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相关概念视频

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

441
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
441
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

382
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
382
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

334
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
334
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

199
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
199
Relative Velocity in Two Dimensions01:11

Relative Velocity in Two Dimensions

7.1K
Relative velocity is the velocity of an object as observed from a particular reference frame, or the velocity of one reference frame with respect to another reference frame. The concept of relative velocity can be used to describe motion in two dimensions. Consider a particle P and two reference frames S and S′. The position of the origin of S′ as measured in S is , the position of P as measured in S′ is , and the position of P as measured in S is , which can be evaluated by...
7.1K
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

316
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
316

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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一种车辆轨迹预测模型,集成了空间相互作用和多尺度时间特征.

Yuan Gao1, Kaifeng Yang2, Yibing Yue2

  • 1School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, China. gaoy@nefu.edu.cn.

Scientific reports
|March 11, 2025
PubMed
概括

这项研究介绍了一种新的神经网络,用于预测混合交通中的车辆轨迹. 该模型通过分析空间相互作用和时间数据,准确地预测未来的运动,超过现有的方法.

关键词:
深度学习是一种深度学习.这是GAT GAT的意思.多尺度 时间特征 时间特征变压器变压器变压器预测车辆轨迹的预测

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 运输工程 运输工程

背景情况:

  • 准确的实时轨迹预测对于异质交通中的智能车辆至关重要.
  • 了解空间相互作用和时间动态是提高预测准确性的关键.

研究的目的:

  • 开发一种神经网络模型,用于精确实时预测人类驾驶汽车在智能汽车周围的轨迹.
  • 将空间交互信息与长期和短期时间序列特征集成.

主要方法:

  • 一个图表注意网络 (GAT) 编码器处理历史车辆状态和空间交互.
  • 变压器编码器提取全球依赖关系,通过剩余连接增强.
  • 一个LSTM编码器-解码器架构捕捉了轨迹生成的短期时间特征.

主要成果:

  • 与公共数据集的基线模型相比,拟议的模型显示出优异的预测性能.
  • 整合GAT和变压器架构可以有效地捕捉复杂的车辆交互.
  • LSTM组件成功地模拟了短期时间依赖性,以准确预测.

结论:

  • 开发的神经网络模型为异质流量的实时轨迹预测提供了重大进展.
  • 该模型能够整合空间和时间特征,为自动驾驶安全提供了强大的解决方案.
  • 进一步的研究可以探索现实世界的部署和适应各种交通场景.