• 主题
高级搜索  >
历史搜索 清空历史

您现在的位置：首页>外文期刊>Intelligent Transportation Systems, IEEE Transactions on

期刊信息

  • 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： -
  • 简介：

年度选择

更多>>

  • 排序：
  • 显示：
  • 每页：
全选（0）
<9/20>
共5416条结果
  • 1.

    【24hr】Guest Editorial Special Issue on Space-Air-Ground Integrated Networks for Intelligent Transportation Systems

    包量

    机译
    摘要：
  • 2.

    【24hr】Editorial Special Issue on AI Innovations in Intelligent Transportation Systems

    包量

    机译
    • 作者：Kim Tai-Hoon;
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第3期
    摘要：
  • 3.

    【24hr】Secure and Temporary Access Delegation With Equality Test for Cloud-Assisted IoV

    包量

    机译
    摘要：With the continuous development of the Internet of Vehicles (IoV), the cloud-assisted IoV is becoming an emerging and attractive paradigm, in which a growing number of IoV users use the cloud server to store the data collected from their smart devices, thereby relieving local costs to a great extent. In this case, considering that the third-party cloud as a commercial mechanism cannot guarantee data security, users choose to encrypt their own data before uploading it to the cloud server. Although the privacy and confidentiality of the uploaded data can be protected, the encryption method hinders data search due to its inherent “decrypt all-or-nothing” feature. To achieve encryption and data search on privacy-preserving data, many existing works of literature design various public key encryption with keyword search schemes that, however, suffers the disadvantage that they can work only between ciphertext encrypted under the same public key, which means that it is not suitable for some computations on cloud. Furthermore, most schemes also have failed to find out how to delegate the searched data securely and efficiently when the data owner is not convenient to address the data. Therefore, this study tends to test the equivalence between messages encrypted under different public keys for basic secure computation and delegating the right of decrypting searched data to the specified user. Then, a lightweight proxy re-encryption scheme with the equality test and temporary delegation (PRE-ET-TD) was put forward for the cloud-assisted IoV. Besides, the proposed scheme is collusion-resistant and proven secure against chosen ciphertext attack (CCA) in the random oracle model. Meanwhile, the experimental simulation indicates that the presented PRE-ET-TD is feasible and efficient.
  • 4.

    【24hr】Sequential Semantic Segmentation of Road Profiles for Path and Speed Planning

    包量

    机译
    摘要：Driving video is available from in-car camera for road detection and collision avoidance. However, consecutive video frames in a large volume have redundant scene coverage during vehicle motion, which hampers real-time perception in autonomous driving. This work utilizes compact road profiles (RP) and motion profiles (MP) to identify path regions and dynamic objects, which drastically reduces video data to a lower dimension and increases sensing rate. To avoid collision in a close range and navigate a vehicle in middle and far ranges, several RP/MPs are scanned continuously from different depths for vehicle path planning. We train deep network to implement semantic segmentation of RP in the spatial-temporal domain, in which we further propose a temporally shifting memory for online testing. It sequentially segments every incoming line without latency by referring to a temporal window. In streaming-mode, our method generates real-time output of road, roadsides, vehicles, pedestrians, etc. at discrete depths for path planning and speed control. We have experimented our method on naturalistic driving videos under various weather and illumination conditions. It reached the highest efficiency with the least amount of data.
  • 5.

    【24hr】KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

    包量

    机译
    摘要：While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/KST-GCN .
  • 6.

    【24hr】Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

    包量

    机译
    摘要：Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced $mathcal {L}^{mathbf {1+alpha }}$ tracking performance. Second, a $mathcal{C}^{ boldsymbol {infty }}$ -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.
  • 7.

    【24hr】Elliptical Encircling of Quadrotors for a Dynamic Target Subject to Aperiodic Signals Updating

    包量

    机译
    摘要：This paper presents an elliptical target encircling control policy of quadrotors subject to uncertainties and aperiodic signals updating based on pure bearing measurements. At the translational level, by resorting to bearing-only data, rather than prior position and velocity information of target, a position estimator is constructed for locating the unknown target. Utilizing the localization result from position estimator, compared to the existing circular surrounding alternatives, a planar elliptical guidance law capable of adapting more sophisticated operational environment, and a longitudinal control law are synchronously established to generate the velocity reference. At the rotational level, an unknown system dynamics estimator (USDE) is introduced to online neutralize total adverse effect induced by exogenous disturbances and internal uncertainties, where high precision estimation and low computational complexity can be guaranteed with only one tuning argument, then an event-triggered robust attitude controller carrying a sampling deviation compensation item is synthesized accomplishing elliptical encircling for a dynamic target without involving Zeno behavior. Finally, stability of closed-loop system is analyzed via input-to-state stable principle, while simulations are given to verify the efficacy of suggested approach.
  • 8.

    【24hr】Reconstruction of Vehicle-Induced Vibration on Concrete Pavement Using Distributed Fiber Optic

    包量

    机译
    摘要：Vibration of concrete pavement contains valuable information in the time, space, and frequency domain, which is beneficial to loading characteristics identification and structural health monitoring. However, these multidimensional features have a significant locality and need to measure effectively. This paper proposes a novel method by using distributed fiber optic to measure the temporal, spatial, and spectral distribution of pavement vibration, as well as reconstruct them into a vibration field for feature analysis. First, a vibration monitoring system with designed units was developed to measure the vibration of concrete pavement. Then reconstruction and analysis methods of the vibration field were proposed to process the one-dimensional measured data. Finally, a series of experiments were conducted to validate the performance of the system and methods under different loading types, speeds, magnitude, and positions. According to the results of the impulse loading test, accelerated pavement test, and traffic loading test, it indicates the system can measure the vibration of concrete pavement with sufficient positioning precision (0.33 m), sampling frequency (2500 Hz), and frequency accuracy (<±1 Hz). Also, based on the data processing method, the vibration field can be reconstructed and analyzed effectively to reflect traffic behaviors like braking and lane change.
  • 9.

    【24hr】Regional Coordinated Bus Priority Signal Control Considering Pedestrian and Vehicle Delays at Urban Intersections

    包量

    机译
    摘要：Bus priority signal control can reduce bus delay at intersections and improve bus operation efficiency. The existing research focus mainly on bus priority control methods at isolated intersections, as well as urban arterial corridors. Rarely existing works consider a network-level bus priority control. Treating regional-wide intersections can be challenging, given that more traffic directions and large multi-modal traffic volume should be considered. The conventional transit signal priority (TSP) research focus on the delay of buses and social vehicles and neglect the delay of pedestrians. To fill these gaps, this paper proposes a regional coordinated bus priority signal control (RCBPSC) method, which is a network-level bus priority control method considering pedestrian and passenger delays. This method is divided into two stages. The first stage is the regional coordinated signal control to obtain the basic signal timing schemes. The second stage is the bus priority signal control. At this stage, the timing schemes at each intersection will be adjusted according to the bus arrival time and the delays. In order to verify the effectiveness of this method, we choose four intersections in Chengdu to study. The results show that the total delay of the proposed method at the first stage (control case 1) can be reduced by 602s (2.3) and 606s (3.7) comparing with the conventional timing method in the peak and non-peak period. At the second stage, the proposed method can reduce more pedestrian delay than the conventional TSP method in both scenario 1 and scenario 2 .
  • 10.

