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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
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  • ISSN： 1524-9050
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  • 1.

    【24hr】A Two-Stage Optimization Method for Schedule and Trajectory of CAVs at an Isolated Autonomous Intersection

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    摘要：Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48 and 46.84, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.
  • 2.

    【24hr】An Improved Model Predictive Control-Based Trajectory Planning Method for Automated Driving Vehicles Under Uncertainty Environments

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    摘要：For automated driving vehicles, trajectory planning is responsible for obtaining feasible trajectories with velocity profiles according to driving environments. From the perspective of trajectory planning, multiple uncertainties of environments and tracking deviations are two significant factors affecting driving safety. The former disturbs the judgment of trajectory planning on the environments, and the latter reduces the tracking accuracy of planned trajectories. To solve these problems, an improved model predictive control (MPC) trajectory planning method is proposed in this paper. Firstly, a Kalman filter fusion method is carried out to predict obstacle trajectory and their uncertainty, which combines model-based and data-based prediction methods. Based on the prediction results, a tube-based MPC trajectory planning method is applied to plan a reference trajectory with a small tracking deviation. The tube-based MPC is composed of two parts. One is the MPC with tightened constraints that is used to plan a feasible trajectory according to a nominal vehicle system and driving environment. The other is a state feedback control that is proposed to adjust the above planned trajectory to reduce the tracking deviations. To our knowledge, this paper proposes Kalman filter fusion and tube-based MPC planning method for the first time to consider the uncertainties of trajectory prediction and tracking control meanwhile in the planning. The planning method is verified by simulations and experiments in multiple scenes. Results show that the method is suitable for both static and dynamic scenes. Compared with applying the basic prediction method, the lateral deviation of the proposed method from the ideal trajectory is decreased by 46.5. Compared with the nominal MPC method, the lateral tracking deviations of the proposed method are decreased by 77.42.
  • 3.

    【24hr】An Explainable Deep Learning Framework for Resilient Intrusion Detection in IoT-Enabled Transportation Networks

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    摘要：The security of safety-critical IoT systems, such as the Internet of Vehicles (IoV), has a great interest, focusing on using Intrusion Detection Systems (IDS) to recognise cyber-attacks in IoT networks. Deep learning methods are commonly used for the anomaly detection engines of many IDSs because of their ability to learn from heterogeneous data. However, while this type of machine learning model produces high false-positive rates and the reasons behind its predictions are not easily understood, even by experts. The ability to understand or comprehend the reasoning behind the decision of an IDS to block a particular packet helps cybersecurity experts validate the system’s effectiveness and develop more cyber-resilient systems. This paper proposes an explainable deep learning-based intrusion detection framework that helps improve the transparency and resiliency of DL-based IDS in IoT networks. The framework employs a SHapley Additive exPlanations (SHAP) mechanism to interpret decisions made by deep learning-based IDS to experts who rely on the decisions to ensure IoT networks’ security and design more cyber-resilient systems. The proposed framework was validated using the ToN_IoT dataset and compared with other compelling techniques. The experimental results have revealed the high performance of the proposed framework with a 99.15 accuracy and a 98.83 F1 score, illustrating its capability to protect IoV networks against sophisticated cyber-attacks.
  • 4.

    【24hr】The SAir-IIoT Cyber Testbed as a Service: A Novel Cybertwins Architecture in IIoT-Based Smart Airports

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    摘要：Rapid technological advancements have resulted in increasingly more efficient and lightweight devices that, coupled with low-power and wide-range wireless connectivity, have given rise to Industrial Internet of Things (IIoT) systems. As a result, the concept of intelligent environments was developed, such as smart airports, where ubiquitous sensors seamlessly cooperate through several types of communication technologies, such as WiFi, BLE, ZigBEE and 5G, enable the collection of data and the dynamic adaption of the system to changing circumstances. However, along with certain benefits, such as augmented communication, enhanced business processes and improved efficiency, IIoT introduces new vulnerabilities, enabling cyber-attackers to compromise not only the digital infrastructure of IIoT architecture-enabled smart airports, but also affecting their physical assets. In this paper, we present a novel smart airport cybertwins security-oriented IIoT testbed, named SAir-IIoT, which comprises multiple heterogeneous IIoT devices and communication protocols, organised into distinct zones, automatically interconnected with each other, that can be remotely accessed as-a-service. To the best of our knowledge, this is the first cybertwins security-oriented testbed that enables researchers and practitioners to remotely practice attack and defence scenarios in smart airport IIoT environments. Additionally, we introduce a new data management technique for dynamically collecting, analysing and tagging heterogeneous data from diverse data sources including IIoT devices and network flows. Finally, we compare SAir-IIoT with other IIoT-based testbeds, revealing its complexity and effectiveness to evaluate new cyber security methods.
  • 5.

    【24hr】Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

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    摘要：Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77 and 6.64 for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed.
  • 6.

