2015 IEEE 18th International Conference on Intelligent Transportation Systems最新文献_第10页

Two-Layer Optimization to Cooperative Conflict Detection and Resolution for UAVs 无人机协同冲突检测与解决的两层优化

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.335

Jian Yang, Dong Yin, Qiao Cheng, Xu Xie

引用次数: 7

A Medium-Scale Network Model for Short-Term Traffic Prediction at Neighbourhood Level 邻域短期交通预测的中尺度网络模型

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.228

D. Giglio

引用次数: 2

Discovering MARS: A Mobility Aware Recommender System 发现MARS:移动感知推荐系统

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.8

Ricardo Leal, P. Costa, Teresa Galvão

引用次数: 3

Computer Vision on Embedded Sensors for Traffic Flow Monitoring 基于嵌入式传感器的计算机视觉交通流量监测

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.35

M. Magrini, D. Moroni, G. Palazzese, G. Pieri, G. Leone, O. Salvetti

引用次数: 16

Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion Combi-Tor:用于汽车传感器融合的轨道到轨道关联框架

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.263

B. Duraisamy, T. Schwarz

{"title":"Combi-Tor: Track-to-Track Association Framework for Automotive Sensor Fusion","authors":"B. Duraisamy, T. Schwarz","doi":"10.1109/ITSC.2015.263","DOIUrl":"https://doi.org/10.1109/ITSC.2015.263","url":null,"abstract":"The data association algorithm plays the vital role of forming an appropriate and valid set of tracks from the available tracks at the fusion center, which are delivered by different sensor's local tracking systems. The architecture of the data association module has to be designed taking into account the fusion strategy of the sensor fusion system, the granularity and the quality of the data provided by the sensors. The current generation environment perception sensors used for automotive sensor fusion are capable of providing estimated kinematic and as well as non-kinematic information on the observed targets. This paper focuses on integrating the kinematic and non-kinematic information in a track-to-track association (T2TA) procedure. A scalable framework called Combi-Tor is introduced here that is designed to calculate the association decision using likelihood ratio tests based on the available kinematic and non-kinematic information on the targets, which are tracked and classified by different sensors. The calculation of the association decision includes the uncertainty in the sensor's local tracking and classification modules. The required sufficient statistical derivations are discussed. The performance of this T2TA framework and the traditional T2TA scheme considering only the kinematic information are evaluated using Monte-Carlo simulation. The initial results obtained using the real world sensor data is presented.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131542755","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

A Universal Approach to Detect and Classify Road Surface Markings 一种检测和分类路面标记的通用方法

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.310

Fabian Poggenhans, M. Schreiber, C. Stiller

引用次数: 20

Learned Optimal Control of a Range Extender in a Series Hybrid Vehicle 串联混合动力汽车增程器的学习最优控制

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.420

A. Styler, A. Sauer, I. Nourbakhsh, H. Rottengruber

{"title":"Learned Optimal Control of a Range Extender in a Series Hybrid Vehicle","authors":"A. Styler, A. Sauer, I. Nourbakhsh, H. Rottengruber","doi":"10.1109/ITSC.2015.420","DOIUrl":"https://doi.org/10.1109/ITSC.2015.420","url":null,"abstract":"Each year, hybrid vehicles command a larger portion of total vehicles on the road. These vehicles combine multiple sources of energy, such as batteries and gasoline, which have different strengths and weaknesses. Active management of these energy sources can increase vehicle efficiency, longevity, or performance. Optimizing energy management is highly sensitive to upcoming power loads on the vehicle, but conventional control policies only react to the present state. Furthermore, these policies are computed at design-time and do not adapt to individual drivers. Advancements in cheap sensing and computation have enabled on-board learning and optimization that was previously impossible. In this work, we developed and implemented a real-time controller that exploits predictions computed from a dataset collected from other drivers. This data-driven controller manages a range-extender in a series gas-electric hybrid vehicle, optimizing fuel use, noise, and ignition frequency. The algorithm is scalable to large amounts of source data, and performance improves with prediction accuracy. We tested the algorithm in simulation and on a modified vehicle with direct programmatic control of the range extender. The experimental results on the vehicle reflected those observed in simulation, achieving fuel savings up to 12% and a noise-cost reduction of 73%.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133572014","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: 9

A Washout and a Tilt Coordination Algorithm for a Hexapod Platform 六足平台的冲刷与倾斜协调算法

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.197

Konrad Stahl, Klaus-Dieter Leimbach, Ansgar Meroth, R. Zöllner

引用次数: 6

Learning Traffic Light Parameters with Floating Car Data 学习交通灯参数与浮动汽车数据

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.393

Valentin Protschky, Christian Ruhhammer, S. Feit

{"title":"Learning Traffic Light Parameters with Floating Car Data","authors":"Valentin Protschky, Christian Ruhhammer, S. Feit","doi":"10.1109/ITSC.2015.393","DOIUrl":"https://doi.org/10.1109/ITSC.2015.393","url":null,"abstract":"The knowledge of traffic light parameters, such as cycle plan or future signal phase and timing information (SPaT) of traffic lights is the base for a vast number of use scenarios. A few examples are traffic signal adaptive routing, green light optimal speed control, red light duration advisory or efficient start-stop control. The basis for all these functionalities is the knowledge on the correct traffic light cycle time, i.e. the periodicity of the traffic light's signaling sequence. With a correct cycle time given, green start and end times can be derived from periodically reoccurring movement patterns. In this paper, we propose a method to reconstruct a traffic light's cycle plan through the interpretation of the recorded information on a vehicle's movement pattern (trajectory) in the intersection area. The recorded trajectories are temporarily sparse and and the cycle plan changes frequently. Therefore, we propose a model that focuses on the performance on very limited available trajectory data and yet is robust with regard to estimation errors. We show that our approach is able to detect the correct cycle time with already 30 trajectories at an accuracy of 99%.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132219953","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: 29

Spatial Prior for Nonparametric Road Scene Parsing 非参数道路场景分析的空间先验

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.199

Shuai Di, Honggang Zhang, Xue Mei, D. Prokhorov, Haibin Ling

引用次数: 4