Collision prediction based on different types of impact

Patent metadata

Field	Value
Publication number	US-2025002011-A1
Application number	US-202318216001-A
Country	US
Kind code	A1
Filing date	Jun 29, 2023
Priority date	Jun 29, 2023
Publication date	Jan 2, 2025
Grant date	—

Abstract

Implementations claimed and described herein provide systems and methods for generating a prediction for a likelihood of a collision based on a respective collision prediction algorithm for a particular type of impact. In one implementation, determining, by a collision prediction algorithm, a prediction score based on a subset of variables associated with the movement of a mobile device. The prediction score associated with a likelihood that the movement is associated with a particular type of impact associated with the first collision prediction algorithm. A prediction that the movement is not association with the first type of impact when the prediction score is below a threshold score is outputted.

First claim

What is claimed is:

1 . A system comprising:
one or more processors; a display with a user interface; and a memory unit storing computer-executable instructions, which when executed by the one or more processors, cause the system to:
store kinematic variables associated with movement of a mobile device;
extract a first subset of the kinematic variables based on a first time window associated with a first type of impact;
receive, by a first collision prediction algorithm, the first subset of variables;
determine, by the first collision prediction algorithm, a first prediction score based on the first subset of variables, the first prediction score associated with a likelihood that the movement is associated with the first type of impact, wherein the first type of impact is associated with the first collision prediction algorithm; and
output a prediction that the movement is not association with the first type of impact when the first prediction score is below a threshold score.

2 . The system of claim 1 , wherein the kinematic variables include at least one of global positioning system (GPS) speed variables, GPS altitude variables, and accelerometer magnitude variables.

3 . The system of claim 2 , wherein the kinematic variables are convolutional neural network (CNN) variables and the first collision prediction algorithm is a collision prediction CNN.

4 . The system of claim 3 , wherein the system is further caused to:
convert the kinematic variables into features that represent GPS speed and altitude properties and accelerometer magnitude properties associated with the movement of the mobile device, wherein the first prediction score is determined based on the features, and wherein the collision prediction CNN is trained to learn which features contribute most to predicting whether the movement is associated with the first type of impact.

5 . The system of claim 2 , wherein the global positioning system (GPS) speed variables and the GPS altitude variables are associated with a particular GPS sensor window of seconds relative to impact time and the accelerometer magnitude variables are associated with a particular accelerometer sensor window of seconds relative to impact time, wherein the particular GPS sensor window and the particular accelerometer sensor window are associated with the type of impact.

6 . The system of claim 1 , wherein the system is further caused to:
extract a second subset of the kinematic variables based on a second time window associated with a second type of impact; receive, by a second collision prediction algorithm, the second subset of variables; determine, by the second collision prediction algorithm, a second prediction score associated with a likelihood that the movement is associated with the second type of impact associated with the second collision prediction algorithm based on the second subset of variables; and output a prediction that the movement is association with the second type of impact when the second prediction score is at or above the threshold score.

7 . The system of claim 6 , wherein the first type of impact is selected from a group of at least: an amusement park ride impact, a skiing-based impact, a boat- or water-based impact, or an action sports type impact, and the second collision prediction algorithm is trained with true automobile collisions.

8 . The system of claim 7 , wherein the system is further caused to:
aggregating predictions from a plurality of collision prediction algorithms; and output a final prediction that the movement is associated with a collision when the second prediction score is at or above the threshold score and when a third prediction score associated with a third collision prediction algorithm trained with false positive data is below an associated threshold score.

9 . The system of claim 1 , wherein the system is further caused to:
train the first collision prediction algorithm with a training dataset comprising a dataset from counterfactual collisions where movement was not associated with the type of impact or actual historical instances associated with the type of impact.

10 . The system of claim 1 , wherein the first prediction score is based on a continuous scale between two numbers, wherein the continuous scale correlates to a continuous likelihood of collision.

11 . A computer-implemented method comprising:
training a first collision prediction algorithm with a training dataset comprising a dataset from counterfactual collisions where movement was not associated with a first type of impact and actual historical instances associated with the first type of impact; storing kinematic variables associated with the movement of a mobile device; extracting a first subset of the kinematic variables based on a first time window associated with the first type of impact; receiving, by the first collision prediction algorithm, the first subset of variables; determining, by the first collision prediction algorithm, a first prediction score based on the first subset of variables, the first prediction score associated with a likelihood that the movement is associated with the first type of impact, wherein the first type of impact is associated with the first collision prediction algorithm; and outputting a prediction that the movement is not association with the first type of impact when the first prediction score is below a threshold score.

12 . The computer-implemented method of claim 11 , wherein the kinematic variables include at least one of global positioning system (GPS) speed variables, GPS altitude variables, and accelerometer magnitude variables.

13 . The computer-implemented method of claim 12 , wherein the kinematic variables are convolutional neural network (CNN) variables and the first collision prediction algorithm is a collision prediction CNN.

14 . The computer-implemented method of claim 13 , further comprising:
converting the kinematic variables into features that represent GPS speed and altitude properties and accelerometer magnitude properties associated with the movement of the mobile device, wherein the first prediction score is determined based on the features, and wherein the collision prediction CNN is trained to learn which features contribute most to predicting whether the movement is associated with the first type of impact.

15 . The computer-implemented method of claim 12 , wherein the global positioning system (GPS) speed variables and the GPS altitude variables are associated with a particular GPS sensor window of seconds relative to impact time and the accelerometer magnitude variables are associated with a particular accelerometer sensor window of seconds relative to impact time, wherein the particular GPS sensor window and the particular accelerometer sensor window are associated with the type of impact.

16 . The computer-implemented method of claim 11 , further comprising:
extracting a second subset of the kinematic variables based on a second time window associated with a second type of impact; receiving, by a second collision prediction algorithm, the second subset of variables; determining, by the second collision prediction algorithm, a second prediction score associated with a likelihood that the movement is associated with the second type of impact associated with the second collision prediction algorithm based on the second subset of variables; and outputting a prediction that the movement is association with the second type of impact when the second prediction score is at or above the threshold score.

Assignees

• Allstate Insurance Co

Inventors

• Antony Lucas
• Rowe Ryan

Classifications

• G01S19/52

  Determining velocity · CPC title

• G01P15/00

  Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• B60R21/013

  including means for detecting collisions, impending collisions or roll-over · CPC title