The role of computer vision in autonomous vehicles has grown markedly in recent years. Classifying and detecting objects, navigation via high-precision 3D maps, and processing real-time traffic insights represent just a few of the recent advancements in self-driving automobiles. Automobile makers worldwide are pouring significant investments into building vehicles powered by AI that can make a real difference in safety through adding innovative features leveraging computer vision technology.
Computer vision in the automotive industry
The application of computer vision(CV) in the automotive industry dates back to the late 1960s. These early systems designers and scientists aimed to mimic human visual systems, ultimately extending their scope to capturing intelligent behavior. By the 1970s studies formed that established the early foundations for many of the computer vision algorithms that we use today – like digital image processing, 3D structurization, motion estimation, non-polyhedral and polyhedral modeling, and more.
With time, the CV technology advancements achieved field of view understanding and helped in better decision-making. Over the next decade, studies based on rigorous mathematical models and quantitative analytics helped engineers build more reliable tools for future technological advancements. Today, computer vision in some AI-driven fields, such as autonomous path planning and robotic deliberation systems that navigate an environment, has proven to be an indispensable deep learning-based technology.
Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Machines accurately identify and locate objects and then react to what they “see” using digital images from cameras, videos, and deep learning models. Pattern recognition and learning techniques are some of the most common practices.
Computer vision and autonomous vehicles
The manufacturing of autonomous vehicles has revolutionized the automobile production industry. Incorporating artificial intelligence in automotive manufacturing drives innovation and creates safer and more reliable autonomous vehicles in many ways.
1. Classifying and detecting objects
Computer vision or machine vision is an advanced technology that accurately identifies objects while classifying them within fractions of a second. Object detection combined with image classification enables expert-level decision-making.
How does it work?
An image recognition sensor captures an image. A processor then analyzes the image compared to other images within a database collection until a match occurs or all comparisons are exhausted.
2. Path planning and lane changing
Advanced CV employed in automotive applications uses distinct techniques to assist navigation by identifying simple objects such as road lanes, boundaries, and traffic signs. These techniques help in effective path planning and road lane-changing while making simultaneous collision avoidance decisions.
3. 3D map analysis
3D mapping and wireframing in real-time help autonomous vehicles make data-driven decisions from collected visual information. Cameras attached within vehicles record live footage and create 3D maps processed through computer vision enabling a better understanding of objects, surroundings, and signals. Through these visual clues, obstacles are avoided, and alternative or best routes for reaching the desired destination are selected.
4. Weather independence
Computer vision sensors operate by measuring light variations to determine distance and angles. Driving in unpredictable climate conditions is made possible through low-light mode processing of images and video. AI-powered algorithms tap into LiDar sensor, thermal camera, and HDR sensor data making computer vision technology more reliable and weather independent.
5. Signal processing
Traffic signal identification, pedestrian detection, and lane detection are vital parts of the signal processing system. A machine vision system trained with thousands of traffic signals enables advanced software to make data-driven decisions in real-time, making autonomous vehicles intelligent and reliable.
It is expected that the growth of computer vision technology will compound annually at a growth rate (CAGR) of 7.6% from 2020 to 2027. With market growth in autonomous vehicles, we expect governments to adopt more machine-readable signs and road markers resulting in countless lives saved and property investments preserved. Further, we can soon expect to see automobiles that coordinate with other vehicles in their environment to avoid collisions and reduce traffic flow time by adopting a technique called swarm intelligence, which is commonly observable in ants. Computer vision and deep learning-enabled autonomous vehicles identify obstacles, avoid collisions, and protect passengers resulting in a more intelligent and reliable form of transport.
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