Search Results (769)

Accurate prognosis of the remaining useful life (RUL) for lithium-ion batteries is critical for mitigating range anxiety and ensuring the operational safety of electric vehicles. However, existing data-driven methods often struggle to maintain robustness when transferring from controlled laboratory conditions to complex, sensor-limited, real-world environments. To bridge this gap, this study presents U-H-Mamba, a novel uncertainty-aware hierarchical framework trained on a massive hybrid repository comprising over 146,000 charge–discharge cycles from both laboratory benchmarks and operational electric vehicle datasets. The proposed architecture employs a two-level design to decouple degradation dynamics, where a Multi-scale Temporal Convolutional Network functions as the base encoder to extract fine-grained electrochemical fingerprints, including derived virtual impedance proxies, from high-frequency intra-cycle measurements. Subsequently, an enhanced Pressure-Aware Multi-Head Mamba decoder models the long-range inter-cycle degradation trajectories with linear computational complexity. To guarantee reliability in safety-critical applications, a hybrid uncertainty quantification mechanism integrating Monte Carlo Dropout with Inductive Conformal Prediction is implemented to generate calibrated confidence intervals. Extensive empirical evaluations demonstrate the framework’s superior performance, achieving a RMSE of 3.2 cycles on the NASA dataset and 5.4 cycles on the highly variable NDANEV dataset, thereby outperforming state-of-the-art baselines by 20–40%. Furthermore, SHAP-based interpretability analysis confirms that the model correctly identifies physics-informed pressure dynamics as critical degradation drivers, validating its zero-shot generalization capabilities. With high accuracy and linear scalability, the U-H-Mamba model offers a viable and physically interpretable solution for cloud-based prognostics in large-scale electric vehicle fleets. Full article

Research on Cooperative Driving Automation (CDA) has advanced to overcome the limited perception range of onboard sensors and the difficulty of inferring surrounding vehicles’ intentions by leveraging vehicle-to-everything (V2X) communications. This paper models how an autonomous vehicle receives cooperative sensing and cooperative maneuvering information generated at an edge roadside unit (edge RSU) that integrates roadside units (RSUs) with multi-access edge computing (MEC), and how the vehicle fuses this information with its onboard situational awareness and path-planning modules. We then analyze the performance gains of edge RSU-enabled services across diverse traffic environments. In a highway-merging scenario, simulations show that employing the edge RSU’s sensor sharing service (SSS) reduces collision risk relative to onboard-only baselines. For unsignalized intersections and roundabouts, we further propose a guidance-driven Hybrid Pairing Optimization (HPO) scheme in which the edge RSU aggregates CAV intents/trajectories, resolves spatiotemporal conflicts via lightweight pairing and time window allocation, and broadcasts maneuver guidance through MSCM. Unlike a first-come, first-served (FCFS) policy that serializes passage, HPO injects edge guidance as soft constraints while preserving arrival order fairness, enabling safe concurrent passage opportunities when feasible. Across intersections and roundabouts, HPO improves average speed by up to 192% and traffic throughput by up to 209% compared with FCFS under identical demand in our simulations. Full article

The development of sensor technology and deep learning has significantly improved the reliability and practicality of automatic driving technology. In an autonomous driving system, agent trajectory prediction is a complex challenge, which includes the understanding of different and unpredictable behavior patterns of various entities, including vehicles, pedestrians, and other traffic participants, among the data collected by sensors. In this paper, we deeply study two kinds of problems: Single-Agent Trajectory Prediction (SATP) and Multi-Agent Trajectory Prediction (MATP). We propose an innovative model, which combines the attention mechanism and integrates the Liquid Time-Constant (LTC) network with spatio-temporal features and the Mixture of Experts (MoE) framework, termed the Atten-LTC-MoE model. The model is general and extensible to support SATP and MATP problems in different autonomous driving environments. In order to improve computational efficiency and prediction accuracy, lane and agent vectorization, spatio-temporal features, agent data fusion, and trajectory endpoint generation technologies are studied. The effectiveness of our method is verified by comprehensive experiments on Argoverse and Interaction datasets. Our proposed model has been superior to the state-of-the-art models in terms of minADE₆ and minFDE₆ metrics and has shown significant advantages in the accuracy of agent trajectory prediction and computational performance. Full article

Ensuring the safety of autonomous driving systems (ADS) requires scenario-based testing that reflects the complexity and variability of real-world driving conditions. However, the nondeterministic nature of actual traffic environments makes physical testing costly and limited in scope, particularly for rare and safety-critical scenarios. To address this, simulation has become a core component in validation by providing scalable, controllable, and repeatable testing environments. This study proposes a trajectory-based scenario classification framework that emphasizes both generality and interpretability. Specifically, we define a set of rule-based maneuver classification criteria using lateral acceleration patterns and apply them to simulated urban driving scenarios modeled with OpenSCENARIO. To address overlapping maneuver characteristics, a priority ordering of classification rules is introduced to resolve ambiguities. The proposed method was evaluated on a dataset comprising 7 types of maneuvers, including straight driving, lane changes, turns, roundabouts, and U-turns. Experimental results demonstrate the effectiveness of rule-driven classification based on vehicle trajectory dynamics and highlight the potential of this approach for structured scenario definition and validation in ADS simulation environments. Full article

Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency. Current approaches often decouple these factors or exhibit high computational overhead, limiting applicability in real-time or large-scale missions. This work proposes a hierarchical trajectory planning framework designed to address these coupled constraints in an integrated manner. The framework consists of two stages: (i) a current-biased sampling-based planner (CB-RRT*) is introduced to incorporate ocean current information into the path generation process. By leveraging flow field distributions, the planner improves path geometric continuity and reduces steering variations compared with benchmark algorithms; (ii) spatial–temporal alternating optimization is performed within underwater safe corridors, where Bézier curve parameterization is utilized to jointly optimize spatial shapes and temporal profiles, producing dynamically feasible and energy-efficient trajectories. Simulation results in dense obstacle fields, heterogeneous flow environments, and large-scale maps demonstrate that the proposed method reduces the maximum steering angle by up to 63% in downstream scenarios, achieving a mean maximum turning angle of 0.06 rad after optimization. The framework consistently attains the lowest energy consumption across all tests while maintaining an average computation time of 0.68 s in typical environments. These results confirm the framework’s suitability for practical AUV applications, providing a computationally efficient solution for generating safe, kinematically feasible, and energy-efficient trajectories in real-world ocean settings. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article

Effective anomaly detection in vehicle trajectories is crucial for developing sustainable and safe urban transportation systems. However, current research faces three main challenges including scarce anomaly data, inadequate spatial feature extraction in complex road networks, and limited capability in identifying complex behaviors. To address these issues, this paper proposes a Multi-scale Temporal and Road Network Interaction Anomaly Detection model (MTRI). Our framework leverages a Contrastive Learning-based Conditional Diffusion Model (CL-CD) to generate synthetic anomalous trajectories across diverse scenarios. It then employs an Urban road Network Interaction Modeling model (UNIM) to capture the profound interactions between trajectories and the road network. Finally, a Long-Short Temporal Anomaly Detection model (LSTAD) is designed to learn multi-scale temporal features for detecting sophisticated anomalies. Extensive experiments on real-world datasets from various urban scenarios demonstrate the superiority of our approach, which achieves high accuracy and adaptability (AUC-ROC > 0.85). This work contributes to sustainable urban mobility by providing a reliable solution for enhancing road safety through proactive anomaly detection. Full article

Fifth-generation (5G) wireless networks have been widely deployed across various applications, including indoor positioning. This paper presents a model for 3D indoor localization of an unmanned aerial vehicle (UAV) using 5G millimeter-wave technology. Wireless InSite software is used to simulate a real-world environment and extract channel state information from multiple 5G next-generation NodeBs (gNBs), which is then used to generate channel frequency response (CFR) images. These images are employed in a fingerprinting method, where a deep convolutional neural network is trained for accurate position prediction. The model is trained across multiple scenarios involving changes in the number of gNBs, receiver positions, and spacing. In all scenarios, the model is tested using a UAV flying along a trajectory at variable speed. It is shown that a mean positioning error (MPE) of 0.36 m in 2D and 0.43 m in 3D is achieved when twelve gNBs with receivers spaced at 0.25 m are used. In addition, the corresponding root mean square error (RMSE) values of 0.32 m (2D) and 0.33 m (3D) further confirm the stability of the localization performance by indicating a low dispersion of positioning errors. This demonstrates that high positioning accuracy is feasible, even when synchronization errors and hardware imperfections exist. Full article

Unmanned aerial vehicles (UAVs) are increasingly used to monitor wildlife, but training robust detectors still requires large, consistently annotated datasets collected across seasons, habitats and flight altitudes. In practice, such data are scarce and expensive to label, especially when animals occupy only a few pixels in high-altitude imagery. We present a behaviour-aware synthetic data pipeline, implemented in Unreal Engine 5, that combines parameterised animal agents, procedurally varied environments and UAV-accurate camera trajectories to generate large volumes of labelled UAV imagery without manual annotation. Each frame is exported with instance masks, YOLO-format bounding boxes and tracking metadata, enabling both object detection and downstream behavioural analysis. Using this pipeline, we study YOLOv8s trained under six regimes that vary by data source (synthetic versus real) and input resolution, including a fractional fine-tuning sweep on a public deer dataset. High-resolution synthetic pre-training at 1280 px substantially improves small-object detection and, after fine-tuning on only 50% of the real images, recovers nearly all performance achieved with the fully labelled real set. At lower resolution (640 px), synthetic initialisation matches real-only training after fine-tuning, indicating that synthetic data do not harm and can accelerate convergence. These results show that behaviour-aware synthetic data can make UAV wildlife monitoring more sample-efficient while reducing annotation cost. Full article

Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics. Full article

The increase in the number of Unmanned Aerial Vehicles (UAVs) for liquid transport tasks, such as agricultural spraying, results in significant stability challenges. The free movement of the liquid, known as sloshing, generates unpredictable forces that destabilize the vehicle and increase collision risks. This study treats this problem by developing and validating a nonlinear control strategy to ensure precise trajectory tracking while actively suppressing liquid sloshing. The coupled dynamics of the system are modeled using the Euler–Lagrange formalism by representing the UAV as a planar vertical take-off and landing (PVTOL) aircraft and the liquid sloshing dynamics as an equivalent pendulum model. The stability of the entire closed-loop system is proven using Lyapunov’s direct method. The analytical results are validated through numerical simulations in MATLAB/Simulink, which demonstrate excellent tracking of desired altitude and horizontal trajectories. Crucially, the simulations confirm that the controller effectively attenuates the sloshing oscillations, offering a robust solution to enhance the safety and operational performance of UAVs in liquid transport applications. Full article

This article presents the results of simulation-based research aimed at assessing the impact of tyre damage on vehicle travel safety. The analysis takes into account various influencing factors, including vehicle speed, load conditions, and road surface condition (dry or wet asphalt). Particular emphasis was placed on the dynamic analysis of the vehicle during collision scenarios, including post-impact vehicle positioning, changes in kinetic energy, and the magnitude of the generated impact force. Simulation results indicate that tyre damage significantly compromises vehicle trajectory stability and, in certain cases, makes vehicle control impossible. The conclusions highlight the critical importance of maintaining proper tyre condition in mitigating the consequences of road collisions and emphasise the need for regular tyre inspections as part of routine vehicle maintenance. Full article

Trajectory planning for intelligent connected vehicles (ICVs) must simultaneously address safety, efficiency, and environmental impact to align with sustainable development goals. This paper proposes a novel hierarchical trajectory planning framework, designed for intelligent connected vehicles (ICVs) that integrates a semantic corridor with a spatiotemporal potential field. First, a spatiotemporal safety corridor, enhanced with semantic labels (e.g., low-carbon zones and recommended speeds), delineates the feasible driving region. Subsequently, a multi-objective sampling optimization method generates candidate trajectories that balance safety, comfort and energy consumption. The optimal candidate is refined using a spatiotemporal potential field, which dynamically integrates obstacle predictions and sustainability incentives to achieve smooth and eco-friendly navigation. Comprehensive simulations in typical urban scenarios demonstrate that the proposed method reduces energy consumption by up to 8.43% while maintaining safety and a high level of comfort, compared with benchmark methods. Furthermore, the method’s practical efficacy is validated using real-world vehicle data, showing that the planned trajectories closely align with naturalistic driving behavior and demonstrate safe, smooth, and intelligent behaviors in complex lane-changing scenarios. The validation using 113 real-world truck lane-changing cases demonstrates high consistency with naturalistic driving behavior. These results highlight the framework’s potential to advance sustainable intelligent transportation systems by harmonizing safety, comfort, efficiency, and environmental objectives. Full article

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article

To address the low trajectory tracking accuracy and limited robustness of conventional reinforcement learning algorithms under complex marine environments involving wind, wave, and current disturbances, this study proposes a proximal policy optimization (PPO) algorithm incorporating an intrinsic curiosity mechanism to solve the unmanned surface vehicle (USV) trajectory tracking control problem. The proposed approach is developed on the basis of a three-degree-of-freedom (3-DOF) USV model and formulated within a Markov decision process (MDP) framework, where a multidimensional state space and a continuous action space are defined, and a multi-objective composite reward function is designed. By incorporating a pure pursuit guidance algorithm, the complexity of engineering implementation is reduced. Furthermore, an improved PPO algorithm integrated with an intrinsic curiosity mechanism is adopted as the trajectory tracking controller, in which the exploration incentives provided by the intrinsic curiosity module (ICM) guide the agent to explore the state space efficiently and converge rapidly to an optimal control policy. The final experimental results indicate that, compared with the conventional PPO algorithm, the improved PPO–ICM controller achieves a reduction of 54.2% in average lateral error and 47.1% in average heading error under simple trajectory conditions. Under the complex trajectory condition, the average lateral error and average heading error are reduced by 91.8% and 41.9%, respectively. These results effectively demonstrate that the proposed PPO–ICM algorithm attains high tracking accuracy and strong generalization capability across different trajectory scenarios, and can provide a valuable reference for the application of intelligent control algorithms in the USV domain. Full article