Owing to the advancements in deep learning methods, predicting vehicle trajectories based on vast amounts of traffic trajectory data is no longer an elusive objective. However, traditional time-series prediction methods struggle to effectively address issues such as V-V interactions, road guidance, and the inherent uncertainty in vehicle trajectories within road traffic. To tackle these issues, this paper proposes a data-driven multistage prediction method. Firstly, in terms of map representation, a slide-window-driven lane node aggregation is employed to compensate for the absence of scale information in lane nodes. Secondly, distance-wise attributes for complex areas are propagated and encoded to obtain enhanced features. Thirdly, by defining the hist features, the perception range of vehicle interactions is expanded both temporally and spatially. Finally, a multi-stage scheme is utilized to achieve the final aggregation of trajectory features, and a feature decoder integrated with a focal loss is employed to obtain multi-modal prediction results. Both qualitative and quantitative analyses of the experimental results demonstrate the effectiveness of our proposed method.

Infrared and visible image fusion (IVIF) aims to create fused images that encompass the comprehensive features of both input images, thereby facilitating downstream vision tasks. However, existing methods often overlook illumination conditions in low-light environments, resulting in fused images where targets lack prominence. To address these shortcomings, we introduce the Illumination-Aware Infrared and Visible Image Fusion Network, abbreviated by IAIFNet. Within our framework, an illumination enhancement network initially estimates the incident illumination maps of input images, based on which the textural details of input images under low-light conditions are enhanced specifically. Subsequently, an image fusion network adeptly merges the salient features of illumination-enhanced infrared and visible images to produce a fusion image of superior visual quality. Our network incorporates a Salient Target Aware Module (STAM) and an Adaptive Differential Fusion Module (ADFM) to respectively enhance gradient and contrast with sensitivity to brightness. Extensive experimental results validate the superiority of our method over seven state-of-the-art approaches for fusing infrared and visible images on the public LLVIP dataset. Additionally, the lightweight design of our framework enables highly efficient fusion of infrared and visible images. Finally, evaluation results on the downstream multi-object detection task demonstrate the significant performance boost our method provides for detecting objects in low-light environments.

Forecasting crowd movement accurately across an urban area is crucial for efficient traffic control and ensuring public security. Current methods involve transforming the city’s roadmap into a grid-based map, enabling Convolutional Neural Networks (CNNs) or Graph Convolutional Networks (GCNs) to capture spatio-temporal relationships efficiently. However, this approach overlooks the connection between irregularly shaped real-world areas, which can be categorized into various functional zones. In this article, we introduce a novel approach for predicting urban crowd flow named STHGN, which utilizes hypergraph convolutional networks. By constructing 3-level hypergraphs from irregular areas and adopting Hyper-GCN, we capture mobility among irregular regions. We construct the hypergraphs based on hour, day, and week, simultaneously using gated-based mechanisms to fuse various embeddings. We evaluate the efficacy of our model by contrasting it with 11 other approaches, including the most sophisticated STGs. After conducting numerous experiments, we find that STHGN outperforms these methods with higher accuracy, resulting in a reduction of approximately 6-9% in mean absolute error (MAE) for crowd flow prediction.

Surface defects of railway trackside equipment pose a serious risk on the safety of railway transportation systems. Image-based surface defect detection methods have made significant progress. However, the image background of trackside equipment is complex, and there is a large amount of noise, which makes existing methods inadequate in accurately detecting small surface defect regions. To tackle with this issue, we propose a coarse-to-fine parallelizable surface defect detection approach to hierarchically detect the defects of trackside equipment. Firstly, a detection network is designed to locate and extract trackside equipment, which aims at roughly focusing the detection field from the original image to the region of interest of individual trackside equipment. Then, a novel semantic segmentation network is proposed to segment the major components of trackside equipment, so as to further finely focus on the defect regions. We apply multiple segmentation networks to parallelly segment various trackside equipment. In the segmentation network, a dense feature enhancement method is introduced to strengthen the high-level semantic information, and a feature partitioning enhancement strategy is designed to improve the segmentation performance for small defect regions. Finally, according to the visual characteristics of the segmentation output, we propose a defect recognizer to discriminate the defects. Extensive experimental results demonstrate that the proposed surface defect detection approach achieves higher accuracy for trackside equipment.

Crowd simulation explores crowd behavior in virtual environments, which has been extensively studied in many areas, such as safety and civil engineering, transportation, social science, and entertainment industry. In this paper, an improved potential field method is proposed to achieve the real-time crowd simulation, which is composed of the global navigation with Dijkstra's algorithm and the potential field based local navigation. First, a region separation is performed to divide the environment into a set of triangles, and thus a topological graph can be built with the triangles as vertices. Then a velocity-density model is introduced for improving the speed controlling mechanism and solving the "maximum speed dilemma" which means the velocity of an individual derived by potential field will be stuck into the maximum due to the ill speed control. Since the movement of an individual in the crowd is influenced by the socio-psychological forces, the individuals' actions express the group attributes. In order to represent the group attributes in the crowd, the repulsive potential function is improved in this paper. Experiments have been carried out and the results show that the improved potential field based method can simulate the crowd in real time and avoid the "maximum speed dilemma".