Derivation of motion equations for collision detection

In summary, the conversation discusses the equations used in a car-accident prediction model, specifically the mintime and mindist equations. These equations are derived using the initial position and speed of two points and the distance between them is calculated using Pythagoras. The minimum possible distance and time can then be solved for, by setting the time-derivative to zero. Plugging the minimum time into the distance equation yields the minimum distance.
  • #1
M Omran
2
1
Dear all,

I am working on a car-accident prediction model. I found the following equations here that worked in my model. However, I could not figure out how the equations were derived. Could anyone help me in understanding the derivation of the mintime and mindist equations (Steps 3 and 5 below).

1. For each car/object/point, you know the following:

initial x position
speed in x direction
initial y position
speed in y direction

2. For two points a and b then, let's define the following variables:

xa0: initial x position of point A
xat: speed in x direction of point A
ya0: initial y position of point A
yat: speed in y direction of point A

xb0 initial x position of point B
xbt speed in x direction of point B
yb0 initial y position of point B
ybt speed in y direction of point B

3. The distance between to points is given by Pythagoras. By expressing this distance as a function of time (based on the given variables above), then we can solve for the minimum possible distance. If we do this then we get a formula which will give us the time of minimum distance. The result is the following formula:

mintime =
-(xa0*xat - xat*xb0 - (xa0 - xb0)*xbt + ya0*yat - yat*yb0 - (ya0 - yb0)*ybt)
/
(xat^2 - 2*xat*xbt + xbt^2 + yat^2 - 2*yat*ybt + ybt^2)

4. The distance at any time t is given by the following expression:

dist = sqrt((t*xat - t*xbt + xa0 - xb0)^2 + (t*yat - t*ybt + ya0 - yb0)^2)

It depends on the given variables in addition to t.

5. To calculate the minimum distance then, you just evaluate it using the minimum time you calculated previously. So with t = mintime:

mindist = sqrt(
(mintime*xat - mintime*xbt + xa0 - xb0)^2
+
(mintime*yat - mintime*ybt + ya0 - yb0)^2
)

Many thanks in advance
 
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  • #2
(4) is clear?
Calculate its time-derivative. At the minimal distance, the derivative is zero. That gives you an equation that can be solved to find the time.

(5) is just plugging the time from (3) into (4) and simplification of the expression.
 
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  • #3
mfb said:
(4) is clear?
Calculate its time-derivative. At the minimal distance, the derivative is zero. That gives you an equation that can be solved to find the time.

(5) is just plugging the time from (3) into (4) and simplification of the expression.

Thanks a million for the crystal clear answer. It is much appreciated.
 
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FAQ: Derivation of motion equations for collision detection

1. What is the purpose of deriving motion equations for collision detection?

The purpose of deriving motion equations for collision detection is to accurately predict the movement of objects and detect potential collisions in a given system. This is important in various fields such as physics, engineering, and computer graphics, where understanding and preventing collisions is crucial for the overall functioning and safety of the system.

2. How are motion equations derived for collision detection?

Motion equations for collision detection are typically derived using principles of classical mechanics, such as Newton's laws of motion. These equations take into account factors such as mass, velocity, and acceleration to determine the trajectories of objects and the likelihood of collisions between them.

3. Are there different types of motion equations for collision detection?

Yes, there are various types of motion equations for collision detection depending on the specific system and type of collision being considered. Some common types include linear motion equations, rotational motion equations, and elastic collision equations.

4. How accurate are motion equations for collision detection?

The accuracy of motion equations for collision detection depends on several factors, such as the complexity of the system and the precision of the input data. In general, these equations can provide a good estimation of the motion and potential collisions, but they may not account for all external factors and uncertainties.

5. Can motion equations for collision detection be applied to real-world scenarios?

Yes, motion equations for collision detection can be applied to real-world scenarios. In fact, they are commonly used in fields such as engineering and computer graphics to model and simulate the behavior of objects and systems. However, they may need to be adjusted or combined with other equations to accurately represent the complexities of real-world situations.

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