Distance-based localization using Time-of-Arrival (ToA) estimation is fundamentally constrained by the accuracy of channel parameter tracking in non-stationary, high-mobility environments. In such scenarios, the estimation landscape becomes highly non-convex and multi-modal, rendering conventional gradient-driven trackers inadequate. This paper proposes a Gated Recurrent Unit (GRU)-enhanced Whale Optimization-based Tracking Algorithm (WoTA), a robust framework that synergistically fuses meta-heuristic global optimization with recursive filtering. Specifically, an Opposition-based Learning Whale Optimization Algorithm (OBL-WOA) is integrated to adaptively explore the complex cost function surface, effectively initializing the tracker and preventing local optima entrapment in multi-modal distributions. To overcome the limitations of manual parameter tuning in traditional WOA and Kalman Filters (KF), a GRU-based adaptive mechanism is introduced. This mechanism learns the temporal dependencies of optimization residuals to dynamically recalibrate the filter’s noise covariance and optimization hyperparameters, thereby enhancing resilience against model uncertainties. By coupling OBL-WOA’s global exploration with the KF’s exploitation of temporal kinematics, WoTA achieves smoothed and precise state transitions. Comprehensive simulations and real-world channel sounding measurements demonstrate that WoTA consistently outperforms state-of-the-art methods, such as SAGE and KEST. These results demonstrate that the proposed integration of heuristic exploration and recursive refinement offers a scalable and accurate framework for channel parameter tracking in dynamic multipath environments, thereby supporting reliable 6G wireless systems.

Feature selection is a critical step in data mining, with the granular ball rough set model widely applied in this area. However, the randomness issue during the initialization of the existing fuzzy granular ball rough set algorithm may lead to the loss of samples during the feature measurement process. Additionally, it also lacks consistency in handling fuzzy samples in different regions of the granular ball, which further weakens the classification performance of high-dimensional fuzzy data. To this issue, we propose a fuzzy advantage granular ball rough set feature selection algorithm via deep reinforcement learning. First, to reduce the randomness in the generation process of granular balls, the center generation method is optimized by defining sample aggregation degree and spacing. Second, to enhance feature credibility, the granular ball advantage degree is constructed and integrated with purity to evaluate feature importance. Third, to improve the handling of fuzzy samples, a deep reinforcement learning mechanism is introduced to uniformly process the fuzzy samples. Finally, a feature selection algorithm (FGFSD) suitable for high-dimensional fuzzy data is proposed. Evaluated on 20 benchmarks, FGFSD achieves a feature reduction rate of 65% on low-dimensional datasets and up to 99.6% on high-dimensional datasets. Furthermore, it attains a mean accuracy of 0.9459, surpassing the suboptimal feature selection method's 0.9013 by 4.95%.

With Petri nets, this paper aims to design a fast response and recovery strategy for abnormal situations where some of right-of-way (ROW) resources are not available due to accidents, thereby enhancing the resilience of unsignalized autonomous intersections. First, based on the analysis of vehicle trajectory characteristics at intersections, we extend the semantic types of places in classical Petri nets and propose the ROW resource Petri net (RRPN) for modeling fundamental autonomous intersections. Then, we introduce lane change transitions and establish a failure and recovery model for a failed ROW. With these two models, the RRPN with resource failure and recovery (RRPN-FR) model for autonomous intersections is built. Subsequently, based on the RRPN-FR model, we introduce maximal perfect resource transition circuit (MPC) and resource recovery circuit (RRC), and analyze the deadlocks caused by these two structures. Then, we introduce the notion of enhanced transition cover and design a resilient intersection control strategy based on the enhanced transition cover. It formally shows that this control strategy can enhance the resilience of a controlled intersection, effectively responding to unexpected situations and abnormal traffic conditions, which in turn prevents traffic congestion. Simulation experiments and comparative analysis further validate the effectiveness of the proposed control strategy in the failure and recovery process and overall performance. Model is available at:https://github.com/yaxwei/Supplement/blob/TITS/SupplementaryMaterials.pdf

As vehicular edge computing (VEC) grows increasingly demanding, distributed task offloading is brought up as a potential solution. However, the high mobility and limited resources of vehicles, coupled with uncertain service reliability, make it difficult to guarantee timely and correct execution of computation-intensive tasks. Existing task offloading models overlook critical aspects such as punctuality and correctness, limiting their ability to select reliable service vehicles in dynamic and potentially adversarial environments. To address this gap, we propose a performance-reputation integrated task offloading scheme for reliable VEC. We introduce a dual-dimensional reputation model (D2Rep) that jointly evaluates vehicles based on the degree of punctuality, result correctness probability, and historical reputation value. Building on this, we develop the performance value-maximized vehicular computation offloading (PFVMax-VCO) scheme, which combines reputation value, link reliability, and projected success rate into a unified performance value to guide multi-objective optimization of delay, energy consumption, and reliability. To support the realistic evaluation, we implement a SUMO-Veins-OMNeT++ integrated simulation framework. Experimental results demonstrate that the proposed PFVMax-VCO scheme outperforms existing solutions in terms of execution cost, result correctness probability, and task success rate, while enabling robust and dynamic reputation management for VEC systems.

Recent advancements in computing and autonomous driving technologies have led to the integration of new functionalities into intelligent automotive, such as environmental perception, path planning, assisted driving, and entertainment services. This integration requires the rapid processing of critical tasks, including collision detection, emergency braking, lane keeping, and Vehicle-to-Everything (V2X) communication, exceeding past functional demands. Consequently, high-performance multi-core processors have emerged as the preferred hardware solution due to their superior processing speeds, energy efficiency, and parallel task execution capabilities. This paper explores various applications of multi core processors in intelligent automotive systems, systematically reviewing recent advancements in multi-core scheduling methods. It categorizes and analyzes approaches to task loading, task migration, efficiency improvement, safety assurance, communication, and resource contention in both general and automotive contexts. To objectively assess these methods, the paper establishes reference standards for evaluating scheduling methods and system architectures and provides analytical methods aligned with these standards. Finally, the paper discusses future challenges, considers trends in intelligent automotive systems and multi-core processors, and offers recommendations for future research in this field.

Human–machine co-driving is expected to be a long-term driving mode. However, existing cooperative steering control strategies often struggle to effectively handle human–machine interaction conflicts and dynamically allocate driving authority, making it difficult to balance safety and driver’s comfort. To address these issues, this article establishes a Stackelberg game-based model predictive control (MPC) framework and derives a human–machine optimal control strategy under the equilibrium conditions. Furthermore, a two-layer adaptive authority allocation model is developed using the deep deterministic policy gradient (DDPG) method, which comprehensively considers environmental risks, human–machine conflict, and driver’s states. This model prevents a vehicle from entering an unstable state by dynamically allocating the driving authority to both the human driver and the autonomous driving system. Results from driver-in-the-loop experiments indicate that, in obstacle avoidance scenarios, the proposed control strategy enhances vehicle driving stability by 31.49%, improves control stability by 48.11%, and reduces the driver’s burden by 42.87%, demonstrating that the proposed strategy can assist the driver in completing obstacle avoidance tasks and ensure driving safety in high-risk scenarios. In low-risk scenarios, it maintains the driver’s freedom, prevents excessive intervention from the autonomous driving system that could lead to significant human–machine conflicts, and alleviates the driver’s operational burden.

Transformer-based methods have achieved remarkable performance in event-based object detection, owing to the global modeling ability. However, they neglect the influence of non-event and noisy regions and process them uniformly, leading to high computational overhead. To mitigate computation cost, some researchers propose window attention based sparsification strategies to discard unimportant regions, which sacrifices the global modeling ability and results in suboptimal performance. To achieve better trade-off between accuracy and efficiency, we propose Sparse Mamba (SMamba), which performs adaptive sparsification to reduce computational effort while maintaining global modeling capability. Specifically, a Spatio-Temporal Continuity Assessment module is proposed to measure the information content of tokens and discard uninformative ones by leveraging the spatiotemporal distribution differences between activity and noise events. Based on the assessment results, an Information-Prioritized Local Scan strategy is designed to shorten the scan distance between high-information tokens, facilitating interactions among them in the spatial dimension. Furthermore, to extend the global interaction from 2D space to 3D representations, a Global Channel Interaction module is proposed to aggregate channel information from a global spatial perspective. Results on three datasets (Gen1, 1Mpx, and eTram) demonstrate that our model outperforms other methods in both performance and efficiency.

In cognitive radio ad-hoc networks (CRAHN), we can change the network topology through flexible spectrum allocation. In this regard, even though CRAHN is not prone to single points of failure, its network performance is entirely dependent on node connectivity. However, the existing solutions mainly focus on the connections between certain nodes, without fully considering the overall network connectivity. To this end, to avoid a large number of connection failures caused by local damage, it is necessary of improving system connectivity performance from a global perspective when allocating available spectrum. As such, we explore the relationship between spectrum allocation and global connectivity of CRAHN, and then propose a robust optimization scheme using optimal spectrum allocation (ROUOS). This scheme aims to create new connections in the connectivity vulnerable areas through spectrum allocation. Specifically, we establish a spectrum allocation model including available spectrum matrix, bandwidth benefit matrix, interference constraint matrix, communication connection matrix, alternate channel vector and optimal allocation matrix. After that, based on the connectivity quantitative index in [11], we design a network benefit function to measure the gain effect of different spectrum allocation solutions on network connectivity. Finally, we select the spectrum allocation solution (i.e., adding communication links) that most effectively improves network connectivity. Simulation results show that, compared to the benchmark scheme, our proposed ROUOS scheme increases the second smallest eigenvalue of the graph Laplacian matrix from 0.09 to 0.29, significantly enhancing the robustness of topological connectivity.

Brain-inspired spiking neural networks (SNNs) have the capability of energy-efficient processing of temporal information. However, leveraging the rich dynamic characteristics of SNNs and prior works in artificial neural networks (ANNs) to construct an effective object detection model for visual tasks remains an open question for further exploration. To develop a directly-trained , low energy consumption and high-performance multi-scale SNN model, we propose a novel interpretable object detection framework Multi-scale Spiking Detector (MSD). Initially, we propose a spiking convolutional neuron as a core component of the Optic Nerve Nucleus Block (ONNB), designed to significantly enhance the deep feature extraction capabilities of SNNs. ONNB enables direct training with improved energy efficiency, demonstrating superior performance compared to state-of-the-art ANN-to-SNN conversion and SNN techniques. In addition, we propose a Multi-scale Spiking Detection Framework to emulate the biological response and comprehension of stimuli from different objects. Wherein, spiking multi-scale fusion and the spiking detector are employed to integrate features across different depths and to detect response outcomes, respectively. Our method outperforms state-of-the-art ANN detectors, with only 7.8 M parameters and 6.43 mJ energy consumption. MSD obtains the mean average precision (mAP) of 62.0% and 66.3% on COCO and Gen1 datasets, respectively.

We proposes Attention Gumbel-Softmax Multi-Agent Deep Deterministic Policy Gradient (AGS-MADDPG), a multi-agent reinforcement learning algorithm for collaborative pathfinding under partial observability. Traditional methods face challenges including non-differentiable policy gradients and inefficient exploration in discrete action spaces. We address these by integrating the MADDPG framework with Gumbel-Softmax for differentiable policy optimization and adaptive exploration via temperature annealing. A multi-head attention mechanism in the critic network explicitly models inter-agent dependencies by dynamically weighting teammates’ action features, enhancing Q-value estimation precision and training stability. Evaluations in Pogema environments with random obstacles and complex layouts demonstrate improved convergence speed, pathfinding success rates, and average path length compared to baseline methods. The results validate the effectiveness of combining differentiable discrete policies with explicit attention-based coordination for multi-agent collaboration in partially observable settings.

Coordination at unsignalized intersections has attracted increasing attention in recent years, which aims at improving the efficiency of intersection operations, while eliminating conflicts and deadlocks for Connected and Automated Vehicles (CAVs). This paper addresses the challenging issue of systematically optimizing CAV trajectories, tackling the high computational cost for finding a good solution, especially as the number of lanes and CAVs increases. To do so, a novel systematic optimal trajectory planning model is designed to efficiently guide CAVs through intersections without conflicts and deadlocks. To tackle the computational hurdles and enable real-time applications, based on the model, we develop a Petri net-aided Iterative Trajectory Optimization (P-ITO) solution algorithm. Leveraging the unique characteristics of the problem, this algorithm first designs a Petri net-based controller for conflict and deadlock avoidance so that initial feasible solutions are generated. Then, refinement is made on the initial feasible solutions to obtain an optimal or near optimal solution by designing an iterative process. This algorithm effectively ensures solution feasibility and enhances computing efficiency by searching for an optimal solution in the feasible region. Numerical experiments for intersections with bidirectional six-lane configurations illustrate the efficacy of our model in facilitating the safe and efficient passage of all CAVs while mitigating the risk of deadlocks. The P-ITO algorithm significantly outperforms the commercial solvers in both solution quality and computational time. Furthermore, our method is versatile, applicable to diverse intersection scenarios, and capable of maintaining high computational efficiency.

