The automotive industry launches new vehicles every year, and also keep upgrading their engines every time to improve its efficiency in terms of safety, power, environment pollution, and also uptime.
The success of such product highly depends on the reliability, and it comes on the product stands on all the parameters.
A new vehicle when assembled to be launched in the market are run on various in house tracks to get the feedbacks on various parameters using various sensors and systems.
Once it passes vehicle is taken for a run to the tuff terrains and climatic conditions to know its durability. Now even if this vehicle is run on the real roads for the test, it can be done for few weeks or months and not very long.
But if we see in the real scenarios, its actual test begins when people start using it. And if a new vehicle starts throwing issues especially from the engine or core platform then it could be a great risk.
Vehicle manufacturers already collect lot of data from the engines to find out the anomaly or the issue and correct them. But these kind of issues are found on existing data and are not able to predict the future impact. Artificial intelligence could help reading the future issues from these data and help automobile company mitigate the possible future risks.
This could not only improve the efficiency of the product, but also save huge amount of money that could be spent to bring back and fix the vehicles or potential lawsuits, and also save from hurting the brand value.
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