Automotive is often at forefront of technology. For decades, it has used robots to build its assembly lines. It has also been a pioneer in industrial intelligence (AI).
Semi-autonomous driving and autonomous driving are two of the biggest problems in the automotive industry.
These vehicles are equipped with a variety of sensors, cameras, and processors that can provide massive amounts of data to help them avoid obstacles, navigate in traffic, respond and signify, stop and park, and other functions required for driving.
Only one parameter is important. To react to an accident on the roads takes 1.5 seconds. Another 1.5 seconds is required to hit the brakes.
Driverless cars need to be able to “see” and react quickly in an actual situation. Advanced AI-based decision-making and processing are required to crunch all driving-related numbers in real-time.
The automotive industry is one sector that has seen rapid growth in AI because of its size, profit margins, and fierce competition with the likes of GM and Toyota.
According to Tractica, the total AI market in automotive will be approximately $27 billion by 2025.
These are just a few examples of AI used in automotive
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Nauto has developed a predictive AI system to help you avoid collisions.
It includes vision tech that is used by over 700 fleets. It can detect more than 40 risk factors within and outside a vehicle and warns of potential collisions. It was used by the Delivery Authority, a last-mile delivery fleet in the Greater Chicago area, to reduce collisions by 81%.
Yoav Banin is the chief product officer of Nauto. Nauto uses AI-native technology and data science to predict collisions and prevent them from happening, Yoav Banin said.
Nauto uses AI to understand driver behavior, rather than relying on vehicle-centric telematics or cameras to determine driving risk. To deliver audible alerts, it analyzes subtle indicators such as distraction, drowsiness, and cell phone use.
Banin stated that “Nauto’s artificial intelligence is at the edge [in the vehicle] to process driver behavior and external road conditions in true-time.” It is allowing the safe introduction and expansion of autonomous driving.
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Tesla is actively involved in AI on many fronts.
It just revealed the DI custom chip, which is part of Tesla’s Dojo supercomputer systems at its Tesla AI Day. This chip is manufactured in 7-nanometers and has 362 teraflops processing power.
This chip has more power than an exaflop, with 25 DI chips per tile and 120 tiles distributed across multiple cabinets. It is enough to transform the automotive AI game.
This technology is being developed in partnership with Intel, Nvidia, and Graphcore. This partnership will speed up the training of AI models to allow them to recognize key details from Tesla’s vehicle video feeds.
Tesla already employs AI in their existing vehicles to make decisions based on what’s happening on the roads. This allows Tesla to offer “full self-driving capability” for its vehicles, which will allow them to change lanes, negotiate highways, and park.
Kawasaki SoftBank use AI to create next-generation motorcycles. They can adapt to the rider’s needs and grow with them.
These bikes use Artificial Intelligence to perform several functions, including advising on how to slow down to avoid stopping at traffic lights when they turn green. They also notify you about the surroundings and potential dangers. They also advise you about road conditions and upcoming hazards like steep curves.
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Jeep Grand Cherokee is another vehicle that uses advanced AI-based technology.
Active-driving assistance has been updated to improve driver safety and performance. Sight Machine technology is being used by the company to continuously inspect the final assembly line for Jeep Grand Cherokees as well as other vehicles.
The system inspects 1,100 vehicles per hour, 15 exterior elements included, and uses enough intelligence to distinguish between 25 models and 11 colors with 99.9% accuracy. AI, manufacturing execution systems (MES), image analysis systems, and edge/cloud systems share the data from the inspection.
Sight Machine’s manufacturing productivity platform “gives everyone from the plant floor up to the C-suite a trusted and dynamically updating view” of production, said Jon Sobel (co-founder, CEO, Sight Machine).
It guides operations through continuous, real-time decision-making. It offers a range of visualization, data discovery, and analytics tools that can be used to improve productivity.
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Ford is at forefront of automotive AI research.
It is using AI to accelerate production in order to help propel the technology forward. Symbio Robotics technology is used to help robots that assemble torque converters in Michigan. This is in addition to the company’s own drive assist systems, and significant investment in autonomous vehicle technology.
The DMS is a set of sensors or cameras that are placed in the vehicle’s interior. They use computer vision (CV), to monitor driver behavior and issue warnings or alerts when drivers exhibit signs of distraction, drowsiness, or inattention.
These AI-enabled systems recognize different driver actions, such as a driver slumbering forward or nodding their heads to indicate sleepiness. They can also detect the direction and position of their hands.
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