2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)最新文献_第3页

Optimize performance of Interior Permanent Magnet Synchronous Motor for electric vehicles transportation system 电动汽车运输系统内嵌式永磁同步电机性能优化

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581335

Rital R. Gajjar, B. Parekh

引用次数: 2

Overview of current progress and development of seaplane safety management 水上飞机安全管理的现状与发展综述

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581307

G. Guo, Yuanchun Xu, Bing Wu

{"title":"Overview of current progress and development of seaplane safety management","authors":"G. Guo, Yuanchun Xu, Bing Wu","doi":"10.1109/ICITE.2016.7581307","DOIUrl":"https://doi.org/10.1109/ICITE.2016.7581307","url":null,"abstract":"The seaplane has the dual characters of both the ship and the aircraft, including the heavy load, fast speed of flight and able to fly in the surface of water. Recently, the seaplane has been developed rapidly. But due to the safety management of seaplane involves two department of maritime and aviation, safety management still lacks clear definition, and the research on this topic is at the beginning, for the various stages of navigation safety in the risk identification is lack of corresponding research. The paper summarized the safety management from the angle of the seaplane as the ship's navigation. Firstly, the performance characteristics of the seaplane are summarized. Secondly, from the six aspects of laws and regulations, inspection and certification and registration, driver training, navigable water security, water pollution management and company management were reviewed on the current seaplane safety management. Thirdly, the generic seaplane model developed according to the experience of International Maritime Organization on ship safety management, based on seaplane with characteristics of similar ships. At last, the paper summarized the significant study on seaplane and, then several theoretical and practical possible research areas of seaplane are definitely proposed. The research results have the theoretical significance for the management pattern of the seaplane.","PeriodicalId":352958,"journal":{"name":"2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892458","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: 3

Seat belt protection of train driver during secondary impact 列车驾驶员在二次碰撞时的安全带保护

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581313

Wen-bin Wang, Dongdong Zhang, Jiawei Ji, Honglei Tian, Hongwei Zhao

引用次数: 3

Simple corrective control for tolerating state transition faults in asynchronous circuits 异步电路中容限状态转换故障的简单纠偏控制

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581336

S. Kwak, Jung‐Min Yang

引用次数: 2

Supervision on abnormal activities in vehicle inspection service by anomaly detection in bipartite graph 基于二部图异常检测的车辆检验业务异常监督

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581309

Chenlu Qiu, Huiying Xu, Weixiang Liu

引用次数: 3

Car trajectory prediction in image processing and control manners 汽车轨迹预测中的图像处理与控制方式

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581305

Ping-Min Hsu, Zhen Zhu

{"title":"Car trajectory prediction in image processing and control manners","authors":"Ping-Min Hsu, Zhen Zhu","doi":"10.1109/ICITE.2016.7581305","DOIUrl":"https://doi.org/10.1109/ICITE.2016.7581305","url":null,"abstract":"This paper studies car trajectory predictions desired in several active safety systems, such as automatic emergency braking (AEB) systems and lane keeping systems (LKS). In the former, the car trajectory is estimated such that objects detected within this trajectory in front of current host vehicle are taken into consideration of collision avoidance. In LKS, the trajectory predictor is utilized to evaluate a lateral displacement so as to keep the host car running within the selected lane by reducing this displacement. To accomplish the prediction task, a trajectory estimation strategy is newly proposed in a fusion manner. First, a road model - capturing road geometry characteristics and combined with a vehicle dynamics - is referred; it includes two parameters related to road curvature, which are both estimated in a nonlinear manner. An image processing mechanism is alternatively adopted as a compensating curvature estimator if the proposed observer was in transient response. After the evaluation of road curvature, the vehicle trajectory in future few seconds are estimated in a series of path positions for the car mass center. Experimental results show the proposed predictor guarantees at least 95% of estimation preciseness compared to a real path. Target sensing in AEB worked rapidly in real-world tests with the aid of the path estimator.","PeriodicalId":352958,"journal":{"name":"2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127132690","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: 1

