IEEE Open Journal of Intelligent Transportation Systems最新文献_第8页

Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control 基于模型的归纳式交通信号控制图强化学习

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-12 DOI: 10.1109/OJITS.2024.3376583

François-Xavier Devailly;Denis Larocque;Laurent Charlin

引用次数: 0

Dilemma of Responsibility-Sensitive Safety in Longitudinal Mixed Autonomous Vehicles Flow: A Human-Driver-Error-Tolerant Driving Strategy 纵向混合自动驾驶车辆流中的责任敏感安全困境：人类-驾驶员-容错驾驶策略

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-03-08 DOI: 10.1109/OJITS.2024.3397959

Hongsheng Qi

{"title":"Dilemma of Responsibility-Sensitive Safety in Longitudinal Mixed Autonomous Vehicles Flow: A Human-Driver-Error-Tolerant Driving Strategy","authors":"Hongsheng Qi","doi":"10.1109/OJITS.2024.3397959","DOIUrl":"10.1109/OJITS.2024.3397959","url":null,"abstract":"The safety of autonomous vehicles (AVs) is a critical consideration for their widespread adoption. Responsibility sensitive safety (RSS) is proposed to serve as a model checking tool for AV safety. However, RSS alone cannot guarantee safety when they are mixed with human-driven vehicles (HDVs). These HDVs may disregard safety rules, creating dilemmas for AVs where they must choose between crashing into their leader or crashing into their follower. This manuscript defines this dilemma regarding the longitudinal driving and extends it to platooning scenarios with an arbitrary number of vehicles, referred to as polylemma. In polylemma, a violation of safety rules by one vehicle inevitably results in at least one crash between neighboring vehicles. To avoid the polylemma scenario, the manuscript proposes a human error-tolerant (HET) driving strategy, wherein AVs maintain an additional gap and prepare for moderate deceleration to account for potential errors by human drivers. The manuscript derives the risk reduction and capacity variation resulting from the implementation of this strategy at a given market penetration rate (MPR) using real world trajectory data. The analysis indicates that a 50% MPR would reduce risks due to human error by 80%, with a decrease in capacity which vary different for background traffic flow speed.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"265-280"},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937710","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

Worst-Case Response Time of Mixed Vehicles at Complex Intersections 复杂交叉口混合车辆的最差响应时间

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-22 DOI: 10.1109/OJITS.2024.3368797

Radha Reddy;Luis Almeida;Harrison Kurunathan;Miguel Gutiérrez Gaitán;Pedro M. Santos;Eduardo Tovar

{"title":"Worst-Case Response Time of Mixed Vehicles at Complex Intersections","authors":"Radha Reddy;Luis Almeida;Harrison Kurunathan;Miguel Gutiérrez Gaitán;Pedro M. Santos;Eduardo Tovar","doi":"10.1109/OJITS.2024.3368797","DOIUrl":"10.1109/OJITS.2024.3368797","url":null,"abstract":"Operating autonomous vehicles (AVs) and human-driven vehicles (HVs) at urban intersections while observing requirements of safety and service level is complex due not only to the existence of multiple inflow and outflow lanes, conflicting crossing zones, and low-speed conditions but also due to differences between control mechanisms of HVs and AVs. Intelligent intersection management (IIM) strategies can tackle the coordination of mixed AV/HV intersections while improving intersection throughput and reducing travel delays and fuel wastage in the average case. An endeavor relevant to traffic planning and safety is assessing whether given worst-case service levels can be met. Given a specific arrival pattern, this can be done via the worst-case response time (WCRT) that any vehicle experiences when crossing intersections. In this research line, this paper estimates WCRT upper bounds and discusses the analytical characterization of arrival and service curves, including estimating maximum queue length and associated worst-case waiting time for various traffic arrival patterns. This analysis is then used to compare six state-of-the-art intersection management approaches from conventional to intelligent and synchronous. The analytical results show the advantage of employing a synchronous management approach and are validated with the vehicles floating car data (timestamped location and speed) and simulations carried out using SUMO.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"186-201"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956877","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

Safety Improvements for Personnel and Vehicles in Short-Term Construction Sites 改善短期施工现场人员和车辆的安全状况

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-19 DOI: 10.1109/OJITS.2024.3366708

Daniel Rau;Jonas Vogt;Philipp Schorr;Juri Golanov;Andreas Otte;Jens Staub;Horst Wieker

