Adapting pre-trained LiDAR segmentation models to dynamic domain shifts during testing is of paramount importance for the safety of autonomous driving. Most existing methods neglect the influence of domain changes on the continual test-time adaption (CTTA) and require backpropagation and large batch sizes for stable adaption.We approach this problem with three insights: 1) Distance of a point to the LiDAR sensor is highly relevant to its local density; 2) The feature distribution of different domains varies, and domain-aware parameters can alleviate domain gaps; 3) Features are highly correlated and make segmentation of different labels confusing. To this end, this work presents D^3CTTA, an online backpropagation-free framework for 3D continual test-time adaption for LiDAR segmentation.D^3CTTA consists of a distance-aware prototype learning module to integrate LiDAR-based geometry prior and a domain-dependent decorrelation module to reduce feature correlations among different domains and different categories.Extensive experiments on three benchmarks showcase that our method achieves a state-of-the-art performance compared to both backpropagation-based methods and backpropagation-free methods.