LONDON--(BUSINESS WIRE)--The global automotive artificial intelligence (AI) market is expected to grow at a CAGR of more than 37% during the period 2018-2022, according to a new market research study by Technavio.
The report presents a comprehensive research of the global automotive artificial intelligence market by application, including HMI and ADAS applications. The report also determines the geographic breakdown of the market in terms of detailed analysis and impact, which includes key geographies such as APAC, the Americas, and EMEA.
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Market driver: rising penetration of ADAS
ADAS forms the base of advanced automotive AI. It uses conditional AI, which specifies certain parameters for the vehicle's control unit and takes a specified action. Conditional AI has limited operational capabilities and therefore, requires the driver's close assistance even when fully operational. Various ADASs that feature conditional AI are lane-change assistance, automatic emergency braking, blind-spot detection, lean-departure warning, driver drowsiness awareness systems, and bird's eye display. The implementation of these features in vehicles increases their safety and utility over conventional vehicle models.
According to a senior analyst at Technavio for automotive electronics research, “Customers are more inclined toward vehicles with increased safety, convenience and connectivity features. Features such as adaptive cruise control, adaptive light control, automatic parking, collision avoidance system, global positioning system navigation, driver, drowsiness detection, hill descent control, and intelligent vehicle speed adaptation are managed by conditional AI. These features increase the utility of the vehicle and thus, would be more desirable to the customers.”
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Market trend: development of autonomous fleet of cab and parcel delivery services
Cab and parcel delivery services widely use vehicles that allow vehicle makers to generate substantial amounts of data regarding various drive patterns, traffic patterns, and road conditions. Autonomous vehicles need to undergo multitudes of real-life scenarios to test their integrity and safety. The autonomous vehicles launched as cabs and parcel delivery services provide the learning foundation by putting the vehicle through all possible types of driving conditions. Therefore, major autonomous vehicle makers are concentrating on developing autonomous cars for cab and delivery services.
Market challenge: high complexity of automotive AI
AI is a complex and time-consuming technology to develop. It would further take time to finish as training an AI with driving skills would take a considerable amount of computing power. Self-driving cars are based on the computational power of the automotive AI used in these vehicles. Thus, these machines utilize deep neural networks for achieving increased computational power.
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