This paper presents a comprehensive survey of Neural Radiance Fields (NeRF) applications in Autonomous Driving (AD). NeRF, a novel view synthesis technology, has gained significant attention due to its implicit representation and ability to generate realistic 3D scenes from 2D images. The paper categorizes NeRF applications in AD into four main areas: perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. In perception, NeRF is used for data augmentation and model training, enhancing the performance of object detection and semantic segmentation. In 3D reconstruction, NeRF is applied to dynamic scene reconstruction, surface reconstruction, and inverse rendering, enabling the creation of detailed 3D models of driving scenes. In SLAM, NeRF is used for mapping and localization, improving the accuracy of pose estimation and enabling real-time scene understanding. In simulation, NeRF is used to generate realistic driving scenarios, reducing the need for expensive real-world testing. The paper also discusses the challenges and future directions of NeRF in AD, highlighting the potential of NeRF to enhance autonomous driving capabilities through its ability to synthesize novel views and understand complex 3D scenes. The survey provides a comprehensive overview of the current state of NeRF applications in AD, offering insights into the key research directions and challenges in this field.This paper presents a comprehensive survey of Neural Radiance Fields (NeRF) applications in Autonomous Driving (AD). NeRF, a novel view synthesis technology, has gained significant attention due to its implicit representation and ability to generate realistic 3D scenes from 2D images. The paper categorizes NeRF applications in AD into four main areas: perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. In perception, NeRF is used for data augmentation and model training, enhancing the performance of object detection and semantic segmentation. In 3D reconstruction, NeRF is applied to dynamic scene reconstruction, surface reconstruction, and inverse rendering, enabling the creation of detailed 3D models of driving scenes. In SLAM, NeRF is used for mapping and localization, improving the accuracy of pose estimation and enabling real-time scene understanding. In simulation, NeRF is used to generate realistic driving scenarios, reducing the need for expensive real-world testing. The paper also discusses the challenges and future directions of NeRF in AD, highlighting the potential of NeRF to enhance autonomous driving capabilities through its ability to synthesize novel views and understand complex 3D scenes. The survey provides a comprehensive overview of the current state of NeRF applications in AD, offering insights into the key research directions and challenges in this field.