Trajectory data has become a key resource for automated map inference due to its low cost, broad coverage, and continuous availability. However, uneven trajectory density often leads to fragmented roads in sparse areas and redundant segments in dense regions, posing significant challenges for existing methods. To address these issues, we propose DGMap, a dual-decoding framework with global context awareness, featuring Multi-scale Grid Encoding, Mask-enhanced Keypoint Extraction, and Global Context-aware Relation Prediction. By integrating global semantic context with local geometric features, DGMap improves keypoint detection accuracy to reduce road fragmentation in sparse-trajectory areas. Additionally, the Global Context-aware Relation Prediction module suppresses false connections in dense-trajectory regions by modeling long-range trajectory patterns.Experimental results on three real-world datasets show that DGMap outperforms state-of-the-art methods by 5% in APLS, with notable performance gains on trajectory data from the Didi Chuxing platform.

Due to the high coverage and low cost nature of trajectory data, an increasing number of works have utilized trajectory data to infer maps. Nevertheless, limited by the sparse trajectories in some areas and intermingled trajectories on parallel roads, the existing inferring methods still face a high missed detection rate of the roads. In view of that, we propose a Complementarity and Disparity-aware Map Inference Framework, called CDMap, consisting of grid dual feature extraction, contextual road difference-embedded grid representation, dual feature complementary network-based road topology prediction and parallel roads disparity-enhanced model optimization. To improve the prediction accuracy of the roads in areas with sparse trajectories, we extract point-wise features and segment-wise features separately for the grids, then design a dual feature complementary network to adaptively model the importance of both types of features in different road scenarios. Further, to proliferate the detection accuracy of parallel roads, we incorporate the contextual roads' differences between parallel roads into grid representations, then put forward a parallel roads disparity-enhanced model optimization strategy. Extensive comparative experiments conducted on three real-world datasets demonstrate the superiority of CDMap over the state-of-the-art methods, especially by achieving the most significant reduction in missed detection rate (30.23%) on the trajectory data collected from DidiChuxing platform.

In this paper, the correlation matrix distance (CMD) metric is employed to characterize the non-stationarity of the Vehicle-to-Vehicle (V2V) channel under three different driving states, namely following, overtaking and meeting. The analysis is based on measurements carried out at 5.9 GHz using a single-input multiple-output (SIMO) system. Firstly, the spatial structure of V2V channel under three different driving states is analyzed through power angle spectrum (PAS). Subsequently, based on the analysis results of CMD, we further calculate the quasi-stationary distance for the three different driving states. The analysis results indicate that the quasi-stationary distance can effectively reflect the spatial structural changes in the V2V channel. Moreover, the statistical characteristics of the quasi-stationary distance for the three driving states are also characterized. The analysis reveals that among the three driving states, the V2V channel in the following driving state exhibits the highest stationarity, followed by overtaking driving states. Compared to the meeting state, the V2V channel in overtaking exhibits greater stationarity.