Pub Date : 2024-03-28DOI: 10.1109/OJITS.2024.3406390
Man Yu;Keyang Gong;Weihua Zhao;Rui Liu
Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.
{"title":"LiDAR and IMU Tightly Coupled Localization System Based on Ground Constraint in Flat Scenario","authors":"Man Yu;Keyang Gong;Weihua Zhao;Rui Liu","doi":"10.1109/OJITS.2024.3406390","DOIUrl":"10.1109/OJITS.2024.3406390","url":null,"abstract":"Accurate estimation of current position and attitude of a vehicle is one of the key technologies for autonomous driving. Due to the defect of LiDAR intrinsic parameter and the sparsity of LiDAR beam in the vertical direction, current LiDAR-based simultaneous localization and mapping (SLAM) system generally suffers from the problem of inaccurate height positioning. In this study, a LiDAR and inertial measurement unit (IMU) tightly coupled localization algorithm considering ground constraint is proposed, which is developed based on a pose graph optimization framework. At the front end, the ground segmentation algorithm Patchwork is improved to obtain a point cloud with higher verticality, which is added to the LiDAR inertial odometry. Moreover, constraints are constructed by using current frame ground points and world map ground points, which are added to factor map optimization to limit elevation errors. At the back end, SC++ descriptors are used to construct loop constraints to eliminate accumulated errors. Verifications based on KITTI dataset show that the height positioning accuracy will be improved through introducing ground constraint factor and loop detection factor. Real vehicle tests indicate that the proposed algorithm has better height positioning accuracy and better robustness compared with the LeGO-LOAM algorithm.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"296-306"},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195009","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}
Pub Date : 2024-03-27DOI: 10.1109/OJITS.2024.3377217
Erik Jenelius;Abdulla Al-Kaff
Public transportation serves many important roles in society: When functioning well, it provides accessibility for people to work, healthcare and other essential activities, as well as high-speed mobility for massive volumes of passengers during peak hours. The efficiency of public transportation, in terms of energy consumption, emissions, surface occupancy, etc., makes it a crucial component of sustainable transportation systems in combination with the active mobility modes. New technologies have the potential to enhance the performance, efficiency and attractiveness of public transportation through new vehicle concepts, better resource utilization, and better use of automated data sources. This special Section on “Intelligent Transportation Systems for Public Transportation” was established to provide a collection of studies that advance the state-of-the-art in the field by developing, implementing and evaluating novel technologies and methods. After a rigorous review process, nine scientific papers have been selected to be published. A couple of themes emerge from the combined contributions, highlighting important and active areas of research:
{"title":"Editorial Special Section on Intelligent Transportation Systems for Public Transportation","authors":"Erik Jenelius;Abdulla Al-Kaff","doi":"10.1109/OJITS.2024.3377217","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3377217","url":null,"abstract":"Public transportation serves many important roles in society: When functioning well, it provides accessibility for people to work, healthcare and other essential activities, as well as high-speed mobility for massive volumes of passengers during peak hours. The efficiency of public transportation, in terms of energy consumption, emissions, surface occupancy, etc., makes it a crucial component of sustainable transportation systems in combination with the active mobility modes. New technologies have the potential to enhance the performance, efficiency and attractiveness of public transportation through new vehicle concepts, better resource utilization, and better use of automated data sources. This special Section on “Intelligent Transportation Systems for Public Transportation” was established to provide a collection of studies that advance the state-of-the-art in the field by developing, implementing and evaluating novel technologies and methods. After a rigorous review process, nine scientific papers have been selected to be published. A couple of themes emerge from the combined contributions, highlighting important and active areas of research:","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"205-207"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10482809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310115","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}
Pub Date : 2024-03-27DOI: 10.1109/OJITS.2024.3377216
Alberto Petrillo;Stefania Santini
Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present.
{"title":"Editorial Special Section on Coordination, Cooperation, and Control of Autonomous Vehicles in Smart Connected Road Environments","authors":"Alberto Petrillo;Stefania Santini","doi":"10.1109/OJITS.2024.3377216","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3377216","url":null,"abstract":"Mobility is facing a transformation in terms of connectivity, allowing vehicles to communicate with each other, to the road infrastructure, and to other road users. This enables coordination and cooperation, hence managing traffic and mobility at an entirely new level. Indeed, Cooperative, Connected and Automated Mobility enables and provides ITS services with better Quality of Service (QoS), compared to the same ITS services by only one of the ITS sub-systems (personal, vehicle, roadside, and central, infrastructures), thus improving the road management, reducing congestion, and contributing to sustainable and eco-mobility. By leveraging a network of Smart Infrastructures, it is possible to be continuously and promptly aware about the circulation and environment conditions, as well as the status of connected devices, along with the related technological services. Such knowledge, gained via the adoption of advanced sensing/communication technologies, has the potential to fundamentally shift the mobility paradigm towards mobility as a service. This contributes to more safe, efficient, and comfortable transportation systems. Along this line, information is continuously communicated/shared to vehicles and travellers thanks to dedicated communication services, thus enabling mobility automation and control. Different services - such as providing information about traffic light signal phases and their predicted changes or barriers on the route in realtime- support smooth and comfortable traveling by avoiding strong accelerations/decelerations, by reducing fuel/energy consumption of vehicles with favoured effects on lowering noise and emissions. In this perspective, the special section aims at exploring how to face Coordination and Cooperation challenges for autonomous vehicles in this new connected environment, also in the transition phase where connected human-driven vehicles are present.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"202-204"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480881","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310116","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}
Pub Date : 2024-03-27DOI: 10.1109/OJITS.2024.3380569
Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed
This paper develops a risk-averse-based framework for optimizing the operation of an integrated power, gas, and traffic (PGT) network with an application to a typical PGT network in downtown Edmonton, the forefront of Canada’s transition to electric vehicles and sustainable urban travel options. The developed non-probabilistic framework provides decision-makers with various secure options to avoid worst-case scenarios and promote social and environmental benefits. The integration of different energy systems allows operators to pursue optimal strategies in critical situations, such as facility outages, maintaining the system within a secure operational range without resorting to expensive workarounds. The proposed algorithm and integrated structure can select optimal travel routes to minimize gas-emission effects and locate charging options to reduce electric vehicle users’ travel time. It can mitigate challenges posed by distributed generator outages and roadway closures. The numerical results from implementing the framework on different case studies and the solar-based PGT network of Edmonton indicate its feasibility and effectiveness.
