What is semantic segmentation and how is it used in autonomous driving perception?

Semantic segmentation classifies every pixel in an image into categories (road, vehicle, pedestrian, building, sky, etc.), creating a dense understanding of the scene. Deep learning architectures like FCN, U-Net, DeepLab, and SegNet are commonly used. For autonomous driving: Freespace detection (drivable area identification), lane understanding, parking space detection, and understanding complex intersections. Unlike object detection (bounding boxes), segmentation provides precise boundaries. Real-time performance (~30 fps) requires efficient architectures and hardware acceleration. Training requires pixel-level labeled datasets. Output informs path planning and enables precise positioning relative to road boundaries.