[Significance] Cross-view geolocalization is the process of using a satellite image with coordinate metadata as reference to determine the geographic coordinates of an unknown ground-view image. This problem is often viewed as an image matching task, where an overhead satellite image is segmented into a number of square blocks of satellite patches, and the ground image is matched with candidate satellite patches to retrieve the most similar satellite patch, using the position of the center pixel in that patch as the query location. [Progress] With the development of cross-view geolocalization, the technique has been extended to fine-grained metric localization of ground imagery, i.e., identifying which image coordinates in a satellite patch correspond to a ground-measured location. Given that satellite images have global coverage and are easy to obtain, their application as reference images in image positioning has significantly broadened the application scope of image geolocation technology. This trend has prompted growing academic interest and attention to cross-view geolocalization research. Along with the development of various algorithmic techniques, cross-view geo-localization has evolved from the manual extraction of features, which was mainly based on the geometric features of buildings, to deep learning approaches that are applicable to richer scenarios, such as suburban and urban areas. The specific localization idea has progressed from the image-level cross-view localization, which uses the retrieval method to directly mark the retrieved center coordinate of the satellite image as the location of the ground image, to pixel-level fine-grained localization, which more accurately assigns the coordinates of the corresponding pixel location of the satellite image to the ground image. However, the drastic change in the viewing angle of ground and satellite images results in a huge difference in visual content, making cross-view image localization more challenging. To improve the accuracy of cross-view geo-localization, various scholars have made algorithmic improvements, such as representation learning and metric calculation. Additionally, for the huge viewpoint differences, some scholars study specialized geometric transformation, image generation, and other viewpoint conversion methods between cross-view images. Others improve localization accuracy with the help of directional information, intermediate viewpoint connection of UAV image information, and more. [Purpose] This paper summarizes the development process of cross-view geolocation, the different methods for improving accuracy, the various data sets involved, and the evaluation methods at different stages. On this basis, we discuss the future development trends and provide corresponding summaries.

[Objectives] Cross-view image geo-localization technology can establish a correlation between image and real geographic space, holding significant research value for exploring the various attributes behind the images. In recent years, most cross-view image geo-localization algorithms based on deep learning have overly focused on image content, leading to overfitting of low-level details within the network and limited capability in extracting geometric spatial layout. As a result, the performance on evaluation datasets is suboptimal. [Methods] To address these issues and improve the performance of cross-view image geolocation algorithms, this paper presents a cross-view image retrieval and localization algorithm based on geometric relation constraints. First, the imaging principle of ground panoramic images is derived, and the mapping relationship between the spherical and plane rectangular coordinate systems is used to convert ground panoramic images to bird's-eye views, achieving initial geometric alignment between cross-domain matched images. Next, a CNN-Transformer hybrid feature extraction operator is designed. This operator not only extracts visual content features but also captures geometric spatial configuration information between local features, thus mitigating content and scale discrepancies caused by viewpoint changes. Furthermore, to suppress distorted information in ground images following geometric mapping transformations, a feature self-interaction module based on relational affinity matrix is proposed. This module separates foreground from background information by calculating the correlations among local features, thereby enhancing key foreground information. Finally, a feature aggregation module is introduced to generate a global feature descriptor and complete the matching process. [Results] Experiments conducted on the CVACT_val, CVUSA, and VIGOR datasets demonstrate that the algorithm achieves superior results, with Top1 image recall rates of 89.28%, 96.42%, and 62.21% on the respective datasets. Compared to GeoDTR, a similar algorithm, the accuracy of our algorithm improves by 3.07%, 1.04%, and 3.2%, respectively. [Conclusions] Highlighting its superiority and adaptability across various application scenarios.