{"title":"Safety Improvements for Personnel and Vehicles in Short-Term Construction Sites","authors":"Daniel Rau;Jonas Vogt;Philipp Schorr;Juri Golanov;Andreas Otte;Jens Staub;Horst Wieker","doi":"10.1109/OJITS.2024.3366708","DOIUrl":"10.1109/OJITS.2024.3366708","url":null,"abstract":"Despite all efforts to enhance safety, construction sites remain a major location for traffic accidents. Short-term construction sites, in particular, face limitations in implementing extensive safety measures due to their condensed timelines. This paper seeks to enhance safety in short-term construction sites by alerting maintenance personnel and approaching vehicles to potentially dangerous scenarios. Focusing on defining the exact dimensions of static construction sites, this method employs high-precision Real-Time-Kinematics-GNSS for localizing traffic cones and deriving the construction site geometry through respective algorithms. By analyzing the geometry, we can identify situations where maintenance personnel are in close proximity to the active lane or when vehicles enter the construction site. To increase awareness of hazardous situations, we present methods for distributing information to maintenance personnel and vehicles, along with technical solutions for warning those involved. Additionally, we discuss the distribution of the construction site’s geometry among approaching vehicles, which can provide future automated vehicles with crucial information on the site’s exact start and end points.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"174-185"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10439273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948044","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

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics 用于客户车队分析的损失感知直方图分选和主成分分析技术

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-15 DOI: 10.1109/OJITS.2024.3366279

Kunxiong Ling;Jan Thiele;Thomas Setzer

{"title":"Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics","authors":"Kunxiong Ling;Jan Thiele;Thomas Setzer","doi":"10.1109/OJITS.2024.3366279","DOIUrl":"10.1109/OJITS.2024.3366279","url":null,"abstract":"We propose a method to estimate information loss when conducting histogram binning and principal component analysis (PCA) sequentially, as usually done in practice for fleet analytics. Coarser-grained histogram binning results in less data volume, fewer dimensions, but more information loss. Considering fewer principal components (PCs) results in fewer data dimensions but increased information loss. Although information loss with each step is well understood, little guidance exists on the overall information loss when conducting both steps sequentially. We use Monte Carlo simulations to regress information loss on the number of bins and PCs, given few parameters of a dataset related to its scale and correlation structure. A sensitivity study shows that information loss can be approximated well given sufficiently large datasets. Using the number of bins, PCs, and two correlation measures, we derive an empirical loss model with high accuracy. Furthermore, we demonstrate the benefits of estimating information losses and the representativeness of total loss in evaluating the accuracy of k-means clustering for a real-world customer fleet dataset. For preprocessing sensor data which are aggregated from sufficient number of samples, continuously distributed, and can be represented by Beta-distributions, we recommend not to coarsen the histogram binning before PCA.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"160-173"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10437985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947963","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

An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV 小视场角多基础设施 RGB-D 摄像机网络的外在校准方法

IF 4.6

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-02 DOI: 10.1109/OJITS.2024.3361842

He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming

{"title":"An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV","authors":"He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming","doi":"10.1109/OJITS.2024.3361842","DOIUrl":"10.1109/OJITS.2024.3361842","url":null,"abstract":"Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras’ extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"617-628"},"PeriodicalIF":4.6,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947719","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

Addressing Rare Outages in C-V2X With Time-Controlled One-Shot Resource Scheduling 利用受时间控制的一次性资源调度解决 C-V2X 中的罕见中断问题

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-02-02 DOI: 10.1109/OJITS.2024.3361473

Md Saifuddin;Mahdi Zaman;Yaser P. Fallah;Jayanthi Rao

引用次数: 0

Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment 在车载网络环境中使用机器学习技术和 HTM 算法进行故障预测和恢复

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-18 DOI: 10.1109/OJITS.2023.3347484

Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri

{"title":"Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment","authors":"Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri","doi":"10.1109/OJITS.2023.3347484","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3347484","url":null,"abstract":"The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"132-145"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572603","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

2024 Editorial IEEE Open Journal of Intelligent Transportation Systems 2024 编辑 IEEE 智能交通系统开放式期刊

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2023.3348988

Jiaqi Ma

引用次数: 0

Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling 通过基于图形的时空建模分析共享单车使用情况

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-01-05 DOI: 10.1109/OJITS.2024.3350213

Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree

{"title":"Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling","authors":"Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree","doi":"10.1109/OJITS.2024.3350213","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3350213","url":null,"abstract":"Bike sharing schemes can be used both to improve mobility around busy city routes but also to contribute to the fight against climate change. Optimization of the network in terms of station locations and routes is a focus for researchers, where usage can highlight the precise times at which bike availability is high in some areas and low in others. Locations for new stations are important for the expansion of the network, but spatio-temporal pattern analysis is required to accurately identify those locations. In other words, one cannot rely on spatial information nor temporal information in isolation, when making interpretations for the purpose of optimizing or expanding the network. In this research, a solution based on graph networks was developed to model activity in transport networks by exploiting properties and functions specific to graph databases. This generic approach adopts a broad series of analyses, comprising different levels of granularity and complexity, to enable better interpretation of network dynamics at a suitably granular level to help the optimization of transport networks. A large dataset provided by an electric bike company is used to address key research questions in both interpreting activity patterns and supporting network optimization.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"115-131"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572602","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