    【24hr】An Adaptive Airspace Model for Quadcopters in Urban Air Mobility

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    摘要：The emerging usage of quadcopters in Urban Air Mobility has urged airspace design to be precise and dynamic to mitigate collision risks. This paper proposes an adaptive AirMatrix model to obtain the block size of AirMatrix that is calculated through the acceptable track deviation of quadcopters by considering GPS signal quality and wind field. First, the acceptable GPS-induced deviation is calculated to meet the safety tolerance by analyzing the distributions of GPS-induced deviation under different signal quality levels. Second, a dynamic modeling and simulation method is applied to assess the wind impact on the track deviation accurately. The wind-induced deviation is calculated using a wind effect model considering surface friction and turbulence. Results show that the proposed model can reduce airspace conserveness by providing 45 percent available blocks more than the previous model. By considering the wind impact, the adaptive AirMatrix improves the safety performance of blocks at levels ranging from 4.9 to 95.5, compared with the model without considering wind impact. The adaptive AirMatrix model provides a delicate and targeted approach to design airspace for quadcopters, which benefits traffic management in Urban Air Mobility.
  • 7.

    【24hr】Security in IoT-Enabled Digital Twins of Maritime Transportation Systems

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    摘要：The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model’s security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system’s multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100 successful transmission probability and $0.01sim 0.05~lambda $ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.
  • 8.

    【24hr】Self-Supervised Spatiotemporal Graph Neural Networks With Self-Distillation for Traffic Prediction

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    摘要：Spatiotemporal graph neural networks (GNNs) have been used successfully in traffic prediction in recent years, primarily owing to their ability to model complex spatiotemporal dependencies within irregular traffic networks. However, the feature extraction processes in these methods are limited in their exploration of the inner properties of traffic data. Specifically, graph and temporal convolutions are local operations and can hardly utilize information from wider ranges, which may affect the long-term prediction performance of such methods. Furthermore, deep spatiotemporal GNNs easily suffer from poor generalization owing to overfitting. To address these problems, this study presents a novel traffic prediction method that integrates self-supervised learning and self-distillation into spatiotemporal GNNs. First, a self-supervised learning module is used to explore the knowledge from the input data. An auxiliary task based on temporal continuity is designed to capture the contextual information in traffic data. Second, a self-distillation framework is developed as an implicit regularization approach that transfers knowledge from the model itself. The combination of self-supervision and self-distillation further mines the knowledge from the data and the model, and the generalization ability and stability of the prediction model can be improved. The proposed model achieved superior or competitive results compared with several strong baselines on six traffic prediction datasets. In particular, the maximum performance improvement ratios for the six datasets were 3.0 (MAE), 5.2 (RMSE), and 3.8 (MAPE). These results demonstrate the effectiveness of the proposed method.
  • 9.

    【24hr】A Learning-Based Discretionary Lane-Change Decision-Making Model With Driving Style Awareness

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    摘要：Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although modeling DLC decision-making has been studied for years, the impact of human factors, which is crucial in accurately modelling human DLC decision-making strategies, is largely ignored in the existing literature. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers’ decision-making maneuvers by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model captures the human decision-making strategies and imitates human drivers’ lane-change maneuvers, which can achieve 98.66 prediction accuracy. Moreover, we also analyze the lane-change impact of our model compared with human drivers in terms of improving the safety and speed of traffic.
  • 10.

    【24hr】Facility Location-Allocation Problem for Emergency Medical Service With Unmanned Aerial Vehicle

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    摘要：This paper models an operation problem of an unmanned aerial vehicle for the emergency medical service (UEMS) system. The model is set up as a location-allocation problem. The coverage distance and capacity of the UEMS facility are modeled as functions of UAVs assigned. The allocation of the demand point is constrained by the variable coverage distance of each facility. The robust optimization approach is used over the cardinality-constrained uncertain demand, which leads to a nonlinear optimization problem. The UEMS location-allocation problem (ULAP) is reformulated to a solvable problem. An extended formulation and corresponding branch-and-price (BP) algorithm are also proposed, which strengthen the linear programming relaxation bound. The subproblem of the BP algorithm is defined as a robust disjunctively constrained integer knapsack problem. Two solution approaches of mixed-integer linear programming reformulation and decomposed dynamic programming are designed for the subproblem. To provide time-efficient solutions for large-scale problems, a restricted master heuristic (RMH) is proposed based on the extended formulation. In computational experiments, the BP algorithm provided a strong lower bound, and the RMH could find an effective feasible solution within an applicable computation time.
  • 11.

    【24hr】A Bibliometric Overview of IEEE Transactions on Intelligent Transportation Systems (2000–2021)

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    摘要：The IEEE Transactions on Intelligent Transport Systems was founded in 2000 to enhance the sharing of international research on theoretical and practical technology developments in the ITS field. Since then, it has become a leading journal in the field and has attracted a high caliber of multi-disciplinary authors and publications. In recognition of twenty plus years of contributions to the field, this paper analyses the evolution of the journal over its lifetime for the period 2000–2021. A bibliometric analysis is conducted on 3,428 peer-reviewed publications (articles and reviews) using data collected from Core Collection Database of Web of Science. The paper identifies the most influential and cited articles and their impacts on the development of research in the ITS field. The analysis also includes detailed information on top leading authors, their organizations and countries where the research was funded and developed. The analysis shows how the growing interest and diversity of transport technology topics has led to an increase in the number and quality of publications in journal over the past twenty plus years. A visualization of bibliographic coupling, co-authorship and keywords analysis is also presented using the VOSviewer software leading to insightful findings regarding the journal’s impact and standing in this field of research.
  • 12.

