Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917290
N. Hernández, Héctor Corrales Sánchez, I. Parra, Mónica Rentero, D. F. Llorca, M. Sotelo
The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi access points at almost any place in our cities. Although WiFi has been mainly used for indoor localisation, there is a growing number of research in outdoor WiFi-based localisation. This paper presents a WiFi-based localisation system that takes advantage of the huge deployment of WiFi networks in urban areas. The idea is to complement localisation in zones where the GPS coverage is low, such as urban canyons. The proposed method explores the CNNs ability to handle large amounts of data and their high accuracy with reasonable computational costs. The final objective is to develop a system able to handle the large number of access points present in urban areas while preserving high accuracy and real time requirements. The system was tested in a urban environment, improving the accuracy with respect to the state-of-the-art and being able to work in real time.
{"title":"WiFi-based urban localisation using CNNs","authors":"N. Hernández, Héctor Corrales Sánchez, I. Parra, Mónica Rentero, D. F. Llorca, M. Sotelo","doi":"10.1109/ITSC.2019.8917290","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917290","url":null,"abstract":"The continuous expanding scale of WiFi deployments in metropolitan areas has made possible to find WiFi access points at almost any place in our cities. Although WiFi has been mainly used for indoor localisation, there is a growing number of research in outdoor WiFi-based localisation. This paper presents a WiFi-based localisation system that takes advantage of the huge deployment of WiFi networks in urban areas. The idea is to complement localisation in zones where the GPS coverage is low, such as urban canyons. The proposed method explores the CNNs ability to handle large amounts of data and their high accuracy with reasonable computational costs. The final objective is to develop a system able to handle the large number of access points present in urban areas while preserving high accuracy and real time requirements. The system was tested in a urban environment, improving the accuracy with respect to the state-of-the-art and being able to work in real time.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"66 1","pages":"1270-1275"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74302494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917084
Lisa Kessler, Barbara Karl, K. Bogenberger
Traffic state reconstruction gets more and more attention for various important applications such as traffic optimization, traffic control, and congestion avoidance. There exist several approaches to detect traffic parameters like speed, flow, and density. A quite common approach in the past was to use stationary detectors like induction loops. An emerging technology is to handle traffic state by floating-car data (probe vehicles) with a high resolution of time and location measurements via GPS. A third methodology is to detect vehicles using the recognition of in-use Bluetooth devices and to derive an average travel time between two Bluetooth detectors. For the first two approaches, several traffic state reconstruction methods exist. This paper aims at reconstructing the prevailing traffic situation out of low-resolution travel times based on Bluetooth captions. A methodology is developed on how to reconstruct the traffic speed and is applied to three months of data from a German autobahn equipped with Bluetooth detectors.
{"title":"Spatiotemporal Traffic Speed Reconstruction from Travel Time Measurements Using Bluetooth Detection","authors":"Lisa Kessler, Barbara Karl, K. Bogenberger","doi":"10.1109/ITSC.2019.8917084","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917084","url":null,"abstract":"Traffic state reconstruction gets more and more attention for various important applications such as traffic optimization, traffic control, and congestion avoidance. There exist several approaches to detect traffic parameters like speed, flow, and density. A quite common approach in the past was to use stationary detectors like induction loops. An emerging technology is to handle traffic state by floating-car data (probe vehicles) with a high resolution of time and location measurements via GPS. A third methodology is to detect vehicles using the recognition of in-use Bluetooth devices and to derive an average travel time between two Bluetooth detectors. For the first two approaches, several traffic state reconstruction methods exist. This paper aims at reconstructing the prevailing traffic situation out of low-resolution travel times based on Bluetooth captions. A methodology is developed on how to reconstruct the traffic speed and is applied to three months of data from a German autobahn equipped with Bluetooth detectors.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"35 1","pages":"4275-4280"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75279240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917166
Haibin Shao, Lulu Pan, Y. Xi, Dewei Li, Shu Lin
In this paper, we examine a data-driven control approach to coordination of multi-vehicle systems based on the distributed neighbor selection. The core of the proposed approach is to utilize a recently developed metric called relative tempo for distributed coordination which is computable from local measurable data for each vehicle in the network. The relative tempo between each agent and its neighbors is shown to be closely related to the directed spanning tree of the underlying network in a quantitative manner. Based on this fact, a local neighbor selection protocol is subsequently provided to construct the global refined communication structure which can both maintain the connectivity and increase the efficiency of the multi-vehicle coordination. The numerical study is finally provided to demonstrate the effectiveness of our approach.
