2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献

WiFi-based urban localisation using CNNs 使用cnn进行基于wifi的城市定位

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917290

N. Hernández, Héctor Corrales Sánchez, I. Parra, Mónica Rentero, D. F. Llorca, M. Sotelo

引用次数: 1

Spatiotemporal Traffic Speed Reconstruction from Travel Time Measurements Using Bluetooth Detection 基于蓝牙检测的时空交通速度重建

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917084

Lisa Kessler, Barbara Karl, K. Bogenberger

引用次数: 4

Velocity Coordination of Multi-vehicle Systems via Distributed Neighbor Selection 基于分布式邻居选择的多车系统速度协调

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917166

Haibin Shao, Lulu Pan, Y. Xi, Dewei Li, Shu Lin

引用次数: 1

A Q-learning Foresighted Approach to Ego-efficient Lane Changes of Connected and Automated Vehicles on Freeways 高速公路网联与自动驾驶车辆自我高效变道的q -学习预见方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917036

Long Wang, Fangmin Ye, Yibing Wang, Jingqiu Guo, I. Papamichail, M. Papageorgiou, Simon Hu, Lihui Zhang

引用次数: 10

Enhanced Temporal Data Organization for LiDAR Data in Autonomous Driving Environments 自动驾驶环境下激光雷达数据的增强时态数据组织

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917283

Michael Kusenbach, T. Luettel, H. Wuensche

引用次数: 3

Push, Stop, and Replan: An Application of Pebble Motion on Graphs to Planning in Automated Warehouses 推动、停止和重新规划:图形上的卵石运动在自动化仓库规划中的应用

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916906

Miroslav Kulich, T. Novák, L. Preucil

引用次数: 4

Dynamic OD Prediction for Urban Networks Based on Automatic Number Plate Recognition Data: Paramertic vs. Non-parametric Approaches 基于自动车牌识别数据的城市网络动态OD预测:参数与非参数方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917229

F. Zheng, Jing Liu, H. Zuylen, Kun Wang, Xaobo Liu, Jie Li

{"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}

引用次数: 6

Drivers’ Trust Model while Using In-Vehicle Traffic Lights in a Partial Deployment Scenario 部分部署场景下使用车载交通灯的驾驶员信任模型

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917462

Bo Yang, T. Kaizuka, Kimihiko Nakano

{"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}

引用次数: 0

Data-oriented network aggregation for large-scale network analysis using probe-vehicle trajectories 基于探测器-车辆轨迹的大规模网络分析的面向数据的网络聚合

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916931

Shohei Yasuda, T. Iryo, Katsuya Sakai, K. Fukushima

{"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}

引用次数: 2

VANET distributed data fusion for traffic management 面向交通管理的VANET分布式数据融合

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917443

Romain Guyard, V. Berge-Cherfaoui

引用次数: 0