2015 IEEE 18th International Conference on Intelligent Transportation Systems最新文献_第8页

Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning 使用在线差分监督学习的基于颜色的自由空间分割

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.152

Willem P. Sanberg, Gijs Dubbelman, P. D. With

{"title":"Color-Based Free-Space Segmentation Using Online Disparity-Supervised Learning","authors":"Willem P. Sanberg, Gijs Dubbelman, P. D. With","doi":"10.1109/ITSC.2015.152","DOIUrl":"https://doi.org/10.1109/ITSC.2015.152","url":null,"abstract":"This work contributes to vision processing for Advanced Driver Assist Systems (ADAS) and intelligent vehicle applications. We propose a color-only stixel segmentation framework to segment traffic scenes into free, drivable space and obstacles, which has a reduced latency to improve the real-time processing capabilities. Our system learns color appearance models for free-space and obstacle classes in an online and self-supervised fashion. To this end, it applies a disparity-based segmentation, which can run in the background of the critical system path, either with a time delay of several frames or at a frame rate that is only a third of that of the color-based algorithm. In parallel, the most recent video frame is analyzed solely with these learned color appearance models, without an actual disparity estimate and the corresponding latency. This translates into a reduced response time from data acquisition to data analysis, which is a critical property for high-speed ADAS. Our evaluation on two publicly available datasets, one of which we introduce as part of this work, shows that the color-only analysis can achieve similar or even better results in difficult imaging conditions, compared to the disparity-only method. Our system improves the quality of the free-space analysis, while simultaneously lowering the latency and the computational load.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"23 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":"121969666","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

Analysis and Evaluation of Intelligent Bus Rapid Transit Systems in Cyprus 塞浦路斯智能快速公交系统的分析与评价

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.24

George Papageorgiou, Athanasios Maimaris, P. Ioannou

引用次数: 7

Inhomogeneous Model Predictive Control Horizon Discretization for an Urban Truck Energy Efficient Driving Application 非均匀模型预测控制水平离散化在城市卡车节能驾驶中的应用

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.78

Michael Henzler, M. Buchholz, K. Dietmayer

引用次数: 2

Smoothening for Efficient Solution of Model Predictive Control for Urban Traffic Networks Considering Endpoint Penalties 考虑端点惩罚的城市交通网络模型预测控制的平滑有效解

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.456

A. Jamshidnejad, H. Hellendoorn, Shu Lin, B. Schutter

{"title":"Smoothening for Efficient Solution of Model Predictive Control for Urban Traffic Networks Considering Endpoint Penalties","authors":"A. Jamshidnejad, H. Hellendoorn, Shu Lin, B. Schutter","doi":"10.1109/ITSC.2015.456","DOIUrl":"https://doi.org/10.1109/ITSC.2015.456","url":null,"abstract":"Traffic congestion together with emissions has become a big problem in urban areas. Traffic-responsive control systems aim to make the best use of the existing road capacity. Here, we propose a model-predictive controller for urban traffic networks, where the goal of the control is to find a balanced trade-off between reduction of congestion and emissions. The cost function is defined as a weighted combination of the total time spent, TTS, (as a criterion for evaluating the congestion level), the total emissions, TE, and the expected values of the TTS and TE caused by the vehicles that remain in the network at the end of the prediction horizon until they leave the network. We propose a method for estimation of the expected time spent and emissions by the remaining vehicles, where our method is based on a K Shortest path algorithm. For the prediction model of the MPC-based controller, we use a macroscopic integrated flow-emission model that includes the macroscopic flow S-model and the microscopic emission model, VT-micro. Since the S-model includes non-smooth functions, it does not allow us to benefit from efficient gradient-based methods to solve the optimization problem of the MPC-based controller. Therefore, in this paper we also propose smoothing methods for the S-model.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"100 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":"120858595","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: 5

Robustness Evaluation and Improvement for Vision-Based Advanced Driver Assistance Systems 基于视觉的高级驾驶辅助系统鲁棒性评价与改进

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.427

Stefan Müller, Dennis Hospach, O. Bringmann, J. Gerlach, W. Rosenstiel

引用次数: 10

Feedback Perimeter Control for Heterogeneous Urban Networks Using Adaptive Optimization 基于自适应优化的异构城市网络周长反馈控制

