This paper provides a comprehensive survey of the applications of Neural Radiance Fields (NeRF) in the domain of Autonomous Driving (AD). NeRF, introduced in 2020, has gained significant attention for its implicit representation and novel view synthesis capabilities. The survey categorizes NeRF's applications in AD into four main areas: perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. Each category is explored in detail, with specific methods and techniques discussed. In perception, NeRF is used for data augmentation and model training, enhancing the performance of object detection, semantic segmentation, and occupancy prediction. For 3D reconstruction, NeRF is applied to dynamic scene reconstruction, surface reconstruction, and inverse rendering, improving the accuracy and realism of 3D scene understanding. In SLAM, NeRF is integrated for pose estimation and scene representation, enhancing real-time performance and mapping accuracy. In simulation, NeRF is used to generate realistic image and LiDAR data, bridging the sim-to-real gap and reducing safety risks. The paper concludes with insights and future research directions, aiming to serve as a comprehensive reference for researchers in the field.This paper provides a comprehensive survey of the applications of Neural Radiance Fields (NeRF) in the domain of Autonomous Driving (AD). NeRF, introduced in 2020, has gained significant attention for its implicit representation and novel view synthesis capabilities. The survey categorizes NeRF's applications in AD into four main areas: perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. Each category is explored in detail, with specific methods and techniques discussed. In perception, NeRF is used for data augmentation and model training, enhancing the performance of object detection, semantic segmentation, and occupancy prediction. For 3D reconstruction, NeRF is applied to dynamic scene reconstruction, surface reconstruction, and inverse rendering, improving the accuracy and realism of 3D scene understanding. In SLAM, NeRF is integrated for pose estimation and scene representation, enhancing real-time performance and mapping accuracy. In simulation, NeRF is used to generate realistic image and LiDAR data, bridging the sim-to-real gap and reducing safety risks. The paper concludes with insights and future research directions, aiming to serve as a comprehensive reference for researchers in the field.