{"title":"Velocity Coordination of Multi-vehicle Systems via Distributed Neighbor Selection","authors":"Haibin Shao, Lulu Pan, Y. Xi, Dewei Li, Shu Lin","doi":"10.1109/ITSC.2019.8917166","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917166","url":null,"abstract":"In this paper, we examine a data-driven control approach to coordination of multi-vehicle systems based on the distributed neighbor selection. The core of the proposed approach is to utilize a recently developed metric called relative tempo for distributed coordination which is computable from local measurable data for each vehicle in the network. The relative tempo between each agent and its neighbors is shown to be closely related to the directed spanning tree of the underlying network in a quantitative manner. Based on this fact, a local neighbor selection protocol is subsequently provided to construct the global refined communication structure which can both maintain the connectivity and increase the efficiency of the multi-vehicle coordination. The numerical study is finally provided to demonstrate the effectiveness of our approach.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"45 1","pages":"2938-2943"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74728691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917036
Long Wang, Fangmin Ye, Yibing Wang, Jingqiu Guo, I. Papamichail, M. Papageorgiou, Simon Hu, Lihui Zhang
Lane changes are a vital part of vehicle motions on roads, affecting surrounding vehicles locally and traffic flow collectively. In the context of connected and automated vehicles (CAVs), this paper is concerned with the impacts of smart lane changes of CAVs on their own travel performance as well as on the entire traffic flow with the increase of the market penetration rate (MPR). On the basis of intensive microscopic traffic simulation and reinforcement learning technique, an ego-efficient lane-changing strategy was first developed in this work to enable foresighted lane changing decisions for CAVs to improve their travel efficiency. The overall impacts of such smart lane changes on traffic flow of both CAVs and human-driven vehicles were then examined on the same simulation platform, which reflects a real freeway infrastructure with real demands. It was found that smart lane changes were beneficial for both CAVs and the entire traffic flow, if MPR was not more than 60%.
{"title":"A Q-learning Foresighted Approach to Ego-efficient Lane Changes of Connected and Automated Vehicles on Freeways","authors":"Long Wang, Fangmin Ye, Yibing Wang, Jingqiu Guo, I. Papamichail, M. Papageorgiou, Simon Hu, Lihui Zhang","doi":"10.1109/ITSC.2019.8917036","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917036","url":null,"abstract":"Lane changes are a vital part of vehicle motions on roads, affecting surrounding vehicles locally and traffic flow collectively. In the context of connected and automated vehicles (CAVs), this paper is concerned with the impacts of smart lane changes of CAVs on their own travel performance as well as on the entire traffic flow with the increase of the market penetration rate (MPR). On the basis of intensive microscopic traffic simulation and reinforcement learning technique, an ego-efficient lane-changing strategy was first developed in this work to enable foresighted lane changing decisions for CAVs to improve their travel efficiency. The overall impacts of such smart lane changes on traffic flow of both CAVs and human-driven vehicles were then examined on the same simulation platform, which reflects a real freeway infrastructure with real demands. It was found that smart lane changes were beneficial for both CAVs and the entire traffic flow, if MPR was not more than 60%.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"53 1","pages":"1385-1392"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73056156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917283
Michael Kusenbach, T. Luettel, H. Wuensche
One of the most important tasks for autonomous cars is the perception of the environment. In particular, the detection and tracking of objects is vital for further applications. We present a new real-time method to organize point cloud data provided by a LiDAR sensor. The main contribution of this method is the linking of 3D points from different time frames. With this connection, it is possible to traverse through the data over time. In addition, an efficient 2D data organization allows fast access to neighboring information of the 3D data. This makes it very suitable for tasks like model creation and clustering. Based on the obtained spatial and temporal neighboring information, tasks such as object detection, tracking and prediction can be solved directly.
{"title":"Enhanced Temporal Data Organization for LiDAR Data in Autonomous Driving Environments","authors":"Michael Kusenbach, T. Luettel, H. Wuensche","doi":"10.1109/ITSC.2019.8917283","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917283","url":null,"abstract":"One of the most important tasks for autonomous cars is the perception of the environment. In particular, the detection and tracking of objects is vital for further applications. We present a new real-time method to organize point cloud data provided by a LiDAR sensor. The main contribution of this method is the linking of 3D points from different time frames. With this connection, it is possible to traverse through the data over time. In addition, an efficient 2D data organization allows fast access to neighboring information of the 3D data. This makes it very suitable for tasks like model creation and clustering. Based on the obtained spatial and temporal neighboring information, tasks such as object detection, tracking and prediction can be solved directly.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"4 1","pages":"2701-2706"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75714832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8916906
Miroslav Kulich, T. Novák, L. Preucil
The pebble-motion on graphs is a subcategory of multi-agent pathfinding problems dealing with moving multiple pebble-like objects from a node to a node in a graph with a constraint that only one pebble can occupy one node at a given time. Additionally, algorithms solving this problem assume that individual pebbles (robots) cannot move at the same time and their movement is discrete. These assumptions disqualify them from being directly used in practical applications, although they have otherwise nice theoretical properties. We present modifications of the Push and Rotate algorithm [1], which relax the presumptions mentioned above and demonstrate, through a set of experiments, that the modified algorithm is applicable for planning in automated warehouses.
