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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.
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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.
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Relative Motion Analysis - Velocity01:24

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Relative Motion Analysis using Rotating Axes01:25

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Relative Motion Analysis - Acceleration01:10

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A slider-crank mechanism 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. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Updated: Sep 22, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction.

Yingying Feng1, Xiaolong Yan1

  • 1College of Information Engineering, Fuyang Normal University, Fuyang 236041, China.

Computational Intelligence and Neuroscience
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances vehicle safety by improving lane change prediction using a gridsearch-PSO optimized Support Vector Machine (SVM) model. The advanced model achieved 97.68% accuracy, significantly outperforming standard SVM for predicting vehicle trajectories.

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Area of Science:

  • Automotive Engineering
  • Machine Learning
  • Artificial Intelligence

Background:

  • Vehicle trajectory prediction is crucial for active safety systems.
  • Accurate prediction supports real-time safety decisions.
  • Existing methods require enhancement for complex maneuvers like lane changes.

Purpose of the Study:

  • To develop an accurate vehicle lane change recognition and trajectory prediction model.
  • To leverage Support Vector Machine (SVM) for analyzing lane change behavior.
  • To optimize SVM performance using a gridsearch-Particle Swarm Optimization (PSO) approach.

Main Methods:

  • Analysis of vehicle lane change behavior using NGSIM data.
  • Identification of 10 key factors influencing lane changes.
  • Development of a gridsearch-PSO optimized SVM model for lane change recognition.
  • Polynomial model and K-fold cross-validation for lateral movement trajectory fitting.

Main Results:

  • The optimized SVM model achieved a test accuracy of 97.68%.
  • The unoptimized SVM model showed a recognition accuracy of 80.87%.
  • The proposed model demonstrated strong classification ability and robustness.

Conclusions:

  • The gridsearch-PSO optimized SVM model significantly improves lane change recognition accuracy.
  • This approach enhances the reliability of vehicle trajectory prediction for active safety.
  • The findings support the development of more advanced driver-assistance systems.