The current field of autonomous driving has achieved superior object detection performance in good weather conditions. However, the environment sensing capability of autonomous vehicles is severely affected in rainfall traffic environments. Although deep-learning-based image derain algorithms have made significant progress, integrating them with high-level vision tasks, such as object detection, remains challenging due to the significant differences between the derain and object detection algorithms. Additionally, the accuracy of object detection in real rain traffic environments is significantly reduced due to the domain transfer problem between the training dataset and the actual rain environment. To address this domain-shifting problem, we propose an adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions (ARODNet). This network architecture consists of an image adaptive enhancement module, an image derain module, and an object detection module. The baseline detection module (CBAM-YOLOv7) is built by incorporating the YOLOv7 object detection network into a feed-forward convolutional neural network, and it includes an attention module (CBAM). We propose a domain adaptive rain image enhancement module, DRIP, for low-quality images acquired under heavy rainfall conditions. DRIP enhances low-quality images on rainy days by adaptively learning multiple preprocessing weights. To remove the effects of rain patterns and fog clouds on image detection, we introduce DRIP-enhanced images into the depth estimation derain module (DeRain) to prevent rain and fog from obscuring the objects to be detected. Finally, the multistage joint training strategy is adopted to improve the training efficiency, and the object detection is performed while the image is derained. The efficacy of the ARODNet network for object detection in rainy weather traffic environments has been demonstrated through numerous quantitative and qualitative studies.