Using AI automotive in the auto industry
The automotive industry is often at the forefront of new technologies. The industry has used robotics on assembly lines for decades. In recent yefars, the automotive industry has also become one of the most widely used industries for (artificial intelligence)AI automotive.
Some of the big issues facing the automotive industry right now are related to semi-autonomous and autonomous driving. With an array of sensors, cameras, processors, radar systems, and more, these vehicles can provide a wealth of data to avoid obstacles, maneuver more nimbly in the road, respond to signs, stay in lane, park intelligently, and meet autonomous driving requirements other requirements.
Taking one of the parameters as an example, it takes about 1.5 seconds to react to an accident on the road, for example. Self-driving cars must be able to "see" what is actually going on and react in such a short period of time. Handling all driving-related digital technologies in real time requires advanced AI automotive -based processing and decision-making.
6 ways AI automotive is driving innovation in the auto industry
Due to the size of the global market, available profit margins and fierce competition among automakers such as GM, Toyota, BMW and Tesla, the automotive industry is one of the industries driving the boom in AI automotive.
According to Tractica, the global AI automotive market, including hardware, software and services, will total about $27 billion by 2025.
Here are some examples of AI automotive being used in cars:
AI automotive:Nauto
Nauto has developed a predictive AI automotive alert system that can help cars avoid traffic accidents.
It addresses more than 40 risk factors inside and outside the car to issue warnings and help reduce the likelihood of a collision by up to 80 percent. For example, last-mile delivery fleets in the Chicago area use it to reduce crashes by 81 percent.
According to Yoav Banin, chief product officer at Nauto, Nauto combines AI automotive -native technology, data science and more than 1 billion kilometers of AI automotive to process miles driven to predict and help prevent collisions. Rather than getting lost in vehicle-centric telematics and cameras as indirect proxies for driving risks and reviewing historical events, Nauto's approach uses AI automotive to directly understand driver behavior. It analyzes subtle indicators of driver distraction, drowsiness, cell phone use and distraction, combining vehicle speed, acceleration, and surrounding vehicles and pedestrians to sound an audible alert only when needed.
Banin said, "Nauto's AI automotive application technology at the edge of the vehicle can process driver behavior and external road conditions in real time, and it can safely introduce higher and higher levels of autonomous driving."
AI automotive: Tesla
Tesla Inc. is involved in AI automotive in many ways.
Tesla Inc. unveiled its DI custom chip at its recent AI automotive Day. As part of Tesla's Dojo supercomputer system, the chip is fabricated on a 7-nanometer process and can deliver 362 teraflops of processing power. There are 25 DI chips on one module, and 120 modules can be installed in several cabinets, providing up to 100 gigabit floating-point computing power per second, which is enough to change artificial intelligence applications in the automotive field.
Tesla is working in AI automotive with Intel Corp, Nvidia, Graphcore and others to develop the technology. The goal is to speed up the training of AI automotive models so they can recognize key details from video feeds from cameras in Tesla vehicles. Tesla Inc. already uses artificial intelligence in chips used in existing vehicles to make decisions in its onboard software based on what's happening on the road. This enables the company to offer its vehicles the option of "full self-driving capability," allowing them to automatically change lanes, navigate highways, park, and more.
AI automotive: Kawasaki
Kawasaki and SoftBank Group are using AI automotive technology to develop a next-generation motorcycle that can grow with and adapt to the rider's needs.
These motorcycles use AI automotive for a variety of functions: such as providing advice on slowing down at 5 mph; informing about surroundings and possible hazards; and providing advice on road conditions and impending hazards such as steep bends Suggest.
AI automotivr: Jeep
Jeep's Grand Cherokee is another vehicle that has introduced advanced AI automotive technology.
Its latest models have updated Active Driver Assistance systems to improve the safety and performance of the car's driving. The company also uses Sight Machine's technology to conduct continuous inspections on the final assembly line of the Grand Cherokee and other vehicles. The system checks 1,100 cars a day, including 15 exterior components, and has enough built-in intelligence to be able to distinguish 25 models and 11 colors with 99.9 percent accuracy. Data from inspections is shared between artificial intelligence and manufacturing execution systems (MES), image analysis systems, and edge/cloud computing systems.
Jon Sobel, Co-Founder and CEO of Sight Machine, said, "Sight Machine's Manufacturing Productivity Platform provides every stakeholder from the factory floor to the top management a trusted and dynamically updated view of production. Continuous, real-time decision-making to guide operations. It includes a suite of visualization, data discovery, analytics and artificial intelligence tools to help increase productivity.”
AI automotive: Ford
Ford is at the forefront of AI automotive research. To help drive the new technology forward, the company is using artificial intelligence on its assembly lines to speed up production. At a Ford plant in Michigan, robots that assemble torque converters learn how to operate more efficiently through AI automotive based on technology from Symbio Robotics. In addition, the company has its own driver assistance systems and is investing heavily in automatic transmissions.
AI automotive: Driver Monitoring System (DMS)
A Driver Monitoring System (DMS) consists of a series of small cameras or sensors located throughout the interior of the vehicle that use computer vision (CV) to monitor the driver's behavior and detect when the driver shows signs of drowsiness, distraction or attention. Raise an alert for signs of inconcentration.
These AI automotive -enabled systems can recognize a variety of driver actions: such as the driver leaning forward or nodding to indicate drowsiness; the direction they are looking to determine whether they are looking at the road; and the position of their hands.
"Computer vision (CV) systems have to account for the vast differences in human appearance, body orientation, motion, clothing, lighting, objects, and the size and detail of cars," said Gil Elbaz, co-founder and CTO of Datagen.
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