    【24hr】Eco-Driving Control Architecture for Platoons of Uncertain Heterogeneous Nonlinear Connected Autonomous Electric Vehicles

    包量

    机译
    摘要：The improvement of energy performance for platoons of autonomous connected vehicles is one of the major challenges the road transport sector is facing with. To this aim, this work addresses and solves the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon via a novel Eco-Driving Control Architecture able to optimize its energy consumption performance while ensuring the fulfillment of the optimal leader tracking trajectory. Specifically, it consists of a Nonlinear Model Predictive Control (NMPC) strategy, driving the leader motion and computing the optimal ecological trajectory to be imposed on the whole platoon, and a novel distributed exponentially-stable robust PID-like protocol, driving the follower vehicles motion for achieving a precise leader-tracking with a desired transient behavior as required for the accurate implementation of the energy-saving control. The exponential stability of the overall vehicular network is analytically proven with the Lyapunov theory and the derived robust stability conditions allow the proper tuning of the control gains on the basis of the desired decay rate. The efficiency of the proposed approach is corroborated via the high-fidelity Mixed Traffic Simulator (MiTraS) co-simulation platform under different operative scenarios and a wide uncertainty range for the vehicles parameters. Simulation results confirm how the proposed architecture ensures the eco-driving behaviour for the whole vehicles platoon.
  • 11.

    【24hr】An Automated Learning Framework With Limited and Cross-Domain Data for Traffic Equipment Detection From Surveillance Videos

    包量

    机译
    摘要：Traffic equipment detection from surveillance videos is of practical significance for temporary traffic element update in high-precision maps. However, there is little relative research developed due to limited labeled data. Based on a detector dubbed Faster R-CNN, we propose an automated learning framework that utilizes easy-to-obtain Internet images containing traffic equipment to acquire the capability of detecting traffic equipment from surveillance videos. In this framework, an appearance weighting module using a comprehensive feature aggregation method is designed to allow Faster R-CNN to converge and generalize quickly by taking limited data (i.e., less than 30 images per class) as input. To further address the cross-domain issue brought by the domain gap between the Internet images and the surveillance video frames, a domain adaptation learning scheme is developed, which aims to align the two domains and guide the framework to learn more robust domain-invariant features. Experimental results show that both the appearance weighting module and the domain adaptation learning scheme could bring a great performance improvement. Moreover, the combination of the two results in a state-of-the-art performance (mAP of 44.6) even if only 30 training images per class are provided. To sum up, the proposed framework is suitable for traffic equipment detection from surveillance videos and provides an inspiration for other detection tasks with limited and cross-domain data, allowing humans to reduce their efforts and time required for arduous data collection and annotation.
  • 12.

    【24hr】A Review of Vision-Based Traffic Semantic Understanding in ITSs

    包量

    机译
    摘要：A semantic understanding of road traffic can help people understand road traffic flow situations and emergencies more accurately and provide a more accurate basis for anomaly detection and traffic prediction. At present, the overview of computer vision in traffic mainly focuses on the static detection of vehicles and pedestrians. There are few in-depth studies on the semantic understanding of road traffic using visual methods. This paper aims to review recent approaches to the semantic understanding of road traffic using vision sensors to bridge this gap. First, this paper classifies all kinds of traffic monitoring analysis methods from the two perspectives of macro traffic flow and micro road behavior. Next, the techniques for each class of methods are reviewed and discussed in detail. Finally, we analyze the existing traffic monitoring challenges and corresponding solutions.
  • 13.

    【24hr】A Hybrid Approach to Trust Node Assessment and Management for VANETs Cooperative Data Communication: Historical Interaction Perspective

    包量

    机译
    摘要：Vehicular ad hoc networks (VANETs) provide self-organized wireless multihop transmission, where nodes cooperate with each other to support data communication. However, malicious nodes may intercept or discard data packets, which might interfere with the transmission process and cause privacy leakage. We consider historical interaction data of nodes as an important factor of trust. Thus, this paper focuses on the trust node management of VANETs, which aims to quantify node credibility as an assessment method and avoid assigning malicious nodes. First, the integrated trust of each node is proposed, which consists of the direct trust and the recommended trust. The former is dynamically computed by historical interaction records and Bayesian inference considering penalty factors. The latter defines trust by third-party nodes and their reputation. Second, the process of trust calculation and data communication calls for timeliness. Therefore, we introduce a time sliding window and time decay function to ensure that the latest interaction information has a higher weight. We can sensitively identify malicious nodes and make quick responses. Finally, the experimental results demonstrate that our proposed method outperforms bassline methods, especially with respect to the packet delivery ratio and security.
  • 14.

    【24hr】Alleviating Data Sparsity Problems in Estimated Time of Arrival via Auxiliary Metric Learning

    包量

    机译
    摘要：With millions of people using ride-hailing platforms for daily travel, estimated time of arrival (ETA) has become a significant problem in intelligent transportation systems and attracted considerable attention recently. Deep learning-based ETA methods have achieved promising results using massive spatial-temporal data. However, we find that the prediction accuracy is not satisfactory in practical applications due to the prevalent data sparsity problems. Instead of focusing on the average prediction performance as many other methods, this study aims to alleviate the data sparsity problems in ETA to enhance user experience. In general, the data sparsity problems arise from two aspects. The first is the road network, where many links are only traversed by few floating cars. The second aspect is drivers, where many drivers’ trajectories are too scarce (e.g., with only 3 trip records). To alleviate the sparsity in road network, we propose a Road Network Metric Learning framework for ETA (RNML-ETA), where an auxiliary metric learning task is used to improve the link-embedding, especially for links with insufficient data. A novel triangle loss is proposed to improve metric learning effectiveness for links. Experiments on massive real-world data show that RNML-ETA outperforms competing methods by promoting the cold links with limited data. Furthermore, we propose a novel unified framework to Alleviate Data Sparsity problems in ETA (ADS-ETA) by extending RNML-ETA with an additional auxiliary task for driver ID embedding. Results with extensive experiments demonstrate that ADS-ETA can effectively alleviate the data sparsity problems caused by road network and driver sparsity.
  • 15.

