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Self-Driving Cars In Space

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Geely, a Chinese automotive manufacturer that also owns Volvo, announced will launch hundreds, and perhaps thousands, of satellites, in order to support V2X and V2V communication. The launches are a little ways down the road -- the current press release touts breaking ground on the facility that will manufacture the satellites. "Geely Technology Group knows how to start the Lunar New Year right -- with important news regarding its future low-orbit exploits. On February 18th 2021, its Taizhou Facility was given its license to begin the commercial manufacturing of its satellites, which will be ultimately used for realizing Vehicle-to-vehicle (V2V) and Vehicle-to X-(V2X) communications to realize full autonomous self-driving. The license, awarded by China's National Development and Reform Commission, essentially means that the factory, located in Geely Group's original hometown of Taizhou in Zhejiang Province, can begin production. When production begins, at present planned for October of this year, the facility will have an estimated production output of over 500 satellites per year."


Training Self Driving Cars using Reinforcement Learning

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In this image, there is a robot at position (1, 1), in a maze. That position is the state. The robot has a set of actions that it can perform, move up or move right. The last thing to note is that, the robot will receive a reward whenever it takes an action. The rewards are defined by the programmer, and we'll define the rewards as such.


A better intelligence test for autonomous driving systems

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In 2015, Elon Musk guessed that the industry should expect fully autonomous vehicles by 2018--but that never happened. In 2014, Nissan promised multiple, commercially viable driverless vehicles on the market by 2020. While the COVID-19 pandemic did not help the situation, this is another unmet promise. Why do auto manufacturers have to keep moving the goalposts on driverless vehicles? According to a research paper recently published in Nature Communications by the Center for Connected and Automated Transportation (CCAT), one of the obstacles that has hindered the development of autonomous vehicles comes down to a severe inefficiency in the way autonomous vehicle testing and evaluation is performed.


Advanced Robotic Vehicles Programming PDF

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Learn how to program robotic vehicles with ardupilot libraries and pixhawk autopilot, both of which are open source technologies with a global scope. This book is focused on quadcopters but the knowledge is easily extendable to three-dimensional vehicles such as drones, submarines, and rovers. Pixhawk and the ardupilot libraries have grown dramatically in popularity due to the fact that the hardware and software offer a real-time task scheduler, huge data processing capabilities, interconnectivity, low power consumption, and global developer support. This book shows you how to take your robotic programming skills to the next level. By the end of this book, you'll learn the pixhawk software and ardupilot libraries to develop your own autonomous vehicles.


Most Downloaded Artificial Intelligence Research Articles

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Summary – Undoubtedly, AI augmentation has become the core subject of the AI world. To be precise, augmented AI is here to show the world that cognitive ability is here just to support human intelligence and it is not here to replace it. Nonetheless, it is the role human intelligence possess using deep learning and machine learning algorithms to solve complex problems. Simply said, AI augmentation is here to make life much more simple as such to support, accelerate, and increase the efficiency of the tasks humans perform. Such instances include tasks like auto-transcription software and self-driving cars.


Convincing A Self-Driving Car To Go Where You Want It To Go When It Won't Go There

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Self-driving cars can be as stubborn as a mule. Sometimes it seems as though a car is about as stubborn as a mule or perhaps acting bull-headed. Here's an example of something I witnessed first-hand the other day. A tow truck was getting ready to take a car for a tow. This was a flatbed style tow truck. You've surely seen these types of tow trucks on the roadways wherein they piggyback a car that needs to be transported. The tail end of the flatbed portion tilts at a somewhat acute angle to allow for driving a car up onto the riding platform. This forms a ramp for the car to traverse upward onto the empty and awaiting flatbed area.


Global Big Data Conference

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Heavily funded autonomous vehicle startup Aurora Innovation Inc. has acquired Ours Technology Inc., a fellow startup developing chip-based lidar sensors based on a new approach known as frequency-modulated continuous-wave sensing. The deal was announced today. Aurora is building an autonomous driving system that can be installed on trucks and cars to let them navigate the roads without human input. The startup has raised more than $1 billion in funding from investors including Amazon.com Inc. and Sequoia Capital. Aurora entered the headlines late last year when it bought Uber Technologies Inc.'s autonomous driving unit in a deal reportedly worth $4 billion.


How Can Driverless Cars Take Account of Human Selfishness?

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Psychologists have long found that people behave differently than when they learn of peers' actions. A new study by computer scientists found that when individuals in an experiment about autonomous vehicles were informed that their peers were more likely to sacrifice their own safety to program their vehicle to hit a wall rather than hit pedestrians who were at risk, the percentage of individuals willing to sacrifice their own safety increased by approximately two-thirds. As computer scientists train machines to act as people's agents in all sorts of situations, the study's authors indicate that the social component of decision-making is often overlooked. This could be of great consequence, note the paper's authors who show that the trolly problem -long shown to be the scenario moral psychologists turn to--is problematic. The problem, the authors indicate, fails to show the complexity of how humans make decisions.


What might sheep and driverless cars have in common? Following the herd

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Psychologists have long found that people behave differently than when they learn of peers' actions. A new study by computer scientists found that when individuals in an experiment about autonomous vehicles were informed that their peers were more likely to sacrifice their own safety to program their vehicle to hit a wall rather than hit pedestrians who were at risk, the percentage of individuals willing to sacrifice their own safety increased by approximately two-thirds. As computer scientists train machines to act as people's agents in all sorts of situations, the study's authors indicate that the social component of decision-making is often overlooked. This could be of great consequence, note the paper's authors who show that the trolly problem -long shown to be the scenario moral psychologists turn to--is problematic. The problem, the authors indicate, fails to show the complexity of how humans make decisions.


Which country leads the innovation charge in AI research? - Tech Wire Asia

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The prevalence of artificial intelligence (AI) applications in many a tech-driven aspect of our daily lives (everything from driving sustainable agriculture to powering a range of smartphone features) would not have been pronounced if not for the research that went into understanding AI abilities and harnessing them to enhance self-driving cars or mobile apps, to name a handful of use cases. These are powerful, yet functional roles that can be easily comprehended. What's perhaps more exciting is AI's growing potential in sourcing and producing new creations and ideas, from writing news articles to discovering new drugs -- in some cases, far quicker than teams of human scientists would have been able to do. New discoveries and understanding of AI capabilities are ever-evolving, much like the technology itself. Hence many national and private interests are charging ahead with AI research and development in a dizzying assortment of fields, from intelligent automation and robotics to AI-powered databases and systems of national importance.