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.148

Anastasios Kouvelas, M. Saeedmanesh, N. Geroliminis

{"title":"Feedback Perimeter Control for Heterogeneous Urban Networks Using Adaptive Optimization","authors":"Anastasios Kouvelas, M. Saeedmanesh, N. Geroliminis","doi":"10.1109/ITSC.2015.148","DOIUrl":"https://doi.org/10.1109/ITSC.2015.148","url":null,"abstract":"A control scheme for heterogeneous transportation networks is presented. The methodology is based on the concept of the Macroscopic Fundamental Diagram (MFD) integrated with an adaptive optimization technique. The heterogeneous transportation network is first partitioned into a number of regions with homogeneous traffic conditions and well-defined MFDs. A macroscopic MFD-based model is used to describe the traffic dynamics of the resulting multi-region transportation system. A multivariable proportional integral (PI) feedback regulator is implemented to control the nonlinear system in real-time. The control variables consist of the inter-transferring flows between neighbourhood regions and the actuators correspond to the traffic lights of these areas (e.g. boundaries between regions). The recently proposed Adaptive Fine-Tuning (AFT) algorithm is used to optimize the gain matrices as well as the vector with the set-points of the PI controller. AFT is an iterative adaptive algorithm that optimizes the values of the tuneable parameters of the controller (e.g. gains and set-points) based on measurements of a performance index (e.g. total delay) for different perturbations of the parameters. The overall control scheme is tested in simulation and different performance criteria are studied. The performance of a fixed-time policy is compared to the final controller that is obtained after the convergence of AFT.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"5 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":"125766861","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: 24

Generalized Path Corridor-based Local Path Planning for Nonholonomic Mobile Robot 基于广义路径走廊的非完整移动机器人局部路径规划

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.311

Qi Wang, C. S. Wieghardt, Yan Jiang, Jian-wei Gong, Bernardo Wagner

引用次数: 6

VITAL: A Simulation-Based Assessment of New Traffic Light Controls 关键:基于模拟的新交通灯控制评估

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.12

J. Erdmann, R. Oertel, P. Wagner

引用次数: 5

Traffic Congestion Analysis Visualisation Tool 交通拥堵分析可视化工具

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.243

N. Petrovska, A. Stevanovic

{"title":"Traffic Congestion Analysis Visualisation Tool","authors":"N. Petrovska, A. Stevanovic","doi":"10.1109/ITSC.2015.243","DOIUrl":"https://doi.org/10.1109/ITSC.2015.243","url":null,"abstract":"The rapid growth of urban population and numbers of private cars in this modern era, results in increasingly urgent transportation problem in cities throughout the world. Road traffic congestion is an omnipresent problem, which leads to delays, time loss, human stress, energy consumption, environmental pollution e.c.t. In order to decrease traffic congestion, there is a need for simulating and optimizing traffic control and improving traffic management. There are different ways for traffic congestion monitoring and analysis such as using video monitoring and surveillance systems, or static and dynamic sensors which allow traffic management in real time. There are also other methods using non real time analysis where traf?c congestion can be extracted from historical patterns of traf?c congestion. The historical patterns can be gained from the stored travel time and speed data. The goal of enhancing driver convenience is achieved by providing applications based on road traffic condition that mainly identifies congestion status. This paper presents a web application which uses live traffic congestion data from Google Maps traffic layer for real time congestion calculation. A technique utilized for estimating the level of congestion is image processing. The main objective is to provide an automated and yet interactive visualization tool for congestion analysis in real time. The aim is reducing the traffic congestion on roads which will lead to decrease in the number of accidents. It can provide important data which can help road traffic management. Thus, it is mainly dedicated to traffic managers, operators and analysts. Nevertheless it can be implemented also by road users. Unlike most sensor based applications, it makes quantified congestion data available even in regions with limited traffic data information.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"11 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":"129341482","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: 34

A Real-Time Applicable Dynamic Hand Gesture Recognition Framework 一个实时适用的动态手势识别框架

2015 IEEE 18th International Conference on Intelligent Transportation Systems Pub Date : 2015-09-15 DOI: 10.1109/ITSC.2015.381

Thomas Kopinski, A. Gepperth, U. Handmann

引用次数: 4