2012 IEEE Intelligent Vehicles Symposium最新文献_第6页

Development of an particle swarm algorithm for vehicle localization 基于粒子群算法的车辆定位研究

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232213

Jorge Godoy, D. Gruyer, A. Lambert, J. Villagrá

引用次数: 12

Indoor micro navigation utilizing local infrastructure-based positioning 利用当地基础设施定位的室内微导航

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232262

Jens Einsiedler, Oliver Sawade, Bernd Schäufele, Marcus Witzke, I. Radusch

引用次数: 16

Image based fog detection in vehicles 基于图像的车辆雾检测

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232256

M. Pavlic, H. Belzner, G. Rigoll, Slobodan Ilic

引用次数: 80

Vehicle detection using discriminatively trained part templates with variable size 采用区别训练的可变尺寸零件模板进行车辆检测

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232284

Hossein Tehrani Niknejad, Taiki Kawano, Mikio Shimizu, S. Mita

引用次数: 9

Cooperative multi-vehicle localization using split covariance intersection filter 基于分割协方差交叉滤波的协同多车定位

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232155

Hao Li, F. Nashashibi

引用次数: 64

Robotic wheeled vehicle ripple tentacles motion planning method 机器人轮式车辆波纹触手运动规划方法

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232292

Hongxiao Yu, Jian-wei Gong, K. Iagnemma, Yan Jiang, Jianmin Duan

{"title":"Robotic wheeled vehicle ripple tentacles motion planning method","authors":"Hongxiao Yu, Jian-wei Gong, K. Iagnemma, Yan Jiang, Jianmin Duan","doi":"10.1109/IVS.2012.6232292","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232292","url":null,"abstract":"This paper describes a nonholonomic robotic wheeled vehicle ripple tentacle motion planning method, aiming to improve the vehicle's trajectory smoothness and avoid frequent weight parameters adjustment in different environments. In the regular tentacle motion planning algorithm, the planning result is selected among the drivable tentacles using a weighted sum cost function. Though the method is simple and easy to understand, it is difficult to adjust the weighted coefficients in different environments. To solve this problem, a geometrical ripple tentacles technique is used to choose a tentacle as a sub-optimal path. Compared with the regular tentacles algorithm, the proposed ripple tentacle algorithm can get a better performance in vehicle's trajectory smoothness with an acceptable runtime expense. And another two traits can also distinguish this method: (a) it can avoid weight parameter adjustment in different environments and varied vehicle's states, and (b) it can be used in both unknown environment and partly known environment with goal point and global reference path. In the totally unknown environment, it acts as a pure obstacle avoidance algorithm, and when there is a global path, it can follow the reference path and avoid hazards simultaneously.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 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":"130845396","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: 10

Transponder- and Camera-based advanced driver assistance system 基于应答器和摄像头的高级驾驶辅助系统

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232140

Daniel Westhofen, Carolin Grundler, Konrad Doll, U. Brunsmann, S. Zecha

{"title":"Transponder- and Camera-based advanced driver assistance system","authors":"Daniel Westhofen, Carolin Grundler, Konrad Doll, U. Brunsmann, S. Zecha","doi":"10.1109/IVS.2012.6232140","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232140","url":null,"abstract":"Cooperative traffic safety is a straightforward approach for a significant reduction of accidents and fatalities. This paper presents a predictive safety system based on a cooperative localization technology using transponders combined with a monocular camera. By means of these sensor components other traffic partners in the surrounding area are recognized and tracked even in case of occlusion. Using the pedestrian detections of the transponder system for the generation of regions of interest (ROI), video-based confirmation is achieved in real-time using histograms of oriented gradients (HOG). An extended Kalman filter is applied to cope with adapted nonlinear process and measurement models for transponder-based tracking, including methods for compensation of the vehicle's ego motion and sensor mounting offsets. The collision risk with other traffic partners especially pedestrians is assessed by using sophisticated motion models based on empirical data. In an experimental study of real-world scenarios it is demonstrated that the fusion of the sensor data results in a reliable prediction of upcoming collision risks and enables a specific warning or a justified autonomous brake maneuver in order to avoid a collision. The results confirm excellent detection, tracking and real-time performance and emphasize the potential of transponder-based active safety systems.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"104 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":"127145759","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: 22

Incorporating environmental knowledge into Bayesian filtering using attractor functions 利用吸引子函数将环境知识融入贝叶斯滤波

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232193

Andreas Alin, Martin Volker Butz, J. Fritsch

{"title":"Incorporating environmental knowledge into Bayesian filtering using attractor functions","authors":"Andreas Alin, Martin Volker Butz, J. Fritsch","doi":"10.1109/IVS.2012.6232193","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232193","url":null,"abstract":"Many automotive systems use linear approaches to track and predict other traffic participants. While this may be appropriate on highways, linear predictions do not work properly on curved roads or lane crossings. This contribution introduces a generic way for including environmental knowledge - such as the lane trajectory ahead - to anticipate yaw rate and acceleration of other traffic participants. The anticipatory knowledge is used to improve prediction in filtering tasks. It is embedded in a Bayesian framework by introducing attractors, which modify the probabilistic propagation of state estimations. The attractors model how traffic participants typically behave, given environmental knowledge such as lane information, traffic lights, or indicator lights. We demonstrate the potential of this approach by modeling the fact that vehicles usually stay in their lane. We show that given correct context information and nonlinear traffic situations, the tracking error is considerably lower compared to conventional tracking methods. In addition, we also show that the intentions of other traffic participants may be inferred by comparing actual sensory data with anticipated probability distributions, which were generated dependent on alternative attractors.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"34 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":"130795375","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: 10

A new collision warning system for lead vehicles in rear-end collisions 一种新型追尾车辆预警系统

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232244

Adrian Cabrera, Sven Gowal, A. Martinoli

{"title":"A new collision warning system for lead vehicles in rear-end collisions","authors":"Adrian Cabrera, Sven Gowal, A. Martinoli","doi":"10.1109/IVS.2012.6232244","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232244","url":null,"abstract":"Collision Warning Systems (CWS) are safety systems designed to warn the driver about an imminent collision. A CWS monitors the dynamic state of the traffic in realtime by processing information from various proprioceptive and exteroceptive sensors. It assesses the potential threat level and decides whether a warning should be issued to the driver through auditory and/or visual signals. Several measures have already been defined for threat assessment and various CWS have been proposed in literature. In this paper, we will focus on two time-based measures that assess both front and rear collision threats. In particular, a new threat metric, the time-to-last-second-acceleration (Tlsa), for lead vehicles in rear-end collision is proposed and compared with its counterpart, the time-to-last-second-braking (Tlsb) [18]. The Tlsa is a novel time-based approach that focuses on the lead vehicle (as opposed to the following vehicle). It inherits the properties of the Tlsb and, as such, is coherent with the human judgement of urgency and severity of threats. It directly quantifies the threat level of the current dynamic situation before a required evasive action (i.e. maximum acceleration) needs to be applied. Furthermore, different warning thresholds are proposed by considering the average driver reaction time. Its effect on decreasing the severity of a rear-end collision is studied and its reliability is tested using a well-established physics-based robotics simulator, namely Webots [13].","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"493 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":"132792241","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: 31

A track-to-track association method for automotive perception systems 一种用于汽车感知系统的轨道到轨道关联方法

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232261

Adam Houénou, P. Bonnifait, V. Berge-Cherfaoui, Jean-François Boissou

引用次数: 32