Introduction to Deep Learning for Perception in Autonomous Vehicles
In autonomous vehicles (AVs), perception is crucial for understanding the environment. Deep learning, particularly Convolutional Neural Networks (CNNs), enhances object recognition and scene understanding by processing visual data from cameras and sensors.
CNNs mimic human visual processing through layers that detect features like edges, shapes, and objects. Unlike traditional computer vision, CNNs learn hierarchical features automatically from data.
Key Components of a CNN:
- Convolutional Layers: Apply filters to input images to extract features. For an input image $I$ and filter $K$, the output feature map is $O(x,y) = (I * K)(x,y) = \sum_m \sum_n I(x+m, y+n) K(m,n)$.
- Pooling Layers: Reduce spatial dimensions while preserving important information, e.g., max pooling selects the maximum value in a window.
- Fully Connected Layers: Classify the extracted features into categories like "pedestrian" or "vehicle".
This foundation builds on actuation and feedback loops by enabling the perception module to provide accurate inputs for decision-making.