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XAOS MOTORS unveiled XCAT LiDAR that can achieve fully self-driving cars - Press Release - Digital Journal

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XAOS MOTORS, headquartered in KOREA, challenges the technological progress of autonomous driving. XAOS MOTORS, by launching XCAT LiDAR Sensor now, give OEMs to make fully self-driving cars earlier than the market expected. MEMS LiDAR Sensor XCAT was developed for self-driving cars. With the ability to scan over 300 meters, XCAT can safely cope with high-speed driving. XCAT is designed for mass production, and OEMs can adopt high-performance 3D LiDARs at a low cost.


MIT and Toyota release innovative dataset to accelerate autonomous driving research

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The following was issued as a joint release from the MIT AgeLab and Toyota Collaborative Safety Research Center. How can we train self-driving vehicles to have a deeper awareness of the world around them? Can computers learn from past experiences to recognize future patterns that can help them safely navigate new and unpredictable situations? These are some of the questions researchers from the AgeLab at the MIT Center for Transportation and Logistics and the Toyota Collaborative Safety Research Center (CSRC) are trying to answer by sharing an innovative new open dataset called DriveSeg. Through the release of DriveSeg, MIT and Toyota are working to advance research in autonomous driving systems that, much like human perception, perceive the driving environment as a continuous flow of visual information. "In sharing this dataset, we hope to encourage researchers, the industry, and other innovators to develop new insight and direction into temporal AI modeling that enables the next generation of assisted driving and automotive safety technologies," says Bryan Reimer, principal researcher.


AI 50: America's Most Promising Artificial Intelligence Companies

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Our second annual list highlights promising, private, U.S.-based companies that are using artificial intelligence in meaningful business-oriented ways.


What Does the Future Hold for Edge Computing?

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Edge computing can roughly be defined as the practice of processing and storing data either where it's created or close to where it's generated -- "the edge" -- whether that's a smartphone, an internet-connected machine in a factory or a car. The goal is to reduce latency, or the time it takes for an application to run or a command to execute. While that sometimes involves circumventing the cloud, it can also entail building downsized data centers closer to where users or devices are. Anything that generates a massive amount of data and needs that data to be processed as close to real time as possible can be considered a use case for edge computing: think self-driving cars, augmented reality apps and wearable devices. Edge computing can roughly be defined as the practice of processing and storing data either where it's created or close to where it's generated -- "the edge" -- whether that's a smartphone, an internet-connected machine in a factory or a car.


NIO Sets Sales Record In May But Is Still Far Behind Tesla

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The Chinese new car market has been topsy turvy lately, primarily because the government keeps playing around with its NEV (new energy vehicle) incentive program. China really, really wants people to buy electric cars -- either plug-in hybrids or battery electrics -- but found its original incentive program was costing too much money. So it modified the program, several times in fact, which caused confusion among car companies and customers. In general, people who are confused postpone buying decisions until things get clearer, and that's exactly what Chinese new car shoppers did. The second factor, of course, was production shutdowns caused by the coronavirus pandemic.


Deep Learning for Object Detection: A Comprehensive Review

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With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.


AI Boundaries and Self-Driving Cars: The Driving Controls Debate - AI Trends

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That's one of the most popular questions I get asked when I am presenting at AI self-driving car events and Autonomous Vehicles (AV) conferences. At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars, and the aspects of driver controls are also of crucial attention to our efforts, along with being notable for the efforts of the auto makers and other tech firms that are developing self-driving cars or so-called driverless or robot cars. If you are willing to strap-in and put on your seat belt, I'll do a whirlwind tour through the nuances of the ongoing debate about driver car controls in AI self-driving cars. It's quite a story and it has both ups and downs, which might leave you in tears or you might be uplifted. In essence, the matter deals with whether or not there should be a steering wheel, a brake pedal, and an accelerator pedal -- which I'll henceforth herein refer to collectively as "driver controls," provided in AI self-driving ...


Digital Circuit World by Artist Nandika Dutt

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Machine learning (ML)is the study of computer algorithms that improve automatically through experience.It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is he Kiwi and orange, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a video stream. The key difference from traditional computer software is that a human developer hasn't written code that instructs the system how to tell the difference between the Kiwi and orange.


How AI is Changing the Mobility Landscape - DATAVERSITY

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Click here to learn more about Gilad David Maayan. There are a significant number of investments in the automotive industry nowadays. The majority of these investments focus on artificial intelligence (AI) and the optimization of self-driving technology. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is trying to improve its autopilot system, Uber is testing robo-taxis, and Google is developing self-driving cars.


The e-bike that's scaring one of America's closest allies

ZDNet

This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Ever since I saw The Exorcist in a theater, I've dreaded such dark manipulation, though I've now come to terms with the benefits of green pea soup. I, therefore, suffered several fits of trepidation on learning that an ad had escaped into the world, an ad which -- according to a powerful official body -- was creating a climate of anxiety. What worried me even more was that this was an ad for an e-bike and the official body was French. Yes, one of America's closest allies and the place where laissez-faire originated.