图上的鹅卵石运动是多代理寻路问题的一个子类别，该问题处理将多个类似鹅卵石的对象从图中的一个节点移动到另一个节点，并约束在给定时间内只有一个鹅卵石可以占用一个节点。此外，解决这个问题的算法假设单个鹅卵石(机器人)不能同时移动，并且它们的运动是离散的。这些假设使它们无法直接用于实际应用，尽管它们在其他方面具有很好的理论性质。我们提出了对Push and Rotate算法的改进[1]，放宽了上述的假设，并通过一组实验证明了改进后的算法适用于自动化仓库的规划。
{"title":"Push, Stop, and Replan: An Application of Pebble Motion on Graphs to Planning in Automated Warehouses","authors":"Miroslav Kulich, T. Novák, L. Preucil","doi":"10.1109/ITSC.2019.8916906","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916906","url":null,"abstract":"The pebble-motion on graphs is a subcategory of multi-agent pathfinding problems dealing with moving multiple pebble-like objects from a node to a node in a graph with a constraint that only one pebble can occupy one node at a given time. Additionally, algorithms solving this problem assume that individual pebbles (robots) cannot move at the same time and their movement is discrete. These assumptions disqualify them from being directly used in practical applications, although they have otherwise nice theoretical properties. We present modifications of the Push and Rotate algorithm [1], which relax the presumptions mentioned above and demonstrate, through a set of experiments, that the modified algorithm is applicable for planning in automated warehouses.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"9 1","pages":"4456-4463"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74353475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917229
F. Zheng, Jing Liu, H. Zuylen, Kun Wang, Xaobo Liu, Jie Li
OD flows provide important information for traffic management and planning. In this paper, we propose four OD prediction models based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make prediction. We further develop three K- Nearest Neighbor (K-NN) based pattern recognition approaches. The proposed four approaches are validated with three days’ field ANPR data from Changsha city, P.R. China. The results show that on one hand our proposed approaches are able to make accurate prediction of OD flows under different demand conditions. On the other hand, the prediction accuracy is highly dependent on the quality of the available OD data: the Kalman filter model performs better for regular and periodic OD patterns; while for irregular OD matrices K-NN models could make more accurate prediction.
{"title":"Dynamic OD Prediction for Urban Networks Based on Automatic Number Plate Recognition Data: Paramertic vs. Non-parametric Approaches","authors":"F. Zheng, Jing Liu, H. Zuylen, Kun Wang, Xaobo Liu, Jie Li","doi":"10.1109/ITSC.2019.8917229","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917229","url":null,"abstract":"OD flows provide important information for traffic management and planning. In this paper, we propose four OD prediction models based on the data obtained by Automated Number Plate Recognition (ANPR) cameras. The principal component analysis (PCA) is applied to reduce the dimension of the original OD matrices and to separate the main structure patterns from the noisier components. A state-space model is established for the main structure patterns and the structure deviations, and is incorporated in the Kalman filter framework to make prediction. We further develop three K- Nearest Neighbor (K-NN) based pattern recognition approaches. The proposed four approaches are validated with three days’ field ANPR data from Changsha city, P.R. China. The results show that on one hand our proposed approaches are able to make accurate prediction of OD flows under different demand conditions. On the other hand, the prediction accuracy is highly dependent on the quality of the available OD data: the Kalman filter model performs better for regular and periodic OD patterns; while for irregular OD matrices K-NN models could make more accurate prediction.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"536 1","pages":"4037-4042"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77453134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917462
Bo Yang, T. Kaizuka, Kimihiko Nakano
In-vehicle information systems have been demonstrated as an effective method to provide driver assistance. However, few studies were focused on drivers’ trust while using these systems in different situations. An in-vehicle traffic light system was proposed in our previous research to assist drivers in crossing unsignalized intersections, by displaying virtual traffic signals inside vehicles based on vehicle to vehicle communications. Nevertheless, previous studies assumed that all the vehicles were equipped with vehicular communications, of which the deployment might actually last decades. Therefore, it is necessary to consider the application of the system in a partial deployment scenario. As a driver assistance system, the effectiveness of the system highly relies on drivers’ trust on it. This study, therefore, proposed a drivers’ trust model based on the decision making process. Driving simulator experiments were performed, to investigate drivers’ initial trust and the change of trust on the system while experiencing successful and failed usages of it. Regression analysis was then conducted with the simulated and observed data to validate the model while using the system in partial deployment situations. The results indicated that the proposed model could be suitable for the prediction of trust in partial deployment scenarios.