{"title":"An Optimal Routing Framework for an Integrated Urban Power–Gas–Traffic Network","authors":"Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed","doi":"10.1109/OJITS.2024.3380569","DOIUrl":"10.1109/OJITS.2024.3380569","url":null,"abstract":"This paper develops a risk-averse-based framework for optimizing the operation of an integrated power, gas, and traffic (PGT) network with an application to a typical PGT network in downtown Edmonton, the forefront of Canada’s transition to electric vehicles and sustainable urban travel options. The developed non-probabilistic framework provides decision-makers with various secure options to avoid worst-case scenarios and promote social and environmental benefits. The integration of different energy systems allows operators to pursue optimal strategies in critical situations, such as facility outages, maintaining the system within a secure operational range without resorting to expensive workarounds. The proposed algorithm and integrated structure can select optimal travel routes to minimize gas-emission effects and locate charging options to reduce electric vehicle users’ travel time. It can mitigate challenges posed by distributed generator outages and roadway closures. The numerical results from implementing the framework on different case studies and the solar-based PGT network of Edmonton indicate its feasibility and effectiveness.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"223-237"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10481512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140315865","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}
With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle’s sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.
{"title":"On the Prediction of the Sideslip Angle Using Dynamic Neural Networks","authors":"Raffaele Marotta;Salvatore Strano;Mario Terzo;Ciro Tordela","doi":"10.1109/OJITS.2024.3405797","DOIUrl":"10.1109/OJITS.2024.3405797","url":null,"abstract":"With the growing interest in self-driving vehicles, safety in vehicle driving is becoming an increasingly important aspect. The sideslip angle is a key quantity for modern control systems that aim to improve passenger safety. It directly affects the lateral motion and stability of a vehicle. In particular, a large sideslip angle can cause the vehicle to experience oversteer or understeer, which can lead to loss of control and potentially result in an accident. For this reason, it is necessary to constantly monitor this quantity while driving in order to implement appropriate action if necessary. Sensors that directly measure this quantity are expensive and difficult to implement. In this paper, two neural networks to estimate the sideslip angle are proposed. The quantities that most influence the vehicle’s sideslip angle were assessed. Furthermore, the neural networks can exploit data from previous instants of time for estimation purposes. In particular, the first uses lateral acceleration and steering wheel angle as input, the second uses longitudinal acceleration, lateral acceleration and yaw rate. Experimental tests carried out on manoeuvres that stimulate the sideslip angle have shown that, although the estimators use few measures, they are able to accurately estimate the quantity of interest.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"281-295"},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10539180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172633","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}
We introduce MuJAM, an adaptive traffic signal control method which leverages model-based reinforcement learning to 1) extend recent generalization efforts (to road network architectures and traffic distributions) further by allowing a generalization to the controllers’ constraints (cyclic and acyclic policies), 2) improve performance and data efficiency over related model-free approaches, and 3) enable explicit coordination at scale for the first time. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as a recent transferable approach.
{"title":"Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control","authors":"François-Xavier Devailly;Denis Larocque;Laurent Charlin","doi":"10.1109/OJITS.2024.3376583","DOIUrl":"10.1109/OJITS.2024.3376583","url":null,"abstract":"We introduce MuJAM, an adaptive traffic signal control method which leverages model-based reinforcement learning to 1) extend recent generalization efforts (to road network architectures and traffic distributions) further by allowing a generalization to the controllers’ constraints (cyclic and acyclic policies), 2) improve performance and data efficiency over related model-free approaches, and 3) enable explicit coordination at scale for the first time. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as a recent transferable approach.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"238-250"},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10470423","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140116726","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}
Pub Date : 2024-03-08DOI: 10.1109/OJITS.2024.3397959
Hongsheng Qi
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.
{"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}
Pub Date : 2024-02-22DOI: 10.1109/OJITS.2024.3368797
Radha Reddy;Luis Almeida;Harrison Kurunathan;Miguel Gutiérrez Gaitán;Pedro M. Santos;Eduardo Tovar
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.
{"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}
Pub Date : 2024-02-19DOI: 10.1109/OJITS.2024.3366708
Daniel Rau;Jonas Vogt;Philipp Schorr;Juri Golanov;Andreas Otte;Jens Staub;Horst Wieker
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.
{"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}
Pub Date : 2024-02-15DOI: 10.1109/OJITS.2024.3366279
Kunxiong Ling;Jan Thiele;Thomas Setzer
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.
{"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}
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