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Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

701
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
701
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

624
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...
624
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

784
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...
784
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

659
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...
659
Equation of Motion: General Plane motion - Problem Solving01:16

Equation of Motion: General Plane motion - Problem Solving

409
Consider a lawn roller with a mass of 100 kg, a radius of 0.2 meters, and a radius of gyration of 0.15 meters. A force of 200 N is applied to this roller, angled at 60 degrees from the horizontal plane. What will be the angular acceleration of the lawn roller?
The friction between the roller and the ground is characterized by two coefficients. The static friction coefficient is 0.15, while the kinetic friction coefficient is 0.1. These values are crucial in understanding the interaction between...
409
Rolling With Slipping01:14

Rolling With Slipping

7.3K
Rolling with slipping is a physical phenomenon that occurs when a rolling object experiences both rotational and linear motion but also experiences frictional forces that cause slipping. This phenomenon can occur in various situations, such as when a tire rolls on a wet road or a ball rolls on a rough surface.
An object's rolling motion is characterized by its rotation around its axis, while linear motion refers to the object's translational motion along a surface. Frictional forces can...
7.3K

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Related Experiment Video

Updated: Dec 15, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach.

Lisardo Prieto González1, Susana Sanz Sánchez2, Javier Garcia-Guzman1

  • 1Computer Science Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain.

Sensors (Basel, Switzerland)
|July 8, 2020
PubMed
Summary

Researchers developed a cost-effective Deep Learning model to estimate vehicle roll and sideslip angles, crucial for autonomous vehicle stability and safety. This method utilizes readily available sensor data for enhanced control systems.

Keywords:
deep Learning based estimatorroll anglesensor fusionsideslip anglevehicle dynamics

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Area of Science:

  • Vehicle Dynamics and Control
  • Artificial Intelligence in Automotive Engineering
  • Deep Learning for State Estimation

Background:

  • Autonomous vehicles require precise state estimation for safe operation across diverse driving scenarios.
  • Sideslip and roll angles are critical for vehicular lateral stability, especially in high-center-of-gravity vehicles prone to rollover.
  • Direct measurement of these angles is costly, driving research towards estimation techniques for mass-market vehicles.

Purpose of the Study:

  • To propose an inexpensive and effective Deep Learning-based model for the simultaneous estimation of roll and sideslip angles.
  • To enable enhanced controller design for autonomous vehicles by providing accurate lateral stability parameters.
  • To address the non-linear nature of vehicle dynamics using advanced Artificial Intelligence methods.

Main Methods:

  • Development of a Deep Learning model utilizing onboard vehicle sensor data (longitudinal/lateral acceleration, steering angle, roll/yaw rates).
  • Training the model with extensive data generated from Trucksim® simulations.
  • Validation using real-world driving data captured with a VBOX3i dual-antenna GPS for ground truth.

Main Results:

  • The proposed Deep Learning model accurately estimates both roll and sideslip angles simultaneously.
  • The model leverages common vehicle sensor inputs, making it suitable for mass production.
  • Validation with real-world data confirms the model's robustness and effectiveness.

Conclusions:

  • Deep Learning offers a powerful and cost-effective solution for estimating critical vehicle states like roll and sideslip angles.
  • The developed model can significantly contribute to the design of advanced control systems for autonomous vehicles.
  • This approach provides a viable alternative to expensive dedicated sensors for monitoring vehicle stability.