Computer Vision in autonomous cars can lead to the designing and development of advanced and next-gen vehicles which can overcome driving obstacles while keeping passengers safe. Such cars can transport passengers to their destination eliminating human intervention.
However autonomous vehicles are still in their infancy stage and cannot be deployed on urban traffic-filled roads for some time. Because even a minor defect in designing or development of this vehicle can cause fatal accidents and life risks.
Researchers and professionals are applying computer vision technology to autonomous vehicles to make it safer for passengers and pedestrians as well. The technology can be used in the following manner in an autonomous vehicle: Creating 3D Maps
It will enable self-driving vehicles to capture visual data in real time. The cameras attached with such vehicles can record live footage and allow computer vision to create 3D maps. Using these maps, autonomous vehicles can understand their surroundings better while spotting obstacles in their path and opt for alternate routes with 3D maps.
Self-driving vehicles can predict accidents using 3D maps and can instantly deploy airbags for the protection of the passengers. This solution makes self-driving cars more safe and reliable. Therefore, technology can help build safe autonomous vehicles to avoid accidents and protect passengers.
Hence, computer vision can help in building self-driving vehicles that can avoid accidents and protect passengers in the event of a crash. Classifying and Detecting Objects
The technology can enable self-driving vehicles to classify and detect different objects. The vehicle can use LiDar sensors and cameras, and the former can use pulsed laser beams to measure distance. The data obtained can be combined with 3D maps to spot objects like traffic lights, vehicles, and pedestrians. These tech-oriented vehicles process such data instantly to make decisions in real time. Thus, computer vision will enable self-driving vehicles to identify obstacles and avoid collisions and accidents. Gathering Data for Training Algorithms
The computer vision technology can gather large sets of data using cameras and sensors including location information, traffic conditions, road maintenance, and crowded areas and others. These detailed data can assist self-driving vehicles to use situational awareness and make vital decisions as soon as possible. These details can be further used in training deep learning models. For instance, a thousand images of traffic signals collected through computer vision can be used in training DL models to detect traffic signals while driving. Additionally, it can help self-driving vehicles in classifying different types of objects. Computer Vision-Enabled Low-Light Mode
In order to process low light images and videos, self-driving vehicles use different algorithms than the ones used for daylight. The images captured in low light may be blurry and such data may not be accurate enough for these vehicles.
As soon as the computer vision detects low-light condition it can shift to low-light mode. Such data can be obtained using LiDar sensors, thermal cameras, and HDR sensors. These types of equipment can be used to create high-quality images and videos.
The self-driving vehicles can be made intelligent, self-reliant and reliable using computer vision technology. However, the vehicles may face further challenges in the development process.