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Ways AI Is Reshaping the Automotive Industry
AI has applications across the automobile lifecycle, from design and development to testing and production to marketing.
Fremont, CA: Make no mistake: we are just getting started in terms of investigating the full potential of AI across all industries. Data Science, Machine Learning, Artificial Neural Networks, Text Mining - these technologies, which are already largely mature in the realms of internet marketing and finance, have a lot to offer manufacturing in general and the automobile industry in particular.
AI has applications across the automobile lifecycle, from design and development to testing and production to marketing. The data created by the numerous sensors presently incorporated in cars and data retrieved from manufacturing lines and collated from consumer input are highly significant sources of information. Their analysis and interpretation are equally effective levers for improving the design, testing, maintenance, and understanding user requirements and expectations. Looking ahead, the challenge – as tricky as it is inspirational – is, of course, the development of autonomous cars and the entire delegation of all safety-related choices to the vehicle itself.
• New functions for new user needs
Environmental perception is a significant subject of research into the development of smart vehicle technology: infrastructures, other cars, people, or anything that may be deemed a hindrance to an automobile. Radar, sensors, cameras, weather conditions, roadworks, and other unusual events: the machine must recognize every sort of external influence and analyze its potential impact on the vehicle's trajectory to make real-time modifications to the driving control system.
• Capitalizing on customer knowledge
End-user customer knowledge is one area where the implications of big data are particularly well understood. Consumer data analysis tools are among the most mature, and brands utilize them to determine their target consumers and expectations. The strategy is a direct response to the growing need for product and service customization. In addition, customer information may be helpful to increase component dependability in the automobile sector.
• Fault prevention and correction
The abundance of data accessible during the testing phase allows access to information particularly useful in fault resolution. Users only need to be able to extract the data. Algorithms discover defects in vast amounts of data, freeing engineers to focus on data interpretation and fault resolution rather than hunting for source information. It implies that clustering and classification technologies may get utilized to analyze and certify vehicle responses during road testing.