How BMW Uses Artificial Intelligence And Big Data To Design And Build Cars Of Tomorrow

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BMW creates some of the most high-tech cars we have yet seen. The German giant builds 2.5 million vehicles every year and sells them all over the world.

But technology is not just limited to the cars it builds, its business model is built on Big Data which drives everything it does across design, engineering, production, sales and customer support.

Thanks to Artificial Intelligence (AI), data-driven predictive analytics and other cutting edge technologies BMW is able to build the cars of today while at the same time envisaging and bringing to reality the cars of tomorrow.

BMW is clearly confident in its belief that cars of the near future will pilot themselves, rather than relying on human drivers. All the big auto manufacturers are staking their claim in a driverless future, but BMW has done so with more confidence than most. It has stated that its aim is for its vehicles to achieve full “level 5” autonomy by 2021.

Level 5 autonomy tops the scale defined by the US Department of Transport as it made preliminary investigations into how legislation for autonomous vehicles would work. It indicates that the car will be capable of driving with no human input or supervision and operate at least as effective as a human driver in any conditions and on any road.

More details of how this would be achieved began to emerge earlier this year when BMW announced a partnership with Intel, which itself had recently acquired Mobileye, a leader in computer vision technology. Computer vision is based on the idea of teaching machines to “see” in the same way humans do, using cameras instead of biological eyes and to interpret the information in a similar way to our brains. It is an advanced form of image recognition – which can be seen in action in Google Image Search as well as many other machine learning applications, where machines have been taught to sort and classify images, becoming more adept as they are exposed to more and more data.

Of course, rather than sorting harmless images of cats and dogs on the internet, the computer vision used in autonomous driving will have to be capable of reading all of the input data from the cars’ cameras and sensors and analyzing it in real-time - quickly enough to take emergency action at 100 kph.

Although full autonomy is probably still a few years away, the cars that BMW is road testing at the moment are described as “highly automated”.  A fleet of 40 BMW series 7, equipped with Mobileye technology, are expected to be on the road by late summer. Although the vehicles won’t pilot themselves or operate without a driver, the autonomy level 3 and 4 vehicles can carry out most driving procedures unassisted, although a human must be ready to take full control at any time – to remain on the right side of the law, if nothing else.

One group of people for whom the concept of not having to drive themselves around is nothing new is Rolls Royce owners. Unsurprisingly, this brand (also owned by BMW) styles its self-driving software as a virtual chauffeur. At the controls of the concept model 103EX is Eleanor, named after the actress and model believed to have been the inspiration for the cars’ famous Spirit of Ecstasy hood ornaments.

The body is made from one seamless, molded piece of metal, in the form of sculpture and the car generates a personal red carpet using LEDs for when its occupant steps out.

Sure, this might not be the sort of car most of us will own in 10 years, but the design of the 103EX gives us some clues about how the super-rich will be served by AI and autonomy.

More likely to have an impact on the lives of us average Joes are BMWs plans for another of its iconic brands – the Mini. Autonomous Mini concepts have also been set out, and here the aim is to produce a consumer product which interacts seamlessly with our lives, just as a smartphone and other connected device manufacturers hope to do.

For other applications which are likely to enjoy a wider exposure among its customers, BMW has partnered with a number of other leaders in the AI field, including IBM. Its Watson cognitive computing platform was used in prototype i8 hybrid vehicles to learn about how drivers and their cars’ systems can interact more comfortably and naturally.

Areas where IBM have said that Watson will be put to work, are self-diagnosis of faults and issues which are limiting car performance, management of communications with other autonomous cars, and detecting and adapting to drivers’ preferences.

Through its partnership with location data service provider Here (which BMW co-owns along with Volkswagen and Daimler after acquiring it from Nokia last year), data is already been collected which could help educate the first wave of consumer-ready self-driving cars

Data including video from onboard cameras, machine data such as braking force, wiper and headlight use, and GPS information, is already being scooped up by vehicles and fed into Here to help with mapping and route planning in today’s cars. Tomorrow it could also be used to train them how they should react and behave when they become fully autonomous.

Another innovation which is in use today but could form the foundation of more complex services in the future is shown through its partnership with Parkmobile, which offers mobile payment for parking services across the US. A recently announced deal will see the systems installed as standard across a range of BMW models. It will allow drivers to find and pay for parking spaces at their destinations before they set off. As well as convenience, this has the advantage of cutting carbon waste as the average driver spends 20 minutes searching for his or her parking space on each trip to somewhere unfamiliar. In the future when vehicles are autonomous it is planned that these systems will allow machine-to-machine payments to take place transparently to the driver.

With a manufacturing operation as enormous as BMWs, a huge amount of data is generated across design, production, logistics and distribution. To coordinate work, cut costs and drive efficiencies between 31 assembly facilities split over 15 countries, BMW worked with Teradata to automate data flow and enhance decision-making. Systems were designed to follow the journey of any part, from the point it is manufactured to when the vehicle is sold. This creates efficiencies in logistics, when millions of parts are being shipped around the world, and helps ensure everything gets to the right place with the minimum use of resources.

Predictive maintenance is enabled across production lines, so worn parts can be replaced and errors highlighted before they cause operational problems. If stock levels of certain parts are too high in certain locations and too low in others, or particular transport resources are under or over-resourced, this can all be quickly rectified.

Whether or not BMW wins the race to bring fully autonomous, self-operating vehicles to our streets, the work they have put into developing systems and frameworks for doing so is likely to be an immensely valuable part of the bigger picture. With an operation of their size, data and analytics are clearly of top importance – a fact which they have capitalized on to position themselves as tech leaders in their own right, through visionary forward-thinking and strategic use of partnerships and acquisitions.