{"id":2890,"date":"2020-01-15T07:03:04","date_gmt":"2020-01-15T12:03:04","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/?p=2890"},"modified":"2026-03-26T12:02:22","modified_gmt":"2026-03-26T16:02:22","slug":"where-today-meets-tomorrow-harnessing-the-power-of-ai-part-ii","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/where-today-meets-tomorrow-harnessing-the-power-of-ai-part-ii\/","title":{"rendered":"Where today meets tomorrow: harnessing the power of AI (part II)"},"content":{"rendered":"\n

In our previous blog, Where today meets tomorrow: harnessing the power of AI (part I)<\/a><\/em>, we discussed the \u201cdata bottleneck\u201d slowing down the implementation of AI in many applications and the role of a comprehensive digital twin in overcoming it. <\/p>\n\n\n\n

In this post, I\u2019d like to elaborate on the role and of the digital twin and the \u201cshape\u201d it takes as products become parts of interconnected systems, how it is present in development as well as production and operation of any complex, connected product.  We will also take a deeper dive into this technology and its use for mobility.<\/p>\n\n\n\n

How AI works in a product closely connected to a comprehensive digital twin<\/strong><\/h3>\n\n\n\n

An AI chip for autonomous driving resides in a vehicle that must interact and communicate with other vehicles and navigate within a larger urban environment. That is why it\u2019s estimated that an autonomous car must go through 11 billion miles<\/em> of testing before being assured it is safe enough for the road, according to RAND Group<\/a>.\u00a0So, it\u2019s clear that road testing is not a feasible approach to the validation of autonomous functions and advanced driver assistant systems. <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The digital twin \u2013 the comprehensive digital representation\nof the product, its design as well as behavior \u2013 can create the data that are\ntoo costly to gather in the real world. By leveraging the digital twin, verification\ntasks can systematically cover different lighting and weather conditions. To do\nthis, they rely on the digital twin of the vehicle: a complete virtual\nrepresentation of all design aspects, whether mechanical, electronic,\nelectrical or software and complete simulation of every functional and\nbehavioral aspect of that vehicle.<\/p>\n\n\n\n

Once there is a high-fidelity virtualization of the physical\nproduct down to the software and AI electronics, it\u2019s possible to virtually\ntest the vehicle in almost any simulated driving conditions imaginable.<\/p>\n\n\n\n

For example, designers can see the deceleration pattern in the\ncase of an emergency braking when a simulated pedestrian runs out in front of\nthe car. If the \u201cjerk\u201d it is too drastic, a design engineer can optimize it \u2013\nvirtually! <\/p>\n\n\n\n

This is key for the productive use of AI and deep learning\nfunctionality where this virtual environment, using a comprehensive digital\ntwin, produces more reliable training and test data than road testing ever could.<\/p>\n\n\n\n

Extending the Digital\nTwin to Production<\/strong><\/h3>\n\n\n\n

In the same way the actual product is represented completely\nin the digital enterprise, the same is true for the production and supply chain\nprocess. Everything from robotic movements and workflow to energy consumption\ncan be tested, simulated, verified or optimized virtually before moving into\nproduction. <\/p>\n\n\n\n

Vinfast<\/a>,\na Vietnamese-based automaker, built an automotive plant in greenfield spaces\nthat was capable of building cars in just 21 months, 50 percent faster than\nusual. A holistic approach of the Siemens Xcelerator<\/a> software portfolio\ncombined with the company\u2019s automation technologies has increased the speed and\nflexibility in development, ensured high global standards in production,\noptimized the manufacturing process, and made the entire plant future-proof for\nfurther expansions and new business models. <\/p>\n\n\n\n

\"\"
Vinfast Factory, Vietnam<\/figcaption><\/figure>\n\n\n\n

There\nare hundreds of steps in the manufacturing environment that require gathering\nand processing data in real time to derive recommendations. The cloud in the\nlong run will not be cost efficient or real-time responsive enough to do that\njob. Instead advanced factories rely on edge devices<\/a> in the manufacturing line\nthat utilize deep learning algorithms to make instant decisions.<\/p>\n\n\n\n

Applying AI and IoT to\nmake cities smarter and more sustainable<\/strong><\/strong><\/h3>\n\n\n\n

Given the continued trend toward urbanization, the challenge\nof managing the environment, energy efficiency, and mobility is\never-increasing. With the pressure to make cities more sustainable, there is gigantic\ngrowth markets with lots of opportunity in the area of smart cities and smart\nmobility.<\/p>\n\n\n\n

Dubai airport is a leader when it comes to applying artificial\nintelligence for better efficiency: They are researching AI to support\nimmigration officers, baggage handling and air traffic management. This may be\nthe first airport where a passenger walks out of the terminal and is greeted by\nan autonomous car, baggage already loaded.<\/p>\n\n\n\n

On top of that, Dubai will be a perfect place to learn about\nthe potential of smart cities at their Expo\n2020<\/a> starting in October with an expected 25 million visitors from around\nthe world. During the six-month exhibition, the city will interact with other\nsystems and use AI and cloud technologies to gather real-time information to\noptimize the flow of energy, people, and information. This will enable a\n\u201cblueprint for future smart cities\u201d.<\/p>\n\n\n\n

\"\"
Expo Dubai 2020<\/figcaption><\/figure>\n\n\n\n

Summary<\/strong><\/h3>\n\n\n\n

In the past, building a car was a separate entity whereas\nnow it is about creating integrated mobility solutions that span all the way\nfrom the chip to the city, the concept of vehicle-to-everything (V2X) that is\nthe foundation of vehicle autonomy. This includes building important strategic\nor ad hoc partnerships.<\/p>\n\n\n\n

The chip is taking a bigger role in driving innovation and\ndifferentiation, and this will be the foundation as we continue to build smart\ncities. We see more and more functionality, especially artificial intelligence\nand deep learning functionality, reside in not just highly-specialized\nsemiconductors but also in more and more products we use every day.<\/p>\n","protected":false},"excerpt":{"rendered":"

In our previous blog, Where today meets tomorrow: harnessing the power of AI (part I), we discussed the \u201cdata bottleneck\u201d…<\/p>\n","protected":false},"author":14392,"featured_media":2896,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spanish_translation":"","french_translation":"","german_translation":"","italian_translation":"","polish_translation":"","japanese_translation":"","chinese_translation":"","footnotes":""},"categories":[1],"tags":[21,11,36],"industry":[120,134],"product":[],"coauthors":[],"class_list":["post-2890","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-autonomous-vehicles","tag-digital-twin","tag-electronics-and-semiconductor","industry-automotive-transportation","industry-electronics-semiconductors"],"featured_image_url":"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/19\/2020\/01\/8502-02-Depaneling-Visual-191017-1-RGB_original.jpg","_links":{"self":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/users\/14392"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/comments?post=2890"}],"version-history":[{"count":3,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2890\/revisions"}],"predecessor-version":[{"id":2948,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/posts\/2890\/revisions\/2948"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media\/2896"}],"wp:attachment":[{"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/media?parent=2890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/categories?post=2890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/tags?post=2890"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/industry?post=2890"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/product?post=2890"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/blogs.sw.siemens.com\/thought-leadership\/wp-json\/wp\/v2\/coauthors?post=2890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}