Introduction to Computer Vision in Self-Driving Cars
Computer vision is a cornerstone of autonomous driving, enabling vehicles to "see" and interpret their surroundings through cameras. In this knowledge point, we focus on Convolutional Neural Networks (CNNs) and semantic segmentation, which are key for scene understanding. Building on prerequisites like object detection and sensor fusion, these techniques allow cars to process visual data for safer navigation.
CNNs are specialized deep learning models designed for image processing. They use convolutional layers to detect features like edges, textures, and shapes automatically, reducing the need for manual feature engineering.
A basic CNN architecture includes: - Input Layer: Raw pixel data from camera images, e.g., RGB values in a 224x224x3 tensor. - Convolutional Layers: Apply filters to extract features. For instance, a 3x3 kernel might detect horizontal edges in road markings. - Pooling Layers: Downsample features to reduce computation, like max-pooling which keeps the strongest signal. - Fully Connected Layers: Classify the extracted features.
In driving scenarios, CNNs power tasks like lane detection. For example, a CNN trained on datasets like KITTI can identify road lanes with over 95% accuracy, helping the car stay centered.
Inline example: The convolution operation can be expressed as $y[i,j] = \sum_m \sum_n x[i+m, j+n] \cdot k[m,n]$, where $x$ is the input and $k$ is the kernel.