It is a significant challenge for high-precision 3D object detection for autonomous vehicles equipped with multiple sensors in the dusty wilderness. The variable wilderness terrain aggravates the regional feature differences of detected objects. Additionally, dust particles can blur the object features. To address these issues, this study proposes a 3D object detection method based on multi-modal feature dynamic fusion and constructs a multi-level feature self-adaptive fusion module and a feature alignment augmentation module. The former module dynamically adjusts the model’s attention to global-level features and regional-level features, leveraging multi-level receptive fields to reduce the impact of regional variances on recognition performance. The latter module bolsters the feature representation of regions of interest before multi-modal feature alignment, effectively suppressing interference factors such as dust. Experimental results show that compared with the average precision of the baseline, that of this approach is improved by 2.79% in the self-built wilderness dataset and by 1.7% in the hard-level test of the KITTI dataset. This shows our method has good robustness and precision.