2012 IEEE Intelligent Vehicles Symposium最新文献_第4页

Identification of target populations for current active safety systems using driver behavior 利用驾驶员行为识别当前主动安全系统的目标人群

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232236

Kristofer D. Kusano, H. Gabler

{"title":"Identification of target populations for current active safety systems using driver behavior","authors":"Kristofer D. Kusano, H. Gabler","doi":"10.1109/IVS.2012.6232236","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232236","url":null,"abstract":"Frontal Pre-Collision Systems (PCS) and Lane Departure Warning (LDW) systems are two of the first active safety systems to penetrate the passenger vehicle market. PCS can warn the driver, amplify the driver's braking effort, and autonomously brake even if there is no driver input. LDW systems deliver a warning to the driver when the vehicle is drifting out of its lane. The potential effectiveness of these two systems in the field not only depends on the crash scenarios they are likely to activate in but also on driver behavior. This study utilized the National Motor Vehicle Crash Causation Survey (NMVCCS), which unlike traditional databases focuses on behavioral aspects that lead to a collision. The target populations for PCS and LDW were found by aggregating crashes that had a) crash scenarios and b) critical reasons attributed to the collisions that were most likely mitigated by the systems. The warning component of PCS was found to be potentially effective in 45% of applicable crash scenarios. The brake assist and autonomous braking components were potentially effective in 71% and 74% of collisions, respectively. LDW was potentially effective in 18% of road departure collisions. These target populations are not estimates of actual system effectiveness but are quantification of the specific crash and driver scenarios most likely to be mitigated by LDW and PCS.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"17 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":"132184300","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

Text recognition on traffic panels from street-level imagery 根据街道图像在交通面板上进行文本识别

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232157

Álvaro González, L. Bergasa, J. J. Torres, J. Almazán

引用次数: 24

A platform for the development and evaluation of passive safety applications 开发和评估被动安全应用的平台

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232305

P. Szczurek, Bo Xu, O. Wolfson, Jie Lin

引用次数: 9

Veiling Luminance estimation on FPGA-based embedded smart camera 基于fpga的嵌入式智能摄像机遮挡亮度估计

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232154

C. Grana, Daniele Borghesani, Paolo Santinelli, R. Cucchiara

引用次数: 2

Grid-based DBSCAN for clustering extended objects in radar data 基于网格的DBSCAN雷达数据扩展目标聚类

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232167

Dominik Kellner, J. Klappstein, K. Dietmayer

引用次数: 77

Car2X-based perception in a high-level fusion architecture for cooperative perception systems 协同感知系统高层融合架构中基于car2x的感知

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232130

A. Rauch, F. Klanner, R. Rasshofer, K. Dietmayer

引用次数: 113

An improved driver-behavior model with combined individual and general driving characteristics 一种改进的个体与一般驾驶特征相结合的驾驶员行为模型

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232177

P. Angkititrakul, C. Miyajima, K. Takeda

{"title":"An improved driver-behavior model with combined individual and general driving characteristics","authors":"P. Angkititrakul, C. Miyajima, K. Takeda","doi":"10.1109/IVS.2012.6232177","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232177","url":null,"abstract":"In this paper, we propose a stochastic driver-behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using Dirichlet process mixture model, a nonparametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development observed data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unmatched parameter spaces from individual training observations. The proposed driver-behavior model was employed to anticipate pedal-operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the adapted model previously proposed.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"150 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":"122765518","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: 18

Multiple vehicle driving control for traffic flow efficiency 多车驾驶控制，提高交通流效率

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232187

Seong-Woo Kim, Gi-Poong Gwon, Seung-Tak Choi, Seung-Nam Kang, Myoung-Ok Shin, In-Sub Yoo, Eun-Dong Lee, Emilio Frazzoli, S. Seo

{"title":"Multiple vehicle driving control for traffic flow efficiency","authors":"Seong-Woo Kim, Gi-Poong Gwon, Seung-Tak Choi, Seung-Nam Kang, Myoung-Ok Shin, In-Sub Yoo, Eun-Dong Lee, Emilio Frazzoli, S. Seo","doi":"10.1109/IVS.2012.6232187","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232187","url":null,"abstract":"The dynamics of multi-agent in nature have been largely studied for a long time to investigate how the aggregation of agents can move smoothly in complex environments without collision. The main insights can be summarized such that the aggregated dynamics of animals and particles can be explained by an individual's simple rules. In a similar vein, we conjecture that such simple rules for vehicle maneuvering can accommodate the fluid flow of traffic and reduce car accidents in highway and urban areas. In this paper, we first show the Reynolds' three rules are applicable to autonomous driving on a single lane. Moreover, we provide additional requirements and algorithms for multiple lanes. Based on these results, we show that the proposed nature-inspired driving maneuver can increase traffic flow by 1) mitigating shockwave at bottlenecks and 2) extending the perception range for better path planning, which requires the support of the vehicle autonomy and wireless communication, respectively. Finally, we prove the feasibility of our work with experiments using multiple UAVs.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"18 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120896541","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: 20

The effect of vehicle acceleration near traffic congestion fronts 交通拥堵前沿车辆加速效应研究

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232152

J. Vergeest, B. Arem

引用次数: 3

Improving moving objects tracking using road model for laser data 利用道路模型改进激光数据的运动目标跟踪

2012 IEEE Intelligent Vehicles Symposium Pub Date : 2012-06-03 DOI: 10.1109/IVS.2012.6232300

Q. Baig, O. Aycard

引用次数: 6