    【24hr】2022 Index IEEE Transactions on Intelligent Transportation Systems Vol. 23

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第12期
    摘要：Presents the 2022 author/subject index for this issue of the publication.
  • 13.

    【24hr】IEEE Transactions on Intelligent Transportation Systems Publication Information

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第10期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 14.

    【24hr】Computation Offloading and Service Caching for Intelligent Transportation Systems With Digital Twin

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    摘要：Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines.
  • 15.

    【24hr】Displacement-Aware Service Endowment Scheme for Improving Intelligent Transportation Systems Data Exchange

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    摘要：Intelligent Transportation Systems (ITS) is a smart-transportation system for road-side assistance and data exchange support by integrating cloud and wireless networks. ITS facilitates vehicle-to-vehicle and vehicle-to-anything (V2X) data exchanges for satisfying user demands. The rate of big data granting to the vehicular users is interrupted by the fundamental attributes such as mobility and link instability of the vehicles. To address the issues in vehicular data exchange big data, this article introduces displacement-aware service endowment scheme with the benefits of data offloading. Displacement-aware big data endowment ensures responsive availability of vehicle request information despite unfavorable location and density factors. The time congruency in V2V and V2X data exchanges are adopted for minimizing data exchange dropouts. In the data offloading phase, extraneous information and big data responses are detained based on data exchange relevance to improve congestion free big data endowment. The distinct methods work in a co-operative manner to improve big data quality of fast configuring smart vehicles to provide reliable big data in smart city environments.
  • 16.

    【24hr】Blockchain-Based Secure Communication of Intelligent Transportation Digital Twins System

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    摘要：The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.
  • 17.

    【24hr】Route Planning and Tracking Control of an Intelligent Automatic Unmanned Transportation System Based on Dynamic Nonlinear Model Predictive Control

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    摘要：Tracking control is one of the important working conditions of unmanned driving and can help vehicles keep distances and run in an orderly manner to improve traffic utilization, ensure clear roads and avoid rear-end collisions. This paper constructs the motion trend of obstacles within the predictive step length of model predictive control (MPC), designs the danger level indicator between vehicles and obstacles, and formulates dynamic planning for the original reference path based on MPC. This paper proposes an integrated collision avoidance control strategy based on dynamic nonlinear MPC (DNMPC), and predicts the locations of moving obstacles within the predictive step length of DNMPC. The proposed method can perform initial planning for the collision-avoidance path according to the road environment information and based on the activation function. It builds the function for the tendency of obstacles within the predictive step length of MPC, introduces it into the objective function for optimization, designs dynamic path planning control based on the theories of MPC, and performs local optimization to the initial reference path under the obstacles in motion with a mass model. In addition, it defines varying discrete step lengths within the predictive step length and achieves the long-distance prediction and high-precision control of collision avoidance controllers. The experimental results show that the dynamic, nonlinear, and integrated collision avoidance control proposed in this paper can ensure excellent collision avoidance and steady vehicle driving and has very good practical value.
  • 18.

    【24hr】PrivacySignal: Privacy-Preserving Traffic Signal Control for Intelligent Transportation System

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    摘要：A new trend of using deep reinforcement learning for traffic signal control has become a spotlight in the Intelligent Transportation System (ITS). However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters’ privacy and thus bring unnecessary troubles to users. In this paper, we propose a privacy-preserving traffic signal control for an intelligent transportation system (PrivacySignal). It relies on the existing road facilities to achieve the privacy of commuters, which guarantees the practicality of the system. Real-time decision-making and confidentiality of the system can be achieved simultaneously via the design of a series of secure and efficient interactive protocols, that are based on additive secret sharing, to perform the deep $Q$ -network (DQN). Moreover, the security of PrivacySignal is testified, meanwhile, the system effectiveness, and the overall efficiency of PrivacySignal is demonstrated through theoretical analysis and simulation experiments. Compared with the existing privacy-preserving schemes of the intelligent traffic signal, PrivacySignal provides a general DQN based privacy-preserving traffic signal control strategy architecture with high efficiency and low-performance loss.
  • 19.

    【24hr】Analysis of Vehicle Location Prediction Errors for Safety Applications in Cooperative-Intelligent Transportation Systems

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    摘要：Cooperative-Intelligent Transportation System (C-ITS) safety applications depend on reliable location information timely exchanged by road users. Due to inter-vehicle communication delays and sampling frequency, there always exists a time gap between the state observation update time and safety decision time. Predicting the vehicle’s locations into a future time epoch common to both host and subject vehicles enables real-time collision detection. Current studies of vehicle positioning performance mostly focus on the accuracy and availability of vehicle navigation solutions at equal observation intervals. Location error propagation over the prediction time intervals and dependence on various factors is not much understood. In this paper, we analyzed how the accuracy of the location prediction degrades depending on prediction intervals and state estimate errors from the measurement updates. We adopted the Kalman Filter method to predict locations with two representative location data sets collected in real road environments. Results from a dual-frequency Global Navigation Satellite System (GNSS)/Real-time Kinematic (RTK) receiver show that the Root Mean Square Error (RMSE) of prediction locations grow from a few centimeters at the state updates to about 50 and 100 cm within the prediction intervals of 1 and 2 seconds, respectively. This implies that GNSS/RTK positioning capability is a prerequisite for C-ITS safety applications. The experimental results from a surveying-grade GNSS/Inertial Navigation System (INS) receiver show that the RMSE can remain within 10 cm for the prediction interval of 2 s. High-rate INS velocity measurements provide significant advantages in efficient control of the error growth of the predicted vehicle locations.
  • 20.

    【24hr】Softwarized Resource Management and Allocation With Autonomous Awareness for 6G-Enabled Cooperative Intelligent Transportation Systems

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    摘要：Cooperative intelligent transport system (C-ITS) is one emerging application scenario in 6G. Within the content of 6G, softwarization is the dominant attribute of networks. 6G networks are required to have the intelligence and autonomy attributes, too. With softwarization and autonomy, not only the network capable of flexibly managing softwarized resources can be achieved, but also the network can learn and adapt itself with respect to the dynamic networking environment. However, multiple issues stand in the way of developing 6G networks, requiring to be addressed. In this paper, the softwarized resource management and allocation with autonomy and intelligence awareness in 6G networks for C-ITS application is researched. Firstly, key enabling technologies and problem model of 6G-enabled C-ITS are described. Then, an architecture design enabling to achieve the intelligent and softwarized resource management and allocation per service request, abbreviated as ReMaAl-AutoNet, is proposed. The proposed architecture design, based on reinforcement learning (RL), can realize the intelligent resource management and allocation by undergoing the training. Afterwards, simulations are illustrated to validate the proposed ReMaAl-AutoNet architecture. For instance, the successful ratio of ReMaAl-AutoNet has an advantage of over ten percentages than the direct counterpart without training.
  • 21.

    【24hr】DCGCR: Dynamic Clustering Green Communication Routing for Intelligent Transportation Systems

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    摘要：For the effective green communications amongst the vehicles, the energy-efficient routing protocol for intelligent transportation system (ITS) is essential. Due to the high speed and recurring topological variations of Vehicular sensor Networks, identifying a connected route with a sufficient latency is a difficult task with many constraints and obstacles. Therefore, to overcome this, we developed the statistical approach to theoretically determine the load congestion and consumption of energy during the lifetime of the sensor network for ITS. Hence, dynamic clustering green communication routing (DCGCR) protocol is proposed for vehicular communication. To manage energy consumption and enhance the lifetime of the network deployed on the roadside units (RSU), we analyze the evolution of energy holes and apply our analytical conclusions for ITS with WSN routing. The proposed routing protocol considers various metrics: i) energy consumption of vehicular sensor nodes,ii) network stability iii) reliability and iv) amount of data exchange among vehicles. The efficiency of the proposed computational model in calculating the lifetime of the vehicular network and energy hole evolution process is demonstrated through extensive computation results. DCGCR approach is compared with the various energy-aware routing algorithms namely, Dynamic Energy Balanced Routing (DEBR), Geographic Greedy Routing (GGR), double cost function-based routing (DCFR) and found that proposed approach achieves more accuracy with 7 less failure rate.
  • 22.

    【24hr】AFFIRM: Provably Forward Privacy for Searchable Encryption in Cooperative Intelligent Transportation System

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    摘要：With the construction of intelligent transportation, big data with heterogeneous, multi-source and massive characteristics has become an important carrier of cooperative intelligent transportation systems (C-ITS) and plays an important role. Big data in C-ITS can break through the restrictions between regions and entities and then learning cooperatively by sharing data. In addition, the combined efficiency and information integration advantages of big data are conducive to the construction of a comprehensive and three-dimensional traffic information system and can enhance traffic prediction. However, such substantial sensitive data, mainly on the cloud infrastructure, exposes several vulnerabilities like data leakages and privacy breaks, especially when data is shared for cooperative learning purposes. To address this, this paper proposes a forward privacy-preserving scheme, named AFFIRM, for multi-party encrypted sample alignment adopting cooperative learning in C-ITS. By introducing the searchable encryption method, we realize the sample alignment of cooperative learning in the multi-party encrypted data space. AFFIRM ensures encrypted sample alignment under the condition of forward privacy security. We have formally proved that the proposed scheme satisfies both forward security and validity. We have assessed AFFIRM by validating the potential threat of malicious tampering by privacy attackers and malicious personnel search for the aligned sample data and verify it. Finally, we numerically tested and compared AFFIRM against the corresponding ones of some state-of-the-art schemes under various record sizes, servers and processing.
  • 23.

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

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    摘要：Intelligent transportation system (ITS) is a key enabler for future road traffic management systems. The core components of ITS include vehicles, roadside units, and traffic command centers. They generate a large amount of data flow that is made up of both mobility and service-related data. Therefore, some data science methods to handle the transportation data are very necessary for ITS. Although some attempts have been done to explore data science methods for ITS, there exist various scientific and engineering challenges including software and hardware development, computational complexity, data multi-source heterogeneity, and privacy protection. Consequently, to fully explore the benefits of ITS applications like connected and autonomous vehicles, traffic control and prediction, road safety, and accident prediction, advanced data science methodologies and applications are in great need.
  • 24.

    【24hr】Event-Triggered Tracking Formation of Networked Nonlinear Intelligent Transportation Systems Surrounded by Random Disturbances

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    摘要：This article proposes an event-triggered (ET) communication mechanism for cooperative control in a group of networked intelligent transportation systems (ITSs). The ITS is described by a class of nonlinear uncertain multi-agent system which is disturbed by a wide stationary process representing the external noise. The control input for each subsystem in the ITS faces the dead-zone constraint. We guarantee the tracking formation control for the ITS under the given circumstances. Additionally, the closed-loop networked system signals are proved to be noise-to-state practically stable in probability (NSpS-P). The transient/steady-state behavior of the tracking errors are managed by the prescribed performance control (PPC) approach. The Zeno phenomenon is then mathematically excluded for the data transmission among the agents. The simulation experiments finally quantify the effectiveness of our proposed approach in terms of reducing the burden of data transmission and providing an appropriate performance with respect to the control objectives.
  • 25.

    【24hr】IEEE Intelligent Transportation Systems Society Information

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第12期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 26.

    【24hr】Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

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    摘要：The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.
  • 27.

    【24hr】Digital Twin Consensus for Blockchain-Enabled Intelligent Transportation Systems in Smart Cities

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    摘要：Digital Twin (DT) has become the key technology in the Intelligent Transportation Systems (ITS) in smart cities to keep the health and reliability of various DT requesters, such as private vehicles, public transportation, energy systems, etc. The combination of DT and ITS can further release the potential of participants in smart cities and guarantee their efficiency and reliability. Despite the advantages of DT-enabled ITS, not all requesters need the same level of DT service due to the highly dynamic nature of ITS. Safe and reliable matching between DT and ITS still needs to be resolved. To address these issues, we propose the blockchain-enabled Digital Twin as a Service (DTaaS) for ITS. First, we propose an on-demand DTaaS architecture to fully utilize the sensing capabilities of ITS and the macro perspective of DT. Second, a double-auction model and a price adjustment algorithm are proposed to realize the optimal DT matching for ITS requesters and ensure the benefits of participants. Third, a permissioned blockchain and a novel DT-DPoS consensus mechanism are established to enhance the security and efficiency of DTaaS. Simulation shows that the proposed DTaaS and double-auction can efficiently stimulate and facilitate DT transactions. The proposed DT-DPoS also has obvious advantages.
  • 28.

    【24hr】Deep Learning for Security in Digital Twins of Cooperative Intelligent Transportation Systems

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    摘要：The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40 ~ 80 and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.
  • 29.

    【24hr】Guest Editorial Introduction to the Special Issue on Intelligent Autonomous Transportation System With 6G

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    摘要：Recently, we have experienced an incredible surge of interest in connected and autonomous vehicles and related enabling technologies, which are expected to revolutionize future Intelligent Transportation Systems (ITS). This surging demand and popularity of ITS with the Internet of Vehicles technology has led to a tremendous rise in the number of connected vehicles. Driven by this massive number of connected vehicles, and the stringent requirements of autonomous vehicles and data-intensive applications such as ultralow latency, high reliability, and high security, intelligent transportation systems are rapidly moving to the 6G networks. The 6G-supported ITS is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles.
  • 30.

    【24hr】IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第12期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 31.

    【24hr】Light-Field Imaging Reconstruction Using Deep Learning Enabling Intelligent Autonomous Transportation System

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    摘要：Light-field (LF) cameras, also known as plenoptic cameras, permit the recording of the 4D LF distribution of target scenes. However, many times, surface errors of a microlens array (MLA) are responsible for degradation in the images captured by a plenoptic camera. Additionally, the limited pixel count of the sensor can cause missing parallax information. The aforementioned issues are crucial for creating accurate maps for Intelligent Autonomous Transport System (IATS), because they cause loss of LF information, and need to be addressed. To tackle this problem, a learning-based framework by directly simulating the LF distribution is proposed. A high-dimensional convolution layer with densely sampled LFs in 4D space and considering a soft activation function based on ReLU segmentation correction is used to generate a superresolution (SR) LF image, improving the convergence rate in the deep learning network. Experimental results show that our proposed LF image reconstruction framework outperforms the existing state-of-the-art approaches; specifically, it is effective for learning the LF distribution and generating high-quality LF images. Different image quality assessment methods are used to evaluate the performance of the proposed framework, such as PSNR, SSIM, IWSSIM, FSIM, GFM, MDFM, and HDR-VDP. Additionally, the computational efficiency was evaluated in terms of number of parameters and FLOPs, and experimental results demonstrated that our proposed framework reached the highest performance in most of the datasets used.
  • 32.

