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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.2K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

13.0K
An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
13.0K
Types of Collisions - II01:19

Types of Collisions - II

8.0K
When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
8.0K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.3K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.5K
Types Of Collisions - I01:04

Types Of Collisions - I

7.4K
When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
7.4K

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Updated: Aug 10, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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Using Machine Learning on V2X Communications Data for VRU Collision Prediction.

Bruno Ribeiro1, Maria João Nicolau2, Alexandre Santos1

  • 1Department of Informatics, University of Minho, 4710-057 Braga, Portugal.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning predicts motorcyclist (Vulnerable Road User) collisions using V2X data. While effective, high false positives necessitate manual driver intervention for safety.

Keywords:
collision predictionmachine learningvehicular communicationsvulnerable road users

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Machine Learning (ML) in traffic safety
  • Vulnerable Road User (VRU) protection

Background:

  • Vulnerable Road Users (VRUs) face heightened risks due to vehicle agility and limited safety features.
  • Anticipating VRU behavior for automatic safety is challenging.
  • Vehicle-to-Anything (V2X) communication data offers potential for ML-driven safety solutions.

Purpose of the Study:

  • To propose and evaluate a VRU (motorcyclist) collision prediction system.
  • To leverage Machine Learning (ML) on Vehicle-to-Anything (V2X) data for accident prediction.
  • To assess the system's performance in simulated traffic scenarios.

Main Methods:

  • Utilized stacked unidirectional Long Short-Term Memorys (LSTMs) for collision prediction.
  • Employed the VEINS simulation framework, integrating Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3).
  • Generated and analyzed V2X communication data from simulated VRU (motorcyclist) interactions.

Main Results:

  • Successfully predicted 96% (Scenario A) and 95% (Scenario B) of simulated collisions.
  • Achieved Average Prediction Times (APT) of 4.53s (Scenario A) and 4.44s (Scenario B).
  • Reported Correct Decision Percentages (CDP) of 41% (Scenario A) and 43% (Scenario B) with 78 and 68 False Positives (FPs), respectively.

Conclusions:

  • ML applied to V2X data effectively predicts the majority of simulated VRU accidents.
  • High numbers of False Positives (FPs) currently preclude automatic safety interventions.
  • Collision avoidance relies on manual driver action based on system predictions.