2012 IEEE Intelligent Vehicles Symposium最新文献_第8页

Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights 使用警告信息、交通统计和智能交通灯的智慧城市

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232229

Carolina Tripp Barba, M. Mateos, Pablo Regañas Soto, A. M. Mezher, M. Aguilar-Igartua

{"title":"Smart city for VANETs using warning messages, traffic statistics and intelligent traffic lights","authors":"Carolina Tripp Barba, M. Mateos, Pablo Regañas Soto, A. M. Mezher, M. Aguilar-Igartua","doi":"10.1109/IVS.2012.6232229","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232229","url":null,"abstract":"Road safety has become a main issue for governments and car manufacturers in the last twenty years. The development of new vehicular technologies has favoured companies, researchers and institutions to focus their efforts on improving road safety. During the last decades, the evolution of wireless technologies has allowed researchers to design communication systems where vehicles participate in the communication networks. Thus, new types of networks, such as Vehicular Ad Hoc Networks (VANETs), have been created to facilitate communication between vehicles themselves and between vehicles and infrastructure. New concepts where vehicular networks play an important role have appeared the last years, such as smart cities and living labs [1]. Smart cities include intelligent traffic management in which data from the TIC (Traffic Information Centre) infrastructures could be reachable at any point. To test the possibilities of these future cities, living labs (cities in which new designed systems can be tested in real conditions) have been created all over Europe. The goal of our framework is to transmit information about the traffic conditions to help the driver (or the vehicle itself) take adequate decisions. In this work, the development of a warning system composed of Intelligent Traffic Lights (ITLs) that provides information to drivers about traffic density and weather conditions in the streets of a city is proposed and evaluated through simulations.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127031147","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: 176

Experts of probabilistic flow subspaces for robust monocular odometry in urban areas 城市区域鲁棒单目里程计的概率流子空间专家

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232238

Christian Herdtweck, Cristóbal Curio

引用次数: 6

Semiotic prediction of driving behavior using unsupervised double articulation analyzer 基于无监督双发音分析器的驾驶行为符号学预测

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232243

T. Taniguchi, Shogo Nagasaka, K. Hitomi, N. P. Chandrasiri, T. Bando

引用次数: 45

Frontal object perception using radar and mono-vision 使用雷达和单视觉的正面物体感知

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232307

R. García, J. Burlet, Trung-Dung Vu, O. Aycard

引用次数: 43

Driver's authority monitoring system for intelligent vehicles: A feasibility study 智能汽车驾驶员权限监控系统的可行性研究

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232290

Pinar Uluer, Can Gocmenoglu, T. Acarman

{"title":"Driver's authority monitoring system for intelligent vehicles: A feasibility study","authors":"Pinar Uluer, Can Gocmenoglu, T. Acarman","doi":"10.1109/IVS.2012.6232290","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232290","url":null,"abstract":"One of the most challenging factors in the development of autonomous vehicles and advanced driver assistance systems is the imitation of an expert driver system which is the observer and interpreter of the technical system in the related driving scenario. In this paper, a multimodal adaptive driver assistance system is presented. The main goal is to determine the human driver's attention and authority level by decoupling the driver's vehicle control in the longitudinal and lateral direction in order to trigger timely warnings according to his/her driving intents and driving skills with respect to the possible driving situation and hazard scenarios. The presented driver assistance system considers the driver's driving performance metric sampled during the longitudinal and lateral vehicle control tasks as well as the processed information about the surrounding traffic environment consisting of the interactions with the other vehicles and the road situations. Experiments on a simulator are performed and the presented metric is calculated for the evaluation of the human driver's driving performance with respect to adaptive cruising and obstacle avoidance maneuvering tasks.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122136356","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

Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches 摩托车侧倾角估计:比较视频和惯性传感器方法

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232200

Marc Schlipsing, J. Salmen, B. Lattke, K. Schröter, H. Winner

{"title":"Roll angle estimation for motorcycles: Comparing video and inertial sensor approaches","authors":"Marc Schlipsing, J. Salmen, B. Lattke, K. Schröter, H. Winner","doi":"10.1109/IVS.2012.6232200","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232200","url":null,"abstract":"Advanced Rider Assistance Systems (ARAS) for powered two-wheelers improve driving behaviour and safety. Further developments of intelligent vehicles will also include video-based systems, which are successfully deployed in cars. Porting such modules to motorcycles, the camera pose has to be taken into account, as e. g. large roll angles produce significant variations in the recorded images. Therefore, roll angle estimation is an important task for the development of various kinds of ARAS. This study introduces alternative approaches based on inertial measurement units (IMU) as well as video only. The latter learns orientation distributions of image gradients that code the current roll angle. Until now only preliminary results on synthetic data have been published. Here, an evaluation on real video data will be presented along with three valuable improvements and an extensive parameter optimisation using the Covariance Matrix Adaptation Evolution Strategy. For comparison of the very dissimilar approaches a test vehicle is equipped with IMU, camera and a highly accurate reference sensor. The results state high performance of about 2 degrees error for the improved vision method and, therefore proofs the proposed concept on real-world data. The IMU-based Kalman filter estimation performed on par. As a naive result averaging of both estimates already increased performance an elaborate fusion of the proposed methods is expected to yield further improvements.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"100 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123191153","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: 23

Predictive maneuver evaluation for enhancement of Car-to-X mobility data 增强Car-to-X机动性数据的预测机动评估

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232217

J. Firl, Hagen Stübing, S. Huss, C. Stiller

引用次数: 46

Validation of reference laboratories to put in service Railway projects (tracks and trains) 为铁路项目(轨道和列车)提供服务的参考实验室

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232258

J. Tamarit, Daniel Molina, Jose Bueno

引用次数: 0

Kinematic constraints in visual odometry of intelligent vehicles 智能汽车视觉里程计量中的运动约束

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232230

Yanhua Jiang, Huiyan Chen, Guang-ming Xiong, Jian-wei Gong, Yan Jiang

引用次数: 2

Lane recognition for moving vehicles using multiple on-car RFID receiver antennas — Algorithm and its experimental results 基于车载RFID接收天线的移动车辆车道识别算法及实验结果

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232139

H. Togashi, C. Borcea, S. Yamada

{"title":"Lane recognition for moving vehicles using multiple on-car RFID receiver antennas — Algorithm and its experimental results","authors":"H. Togashi, C. Borcea, S. Yamada","doi":"10.1109/IVS.2012.6232139","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232139","url":null,"abstract":"Accurate lane recognition for moving vehicles is important for lane keeping and lane changing assistance systems. Additionally, this information could be leveraged by Intelligent Transportation Systems to suggest lane changes for improved traffic load balancing across lanes. This paper presents a position estimation algorithm for moving vehicles based on RFID (Radio Frequency Identification) active sensors placed on roadsides and lane boundaries, and multiple on-car RFID receiver antennas. To improve localization accuracy, the algorithm proposes two novel ideas: (1) compute pair-wise position estimates using the RSSI (Received Signal Strength Indication) of all pairs of signals received from RFIDs, and (2) compute the final position as a weighted average of these pair-wise estimates using a dynamic weighting function that assigns higher weights to positions estimated based on closer RFIDs. The results from our field experiments indicate that the proposed method achieves 0.7-meter localization accuracy when RFIDs are placed at 0.5-meter intervals and a vehicle has 8 antennas. This accuracy allows a moving vehicle to recognize which lane it is in. The localization accuracy of the proposed method was found to be mostly stable for any type of road shape and any number of lanes. A further 14% accuracy improvement is achieved when RFIDs are placed at 0.25-meter intervals and the RFIDs located farther than 30-meter are excluded from computation.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1032 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123332660","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