Traffic flow forecast with urban transport network 基于城市交通网络的交通流预测

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581322

Dingsu Wang, Qi Zhang, Shunyao Wu, Xinmin Li, Ruixue Wang

引用次数: 11

Tunnel moving target detection based on local structure of image and gray scale information 基于图像局部结构和灰度信息的隧道运动目标检测

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581316

Haiyang Yu, Yawen Hu, Hongyu Guo, Lin Fang

引用次数: 0

Real time bus arrival time prediction system under Indian traffic condition 印度交通状况下公交到达时间实时预测系统

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581300

B. Dhivyabharathi, B. A. Kumar, L. Vanajakshi

{"title":"Real time bus arrival time prediction system under Indian traffic condition","authors":"B. Dhivyabharathi, B. A. Kumar, L. Vanajakshi","doi":"10.1109/ICITE.2016.7581300","DOIUrl":"https://doi.org/10.1109/ICITE.2016.7581300","url":null,"abstract":"The estimation of bus travel time and providing accurate information about bus arrival time to passengers are important to make public transport system more user-friendly and thus enhance its competitiveness among various transportation modes. However, for the system to be effective, the information provided to passengers should be highly reliable. The model and technique used for prediction plays a major role in enhancing the accuracy and reliability of the system. The present study proposes a model based approach for accurate prediction of bus travel times for the development of a real time passenger information system under heterogeneous traffic conditions that exist in India. The proposed model considers the predicted bus travel time as the sum of the median of historical bus travel times, random variations in travel time over time, and a model evolution error. In order to capture the random variations in travel time, a model based approach with Particle filtering technique is used, wherein inputs are obtained using k-NN algorithm. The results obtained from the implementation of the above method are compared with the measured travel time data and the prediction accuracy is quantified using the Mean Absolute Percentage Error (MAPE). The Performance of the proposed method showed a clear improvement in prediction accuracy when compared with an existing model based approach using Kalman filter that was reported to be work well under similar traffic conditions.","PeriodicalId":352958,"journal":{"name":"2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131368397","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: 16

Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm 基于遗传算法的隐马尔可夫模型交叉口驾驶员行为建模

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) Pub Date : 2016-08-01 DOI: 10.1109/ICITE.2016.7581332

S. Amsalu, A. Homaifar

{"title":"Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm","authors":"S. Amsalu, A. Homaifar","doi":"10.1109/ICITE.2016.7581332","DOIUrl":"https://doi.org/10.1109/ICITE.2016.7581332","url":null,"abstract":"Driver behavior modeling plays a significant role in the development of Advanced Driver Assistance Systems (ADAS) for assisting drivers in different driving scenarios. One of the scenarios where high numbers of traffic accidents occur is road intersection. It is vital to develop driver behavior models near intersections in order for the ADAS to plan a proper action in avoiding accidents. In this paper, Hidden Markov Models (HMMs) for driver behavior near intersections are trained using Genetic Algorithm combined with Baum-Welch Algorithm based on the hybrid-state system (HSS) framework. HMM is usually trained using Baum-Welch which is easily trapped at local maxima. GA solves this problem by searching the entire solution space. Consequently, the best driver behavior model is trained. In the HSS framework, the vehicle dynamics are represented as a continuous-state system (CSS) and the decisions of the driver are represented as a discrete-state system (DSS). The continuous observations from the vehicle, such as acceleration, velocity and yaw-rate, are used by the proposed technique to estimate the driver's intention at each time step. The models are trained and tested using naturalistic driving data obtained from the Ohio State University, in an experiment with a sensor-equipped vehicle that was driven in the streets of Columbus, OH. The proposed framework improves the HMM accuracy in estimating the driver's intention when approaching an intersection with over 10% higher accuracy.","PeriodicalId":352958,"journal":{"name":"2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519114","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: 17