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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

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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...
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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...
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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...
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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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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...
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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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Determination of functional scenarios for intersection collisions.

L Garrett Bangert1, Theodore Lubash1, John M Scanlon2

  • 1Virginia Tech, United States.

Accident; Analysis and Prevention
|October 4, 2023
PubMed
Summary

This study identified 44 functional intersection crash configurations for advanced driver assist systems (ADAS) and automated driving systems (ADS) safety evaluation. These clusters represent real-world crashes to improve testing scenarios.

Keywords:
ADSCISSI-ADASIntersection crashesUnsupervised decision trees

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Area of Science:

  • Automotive Engineering
  • Traffic Safety Research
  • Artificial Intelligence in Transportation

Background:

  • Intersection crashes cause significant economic and societal damages.
  • Advanced driver assist systems (I-ADASs) and automated driving systems (ADS) aim to mitigate these crashes.
  • Developing comprehensive simulation testing for I-ADASs and ADS is challenging due to the vast parameter space of intersection scenarios.

Purpose of the Study:

  • To identify functional intersection crash configurations for I-ADAS and ADS safety evaluation.
  • To condense complex intersection crash scenarios into digestible, executable conditions for simulation testing.

Main Methods:

  • Utilized an unsupervised decision tree model to group similar crash scenarios based on critical features.
  • Analyzed two-vehicle intersection crash data from the Crash Investigation Sampling System (CISS) (2017-2020).
  • Pruned decision tree branches to reduce overfitting and improve model performance, identifying key features like two-way-left-turn-lane (TWLTL) presence and traffic control device functionality.

Main Results:

  • Identified 44 distinct functional intersection crash configurations after model pruning.
  • Classified crashes into five main categories: straight-crossing path, left-turn-across-path/opposite direction, other 4-legged intersections, roundabout/multi-leg intersections, and 3-legged intersections.
  • The largest cluster involved straight-crossing path crashes at 4-legged, undivided intersections with functional traffic control and no lane violations, with variations including unexpected vehicle maneuvers.

Conclusions:

  • The 44 identified crash configurations provide a compact representation of police-reported intersection crashes.
  • These configurations can enhance the robustness of I-ADAS and ADS intersection safety testing.
  • Future research can use these clusters to generate realistic initial conditions and behaviors for evaluating I-ADAS and ADS performance.