Introduction to Neural Networks for Perception in Autonomous Vehicles
In self-driving cars, perception is crucial for understanding the environment. Building on sensor fusion, which combines data from cameras, LiDAR, and radar, neural networks—particularly Convolutional Neural Networks (CNNs)—enable advanced object recognition and prediction. CNNs are a type of deep learning model designed to process grid-like data, such as images from cameras.
Why CNNs? Traditional computer vision methods struggle with complex scenes, but CNNs automatically learn hierarchical features: from edges to shapes to full objects. This is vital for autonomous vehicles (AVs) to detect pedestrians, vehicles, and traffic signs in real-time.
Key components include:
- Convolutional layers: Apply filters to extract features, using kernels of size $k \times k$.
- Pooling layers: Reduce spatial dimensions while preserving important information, e.g., max pooling.
- Fully connected layers: Make final classifications.
For example, a CNN might process a camera image to identify a stop sign, outputting a probability score greater than 0.9 for "stop sign" class.
Display math for convolution operation: $$ (f * g)(i,j) = \sum_m \sum_n f(m,n) g(i-m, j-n) $$