[Objectives] Visual saliency modeling in urban driving scenarios is a key direction for enhancing the intelligence of driving systems. Existing methods often overlook the feature extraction of combined objects and the dynamic variations in spatiotemporal features. This results in low efficiency in the interaction and transmission of multi-factor features, failing to meet the demands for high precision and efficiency in saliency prediction. [Methods] To address these challenges, we propose a novel method for visual saliency prediction and modeling in driving scenarios, based on the concept of multi-factor feature fusion. First, we employ computer vision techniques to extract and quantify color and texture features from scenario images. Next, we introduce high-difference thresholds, Hu moments, and Fourier descriptors to focus on combined objects and extract their shape features. Subsequently, a three-dimensional convolutional network is used to extract spatiotemporal features from continuous scenario images. Finally, we design a saliency fusion module based on a Long Short-Term Memory (LSTM) network, which integrates color, texture, shape, and spatiotemporal features. Saliency prediction and heatmap generation are accomplished through a saliency map decoding module. [Results] Comparative experiments were conducted using five different methods on a driving dataset from Zhengzhou. The proposed method was evaluated using the Area Under the Curve (AUC) metric, achieving a prediction accuracy of 91.12%, significantly outperforming other methods. It was also effectively validated on public datasets such as DADA, Dr(eye)ve, and Deng. [Conclusions] Experimental results demonstrate that the proposed method not only improves prediction accuracy but also maintains computational efficiency, while effectively capturing the distribution of combined objects and the details of spatiotemporal changes within the scenarios. This advancement supports the evolution of perception technology from isolated task models toward cognitively coupled systems.

[Objectives] In recent years, researchers have begun to focus on individuals' psychological experience of interaction with maps. It has become evident that individuals' experience with maps are significantly influenced by their spatial cognitive styles and the information they receive. [Methods] This study employed the Spatial Cognitive Style Test (SCS) to classify participants based on their spatial cognitive styles and investigated the mode and scale of navigation maps. We aimed to investigate the optimal alignment between an individual's spatial cognitive style and the presentation mode of navigation maps from an offline representation perspective. [Results] The results show that: (1) Participants with a landmark style exhibited the poorest performance, followed by those with a route style, and participants with a survey style achieved the best results; (2) Compared to Judgment of Relative Direction (JRD) tasks, participants demonstrated higher proficiency in Scene and Orientation-dependent Pointing (SOP) tasks; (3) Participants performed better after learning large-scale maps than learning small-scale maps; (4) Regardless of learning large-scale or small-scale maps, landmark style individuals demonstrated significantly higher accuracy in SOP tasks than in JRD tasks. By contrast, route style participants did not exhibit significant difference in accuracy between these two tasks. Moreover, survey style participants did not exhibit significant difference in accuracy between these two tasks after learning large-scale maps, but their accuracy in SOP tasks was significantly higher than that in JRD tasks after learning small-scale maps. [Conclusions] Our results indicated significant differences in task performance among individuals with three different spatial cognitive styles, and individuals tend to judge target locations based on their current positions. In addition, the research results also suggest that using large-scale maps is more beneficial for individuals to judge the target location, and that individuals with different spatial cognitive styles have different performance on different tasks after using different scale maps. These results indicate that there is an optimal adaptability between an individual's spatial cognitive style and the mode of presentation of navigation maps and that people with different spatial cognitive styles have preferences for specific modes of map presentation. These findings are of great importance for enhancing individuals' experience of navigation map usage and also provide valuable suggestions and guidance for map designers and developers.

