IEEE Open Journal of Intelligent Transportation Systems最新文献_第5页

FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications FedRSC：针对多标签路面分类的联合学习分析

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-22 DOI: 10.1109/OJITS.2024.3432176

Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos

{"title":"FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications","authors":"Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos","doi":"10.1109/OJITS.2024.3432176","DOIUrl":"10.1109/OJITS.2024.3432176","url":null,"abstract":"The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"433-444"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Parameters Estimation of a Microscopic Traffic Flow Sub-Model Within a Multiscale Approach Using Experimental Data 在多尺度方法中利用实验数据估算微观交通流子模型的参数

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-15 DOI: 10.1109/OJITS.2024.3427790

Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca

{"title":"Parameters Estimation of a Microscopic Traffic Flow Sub-Model Within a Multiscale Approach Using Experimental Data","authors":"Facundo Storani;Roberta Di Pace;Shi-Teng Zheng;Rui Jiang;Stefano de Luca","doi":"10.1109/OJITS.2024.3427790","DOIUrl":"10.1109/OJITS.2024.3427790","url":null,"abstract":"Future traffic contexts will likely involve the coexistence of human-driven vehicles and connected and automated vehicles (CAVs). To assess the impact of CAVs, especially in large-scale applications, intermediate hybrid multi-scale models can be used. These models are easily adaptable to traffic control strategies by employing disaggregated modeling in regions where such strategies are implemented and macroscopic modeling in other regions indirectly affected by the controlled infrastructure. This paper focuses on a model previously established in the literature, the H - CA&CTM (Hybrid Cellular Automata -CA- Cell Transmission Model-CTM), with an emphasis on the micro model that can be implemented in the hybrid traffic flow model. The research has two primary aims: 1) Investigate the calibration of the CA model with respect to various cell lengths using two distinct approaches: simulating all vehicles together in a closed ring layout and simulating each vehicle using data obtained from its respective follower; 2) Utilize vehicle trajectory data for the calibration procedure, enabling a comprehensive comparison of methods. Two detailed approaches were considered: 1. Measured Leader – Simulated Follower interaction approach. 2. Simulated Leader – Simulated Follower interaction approach. The major finding of the paper is that the calibrated parameters obtained using the Simulated Leader approach display greater regularity across different cell lengths.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"409-421"},"PeriodicalIF":4.6,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10597615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Managing Risk in the Design of Modular Systems for an Autonomous Shuttle 自主航天飞机模块化系统设计中的风险管理

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-08 DOI: 10.1109/OJITS.2024.3425165

Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl

{"title":"Managing Risk in the Design of Modular Systems for an Autonomous Shuttle","authors":"Thomas H. Drage;Kieran Quirke-Brown;Lemar Haddad;Zhihui Lai;Kai Li Lim;Thomas Bräunl","doi":"10.1109/OJITS.2024.3425165","DOIUrl":"10.1109/OJITS.2024.3425165","url":null,"abstract":"This paper presents an analysis and implementation of a robust autonomous driving system for an electric passenger shuttle in shared spaces. We present results of a risk assessment for our vehicle scenario and develop a flexible architecture that integrates safety features and optimises open-source software, facilitating research and operational functionality. Identifying limitations of the Robot Operating System (ROS) framework, we incorporate our own control measures for autonomous, unsupervised operation with enhanced intelligence. The study emphasizes algorithm selection based on application requirements to ensure optimal performance. We discuss system improvements, including monitoring node implementation and localization algorithm selection. Future work should explore transitioning to a real-time operating system (RTOS) and establishing standardized software engineering practices for consistent reliability. Our findings contribute to effective autonomous shuttle systems in shared spaces, promoting safer and more reliable transportation solutions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"555-565"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance 全局和局部意识：结合强化学习和基于模型的控制来避免碰撞

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-05 DOI: 10.1109/OJITS.2024.3424587

Luman Zhao;Guoyuan Li;Houxiang Zhang

{"title":"Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance","authors":"Luman Zhao;Guoyuan Li;Houxiang Zhang","doi":"10.1109/OJITS.2024.3424587","DOIUrl":"10.1109/OJITS.2024.3424587","url":null,"abstract":"In this research, we focus on developing an autonomous system for multiship collision avoidance. The proposed approach combines global path planning based on deep reinforcement learning (DRL) and local motion control to improve computational efficiency and alleviate the sensitivity to heading angle changes. To achieve this, firstly, DRL is used to learn a policy that maps observable states of target ships to a sequence of predicted waypoints. This learning task aims to generate a specific trajectory while avoiding collision with target ships complying with the international regulations for preventing collisions at sea (COLREGs). The learned policy is used as a global path planner during navigation. Secondly, the line-of-sight (LOS) guidance system is applied to calculate the desired course command based on the collision-free trajectory generated according to the policy. Lastly, a model-based control strategy is implemented to control the ship to the specific goal in collision-free space while satisfying the desired commands. We demonstrate the performance of the approach using an example of an autonomous surface vehicle. In comparison to other methods, our proposed control can provide a more stable and smoother maneuvering effect.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"422-432"},"PeriodicalIF":4.6,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10587203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing 基于因子图的规划作为自动驾驶汽车竞赛的推理方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-25 DOI: 10.1109/OJITS.2024.3418956

