the transportation market in the upcoming five years. Digital technology plays a pivotal role in influencing consumers' potential buyers of premium and executive vehicles. The added services of vehicle safety, traffic management, and on-road vehicle modulation further substantiate the growth of the United Kingdom AI in the transportation market in the next five years. Furthermore, rapid growth in vehicle data generation, unrestricted access to computing resources, and a substantial reduction in the cost of data storage give potential to the already expanding market in the future years.
Increasing instances of human mistakes while parking or other vehicular functions leads to increased cases of on-road accidents. Also, with the high population and growing sales of passenger cars, and personal vehicles, the rising disposable income of the market adds to the growing traffic congestion on road. In the year 2021, more than 1.6 million units of automobiles were sold in the United Kingdom.
Since the adaptation of advanced traffic management systems is a must in recent times, the future holds great opportunities for the market players providing the services for AI in transportation thereby aiding the growth of the United Kingdom AI in the transportation market in the future five years. The data thus collected during the traffic management also needs to be managed then further adding to the growth of the market.
The United Kingdom AI in the transportation market is segmented by machine learning technology, process, application, offering, competitional landscape, and regional distribution. Based on machine learning technology, the market is further segmented into computer vision, context awareness, deep learning, and natural language processing. By process, the market is fragmented into data mining, image recognition, and signal recognition. Based on application, the market is bifurcated into autonomous trucks, HMI in trucks, and semi-autonomous trucks. By offering, the market is differentiated between hardware and software. The market analysis also studies the regional segmentation to devise regional market segmentation, divided among London, East Anglia, Southwest, Southeast, Scotland, East Midlands, and Yorkshire & Humberside.
Daimler AG, Robert Bosch GmbH, Intel Corporation, Continental AG, The Volvo Group, ZF Friedrichshafen AG, Magna International Inc., Valeo, Nvidia Corporation, and Scania AB, among others is a partial list of major market players of the companies responsible for the growth of United Kingdom AI in the transportation market.
In this report, United Kingdom AI in the transportation market has been segmented into the following categories, in addition to the industry trends which have also been detailed below: