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  • 期刊名称：

    Intelligent Transportation Systems, IEEE Transactions on

  • 中文名称： 智能交通系统，IEEE事务
  • 刊频：
  • ISSN： 1524-9050
  • 出版社： -
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  • 1.

    【24hr】AI-Empowered Trajectory Anomaly Detection for Intelligent Transportation Systems: A Hierarchical Federated Learning Approach

    包量

    机译 面向智能交通系统的AI轨迹异常检测：一种分层联邦学习方法
    摘要：The vigorous development of positioning technology and ubiquitous computing has spawned trajectory big data. By analyzing and processing the trajectory big data in the form of data streams in a timely and effective manner, anomalies hidden in the trajectory data can be found, thus serving urban planning, traffic management, safety control and other applications. Limited by the inherent uncertainty, infinity, time-varying evolution, sparsity and skewed distribution of trajectory big data, traditional anomaly detection techniques cannot be directly applied to anomaly detection in trajectory big data. To solve this problem, we propose a hierarchical trajectory anomaly detection scheme for Intelligent Transportation Systems (ITS) using both machine learning and blockchain technologies. To be specific, a hierarchical federated learning strategy is proposed to improve the generalization ability of the global trajectory anomaly detection model by secondary fusion of the multi-area trajectory anomaly detection model. Then, by integrating blockchain and federated learning, the iterative exchange and fusion of the global trajectory anomaly detection model can be realized by means of on-chain and off-chain coordinated data access. Experiments show that the proposed scheme can improve the generalization ability of the trajectory anomaly detection model in different areas, while ensuring its reliability.
  • 2.

    【24hr】Guest Editorial Introduction to the Special Issue on AI-Empowered Trajectory Analytics in Intelligent Transportation Systems

    包量

    机译 智能交通系统中人工智能轨迹分析特刊的客座编辑介绍
    摘要：With the rapid growth of location sensing in the Internet of Things (IoT) and Internet of Vehicles (IoV) techniques, trajectory data has been generated that can be used to describe diversity and characteristics of moving objects. The analysis and management of trajectory patterns has become an important issue in recent decades, as it supports efficient strategies and decisions based on discovered patterns and knowledge from the mobility behavior of customers or citizens in many fields and applications (e.g., smart city, intelligent transportation, location-based services, health management, etc.). Since the recent development in AI, it is possible to use AI-based techniques to analyze trajectory data at an unprecedented scale to address applicable issues of effectiveness, efficiency, accuracy, and privacy in Intelligent Transportation Systems (ITS), therefore, it is a highly competitive area to propose innovative methods, principles, procedures, techniques, frameworks, theories, and applications to address the aforementioned challenges of trajectory data in ITS. This Special Issue is intended to provide a forum for all researchers from academia and industry to share their original, creative, innovative, cutting-edge insights, theories, ideas, and developments for the analysis of trajectory data using AI-empowered techniques in ITS. In this special issue, we received 52 submissions, and finally accepted 17 articles to be published in the special issue. Below is a brief introduction to each of them.
  • 3.

    【24hr】Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey

    包量

    机译 面向智能交通系统的情境感知机器学习：一项调查
    摘要：Context awareness adds intelligence to and enriches data for applications, services and systems while enabling underlying algorithms to sense dynamic changes in incoming data streams. Context-aware machine learning is often adopted in intelligent services by endowing meaning to Internet of Things(IoT)/ubiquitous data. Intelligent transportation systems (ITS) are at the forefront of applying context awareness with marked success. In contrast to non-context-aware machine learning models, context-aware machine learning models often perform better in traffic prediction/classification and are capable of supporting complex and more intelligent ITS decision-making. This paper presents a comprehensive review of recent studies in context-aware machine learning for intelligent transportation, especially focusing on road transportation systems. State-of-the-art techniques are discussed from several perspectives, including contextual data (e.g., location, time, weather, road condition and events), applications (i.e., traffic prediction and decision making), modes (i.e., specialised and general), learning methods (e.g., supervised, unsupervised, semi-supervised and transfer learning). Two main frameworks of context-aware machine learning models are summarised. In addition, open challenges and future research directions of developing context-aware machine learning models for ITS are discussed, and a novel context-aware machine learning layered engine (CAMILLE) architecture is proposed as a potential solution to address identified gaps in the studied body of knowledge.
  • 4.

    【24hr】C-FAR: A Compositional Framework for Anomaly Resolution in Intelligent Transportation Systems

    包量

    机译 C-FAR：智能交通系统中异常解析的组合框架
    摘要：In this paper, we present C-FAR, a framework for reasoning about anomalies in road-based intelligent transportation systems (ITS) based on video monitoring by the roadside camera infrastructure. The anomalies could span broad temporal and spatial ranges, including fine-grain (e.g., unsafe interactions among moving vehicles in real-time), medium-grain (e.g., aggressive/unsafe driving styles of individual vehicles over extended periods/distances), and coarse-grain (e.g., ensemble properties of the traffic over even longer time horizons). Unlike traditional approaches that utilize deep learning to recognize individual activities, C-FAR does so only for primitive movements and activities and then builds a comprehensive event logic framework. It also provides an optimal resolution of the detected/predicted anomalies by identifying the minimal changes in the controllable parameters of the system. We implemented a prototype system and tested it on three distinct real-world traffic data sets. We demonstrate that the proposed scheme can predict anomalies with over 84 recall level at 95 confidence level approximately 4.05 seconds before the incident.
  • 5.

    【24hr】Bl-IEA: A Bit-Level Image Encryption Algorithm for Cognitive Services in Intelligent Transportation Systems

    包量

    机译 Bl-IEA：一种用于智能交通系统认知服务的位级图像加密算法
    摘要：In Intelligent Transportation Systems, images are the main data sources to be analyzed for providing intelligent and precision cognitive services. Therefore, how to protect the privacy of sensitive images in the process of information transmission has become an important research issue, especially in future no non-private data era. In this article, we design the Rearrangement-Arnold Cat Map (R-ACM) to disturb the relationship between adjacent pixels and further propose an efficient Bit-level Image Encryption Algorithm ( $text{B}{l}$ -IEA) based on R-ACM. Experiments show that the correlation coefficients of two adjacent pixels are 0.0022 in the horizontal direction, -0.0105 in the vertical direction, and -0.0035 in the diagonal direction respectively, which are obviously weaker than that of the original image with high correlations of adjacent pixels. What’s more, the NPCR is 0.996120172, and the UACI is 0.334613406, which indicate that $text{B}{l}$ -IEA has stronger ability to resist different attacks compared with other solutions. Especially, the lower time complexity and only one round permutation make it particularly suitable to be used in the time-limited intelligent transportation field.
  • 6.

    【24hr】Agreement-Induced Data Verification Model for Securing Vehicular Communication in Intelligent Transportation Systems

    包量

    机译 智能交通系统车辆通信安全的协议诱导数据验证模型
    摘要：Intelligent Transportation security requires cooperative credentials for sharing navigation and communication data between the vehicles. However due to the dynamic environment, communication is interrupted by the adversaries, resulting in non-privacy issues. This article introduces an Agreement-induced Data Verification Model (ADVM) for securing vehicular communication against adversaries. The connected vehicles in a grid communicate with each other based on direct and indirect recommendation. This recommendation is based on mutual identity sharing between the vehicles for masked information exchange. Non-replicated and recommendation based verifications are performed using the vector classification learning. In this learning process, the credential validity and communication tolerance amid adversaries are augmented. The constraint-failing vehicles are disconnected from the communication grid, preventing its insecure impact over the communication. The proposed model’s performance is verified using false rate, success ratio, processing time, complexity, and recommendation ratio. For the different vehicles, the proposed model achieves 9.69 less false rate, 10.3 success ratio, 10.49 less processing time, 10.3 less complexity, and 12.87 high recommendation ratio.
  • 7.

    【24hr】Graph Optimized Data Offloading for Crowd-AI Hybrid Urban Tracking in Intelligent Transportation Systems

    包量

    机译 面向智能交通系统中Crowd-AI混合城市跟踪的图优化数据卸载
    摘要：Urban tracking plays a vital role for people’s urban life in intelligent transportation systems, e.g., public safety, case investigation, finding missing items, etc. However, the current tracking methods consume a large amount of communication and computing resources since they mainly offload all related sensing data, i.e., videos, generated by widely deployed cameras to the cloud where data are stored, processed, and analyzed. In this paper, we propose a graph optimized data offloading algorithm leveraging a crowd-AI hybrid method to minimize the data offloading cost and ensure the reliable urban tracking result. To be specific, we first formulate a crowd-AI hybrid urban tracking scenario, and prove the proposed data offloading problem in this scenario is NP-hard. Then, we solve it by decomposing the problem into two parts, i.e., trajectory prediction and task allocation. The trajectory prediction algorithm, leveraging the state graph, computes possible tracking areas of the target object, and the task allocation algorithm, using the dependency graph, chooses the optimal set of crowds and cameras to cover the tracking area while minimizing the data offloading cost separately. Finally, the extensive simulations with large real world data set are conducted showing that the proposed algorithm outperforms benchmarks in reducing data offloading cost while ensuring the tracking success rate in intelligent transportation systems.
  • 8.

    【24hr】Light Deep Models for Cognitive Computing in Intelligent Transportation Systems

    包量

    机译 用于智能交通系统中认知计算的光深度模型
    摘要：The paper proposes light convolutional neural network (CNN) models for the use of cognitive networking in an intelligent transportation system (ITS). There are two CNN models, one with 1D convolution and connectors, and the other with a tree-like structure. The 1D CNN model is deployed to process 1D temporal data such as the driver’s body temperature and electrocardiogram (ECG) data to measure emotion, while the deep tree CNN model is used to process image data obtained from car camera sensors. As the driver’s cognitive state can frequently change depending on the situation and location of the car, different edge controllers should handle the car sensors’ data within a short period of time. The tree-based deep learning model that can be branched and processed independently in the edge devices can be executed with less computation. This reduces the load and the time of the execution of the model. The light 1D CNN model has less learnable parameters, and hence can be executed in real-time. The cognitive state of a driver is measured by the facial emotion, body temperature, and ECG signal of the driver. The proposed is tested using a publicly available facial emotion database, and the accuracy and the information density are around 94-96 and 4.4, respectively.
  • 9.

    【24hr】Multi-Granularity Collaborative Decision With Cognitive Networking in Intelligent Transportation Systems

    包量

    机译 基于认知网络的智能交通系统多粒度协同决策
    摘要：Cognitive networking is a valuable enabler to improve the capability of intelligent transportation system (ITS) by analyzing and utilizing the heterogeneous traffic information. However, the significant increase in the amount of decision-making tasks makes it difficult to guarantee real-time performance of decision response. This paper focuses on the problem of the quality and real-time assurance of collaborative decision-making response in large-scale ITS during multi-task parallelism execution. First, a collaborative decision architecture with cognitive networking is developed, which introduces the advanced 6G communication technology to enhance information interaction capability of vehicle-road-cloud collaboration, and lays the foundation for multi-task real-time decision-making with inevitable fuzzy information in the perception process. Then, a multi-task parallel multi-granularity collaborative decision model (MPMCD) is designed to improve knowledge discovery ability for decision-making process by building multi-granularity information structures. An AI-driven cognitive networking collaborative decision-making (ACNCD) algorithm is further proposed based on MPMCD model to support multi-task parallel vehicle-road-cloud collaborative real-time decision. Extensive simulation experiments are carried out to evaluate ACNCD algorithm in terms of several performance criteria including decision response time, accuracy, and accident rate. The obtained results show that the comprehensive decision-making performance of ACNCD outperforms other relevant existing algorithms.
  • 10.

    【24hr】IVF-Net: An Infrared and Visible Data Fusion Deep Network for Traffic Object Enhancement in Intelligent Transportation Systems

    包量

    机译 IVF-Net：一种用于智能交通系统交通对象增强的红外-可见数据融合深度网络
    摘要：Infrared and visible data fusion (IVF) aims to generate a fused output that simultaneously highlights salient thermal radiation features and preserves texture information, which can not only grasp the necessary information for traffic movement, but also highlight the invisible objects that need to be dodged in intelligent transportation system (ITS). Therefore, IVF is capable of improving the environmental perception ability for various challenging traffic situations, e.g., foggy scenarios, rainy environments, and low-light illumination. However, current available IVF algorithms cannot offer a theoretical manner to integrate a priori knowledge and the network structure into a unified model. Moreover, they always fail to handle infrared and visible data pairs with different resolutions, which is a common occurrence in real ITS scenarios. To this end, this study develops a novel model-inspired unsupervised network termed IVF-Net. Specifically, an enhanced IVF model (IVFM), which pays more attention on detailed texture information and salient objects, is first established. According to proximal gradient theory, then we map this model into a deep network with learnable feature extraction parameters, aiming to draw on the strengths of the fusion model and deep learning to better describe the IVF task. Finally, a multiple task-driven loss function is designed to train the mapped network. Unlike previous work, our IVF-Net is motivated by IVFM, each layer in which has a semantic interpretability and a clear mission, thereby leading to a significantly enhanced fusion effect. Another advantage is that it is only composed of simple convolution-based structures, which ensures its lightweight and efficiency. Experiments demonstrate that IVF-Net can have a stronger ability to capture the key traffic information and highlight the salient feature of imperceptible objects, which makes it an excellent candidate to improve the reliability of subsequent applications in ITS.
  • 11.

    【24hr】A Privacy-Preserving Solution for Intelligent Transportation Systems: Private Driver DNA

    包量

    机译 智能交通系统的隐私保护解决方案：私人司机的DNA
    摘要：The rising connection of vehicles with the road infrastructure enables the creation of data-driven applications to offer drivers customized services. At the same time, these opportunities require innovative solutions to protect the drivers’ privacy in a complex environment like an Intelligent Transportation System (ITS). This need is even more relevant when data are used to retrieve personal behaviors or attitudes. In our work, we propose a privacy-preserving solution, called Private Driver DNA, which designs a possible architecture, allowing drivers of an ITS to receive customized services. The proposed solution is based on the concept of Driver DNA as characterization of driver’s driving style. To assure privacy, we perform the operations directly on sanitized data, using the Order Revealing Encryption (ORE) method. Besides, the proposed solution is integrated with ITS architecture defined in the European project E-Corridor. The result is an effective privacy-preserving architecture for ITS to offer customized products, which can be used to address drivers’ behaviors, for example, to environmental-friendly attitudes or a more safe driving style. We test Private Driver DNA using a synthetic dataset generated with the vehicle simulator CARLA. We compare ORE with another encryption method like Homomorphic Encryption (HE) and some other privacy-preserving schemas. Besides, we quantify privacy gain and data loss utility after the data sanitization process.
  • 12.

    【24hr】Intelligent Deep Fusion Network for Anomaly Identification in Maritime Transportation Systems

    包量

    机译 用于海上运输系统异常识别的智能深度融合网络
    摘要：This paper introduces a novel deep learning architecture for identifying outliers in the context of intelligent transportation systems. The use of a convolutional neural network with decomposition is explored to find abnormal behavior in maritime data. The set of maritime data is first decomposed into similar clusters containing homogeneous data, and then a convolutional neural network is used for each data cluster. Different models are trained (one per cluster), and each model is learned from highly correlated data. Finally, the results of the models are merged using a simple but efficient fusion strategy. To verify the performance of the proposed framework, intensive experiments were conducted on marine data. The results show the superiority of the proposed framework compared to the baseline solutions in terms of several accuracy metrics.
  • 13.

    【24hr】Intelligent Anomaly Detection of Trajectories for IoT Empowered Maritime Transportation Systems

    包量

    机译 物联网赋能海上运输系统轨迹的智能异常检测
    摘要：The convergence of Maritime Transportation Systems (MTS) and Internet of Things (IoT) has led to the promising IoT-empowered MTS (IoT-MTS). However, abnormal trajectories of maritime transportation ships can have highly negative impacts on the management of IoT-MTS. Therefore, anomaly detection of trajectories is important for the successful deployment of IoT-MTS. In this paper, we propose a Transfer Learning based Trajectory Anomaly Detection strategy, named TLTAD, for IoT-MTS. Specifically, a variational autoencoder is used to discover the potential connections between each dimension of the normal trajectory, while a graph variational autoencoder is used to explore the spatial similarity between normal trajectories. Based on internal connection of trajectories, a deep reinforcement learning algorithm, Twin Delayed Deep Deterministic policy gradient (TD3), is employed to train the trajectory anomaly detection model. To reduce the model training time, transfer learning is used to migrate the trained anomaly detection model between different regions of an ocean area or between similar ocean areas. Moreover, an efficient data transformation module is designed to improve the efficiency of model transfer. The experiments were conducted on a real-world automatic identification system (AIS) dataset. The results indicate that the proposed TLTAD can provide accurate anomaly detection on ships’ trajectories in IoT-MTS with reduced model training times.
  • 14.

    【24hr】IEEE Intelligent Transportation Systems Society Information

    包量

    机译 IEEE智能交通系统学会信息
    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2023年第2期
    摘要：
  • 15.

    【24hr】Guest Editorial Introduction to the Special Issue on Cognitive Networking for Intelligent Transportation Systems

    包量

    机译 《智能交通系统认知网络》特刊的客座编辑介绍
    摘要：Cognitive networking is expected to analyze and utilize the various information for improving the intelligence of transportation systems. For example, through the Vehicleto-Vehicle (V2V), Infrastructure-to-Vehicle, and Vehicular-to-Infrastructure (V2I) communications, which are the foundation and key support technologies determining the overall performance of advanced Intelligent Transportation Systems (ITS), road safety and traffic efficiency are significantly improved.
  • 16.

    【24hr】A Provably Secure Mobile User Authentication Scheme for Big Data Collection in IoT-Enabled Maritime Intelligent Transportation System

    包量

    机译 物联网海事智能交通系统中大数据采集的可验证安全移动用户认证方案
    摘要：The emergence of contemporary technologies like cloud computing and the Internet of Things (IoT) has revolutionized the trends in the cyber world to serve humanity. There are plenty of applications in which they are being used, especially in smart cities and their constituents, Maritime Transportation System (MTS) is one of them. The IoT-enabled MTS has the potential to entertain the growing challenges of modern-day ship transportation. Secure real-time data access from numerous smart IoT devices is the most critical and crucial exercise for Big Data acquisition in IoT-enabled MTS. Therefore, we have developed a Physically Unclonable Function (PUF) based authenticated key agreement solution to deal with this challenge. This solution enables the mobile user and IoT node to mutually authenticate each other via Cloud-Gateway before real-time data exchange and transmission in IoT-enabled MTS. The use of PUF in our solution brings invincibility against physical security threats. An inclusive security analysis under the assumption of the specified threat model is carried out to substantiate the security resilience of our solution. The conduct of our solution is realized through security features, communication, and computation cost and It has been observed that our solution achieves efficiency of 37.3 and 9.7 in communication and computation overhead, respectively. Moreover, the network performance effectiveness of our solution is demonstrated in NS3 implementation.
  • 17.

    【24hr】A Novel Parameter Adaptive Dual Channel MSPCNN Based Single Image Dehazing for Intelligent Transportation Systems

    包量

    机译 一种基于智能交通系统参数自适应双通道MSPCNN的单图像去雾
    摘要：Visibility issues in intelligent transportation systems are exacerbated by bad weather conditions such as fog and haze. It has been observed from recent studies that major road accidents have occurred in the world due to low visibility and inclement weather conditions. Single image dehazing attempts to restore a haze-free image from an unconstrained hazy image. We proposed a dehazing method by cascading two models utilizing a novel parameter-adaptive dual-channel modified simplified pulse coupled neural network (PA-DC-MSPCNN). The first model uses a new color channel for removing haze from images. The second model is the improved brightness preserving model (I-GIHE), which retains the brightness of the image while improving the gradient strength. To integrate the results from these two models and provide a pleasing haze-free image, a PA-DC-MSPCNN-based fusion is used. Furthermore, the proposed approach is deployed on a Xilinx Zynq SoC by exploiting the recently released PYNQ platform. The dehazing system runs on a PYNQ-Z2 all-programmable SoC platform, where it will input the camera feed through the FPGA unit and carry out the dehazing algorithm in the ARM core. This configuration has allowed reaching real-time processing speed for image dehazing. The results of dehazing are analyzed using both synthetic and real-world hazy images. Synthetic hazy images are acquired from the O-HAZE, I-HAZE, SOTS, and FRIDA datasets, while real-world hazy images are taken from the RailSem19, E-TUVD dataset, and the internet. For evaluation, twelve cutting-edge approaches are chosen. The proposed method is also analyzed on underwater and low-light images. Extensive experiments indicate that the proposed method outperforms state-of-the-art methods of qualitative and quantitative performances.
  • 18.

    【24hr】Multifeature Fusion-Based Object Detection for Intelligent Transportation Systems

    包量

    机译 基于多特征融合的智能交通系统目标检测
    摘要：The detection of 3D objects with high precision from point cloud data has become a crucial research topic in intelligent transportation systems. By effectively modeling global and local features, it can be acquired the state-of-the-art detector for 3D object detection. Nevertheless, regarding the previous work on feature representations, volumetric generation or point learning methods have difficulty building the relationships between local features and global features. Thus, we propose a multi-feature fusion network (MFFNet) to improve detection precision for 3D point cloud data by combining the global features from 3D voxel convolutions with the local features from the point learning network. Our algorithm is an end-to-end detection framework that contains a voxel convolutional module, a local point feature module and a detection head. Significantly, MFFNet constructs the local point feature set with point learning and sampling and the global feature map through 3D voxel convolution from raw point clouds. The detection head can use the obtained fusion feature to predict the position and category of the examined 3D object, so the proposed method can obtain higher precision than existing approaches. An experimental evaluation on the KITTI 3D object detection dataset obtain 97 MAP (Mean Average Precision) and Waymo Open dataset obtain 80 MAP, which proves the efficiency of the developed feature fusion representation method for 3D objects, and it can achieve satisfactory location accuracy.
  • 19.

    【24hr】Federated Deep Reinforcement Learning-Based Spectrum Access Algorithm With Warranty Contract in Intelligent Transportation Systems

    包量

    机译 基于联邦深度强化学习的智能交通系统频谱接入算法
    摘要：Cognitive radio (CR) provides an effective solution to meet the huge bandwidth requirements in intelligent transportation systems (ITS), which enables secondary users (SUs) to access the idle spectrum of the primary users (PUs). However, the high mobility of users and real-time service requirements result in the additional transmission collisions and interference, which degrades the spectrum access rate and the quality of service (QoS) of users in ITS. This paper proposes a spectrum access algorithm (Feilin) based on federated deep reinforcement learning (FDRL) to improve spectrum access rate, which maximizes the QoS reward function with considering the hybrid benefits of delay, transmission power and utility of SUs. To guarantee the utility of SUs, the warranty contract is designed for SUs to obtain compensation for data transmission failure, which promotes SUs to compete for more spectrum resources. To meet the real-time requirements and improve QoS in ITS, a spectrum access model called FDQN-W is proposed based on federated deep Q-network (DQN), which adopts the asynchronous federated weighted learning algorithm (AFWLA) to share and update the weights of DQN in multiple agents to decrease time cost and accelerate the convergence. Detailed simulation results show that, in the multiuser scenario, compared with the existing methods, the proposed algorithm Feilin increases the spectrum access success rate by 15.1, and reduces the collision rate with SUs and the collision rate with PUs by 46.4 and 6.8, respectively.
  • 20.

    【24hr】CITS-MEW: Multi-Party Entangled Watermark in Cooperative Intelligent Transportation System

    包量

    机译 CITS-MEW：协同智能交通系统中的多方纠缠水印
    摘要：Federated learning is good for building better cooperative intelligent transportation system (C-ITS). Intellectual property protection in C-ITS brings many benefits to all vehicles. Although the protection of model intellectual property by watermark has received much research attention, the existing works only deploy watermark in centralized models. Due to the difference of watermark distribution among vehicles, the global model accuracy of watermark in federated learning is significantly reduced or the local watermark is invalid. To solve these problems, we propose a multi-party entangled watermark algorithm in federated learning. Specifically, in the local training, we propose a watermark enhancement algorithm, which solves the problem of local watermark failure. Then, in the global aggregation, we propose an entanglement aggregation algorithm, which solves the problem of a great loss of global model accuracy. We conduct extensive experiments on public datasets to show the superiority of our proposal. The results show that our scheme can obtain more than 16 and 31 advantages in model accuracy and watermark success rate, respectively, compared with existing watermark schemes in federated learning.
  • 21.

    【24hr】Communication Security Analysis of Intelligent Transportation System Using 5G Internet of Things From the Perspective of Big Data

    包量

    机译 基于大数据视角的5G物联网智能交通系统通信安全分析
    摘要：The transportation system has entered the era of 5G intelligent Internet of things (IoT), which can realize the comprehensive monitoring, perception, and intelligent decision-making of people, vehicles, roads, and the environment. The purpose is to solve the problems in the communication security of intelligent transportation system (ITS) and improve the vulnerability of traditional distributed architecture. The security issues of the Internet of vehicles (IoV) in 5G environment are analyzed from the perspective of big data. An access control mechanism based on risk prediction is proposed aiming at the problems existing in the node access control process. A Wasserstein Distance-based Combined Generative Adversarial Network (WCGAN) is proposed. It modifies the loss function to solve the gradient disappearance problem, and a combination of multiple generators is designed to solve the pattern collapse. The simulation experiment is carried out on the dataset of the intrusion detection evaluation project. The WCGAN model has the smallest prediction error than the other models regarding the node packet transmission rate. Its loss value is close to 0 after 10 iterations, while the loss value of the BP neural network (BPNN) is about 0.28. The prediction accuracy of the WCGAN model can reach 86.3 when the training set is 5000, which is much higher than that of BPNN (77.8). The reason is that the WCGAN model increases the number of generators according to GAN, which improves the low accuracy caused by pattern collapse. The IoT-based ITS can implement corresponding strategies according to the prediction results and control the access rights of nodes, thus ensuring the security of information resources effectively. The research content reduces the communication delay under ensuring the integrity and confidentiality of information in the process of data transmission, and provides a reference for ensuring the safe communication of IoV.
  • 22.

    【24hr】IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

    包量

    机译 不，智能交通系统协会
    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2023年第3期
    摘要：
  • 23.

    【24hr】An UAV-Enabled Intelligent Connected Transportation System With 6G Communications for Internet of Vehicles

    包量

    机译 面向车联网的6G通信无人机智能互联交通系统
    摘要：6G networks provide faster communication for connected vehicles. These vehicles are connected to the Internet, forming the Internet of Vehicles (IoV). Due to the development of Intelligent Transportation Systems (ITS), more and more vehicles are deployed with data-intensive applications. These applications interact heavily with IoT devices at the edge of the network, which causes IoT devices to consume a lot of limited and valuable power. Task offloading can help overcome resource constraints of IoT devices by offloading task to edge server which has sufficient computational power in ITS. Unmanned Aerial Vehicles (UAV) is a promising solution by serving as Computing-Communications Edge Server (CCES) for resource-constrained IoT devices that there is no edge server nearby that can offload task. Due to the IoT devices’ limited battery capacity and UAV energy budget, it is a challenging issue to reduce the energy for task offloading in UAV-enable edge network. In this paper, an UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme is proposed to offload task energy-efficiently. First, some nodes with a large amount of data are selected as Task Gathering Nodes (TGNs), and TGNs collect all the tasks of the left nodes. In this way, the UAV can only fly the TGNs and so all the IoT devices’ tasks can be offloaded. The distance needed for the UAV can be greatly reduced and energy is saved. On the other hand, tasks that route to TGNs have a relatively small amount of data, while nodes with a large amount of data have already been selected as TGNs without routing, thus saving energy. Second, an optimization strategy for collection tasks is proposed to reduce UAV’s energy. The extensive experimental simulations indicate that the performance of UAV-CCIO scheme is better than the existing scheme.
  • 24.

    【24hr】Optimized Security Algorithms for Intelligent and Autonomous Vehicular Transportation Systems

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    机译 智能和自主车辆运输系统的优化安全算法
    摘要：With the growth of the Internet of Vehicles (IoV) in Intelligent Autonomous Transport Systems (IATS), a huge volume of data is exchanged between vehicles in these newly developed infrastructures. As a result, the requirements of securing data exchange between vehicles, autonomous or otherwise, have also increased tremendously. Securing data transfer and keeping a record of each transaction becomes a necessity in IoV/IATS. In this paper, we propose some optimized security algorithms using symmetric encryption for secure multimedia data transfer between vehicles. The main feature of these optimized algorithms is that they use a lower amount of data to generate fingerprints. The algorithms convert approximately $3.7 times 10^{5}$ samples of data into 3600 samples to generate the fingerprint. Fast Fourier Transform (FFT) is used to fetch the highest three peak values of the signal in the frequency domain. A centralized server authenticates the data transfer by comparing the $HASH$ of the fingerprints and also keeps the transaction record. Through experimental analysis, the performance of proposed algorithms is confirmed by achieving reduced size samples to generate fingerprints and their authentication at the server-side.
  • 25.

    【24hr】OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

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    机译 OQFL：一种优化的基于量子的联合学习框架，用于防御智能交通系统中的对抗性攻击
    摘要：Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems depend on machine learning algorithms to autonomously control their operations. Given the amount of personal information AVs collect, coupled with the distributed nature of such ecosystems, there is a movement to employ federated learning algorithms to develop secure decision-making models. Although federated learning is a viable candidate for data privacy, it is vulnerable to adversarial attacks, particularly data poisoning attacks, where malicious vectors would be injected in the training phase. Additionally, hyperparameters play an important role in establishing an efficient federated learning model that can be resilient against adversarial attacks. In this paper, to address these challenges, we propose a novel Optimized Quantum-based Federated Learning (OQFL) framework to automatically adjust the hyperparameters of federated learning using various adversarial attacks in AV settings. This work is innovative in two ways: first, a quantum-behaved particle swarm optimization technique is used to update the hyperparameters of the learning rate, local and global epochs. Second, the proposed technique is utilized within a cyber defense framework to defend against adversarial attacks. The performance of the proposed framework was evaluated using two benchmark datasets: MINST and Fashion-MINST, where they include images that would be extracted from smart cameras of AVs. This framework is shown to be more resilient against various adversarial attacks compared with peer techniques.
  • 26.

    【24hr】Guest Editorial Intelligent Autonomous Transportation System With 6G—Series—Part III

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    机译 客座编辑 6G智能自主交通系统—系列—第三部分
    摘要：We are delighted to introduce the third part of the Special Section on Intelligent Autonomous Transportation Systems with 6G, which aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Twenty articles were selected for publication in this issue. All the articles were rigorously evaluated according to the standard reviewing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.
  • 27.

    【24hr】Construction of Regional Intelligent Transportation System in Smart City Road Network via 5G Network

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    机译 通过5G网络构建智慧城市路网区域智能交通系统
    • 作者：Miao Yu;
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2023年第2期
    摘要：This purpose of the research is to explore the construction status and prediction performance of intelligent transportation systems in the road network of smart cities based on 5G (5th Generation Mobile Communication Technology) network, and further intellectualize the smart city. Aiming at the diversity and complexity of regional traffic influencing factors of road network in the construction of smart city, this research carries out resource real-time load balancing scheduling from the perspective of 5G heterogeneous network. Meanwhile, CNN (convolutional neural network) in the introduced deep learning algorithm is improved, and finally an intelligent traffic prediction model is constructed based on 5G load balancing and AlexNet network. The model is simulated and its performance is analyzed. The results show that the algorithm proposed is compared with LSTM (Long Short-Term Memory), CNN, RNN (Recurrent Neural Network), VGGNet (Visual Geometry Group Network), and BN (Bayesian network) models regarding Accuracy, Precision, Recall, and F1. It is found that the road network prediction accuracy of the algorithm proposed is 94.05, which is at least 4.29 higher than that of the model algorithm proposed by other scholars. The analysis of network data transmission synchronization performance suggests that there are obvious performance improvements in access delay, access collision rate, reliability, and network throughput. Among them, the packet loss rate is lower than 0.1, the access collision rate is basically stable at about 0, the access time is stable at about 75ms, and the sending throughput is basically maintained at about 1, which is significantly better than the performance of other algorithms. Therefore, the intelligent transportation system can achieve better data transmission performance under the premise of ensuring high prediction performance, with prominent instantaneity, which can provide experimental basis for the intelligent development of transportation in smart cities.
  • 28.

    【24hr】GTSM—Graph-Transient Security Model for Intelligent Transportation System Information Exchange

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    摘要：Intelligent Transportation Systems (ITSs) rely on environmental information for communication, navigation, and driving assistance. This technology-based interconnected vehicular network provides a wide range of support for heterogeneous real-time applications. However, the network relies on secure and robust information for providing driving application support, which is defaced at times due to fraudulent devices and information. In this article, Graph-Transient Security Method (GTSM) is proposed for improving the cybersecurity features of ITS. The proposed method uses a trusted graph model for identifying reliable infrastructure and neighboring units in communication. The units are verified based on the information exchanged for identifying the frauds through the mutual trust sharing paradigm. In the mutual trust sharing process, fastened classifier learning is employed for assessing the trust of the communicating vehicles and infrastructure units. Based on the output of the classifier learning, a connected trust-based transportation network is constructed. This helps to replace, transform the connected scenario depending on the cybersecurity requirement. The proposed method improves sharing rate by 11.4 and detection ratio by 7.84 and reduces down-time and latency by 11.53 and 10.7 in different sharing intervals.
  • 29.

    【24hr】Transportation Internet: A Sustainable Solution for Intelligent Transportation Systems

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    摘要：New challenges such as automation, connection, electrification, and sharing (ACES) have brought disruptive changes to vehicles, transportation, and mobility services, which urgently requires an ideal solution for sustainable transportation. This paper introduces the Internet as a paradigm and, for the first time, proposes the Transportation Internet (TI), inspired by the similarity between the Internet and transportation. Referring to the construction ideas of the Internet, this paper establishes the framework of TI, proposes the transportation router based on the transportation switching and routing models, and preliminarily forms a large-scale automatic transportation solution. Following the latest technologies of the Internet, this paper further presents the software-defined transportation (SDT) by separating the control plane and transport plane of the transportation router, which can enhance transportation routing and provide Internet-like capabilities such as centralized intelligent control, terminals plug-and-play, and open application ecology. The evaluation of the prototype system shows promising results. The software-defined signals (SDS) can save 36 energy compared to signal machines, and the software-defined vehicles (SDV) automatic driving can save 24 energy compared to manual driving. Overall, TI brings innovations to sustainable transportation, and provides a framework for a new generation of Intelligent Transportation Systems (ITS).
  • 30.

    【24hr】Two-Stage Intrusion Detection System in Intelligent Transportation Systems Using Rule Extraction Methods From Deep Neural Networks

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    摘要：In recent years, intrusion detection systems (IDSs) are offering effective solutions to protect various types of cyber-attacks in different networks such as Internet of Vehicles (IoVs) network in Intelligent Transportation Systems (ITS). Deep learning models have largely been leveraged by these intrusion detection systems to achieve better effectiveness results. However, deep learning models are black boxes, which limits their acceptability in decision systems. Also, they require powerful processing capabilities such as GPU, which limit their deployments in resource-constrained devices in IoV environment. To deal with these issues, we propose a two-stage IDS in ITS to discover suspicious network activity of In-Vehicles Networks (IVN) and vehicles to everything (V2X) networks. Our proposed IDS system uses rule extraction methods from deep learning models, i.e., deep neural networks in two stages. In the first stage, we analyze network traffic to distinguish between normal and attack traffic. If the traffic is found malicious, the second stage is invoked to identify the type of attack. To this end, we propose three variants of rule extraction. The first and the second variants are homogeneous, and they apply $DeepRed$ and $HypInv$ rule extraction methods in both stages respectively. The third variant is heterogeneous, and it applies $HypInv$ in the first stage to perform binary classification, and $DeepRed$ in the second stage to perform attack classification. The key idea is to combine the advantages of rule extraction technique and two-stage IDS architecture to resource consumption and improve classification accuracy. The proposed IDS model was tested using four benchmark datasets, i.e. ISCXIDS2012, CIC-IDS2017, and CSE-CIC-IDS2018 datasets are used for external network communications and the car hacking dataset are used for in-vehicle communications. The evaluation results show that the homogeneous $DeepRed$ is the optimal one in all cases of IDS system with an accuracy scores ranging between 92.43-98.32 under CIC-IDS2017 dataset, between 91.32-99.46 under CSE-CIC-IDS2018 dataset, and between 96.05-99.21 under Car-hacking dataset.
  • 31.

    【24hr】Graph Neural Networks for Intelligent Transportation Systems: A Survey

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    摘要：Graph neural networks (GNNs) have been extensively used in a wide variety of domains in recent years. Owing to their power in analyzing graph-structured data, they have become broadly popular in intelligent transportation systems (ITS) applications as well. Despite their widespread applications in different transportation domains, there is no comprehensive review of recent advancements and future research directions that covers all transportation areas. Accordingly, in this survey, for the first time, we provide an overview of GNN studies in the general domain of ITS. Unlike previous surveys, which have been limited to traffic forecasting problems, we explore how GNN frameworks have evolved for different ITS applications, including traffic forecasting, demand prediction, autonomous vehicles, intersection management, parking management, urban planning, and transportation safety. Also, we micro-categorize the studies based on their transportation application to identify domain-specific research directions, opportunities, and challenges, which have been missing in previous surveys. Moreover, we identify unique and undiscussed research opportunities and directions, which is the result of reviewing a wide range of transportation applications. The neglected role of edge and graph learning in ITS applications, developing multi-modal models, and exploiting the power of unsupervised and reinforcement learning methods for developing more powerful GNNs are some examples of such new discussions in this survey. Finally, we have identified popular baseline models and datasets in each transportation domain, which facilitate the development and evaluation of future GNN-based frameworks.
  • 32.

    【24hr】Deep Learning Enabled IRS for 6G Intelligent Transportation Systems: A Comprehensive Study

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    摘要：Intelligent Transportation Systems (ITS) play an increasingly significant role in our life, where safe and effective vehicular networks supported by sixth-generation (6G) communication technologies are the essence of ITS. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications need to be studied to implement ITS in a secure, robust, and efficient manner, allowing massive connectivity in vehicular communications networks. Besides, with the rapid growth of different types of autonomous vehicles, it becomes challenging to facilitate the heterogeneous requirements of ITS. To meet the above needs, intelligent reflecting surfaces (IRS) are introduced to vehicular communications and ITS, containing the reflecting elements that can intelligently configure incident signals from and to vehicles. As a novel vehicular communication paradigm at its infancy, it is key to understand the latest research efforts on applying IRS to 6G ITS as well as the fundamental differences with other existing alternatives and the new challenges brought by implementing IRS in 6G ITS. In this paper, we provide a big picture of deep learning enabled IRS for 6G ITS and appraise most of the important literature in this field. By appraising and summarizing the existing literature, we also point out the challenges and worthwhile research directions related to IRS aided 6G ITS.
  • 33.

    【24hr】Efficient Visual Tracking Based on Fuzzy Inference for Intelligent Transportation Systems

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    摘要：Remote monitoring is an important application of intelligent transportation systems (ITSs). The combination of monitoring equipment and tracking algorithms can be used to automatically track moving targets. The tracking algorithm based on the Siamese network is both accurate and efficient, and its development potential is better than that of other algorithms. Its output is a detection map that reflects the probability that any position in the search area is the center of the target’s bounding box, and the maximum value of the detection map is the center of the target’s bounding box predicted by the algorithm. Owing to partial occlusion, target deformation, out-of-view, and background clutter, local maxima in the detection map may also be the center of the target’s bounding box. A tracker’s ability to make accurate judgments is currently limited. Furthermore, previous trackers extracted only the target features in the initial frame as the matching template. Although this matching template is highly reliable, it cannot effectively combine the target features available in the subsequent frames. Therefore, in this study, fuzzy inference is introduced into the tracking process to analyze the reliability of the detection map. When this map is reliable, the target feature of the search area is transformed into a substitute template; otherwise, multiple substitute templates are selected from the template pool for parallel matching as per the set rules. The optimal result is selected from multiple detection results, based on the priority of the detection results when the initial frame is used as the matching template. Experimental results on multiple datasets show that the proposed algorithm is superior to other similar algorithms in terms of multiple assessment metrics and can improve the robustness of remote monitoring tasks in ITSs.
  • 34.

    【24hr】BSL: Sustainable Collaborative Inference in Intelligent Transportation Systems

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    摘要：As the recent rise of intelligent transportation systems (ITS), the sensing capability of vehicles has become crucial in realizing sophisticated intelligent transportation services. Collaborative sensing, an important approach to extend the sensing coverage of individual vehicles, has become an essential component of connected vehicle systems. However, due to challenges such as privacy concerns, frequent communication interruptions, customized models, and limited available data, the application of collaborative sensing in current ITS systems is still limited. In this paper, we propose BSL, a novel multi-exit split learning-based collaborative inference system. The key innovation of BSL is the introduction of multi-exit to the split network, enabling network training and collaborative inference between distributed device nodes and the cloud in a split manner. Specifically, BSL allows the device node to dynamically collaborate with the cloud by introducing the edge mode and collaboration mode, ensuring that intelligent services provided to the device will be sustained even if the communication is interrupted, which is crucial in ITS systems. We have implemented the system and evaluated it with public dataset on different embedded devices. The results demonstrate the promising performance of BSL.
  • 35.

    【24hr】A Scheme for Cooperative-Escort Multi-Submersible Intelligent Transportation System Based on SDN-Enabled Underwater IoV

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    摘要：As an emerging multi-submersible system, Human Occupied Vehicle (HOV) under a convoy of a set of Autonomous Underwater Vehicles (AUVs) is regarded as the future framework for underwater exploration. In this work, to improve the interoperability and communication efficiency of the multi-submersible formations, we treat the multi-submersible system as a paradigm of the underwater Internet of Vehicle (IoV) and show how to utilize the Software-Defined Networking (SDN) technique to optimize the system architecture. With the assistance of SDN, we consider the ocean current factors and propose an artificial flow potential field algorithm that combines the artificial potential field algorithm and the gradient descent algorithm, to plan the path for the multi-submersible system. In particular, to improve the safety and efficiency of path planning, we propose a dual leader-follower algorithm-based escort formation obstacle avoidance mechanism for dealing with all categories of obstacle avoidance situations. Simulation tests show that the proposed scheme performs better in data delivery among the multi-submersible system, at a lower energy cost. And it shows high stability and strong practicability in multi-submersible formation control and path planning, respectively.
  • 36.

    【24hr】Joint Semantic-Instance Segmentation Method for Intelligent Transportation System

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    摘要：Getting the point cloud data from sensors and correctly understanding the scene is the core of the intelligent transportation system. Point cloud segmentation can help intelligent transportation systems distinguish different objects in the scene. Some methods process the point cloud through a feature extraction network and complete the segmentation task. However, these methods have high requirements on the feature extraction network, and the fineness of the features will directly affect the final segmentation result. In this paper, we propose a new feature extraction network for segmentation by adding an encoder-decoder structure, which can extract the multiscale local feature information from the feature map. In our opinion, the merged multiscale features obtain a better feature matrix, which improves the performance of the segmentation tasks. We report results on the S3DIS dataset, new feature extraction network greatly improves both semantic segmentation and instance segmentation tasks.
  • 37.

    【24hr】Guest Editorial Introduction to the Special Issue on Graph-Based Machine Learning for Intelligent Transportation Systems

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    摘要：With the advance of artificial intelligence (AI), the Internet of Things (IoT), and 5G communication technologies, various kinds of traffic data from diverse devices can be acquired nowadays, and they can help us look into intelligent transportation systems (ITSs) with a new eye. Graph-based machine learning holds out the potential as a powerful tool for modeling complex structural data relationships and also mining both useful information and temporal patterns which could be used for building powerful analytics for ITS construction. Considering the benefit of graph-based machine learning for ITS, some graph-based machine learning methods/architectures have been proposed. Even though these methods have achieved certain success, there exist various scientific and engineering challenges.
  • 38.