    【24hr】Intelligent Virtual Resource Allocation of QoS-Guaranteed Slices in B5G-Enabled VANETs for Intelligent Transportation Systems

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    摘要：5G communication technologies and networks help researchers and engineers look into intelligent transportation systems (ITS) with a new eye, including vehicular ad hoc networks (VANET) application. Network function virtualization (NFV) and network slicing (NS) are accepted as two most promising technologies towards the agile and elastic network architecture of 5G and beyond 5G (B5G). However, previous researchers studied NFV and NS separately. In addition, learning technologies, such as reinforcement leaning (RL), graph-based learning, emerge so as to enhance the network intelligence and resource allocation in recent years. Inspired from these, we jointly explore intelligent resource allocation issue within B5G-enabled VANETs. At first, the novel virtual resource allocation framework supporting NFV and NS for providing quality of service (QoS)-guaranteed slices is constructed. Then, we formulate the virtual resource allocation of slices as the optimization problem, having the goals of providing guaranteed QoS performance and maximizing the net profit. Considering the non convex attributes of the formulated optimization problem, we propose one intelligent and feasible algorithm instead, including the details of the proposed intelligent algorithm. We record the results in order to validate the feasibility and highlights of our proposed algorithm. For example, our intelligent algorithm has the slice acceptance advantage of 5, comparing with the best existing work.
  • 33.

    【24hr】IEEE Intelligent Transportation Systems Society Information

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第10期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 34.

    【24hr】IEEE Intelligent Transportation Systems Society Information

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第5期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 35.

    【24hr】Guest Editorial Introduction to the Special Issue on Applications and Developments of Novel Technologies in Intelligent Transportation Infrastructure Systems

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    摘要：The service life and service quality of transportation infrastructures are crucial for the regular function and public safety of transportation systems. Thus, the intelligent and accurate evaluation, prediction, and analysis of transportation infrastructure service conditions have always been hot topics for transportation engineers, where such problems are sometimes hard to be solved using traditional approaches, considering different factors including complex traffic conditions, severe environmental conditions, etc. In recent years, intelligent technologies, including advanced sensors, novel artificial intelligent approaches (deep learning, etc.), the IoT-based monitoring and analysis platform, auto piloting, green energy, synergistic technologies, and systems integrating both traffic and infrastructures monitoring, have emerged as powerful tools in transportation engineering area. These efforts have greatly advanced the service convenience of public transportation, prolonged the service life of transportation infrastructures, and improved the efficiency and safety of transportation systems.
  • 36.

    【24hr】An Intelligent Control Scheme for Optimum Efficiency and Reduced Emission Operation of Marine Transportation System

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    摘要：This paper proposes an intelligent control scheme (ICS) for optimum efficiency and reduced emission operation in small marine transportation systems such as tugs, research vessels, and small scale cruiser ships. The diesel engines used in marine and other transport related applications are designed to provide optimum efficiency at a specific value of mechanical loading. Generally, these engines provide the best fuel efficiency near 80 of their rated load and also the break specific emission of CO (carbon mono oxide) and other harmful greenhouse gases are found to be lowest at this specific amount of mechanical loading. The proposed controller makes the diesel engine to operate continuously at 80 of loading in order to ensure an optimum efficiency operation even if the mechanical load is kept changing due to the variations and fluctuations in the ship propulsion load or electrical load. The intelligent control scheme (ICS) uses a battery bank as an energy balancing element to maintain a constant total mechanical loading on the engine shaft using a bidirectional power flow control through the battery bank thus changing effective mechanical loading on the engine for optimum efficiency operation of the marine transportation system. The output mechanical power (P) delivered by the engine on its shaft is sensed using a power sensor and compared with its optimum value in order to calculate the error. Thereafter, based on the information about the error in engine shaft power, the ICS regulates the power flow to the SPAG (single phase asynchronous generator) in such a manner that the mechanical load on the engine is restored to its optimum value immediately. The optimum efficiency operation is maintained under randomly varying mechanical power demanded by the ship propulsion system and it is also maintained under varying electrical loads in the marine transportation system. This ICS scheme has been implemented in the laboratory using a DSP controller. The detailed test results are presented to validate the claims.
  • 37.

    【24hr】Pre-Predictive Congestion-Based Data Allocation for Sixth Generation Cooperative Intelligent Transportation Systems

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    摘要：Cooperative intelligent and autonomous transportation systems rely on intelligent sensing, computing, and actuating technologies for unmanned freight and public movements. The information gained from neighbors and communication infrastructures provides efficient actuation for safe and sustained transportation. This article resolves traffic data management congestion using sixth-generation (6G) communication and computing techniques. Terahertz and machine-type communications are exploited for swift information exchange, bypassing the congestion effects. Congestion occurs when demand for road space exceeds supply. This proposal incorporates prediction-based learning to compute the feasibility of handling traffic information and cooperative intelligent transportation. This model is named Congestion-aware Pre-predictive Data Allocation (CPPDA). The traffic flows causing congestion in the data exchange process are predicted for re-allocation and independent channel utilization. In this learning, the pre-predicted instances are updated with the actual identified utilization-to-congestion rate. Therefore, the congestion-causing channels for sensing are identified with ease, reducing the outage. The outage is examined for a basic inter-vehicle data link. Through the optimal allocation of channels for actuation, cloud-aided resources are utilized to a maximum level, leveraging infrastructure support. In addition to an outage of 10.83, the response time of 14.75, congestion factor of 8.2, computational overhead of 6.4, and information gain factors of 6.86 are analyzed through a comparative study.
  • 38.

