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.
Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it's time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case. Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy.
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.
When we purchase a new car in today's market, it is often the case that you can choose to add a personal touch for an additional fee: seat warmers, a particular color, wheel rims, and more. However, once the car leaves the showroom and is in a customer's hands, beyond repairs or, perhaps, a rare upgrade, there is little room for manufacturers to generate extra revenue from a sale. With the arrival of mobile solutions, the Internet of Things (IoT), and in-car connectivity, however, the game changed -- and the vehicle industry is now able to capitalize on the same subscription-based models that others, such as streaming content providers, are already shifting to. BMW appears to be keen to cash in on this change in consumer trends. This week, the automaker announced a set of new services in tandem with its upgraded vehicle operating system, OS version 7, including what could become subscription-based bolt-ons for owners.
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.
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.
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.
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 ...
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.