2015 IEEE Intelligent Vehicles Symposium (IV)最新文献

Surround view based parking lot detection and tracking 基于环绕视图的停车场检测和跟踪

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225832

K. Hamada, Zhencheng Hu, Mengyang Fan, Hui Chen

引用次数: 56

Automatic LED text recognition method on electronic road sign using local spatial pattern and random forest classifier 基于局部空间模式和随机森林分类器的电子道路标志LED文本自动识别方法

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225686

Wahyono, A. Filonenko, K. Jo

引用次数: 1

The Foresighted Driver Model 前瞻性驾驶员模型

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225706

J. Eggert, Florian Damerow, Stefan Klingelschmitt

引用次数: 43

Optimal energy consumption algorithm based on speed reference generation for urban electric vehicles 基于速度参考生成的城市电动汽车最优能耗算法

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225771

Carlos Flores, V. M. Montero, Joshué Pérez, D. González, F. Nashashibi

引用次数: 13

Uncertainty propagation in criticality measures for driver assistance 驾驶员辅助临界测量中的不确定性传播

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225844

J. Stellet, Jan Schumacher, Wolfgang Branz, Johann Marius Zöllner

引用次数: 19

Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems 能力和技能图表系统建模，在线监测，和决策支持车辆引导系统

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225804

A. Reschka, Gerrit Bagschik, Simon Ulbrich, Marcus Nolte, M. Maurer

{"title":"Ability and skill graphs for system modeling, online monitoring, and decision support for vehicle guidance systems","authors":"A. Reschka, Gerrit Bagschik, Simon Ulbrich, Marcus Nolte, M. Maurer","doi":"10.1109/IVS.2015.7225804","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225804","url":null,"abstract":"In this paper, the ability and skill graphs are introduced for modeling vehicle guidance systems in the concept phase of the development process (abilities), for online monitoring of system operation (skills), and to support driving decisions (skill levels) of automated road vehicles and advanced driver assistance systems. Both graphs rely on a decomposition of the human driving task. An ability is the entirety of conditions which are necessary to provide a certain part of the driving task. The ability graph can be developed in parallel to the item definition according to the ISO 26262 standard in the concept phase of the development process and can be used for supporting further development steps. A skill is defined as an abstract representation of a part of the driving task including information about the skills current performance. The skill graph is used to monitor the current system performance during operation and skill levels are input to driving decisions. Abilities and skills cover all aspects of the driving task including environment and self perception, data processing, decision making, and behavior execution. During operation of the developed item, the skill graph is instantiated as a (distributed) software component to process online information for assessing current skill levels. Each skill uses one or more performance metrics, which represent its current performance capability in relation to the maximum (inherent) ability level. The resulting information could replace the monitoring of the system by a human driver and can be used as an input to driving decisions of the vehicle to support appropriate and safe decisions.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114934161","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: 28

Torque-vectoring stability control of a four wheel drive electric vehicle 四轮驱动电动汽车转矩矢量稳定性控制

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225818

B. Jager, P. Neugebauer, R. Kriesten, N. Parspour, Christian Gutenkunst

引用次数: 12

Smartphone-based modeling and detection of aggressiveness reactions in senior drivers 基于智能手机的老年司机攻击反应建模与检测

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225655

Dong-Woo Koh, Hang-Bong Kang

引用次数: 24

Estimating energy consumption of a PHEV using vehicle and on-board navigation data 利用车辆和车载导航数据估算插电式混合动力汽车的能耗

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225775

A. Ourabah, B. Quost, A. Gayed, T. Denoeux

{"title":"Estimating energy consumption of a PHEV using vehicle and on-board navigation data","authors":"A. Ourabah, B. Quost, A. Gayed, T. Denoeux","doi":"10.1109/IVS.2015.7225775","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225775","url":null,"abstract":"This paper presents a novel approach for predicting the energy consumption of a plug-in hybrid electric vehicle (PHEV). We propose to estimate energy consumption strategy from data via regression applied to trip recordings. Descriptors of the trip elements are obtained from both recordings and statistics provided by a GPS navigation system. Trips are then split into elementary units corresponding to an homogeneous driving context. For each trip element, the optimal energy consumption strategy is computed via (expensive) dynamic programming simulations. Here, data analysis is used so as to identify descriptors of this trip element that are relevant to predict the energy consumption. Then, a polynomial model is fit to the data so as to estimate, for each new trip element, the optimal energy consumption strategy from the expected driving condition, rather than using dynamic programming. Our approach distinguishes itself by the fact that road context, driver style, road slope and auxiliary electrical power are taken into account to estimate the energy consumption of a PHEV. The accuracy of the prediction process is evaluated over test data, and demonstrates the interest of our approach in predicting energy consumption.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116500449","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

Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection 结合RGB和LIDAR数据进行行人检测的本地专家多视图随机森林

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225711

Alejandro González, Gabriel Villalonga, Jiaolong Xu, David Vázquez, J. Amores, Antonio M. López

{"title":"Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection","authors":"Alejandro González, Gabriel Villalonga, Jiaolong Xu, David Vázquez, J. Amores, Antonio M. López","doi":"10.1109/IVS.2015.7225711","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225711","url":null,"abstract":"Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multi-modality and strong multi-view classifier) affect performance both individually and when integrated together. In the multi-modality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114695820","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: 81