Neural networks play a central role in the development of autonomous vehicles by enabling them to interpret sensor data, make decisions, and navigate safely. CNNs are often used for image and video processing tasks, such as detecting pedestrians, other vehicles, traffic signs, and road markings. RNNs and LSTMs are used for understanding the temporal relationships in driving scenarios, such as predicting the movement of other vehicles.
Additionally, neural networks help with sensor fusion, where data from multiple sensors, like cameras, lidar, and radar, is combined to create a comprehensive understanding of the vehicle's environment. Reinforcement learning is also applied to train autonomous vehicles to make real-time driving decisions.
Neural networks are essential for enabling autonomous vehicles to perform complex tasks such as lane keeping, obstacle avoidance, and navigation in dynamic environments.