Salman Bari;Xiagong Wang;Ahmad Schoha Haidari;Dirk Wollherr

{"title":"Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing","authors":"Salman Bari;Xiagong Wang;Ahmad Schoha Haidari;Dirk Wollherr","doi":"10.1109/OJITS.2024.3418956","DOIUrl":"10.1109/OJITS.2024.3418956","url":null,"abstract":"Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"380-392"},"PeriodicalIF":4.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10571575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models 用于解释自主感知模型的全局映射-一致性约束视觉-语义嵌入技术

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-24 DOI: 10.1109/OJITS.2024.3418552

Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu

{"title":"Global-Mapping-Consistency-Constrained Visual-Semantic Embedding for Interpreting Autonomous Perception Models","authors":"Chi Zhang;Meng Yuan;Xiaoning Ma;Ping Wei;Yuanqi Su;Li Li;Yuehu Liu","doi":"10.1109/OJITS.2024.3418552","DOIUrl":"10.1109/OJITS.2024.3418552","url":null,"abstract":"From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the need to evaluate the intelligence level of such testees are of equal importance. In this paper, we propose a possible solution to these challenges: Explainable Evaluation for visual intelligence. Specifically, we focus on the problem setting where the internal mechanisms of AI algorithms are sophisticated, heterogeneous or unreachable. In this case, a latent attribute dictionary learning method with constrained by mapping consistency is proposed to explain the performance variation patterns of visual perception intelligence under different test samples. By jointly iteratively solving the learning of latent concept representation for test samples and the regression of latent concept-generalization performance, the mapping relationship between deep representation, semantic attribute annotation, and generalization performance of test samples is established to predict the degree of influence of semantic attributes on visual perception generalization performance. The optimal solution of proposed method could be reached via an alternating optimization process. Through quantitative experiments, we find that global mapping consistency constraints can make the learned latent concept representation strictly consistent with deep representation, thereby improving the accuracy of semantic attribute-perception performance correlation calculation.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"393-408"},"PeriodicalIF":4.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10570287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141508701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Container Relocation and Retrieval Tradeoffs Minimizing Schedule Deviations and Relocations 集装箱搬迁和取回的权衡尽量减少时间表偏差和搬迁

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-12 DOI: 10.1109/OJITS.2024.3413197

Robert Klar;Anders Andersson;Anna Fredriksson;Vangelis Angelakis

{"title":"Container Relocation and Retrieval Tradeoffs Minimizing Schedule Deviations and Relocations","authors":"Robert Klar;Anders Andersson;Anna Fredriksson;Vangelis Angelakis","doi":"10.1109/OJITS.2024.3413197","DOIUrl":"10.1109/OJITS.2024.3413197","url":null,"abstract":"Ports are striving to improve operational efficiency in the context of constantly growing volumes of trade. In this context, port terminal storage yard operation is key, since complexity and poor coordination lead to containers stacked without consideration of retrieval schedules, resulting in time- and energy-consuming reshuffling operations. This problem, known as the block relocation (and retrieval) problem (BRP), has recently gained considerable attention. Indeed, there are promising solutions to the BRP. However, the literature views the problem in isolation, optimizing one operational parameter for one of the many port stakeholders. This often leads to efficiency losses since port processes involve different stakeholders and port parts. In this work, we explicitly focus on scheduling trucks for pick-up for hinterland distribution. Appointments are often postponed in order to minimize reshuffling operations, leading to losses for the transport forwarders and decreasing the competitiveness of the port. We discuss the trade-off between minimizing container reshuffling operations while maintaining scheduled time windows for container retrieval. We describe the multi-objective optimization problem as a weighted sum of the two objectives. Given the complexity of the problem, we also present a greedy heuristic. Our results indicate that the number of schedule deviations can be reduced without significantly affecting the number of relocations compared to solutions that consider only the latter. Ideally, a weighting of 0.4 and 0.6 should be applied, reflecting schedule deviations and relocations, respectively, to achieve the highest joint optimization potential. This demonstrates that in complex environments, such as ports, with multiple interacting stakeholders and processes, coordination of solutions yields significant benefits.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"360-379"},"PeriodicalIF":4.6,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Big Data Architecture for Digital Twin Creation of Railway Signals Based on Synthetic Data 基于合成数据的铁路信号数字孪生创建大数据架构

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-11 DOI: 10.1109/OJITS.2024.3412820

Giulio Salierno;Letizia Leonardi;Giacomo Cabri

引用次数: 0

SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing SenseNow：用于车载人群感应的与时间相关的激励方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-06-07 DOI: 10.1109/OJITS.2024.3411525

Luis G. Jaimes;Harish Chintakunta;Paniz Abedin

{"title":"SenseNow: A Time-Dependent Incentive Approach for Vehicular Crowdsensing","authors":"Luis G. Jaimes;Harish Chintakunta;Paniz Abedin","doi":"10.1109/OJITS.2024.3411525","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3411525","url":null,"abstract":"This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"307-321"},"PeriodicalIF":4.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach 利用噪声多导测量的自适应巡航控制：基于学习的方法

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-04-29 DOI: 10.1109/OJITS.2024.3395149

Ying-Chuan Ni;Victor L. Knoop;Julian F. P. Kooij;Bart van Arem

{"title":"Adaptive Cruise Control Utilizing Noisy Multi-Leader Measurements: A Learning-Based Approach","authors":"Ying-Chuan Ni;Victor L. Knoop;Julian F. P. Kooij;Bart van Arem","doi":"10.1109/OJITS.2024.3395149","DOIUrl":"10.1109/OJITS.2024.3395149","url":null,"abstract":"A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"251-264"},"PeriodicalIF":0.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140828406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0