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相关概念视频

Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

79
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

427
When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
Elevation of Intermediate Points on Vertical Curves01:20

Elevation of Intermediate Points on Vertical Curves

52
Vertical curves are essential in roadway design because they provide smooth transitions between varying roadway grades. Designing vertical curves involves calculating intermediate elevations and identifying the curve's highest or lowest point, which is essential for optimal roadway performance.Intermediate elevations on a vertical curve are determined using the tangent offset method. This method considers the initial elevation at the start of the curve, the grades, and the curve's geometry. The...
52
Introduction to Horizontal Curves01:19

Introduction to Horizontal Curves

124
Horizontal curves are essential in highway and railroad design, ensuring smooth and safe transitions between straight path segments, or tangents. These curves allow vehicles to maintain speed without abrupt changes, minimizing accidents and improving travel efficiency.A horizontal curve is typically defined by its geometric relationship to two tangents that meet at an intersection point (P.I.), where a simple curve is introduced to connect them. The back tangent refers to the initial tangent...
124
Dynamics Of Circular Motion: Applications01:17

Dynamics Of Circular Motion: Applications

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Suppose a car moves on flat ground and turns to the left. The centripetal force causing the car to turn in a circular path is due to friction between the tires and the road. For this, a minimum coefficient of friction is needed, or the car will move in a larger-radius curve and leave the roadway. Let's now consider banked curves, where the slope of the road helps in negotiating the curve. The greater the angle of the curve, the faster one can take the curve. It is common for race tracks for...
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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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一种动态方法来预测在曲线上驾驶风险,使用多源数据来预测.

Yongfeng Ma1, Fan Wang1, Shuyan Chen1

  • 1Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China.

Accident; analysis and prevention
|July 23, 2023
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概括
此摘要是机器生成的。

使用连接车辆数据预测在曲线上驾驶风险对于安全至关重要. 一个新的LSTM模型有效地识别高风险的驾驶行为,优于其他算法.

关键词:
聚类算法 聚类算法 聚类算法驱动风险 驱动风险是什么?动态预测 动态预测长时间短期记忆 (LSTM) 算法一个利的曲线曲线.

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科学领域:

  • 道路安全工程工程 道路安全工程
  • 智能运输系统 智能运输系统
  • 在汽车安全领域的机器学习应用.

背景情况:

  • 利的曲线带来了显著的交通事故风险,特别是在不利的驾驶条件下.
  • 在曲的道路上实时预测风险对于提高互联汽车驾驶员安全至关重要.
  • 现有的方法可能无法充分捕捉曲线上驾驶风险的动态性质.

研究的目的:

  • 开发一种动态的实时方法,用于预测在曲线上驾驶的风险.
  • 合并多个数据源,包括驾驶员机动,车辆动力学和生理数据,以进行全面的风险评估.
  • 评估长短期记忆 (LSTM) 网络模型与其他机器学习算法进行风险预测的性能.

主要方法:

  • 在六个曲线上进行了一项驾驶模拟实验,曲线的半径和方向各不相同.
  • 从55名参与者中收集了驾驶员机动,车辆动力学和驾驶员生理学数据.
  • 数据被细分,并使用临界横向加速作为风险指数,使用K-means集群将其分类为低,中和高水平.
  • 开发了一个LSTM网络模型,使用合并数据预测风险水平,确定最佳回顾和延迟窗口.
  • 将LSTM模型性能与随机森林,XGBoost和LightGBM算法进行了比较.

主要成果:

  • 提出的基于LSTM的模型在预测在曲线上危险的驾驶行为方面表现出了卓越的表现.
  • 对于LSTM模型来说,最优的窗口组合被确定为20米的回顾和20米的延迟.
  • 在LSTM模型中,中等风险的F1得分为84.8%,高风险的F1得分为86.0%,超过了比较算法.
  • 多源数据融合方法显著优于仅使用车辆动力学数据的模型.

结论:

  • 使用LSTM开发的动态实时风险预测方法在联网车辆环境中对曲线有效.
  • 通过利用合并的多源数据,LSTM模型为预测驾驶风险提供了强大的解决方案.
  • 这种方法可以为智能互联汽车的实时预测和预警系统的开发做出重大贡献.