Researchers and professionals have been attempting several prototypes of self-driving cars. The idea behind it is to fit a car with cameras that can make it identify all the objects around it and perform necessary actions as a reaction. This is just a simple description that explains the whole lot of complexity behind it.
Engineers are applying computer vision technology to self-driving vehicles to make it even safer to use our roads. The technology can be used in the following manner in an autonomous vehicle:
Creating 3D Maps
This will enable vehicles to capture data as images in real time. The cameras attached with such vehicles can capture images and record live footage allowing computer vision to create 3D maps. Using these maps, autonomous vehicles can now be conscious of their environment aiding them to determine their driving space while spotting obstacles in their path and opt for alternate routes with 3D maps.
Self-driving vehicles can predict any probable accident beforehand using 3D maps. To ensure safety of its passengers it can instantly perform actions like deploying airbags. 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.
The technology can enable self-driving vehicles to identify different objects. There can be stationary or moving objects on the road such as other vehicles, pedestrians, traffic lights and more. The vehicle can use sensors and cameras to obtain data which can be combined with 3D maps to spot these. 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.
Data for Training Algorithms
The computer vision technology to ensure a total unmanned transport system collects varied sets of data about location, road and traffic conditions, the terrain and crowded areas and more. These large sets of data are used for situational awareness and can be used to make vital decisions as soon as possible. Remember data should be in real time. These details can be further used in training deep learning models.
Low-Light Mode Driving
Depending on the time or weather of the day, the light condition of a particular route will vary. This therefore influences the need to switch between the normal and low light modes. Self-driving vehicles use different algorithms to process the low light condition images and videos, compared to the normal light condition. The images captured in low light may be blurry and such data may be unsafe. Computer vision can detect low-light condition and immediately switch to low-light mode. Data in this mode 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.