Visible–infrared person re-identification (VI-ReID) aims to match pedestrian images across heterogeneous modalities. As a key technology in intelligent transportation systems, VI-ReID supports cross-camera tracking, behavior analysis, and security monitoring, particularly in nighttime or low-illumination scenarios. Despite recent advances, existing methods still encounter two critical challenges: (i) semantic misalignment between low-level and high-level features across modalities, and (ii) distribution discrepancies between visible and infrared images. To address these challenges, we propose a novel framework, Mutual Distillation Driven Dual-Space Matching (MDDM), which performs modality alignment in two complementary spaces. For challenge (i), we design a Dual Level Fusion (DLF) module to capture and adaptively fuse hierarchical features, aligning modalities by integrating both low- and high-level semantics across spatial and channel dimensions. In addition, a Modality Invariant Augmentation (MIA) module is developed to extract fine-grained semantic cues and enhance identity discrimination, thereby reinforcing the correlation between visible and infrared modalities and facilitating the learning of robust shared representations. For challenge (ii), we introduce Dual-Space Matching (DSM), which aligns features in both Hilbert and Euclidean spaces. Furthermore, a mutual distillation strategy is incorporated to promote cross-space consistency and alleviate modality-specific discrepancies. Extensive experiments on widely used VI-ReID benchmarks demonstrate the superiority and flexibility of the proposed method, which consistently achieves competitive performance across multiple datasets. Our code is available at https://github.com/lfjiang-cn/MDDM.

Traffic sign detection is a vital component of intelligent transportation systems. However, in real-world driving scenarios, challenges such as illumination variations, occlusions, and low resolution of small objects can significantly reduce detection accuracy. To overcome these challenges, we propose YOLO-MAFF, a traffic sign detection network that integrates a multi-scale attention mechanism and adaptive feature fusion. Firstly, a backbone network incorporating a multi-scale channel attention mechanism is designed. By integrating multi-scale contextual information with channel attention, efficient feature extraction and representation learning are facilitated. Secondly, a pyramid network based on adaptive feature fusion is developed to learn spatial attention maps. By fusing feature maps at various scales and emphasizing or suppressing region-specific features, the network can alleviate inconsistencies in feature representations. Finally, a small object detection layer is designed to preserve shallow-level detail information in the feature maps, enabling the network to detect small traffic signs. In the experimental section, YOLO-MAFF is evaluated on four datasets, i.e., TT100K, CCTSDB2021, CURE-TSD, and COCO. The experimental results show that YOLO-MAFF exhibits superior performance in traffic sign detection tasks. Compared to the baseline YOLOv8s, our method improves the mAP by 4.8% on TT100k (reaching 90.2%), 2.8% on CCTSDB2021 (reaching 86.0%), 2.7% (reaching 53.7%) on CURE-TSD, and 2.0% (reaching 72.5%) on the COCO dataset. The source code is available athttps://github.com/lfjiang-cn/yolo-maff

Recent research has shown that the Macroscopic Fundamental Diagram (MFD) is an effective way to manage traffic flow and mitigate congestion, which regulates bidirectional transfer flows at regional boundaries via perimeter control. In practice, perimeter control may fail to achieve desired control targets due to adverse conditions such as inclement weather or accidents, which may result in loss of actuator effectiveness faults. To address this challenge, this paper proposes two adaptive fault-tolerant perimeter control schemes to handle both time-invariant and time-varying actuator faults. To cope with time-invariant actuator faults, a data-driven fault-tolerant perimeter control approach is presented, which employs adaptive dynamic programming to approximate the solution of the Hamilton-Jacobi-Bellman equation. For dealing with time-varying faults, an adaptive fault-tolerant perimeter controller is designed by linearizing the MFD function and ensuring tracking of a given reference model. Theoretical analysis proves that the tracking error converges to zero asymptotically. Finally, simulation studies validate the effectiveness of the proposed schemes in improving traffic management under actuator fault conditions.

In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks’ nontrivial charging times and the stations’ limited charging facilities. Our goal is to reduce the trucks’ waiting times at the stations while minimizing individual trucks’ operational costs. We propose a distributed coordination framework that relies on computation and communication between the stations and the trucks, and handles uncertainties in travel times and energy consumption. Within the framework, the stations assign a limited number of charging ports to trucks according to the first-come, first-served rule. In addition, each station constructs a waiting time forecast model based on its historical data and provides its estimated waiting times to trucks upon request. When approaching a station, a truck sends its arrival time and estimated arrival-time windows to the nearby station and the distant stations, respectively. The truck then receives the estimated waiting times from these stations in response, and updates its charging plan accordingly while accounting for travel uncertainties. We performed simulation studies for$1,000$trucks traversing the Swedish road network for 40 days, using realistic traffic data with travel uncertainties. The results show that our method reduces the average waiting time of the trucks by 46.1% compared to offline charging plans computed by the trucks without coordination and update, and by 33.8% compared to the coordination scheme assuming zero waiting times at distant stations.

Rapid detection of small objects in remote sensing (RS) images is crucial for intelligence acquisition, for instance, enemy ship detection. Instead of employing images with high resolution, low-resolution images of the same size typically cover a wider area and thus facilitate efficient object detection. However, accurately detecting small objects in such images remains a challenge due to their limited visual information and the difficulty in distinguishing them from the background. To address this issue, we propose a small object detection method called the Deeper and Stronger Feature Path Aggregation Pyramid Network for low-resolution remote sensing images. First, our approach involves designing aggregation networks with deeper paths and utilizing feature layers closer to the shallow layers to enhance the acquisition of information about small objects. Second, to enhance the network’s focus on small objects, we propose a Resolution-Adjustable 3-D Weighted Attention (RA3-DWA) mechanism. This mechanism enables independent learning of spatial feature information and assigns 3-D weights specifically to small objects, resulting in improved detection accuracy for small objects. Finally, we propose the Fast-EIoU loss function to accelerate the regression of the model boundary. This loss function assigns an acceleration factor to the length loss and width loss, respectively, thereby improving the detection accuracy of small objects. Experiments on Levir-Ship and DOTA demonstrate the effectiveness and efficiency of the proposed method. Compared to the baseline YOLOv5, our method has improved the average detection accuracy of the Levir-Ship dataset by 6.7% (reaching up to 82.6%) and the accuracy of the DOTA dataset by 6.4% (reaching up to 73.7%).

Truck platooning is a promising technology that enables trucks to travel in formations with small inter-vehicle distances for improved aerodynamics and fuel economy. The real-world transportation system includes a vast number of trucks owned by different fleet owners, for example, carriers. To fully exploit the benefits of platooning, efficient dispatching strategies that facilitate the platoon formations across fleets are required. This paper presents a distributed framework for addressing multi-fleet platoon coordination in large transportation networks, where each truck has a fixed route and aims to maximize its own fleet’s platooning profit by scheduling its waiting times at hubs. The waiting time scheduling problem of individual trucks is formulated as a distributed optimal control problem with continuous decision space and a reward function that takes non-zero values only at discrete points. By suitably discretizing the decision and state spaces, we show that the problem can be solved exactly by dynamic programming, without loss of optimality. Finally, a realistic simulation study is conducted over the Swedish road network with$5,000$trucks to evaluate the profit and efficiency of the approach. The simulation study shows that, compared to single-fleet platooning, multi-fleet platooning provided by our method achieves around$15$times higher monetary profit and increases the CO$_2$emission reductions from$0.4\%$to$5.5\%$. In addition, it shows that the developed approach can be carried out in real-time and thus is suitable for platoon coordination in large transportation systems.