Search Results (187)

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions. Full article

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

This study provides a comparative performance evaluation of commercial Precise Point Positioning Real-Time Kinematic (PPP-RTK) and public Network RTK (NRTK) services for vehicle-based positioning in urban and suburban environments. Using low-cost u-blox ZED-F9 receivers, the research assesses the accuracy, availability, and robustness of the u-blox PointPerfect service against a regional NRTK network across diverse real-world scenarios, including high-speed highway conditions and signal-challenging urban corridors. The experimental framework utilizes a rigid-bar setup for high-precision ground-truth validation and incorporates an independent vertical accuracy assessment against a LiDAR-derived digital elevation model (DEM). The results demonstrate that all tested configurations achieve decimeter-level accuracy. Notably, the integration of PPP-RTK with an inertial measurement unit (IMU) delivers performance nearly equivalent to NRTK, effectively mitigating vertical biases and ensuring positioning continuity in GNSS-denied areas such as tunnels. These results confirm that low-cost GNSS solutions, when paired with modern augmentation services and IMU integration, can meet the stringent demands of mass-market applications like Cooperative Intelligent Transport Systems (C-ITS) and autonomous mobility. Full article

Precise time synchronization is foundational for future intelligent transportation systems (ITS), where safety-critical functions like cooperative Vehicle-to-Everything (V2X) communication and multi-sensor fusion demand a leap from sub-microsecond- to nanosecond-level precision. Standard protocols like the Precision Time Protocol (PTP) are limited by inherent errors (e.g., timestamping inaccuracies and clock drift) that are typically only solvable with expensive hardware upgrades. This paper proposes a cost-effective, software-based solution. We introduce a novel method that leverages deep reinforcement learning (DRL) to actively predict and compensate for these synchronization errors in real time. An experimental environment is constructed to rigorously evaluate the performance of the proposed method. The results demonstrate that our approach achieves a significant leap in synchronization accuracy, showcasing its potential to meet the stringent timing demands of future intelligent transportation. Full article

Unmanned Aerial Vehicle (UAV) tracking of ground moving targets holds significant applications in domains such as intelligent transportation, logistics distribution, and environmental monitoring, placing greater demands on efficient and stable path-planning methods for vehicular tracking. This study investigates a UAV path tracking approach based on a deep reinforcement learning algorithm, Proximal Policy Optimization (PPO). Starting from the kinematic characteristics of UAVs and ground vehicles, a 3D path planning model was constructed that considers spatial coordinates, velocity, and attitude constraints. A well-designed objective function—including tracking error minimization, energy optimization, and safety distance constraints—was incorporated. By designing the state space, action space, and reward function, the PPO algorithm is capable of adaptive learning in complex environments. Compared with traditional Artificial Potential Field (APF), Q-learning, and TD3 algorithms, PPO better balances exploration and exploitation and demonstrates stronger learning stability and global optimization capability in dynamic multi-obstacle scenarios. Simulation results show that PPO-based UAV path planning outperforms Q-learning and other comparative algorithms in terms of tracking accuracy, convergence speed, and robustness. In specific scenarios, Q-learning achieves a trajectory error of approximately 1 m, TD3 and APF exhibit errors around 0.3 m with noticeable oscillations, and PPO achieves an error of about 0.2 m. The UAV can follow the vehicle trajectory smoothly, with a more continuous path and rapidly converging, stable error curves, indicating the promising application potential of PPO in intelligent UAV control. The PPO-based UAV-tracking path planning method effectively enhances the UAV’s intelligent decision-making and path optimization capabilities, providing new technical approaches and a research foundation for intelligent UAV traffic and cooperative control systems. Full article

Vehicular Ad Hoc Networks (VANETs) have become the foundation for the implementation of intelligent transportation systems and new vistas for road safety and traffic efficiency. However, these networks are still susceptible to Sybil attacks, a form of attack that requires malicious entities to create a series of fake identities in order to have an out-of-proportion influence. The present paper puts forth a new Sybil attack detection framework that combines Verifiable Delay Functions (VDFs) in synergistic cooperation with a hierarchical fog-cloud computing structure. Our method does not rely on any additional properties of VDFs but uses them to prove uniqueness computationally, deploying purposefully placed fog nodes for effective localized detection. We mathematically formulate a multi-layered detection algorithm that processes interactions between vehicles on two fog (and cloud) layers to produce suspicion scores using spatiotemporal consistency and VDF challenge-response patterns. Security analysis proves the system’s ability to resist a range of Sybil attack variants with performance evaluation outperforming at detection above 97.8% and false positives below 2.3%. The incorporation of machine learning techniques also extends detection capabilities, and our hybrid VDF-ML method proves better adaptation to the changing attack patterns. Details of implementation and detailed simulations in various traffic situations prove the feasibility and efficiency of our proposed solution to set a new level playing ground for secure VANET communications. Full article

Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article

Vehicular Ad Hoc Network (VANET) traffic–network co-simulation is a foundational methodology for the engineering evaluation of vehicle-to-everything (V2X) protocols and cooperative Intelligent Transportation System (ITS) applications before field deployment. However, with research objectives and experimental conditions varying widely, existing studies still lack a systematic paradigm for parameter configuration and experimental workflows. As a result, researchers often rely on experience-based settings, which can bring high time and computational overhead, long experimental cycles, and limited reproducibility. To address these issues, this paper proposes a simulation cost modeling and configuration optimization methodology for traffic–network co-simulation. By profiling and structurally modeling key overheads, such as initialization and traffic- and network-side execution, we characterize how traffic, network, and control parameters jointly affect total simulation overhead. We formulate a minimum-cost configuration optimization model under constraints of statistical validity and experimental comparability. We further develop a configuration solving mechanism and a structured workflow for simulation experiment configuration to complement empirical tuning with a more systematic approach, thereby improving the reproducibility of simulation studies. The study is grounded in a representative urban road-network co-simulation scenario based on Simulation of Urban MObility (SUMO), Veins, and Objective Modular Network Testbed in C++ (OMNeT++). Simulation results show that the proposed method reduces simulation overhead while keeping conclusions on key performance metrics consistent, thereby providing a more efficient and statistically credible evaluation basis for application-oriented urban VANET studies related to traffic safety, transportation efficiency, and wireless-system performance. Full article

The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures. Full article

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

The Cooperative-Intelligent Transport Systems (C-ITS) require emerging Vehicular-to-Everything (V2X) applications, such as Advanced Driving Systems (ADS) and Connected Autonomous Driving (CAD), to support efficient road safety measures. These applications often require high reliability, throughput, and low latency by exchanging a significant amount of data among End-to-End (E2E) vehicles. However, current V2X communication technologies, such as DSRC and C-V2X, are not able to meet these stringent demands. Two or more Radio Access Technologies (RATs) are essential to guarantee the required Quality of Service (QoS) in high-density vehicular environments. To address this critical gap, this survey presents the 3M Framework—a hybrid vehicular architecture approach based on Multi-Radio Access Technology (M-RAT), Mobility Management, and Machine Learning (ML). The manuscript provides a detailed overview of V2X Multi-RAT evolutions, analyzing their state-of-the-art and limitations in heterogeneous scenarios. We specifically highlight that the existing Long Term Evolution (LTE)-based mobility management fails to meet V2X handover requirements for high-speed vehicles, necessitating a comprehensive overview of Vertical Handover (VHO). Furthermore, the survey details how the integration of ML promotes the prediction of network states, enabling optimized context-aware decisions for connectivity and resource allocation, thereby reducing Handover Failures (HoFs) and enhancing reliability using techniques like Deep Reinforcement Learning (DRL). Finally, based on a comprehensive review of existing methods, the paper identifies critical research directions and challenges required to realize intelligent, hyper-fast, and ultra-reliable Beyond 5G (B5G) and Sixth Generation (6G) V2X networks, delivering a more profound understanding for future endeavors. Full article

Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems. Full article

The Intelligent Road Side Unit (RSU) is a crucial component of Intelligent Transportation Systems (ITSs), where roadside LiDAR are widely utilized for their high precision and resolution. However, water droplets and atmospheric particles in fog significantly attenuate and scatter LiDAR beams, posing a challenge to multi-target tracking and ITS safety. To enhance the accuracy and reliability of RSU-based tracking, a collaborative RSU method that integrates denoising and tracking for multi-target tracking is proposed. The proposed approach first dynamically adjusts the filtering kernel scale based on local noise levels to effectively remove noisy point clouds using a modified bilateral filter. Subsequently, a multi-RSU cooperative tracking framework is designed, which employs a particle Probability Hypothesis Density (PHD) filter to estimate target states via measurement fusion. A multi-target tracking system for intelligent RSUs in Foggy scenarios was designed and implemented. Extensive experiments were conducted using an intelligent roadside platform in real-world fog-affected traffic environments to validate the accuracy and real-time performance of the proposed algorithm. Experimental results demonstrate that the proposed method improves the target detection accuracy by 8% and 29%, respectively, compared to statistical filtering methods after removing fog noise under thin and thick fog conditions. At the same time, this method performs well in tracking multi-class targets, surpassing existing state-of-the-art methods, especially in high-order evaluation indicators such as HOTA, MOTA, and IDs. Full article

The convergence of Vehicular Ad Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks. Full article

The rapid advancement of connected and autonomous vehicles (CAVs) has the potential to revolutionize road transportation, promising significant improvements in safety, efficiency, and sustainability. However, traditional CAV architectures are predominantly modular and rule-based. They struggle with interaction, cooperation, and adaptability in complex mixed-traffic environments. Moreover, the substantial infrastructure investment required and the absence of compelling killer applications have limited large-scale deployment of CAVs and roadside units (RSUs), resulting in insufficient penetration to realize the full safety benefits of CAV applications and creating a deployment stalemate. To address the above challenges, this paper proposes an innovative connected autonomous vehicle system, termed AQ-CAV, which leverages recent advances in AI agents and QR codes. AI agents are employed to enable cooperative, self-adaptive, and intelligent vehicular behavior, while QR codes provide a cost-effective, accessible, robust, and scalable mechanism for supporting CAV deployment. We first analyze existing CAV systems and identify their fundamental limitations. We then present the architectural design of the AQ-CAV system, detailing the components and functionalities of vehicle-side and infrastructure-side agents, inter-agent communication and coordination mechanisms, and QR code-based authentication for AQ-CAV operations. Representative applications of the AQ-CAV system are investigated, including a case study on emergency response. Preliminary results demonstrate the feasibility and effectiveness of the proposed system, which achieves significant safety improvements at low system cost. Finally, we discuss the key challenges faced by AQ-CAV and outline future research directions that require exploration to fully realize its potential. Full article