Automated driving systems are an integral part of the automotive industry. Tools such as Robot Operating System and simulators support their development. However, in the end, the developers must test their algorithms on a real vehicle. To better observe the difference between reality and simulation--the reality gap--digital twin technology offers real-time communication between the real vehicle and its model. We present low fidelity digital twin generator and describe situations where automatic generation is preferable to high fidelity simulation. We validated our approach of generating a virtual environment with a vehicle model by replaying the data recorded from the real vehicle.

Digital Twins (DTs) are virtual representations of physical objects or processes that can collect information from the real environment to represent, validate, and replicate the physical twin's present and future behavior. The DTs are becoming increasingly prevalent in a variety of fields, including manufacturing, automobiles, medicine, smart cities, and other related areas. In this paper, we presented a systematic reviews on DTs in the autonomous vehicular industry. We addressed DTs and their essential characteristics, emphasized on accurate data collection, real-time analytics, and efficient simulation capabilities, while highlighting their role in enhancing performance and reliability. Next, we explored the technical challenges and central technologies of DTs. We illustrated the comparison analysis of different methodologies that have been used for autonomous vehicles in smart cities. Finally, we addressed the application challenges and limitations of DTs in the autonomous vehicular industry.

Digital Twin technology has been around for decades. The concept is believed to have its origin at NASA when simulations were carried out to bring back the Apollo 13 astronauts. Gartner defines digital twin as a digital representation of a real-world entity or system. Simulations of what-if scenarios can be performed on this digital/virtual copy of the asset in deriving at the next-best action. Machine Learning models can perform predictive and prescriptive analytics on this digital copy which can then be applied back to the actual asset.

In this special guest feature, Eric Whitley, Director of Smart Manufacturing at L2L, believes that machine learning is so powerful precisely because it grows machine knowledge in a continuous feedback loop and becomes exponentially smarter. But what can it do for your business? This article will provide insights into the five benefits of machine learning for manufacturers. For over 30 years, Eric has been a noteworthy leader in the Manufacturing space. In addition to the many publications and articles Eric has written on various manufacturing topics, you may know him from his efforts leading the Total Productive Maintenance effort at Autoliv ASP or from his involvement in the Management Certification programs at The Ohio State University, where he served as an adjunct faculty member.

A sustainable future: How AI and digital twins are helping ESG and sustainability efforts in construction, industry and city planning.

Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services). Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model.

Just how much will AI influence tomorrow's consumer economy? Will it know much we like avocado? When you talk to the average person about harnessing AI, many don't consider these questions. They are prone to offload fears of Skynet rather than contemplate how this tech will be used in the real world, as in our sushi bar example. Those with a little more subject matter knowledge may point to purely digital applications, such as social media platforms identifying terrorist content sans human intervention, or drug companies using machine learning to sift through mountains of health data for tomorrow's cures.

Let's face it: making decisions is hard. Naturally, with decisions come mistakes, and mistakes are both costly and painful. "It's good to learn from your mistakes. It's better to learn from other people's mistakes." Failing in real life is expensive, but failing in the virtual world is cheap.

Digital Twins are increasing in usage but are often used in multiple contexts and in a simplified manner. Most references to the Digital Twin actually refer to a Digital shadow i.e. maintaining a digital copy of a physical object that is updated periodically. I am interested in the idea of Digital Twin because my teaching at the #universityofoxford applies more to AI in engineering (as opposed to say financial services). Also, Digital Twins relate to the idea of Physics based modelling in Engineering. A wind tunnel is an example of Physics based model.

Despite many challenges that we faced due to the pandemic in 2020, the momentum of growth for advanced technologies has continued. Especially, artificial intelligence (AI) is continuously finding increased usage in both the private and public sectors. During 2020, there were developments around natural language processing (NLP) techniques (for example, GPT-3 model built to produce human-like texts), virtual assistants, job automation, and more. And it appears that AI growth is not going to slow down anytime soon. In 2021, AI will become the core business technology.