This paper investigates the problems of privacy protection and disturbance rejection in vehicle platoon systems. A state decomposition-based vehicle privacy preservation mechanism is proposed. This mechanism decouples the vehicle system into a communication subsystem and a self-updating subsystem, thereby achieving comprehensive data protection for the vehicle system. Meanwhile, a correction term is designed within the self-updating subsystem to improve the robustness of the vehicle system. Furthermore, to improve the tracking accuracy of the vehicle system, a hierarchical funnel control scheme is proposed. By separately constructing the upper-layer funnel function and the lower-layer funnel function, this scheme enhances the tracking accuracy of vehicles while releasing the initial conditions. Based on this, a hierarchical control strategy is constructed. It aims to achieve consensus control between vehicles and tracking control within each vehicle, while ensuring the privacy of information within the vehicle system. Finally, a simulation example of the vehicle platoon system is presented to demonstrate the effectiveness of the designed control strategy.
Railway systems are gradually adopting Internet of Things (IoT) and artificial intelligence (AI)-driven technologies to improve operational efficiency, implement preventive maintenance, and enable real-time monitoring. However, this technological shift has raised significant challenges for security, privacy, and data integrity. As cloud-based infrastructure is susceptible to unauthorized access, manipulation, and breach of confidentiality, there is an urgent demand for a secure and efficient data-sharing platform. Traditional architecture predominantly focuses on the aviation and automotive sectors, leaving the implementation of AI in railway networks relatively unexplored despite their essential role in urban and inter-city transportation infrastructure. This paper presents a blockchain-based system for secure data sharing in railway networks. This framework implements ciphertext-policy attribute-based encryption (CP-ABE) and an Intel SGX-based trusted execution environment to manage and prevent unauthorized data access. The proposed system implements smart contracts to facilitate secure data sharing and access control, thereby enhancing railway data security. Our experimental evaluation reveals that the proposed system improves efficiency, authentication speed, and cost-effectiveness. The results show a 27% reduction in computational cost, a 4% decrease in authentication time, an 8% improvement in peer-to-peer connection establishment, and a 23% decline in data sharing expenses. These improvements strike a balance between security, efficiency, and accessibility, thereby optimizing real-time railway operations.
Ensuring the safety of electric vehicles (EVs) is a critical concern for next-generation mobility solutions, where the digital twin (DT) plays a vital role in addressing this challenge. This study provides a detailed analysis of the current state of knowledge in this domain. The findings highlight several effective applications of DT technology in enhancing EVs safety, particularly in battery maintenance and performance. However, our analysis reveals that most existing research is focused on battery-related applications, indicating the need to explore broader dimensions of EV safety. We identify key safety challenges, for example, high-voltage hazards, thermal runaway, system malfunction, and examine the role of DT in real-time monitoring and predictive maintenance to address them. We outline the DT-EVs enabling technologies, analyse the simulation platform, and present a unified DT-EVs architecture. This study classifies DT architectures and evaluates DT using artificial intelligence (AI) driven algorithms such as convolutional neural networks (CNNs), long short-term memory (LSTM), and generative adversarial networks (GANs), have achieved high accuracies in risk detection. By synthesising these insights, the objective of this study is to provide foundational conceptual support to researchers and, encourage the development of comprehensive, user-centric, and scalable DT-based safety solutions.
The logistics and transportation industry faces several critical challenges, including the absence of real-time vehicle data, inadequate safety measures, and inefficient resource management. This paper presents the Smart Logistic System (SLS), a unified intelligent transportation architecture that integrates embedded systems, Augmented Reality (AR), Machine Learning (ML), Internet of Things (IoT), and digital-twin technology within a single operational framework, rather than as independent subsystems. The system continuously monitors key vehicle parameters — location, speed, fuel consumption, and engine performance — to support proactive maintenance and minimize downtime. An edge-deployable driver-behavior-monitoring module employs computer vision and a lightweight MobileNetV3-Small classifier to detect drowsiness, distraction, and other hazardous behaviors in real time. Special-needs support includes seizure detection, GPS-based wandering alerts, and AR-guided vehicle-operation assistance. A route-optimization module formulates logistics planning as a constrained, multi-objective vehicle routing problem that jointly considers transportation cost, travel time, and estimated carbon emissions, while a digital-twin layer maintains a continuously synchronized virtual representation of fleet state to support fleet-manager decision-making. A mobile application delivers real-time data, alerts, and navigation to drivers; a desktop application allows managers to decode raw Controller Area Network (CAN) data; and a cloud platform provides secure storage, synchronization, and visualization. On a held-out benchmark, the driver-behavior-monitoring module achieves accuracy and inference-latency performance competitive with, and in several respects exceeding, recent state-of-the-art driver-monitoring methods; the route-optimization, predictive-maintenance, and digital-twin modules are validated through architectural and feasibility analysis rather than large-scale benchmarking. Taken together, these results indicate that SLS’s principal contribution is the integration of safety monitoring, logistics optimization, predictive maintenance, digital-twin synchronization, and accessibility features within a single operational architecture — a step toward more secure, efficient, and inclusive logistics and transportation systems.
Multi-agent cooperative trajectory planning in safety-critical systems faces safety assurance and scalable coordination challenges, especially under dynamic environmental disturbances. Traditional sampling and optimization-based methods lack real-time adaptability and do not scale well in multi-agent settings. Reinforcement learning methods show promise, but often suffer from safety constraint violations and sample inefficiency. This paper proposes MAF-DAC, a Multi-head Action-Filtered Deterministic Actor-Critic framework for safe multi-aircraft trajectory planning. MAF-DAC adopts centralized training with parameter sharing and decentralized execution. A shared actor-critic model is trained using experiences collected from all agents, and the trained actor is deployed to each aircraft agent for execution based only on local observations. A pre-trained Multi-head Action Filter Network (MAF-Net) serves as a differentiable safety filter to reduce unsafe and inefficient actions before execution. MAF-DAC is adapted to a multi-aircraft rerouting task under dynamically evolving thunderstorm scenarios with positional uncertainty. The simulation results show that the proposed method improves safety, scalability, and learning efficiency over representative baselines. It maintains 99% separation compliance, achieves a 95.5% task success rate under moderate uncertainty levels, and scales to 45 aircraft in a 30-minute window in the tested scenarios. These findings demonstrate the potential of MAF-DAC as a safe and scalable AI-driven decision support approach for tactical multi-aircraft rerouting in convective weather conditions.