    【24hr】Fitting Spatial-Temporal Data via a Physics Regularized Multi-Output Grid Gaussian Process: Case Studies of a Bike-Sharing System

    包量

    机译
    摘要：Fitting and modeling spatial-temporal processes are essential research topics in transportation studies. Recently, due to the analytically tractable formulation and good fitting accuracy, the Gaussian Process (GP) is becoming increasingly preferable in fitting transportation processes. However, conventional GPs are inapplicable for large-scale problems due to computational issues. Moreover, they dismiss physics laws when conducting multi-output fitting tasks. This paper proposes a physics regularized multi-output grid Gaussian Process Model (PRMGGP) model for fast and multi-output fitting of large-scale spatial-temporal processes in transportation systems. The PRMGGP model adopts a grid input structure to capture inherent spatial-temporal correlations in the fitting process, takes advantage of the Kronecker algebra to notably accelerate the computation speed, and utilizes a shadow GP to incorporate physics laws of the process. Model training and predictive algorithms are developed coordinately and are tested via synthetic datasets. Furthermore, we apply the proposed model and other widely used machine learning models to fit the numbers of pickups, returns, and idle bikes of a large-scale bike-sharing system based on Citi Bike data from New York City. The results demonstrate the computational efficiency, interpretable results, and the prediction accuracy of the PRMGGP model, which can be a promising methodology for modeling multi-output processes in transportation systems.
  • 16.

    【24hr】Survey on Image and Point-Cloud Fusion-Based Object Detection in Autonomous Vehicles

    包量

    机译
    摘要：With the improvements in sensor performance (cameras, Lidars) and the application of deep learning in object detection, autonomous vehicles (AVs) are gradually becoming more notable. After 2019, AV has produced a wave of enthusiasm, and many papers on object detection were published, boasting both practicality and innovation. Due to hardware limitations, it is difficult to accomplish accurate and reliable environment perception using a single sensor. However, multi-sensor fusion technology provides an acceptable solution. Considering the AV cost and object detection accuracy, both the traditional and existing literature on object detection using image and point-cloud was reviewed in this paper. Additionally, for the fusion-based structure, the object detection method was categorized in this paper based on the image and point-cloud fusion types: early fusion, deep fusion, and late fusion. Moreover, a clear explanation of these categories was provided including both the advantages and limitations. Finally, the opportunities and challenges the environment perception may face in the future were assessed.
  • 17.

    【24hr】High Time-Resolution Queue Profile Estimation at Signalized Intersections Based on Extended Kalman Filtering

    包量

    机译
    摘要：The dynamic spatiotemporal characteristics of queues at urban intersections are crucial to traffic operation tasks such as signal performance measure and signal optimization. This paper addresses the high time-resolution estimation of queue profile at urban signalized intersection using Extended Kalman Filtering (EKF) with data of connected vehicles (CVs). The main features of this work are as follows: (i) a machine learning method was applied to construct a dynamic shockwave propagation model based on shockwave theory and historical data of CVs; (ii) a heuristic approach was proposed to measure the shockwave speed for use in EKF; (iii) an urban queue estimator was designed to combine the dynamic shockwave propagation model and real-time shockwave information via EKF to deliver second-by-second queue profile estimates. The queue estimator does not require any priori information about vehicle arrival patterns and the market penetration rate (MPR) of CVs. The performance and robustness of the queue estimator were evaluated using both simulation and real-world CV data. The results show that the method can provide satisfactory queue estimation results at various MPR levels of CVs, with the estimation error of 2.5 vehicles at the MPR of 5, and of 0.5 vehicle at the MPR of 40.
  • 18.

    【24hr】Data-Driven Flexible Vehicle Scheduling and Route Optimization

    包量

    机译
    摘要：The flexible transit service reflects a trend of demand on the flexibility and convenience in urban public transport systems, within which the vehicle scheduling and passenger insertion are two challenging issues. Especially, finding the optimal solution for a flexible transit system can be viewed as an extension of the traveling salesman problem which is NP-complete. Yet most of the existing research mainly focuses on one aspect, i.e. route planning, stop selection or vehicle scheduling, where a combined integration and optimization of the whole system is largely neglected. In this paper, we propose a data-driven flexible transit system that integrates the origin-destination insertion algorithm and the milp-based (mixed-integer linear programming) scheduling scheme. Specifically, stops are mined from the historical datasets and some stops act as $backbone$ stops that should be visited by the vehicles; and a heuristic backbone-based origin-destination insertion algorithm is proposed to schedule the routing path of vehicles, where the time loss caused by the optimal insertion positions is calculated for the vehicles to decide whether to accept the requests or not when constructing a path for the flexible routes. Moreover, a vehicle scheduling model based on milp is proposed to minimise the gap between the passenger flow and available seats. The proposed flexible transit systems are simulated in real-world taxi datasets, and experimental results show that the proposed flexible transit system can effectively increase the delivery ratio and decrease the passengers’ waiting time compared with existing methods.
  • 19.

    【24hr】Multi-Objective Asymmetric Sliding Mode Control of Connected Autonomous Vehicles

    包量

    机译
    摘要：The platoon of connected autonomous vehicles plays an essential role in future intelligent transportation. It can improve traffic efficiency and release traffic congestion. However, there are lots of existing challenging problems of the control of connected autonomous vehicles, such as the negative impact caused by wireless communication and disturbance. To solve these challenges, a multi-objective asymmetric sliding mode control strategy is proposed in this paper. Firstly, the asymmetric degree is introduced in the topological matrix. Then, a sliding mode controller is designed targeting platoon’s tracking performance. Moreover, Lyapunov analysis are used via Riccati inequality to find the controller’s gains and guarantee internal stability and Input-to-output string stability. Finally, a non-dominated sorting genetic algorithm is utilized to find the Pareto optimal asymmetric degree regarding the overall performance of the platoon, including tracking index, fuel consumption, and acceleration standard deviation. Four different information flow topologies, including a random topology are studied. The results indicate that the proposed asymmetric sliding mode controller can ensure platoon’s stability while improving its performance. The tracking ability is improved by 54.61 and 75.17, fuel economy is improved by 0.78 and 6.34 under the Urban Road and Highway Case Study, respectively.
  • 20.

