In this paper we study data collection in an energy harvesting sensor network where sensors are deployed along a given path and a mobile sink travels along the path periodically for data collection. Such a typical application scenario is to employ a mobile vehicle for traffic surveillance of a given highway. As the sensors in this network are powered by renewable energy sources, the time-varying characteristics of energy harvesting poses great challenges on the design of efficient routing protocols for data collection in harvesting sensor networks. In this paper we first formulate a novel optimization problem as a network utility maximization problem, by incorporating multi-rate communication mechanism between sensors and the mobile sink and show the NP-hardness of the problem. We then devise a novel centralized algorithm for it, assuming that the global knowledge of the entire network is available. We also develop a distributed solution to the problem without the global knowledge assumption. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms are promising and very efficient.

Sensor fusion plays an increasingly important role in real-time traffic perception using roadside sensing devices because the use of single type of sensors often fail to deliver satisfactory performance in certain harsh environment. This paper investigates asynchronous data fusion for lane-level vehicle tracking with randomly delayed measurements and inaccurate detections from millimeter wave (MMW) radars and magnetic sensors in tunnels, where vehicle tracking with single type of sensors can not meet the requirements of reliable and accurate lane-level tracking due to inaccurate radar detections at far distances, noisy radar detections in tunnel environment, and missed or false vehicle detections by magnetic sensors. A multisensor data association algorithm is first designed to assign the measurements of MMW radar and magnetic sensors to a particular vehicle. A multi-lane estimation model is then developed, which employs Bayesian weight mixture filtering to fuse MMW radar and magnetic sensor measurements and to estimate the lane in which a vehicle is located. Finally, the proposed algorithm is implemented in a real environment -the Xianfengding Tunnel located in Jiangxi Province, China. Experiments are conducted to validate the accuracy of the proposed method using real data. The proposed method and the collected data are further integrated to establish a real-time digital twin system aimed at supporting advanced traffic management. The fusion results and the real radar measurement dataset of the tunnel are made available at https://github.com/futianxuan/data.

While the increased automation levels of production and operation equipment have led to the improved productivity of mining activity in open pit mines, the capacity of the mine transport system has become a bottleneck. The optimisation of the mine transport system is of great practical significance to reduce the production and operation cost and improve the production and organizational efficiency of mines. In this paper, we first formulate a multiobjective optimisation problem for mine railway scheduling by introducing a set of mathematical constraints. As the problem is NP-hard, we then devise a mixed integer programming-based solution to solve this problem and develop an online framework accordingly. We finally conduct test cases to evaluate the performance of the proposed solution. Experimental results demonstrate that the proposed solution is efficient and able to generate a train schedule in a real-time manner.

Smart road studs have been extensively deployed as road safety and data collection devices. Accurate and reliable detection of smart road studs and its further integration into the perception and control modules of connected and autonomous vehicles (CAVs) undoubtedly benefit road boundary detection, localization of CAVs and augument the safety of CAVs’ driving. This work investigates real-time, accurate and reliable detection of smart road studs, which is a challenging task for CAVs because existing methods fail to achieve accurate and real-time smart road stud detection, especially in harsh road environment. To address these challenges, we first build a real-world smart road stud dataset, and then propose and validate a lightweight and efficient smart road stud detection model based on the you only look once 8th version (YOLOv8), called SRS-YOLO. First, a Squeeze-and-Excitation (SE) attention module is used to improve the coarse-to-fine (C2F) module to differentiate the channel importance of feature maps and improve the detection accuracy of smart road studs. Second, a novel downsampling module (DownS) that integrates the average pooling and the max pooling is designed to reduce the number of parameters and minimize information loss during the downsampling process. Third, the loss function is replaced with the Normalized Wasserstein Distance (NWD) loss to alleviate the sensitivity to location deviations when computing the loss for small targets. The experimental results demonstrate that the proposed SRS-YOLO outperforms other state-of-the-art methods, and achieves a 87.92% mean average precision at a real-time speed of 78 frames/s. Our dataset is available at: https://github.com/wky-xidian/smart-road-stud-dataset.

Current vehicle trajectory prediction methods often fail to capture the complex spatial and temporal interactions among surrounding vehicles in dynamic environments, leading to suboptimal prediction accuracy. In response to these challenges, we propose a novel Transformer-based model for vehicle trajectory prediction, denoted as the Dynamic Spatial-Temporal Interaction-aware Transformer Network (DSTIFormer). The three main parts of the DSTIFormer architecture are a spatial-temporal embedding module, a spatial-temporal dynamics interaction-aware module, and a trajectory encoderdecoder module. The spatial-temporal embedding module aims to encode vehicular trajectory interactions into latent representations that simultaneously preserve spatial dependencies and temporal features. The second module utilizes a temporal transformer network (TTN) along with a spatial graph transformer network (STN) to capture the dynamic spatial-temporal interactions between vehicles. To make trajectory predictions more accurate, the third module uses a sequence-to-sequence (Seq2Seq) architecture as the encoder and an attention-based decoder layer to effectively extract the long-range sequential dependencies in the process. The model's prediction accuracy with the root mean squared error (RMSE) metric on two publicly available datasets: NGSIM and HighD, and the results demonstrate that our proposed model outperforms several benchmark methods in long-term prediction.

Weakly supervised learning has emerged as a powerful paradigm for image segmentation, providing a practical solution to reduce the dependence on costly, pixel-level annotated datasets. By leveraging partial annotations, such as image-level tags, this approach significantly reduces the labeling burden while maintaining competitive performance. However, weakly supervised methods inherently face challenges, including overlapping activation regions and insufficient localization due to sparse and noisy signals from image-level tags, often leading to suboptimal segmentation performance. These issues are further exacerbated in industrial defect localization, where datasets present unique complexities, including low-contrast object boundaries, inconsistent shapes, interclass similarities, and substantial intraclass variations. To address these limitations, we introduce a novel saliency-guided transformer attention with contrastive learning framework. The proposed framework leverages Transformer attention to generate localization maps and enhances the learning of challenging foreground and background information through saliency-guided cues. In addition, a pixel-level contrastive learning module is employed to refine feature map representations by bringing positive pairs closer together and pushing negative pairs apart, effectively addressing challenges, such as overlapping activations and ambiguous boundaries. Through extensive experiments and ablation studies, the proposed method demonstrates superior performance compared to state-of-the-art approaches across three defect segmentation datasets. We also evaluated the generalization of our approach on the PASCAL VOC segmentation and MVTec anomaly detection datasets.