IEEE Open Journal of Intelligent Transportation Systems最新文献

{"title":"Online LiDAR-Camera Extrinsic Calibration Using Selected Semantic Features","authors":"Ping-Tzu Lin;Ying-Shiuan Huang;Wen-Chieh Lin;Chieh-Chih Wang;Huei-Yung Lin","doi":"10.1109/OJITS.2025.3555574","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3555574","url":null,"abstract":"Autonomous vehicles have gained great attention from all walks of life in recent years. The relative position and orientation between sensors often change gradually over time due to vibrations or thermal stress of materials. Thus, online re-calibrating extrinsic parameters periodically is required. In this situation, automatic targetless methods are more preferable as they do not require a calibration target or tedious calibration procedure. In this paper, we propose an online targetless camera-LiDAR extrinsic calibration approach with the help of semantic information. Our method could effectively ameliorate the problem of targetless methods which usually lack robust features and the correspondences. We also propose a feature selection technique to filter out improper feature correspondences by matching the image contours and point cloud projection contours. The experiment results show that our approach is more robust than previous work, and the calibration algorithm is applicable to more scenarios.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"456-464"},"PeriodicalIF":4.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10944781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830516","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}

{"title":"Fuzzy Logic-Enhanced Sustainable and Resilient EV Public Transit Systems for Rural Tourism","authors":"Rapeepan Pitakaso;Thanatkij Srichok;Surajet Khonjun;Peerawat Luesak;Chutchai Kaewta;Sarayut Gonwirat;Prem Enkvetchakul;Rerkchai Srivoramas","doi":"10.1109/OJITS.2025.3554204","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554204","url":null,"abstract":"The integration of electric vehicles (EVs) into public transit systems is crucial for enhancing sustainability and operational efficiency, particularly in rural tourism regions where demand is highly variable and infrastructure constraints pose unique challenges. Traditional transportation planning approaches often lack the adaptability required to handle the fluctuating nature of tourist mobility, leading to inefficiencies in service coverage and resource utilization. While fuzzy logic-based models have been extensively applied in urban transit optimization, their applicability to rural EV public transit remains underexplored. This study addresses this gap by developing the Fuzzy-Artificial Multiple Intelligence System (F-AMIS), an enhanced version of the Artificial Multiple Intelligence System (AMIS). F-AMIS integrates new intelligence boxes and an optimized selection formula, allowing for real-time adaptive decision-making in EV bus networks. A real-world case study demonstrates that F-AMIS significantly outperforms conventional optimization methods, achieving a 20% reduction in operational costs and increasing service coverage from 75% to 90%, while also enhancing resilience and sustainability indices. These findings highlight the potential of F-AMIS as a scalable, intelligent optimization framework for improving the efficiency and sustainability of rural EV transit systems. Future research should explore integrating F-AMIS with advanced AI-driven decision models, refining fuzzy logic techniques for rural-specific constraints, and assessing the model’s adaptability across diverse global tourism networks to further enhance its applicability and impact.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"407-432"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800743","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}

{"title":"Sustainable Intelligent Transportation Systems via Digital Twins: A Contextualized Survey","authors":"Victor M. G. Martinez;Divanilson R. Campelo;Moises R. N. Ribeiro","doi":"10.1109/OJITS.2025.3553696","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3553696","url":null,"abstract":"Intelligent transportation systems (ITS) have been attracting the attention of industry and academia alike for addressing issues raised by the 2030 agenda for sustainable development goals (SDG) approved by the United Nations. However, the diversity and dynamics of present-day transportation scenarios are already very complex, turning the management of ITS into a virtually impossible task for conventional traffic control centers. Recently, the digital twin (DT) paradigm has been presented as a modern architectural concept to tackle complex problems, such as the ones faced by ITS. This survey aims to provide a piece-wise approach to introducing DTs into sustainable ITS by addressing the following cornerstone aspects: i) Why should one consider DTs in ITS applications? ii) What can DTs represent from ITS’ new physical environments? And iii) How can one use DTs to address ITS SDG related to efficiency, safety, and ecology? Our methodological approach for surveying the literature addresses these questions by categorizing contributions and discriminating their ITS elements and agents against the SDG they addressed. Thus, this survey provides an in-depth and contextualized overview of the challenges when approaching ITS through DTs, including scenarios involving autonomous and connected vehicles, ITS infrastructure, and traffic agents’ behavior. Moreover, we propose a functional reference framework for developing DTs of ITS. Finally, we also offer research challenges regarding standardization, connectivity infrastructure, security and privacy aspects, and business management for properly developing DTs for sustainable ITS.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"363-392"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792783","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}

