Computer vision is a subset of artificial intelligence (AI) that produces sight and vision for machines. This groundbreaking technology is present nearly everywhere in our digital world from smartphone cameras to CCTVs, but it’s also helping create some of the most advanced automation systems for self-driving cars and vehicles. 

With powerful sensors and cameras, computer vision is able to capture real-time road information that can be used to navigate everything from busy city streets to open, rural highways. So, how is computer vision improving autonomous vehicles and navigation?

What is Computer Vision?

Computer vision is a field of AI that teaches machines how to interpret and understand the visual world. Humans are highly reliant on their sense of sight, making the need to teach machines how to view the world like we do highly important. 

Computer vision begins with image acquisition when it obtains a video or image as input through cameras or sensors. Next, the data begins pre-processing by adjusting the contrast and brightness to enhance the quality of the visual data. This helps the algorithm with feature extraction when the model begins identifying edges, textures, and shapes. 

Afterward, the algorithm will then begin classifying different aspects of the image or video with image recognition. Then, the algorithm begins post-processing, using certain weights and biases to make additional adjustments and decision-making for the final output. 

Importance of Computer Vision in Autonomous Vehicles

Years of innovation in computer vision over the last two decades have led to many features that make self-driving cars possible. These improvements include:

• Safety: Obstacle detection and avoiding pedestrians is the most essential feature needed for any autonomous vehicle. Collisions with other vehicles or objects can result in injury or death, so maximizing these abilities are vital to the success of self-driving cars and their future in commercial use. 

• Navigation: Computer vision can optimize route navigation by analyzing real-time traffic and road conditions to find the shortest routes possible when driving to a certain destination. This can be especially helpful in avoiding emergency scenes or areas that are under road maintenance. 

• Parking: Parallel parking can be a serious challenge for many drivers, especially those in large cars with poor visibility. Computer vision can solve this challenge by measuring the distance between other vehicles and maneuvering the car itself without causing collision. 

• Difficult conditions: Driving at night or through difficult weather can be a serious risk to driver and passengers. Computer vision can improve low visibility by relaying traffic data and expected road conditions to the driver for safer travel. 

How Computer Vision Works in Autonomous Vehicles

Autonomous vehicles begin by using cameras and sensors placed around the vehicle for a 360-degree view of the vehicle’s surroundings. These cameras all vary in purpose, offering different focal lengths, resolutions, and laser technology known as LIDAR to provide AI with a comprehensive understanding of the environment including other cars, barriers, and weather conditions. 

Computer vision algorithms within the vehicle’s AI then begin processing real-time input data. Convolutional neural networks are often involved with this phase of computer vision because they are designed to analyze grid-like data that is captured by image and video for image recognition. 

Decision-making algorithms using deep learning are then tasked with adjusting the speed and direction of the vehicle based on the information gathered by computer vision. They determine how much space is needed between each vehicle and can make sudden changes to avoid reckless drivers. 

Challenges Faced by Computer Vision in Autonomous Driving

While computer vision aims to solve many issues with low visibility, there are still many challenges that need to be overcome before it can do so efficiently and constantly enough for human drivers to trust autonomous vehicles. Technical limitations in cameras and sensors prevent autonomous vehicles from seeing through dense fog or heavy rain, leading to impacts if left unattended by a human driver. 

Likewise, training computer vision models to interpret the different styles of driving in urban environments vs. rural ones also poses a problem because models might have difficulties switching to these new environments when driving through them for the first time.  

Future Developments in Computer Vision for Autonomous Vehicles

Autonomous vehicles have a bright future ahead of them with the advent of AI technology and its ability to minimize driving risks and it can’t be fulfilled without computer vision which is essential to a vehicle’s ability to interpret roads for navigation. 

However, computer vision and autonomous driving are not just limited to self-driving cars. They can be used in a myriad of different vehicles from ships and airplanes to advanced space shuttles that require pinpoint accuracy when traversing the depths of space.