    【24hr】Improving the Utilization of Regenerative Energy and Shaving Power Peaks by Railway Timetable Adjustment

    包量

    机译
    摘要：Employing regenerative braking in trains contributes to reducing the amount of energy used, especially when applied to commuter trains and to those used on very dense suburban networks. This paper presents a method to fine-tune the periodic timetable to improve the utilization of regenerative energy and to shave power peaks while maintaining the structure and robustness of the original timetable. First, a mixed-integer linear programming model based on the periodic event scheduling framework is proposed. A set of feasible timetables is determined and optimized with the aim of increasing synchronized acceleration and braking events at the same station, and maintaining the timetable robustness at the specified level. Next, a local search algorithm is developed to optimize the timetable such that the power peak value is minimized. The max-plus system model is adopted to estimate the delay propagation. Monte Carlo simulation is used to evaluate the utilization of regenerative energy and power peaks in random delayed circumstances. The proposed method was adopted to fine-tune the 2019 timetable for a sub-network of the Dutch railway. In the case of on- time scenarios, the optimized timetable increases the regenerative energy usage by almost 290 and decreases the 15-minute power peaks by 8.5. In the case of delay scenarios, the optimized timetable outperforms the original timetable in terms of using regenerative energy and shaving power peaks.
  • 21.

    【24hr】A Multi-Agent Reinforcement Learning Method With Route Recorders for Vehicle Routing in Supply Chain Management

    包量

    机译
    摘要：In the modern supply chain system, large-scale transportation tasks require the collaborative work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle route planning was mainly implemented by heuristic algorithms. However, these algorithms face the dilemma of long computation time. In recent years, some machine learning-based methods are also proposed for vehicle route planning, but the existing algorithms can hardly solve multi-vehicle time-sensitive problems. To overcome this problem, we propose a novel multi-agent reinforcement learning model, which optimizes the route length and the vehicle’s arrival time simultaneously. The model is based on the encoder-decoder framework. The encoder mines the relationship between the customer nodes in the problem, and the decoder generates the route of each vehicle iteratively. Specially, we design multiple route recorders to extract the route history information of vehicles and realize the communication between them. In the inferring phase, the model could immediately generate routes for all vehicles in a new instance. To further improve the performance of the model, we devise a multi-sampling strategy and obtain the balance boundary between computation time and performance improvement. In addition, we propose a simulation-based vehicle configuration method to select the optimal number of vehicles in real applications. For validation, we conduct a series of experiments on problems with different customer amounts and various vehicle numbers. The results show that the proposed model outperforms other typical algorithms in both performance and calculation time.
  • 22.

    【24hr】Urban Road Network Partitioning Based on Bi-Modal Traffic Flows With Multiobjective Optimization

    包量

    机译
    摘要：The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the contribution of large occupancy vehicles such as buses in improving urban transportation efficiency. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized for further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. It is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjusting is performed by changing the region to which a boundary is clustered to optimize the result. Multi-sensor data collected from Shenzhen in China are utilized to verify the effectiveness of the proposed partition method.
  • 23.

    【24hr】An Efficient Power Allocation Algorithm for Green Reconfigurable Intelligent Surface Assisted Vehicular Network

    包量

    机译
    摘要：It is an irreversible trend to build a green and sustainable vehicular network facing with the dramatic increase in urban traffic. Reducing energy consumption has been an important aspect for green transportation. Reconfigurable intelligent surface (RIS) is considered as a promising technology to enhance the communication quality with higher energy efficiency. In this paper, we focus on the RIS-assisted vehicular networks. We obtain the closed-form analytical expressions for outage probability, ergodic achievable rate and average energy efficiency. A series of insights are further explored. Based on these, we discuss the performance under high SNR case, as well as, weak interference case. And then, the approximations in simpler form expressions are provided for each case, respectively. Outage diversity order and high SNR rate slope are also investigated. In addition, we propose a power allocation algorithm to maximize the ergodic achievable sum rate guaranteeing the outage probability and average energy efficiency. Numerical results show that our analytical results agree well with the Monte Carlo simulations in various network configurations. Besides, our proposed power allocation scheme significantly enhances the ergodic achievable sum rate compared with the equal power strategy.
  • 24.

    【24hr】Sensitivity of Electrodermal Activity Features for Driver Arousal Measurement in Cognitive Load: The Application in Automated Driving Systems

    包量

    机译
    摘要：Driver’s under-arousal occurred in automated driving systems (ADS) impairs takeover safety. This study aims to determine electrodermal activity (EDA) features’ importance for driver’s arousal quantification. A car-following simulator study was conducted with participants concurrently executing four levels of cognitive tasks, triggering four levels of arousal. Participants’ skin conductance (SC) data were collected and decomposed into tonic (skin conductance level, SCL) and phasic (skin conductance response, SCR) components. Seventeen features extracted from SC, SCL and SCR were compared. As a result, SCR-relevant features showed higher significance and larger effect size than SC and SCL features in response to cognitive load, which suggests the phasic component dominates changes in EDA under varying cognitive load. Moreover, the SCR rate TTP.nSCRs, identified by $0.03 ~mu text{S}$ thresholds, attained the largest effect size among all features for driver’s arousal measurement. A varying time windows (TW) analysis showed that TTP.nSCRs was the most suggested arousal metric when TW was over 20 s, whereas the sum of SCRs amplitudes TTP.AmpSum was preferred when TW was less than 20 s. For driver’s arousal quantification with multi-features, the top five suggested features were TTP.nSCRs, SC_Rate5, CDA.SCR (or CDA.ISCR), CDA.AmpSum, and TTP.AmpSum. Although male drivers showed higher values of EDA features than female drivers, the sensitivity of the proposed EDA features stands across gender and individuals. This study promotes an improved understanding of EDA changes in human cognitive process. The sensitive EDA features proposed could be used from uni- or multi-modalities in driver state management and takeover-safety prediction for ADS.
  • 25.

    【24hr】Eco-Driving: A Scientometric and Bibliometric Analysis

    包量

    机译
    摘要：The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”, “ecological-routing”, “ecological-bus”, “ecological-car”, “ecological-vehicle”, “eco-driving”, “eco-routing”, “eco-driver”, “eco-bus”, “eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.
  • 26.

    【24hr】Passenger Evacuation Path Planning in Subway Station Under Multiple Fires Based on Multiobjective Robust Optimization

    包量

    机译
    摘要：The optimization of passenger evacuation path in subway station under emergencies directly affects the evacuation efficiency. Aiming at the evacuation scenario of passengers in subway station under multiple fires, a path planning method based on multi-objective robust optimization is established in this paper. The shortest total evacuation time, the minimum total risk and the minimum total congestion cost are taken as the objectives where the robustness of travel time and risk is also considered. NSGA-II algorithm is used to solve the model, the optimal Pareto solution under a certain robust control parameter is obtained according to the principle of minimizing the total cost function, and the overall optimization degree of route is obtained by using the evaluation index. Taking Qingdao May 4th Square subway station of China as a verification example, a fire diffusion model is built by using Pyrosim, and a passenger evacuation system is built by using Pathfinder. The multi-objective robust path optimization model of passenger evacuation is applied to the built passenger evacuation system under fires. The simulation results show that the overall optimization degree of route can reach 12.8, when the time and risk robust control parameters are 30 and 30, respectively. The method can be used to guide the passenger evacuation in subway stations under multiple hazard sources and improve the safety index of passenger travel.
  • 27.

