Computer vision is a field of artificial intelligence that enables computers to derive meaningful information from digital images, videos, and other visual inputs.

For automotive applications, it essentially gives a vehicle a sense of sight.

This is achieved using deep learning models, specifically convolutional neural networks (CNNs), which are trained on massive datasets of road imagery.

The system's primary function is object detection and classification.

It can identify and locate pedestrians, cyclists, other cars, trucks, traffic signs, and lane markings.

But it goes further than just identification.

It also performs semantic segmentation, where every pixel in an image is assigned a class (e.

g.

, 'road', 'sidewalk', 'sky', 'building').

This creates a highly detailed, pixel-level understanding of the scene.

This 'sight' is the foundation for Advanced Driver-Assistance Systems (ADAS) and autonomous driving.

Features like Automatic Emergency Braking (AEB), Lane Keeping Assist, and Traffic Sign Recognition all rely on computer vision.

In fleet management, AI-powered dashcams use computer vision to detect risky driver behaviors like tailgating, distraction, or drowsiness, providing real-time alerts and data for coaching.

It is the key technology that allows a vehicle to perceive its environment and react safely.