High reliability and low latency are essential to railway wireless communications, which transmit train control and dispatch commands to ensure operational safety. However, as railway systems become increasingly electrified and more complex, the exposure to electromagnetic interference (EMI) also grows, potentially causing service disruptions and compromising safety. Intentional EMI (IEMI), which is deliberately and often maliciously generated, further increases the vulnerability of these critical communication networks. Real-time detection and classification of EMI and IEMI therefore become increasingly important. This paper presents composite models that reflect realistic railway scenarios and proposes an adaptive classification approach for EMI and IEMI using a deep learning algorithm based on bidirectional long-short-term memory (BiLSTM) networks and attention mechanisms. By employing time-series feature extraction to analyze both time and frequency information at fine resolution, the proposed method demonstrates a classification accuracy of 94.98%. Simulation results outperform existing techniques with a 3% improvement in accuracy, showcasing its adaptability across four typical railway scenarios at train speeds of up to 500 km/h. Moreover, online monitoring phase performs real-time detection in just 7.43 ms, meeting the stringent latency requirements for railway systems. Validation using real-world data further confirms the practical applicability of the proposed methods under actual operating conditions.

This article introduces a radar perception learning framework guided by data collected from commonly equipped visual-inertial (VI) sensor suites on smart vehicles. Unlike existing approaches that rely on dense point clouds from 3D LiDARs, which are costly and not widely deployed, this method leverages the broader availability of VI data. However, visual images alone lack the ability to capture the three-dimensional motion of moving targets, which limits their effectiveness in supervising motion-related tasks. To overcome this limitation, the framework integrates multiple perception tasks such as odometry estimation, motion segmentation, and scene flow prediction into a unified learning process. The first component is an odometry estimation module that combines deterministic ego-motion models with data-driven learning results. This fusion helps accurately infer the scene flow of static background points while minimizing drift. The second component is a supervision signal extraction module that aligns optical and millimeter-wave radar measurements to guide the learning of radar scene flow and rigid transformations. This module improves the reliability of dynamic point supervision through joint constraints across sensing modalities. The third component introduces a feature-selection module designed for cross-modal learning. It enhances the accuracy of motion segmentation and enforces consistency between odometry and scene flow, resulting in more coherent radar perception outputs. Experimental evaluations show that this framework achieves superior performance in challenging conditions such as smoke-obscured environments. It surpasses state-of-the-art (SOTA) methods that depend on high-cost LiDAR systems. The implementation of VISC+ will be open-source at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weini-Eve/VISC</uri>

Intelligent transportation systems (ITSs) play a vital role in addressing urban traffic safety challenges. The deployment of lightweight and efficient algorithms for conducting vehicle detection and tracking on edge devices is a key requirement, due to the limited computational resources of these devices. To address this challenge, we propose YOLO-LIGHT, a real-time vehicle detection model, and Ve-Track, a lightweight tracking algorithm. YOLO-LIGHT improves the feature extraction process and reduces the incurred computational cost. It integrates a novel FastPConv convolution in its detection head, while the DySample and SCAM modules in the neck provide enhanced feature representations. Soft-NMS and AMA_SPPF further strengthen the ability of the model to capture fine details. Additionally, global channel sparsity and pruning are employed to compress the parameters of the model while preserving its detection accuracy. For tracking purposes, Ve-Track builds on ByteTrack by filtering background detection boxes using a “life value” indicator and recalculating scores. An enhanced Kalman filter (EV-KF) is also introduced to better model nonlinear vehicle motions. Experimental results obtained on the modified VisDrone dataset show that YOLO-LIGHT improves the mAP95 metric by 17.6% and exhibits a computational complexity reduction of 82.6%, achieving 51 FPS on an embedded platform. Ve-Track outperforms ByteTrack, with a 6.2% MOTA increase and a 17.2% FPS increase, demonstrating its suitability for real-time ITS applications.

As the demand for traffic simulation has shifted to large-scale urban areas, achieving both high accuracy and computational efficiency has become increasingly challenging. Among various traffic simulation levels, microscopic simulation provides the most detailed and accurate representation of traffic dynamics; however, its high computational cost prevents its applicability to large-scale urban networks. Consequently, the need to find a compromise between accuracy and computational cost leads to hybrid or multi-level traffic simulation. Multi-level traffic simulation integrates multiple simulation levels within a single framework, where micro-meso hybrids are generally suitable for urban networks and micro-macro hybrids for highways. To maintain constant simulation performance in terms of accuracy and computational cost, recent studies have emphasized the importance of dynamic properties that adjust the simulation levels of road segments in response to the traffic conditions over time. Accordingly, this study proposes a dynamic multi-level traffic simulation for urban networks by combining microscopic and mesoscopic traffic simulations. A unified simulation framework and data structure were proposed to ensure compatibility and consistency between different simulation levels. Temporal and spatial interfaces were designed and verified for proper functioning, including the preservation of vehicle information and consistency in traffic dynamics. The proposed simulation was evaluated in terms of computational cost and accuracy under various demand scenarios, using multiple methods for dynamically determining micro- and meso-level representations. The dynamic multi-level simulation consistently achieved higher accuracy than the mesoscopic model with reduced computational cost than the microscopic model across all demand scenarios. In addition, the proposed approach showed higher reliability than the fixed multi-level simulations, especially under an ROI-unfocused demand patterns. These outcomes were consistently observed across networks of varying sizes. Finally, an application to a real-world urban network demonstrated the potential of the proposed model for practical use in traffic management systems.