{"title":"Modeling and Simulation of Automotive FMCW RADAR Sensor for Environmental Perception","authors":"Arsalan Haider;Abdulkadir Eryildirim;Marcell Pigniczki;Lukas Haas;Birgit Schlager;Thomas Zeh;David Nickel;Alexander W. Koch","doi":"10.1109/OJITS.2025.3554452","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554452","url":null,"abstract":"Frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensors have become indispensable technologies for automated driving systems (ADS) due to their reliability in adverse weather conditions and their ability to simultaneously measure the distance to objects, relative radial velocity, and azimuth and elevation angles. The automotive industry has increasingly considered simulation-based testing of autonomous vehicles due to safety, cost, and time constraints. This raises the need for virtual environmental perception sensors that provide results close to reality. This work presents the design and structure of a ray-tracing-based, high-fidelity, tool-independent baseband FMCW RADAR sensor model. The RADAR sensor model is developed using the standardized functional mock-up interface (FMI) and open simulation interface (OSI) and is integrated into the co-simulation environment of commercial software to demonstrate its exchangeability. The RADAR FMU model incorporates a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna array, non-coherent integration (NCI) of range-Doppler maps (RDMs) over receiver antennas, a constant false alarm rate (CFAR) to obtain an interim object detection list, and density-based spatial clustering of applications with noise (DBSCAN) to provide a single detection per object. The presented RADAR FMU model also includes RADAR sensor-specific impairments such as phase noise (PN), radio frequency (RF) group delay, phase imbalance (PI) of transmitter antennas, mixer non-linearity including third-order intermodulation products (IM3), and noise figure (NF) of receiver antennas. Additionally, this work presents a methodology for plausibly verifying the RADAR sensor model at the raw data level (range map (RM) and RDM) and object detection list level. The simulation results are compared with real sensor measurements to validate the modeling of sensor-specific impairments. The mean absolute percentage error (MAPE) metric is used to quantify the difference between the simulation and real sensor measurements. The results demonstrate that the complete signal processing toolchain and sensor-specific impairments of the RADAR sensor must be considered to achieve simulation results that closely resemble those of the real sensor.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"433-455"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792843","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}

{"title":"Pedestrian Vision Language Model for Intentions Prediction","authors":"Farzeen Munir;Shoaib Azam;Tsvetomila Mihaylova;Ville Kyrki;Tomasz Piotr Kucner","doi":"10.1109/OJITS.2025.3554387","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3554387","url":null,"abstract":"Effective modeling of human behavior is crucial for the safe and reliable coexistence of humans and autonomous vehicles. Traditional deep learning methods have limitations in capturing the complexities of pedestrian behavior, often relying on simplistic representations or indirect inference from visual cues, which hinders their explainability. To address this gap, we introduce PedVLM, a vision-language model that leverages multiple modalities (RGB images, optical flow, and text) to predict pedestrian intentions and also provide explainability for pedestrian behavior. PedVLM comprises a CLIP-based vision encoder and a text-to-text transfer transformer (T5) language model, which together extract and combine visual and text embeddings to predict pedestrian actions and enhance explainability. Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. The dataset and code will be made available at <uri>https://github.com/munirfarzeen/Ped_VLM</uri>.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"393-406"},"PeriodicalIF":4.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792931","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}

{"title":"A 5G Roadside Infrastructure Assisting Connected and Automated Vehicles in Vulnerable Road User Protection","authors":"Raffaele Viterbo;Federico Campolo;Mattia Cerutti;Satyesh Shanker Awasthi;Stefano Arrigoni;Mattia Brambilla;Monica Nicoli","doi":"10.1109/OJITS.2025.3552849","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3552849","url":null,"abstract":"Road safety is one of the major concerns when considering road vehicles, with issues referring to both drivers and Vulnerable Road Users (VRUs) (e.g., pedestrians). The role of roadside infrastructures in augmenting VRU protection is gaining popularity thanks to the development of smart and connected sensing systems, which enable the detection of critical events and their forwarding to nearby Connected and Automated Vehicles (CAVs) by Vehicle-to-Everything (V2X) communications. In this context, the fifth generation (5G) of mobile radio networks is emerging as the communication enabler for advanced V2X services for CAVs. In this work we propose the design, development, and assessment on road of a 5G roadside infrastructure for VRU protection, performing an in-depth analysis of the single components and their integration into the service. More specifically, we present a Roadside Unit (RSU) capable to detect, localize, and track VRUs; a communication architecture that exploits 5G V2X connectivity for sending VRUs detections; a CAV with on-board algorithms warning the driver of the presence of the VRU and an Autonomous Emergency Braking (AEB) solution triggered by the 5G V2X architecture. We validate the efficacy of the proposed solution in a road scenario inside the campus of Politecnico di Milano.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"346-362"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800770","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}

{"title":"Security Enhancement in AAV Swarms: A Case Study Using Federated Learning and SHAP Analysis","authors":"Sushmitha Halli Sudhakara;Lida Haghnegahdar","doi":"10.1109/OJITS.2025.3550792","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550792","url":null,"abstract":"As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort AAV Attack Dataset, this study aims to develop a robust distributed ML security solution for AAV swarms, significantly advancing the cybersecurity landscape for CPSs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"335-345"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769405","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}

{"title":"Integrating Multimodality and Partial Observability Solutions Into Decentralized Multiagent Reinforcement Learning Adaptive Traffic Signal Control","authors":"Kareem Othman;Xiaoyu Wang;Amer Shalaby;Baher Abdulhai","doi":"10.1109/OJITS.2025.3550312","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3550312","url":null,"abstract":"Adaptive Traffic Signal Control (ATSC) systems leverage sensor data to dynamically adjust signal timings based on real-time traffic conditions but they often suffer from partial observability (PO) due to sensor limitations and restricted detection ranges. This study addresses PO in fully decentralized ATSC systems by introducing eMARLIN-T, a controller designed to enhance performance by incorporating historical information in the decision-making process. Additionally, ATSC systems are commonly optimized to improve the performance of the general traffic, ignoring the impact on transit. On the other hand, traditional transit signal priority (TSP) strategies, which overlay preferential strategies for transit vehicles onto general traffic fixed signal plans, often lead to negative impacts on the general traffic. Thus, this paper tackles the challenge of optimizing traffic signals to benefit both public transit and general vehicular traffic. To address this, a novel decentralized multimodal multiagent reinforcement learning (RL) signal controller, eMARLIN-T-MM, is developed. This controller integrates a transformer-based encoder for transforming the state observations into a latent space and an executor Q-network for decision-making. Tested on a simulation of five intersections in North York, Toronto, eMARLIN-T-MM significantly reduces the total person delays by 58% to 74% across various bus occupancy levels compared to pre-timed signals, outperforming the other decentralized RL-based ATSCs. In addition, eMARLIN-T-MM can automatically adapt to changes in the levels of occupancy, allowing it to optimize the intersection performance in response to varying transit and traffic demands.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"322-334"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10922202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716453","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}

{"title":"On-Demand Technologies for Public Transport: Insights From a Melbourne Survey","authors":"Sohani Liyanage;Hussein Dia","doi":"10.1109/OJITS.2025.3567075","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3567075","url":null,"abstract":"The integration of on-demand technologies in urban mobility requires a comprehensive understanding of user acceptance and willingness to pay for innovative modes like on-demand public transport designed to enhance conventional services. This study presents findings from a survey conducted in Melbourne, highlighting passenger behaviours, preferences, and attitudes towards the use of on-demand transport technologies as a sustainable alternative to conventional bus services. Data from 510 diverse participants revealed a strong preference for private vehicles, mainly for convenience and flexibility. However, concerns regarding waiting times, crowding, and reliability in public transport highlighted the need for service improvements. The survey included hypothetical scenarios where respondents evaluated on-demand transport options with varying factors like waiting time, travel cost, and journey duration. Using binary logistic regression and neural networks (NN), the study analysed preferences for the proposed hypothetical on-demand transport scenarios, revealing that while travel cost negatively impacts mode choice, reduced waiting times positively influence it. The binary logistic model showed classification accuracies between 64% and 72%, while the NN models achieved a high prediction accuracy, reaching approximately 91%. The results indicate that 67% would switch to on-demand transport if it offered reliability, convenience, reduced crowding, and fair pricing. Additionally, 53% were willing to pay a 25% premium for shorter walking and waiting times, with 69% identifying service reliability as the key factor influencing their transport decisions. These insights are essential for developing transport technology frameworks that incorporate on-demand technologies within existing public transport systems, thus advancing sustainable and resilient urban mobility solutions.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"653-672"},"PeriodicalIF":4.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10988636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117150","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}

{"title":"Policy-aware Optimization-based Modeling of Autonomous Vehicles’ Longitudinal Driving Behavior","authors":"Hashmatullah Sadid;Constantinos Antoniou","doi":"10.1109/OJITS.2025.3567009","DOIUrl":"https://doi.org/10.1109/OJITS.2025.3567009","url":null,"abstract":"Microscopic traffic models (MTMs) are widely used to evaluate the potential impacts of autonomous vehicles (AVs) deployment scenarios in our transportation network. Car-following (CF) and lane-changing (LC) models are the backbones of MTMs. Several studies attempt to accurately replicate these behaviors (especially CF behavior) using state-of-the-art modeling methods. A CF model consists of a set of relations and modifiable parameters that are calibrated by mass field driving data. Since mass field driving data of AVs are not available, researchers often assume these parameters and conduct impact assessments, leading to different conclusions on the potential effects of AVs. Meanwhile, AVs are agents and unlike human-driven vehicles, their behaviors are controllable and trainable. AVs might have safe and efficient driving behavior throughout a trip, therefore, we can train them to reach a destination optimally in a simulation environment. In this research, we develop an optimization framework that finds a set of optimized driving parameters for AVs under various scenarios, aiming to improve certain optimization targets (e.g., reducing travel time, number of conflicts) using a well-defined simulation-based objective function. The methodological framework consists of an optimization module and a simulation environment. The differential evolution (DE) method is employed within the optimization module to identify the optimized values of the CF parameters. The simulation environment is a SUMO-based platform where several simulation replications are conducted under certain scenario conditions. An experimental setup is designed to implement the proposed framework under different scenarios of mixed traffic and demand cases for the IDM (intelligent driving model), Krauss, and ACC (adaptive cruise control) models. The findings of this research reveal that safety could potentially be improved by optimized values of the CF model. For each policy where a higher weight is allocated to safety, generated optimized parameters significantly enhance safety as well as efficiency. In addition, the results show that minimum gap and desired time headway are the most sensitive parameters in regards to the policy targets, and their optimized values could replicate the potential CF behavior of AVs.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"6 ","pages":"634-652"},"PeriodicalIF":4.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117097","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}