We are now in the Fourth Industrial Revolution. Artificial Intelligence (AI) is the fuel behind all the developments that we are witnessing in this era. The continuous and vast development of computing infrastructure changed our goal from machine programming to machine learning. Today we see self-driving cars, translation software, virtual assistants, drones, and other things which are powered by AI. As our technologies continue to grow, AI will dominate our cities even further.
It's an exciting time to be investing in mobility startups. Below are the current trends in the mobility industry and an overview of the startup ecosystem in Europe. The mobility industry is undergoing rapid change these days. While they bring opportunities for newcomers, they create challenges for the incumbents. Let's have a look at the four trends in more detail: While Mercedes Benz had already started to experiment with self-driving technology three decades ago, it wasn't until recently that autonomous driving (AD) efforts really speed up.
As the rise of e-commerce continues, companies around the globe have become increasingly sensitive to evolving consumer preferences. In a world where instant gratification has come to represent a generation, autonomous technologies are set to make a significant impact. When it comes to consumer shipping, McKinsey reports that 25 percent of all consumers would pay a premium for same-day or instant delivery made possible by autonomous tech. However, this figure is likely to grow, given that 30 percent of younger consumers are willing to pay more for the same shipping options. As industry use cases continue to expand, many have come to define the ecosystem as the autonomous "last-mile."
Roger Bootle is not afraid to think and say unconventional things. He is that rare phenomenon: a professional economist who thinks that Brexit is a Good Idea. Indeed, he belongs to a group called Economists for Brexit, now renamed as Economists for Free Trade, which argues for a no-deal Brexit. Whatever you think of that, the economics consultancy that Bootle founded, Capital Economics, has been very successful financially, and in 2012 it was awarded the £250,000 Wolfson Economics Prize, the second most valuable economics prize in the world after the Nobel, for a proposal that EU member states who wanted to exit should default on a large part of their debts. A book on tech unemployment from such a high-profile economist is to be warmly welcomed.
School of Information Technology, Deakin University, Geelong, Australia Robots are increasingly tested in public spaces, towards a f uture where urban environments are not only for humans but for autonomous syst ems. While robots are promising, for convenience and efficiency, there are challenges associated with building cities crowded with machines. This p aper provides an overview of the problems and some solutions, and calls for gr eater attention on this matter . Urban environments will increasingly be spaces for autonom ous systems, of which automated vehicles is only one popular type. Robot wheelchairs could be used in public as well other robot -transporters to help the elderly.
The autonomous vehicle industry is in the process of rerouting. Early AV leaders said fully autonomous cars would hit the mass market by 2020 or 2021--Elon Musk even promised a self-driving Tesla by 2017. But with the end of the decade in sight, two things are certain: The autonomous future remains a long way off, and AV-makers are going to have to change their plan for how to get there. In this presentation, we show you what this new path looks like and lay out the step-by-step changes we'll see on the way to full autonomy. We make the case that AV developers' early shortcomings have ushered in a new era of collaboration and realism.
This is an updated version of a story that initially appeared in Interglobix Magazine, the publication for data centers, connectivity and lifestyle. The road to the self-driving car of the future is paved with hardware and data centers. Autonomous vehicles promise to be one of the transformational technologies of the 21st century, with the potential to remake much of our urban and economic landscape. But many questions remain about how the connected car of 2019 will evolve to meet the vision for the autonomous vehicles of the future, and tough issues to be resolved on multiple fronts – including technology, regulation and infrastructure. The long-term vision is to create networks of connected vehicles that "talk" to one another using vehicle-to-vehicle (V2V) communications over low-latency wireless connections, which can also allow vehicle-to-infrastructure (V2I) that enable robot cars to connect with traffic lights and parking meters.
When it comes to the automotive industry and the role of artificial intelligence within it, your mind will begin to conjure up exciting thoughts. Whether images of self driving vehicles or driver monitoring, AI can impact and develop our experience when behind the wheel. We will be taking a look at how artificial intelligence is being used within cars, the current developments taking place and the future landscape of artificial intelligence within the automotive industry. Artificial Intelligence, AI for short, has an array of different meanings. Artificial intelligence for many is most commonly seen as a technique that enables computers to mimic human behavior.
Seemingly, one of the most controversial things about Tesla cars is its Autopilot feature, a driver-assist feature that helps drivers navigate and pilot their vehicle. Oddly, while news of exciting Autopilot features comes out regularly, general information about exactly what Autopilot is, what the options are, and what it can and cannot do seem to be few and far between. I have tried to collect and answer the biggest questions about Autopilot below to help prospective buyers know what the system is and is not, as well as to inform journalists about the system in case they find themselves trying to cover a news story regarding the system. When the next questionable news story comes out, please feel free to link this article for anyone wondering about the system. Please note that all of the below information refers to Tesla vehicles containing Autopilot 2.0 hardware or higher in them (vehicles built since October of 2016). Although, the majority of the information will apply to all Tesla vehicles that are Autopilot enabled.
Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed.