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Modeling slow lead vehicle lane changing.

机译:模拟慢车道的车道变化。

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摘要

Driving field experiment data were used to investigate lane changes in which a slow lead vehicle was present to: (1) characterize lane changes, (2) develop predictive models, and (3) provide collision avoidance system (CAS) design guidelines. A total of 3,227 slow lead vehicle lane changes over 23,949 miles were completed by sixteen commuters. Two instrumented vehicles, a sedan and an SUV, were outfitted with video, sensor, and radar data systems that collected data in an unobtrusive manner.; Results indicate that 37.2% of lane changes are slow lead vehicle lane changes, with a mean completion time of 6.3 s; most slow lead vehicle lane changes are leftward, rated low in urgency and severity. A stratified sample of 120 lane changes was selected to include a range of maneuvers. On the interstate, lane changes are performed less often, t(30) = 2.83, p = 0.008, with lower urgency ratings, F(1, 31) = 5.24, p = 0.05, as compared to highway lane changes, as interstates are designed for smooth flow. Drivers who usually drive sedans are more likely to make lane changes than drivers of SUVs, X 2(1) = 99.6247, p 0.0001, suggesting that driving style is maintained regardless of which experimental vehicle is driven.; Turn signals are used 64% of the time but some drivers signal after the lane change starts. Of cases in which signals are not used, 70% of them are made with other vehicles nearby. Eyeglance analysis revealed that the forward view, rearview mirror, and left mirror are the most likely glance locations. There are also distinct eyeglance patterns for lane changing and baseline driving. Recommendations are to use forward view or mirror-based visual displays to indicate presence detection, and auditory displays for imminent warnings.; The "vehicle + signal" logistic regression model is best overall since it takes advantage of the distance to the front and rear adjacent vehicle, forward time-to-collision (TTC), and turn signal activation. The use of additional regressors would also improve the model. Five design guidelines are included to aid in the development of CAS that are useable, safe, and integrated with other systems, given testing and development.*; *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.
机译:驾驶场实验数据用于调查存在慢引导车辆的车道变化,以:(1)表征车​​道变化;(2)开发预测模型;(3)提供防撞系统(CAS)设计指南。 16个通勤者在23,949英里内完成了3,227辆慢速领先的车道变更。两辆仪表车(轿车和越野车)配备了视频,传感器和雷达数据系统,以不干扰用户的方式收集数据。结果表明,37.2%的车道变化是缓慢的引导车辆车道变化,平均完成时间为6.3 s;引导车辆车道最慢的变化是向左,紧急程度和严重性较低。选择了120个车道变更的分层样本,以包括一系列机动。在州际公路上,与高速公路车道变更相比,车道变更的执行频率较低,t(30)= 2.83,p = 0.008,紧急程度较低,F(1,31)= 5.24,p = 0.05设计用于平稳流动。与SUV相比,通常驾驶轿车的驾驶员更可能改变车道,X 2(1)= 99.6247,p <0.0001,这表明无论驾驶哪种实验车辆,驾驶风格都能得到保持。 64%的时间使用转向信号灯,但在换道开始后一些驾驶员会发出信号。在不使用信号的情况下,其中70%是由附近的其他车辆发出的。眼动分析表明,前视,后视镜和左后视镜是最可能的扫视位置。对于换道和基线驾驶也有不同的眼动模式。建议使用前视或基于镜子的视觉显示来指示存在检测,使用听觉显示来警告。 “车辆+信号”逻辑回归模型总体上最好,因为它利用了与前后相邻车辆的距离,前进碰撞时间(TTC)和转向信号激活功能。使用其他回归变量也将改善模型。包括五项设计准则,以帮助CAS的开发,这些准则是可用的,安全的,并且可以在进行测试和开发的情况下与其他系统集成。 *本论文是复合文件(作为论文的一部分,包含纸质副本和CD)。该CD需要满足以下系统要求:Windows MediaPlayer或RealPlayer。

著录项

  • 作者

    Olsen, Erik C. B.;

  • 作者单位

    Virginia Polytechnic Institute and State University.;

  • 授予单位 Virginia Polytechnic Institute and State University.;
  • 学科 Engineering Industrial.; Engineering Automotive.; Engineering System Science.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 236 p.
  • 总页数 236
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;自动化技术及设备;系统科学;
  • 关键词

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