    【24hr】Joint Deployment Optimization and Flight Trajectory Planning for UAV Assisted IoT Data Collection: A Bilevel Optimization Approach

    包量

    机译
    摘要：This work investigates an unmanned aerial vehicle (UAV) assisted IoT system, where a UAV flies to each foothold to collect data from IoT devices, and then return to its start point. For such a system, we aim to minimize the energy consumption by jointly optimizing the deployment and flight trajectory of UAV. It is a mixed-integer non-convex and NP-hard problem. In order to address it, a bilevel optimization approach is proposed, where an upper-level method aims to optimize the deployment of UAV and a lower-level one aims to plan UAV flight trajectory. Specifically, the former optimizes the number and locations of footholds of UAV. This work proposes an improved dandelion algorithm with a novel encoding strategy, in which each dandelion represents a foothold of UAV and the entire dandelion population is seen as an entire deployment. Then, two mutation strategies are designed to adjust the number and locations of footholds. Based on the footholds of the UAV provided by the former, the latter transforms flight trajectory planning into a traveling salesman problem (TSP). This work proposes an iterated greedy algorithm to solve it efficiently. The effectiveness of the proposed bilevel optimization approach is verified on ten instances, and the experimental results show that it significantly outperforms other benchmark approaches.
  • 28.

    【24hr】NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks

    包量

    机译
    摘要：Modern vehicular electronics is a complex system of multiple Electronic Control Units (ECUs) communicating to provide efficient vehicle functioning. These ECUs communicate using the well-known Controller Area Network (CAN) protocol. The increasing amount of research in the Intelligent Transportation System (ITS) domain has demonstrated that this protocol is vulnerable to various types of security attacks, compromising the safety of passengers and pedestrians on the roads. Hence, there is a need to develop novel anomaly detection systems to address this problem. This work presents a novel deep learning-based Intrusion Detection System incorporating thresholding and error reconstruction approaches. We train and explore multiple neural network architectures and compare their performance. The proposed anomaly detection system is tested on four kinds of attacks - Denial of Service (DoS), Fuzzy, RPM Spoofing and Gear Spoofing using evaluation metrics such as Precision, Recall and F1-Score. We also present reconstruction-error distribution plots to give a qualitative intuition about the proposed system’s ability to distinguish between genuine and anomalous sequences.
  • 29.

    【24hr】A bi-Hierarchical Game-Theoretic Approach for Network-Wide Traffic Signal Control Using Trip-Based Data

    包量

    机译
    摘要：Network-wide traffic signal control (NTSG) is one of the most important factors that impact the transportation network efficiency. Nevertheless, the NTSG problem still faces some issues impeding its development: (1) the traditional flow-based data has poor applicability to the evaluation of the network-wide traffic state, and the identification of the most delayed bottleneck intersections; (2) the existing control methods are difficult to capture the global optimal solution, owing to the lack of collaboration among multiple controllers. In this paper, to overcome the aforementioned challenges, we collect the trip-based data to evaluate the network-wide traffic state, and identify the most delayed trip-based bottlenecks as the controllers. Then, a bi-hierarchical game-theoretic (BHGT) method is proposed to solve the NTSG problem. At the lower layer, the NTSG problem is decomposed into several sub-problems of bottleneck control. A coalition game of intersections is formulated to solve the optimal signal control strategy of each single bottleneck. Furthermore, at the upper layer, a potential game is formulated to collaborate the multiple bottlenecks’ strategies solved from the lower-layer. After several iterations between two layers, the BHGT method will converge to a solution which minimizes the total travel delay of the network. Experimental results on a real-world dataset in Xuancheng City prove that the BHGT method outperforms other baseline methods in reducing the network-wide travel delay, both in low-traffic and high-traffic scenarios.
  • 30.

    【24hr】Two-Stage Merging Network for Describing Traffic Scenes in Intelligent Vehicle Driving System

    包量

    机译
    摘要：Intelligent vehicle driving systems aim to control the driving behavior of a vehicle in real time without human intervention by perceiving and monitoring the surrounding environment. Describing images of traffic scenes automatically, which is one of the key problems of intelligent vehicle driving technology, has drawn attention since its inception. In recent years, a variety of automatic image description technologies have been proposed, among which the attention-based encoder-decoder framework achieved good results. In this paper we will discuss the fusing of a variety of information from multiple aspects of the images of traffic scenes. First, we will introduce visual attention, text attention and image topics attention which generates the weighted visual features, the attentive text information and the global image topics information respectively. We will then propose an adaptive two-stage merging network based on an encoder-decoder framework, which can fully integrate the three kinds of information in two stages, while automatically calculating the proportions of the information at each time step. Numerous experiments conducted on COCO2014 and Flickr30K datasets have demonstrated the effectiveness and advantages of the proposed method.
  • 31.

    【24hr】FastVOD-Net: A Real-Time and High-Accuracy Video Object Detector

    包量

    机译
    摘要：Video object detection is a tough task due to the severe appearance degradation caused by rapid motion, sudden occlusion or rare poses. The great challenge facing video object detection is the simultaneous requirements on both accuracy and speed because the pursuit of one aspect usually causes significant expense to the other. Most existing methods mainly focus on improving detection accuracy with little attention to computationally efficient solutions, and thus they are impractical for many real-world applications. This motivates us to develop a real-time and high-accuracy video object detection method. In this paper, we propose a novel video object detector, called FastVOD-Net, which can yield highly accurate detection results at real-time speed. Specifically, we first develop a temporally-cascaded deformable alignment (TCDA) module to model the object displacements induced by video motion. Then, we introduce another two modules, namely spatially-refined temporal aggregation (SRTA) and attention-guided semantic distillation (AGSD), to improve the appearance feature of the currently processed frame and enhance the semantic representation of non-keyframes, respectively. For keyframe scheduling, we design an adaptive keyframe selection scheduler (AKSS) to adjust the keyframe interval online, making the keyframe usage more rational. On one hand, the characteristics of our FastVOD-Net enable it to sparsely perform expensive feature extraction, which significantly reduces the computational cost and thus guarantees real-time speed. On the other hand, the collaboration of the above tightly-coupled modules and adaptive keyframe scheduler makes FastVOD-Net fully exploit inter-frame temporal dependencies and thus guarantees high accuracy. Experiments on the ImageNet VID dataset show that our FastVOD-Net achieves 79.3 mAP at 29.6 fps or 81.2 mAP at 23.0 fps on an Nvidia RTX 2080 Ti GPU, which is the state-of-the-art performance in real time.
  • 32.

    【24hr】Constructing Cooperative Intelligent Transport Systems for Travel Time Prediction With Deep Learning Approaches

    包量

    机译
    摘要：Drivers and traffic system planners require accurate forecasting of future travel times. To alleviate traffic congestion on Taiwan’s Provincial Highway No. 61, this study considers temporal (weekdays, weekends or continuous holidays) and spatial characteristics (road types), and establishes short-term and long-term travel time prediction models. Data pre-processing is accomplished using the Google Maps API and floating car method to verify travel time comparisons, finding that post-processing travel times are reliable, credible and reasonable. This study develops a collaborative intelligent transportation system (CITS) based on 9 different algorithms for the prediction of current and future travel-time. The results show that $k$ NN-R provides the most accurate short-term predictions, with average prediction error within 20 seconds per kilometer. For long-term forecasting, SARIMAX and fbProphet provide the most accurate results for weekday and continuous holiday modes. Travel time prediction can assist traffic management agencies in the timely implementation of appropriate traffic management measures. CITS also provides real-time traffic query and travel time prediction functions to help drivers avoid traffic congestion.
  • 33.

    【24hr】A Comprehensive Resilient Control Strategy for CBTC Systems Through Train-to-Train Communications Under Malicious Attacks

    包量

    机译
    摘要：In recent years, intelligent transportation systems and automatic train control technologies have been developed rapidly. The communication-based train control (CBTC) system through train-to-train communications is very important in the intelligent transportation system and it uses wireless communication links to transmit the train control commands. However, the CBTC system is sensitive to information security attacks. Therefore, a comprehensive resilient control strategy is proposed for the CBTC system under Denial of Service (DoS) attacks. As a cyber physical system, the CBTC system includes two layers. In the communication layer, a strongly robust train-to-train communication topology is proposed which adopts a bidirectional communication strategy and a three times communication protocol to achieve the goal of resisting multi-channel DoS attacks. In the physical layer, an observer-based distributed intrusion detection method is firstly proposed to detect the attack. Further, a resilient control method based on a distributed state observer of the leader train is proposed to avoid triggering the train safety protection mechanism frequently. Finally, simulation results prove that the proposed comprehensive resilient control algorithm is effective in the CBTC system.
  • 34.

    【24hr】An Efficient and Secure Identity-Based Signature System for Underwater Green Transport System

    包量

    机译
    摘要：The smart ocean has aroused the interest of government, business, and academia because of the wealth of marine resources. It has been suggested to use underwater Internet of Things (IoT) frameworks to collect a variety of data from smart seas that can aid in the underwater green transport system, ecological sustainability, military intelligence gathering, and a variety of other operations. Because of the limited resources accessible to IoT devices regarding communication overhead, processing expenses, and battery capacity, security and privacy concerns in underwater green transport systems have lately been a critical source of worry. In this context, We presented a unique identity-based authentication mechanism for underwater green transport systems. Our suggested solution uses lightweight authentication mechanisms that prove secure communication between different elements of the green transport system.
  • 35.

    【24hr】ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-Based Pedestrian Crossing Intention Prediction

    包量

    机译
    摘要：Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method.
  • 36.

    【24hr】A Minimax Scheduling Framework for Inertially-Constrained Multi-Agent Systems

    包量

    机译
    • 作者：Feihong Yang;Yuan Shen;
    • 刊名：IEEE transactions on intelligent transportation systems
    • 2022年第12期
    摘要：Scheduling of physical space in multi-agent systems is crucial in widespread applications including transportation and industrial manufacturing. However, few existing works focused on improving the scheduling efficiency when agents are inertially constrained and some non-cooperative agents with unknown and uncontrollable trajectories exist. In this article, we establish a minimax framework aiming to ensure the robustness of scheduling against the uncertainty of non-cooperative agents. Specifically, we propose a function characterizing the preference of different states based on a given situation information, and formulate a trajectory planning policy by establishing a minimax optimization problem. Furthermore, the tractability of the proposed policy is ensured by developing an approximate algorithm and a truncation method, and the safety guarantee of the policy is also proved. Finally, numerical simulations suggest a 90 reduction on the empirical probability of high-cost scenarios compared with heuristic policies, validating the robustness of the proposed policy.
  • 37.

    【24hr】Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction

    包量

    机译
    摘要：Temporal traffic prediction is critical for ITS yet remains challenging in handling complex spatio-temporal dynamics of traffic systems. The continuous traffic data (e.g., traffic flow, and speed) from various channels and nodes in a traffic network are coupled with each other over the time points of each channel, spatially between traffic nodes, and jointly in both spatial and temporal dimensions. Such multi-aspect traffic data couplings reflect the conditions of a real-life traffic system and evolve over traffic movement and network dynamics. The recent studies formulate traffic prediction by high-profile graph neural networks. However, they mainly focus on hidden relations captured by neural graph mechanisms while overlooking or simplifying the above multi-aspect traffic data couplings. By modeling a traffic system as a coupled traffic network, we learn the multi-aspect traffic data couplings by a Multi-relational Synchronous Graph Attention Network (MS-GAT). Specifically, MS-GAT learns three embeddings to respectively but synchronously represent the traffic data-based channel, temporal, and spatial relations between nodes by specific graph attention designs. The embeddings are further adaptively coupled according to their respective importance to prediction. Tested on five real-world datasets, MS-GAT outperforms six SOTA graph networks-based traffic predictors. MS-GAT captures not only spatial and temporal couplings but also traffic data-based channel interactions over traffic evolution.
  • 38.

    【24hr】Safety Performance Boundary Identification of Highly Automated Vehicles: A Surrogate Model-Based Gradient Descent Searching Approach

    包量

    机译
    摘要：Highly automated vehicles (HAVs) have been introduced to the transportation system for the purpose of providing safer mobility. Considering the expected long co-existence period of HAVs and human-driven vehicles (HDVs), the safety operation of HAVs interacting with HDVs needs to be verified. To achieve this, HAVs’ Operational Design Domain (ODD) needs to be identified under the scenario-based testing framework. In this study, a novel testing framework aiming at identifying the Safety performance boundary (SPB) is proposed, which assures the coverage of safety-critical scenarios and compatible with the black-box feature of HAV control algorithm. A surrogate model was utilized to approximate the safety performance of HAV, and a gradient descent searching algorithm was employed to accelerate the search for SPB. For empirical analyses, a three-vehicle following scenario was adopted and the Intelligent Driver Model (IDM) was tested as a case study. The results show that only 4 of the total scenarios are required to establish a reliable surrogate model. And the gradient descent algorithm was able to establish the SPB by identifying 97.42 of collision scenarios and only false alarming 0.29 of non-collision scenarios. Furthermore, the concept of safety tolerance was proposed to measure the possibilities of boundary scenarios dropping in safety performance. The applications of helping to construct ODD and compare different control algorithms were discussed. It shows that the IDM performs better than the Wiedemann 99 (W99) model with larger ODD.
  • 39.