    【24hr】Efficient Fire Segmentation for Internet-of-Things-Assisted Intelligent Transportation Systems

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    摘要：Rapid developments in deep learning (DL) and the Internet-of-Things (IoT) have enabled vision-based systems to efficiently detect fires at their early stage and avoid massive disasters. Implementing such IoT-driven fire detection systems can significantly reduce the corresponding ecological, social, and economic destruction; they can also provide smart monitoring for intelligent transportation systems (ITSs). However, deploying these systems requires lightweight and cost-effective convolutional neural networks (CNNs) for real-time processing on artificial intelligence (AI)-assisted edge devices. Therefore, in this paper, we propose an efficient and lightweight CNN architecture for early fire detection and segmentation, focusing on IoT-enabled ITS environments. We effectively utilize depth-wise separable convolution, point-wise group convolution, and a channel shuffling strategy with an optimal number of convolution kernels per layer, significantly reducing the model size and computation costs. Extensive experiments on our newly developed and other benchmark fire segmentation datasets reveal the effectiveness and robustness of our approach against state-of-the-art fire segmentation methods. Further, the proposed method maintains a balanced trade-off between the model efficiency and accuracy, making our system more suitable for IoT-driven fire disaster management in ITSs.
  • 39.

    【24hr】Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems

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    摘要：Intelligent Transport Systems (ITS) is a developing technology that will significantly alter the driving experience. In such systems, smart vehicles and Road-Side Units (RSUs) communicate through the VANET. Safety apps use these data to identify and prevent hazardous situations in real-time. Detection of malicious nodes and attack traffic in Intelligent Transportation Systems (ITS) is a current research subject. Recently, researchers are proposing graph-based machine learning techniques to identify malicious users in the ITS environment, through which it is easy to analyze the network traffic and detect the malicious devices. Therefore, graph-based machine learning techniques could be a technique that efficiently detect malicious nodes in the ITS environment. In this context, this article aims to provide a technique for resolving authentication and security issues in ITS using lightweight cryptography and graph-based machine learning. Our solution uses the concepts of identity based authentication technique and graph-based machine learning in order to provide authentication and security to the smart vehicle in ITS. By authenticating smart vehicles in ITS and identifying various cyber threats, our proposed method substantially contributes to the development of intelligent transportation communication environment.
  • 40.

    【24hr】Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System

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    摘要：With the development of the Internet of Things (IoT) and 5G technologies, IoT devices deployed on roads are able to collect a large amount of traffic data at any time. Road networks can be easily constructed into a graph structure with spatial-temporal features, and how to use these spatial-temporal features for dynamic traffic flow forecasting has become a heated issue. Although existing studies bring in the consideration of periodicity to deal with spatial-temporal sequence dependence, the similarity of time-varying relationships among cross-spatial nodes has not been well discussed. In this paper, we propose a Graph Attention Network with Spatial-Temporal Clustering (GAT-STC), which considers the so-called recent-aware features and periodic-aware features, to improve the Graph Neural Network (GNN)-based traffic flow forecasting in Intelligent Transportation System (ITS). Specifically, for the recent-aware feature extraction, a distance-based Graph Attention Network (GAT) is improved and constructed to better utilize the hidden features of neighbor nodes within a reliable distance during the recent time interval, thus can effectively capture the dynamic changes in spatial feature representation. For the periodic-aware feature extraction, a spatial-temporal clustering algorithm, in which both features in terms of nodes’ current traffic states and similar trends in terms of their dynamic changes are taken into account, is developed and applied to achieve better learning efficiency. Experiments using three public traffic datasets demonstrate the higher accuracy and better efficiency of our proposed model for traffic flow forecasting, compared with five baseline methods in ITS.
  • 41.

    【24hr】Smart Traffic Navigation System for Fault-Tolerant Edge Computing of Internet of Vehicle in Intelligent Transportation Gateway

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    摘要：To investigate the diversified technologies in Internet of Vehicles (IoVs) under intelligent edge computing, brain-inspired computing techniques are proposed in this study, which is a promising biologically inspired method by using brain cognition mechanism for various applications. A neuromorphic approach in a scalable and fault-tolerant framework is presented, targeting to realize the navigation function for the edge computing in IoV applications. A novel fault-tolerant address event representation approach is proposed for the spike information routing, which makes the presented model both scalable and fault-tolerant. Experimental results reveal that the proposed approaches can enhance the communication distance, the load balancing and the maximum throughput of the neuromorphic system accordingly. Based on the proposed neuromorphic model, the effects of the dopamine level are investigated. Besides, the results show that the proposed work can realize the accurate obstacle avoidance for the edge IoV computing, and the performance of the proposed network is superior to the network without the proposed scalable and fault-tolerant design. Therefore, the proposed IoV model provides an experimental basis for the improvement of the IoV system.
  • 42.

    【24hr】Scale Region Recognition Network for Object Counting in Intelligent Transportation System

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    摘要：Self-driving technology and safety monitoring devices in intelligent transportation systems require superb capacity for context awareness. Accurately inferring the counts of crowds and vehicles are the two practical and fundamental tasks in the transportation system. However, the scale variation and background interference in the traffic image hinder the counting performance. To solve the aforementioned problems, a scale region recognition network (SRRNet) is proposed in this paper. It has two key components, termed scale level awareness (SLA) module and object region recognition (ORR) module. The SLA module aims to encode the representations at multiple scales, which are beneficial to address the scale variation. The ORR module is designed to suppress background interference through the visual attention mechanism. Extensive experimental results on four crowd counting datasets and five vehicle counting datasets have demonstrated the superiority of the proposed SRRNet in both counting accuracy and robustness compared with the mainstream competitors. Meanwhile, substantial ablation studies have proved the effectiveness of the proposed SLA and ORS modules.
  • 43.

    【24hr】Network Car Hailing Pricing Model Optimization in Edge Computing-Based Intelligent Transportation System

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    摘要：The purpose of this study is to investigate Network Car Hailing (NCH) price or the deficiency in NCH Platform in Edge Computing (EC)-based Intelligent Transportation System. Aiming at the uncertain capacity and unbalanced load in the car-hailing platform, this work innovatively introduces the EC to unload, constructs an EC-based online car-hailing resource allocation and pricing optimization model by combining with factors such as the number of users and reputation in the network, and further analyzes the performance of the resource allocation and pricing optimization model in the constructed car-hailing platform through simulation experiments. The experimental results show that with the increase in the number of vehicles with computing tasks, the amount of resources purchased from various car-hailing vehicles also increases, the cost of paying is showing an increasing trend, and the utility function of NCH platforms and operators has declined. In the task resource analysis, the average unloading utility of the algorithm in this work is the highest, and the average unloading utility is basically stable at about 70 when the number of vehicles is 98. With the increase of the delay weight, the delay is smaller and the energy consumption is lower. Therefore, the model constructed in this work can minimize the average cost and consumes less energy while the delay is small. It can provide a reference for intelligent pricing and resource allocation of the online car-hailing platform in the later period of intelligent transportation.
  • 44.

    【24hr】Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems

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    摘要：Supported by some of the major revolutionary technologies, such as Internet of Vehicles (IoVs), Edge Computing, and Machine Learning (ML), the traditional Vehicular Networks (VNs) are changing drastically and converging rapidly into one of the most complex, highly intelligent, and advanced networking systems, mostly known as Intelligent Transportation System (ITS). Recently, distributed ML techniques, such as Federated Learning (FL) have gained huge popularity mainly for their advantages in terms of intelligence sharing and privacy concerns. VNs are a natural contender for exploiting FL for solving challenging problems; however, their limited resources, dynamic nature, high speed, and reduced latency requirements often become the bottleneck. V2X communication technologies allow vehicular terminals (VTs) to share their valuable local environment parameters and become aware of their surroundings. Such information can be utilized to build a more sustainable and affordable FL platform for serving VTs. Gaining from recently introduced 3D architectures, integrating terrestrial and aerial edge computing layers, we present here a distributed FL platform able to distribute the FL process on a 3D fashion while reducing the overall communication cost for providing vehicular services. The framework is defined as a constrained optimization problem for reducing the overall FL process cost through a proper network selection between various nodes. We have modeled the FL network selection problem as a sequential decision-making process through a Markov Decision Process (MDP) with time-dependent state transition probabilities. A computation-efficient value iteration algorithm is adapted for solving the MDP. Comparison with various benchmark methods shows the overall improvement in terms of latency, energy, and FL performance.
  • 45.

