How Self driving cars use computer vision to see


An autonomous vehicle, or a driverless vehicle, can operate itself and perform the required functions without any human help, by sensing its surroundings with the help of cameras and sensors. A fully automated driving system is used in an autonomous vehicle to enable it to adapt to environmental situations that a human driver would handle. 

In autonomous cars, computer vision plays a vital role in the designing and construction of futuristic and next-generation vehicles that can solve roadblocks while keeping passengers safe. Passengers may be transported to their destination without the need for human interaction. It includes the Creation of 3D Maps, Classifying and Detecting Objects, and Gathering Data for Training Algorithms.


The most realistic way for embedded vision to capture a visual image of the universe is through cameras. Autonomous vehicles have cameras on all four sides of the vehicle. These videos are combined to provide a 360-degree view of their surroundings. Wide-angle lenses with a shorter range are available on some cameras, while others have a narrower vision capable of long-range visuals. And even Fish-eye cameras provide panoramic views of the rear side of the vehicle and assists in parking. 

Vehicle manufacturers frequently use a range of complementary metal-oxide-semiconductor ( CMOS ) imaging sensors to generate images with resolutions ranging from 1 to 2 megapixels, most use comparatively cheap 2D cameras, although some are now adding 3D cameras. And to maintain a consistent picture under all environments, including direct sunlight, embedded vision systems sensors with a high dynamic range of more than 130 dB are used frequently nowadays by vehicle manufacturers.


The datasets that provide information about the atmosphere and object detection are mainly done by the LiDAR sensors and cameras. Objects are tracked, identified, and the distance and position of the object relative to the car are determined using the data from these sensors. Item recognition is usually accomplished by combining feature-based and appearance-based modeling. As compared to a laser scan, the image contains more detail that can be used to distinguish objects and allows for both feature-based and appearance-based modeling. 

The imaging is mainly used to detect and distinguish between the objects, while the LiDAR is used to measure the object’s position relative to the car. And the 3D point cloud of the environment is generated using the laser scan and the vehicle’s pose and ultimately a picture is then projected onto it.


Interstate Driving

When the driver enters the highway lane, he or she will activate autonomous driving. This is done by indicating the desired location destination. And once they exit the highway and the destination is reached, the vehicle takes over navigation, guidance, and control. And then autonomous driving system handovers the driving control to the driver. And if the driver is unable to follow the conditions for safe handover, such as if he or she is unconscious or seems to be unaware of the situation, the driving robot shifts the car to a risk-minimal condition in the emergency lane or immediately after leaving the highway.

Autonomous Parking

When a driver arrives at his or her destination (for example, office, the gym, or home), he or she stops the car, exits, and instructs the car to automatically park itself. As a result, the car can now be driven to a residential, municipal, or service-provider-owned parking lot by the driving robot. It’s crucial to give the driving robot a parking spot. The car then detects its surroundings and measures the distance and speed necessary to park itself at the specified spot.

Fully Autonomous Driving

The driver can hand over the driving role to autonomous driving if he or she wants. The vehicle is licensed in the authorized country, however, this permission can be subjective to certain conditions. If, for example, If any new traffic rule is introduced or the traffic flow is rerouted, a new parking system is built, then the respective areas cannot be navigated autonomously unless human assistance is provided to the car.

Autonomous Transportation

In this case, the vehicle collects the requested destination from the vehicle’s occupants or external individuals (users, service providers) and drives autonomously to it. Here humans are unable to take over the task of driving. Only the person may signal the destination or unlock the secure escape, allowing him or her to exit the car as safely as possible. A wide range of business models is possible for this driving robot. A combination of taxi and car-sharing services, electric cargo trucks, or even used models that go beyond transportation.


Autonomous vehicles are still in their infancy and will take some time to be deployed on congested city streets. Since even any small flaw in the vehicle’s construction or production will result in fatal accidents and life-threatening situations, it would take time to build up the transition from self-driving car automation to fully autonomous vehicles. Modern AI and machine learning research are rapidly progressing in this direction, and it is this that is propelling the industry ahead. Top automakers like GM, Ford, and Tesla are in the final stages of testing their self-driving cars, they are planning to bring this future very near to us.