Continental AG is taking a minority stake in AEye Inc., a Dublin, California-based developer of LiDAR technology, in order to bring its autonomous vehicle technology to commercial vehicles sooner. Specifically, AEye, founded in 2013, has developed a long-range LiDAR system that can detect vehicles at a distance of more than 300 meters and pedestrians at more than 200 meters. Continental hopes the investment will enhance its current short-range LiDAR technology that is slated to go into production by the end of 2020. Then the AEye system would be deployed in a automotive passenger or commercial vehicle later this decade. "We now have optimum short-range and long-range LiDAR technologies with their complimentary sets of benefits under one roof," said Frank Petznick, head of Continental's advanced driver assistance systems, in a statement.
Daimler is clearly eager to expand its plans for self-driving trucks. The automotive giant is teaming up with Waymo to develop trucks capable of level 4 autonomy, or full self-driving in specific conditions. The early strategy will focus on a modified Freightliner Cascadia that uses Waymo Driver for navigation. This first truck will be available in the US in the "coming years," the companies said. The two would also "investigate" expanding their efforts to other brands and markets.
AMD is acquiring chip designer Xilinx for $35 billion in stock to "significantly" expand the range of products it makes and customers it reaches, particularly in high performance computing. As the Wall Street Journal noted, Xilinx's easily customizable FPGA (field-programmable gate array) chips are used in a variety of places AMD wouldn't have even considered before, from 5G systems to the F-35 to self-driving cars. The newly-bought company also specializes in adaptive systems-on-chip, accelerators and smart networking devices found in data centers, edge computing and end devices. AMD expects the Xilinx deal to take a while to wrap up. It should close by the end of 2021, the company said.
Daimler Trucks, which makes semis and commercial trucks for Mercedes-Benz and other truck brands around the world, will soon start offering Waymo's self-driving platform on some of its Class 8 trucks in the U.S. The partnership between the leaders in self-driving and commercial truck manufacturing was announced Tuesday. In a media briefing beforehand, Waymo CEO John Krafcik called it "sort of an epic moment." Waymo is bringing its Driver platform, a machine-learning-based sensor and camera system that enables Level 4 autonomous driving ability without human intervention, into Daimler Freightliner Cascadia trucks. The integration will start in the U.S. and could eventually expand to Europe and other markets. There was no set timeline for when Waymo autonomous features will be built into the next generation of long-haul trucks that carry loads across the U.S. Krafcik said he planned to work with Daimler to set the standard for what an autonomous truck looks like, which includes changes to steering, braking, and control systems.
Scientists are building autonomous repair robots that will use AI to identify and fix potholes in UK roads. The electric, self-driving bots – which are being built by a spin-out company from the University of Liverpool called Robotiz3d – can find small cracks in the road and cover them with asphalt. Researchers say the machines, which look like a cross between a tank and a road roller, will transform road maintenance when they hit the roads in 2021, and finally offer a cost effective fix for the UK's pothole problem. Currently, no autonomous technology solutions exist to tackle potholes, which are estimated to have cost UK taxpayers more than £1 billion to fix over the last decade. Artist's impression of the autonomous road repair system, which looks part-tank, part road roller.
Description This course is about the fundamental concept of image processing, focusing on face detection and object detection. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation. Self-driving cars (for example lane detection approaches) relies heavily on computer vision. With the advent of deep learning and graphical processing units (GPUs) in the past decade it's become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?
When most people think of Artificial Intelligence (AI) they probably think about their Amazon's Alexa, self-driving cars or Apple's Siri. However, AI can help be used for many other functions, including marketing and construction. Home builders and developers can incorporate AI in their marketing and construction efforts to impact growth in business and employee retention. The benefits of AI include improving efficiency in the workplace, solving complex problems, and even freeing up your time. Many businesses use AI-related machines or bots and other technologies today so they can use their time more wisely.
Sensor-based technologies are playing a key role in making artificial intelligence (AI) possible in various fields. LiDAR is one of the most promising sensor-based technology, used in autonomous vehicles or self-driving cars and became essential for such autonomous machines to get aware of its surroundings and drive properly without any collision risks. Autonomous vehicles already use various sensors and LiDAR is one of them that helps to detect the objects in-depth. So, right here we will discuss LiDAR technology, how it works, and why it is important for autonomous vehicles or self-driving cars. LIDAR stands for Light Detection and Ranging is a kind of remote sensing technology using the light in the form of a pulsed laser to measure ranges (variable distances) to the Earth.
The auto industry is currently experiencing a rapid shift to autonomous vehicles (AV). This evolution is spearheaded by new, innovative technology companies that are bringing cutting-edge automotive platforms to the market at an unprecedented pace. Currently, vehicles on the road are equipped with the ability to maneuver on their own on highways while in the presence of a human driver. The next logical step in the race to autonomy is self-driving capability in an urban setting -- first with a driver and eventually with humans acting solely as passengers. However, driving in cities is an exponentially more difficult problem to solve than maneuvering on highways.
Since it launched in 2017, Facebook's machine-learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot-farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. Combining PyTorch with Microsoft Azure Machine Learning, AstraZeneca's technology can comb through massive amounts of data to gain new insights about the complex links between drugs, diseases, genes, proteins or molecules. Those insights are used to feed an algorithm that can, in turn, recommend a number of drug targets for a given disease for scientists to test in the lab. The method could allow for huge strides in a sector like drug discovery, which so far has been based on costly and time-consuming trial-and-error methods.