How AI Accelerates the Automotive Sector

Auto Tech Outlook | Tuesday, March 02, 2021

Automotive AI is already in the drive from simulating advanced systems to assembling them on the factory floor.

FREMONT, CA: The automotive industry has embraced a great number of AI advancements that are not only utilized for vehicle path planning and obstacle avoidance, but incorporated in every step of development, from gathering parts for manufacturing, to testing software, to modeling how these systems will perform on the road. Today, automakers are forced to investigate the use of AI technology because their annual sales have doubled in the past few years. Each of the car models they manufacture comes with an average of different options. Here is how AI is used to accelerate automotive technology.

Systems like automotive manufacturing robots must be tested to ensure that they meet safety and security requirements. And as it turns out, AI is being leveraged to help automate the testing of increasingly complex software stacks as well. The software test automation product is presently being used in the automotive industry to minimize the countless hours conventionally spent analyzing complex test vectors. In use, a test automation tool helps test engineers by reviewing thousands of code lines for violations and learning about the decisions of a programmer. After e

valuating any issues, a machine learning engine prioritizes the significant violations and finds potential solutions.

In addition to helping with vehicle development and manufacturing, AI and machine learning are also keeping an eye on recently deployed systems. For instance, there is analytics technology that helps identify and prevent automotive chip failures before they happen. This platform stores everything about these electronic components, such as the PCB they are mounted on, how they are positioned, where and when they were manufactured, and many more.

As vehicles become more complicated, so too do the simulation and modeling techniques required to design them. For instance,  automotive simulation and modeling tools must create high-fidelity driving scenarios that place precise representations of a whole vehicle into interactive, 3D environments that mimic the real world. There is a comprehensive automotive validation environment that uses simulation tools and digital twin technology to bring pre-silicon testing to reality. The platform can be leveraged to model and test deterministic and AI-based self-driving technologies.

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