[Objectives] The setting of lane-turning signs at planar intersections is a critical measure to achieve orderly vehicle flow on a large scale. These signs not only assist traffic management authorities in controlling traffic at planar intersections, but also help drivers avoid detours caused by selecting the wrong lane. Therefore, lane-level turning relationships provide crucial information for precise navigation services. Setting turning signs at intersection lanes is a fundamental aspect of traffic management that promotes safety, orderliness, and efficiency. Considering the low cost and fast updates possible with crowd-sourced trajectories, this paper presents a study on the recognition of lane-level turning relationships at planar intersections using crowd-sourced data. [Methods] First, the intersection lane space was determined. Road network topology processing, trajectory data cleaning, and map matching were performed to establish connections between trajectory points and their corresponding road segments. Based on processed road network and trajectory data, trajectories within the intersection guide area were extracted, and noise was removed from multiple perspectives. Using a Gaussian mixture model, cluster analysis was then conducted to detect lane information at road segment intersections. Second, intersection lane noise turnings were removed. Horizontal and vertical statistics of lane turning trajectories were analyzed, and trajectories falling below the threshold were identified as noise turnings and removed. Finally, intersection lane-turning identification rules were designed, taking into account the distribution of different lane-turning trajectories. An unsupervised classification method was used to detect lane-turning information based on these rules. [Results] To validate the effectiveness of the method, we selected OpenStreetMap road networks and crowd-sourced trajectories from two areas in Beijing for experiments, focusing on 10 representative intersections on major city thoroughfares. The main findings are as follows: (1) Based on the original trajectory data, lane-level turning relationships were identified using one-day trajectories, peak period trajectories, and off-peak trajectories, with recognition accuracy rates of 74.3%, 72.7%, and 55.7%, respectively; (2) After data encryption and simplification, recognition accuracy gradually improved with increased sampling frequency, reaching a maximum of 77.0% with 3-second encryption; (3) The proposed method demonstrated advantages over the threshold segmentation method, the method without noise turning elimination, and the topological connection method. [Conclusions] This research on lane-level turn relationship recognition based on crowd-sourced trajectories has significant implications for intelligent transportation and autonomous driving. The algorithms, technologies, and research findings in this paper provide a valuable reference for future research and foster continued innovation and application of intelligent transportation systems.

[Objectives] Overpasses serve as hubs within the urban traffic system and constitute a vital component of the transportation infrastructure. Accurate extraction of an intricate overpass structure holds paramount significance in the realm of transportation planning and vehicle navigation. Crowdsourced trajectory data have the merits of low cost, large data volume, and high real-time performance. In particular, vehicle trajectory contains a large amount of semantic information about travelling vehicles and road connectivity info, making it a significant data source for road network information extraction. Nevertheless, extracting fine overpass structure using trajectory data lacking elevation information presents many challenges, due to the fact that an overpass has a multi-layered three-dimensional structure with dense and intertwined internal roads, and its hierarchical structure and turning relationship are more complex than those of a typical intersection. [Methods] Considering these challenges, this paper proposes a forward and backward tracking and fusion method for automatic overpass structure extraction, taking macroscopic ordered connection and microscopic similarity clustering of trajectory points into account. Firstly, a set of high-confidence vehicle trajectories located on the main body of overpass is filtered out based on feature constraint rules. Then, taking the manually marked overpass entrances and exits as the tracking seed points, through potential road junction perceiving and verifying, and diverting trajectory based on flow clustering, the trajectory is tracked recursively forward or backward. After removing redundant branches, the overpass substructure is obtained. Finally, a two-stage fusion strategy is used, where all the substructures extracted from the forward or backward tracking are fused separately based on the road junctions with the same geographical location, and then by incorporating the road confluences information obtained from the backward tracking into the fused structure derived from the forward tracking, a complete two-dimensional structure of the overpass at roadway level is extracted. [Results] Taking the crowdsourced trajectory data collected from Shenzhen as the data source, structural extraction experiments and analyses were conducted on seven types of overpasses distributed in the urban or suburban areas, including three-forked trumpet overpasses, four-forked double trumpet overpasses, four-forked partial cloverleaf overpasses, four-forked combined overpasses, turbo overpasses, complete cloverleaf overpasses and deformed cloverleaf overpasses. The extracted structures are basically located in the center of the real overpass roads in the remote sensing images, with an overall GEO precision of 95.50% and an overall TOPO of 88.96%. [Conclusions] The results indicate that our proposed method can combine the connectivity of the trajectory and the bearing divergence of the local trajectory point group and effectively extract the geometric structure and topological information of overpasses without incorrect connection at the road crossing stacks.