{"title":"Drivers’ Trust Model while Using In-Vehicle Traffic Lights in a Partial Deployment Scenario","authors":"Bo Yang, T. Kaizuka, Kimihiko Nakano","doi":"10.1109/ITSC.2019.8917462","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917462","url":null,"abstract":"In-vehicle information systems have been demonstrated as an effective method to provide driver assistance. However, few studies were focused on drivers’ trust while using these systems in different situations. An in-vehicle traffic light system was proposed in our previous research to assist drivers in crossing unsignalized intersections, by displaying virtual traffic signals inside vehicles based on vehicle to vehicle communications. Nevertheless, previous studies assumed that all the vehicles were equipped with vehicular communications, of which the deployment might actually last decades. Therefore, it is necessary to consider the application of the system in a partial deployment scenario. As a driver assistance system, the effectiveness of the system highly relies on drivers’ trust on it. This study, therefore, proposed a drivers’ trust model based on the decision making process. Driving simulator experiments were performed, to investigate drivers’ initial trust and the change of trust on the system while experiencing successful and failed usages of it. Regression analysis was then conducted with the simulated and observed data to validate the model while using the system in partial deployment situations. The results indicated that the proposed model could be suitable for the prediction of trust in partial deployment scenarios.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"379 1","pages":"1588-1593"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77884496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8916931
Shohei Yasuda, T. Iryo, Katsuya Sakai, K. Fukushima
Network representation is required to be simple and to have a high affinity to observed data, considering large-scale transportation network analysis. With the spread of technologies such as probe vehicles, continuous acquisition of detailed traffic data in a large-scale network is now possible. It is needed to link characteristic values to each link of network data for utilizing that. However, handling the data linked to all links of a detailed network can be very difficult when the number of links in the network is very large. In that case, aggregating a network structure is an effective approach, however, existing methods have some issues regarding the subjectivity of network selection or the dependence on the original network structure. In this paper, we developed a method to generate an aggregated network consisting of observed vehicle trajectories. Using observed vehicle trajectories to represent network can improve the objectivity of network representation and relieve the dependence on the original network data. As shown by numerical examples of Kobe area network, the complexity of the structure of the aggregated network is not too simple to lose information under network-wide traffic conditions and not too complex to incur a huge calculating cost.
{"title":"Data-oriented network aggregation for large-scale network analysis using probe-vehicle trajectories","authors":"Shohei Yasuda, T. Iryo, Katsuya Sakai, K. Fukushima","doi":"10.1109/ITSC.2019.8916931","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916931","url":null,"abstract":"Network representation is required to be simple and to have a high affinity to observed data, considering large-scale transportation network analysis. With the spread of technologies such as probe vehicles, continuous acquisition of detailed traffic data in a large-scale network is now possible. It is needed to link characteristic values to each link of network data for utilizing that. However, handling the data linked to all links of a detailed network can be very difficult when the number of links in the network is very large. In that case, aggregating a network structure is an effective approach, however, existing methods have some issues regarding the subjectivity of network selection or the dependence on the original network structure. In this paper, we developed a method to generate an aggregated network consisting of observed vehicle trajectories. Using observed vehicle trajectories to represent network can improve the objectivity of network representation and relieve the dependence on the original network data. As shown by numerical examples of Kobe area network, the complexity of the structure of the aggregated network is not too simple to lose information under network-wide traffic conditions and not too complex to incur a huge calculating cost.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"33 1","pages":"1677-1682"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80471443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-01DOI: 10.1109/ITSC.2019.8917443
Romain Guyard, V. Berge-Cherfaoui
In this article, we propose a distributed fusion algorithm to detect traffic congestion through the exchange of messages in vehicle network. This algorithm is based on the Dempster-Shafer theory that manages the uncertainties on data and sources of information. Each vehicle updates its database with local measurements (speed and interdistance) and information received from other vehicles and can calculate its route. Thanks to the collaboration, smart cars can avoid congested roads and take a better path to their destination. Several variants of the algorithm are studied and compared to a centralized approach through experiments carried out on the SUMO simulator using real urban road networks.
{"title":"VANET distributed data fusion for traffic management","authors":"Romain Guyard, V. Berge-Cherfaoui","doi":"10.1109/ITSC.2019.8917443","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917443","url":null,"abstract":"In this article, we propose a distributed fusion algorithm to detect traffic congestion through the exchange of messages in vehicle network. This algorithm is based on the Dempster-Shafer theory that manages the uncertainties on data and sources of information. Each vehicle updates its database with local measurements (speed and interdistance) and information received from other vehicles and can calculate its route. Thanks to the collaboration, smart cars can avoid congested roads and take a better path to their destination. Several variants of the algorithm are studied and compared to a centralized approach through experiments carried out on the SUMO simulator using real urban road networks.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"22 1","pages":"1851-1856"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81271499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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