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Published on February 12th, 2019 | by Emergent Enterprise0
AI in the Automotive Industry – an Analysis of the Space
Artificial intelligence has potential in essentially any aspect of the automotive industry. This overview by Niccolo Mejia at emerj.com presents the many different ways AI is being integrated into business processes and tasks. Combining with other technologies like IoT and robotics, human workers are relieved of duties that are tedious and often unsafe. And those tasks are done with greater accuracy and consistency. AI and other tech takes on the jobs that people really don’t want to do. Those same people can move on to more fulfilling roles within the company.
In this article, we’ll explore the applications of AI software within the automotive industry from production and manufacturing to insurance and transportation. We will discuss the equipment involved in collecting and analyzing data along with the potential value they offer to manufacturers, shared mobility companies, insurers, and drivers.
We begin our overview of AI in the automotive industry with how machine vision technology could improve the robots that car manufacturers use to build vehicles and maintain quality control.
The car manufacturing process is a multifaceted business in itself, so it follows that there would be numerous areas where one can find applications for AI technology. Factories can monitor the condition of production equipment and heavy machinery with IoT sensors and predictive maintenance.
Each sensor is attached to a piece of equipment and collects vibrational data whenever the equipment moves or is used. Changes or anomalies in these vibrations can indicate damage or natural wear over time and help business leaders prevent equipment failure by predicting it and performing necessary maintenance before the assembly line needs to stop out of emergency.
Computer vision also has many applications at the factory level. This includes quality control during production. Factories can leverage this type of software to detect flaws or inconsistencies across produced cars and highlight areas where the new car may need to be worked on further before going to market.
For example, the computer vision software of a camera stationed at the end of the assembly line could find flaws in a car’s paint job or identify parts that were not attached correctly, such as windshield wipers.