Monocular 3D object detection has been studied as a promising task for diverse applications, such as autonomous driving, due to its lower cost and more straightforward configuration than multiple sensors. However, existing studies have focused on clear weather without considering diverse weather conditions with varying intensity, such as rain, snow, and fog, affecting detection performance. In this paper, we propose MonoRange, a monocular 3D object detection method that uses object-centric images and range maps in adverse weather conditions. Leveraging the 2D detection results, MonoRange generates range maps from images via an object-centric range map reconstruction. Furthermore, MonoRange flexibly removes adverse weather noise in images via weather intensity adaptive image restoration with a weight modulation transformer. Then, MonoRange fuses the range map and restored image and predicts 3D bounding boxes via the range map aligned detector. Introducing the projected box consistency loss between 2D and 3D boxes also enables to consistent and accurate 3D object detection. Experimental results on diverse weather datasets demonstrate that MonoRange surpasses existing monocular 3D object detection approaches. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jhyoon964/MonoRange</uri>

Accurate mobile network traffic prediction is crucial for transit infrastructure service optimization in industrial informatization. Traditional linear models fail to capture complex non-linear dynamics, while existing deep learning methods struggle with rapid temporal changes, signal fluctuations, and diverse network conditions, limiting real-time applicability. To address these challenges, this paper proposes a hybrid model integrating Convolutional Neural Networks (CNNs) and Transformers, tailored for High-Speed Railway (HSR) environments. The proposed hybrid model is evaluated using both public datasets and a real-world HSR dataset collected through empirical field measurements, it not only achieves state-of-the-art (SOTA) predictive accuracy, reducing root mean square error by 4.7% over strong baselines in the challenging HSR environment, but also delivers this performance with superior computational efficiency, achieving over 3.6 times lower inference latency than leading SOTA models. This establishes an optimal performance-to-cost ratio, demonstrating its practical value for real-time HSR systems.

With the rapid development of the Internet of Vehicles (IoV), the tight coupling between In-Vehicle Networks (IVN) and External Vehicle Networks (EVN) has made vehicular systems vulnerable to sophisticated cross-network attack chains. Existing Intrusion Detection Systems (IDS), however, typically operate in isolation on either IVN or EVN, and lack effective context-aware mechanisms for capturing inter-domain dependencies. To overcome this limitation, we propose CATwin-IDS, a context-aware intrusion detection framework that integrates digital twin technology with a lightweight Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) model. In our design, Conditional Mutual Information (CMI) and Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) are applied for feature optimization and data balancing, while Temporal Self-Attention (TSA) enhances the modeling of spatiotemporal dependencies across heterogeneous traffic. The digital twin provides real-time bidirectional synchronization and a simulation environment, enabling proactive adaptation to dynamic threats. Experimental results on benchmark datasets (Car-Hacking, CICIoV2024, CICIDS2018, CICIoT2023) demonstrate that CATwin-IDS achieves higher accuracy and real-time efficiency compared with state-of-the-art methods, providing a holistic solution for securing IoV against cross-network intrusions.

In this paper, the distributed prescribed-time resilient control problem of the two-dimensional (2-D) plane heterogeneous connected vehicular platooning system (HCVPS) subject to the weaken communication and false data injection (FDI) communication attack threats is investigated. Firstly, for achieving the multi-lane vehicle merging, longitudinal following, and vehicular platooning lane changing, the third-order nonlinear dynamics of the HCVPS on the 2-D plane are considered. By developing the distributed information reconstruction observer, the weaken communication problem of leading vehicle can be mitigated by each following vehicles within a prescribed time. A distributed prescribed-time resilient control modeling framework is proposed that maintains the resilience performance and the desired safety inter-vehicle distance, ensures string stability within a prescribed-time. Finally, the results of Simulation of Urban Mobility (SUMO)-Simulink joint experimental platform and comparison cases are shown to verify the effectiveness of the developed scheme.