Automotive

Part 3: Developing IoT-enabled autos: a tsunami of change and the journey to cognitive

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In my series on IoT-enabled autos, we’ve already recognized the emergence of model-based systems engineering (MBSE) and the wisdom of using the Scaled Agile Framework (SAFe) in modern engineering practices. In part three of my series highlighting the ways that automotive product engineering is evolving, we’ll examine the promise of cognitive systems. It’s the final component driving automotive development forward, helping at key junctures in the process.

In the third part of a 4-part series, we’ll examine the promise of cognitive systems. It’s the final component driving automotive development forward.

Cognitive analytics and systems are shaping the future of product development.

Speeding product development with cognitive

To date there are only limited examples, but AI systems will be able to shortcut and further speed vehicle development.

Cognitive analytics can examine volumes of unstructured information. This includes machine learning, natural language processing and speech/image recognition to find new patterns that mimic human thinking. This can be invaluable wherever AI is applied. Let’s consider a few examples.

AI systems like Watson can be a question-and-answer engine as a single source of knowledge. Engineers in the early stages of development often try to determine new interfaces, materials and capabilities for vehicle software. It’s exceedingly difficult to sort through academic and industry research, consumer studies and global trends to coalesce around the best direction. But learning systems can effectively facilitate that process.

Embracing requirements management

Requirements management is also a great opportunity for cognitive analytics. Quality assessment, resolving duplication and conflicts, as well as finding missing requirement gaps, all can be helped by AI. For example, cognitive systems would be great at classifying and tagging in order to better cross-reference requirements that may have interdependencies.

Using content analytics to limit recalls

And what about recall management? Engineering issues have to be dealt with throughout the vehicle’s life. Automotive recalls have spiked the past few years, leading to some very high-profile, expensive incidents that have harmed certain automakers’ brands and bottom lines. Many of these are software related, and many recalls are identified from consumer complaints and automakers’ submissions to the National Highway Transportation Safety Administration (NHTSA). For faster resolution, these can be analyzed through content analytics to diagnose and mitigate vehicle defects.

But if you’re ready to learn more now, visit IBM’s suite of Continuous Engineering solutions. Or visit us at the upcoming Consumer Electronics Show or the Automotive News World Congress, January 16-17 in Detroit.

Next up: Part 4 – a video overview of autos in an IoT-enabled world.

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