Beyond Autonomy: How Artificial Intelligence is Changing the Automotive Industry

As today's vehicles grow increasingly challenging to design and produce, artificial intelligence (AI) is working behind the scenes to enhance productivity by reducing design and manufacturing components linked with the industry.

Fremont, CA: Artificial intelligence is at the forefront of many of the most innovative aspects of the automobile industry. Although autonomous vehicles have usually been linked with the business, the industry is also focusing on AI applications to revolutionize other sectors of the sector, such as mobility services, supply chain, predictive maintenance, and customer experience, to mention a few.

Manufacturing: Steady-state Track Testing and Transient Testing

To evaluate a car's performance, each development phase within manufacturing requires a distinct assessment method: CFD calculations before any prototype is made, wind tunnel trials, and, finally, track testing. Track testing, in general, gives the most precise and relevant performance information from which to begin the design process. Therefore, these preliminary tests are designed to guide development toward designs that will perform best in steady-state track testing.

Steady-state Track Testing

Track testing generates enormous datasets from sensors that measure acceleration, velocity, temperature, and other variables. It is complicated to comprehend the car's underlying performance in this "dynamic" setting. The complex interplay of all car components, weather conditions, track conditions, and driving style results in a massive collection of sophisticated high-dimensional data. Instead of evaluating the car's emotional reaction, some manufacturers test it in a "steady-state" way, like travelling in a straight line at constant speeds. This makes extracting the car's performance from sensor data easier.

Behind the Scenes, AI is Driving Innovation.

Almost every aspect of the automobile industry is undergoing some digital transformation, with AI substantially shortening the time it takes for these advancements to occur.

While the goals may vary based on what is accomplished, we are witnessing an increase in the share of development work done digitally rather than physically throughout the industry. So, the next time you read an article about how AI is accelerating technical advancements in autonomous vehicles, keep in mind all the invisible ways that machine learning technologies are progressing the automotive sector behind the scenes.

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However, steady-state testing has limits. For example, most of these real-world tests are designed to evaluate other areas of the car's performance (such as the cooling system or the driver's comfort). Then, separate tests are run at the track to collect data for the car's steady-state response. Even after that, the results of these steady-state tests are frequently too basic to depict the car's whole dynamic reaction accurately.