    【24hr】Attention Mechanism With Spatial-Temporal Joint Model for Traffic Flow Speed Prediction

    包量

    机译
    摘要：Intelligent transportation system (ITS) plays an important role in solving today’s transportation problems, and short-term traffic flow prediction is at its core. Deep learning can extract and capture abstract high-order features, and introducing attention mechanism to improve the performance of deep learning algorithm has been verified in many fields. Due to the complexity and randomness of traffic flow, accurate traffic flow prediction is not a simple task. Reasonable use of deep learning to predict traffic flow is of great significance to the whole transportation system. In this paper, the reason of choosing recurrent neural network (RNN) as the basic network for traffic flow prediction is explained. Aiming at the problem of gradient disappearance in practical application, the long short-term memory network (LSTM) is introduced to improve the model, and the model framework, algorithm and training process are described in detail. Attention mechanism is introduced into LSTM-RNN to build a short-term traffic flow prediction model. Applying the proposed model to observed traffic flow data, we found that the proposed model has higher prediction accuracy and model efficiency.
  • 40.

    【24hr】FEEL: Federated End-to-End Learning With Non-IID Data for Vehicular Ad Hoc Networks

    包量

    机译
    摘要：Recent studies have demonstrated the potentials of federated learning (FL) in achieving cooperative and privacy-preserving data analytics. It would also be promising if FL can be employed in vehicular ad hoc networks (VANETs) for cooperative learning tasks, such as steering angle prediction, trajectory prediction, drivable road detection, etc., among integrated vehicles. However, since VANETs are characterized by ad hoc cooperating vehicles with non-independent and identically distributed (Non-IID) data, directly employing existing FL frameworks to VANETs may cause extensive communication overhead and compromised model performance. Further, most of the existing deep learning models incorporated in FL frameworks rely heavily on data with manual annotations, leading to a huge labor cost. To address these issues, in this paper we propose an efficient and effective Federated End-to-End Learning framework for cooperative learning tasks in VANETs, named FEEL. Specifically, we first formulate a distributed optimization problem for cooperative deep learning tasks with Non-IID data in multi-hop cluster VANETs. Second, two algorithms for inter-cluster learning and inner-cluster learning are respectively designed, to reduce the communication overhead and fit Non-IID data. Third, a Paillier-based communication protocol is crafted, allowing secure model parameter updates at the central server without knowing the real updates at each cooperating base station. Extensive experiments on two real-world datasets are conducted by considering various data distributions and VANET topologies, demonstrating the high efficiency and effectiveness of the proposed FEEL framework in both regression and classification tasks.
  • 41.

    【24hr】DNN-Based Approach to Mitigate Multipath Errors of Differential GNSS Reference Stations

    包量

    机译
    摘要：One of the major error components of differential global navigation satellite systems is a multipath error in a reference station. This paper introduces a deep neural network based multipath modeling method. A signal to noise ratio, as well as satellite geometry, is used as a feature parameter to capture the variation of the multipath error caused by unavoidable changes in the vicinity of the reference station. The performance of the proposed method is demonstrated for both normal and varying multipath cases using experimental data. The remaining multipath error after mitigation is well bounded by the standardized error model.
  • 42.

    【24hr】Real-Time Holding Control for Transfer Synchronization via Robust Multiagent Reinforcement Learning

    包量

    机译
    摘要：This study presents a robust deep reinforcement learning (RL) approach for real-time, network-wide holding control with transfer synchronization, considering stochastic passenger demand and vehicle running time during daily operations. The problem is formulated within a multi-agent RL framework where each active trip is considered as an agent, which not only interacts with the environment but also with other agents in the considered transit network. A specific learning procedure is developed to learn robust policies by introducing $maxmin$ optimization into the learning objective. The agents are trained via deep deterministic policy gradient algorithm (DDPG) using an extended actor-critic framework with a joint action approximator. The effectiveness of the proposed approach is evaluated in a simulator, which is calibrated using data collected from a transit network in Twin Cities Minnesota, USA. The learned policy is compared with no control, rule-based control and the rolling horizon optimization control (RHOC). Computational results suggest that RL approach can significantly reduce the online computation time by about 50 compared with RHOC. In terms of policy performance, under deterministic scenario, the average waiting time of RL approach is 1.3 higher than the theoretical lower bound of average waiting time; under stochastic scenarios, RL approach could reduce as much as 18 average waiting time than RHOC, and the performance relative to RHOC improves when the level of system uncertainty increases. Evaluation under disrupted environment also suggests that the proposed RL method is more robust against short term uncertainties. The promising results in terms of both online computational efficiency and solution effectiveness suggest that the proposed RL method is a valid candidate for real-time transit control when the dynamics cannot be modeled perfectly with system uncertainties, as is the case for the network-wide transfer synchronization problem.
  • 43.

    【24hr】Fine-Grained Vessel Traffic Flow Prediction With a Spatio-Temporal Multigraph Convolutional Network

    包量

    机译
    摘要：The accurate and robust prediction of vessel traffic flow is gaining importance in maritime intelligent transportation system (ITS), such as vessel traffic services, maritime spatial planning, and traffic safety management, etc. To achieve fine-grained vessel traffic flow prediction, we will first generate the maritime traffic network (which is essentially a graph), and then propose a graph-driven neural network. In particular, to represent various correlations among spatio-temporal vessel traffic flow, we tend to extract the feature points (i.e., starting, way and ending points) by utilizing the knowledge of vessel positioning data. These feature points are essentially related to the geometrical structures of massive vessel trajectories collected from massive automatic identification system (AIS) records, contributing to the generation of maritime traffic network. We then propose a spatio-temporal multi-graph convolutional network (STMGCN)-based vessel traffic flow prediction method by exploiting multiple types of inherent correlations in the generated maritime graph. The proposed STMGCN mainly contains one spatial multi-graph convolutional layer and two temporal gated convolutional layers, beneficial for extracting spatial and temporal traffic flow patterns. The main benefit of our graph-driven prediction method is that it takes full advantage of the maritime graph and multi-graph learning. Comprehensive experiments have been implemented on realistic AIS dataset to compare our method with several state-of-the-art prediction methods. The fine-grained prediction results have demonstrated our superior performance in terms of both accuracy and robustness.
  • 44.

    【24hr】BR-GAN: A Pedestrian Trajectory Prediction Model Combined With Behavior Recognition

    包量

    机译
    摘要：Pedestrian trajectory prediction is a crucial task for many domains, such as self-driving, navigation robots and video surveillance. The performance of trajectory prediction can be improved in various patterns, including using a more effective network, considering more complicated social interactions, and utilizing sufficient information. On the one hand, the change of subsequent trajectory depends on the geographical scene and the social interaction with other pedestrians in the same scene. On the other hand, the subsequent trajectory also makes some real-time adjustments according to the judgment of pedestrian behavior. Therefore, we propose a novel behavior recognition module to obtain extra pedestrian behavior information. To guarantee the precision and diversity of prediction, this paper builds the Geographical, the Social and the Behavior feature modules based on the GAN framework to process information. As a result, we present a trajectory prediction approach, referred to as the BR-GAN, which exploits geographical, social and behavior context-aware. The BR-GAN achieves greater accuracy in parts of the ETH/UCY datasets compared with some baselines. We will republic all of them on https://github.com/HITjian/Pedestrian-trajectoty-prediction-based-on-behavior-recognition .
  • 45.

    【24hr】Adaptive Robust Control for Pointing Tracking of Marching Turret-Barrel Systems: Coupling, Nonlinearity and Uncertainty

    包量

    机译
    摘要：Pointing tracking control of marching turret-barrel system is one of the important topics in exploration of intelligent ground combat platform. This paper focuses on an adaptive robust control scheme for pointing tracking of marching turret-barrel system driven by a motor and an electric cylinder. Three types of possibly fast time-varying but bounded uncertainty are considered: system modeling error, external disturbance and road excitation. The uncertainty bounds are not necessary to be known. First, the pointing tracking system is constructed as a coupled, nonlinear and uncertain dynamical system with two interconnected (horizontal and vertical) subsystems. Second, a tracking error $e$ is defined as a gauge of control objective, and then the dynamical equation of the pointing tracking system is built in state-space form. Third, for uncertainty control, a comprehensive uncertainty bound $alpha $ is derived to measure the most conservative influence of the uncertainty, and then an adaptive law is proposed to evaluate it in real time. Finally, for pointing tracking control, an adaptive robust control is proposed to render the pointing tracking system to be practically stable; thereout, the objective of pointing tracking is achieved. This work should be among the first ever endeavours to cast all the coupling , nonlinearity and bound-unknown uncertainty into the pointing tracking framework of marching turret-barrel system.
  • 46.

    【24hr】Novel Formulations and Improved Differential Evolution Algorithm for Optimal Lane Reservation With Task Merging

    包量

    机译
    摘要：This paper investigates a new lane reservation problem with task merging that consists of optimally determining which lanes in a transportation network have to be reserved and designing reserved lane-based routes in the network for time-crucial transport tasks. Part of the tasks whose destinations are geographically close is merged to reduce the number of vehicles and transport costs. Reserved lanes can reduce the travel time of task vehicles passing through them, while they will generate negative impact on normal traffic, such as traffic delay to the vehicles on adjacent non-reserved lanes. The objective is to minimize the total negative impact of all reserved lanes. For this problem, two new integer linear programming (ILP) models are first developed. The complexity of the problem is proved to be NP-hard. Since commercial solver (like CPLEX) is time-consuming for solving it when the problem size increases, a fast and effective improved differential evolution algorithm (IDEA) is developed based on explored problem properties. Extensive experimental results for a real-life case and benchmark instances of up to 500 nodes in the network and 30 transport tasks show the favorable performance of the IDEA, as compared to CPLEX, differential evolution algorithm and genetic algorithm. Management insights are also drawn to support practical decision-making.
  • 47.

    【24hr】Concurrent Optimization of Subway Vertical Alignments and Station Elevations With Improved Particle Swarm Optimization Algorithm

    包量

    机译
    摘要：Station elevations and vertical alignment between adjacent stations often influence each other. Therefore, they should be optimized concurrently. However, existing studies mainly focus on optimizing station elevations or links separately. In this paper, a concurrent optimization model of stations and links is developed, in which the vertical points of intersection in stations and links are design variables. The comprehensive cost including construction, energy consumption, and travelling time, is the optimized objective function. Particle swarm optimization (PSO) is a method for searching in continuous space and is widely used for alignment optimization. Considering the characteristics of concurrent optimization for stations and links, the PSO algorithm is improved by modifying the updating formulas for particles and designing two strategies for particle updating, namely “Stations before Links” and “Links before Stations” strategy. A dynamic adaptive feasible region is proposed to handle the complex constraints during optimization. This method is applied here to a real-world case. The applications demonstrate that this method can automatically generate vertical alignments which jointly optimize the locations of stations and links and satisfy all the complex constrains.
  • 48.

    【24hr】An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning

    包量

    机译
    摘要：A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.
  • 49.

    【24hr】Deployment of Charging Stations for Drone Delivery Assisted by Public Transportation Vehicles

    包量

    机译
    摘要：To enable the drone delivery service in a remote area, this paper considers the approach of deploying charging stations and collaborating with public transportation vehicles. From the warehouse which is far from a customer, a drone takes some public transportation vehicles to reach some position close to the remote area. When the customer is unreachable from the position where the drone leaves the public transportation vehicle, the drone swaps the battery at a charging station. The focus of this paper is the deployment of charging stations. We propose a new model to characterize the delivery time for customers. We formulate the optimal deployment problem to minimize the average delivery time for the customers, which is a reflection of customer satisfaction. We then propose a sub-optimal algorithm that relocates the charging stations in sequence, which ensures that any movement of a charging station leads to a decrease in the average flight distance. The comparison with a baseline method confirms that the proposed model can more accurately estimate the flight distance of a customer than the commonly used model, and the proposed algorithm can relocate the charging stations achieving lower flight distance.
  • 50.

    【24hr】A Novel Control Framework of Brain-Controlled Vehicle Based on Fuzzy Logic and Model Predictive Control

    包量

    机译
    摘要：Brain-controlled vehicles (BCVs) have vital practical values for the disabled and healthy people. To improve the performance of existing BCVs and lower the workload generated by BCVs to drivers, in this paper, we propose a novel control framework of BCVs, which consists of a brain-computer interface (BCI) with a probabilistic output model, an adaptive fuzzy logic-based interface model, and a model predictive control (MPC) shared controller. The BCI with a probabilistic output model can output all commands in a probabilistic form rather than a specific single command once. The adaptive fuzzy logic-based interface can convert the probabilities into the vehicle’s input signals (including the vehicle acceleration and the increment of steering wheel angle) according to the vehicle state and road information. The MPC shared controller can ensure the control authority of brain-control drivers and reduce drivers’ workload on the premise of maintaining safety. We establish an experimental platform to validate the proposed method by using the intersection selection and obstacle avoidance scenarios with eight subjects. The experimental results show the effectiveness of the proposed method in improving driving performance and decreasing drivers’ workload. This work can contribute to the research and development of BCVs and provide some new insights into the study of intelligent vehicles and human-vehicle integration.
题名 作者 来源 发表时间

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号