    【24hr】Multi-Agent Reinforcement Learning for Intelligent V2G Integration in Future Transportation Systems

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    机译 面向未来交通系统智能V2G集成的多智能体强化学习
    摘要：Electric vehicles (EVs) are the backbone of the future intelligent transportation system (ITS). They are environmentally friendly and can also be integrated as distributed energy resources (DERs) into the smart grid using vehicle-to-grid (V2G) scheme. Specifically, utility companies can push back EV batteries into the electric grid to reduce the peak load. However, integrating EVs into the power grid efficiently requires accurate artificial intelligence (AI) mechanisms to forecast, coordinate, and dispatch the EVs into the grid. This paper proposes a Multi-agent Reinforcement Learning (MARL) mechanism that schedules the day-ahead discharging process of EV batteries to optimize the peak shaving performance of the electric grid. The proposed MARL overcomes the inaccuracy of energy prediction by allowing the agents, i.e. EVs, to make autonomous decisions. These agents are trained in a centralized fashion but make decisions locally to maintain autonomy and privacy. In particular, the model does not require that the EVs communicate with a centralized entity during the execution stage, which assures the model’s integrity and protects the EVs’ private information. To evaluate the model, a comprehensive series of experiments were carried out to prove the effectiveness of the MARL coordination and scheduling mechanism and to show that the model can indeed flatten the peak load.
  • 46.

    【24hr】Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

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    摘要：Federated learning (FL) provides a promising solution to meet the requirements of data privacy and security in intelligent transportation systems (ITS), which enables edge devices and road side units (RSUs) to collaboratively train learning models without exposing the raw data. However, the deep leakage from gradients (DLG) still leads to the risk of divulging the original data. Meanwhile, the existing gradient protection methods based on secure multi-party computation (SMC) result in huge communication overheads and latency, which are difficult to satisfy the real-time demands of both FL and the diverse services in ITS. Focusing on improving edge data security in ITS, this paper proposes an enhanced federated learning (FL) model (SemBroc-RF) with reinforcement learning, which considers the advantages of both end-to-end homomorphic encryption (HE) and SMC. To reduce communication overheads and strengthen data security in RSUs and end devices simultaneously, a partially encrypted secure multi-party broadcast computation algorithm (SemBroc) is designed, which achieves the time complexity O(n) by constructing the decoding function and sharing the gradients among the local models. To improve the model accuracy by gradients aggregation, a FL algorithm (GreFLa) with reinforcement learning is proposed based on the adaptive assigned weight of the local gradients. Theoretical analysis and detailed simulation results verify that SemBroc-RF can effectively prevent gradient leakage. On the MNIST and CIFAR-10 datasets, compared with the benchmark, the accuracy of SemBroc-RF is increased by 3.63 and 1.35, and the training round of SemBroc-RF is reduced by 70.8 and 45.6, respectively.
  • 47.

    【24hr】Sustainable and Transferable Traffic Sign Recognition for Intelligent Transportation Systems

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    摘要：Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in popularity in recent years. However, since these models belong to the closed-world-oriented learning paradigm, they are only capable of accurately identifying traffic signs that are easy to collect and cannot adapt to the real world. Furthermore, the sample utilization of these methods is insufficient, the resource consumption of model training may become unbearable as the data scale grows. To address this problem, we propose a novel “knowledge + data” co- driven solution (i.e., Joint Semantic Representation algorithm, JSR) for TSR. JSR creates a hybrid feature representation by extracting general and principal visual features from traffic sign images. It also realizes the model’s reasoning ability to zero-shot TSR based on prior knowledge of traffic sign design standards. The effectiveness of JSR is demonstrated by experiments on four benchmark datasets and two self-built TSR datasets.
  • 48.

    【24hr】Guest Editorial Special Issue on Intelligent Autonomous Transportation Systems With 6G—Part IV

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    机译 《基于6G的智能自主交通系统（第四部分）》特刊特刊
    摘要：We are delighted to introduce the fourth part of the Special Issue on Intelligent Autonomous Transportation Systems ith 6G, hich aims to provide the scientific community ith a comprehensive overvie of innovative technologies, advanced architectures, and potential challenges for the 6G-supported Intelligent Autonomous Transport Systems. Forty-three papers ere selected for publication in this issue. All the papers ere rigorously evaluated according to the standard revieing process of the IEEE Transactions on Intelligent Transportation Systems. The evaluation process considered originality, technical quality, presentational quality, and overall contribution. e ill introduce these articles and highlight their main contributions in the folloing.
  • 49.

    【24hr】Multi-Relay Cognitive Network With Anti-Fragile Relay Communication for Intelligent Transportation System Under Aggregated Interference

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    机译 基于抗脆弱中继通信的多中继认知网络面向聚合干扰下的智能交通系统
    摘要：The rapid development and continuous innovation of wireless services have led to a boom in the number and types of smart terminals. The huge amount of data that deep learning needs to calculate relies on the continuous improvement of hardware devices to solve. Therefore, the realization of intelligent transportation system(ITS) has become the general trend. The increase in the number of Internet of Things devices and the changes in the location of vehicles in the Internet of Vehicles(IOV) system have put forward higher requirements for the reliability and effectiveness of information transmission and the effective use of spectrum resources. Aiming at the influence and elimination of aggregated interference in intelligent transportation system, an anti-fragile communication algorithm is proposed to improve the reliability of signal transmission. At the same time, the outage probability of energy harvesting and cognitive radio technology enhanced with relay cooperative transmission under cognitive wireless network system with the aggregate interference can be deduced in detail. Finally, the correctness of theoretical analysis and the reliability of the proposed anti-fragile communication algorithms are verified by simulation experiments.
  • 50.

    【24hr】Swarm Learning-Based Dynamic Optimal Management for Traffic Congestion in 6G-Driven Intelligent Transportation System

    包量

    机译 基于群体学习的6G驱动智能交通系统中交通拥堵动态最优管理
    摘要：As city boundaries expand and the vehicles continues to proliferate, the transportation system is increasingly overloaded, greatly increasing people’s commuting burden and extending the resulting negative effects to all areas of work and life. It is a big issue that needs to be solved urgently. However, due to the development of infrastructure and technologies in 6G-driven Intelligent Transportation Systems (ITS), it becomes possible to alleviate urban congestion. Existing solutions either optimize the path planning of each vehicle, or only focus on solving the problem of resource allocation of a single road, neither can take advantage of self-organizing networks and easily fall into local optimum. Combining the above reasons, we propose the Direction Decide as a Service (DDaaS) scheme. First, it contains a novel three-layer service architecture based on Swarm Learning (SL), which enables orderly transmission of traffic data and control instructions and protects user privacy. Second, an improved local model and aggregation method is incorporated into DDaaS, which enables to make accurate predictions when the road resources at a single intersection are insufficient. Third, we propose a dynamic traffic control algorithm to provide signal light switching decisions for rapidly changing ITS. Finally, constructing an urban road simulation experiment combined with SUMO, we prove that DDaaS can reduce traffic congestion effectively and has significant advantages compared to other schemes.
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