    【24hr】Micro-Safe: Microservices- and Deep Learning-Based Safety-as-a-Service Architecture for 6G-Enabled Intelligent Transportation System

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    摘要：In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 – 31.2, energy consumption is reduced by 22.1 – 29.9, throughput is increased by 26.1 – 31.7, compared to the existing schemes.
  • 39.

    【24hr】Guest Editorial Intelligent Autonomous Transportation System With 6G—II

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    摘要：In the near future, the 6G-supported Intelligent Transportation System (ITS) is expected to be a transformative factor for both society and the economy by delivering unprecedented, seamless, reliable, efficient massive connectivity to millions of users and connected vehicles. This Special Issue 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 System. Twenty articles were selected for publication in the second part of the 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 factors pertaining to originality, technical quality, presentational quality, and overall contribution. We will introduce these articles and highlight their main contributions in the following.
  • 40.

    【24hr】Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey

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    摘要：Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.
  • 41.

    【24hr】Dynamic Pricing for Intelligent Transportation System in the 6G Unlicensed Band

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    摘要：The use of an unlicensed band has significantly boosted the capacity of cellular technology via LTE in unlicensed band, license assisted access, and new radio in unlicensed band. Likewise, cellular vehicle to everything in the shared band is also gaining momentum for intelligent transportation systems. Nevertheless, the cellular operator has to wisely decide the proper allocation of this unlicensed band as well as its licensed band, to its users. As the cellular operator cannot guarantee the quality of service in the unlicensed band, motivating the users to offload into the unlicensed band is one of the challenging tasks for the operator. In this paper, we propose an economical approach to encourage users to offload in the unlicensed band while maximizing the utility function for the users and revenue for the operator. Under the proposed scheme, fairness with legacy WiFi users operating in common channel is considered. We investigate the interaction between the operator and the user using a Stackelberg game. We derive the best response function for both operator and user to maximize its utility under complete information, such as service contract and usage pattern. However, it is not always practical to know the comprehensive knowledge of the user in a highly dynamic environment. Thus, a various multi-armed bandit algorithms are used and compared to drive convergence towards an optimal solution. Simulation results have been presented to compare and verify the performance of our proposed scheme.
  • 42.

    【24hr】AI Based Energy Efficient Routing Protocol for Intelligent Transportation System

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    摘要：The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.
  • 43.

    【24hr】IEEE Intelligent Transportation Systems Society Information

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    • 作者：
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第1期
    摘要：Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  • 44.

    【24hr】An Evolutionary Game Approach to Safety-Aware Speed Recommendation in Fog/Cloud-Based Intelligent Transportation Systems

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    摘要：Vehicle speed is known as one of the most important parameters in the various goals for driving. Although the most critical goal of driving is safety, each driver can consider another secondary goal, such as reducing travel time, economical driving, green driving, passenger comfort, etc. In this paper, we propose a smartphone-based application that utilizes the cloud/fog computing service infrastructure in order to recommend speed according to primary safety goal and another secondary objective. Due to the strategic nature of the driving speed selection issue, we have modeled the problem as a game that the drivers are the players, and the speed of the vehicle is their strategy. We solve the proposed game via evolutionary dynamics with appropriate convergence time, and the resulting equilibrium profile is announced to the drivers as the recommended speed at specified time intervals. Finally, the experimental results obtained from the simulation of the proposed scheme confirm that the deviation from the proposed equilibrium velocity is not profitable for the offending player. Indeed, deviation from equilibrium conditions leads to the negative impacts on the formal evaluation parameters presented in this paper.
  • 45.

    【24hr】Driving Behavior Analysis Guidelines for Intelligent Transportation Systems

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    摘要：The advent of in-vehicle networking systems as well as state-of-the-art sensors and communication technologies have facilitated the collection of large volume and almost real-time data on vehicles and drivers, thus opening up future possibilities. Processing and analyzing this data provides unprecedented opportunities to offer remarkable insights and solutions for driving behavior analysis (DBA). Characterizing driving behavior plays a key role in a variety of research areas such as traffic safety, the development of automated vehicles, energy and fuel management, risk assessment, and driver identification and profiling. Advances in DBA-based driver inattention or drunk driver detection can help reduce fatal car crashes, and understanding the driving style (e.g. eco-friendly or aggressive) of drivers can contribute to fuel management and risk assessment of the drivers. These facts have led to a growing interest in addressing DBA challenges. This paper aims to present the state-of-the-art methodologies for DBA and provide a clear roadmap about the main current and future trends in DBA. To this end, we propose categorizing the current research on driving behavior based on the types of data employed for the analysis, the ultimate goals of the analysis, and the techniques based on which the driving data are modeled. We provide an overview of different data resources and available datasets for DBA. Moreover, we discuss the application of DBA along with the key research challenges in this field and potential future directions.
  • 46.

