Deep learning is critical in autonomous driving, enabling vehicles to process and interpret vast amounts of sensor data in real time. Models like CNNs detect objects such as pedestrians, vehicles, and traffic signs, while RNNs analyze temporal data for trajectory prediction.
These models handle complex scenarios like bad weather or crowded environments, making decisions based on diverse inputs from cameras, LiDAR, and radar. For instance, YOLO and Faster R-CNN are commonly used for object detection in autonomous systems.
Deep learning’s adaptability and accuracy are essential for achieving safe and reliable autonomous navigation, making it a cornerstone of self-driving technology.