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Seoul launches VR simulator to test autonomous driving


The Seoul Metropolitan Government (SMG) has announced it is building a pilot driving zone for autonomous cars. Forming part of the cooperative intelligent transport system (C-ITS) construction project, the virtual reality autonomous driving simulator will reflect road, traffic, and weather conditions by using digital twin technologies. According to SMG, by expanding the virtual territory to Gangnam and the city centre, it will enable Seoul to "leap forward" as a city of commercialised self-driving vehicles. The autonomous driving simulator will be open to the public, and anyone from companies to research institutes, start-ups, and universities can use it free of charge. SMG's rationale is the greater the numbers of developers who test the simulator the more opportunity there is to improve their technologies, and help the industry to further advance.

Red Hat Linux is coming to your Vette and Caddy Escalade


Steven J. Vaughan-Nichols, aka sjvn, has been writing about technology and the business of technology since CP/M-80 was the cutting edge, PC operating system; 300bps was a fast Internet connection; WordStar was the state of the art word processor; and we liked it. Linux has long played a role in cars. Some companies, such as Tesla, run their own homebrew Linux distros. Audi, Mercedes-Benz, Hyundai, and Toyota all rely on Automotive Grade Linux (AGL). AGL is a collaborative cross-industry effort developing an open platform for connected cars with over 140 members.

Pinaki Laskar on LinkedIn: #selfdrivingcars #autonomousdriving #autonomouscars


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner What do you think of the update to the SAE's levels of autonomous driving? Do you find these levels helpful when it comes to knowing what an AV can do? What's the difference between driver support features and automated driving? Society of Automotive Engineers (SAE) recognise that levels 0-2 are better defined as'driver support features.' Level 3 and above encompass what they would now refer to as'automated driving features.' a six degrees of automated driving: from zero automation to full automation.

Self-Driving Cars With Convolutional Neural Networks (CNN) -


Humanity has been waiting for self-driving cars for several decades. Thanks to the extremely fast evolution of technology, this idea recently went from "possible" to "commercially available in a Tesla". Deep learning is one of the main technologies that enabled self-driving. It's a versatile tool that can solve almost any problem – it can be used in physics, for example, the proton-proton collision in the Large Hadron Collider, just as well as in Google Lens to classify pictures. Deep learning is a technology that can help solve almost any type of science or engineering problem. CNN is the primary algorithm that these systems use to recognize and classify different parts of the road, and to make appropriate decisions. Along the way, we'll see how Tesla, Waymo, and Nvidia use CNN algorithms to make their cars driverless or autonomous. The first self-driving car was invented in 1989, it was the Automatic Land Vehicle in Neural Network (ALVINN). It used neural networks to detect lines, segment the environment, navigate itself, and drive. It worked well, but it was limited by slow processing powers and insufficient data.

Powering Data-Driven Autonomy at Scale with Camera Data


At Woven Planet Level 5, we're using machine learning (ML) to build an autonomous driving system that improves as it observes more human driving. This is based on our Autonomy 2.0 approach, which leverages machine learning and data to solve the complex task of driving safely. This is unlike traditional systems, where engineers hand-design rules for every possible driving event. Last year, we took a critical step in delivering on Autonomy 2.0 by using an ML model to power our motion planner, the core decision-making module of our self-driving system. We saw the ML Planner's performance improve as we trained it on more human driving data.

Best usages of Artificial Intelligence in everyday life (2022) - Dataconomy


There are so many great applications of Artificial Intelligence in daily life, by using machine learning and other techniques in the background. AI is everywhere in our lives, from reading our emails to receiving driving directions to obtaining music or movie suggestions. Don't be scared of AI jargon; we've created a detailed AI glossary for the most commonly used Artificial Intelligence terms and the basics of Artificial Intelligence. Now if you're ready, let's look at how we use AI in 2022. Artificial intelligence (AI) appears in popular culture most often as a group of intelligent robots bent on destroying humanity, or at the very least a stunning theme park. We're safe for now because machines with general artificial intelligence don't yet exist, and they aren't expected to anytime soon. You can learn the risk and benefits of Artificial Intelligence with this article.

Small Drones Are Giving Ukraine an Unprecedented Edge


In the snowy streets of the north Ukrainian town of Trostyanets, the Russian missile system fires rockets every second. Tanks and military vehicles are parked on either side of the blasting artillery system, positioned among houses and near the town's railway system. The weapon is not working alone, though. Hovering tens of meters above it and recording the assault is a Ukrainian drone. The drone isn't a sophisticated military system, but a small, commercial machine that anyone can buy.

Rapid adaptation of deep learning teaches drones to survive any weather


To be truly useful, drones--that is, autonomous flying vehicles--will need to learn to navigate real-world weather and wind conditions. Right now, drones are either flown under controlled conditions, with no wind, or are operated by humans using remote controls. Drones have been taught to fly in formation in the open skies, but those flights are usually conducted under ideal conditions and circumstances. However, for drones to autonomously perform necessary but quotidian tasks, such as delivering packages or airlifting injured drivers from a traffic accident, drones must be able to adapt to wind conditions in real time--rolling with the punches, meteorologically speaking. To face this challenge, a team of engineers from Caltech has developed Neural-Fly, a deep-learning method that can help drones cope with new and unknown wind conditions in real time just by updating a few key parameters.

Council Post: The Future Of AI: 5 Things To Expect In The Next 10 Years


There has been no better time to be in the world of artificial intelligence than now. AI has achieved an inflection point and is poised to transform every industry. Much has already been written about specific applications of AI. In this article, I take a step back to consider how artificial intelligence is poised to fundamentally restructure broader swaths of our economy and society over the next decade with five bold predictions that are informed by my expertise and immersion in the field. Important science--think large-scale clinical trials or building particle colliders--is expensive and time-consuming.

Systems Engineer, Core Autonomy OEM Interface (m/f/d)


Argo AI is a global self-driving products and services company on a mission to make the world's streets and roadways safe, accessible, and useful for all. Our technology is built to enable commercial services for autonomous delivery and ridesharing in cities. With experienced leaders in the field and collaborative partnerships with some of the world's top consumer brands, we're working block by block, city by city to empower people and businesses to be more successful. We're individuals driven by strong values to solve complex problems together. Come join us to reimagine the human journey.