How the BMW Group has embraced AI for positive use cases and to improve sustainability | AWS re: Invent

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Artificial intelligence (AI) helps many different industries and has a particularly strong impact in the automotive industry. Among the most exciting use cases are for fully autonomous vehicles, but that’s not the only area where AI is having an impact. For example, Microsoft and Mercedes-Benz are working together to improve efficiency in car production.

At the AWS re:Invent cloud conference this week, the BMW Group outlined the impact AI has had on the organization and detailed new use cases where AI will deliver future positive business outcomes.

In a session, Marco Görgmaier, GM, data transformation and artificial intelligence, BMW Group, said his team had built a library of thousands of data assets across the company that can be reused for analytics and AI. Since 2019, he said his team has been able to deliver more than 800 use cases that have generated over $1 billion in US dollar value. The use cases span research and development, logistics, sales, quality and supplier networks.

“The vision and mission of our team is to drive and scale value creation through the use of artificial intelligence across our value chain,” Görgmaier said.

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BMW is driving towards a sustainable future with a little help from AI

An emerging area where BMW is now investing resources is helping to improve sustainability.

Görgmaier commented that 60% of the world’s population lives in cities and urban areas, and it is also where 70% of greenhouse gas emissions are generated. What BMW is now trying to do is help city planners solve problems to reduce emissions.

BMW is already helping with machine learning models capable of predicting how traffic regulations could potentially help reduce both traffic and petrol emissions. ML models are also used to identify where there is not yet sufficient charging infrastructure for electric cars. Görgmaier said a lack of charging infrastructure prevents people from switching to an electric vehicle, which in turn has an impact on sustainability.

There is also a BMW ML effort to help predict the impact of parking availability and pricing on driving patterns. These patterns include commuting routes and traffic, which will also have an impact on emissions.

Running geospatial information with Amazon SageMaker

Görgmaier said many of the urban sustainability issues that BMW is trying to help solve could benefit from geospatial information. That’s where BMW is starting to use new geospatial features in the Amazon SageMaker ML toolkit that was just publicly revealed this week.

One area where BMW wants to take advantage of geospatial ML is to help predict when an organization with a fleet of vehicles will be able to transition to electric vehicles.

“We set out to train machine learning models to learn correlations between engine type and driving profiles,” he said. “The rationale behind it was that if such a correlation would exist, then the model could learn to predict the affinity of certain drivers for an electric vehicle based on their profiles.”

Since BMW was working with completely anonymized fleet-level data, it had to use GPS tracks and geospatial data to create the correlations.

“At the end of training, the model was able to predict how likely specific fleets were to convert to EVs with an accuracy of more than 80%,” Görgmaier said.

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