Proper planning of charging infrastructure can significantly facilitate the popularization of electric vehicles and alleviate users’ mileage anxiety. Charging station siting and sizing are two key challenges in the planning with each of them being a complex optimization problem. In this paper, a multi-objective optimization approach is proposed to solve them together. First, considering that accurate charging demand estimation is crucial for planning, a traffic road network is established for this purpose. A Monte Carlo method is used to estimate the spatiotemporal distribution of charging demand in a region based on the probabilistic characteristics of user trips. Since uncoordinated charging not only increases the load but also leads to unstable operation of the local power system, a heuristic algorithm is proposed to coordinate charging scheduling. Then, based on the scheduled demand, this paper proposes a framework for the siting and sizing of charging stations to optimize the benefits for both operators and users by minimizing the construction, operation and maintenance costs, and the user’s detour time. As the given problem is a complex multi-objective combinatorial optimization problem, it is easy to fall into local optimum if traditional evolutionary algorithms are employed. Therefore, a multi-objective dynamic binary particle swarm optimization method is designed to solve this problem effectively. Finally, experimental simulations show that the proposed method outperforms the other comparative algorithms in terms of solution quality. A case study is presented to demonstrate the applicability and effectiveness of the proposed method in optimizing the location and capacity of charging stations.

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle charging has widely become a concern. Therefore, considering the fact that charging time for one EV cannot be shortened quickly and the number of charging stations will not expand rapidly, how to schedule charging operations of electric vehicles in urban areas becomes a very important issue, since it can improve charging efficiency and relieve charging anxiety of EV users. Up to now, there is no scheduling software tool for practical use in this field. Based on the analysis of electric vehicle charging behavior characteristics, this paper investigates the EV charging problem at the scheduling level. First, a mathematical model for coordinated charging of EVs is proposed to minimize the total charging time for a given number of vehicles. Second, an earliest finish charging scheduling algorithm is presented to solve the charging problem. Then, by considering the combinatorial nature and practical applications with large number of EVs, two practical swarm-optimization-based EV charging scheduling algorithms are proposed. A real-life case study is presented to illustrate the proposed approaches.

This paper mainly addresses the issue of designing safety Petri net-based controllers to prevent vehicle flow deadlocks at intersections. The designed controller can monitor the flow of vehicles at an intersection and guide them to pass an intersection safely without causing deadlocks. This study begins by investigating the intersection deadlock scenarios, analyzing the physical size of the “right-of-way” cells with the discretization of the interior area of an intersection, and developing the intersection initial Petri net (PN) model based on simple sequential processes with resources (S3PR). Then, based on the initial PN model, we propose a deadlock prevention strategy for controller design to control the vehicle flows to ensure that the vehicle flows in different directions do not result in deadlocks. Finally, the effectiveness of the proposed strategy is illustrated by examples and theoretical proof. This study contributes to the advancement of the state-of-the-art in the design of safety controllers for self-driving vehicles passing through intersections.

In this paper, the problem of developing a model for signal control system with transit priority using Colored Petri Nets (CPNs) is considered. In a regular four phases signal lights control model, transit detection and two kinds of transit priority strategies are integrated to obtain Colored Petri Nets based transit priority signal control model. The resulting model ensures that transit can pass through intersection with no or less delay. In order to verify the correctness and reliability of the proposed model, the reachability graph is generated and analyzed. We also compare our model with some existing models in literature. This work helps advance the state-of-the-art in design of signal control model related to the intersections.

The research presents in this paper develops an Ant Colony System (ACS) based system optimal route choice strategy to ensure the rational traffic flow assignment for urban traffic network. In this work, the traffic flow and impedance function of each road section are calculated firstly, and then the individual traveler's route choice behaviors on network nodes are simulated based on applying the pseudo-random state transition rule, route and road section pheromone update formula, which implement the synthesizing of static prior knowledge, dynamic traffic state and the randomness of route choice. This paper's findings reveal that the designed strategy is in a position to reflecting the overlay and delay effect of route choice under different Origin-Destination (OD) demands. In addition, the findings can obtain better network equilibrium comparing with the incremental assignment method, and will benefit for achieving the route guidance system with time varying traffic conditions.