Richard Felton explores how generative AI is likely to shape the automotive industry, and how car manufacturers might be able to leverage the technology
Generative AI applications continue to capture widespread attention and imagination. In the automotive industry specifically, generative AI has the potential to help transform the way vehicles are designed and developed.
For instance, AI can be applied to create new content and ideas, including exploring design options, based on criteria that has been stipulated by the developer. Like all AI, generative AI is powered by machine learning models—very large models that are pre-trained on vast amounts of data and commonly referred to as foundation models (FMs). Today’s FMs can perform a wide range of tasks that span multiple domains, like writing blog posts, generating images, solving math problems, engaging in dialogue, and answering questions based on a document. When applied to the automotive development process, generative AI could help automakers quickly identify the best design options for complex systems such as engines, lightweight structures, and vehicle features.
The journey towards software defined vehicles
The automotive industry is increasingly adopting software defined vehicles (SDVs) with millions of lines of code, offering customers an agile and responsive experience. SDVs have the ability to update and upgrade vehicle features through over-the-air (OTA) updates, similar to how smartphones are updated with new features and become better products over time.
Generative AI can be used to create and optimise the software and control systems, as well as to help improve the performance of the vehicle’s hardware. As a vehicle’s code increases in complexity, it is important for software engineers to focus on developing new, innovative functionalities and not spend their time trying to keep up with a complex and ever-changing tool and technology landscape. Automotive customers can use AI coding companions that use generative AI to help improve developer productivity by generating code suggestions in real-time based on developers’ comments in natural language and prior code in their Integrated Development Environment (IDE). This can identify problematic code with high accuracy, and provides intelligent suggestions on how to remediate it.
Testing autonomous driving using generative AI
Highly automated and autonomous mobility is a major focus of the automotive industry. Autonomous driving requires complex software and hardware systems that must be designed to work seamlessly together.
Generative AI can be an important tool in designing and testing these systems. For example, generative AI may be used by OEMs to create simulations that test the vehicle’s response to various driving scenarios. These scenarios and the accompanied simulated test data can be edge cases that statistically happen so rarely as to not be represented in typical circumstances, or so extreme as to be unsafe to test in real-world (e.g. near miss of a pedestrian crossing at night, in the rain, or in the dark). This is not just an efficiency improvement but will also allow automotive companies to create more test scenarios with the potential to improve the overall system capabilities.
Generative AI offers a huge opportunity for innovation in the automotive industry. But it does require a large amount of computational resources and data, which can be costly and time-consuming to acquire and manage. Customers need performant, cost-effective infrastructure that is purpose-built for ML. This is where cloud computing comes in, helping optimise systems specifically for large scale generative AI applications with models containing hundreds of billions of parameters.
The opportunities afforded by AI and the cloud mean automotive manufacturers now have greater access to powerful computing resources needed to help scale their generative AI capabilities across a variety of applications including designing, training, and testing automated and autonomous driving systems.
The opinions expressed here are those of the author and do not necessarily reflect the positions of Automotive World Ltd.
Richard Felton is Senior Practice Manager, Automotive, at Amazon Web Services (AWS)
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