[Objectives] Road object detection is a significant application of LiDAR (Light Detection and Ranging) point clouds. As a rapidly developing mapping system, LiDAR can quickly collect point cloud data of objects' surface in road environments. Currently, many detection methods based on LiDAR point clouds perform well for large objects, close-range objects, or objects in simple scenes, accurately detecting objects in such conditions. However, due to sensor limitations during the acquisition process, LiDAR point clouds are often sparse and lack the texture information present in RGB images. This limitation leads to high false detection and omission rates when dealing with small objects, long-distance objects, or objects in complex road environments. The rich texture information in RGB images can compensate for these deficiencies in point clouds, making it necessary to explore road object detection technologies based on the fusion of RGB images and LiDAR point clouds. [Methods] To improve road object detection in complex scenes, this paper uses PointRCNN as the baseline network and proposes a two-branch multi-stage fusion detection network, named EPG2LFusion, based on RGB images and LiDAR point clouds. The network introduces two key innovations: Firstly, to address the limitation of existing convolutional neural networks in extracting image features due to receptive field constraints, a convolutional module named WaveDSConv is designed in the image branch. This module combines wavelet transform convolution and depth separable convolution to enhance global image feature extraction, thereby improving the performance of fused object detection. Secondly, to overcome challenges in fusing the two different modalities of point clouds and images, a fusion module G2L-Fusion is proposed. This module establishes accurate point-pixel correspondence between point clouds and images using a projection matrix and effectively employs a channel attention mechanism to fuse global information and local information across multiple stages. [Results] The proposed method was evaluated on the KITTI benchmark dataset for road object detection across multiple categories (cars, pedestrians, cyclists). Results indicate that the proposed method achieves an average detection accuracy of 65.21% for all categories on the KITTI test set, which is 4.88% higher than the baseline network. For the challenging pedestrian category under medium difficulty, an average detection accuracy of 45.86% was achieved, demonstrating competitive performance compared to existing advanced algorithms. [Conclusions] The results confirm that the proposed algorithm leverages the rich texture features of RGB images to address the sparsity of LiDAR point clouds, significantly improving the detection accuracy of common road objects.

[Objectives] Multiple Unmanned Aerial Vehicles (UAVs) path planning is a pivotal technology enabling efficient and cooperative operation of UAV swarms in complex environments. Fundamentally, it constitutes a constrained multi-objective optimization problem characterized by a sparse feasible region. Due to their robust global search capabilities, evolutionary algorithms have been widely adopted to address this class of problems. However, existing methodologies frequently neglect the spatial structural attributes of paths during the initialization phase and primarily rely on generic stochastic search operators for path regeneration. These limitations restrict their ability to effectively exploit the fitness function and the spatial relationships between UAVs and their operational environment, thereby hindering the generation of high-quality, globally feasible solutions under constrained computational resources. [Methods] To address these challenges, this paper introduces a Multi-Source Heuristic Evolutionary Algorithm (MSHEA) for multiple UAV path planning. MSHEA systematically integrates multi-source heuristic information related to spatial path structure, fitness data, and environmental context to enhance both the path initialization and regeneration processes. Specifically, we propose a Sequential Directed Expansion-based Initialization (SDEI) strategy to generate high-quality initial paths that exhibit spatial rationality and structural diversity. Furthermore, we develop a path regeneration mechanism integrating fitness and Flight Environment Information (FFEI), which substantially improves the repair efficiency of infeasible paths and the local optimization of feasible ones. [Results] Empirical validation was conducted using eight publicly available benchmark datasets for multiple UAV path planning, covering a range of complexity levels. The experimental results demonstrate MSHEA's superior performance and elevated stability across diverse flight scenarios: (1) Compared to the suboptimal benchmark algorithm, MSHEA achieved a 1%~6% improvement in hypervolume and a 6%~81% reduction in inverted generational distance. (2) Both the SDEI and FFEI components were confirmed to have significant positive impacts on the algorithm's overall performance. (3) MSHEA shows low sensitivity to its newly introduced hyperparameters, indicating strong adaptability and generalization capability. [Conclusions] In conclusion, MSHEA improves the effectiveness of solving multiple UAV path planning problems by incorporating multi-source heuristic information. This leads to robust and reliable performance for the challenges inherent in collaborative UAV missions.