    【24hr】A Decentralized Trust Management System for Intelligent Transportation Environments

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    摘要：Commercialized 5G technology will provide reliable and efficient connectivity of motor vehicles that could support the dissemination of information under an intelligent transportation system. However, such service still suffers from risks or threats due to malicious content producers. The traditional public key infrastructure (PKI) cannot restrain such untrusted but legitimate publishers. Therefore, a trust-based service management mechanism is required to secure information dissemination. The issue of how to achieve a trust management model becomes a key problem in the situation. This paper proposes a novel prototype of the decentralized trust management system (DTMS) based on blockchain technologies. Compared with the conventional and centralized trust management system, DTMS adopts a decentralized consensus-based trust evaluation model and a blockchain-based trust storage system, which provide a transparent evaluation procedure and irreversible storage of trust credits. Moreover, the proposed trust model improves blockchain efficiency by only allowing trusted nodes participating in the validation and consensus process. Additionally, the designed system creatively applies a trusted execution environment (TEE) to secure the trust evaluation process together with an incentive model that is used to stimulate more participation and penalize malicious behaviours. Finally, to evaluate our new design prototype, both numerical analysis and practical experiments are implemented for performance evaluation.
  • 47.

    【24hr】Age of Information Aware UAV Deployment for Intelligent Transportation Systems

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    摘要：The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.
  • 48.

    【24hr】A Taxonomy and Survey of Edge Cloud Computing for Intelligent Transportation Systems and Connected Vehicles

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    摘要：Recent advances in smart connected vehicles and Intelligent Transportation Systems (ITS) are based upon the capture and processing of large amounts of sensor data. Modern vehicles contain many internal sensors to monitor a wide range of mechanical and electrical systems and the move to semi-autonomous vehicles adds outward looking sensors such as cameras, lidar, and radar. ITS is starting to connect existing sensors such as road cameras, traffic density sensors, traffic speed sensors, emergency vehicle, and public transport transponders. This disparate range of data is then processed to produce a fused situation awareness of the road network and used to provide real-time management, with much of the decision making automated. Road networks have quiet periods followed by peak traffic periods and cloud computing can provide a good solution for dealing with peaks by providing offloading of processing and scaling-up as required, but in some situations latency to traditional cloud data centres is too high or bandwidth is too constrained. Cloud computing at the edge of the network, close to the vehicle and ITS sensor, can provide a solution for latency and bandwidth constraints but the high mobility of vehicles and heterogeneity of infrastructure still needs to be addressed. This paper surveys the literature for cloud computing use with ITS and connected vehicles and provides taxonomies for that plus their use cases. We finish by identifying where further research is needed in order to enable vehicles and ITS to use edge cloud computing in a fully managed and automated way. We surveyed 496 papers covering a seven-year timespan with the first paper appearing in 2013 and ending at the conclusion of 2019.
  • 49.

    【24hr】Lightweight Tensor Deep Computation Model With Its Application in Intelligent Transportation Systems

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    摘要：Deep computation models (DCMs) are widely used in intelligent transportation systems (ITS), like driving behavior detection, intelligent parking navigation and real-time road condition detection. Due to the multi-source heterogeneous nature of big data of the ITS, it is difficult for traditional DCMs to learn effective multi-modal data features. Although, the DCMs in tensor space can efficiently represent multi-modal data, it further worsens the problem of model learning parameter explosion. In this paper, we propose a lightweight tensor DCM. The model compresses the redundant learning parameters of the model and reduces the consumption of computational resources while maintaining the learning characterization capability of the DCM in tensor space, thus making the network model more general and lightweight for deploying the DCM to smart cars and edge devices. The proposed lightweight tensor DCM is evaluated on several real datasets. The experimental results show that the number of learning parameters is massively compressed while keeping the performance of the network model almost constant, while also reducing the computational complexity and training time of the model.
  • 50.

    【24hr】Speech Emotion Recognition Enhanced Traffic Efficiency Solution for Autonomous Vehicles in a 5G-Enabled Space–Air–Ground Integrated Intelligent Transportation System

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    摘要：Speech emotion recognition (SER) is becoming the main human-computer interaction logic for autonomous vehicles in the next generation of intelligent transportation systems (ITSs). It can improve not only the safety of autonomous vehicles but also the personalized in-vehicle experience. However, current vehicle-mounted SER systems still suffer from two major shortcomings. One is the insufficient service capacity of the vehicle communication network, which is unable to meet the SER needs of autonomous vehicles in next-generation ITSs in terms of the data transmission rate, power consumption, and latency. Second, the accuracy of SER is poor, and it cannot provide sufficient interactivity and personalization between users and vehicles. To address these issues, we propose an SER-enhanced traffic efficiency solution for autonomous vehicles in a 5G-enabled space-air-ground integrated network (SAGIN)-based ITS. First, we convert the vehicle speech information data into spectrograms and input them into an AlexNet network model to obtain the high-level features of the vehicle speech acoustic model. At the same time, we convert the vehicle speech information data into text information and input it into the Bidirectional Encoder Representations from Transformers (BERT) model to obtain the high-level features of the corresponding text model. Finally, these two sets of high-level features are cascaded together to obtain fused features, which are sent to a softmax classifier for emotion matching and classification. Experiments show that the proposed solution can improve not only the SAGIN's service capabilities, resulting in a large capacity, high bandwidth, ultralow latency, and high reliability, but also the accuracy of vehicle SER as well as the performance, practicality, and user experience of the ITS.
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