2014 IEEE Intelligent Vehicles Symposium Proceedings最新文献_第2页

Periodicity based cruising control of passenger cars for optimized fuel consumption 基于周期的乘用车巡航控制优化油耗

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856424

S. Li, Shaobing Xu, Guofa Li, B. Cheng

引用次数: 18

Drive analysis using lane semantics for data reduction in naturalistic driving studies 自然驾驶研究中使用车道语义进行数据缩减的驾驶分析

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856609

R. Satzoda, Pujitha Gunaratne, M. Trivedi

引用次数: 18

Energy-efficient torque distribution for axle-individually propelled electric vehicles 单轴驱动电动汽车的节能扭矩分配

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856499

Stefan Koehler, A. Viehl, O. Bringmann, W. Rosenstiel

引用次数: 16

Real-time capable path planning for energy management systems in future vehicle architectures 未来车辆架构中能源管理系统的实时路径规划

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856456

J. Brembeck, C. Winter

{"title":"Real-time capable path planning for energy management systems in future vehicle architectures","authors":"J. Brembeck, C. Winter","doi":"10.1109/IVS.2014.6856456","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856456","url":null,"abstract":"In this paper an energy optimal path planning and velocity profile generation for our highly maneuverable Robotic Electric Vehicle research platform ROboMObil is presented. The ROMO [1] is a development of the German Aerospace Center's Robotics and Mechatronics Center to cope with several research topics, like energy efficient, autonomous or remote controlled driving for future (electro-) mobility applications. The main task of the proposed algorithms is to calculate an energy optimal trajectory in a real-time capable way. It is designed to incorporate data from actual traffic situations (e.g. oncoming traffic) or changed conditions (e.g. snowy conditions). The resulting trajectory is then fed forward to a lower level time independent path following control [2] that calculates the motion demands for our energy optimal control allocation. This in turn distributes the demand to the actuators of the over-actuated vehicle. We show a numerical reliable way to formulate the energy optimal path planning optimization objective, which is able to provide a consistent replanning feature considering the actual vehicle states. Besides this, different types of optimization methods are evaluated for their real-time capabilities. The velocity profile will be calculated afterwards and the generation of the profile is also enabled to handle dynamic replanning. Finally, we show several experimental results, using a virtual road definition and tests on a commercial real-time platform.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130337646","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

Temporal preview estimation for design of a low cost lane-following system using a forward-facing monocular camera 基于时间预估的低成本车道跟踪系统设计

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856606

A. Brown, S. Brennan

引用次数: 0

Narrow passage path planning using fast marching method and support vector machine 基于快速行军法和支持向量机的窄通道路径规划

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856611

Quoc Huy Do, S. Mita, Keisuke Yoneda

引用次数: 13

Results and issues of an automated truck platoon within the energy ITS project 能源ITS项目中自动卡车排的结果和问题

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856400

S. Tsugawa

{"title":"Results and issues of an automated truck platoon within the energy ITS project","authors":"S. Tsugawa","doi":"10.1109/IVS.2014.6856400","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856400","url":null,"abstract":"This paper presents an automated truck platoon that has been developed within a national ITS project named \"Energy ITS,\" and the results and future issues. The five-year project started in 2008 aimed at energy saving and global warming prevention with automated driving. A platoon of three fully-automated heavy trucks and also a fully-automated light truck drove at 80 km/h with the gap of up to 4.7 m on a test truck. The lateral control was based on the lane marker detection by computer vision, and the longitudinal control was based on gap measurement by 76 GHz radar and lidar in addition to the inter-vehicle communications of 5.8 GHz DSRC and infrared. The radar and lidar also worked as the obstacle detection. The feature of the technologies is high reliability. Fuel consumption measurement on a test track shows that the fuel can be saved by about 15 % when the gap was 4.7 m. A simulation study shows that the effectiveness of the platooning with the gap of 10 m when the 40 % penetration in heavy trucks is 2.1 % reduction of CO2 along an expressway. In addition, experiments of four heavy trucks with CACC were also conducted for the introduction scenario. The technological and non-technological issues on automated driving and its introduction are also discussed.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297402","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: 82

Understanding head and hand activities and coordination in naturalistic driving videos 了解在自然驾驶视频中头部和手部的活动和协调

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856610

Sujitha Martin, Eshed Ohn-Bar, Ashish Tawari, M. Trivedi

引用次数: 40

Road geometry classification using ANN 基于人工神经网络的道路几何分类

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856513

A. Hata, Danilo Habermann, F. Osório, D. Wolf

{"title":"Road geometry classification using ANN","authors":"A. Hata, Danilo Habermann, F. Osório, D. Wolf","doi":"10.1109/IVS.2014.6856513","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856513","url":null,"abstract":"An autonomous car must have a robust perception system to navigate safely in urban streets. An important issue of environment perception is the road (navigable area) detection and the identification of the road geometry. The road geometry information can be used to determine the vehicle control according to the street and also for topological localization. Existing road geometry identifiers only work with a limited number of classes and, due to the use of cameras, some solutions depend on filters to deal with shadows and light variations. This paper presents a road detector that extracts curb and navigable surface information from a multilayer laser sensor data. The road data was trained with an artificial neural network (ANN) and classified into eight road geometries: straight road, left turn, right turn, left side road, right side road, T intersection, Y intersection and crossroad. The main advantage of our method is its robustness to light variations for detecting distinct roads even in the presence of noisy data thanks to the ANN. In order to determine which road information has the best features for ANN training, three approaches were explored: ANN trained with curb data, ANN trained with surface data and ANN trained with both curb and surface data. Performed experiments resulted in the superiority of the network trained with both curb and surface data, with an accuracy of 0.91799. The trained ANN was validated in different urban scenarios and, evaluating a 1 Km track, we obtained a 94.48% of correct classifications. These results are superior than other works that detect fewer number of road shapes.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126528274","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

Vehicle safety evaluation based on driver drowsiness and distracted and impaired driving performance using evidence theory 基于证据理论的驾驶员困倦、分心和驾驶性能受损的车辆安全评价

2014 IEEE Intelligent Vehicles Symposium Proceedings Pub Date : 2014-06-08 DOI: 10.1109/IVS.2014.6856435

Xuanpeng Li, E. Seignez, Wenjie Lu, P. Loonis

引用次数: 8