System and methods for detecting vehicle braking events using data from fused sensors in mobile devices

Patent metadata

Field	Value
Publication number	US-9809159-B1
Application number	US-201615391986-A
Country	US
Kind code	B1
Filing date	Dec 28, 2016
Priority date	Dec 28, 2016
Publication date	Nov 7, 2017
Grant date	Nov 7, 2017

Abstract

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

First claim

What is claimed is:

1. A braking event detection system comprising:
at least one processor; and
memory storing computer-readable instructions, that when executed by the at least one processor, cause the system to:
collect, by a sensor data collection device of the system, raw sensor data from sensors associated with a mobile device within a vehicle during a window of time using a polling frequency, wherein the sensors comprise a GPS receiver, an accelerometer, and a gyroscope, wherein the raw sensor data comprises information relating to a location, a speed, and an acceleration of the vehicle, and wherein the polling frequency is determined based on at least one of: the speed of the vehicle, a battery status of the mobile device, traffic information within a first threshold radius of the vehicle, and weather information within a second threshold radius of the vehicle;
process, by a sensor data processing device of the system, the raw sensor data collected from the sensors associated with the mobile device to remove duplicate data points and generate processed sensor data;
apply, by a braking event classification device of the system, a classification machine learning algorithm to the raw sensor data and processed sensor data to determine that a window should be classified as a braking event, wherein the classification machine learning algorithm is stored in a braking event classification model of the system; and
generate and transmit, by a braking event notification device of the system, a notification to at least one of: a second mobile device within a first predetermined distance of the vehicle and a second vehicle within a second predetermined distance of the vehicle relating to the braking event of the vehicle.

2. The system of claim 1 wherein processing the raw sensor data collected from the sensors associated with the mobile device to remove duplicate data points includes:
replacing a duplicate data point with an average value of a first data point and a second data point, wherein the first data point immediately precedes the duplicate data point, and wherein the second data point immediately follows the duplicate data point.

3. The system of claim 1 , further including instructions that, when executed by the at least one processor, cause the system to:
perform, by a sensor data calibration device of the system, an alignment of a first axis, a second axis, and a third axis of the raw sensor data with an x-axis, a y-axis, and a z-axis of a reference frame of the vehicle based, at least in part, on a vector indicating acceleration due to gravity and a detection of acceleration of the vehicle along one of the first axis, the second axis, and the third axis.

4. The system of claim 1 , further including instructions that, when executed by the at least one processor, cause the system to:
collect, by the sensor data collection device, supplemental sensor data from sensors associated with the vehicle during the window of time;
process, by the sensor data processing device, the supplemental sensor data collected from the sensors associated with the vehicle to remove duplicate data points; and
apply, by the braking event classification device, the classification machine learning algorithm to the raw sensor data, the processed sensor data, and the supplemental sensor data to determine that the window should be classified as a braking event.

5. The system of claim 1 , further including instructions that, when executed by the at least one processor, cause the system to:
apply, by the braking event classification device, the classification machine learning algorithm to determine a probability that the raw sensor data and the processed sensor data associated with the window is a braking event; and
determine, by the braking event classification device, that the probability is greater than a probability threshold.

6. The system of claim 1 , further including instructions that, when executed by the at least one processor, cause the system to:
transmit, by the braking event notification device, the notification via a short-range communication protocol.

7. The system of claim 1 wherein the classification machine learning algorithm comprises a random forest model.

8. A method comprising:
collecting, by a sensor data collecting device of a braking event detection system, raw sensor data from sensors associated with a mobile device within a vehicle during a window of time using a polling frequency, wherein the sensors comprise a GPS receiver, an accelerometer, and a gyroscope, wherein the sensor data comprises information relating to a location, a speed, and an acceleration of the vehicle, and wherein the polling frequency is determined based on at least one of: the speed of the vehicle, a battery status of the mobile device, traffic information within a first threshold radius of the vehicle, and weather information within a second threshold radius of the vehicle;
processing, by a sensor data processing device of the system, the raw sensor data collected from the sensors associated with the mobile device to remove duplicate data points and generating processed sensor data;
applying, by a braking event classification device of the system, a classification machine learning algorithm to the raw sensor data and processed sensor data to determine that a window should be classified as a braking event, wherein the classification machine learning algorithm is stored in a braking event classification model of the system; and
generating and transmitting, by a braking event notification device of the system, a notification to at least one of: a second mobile device within a first predetermined distance of the vehicle and a second vehicle within a second predetermined distance of the vehicle relating to the braking event of the vehicle.

9. The method of claim 8 , further comprising:
replacing a duplicate data point with an average value of a first data point and a second data point, wherein the first data point immediately precedes the duplicate data point, and wherein the second data point immediately follows the duplicate data point.

10. The method of claim 8 , further comprising:
performing an alignment of a first axis, a second axis, and a third axis of the raw sensor data with an x-axis, a y-axis, and a z-axis of a reference frame of the vehicle based, at least in part, on a vector indicating acceleration due to gravity and a detection of large magnitudes of the speed and the acceleration of the vehicle along one of the first axis, the second axis, and the third axis.

11. The method of claim 8 , further comprising:
collecting, by the sensor data collection device, supplemental sensor data from sensors associated with the vehicle during the window of time;
processing, by the sensor data processing device, the supplemental sensor data collected from the sensors associated with the vehicle to remove duplicate data points; and
applying, by the braking event classification device, the classification machine learning algorithm to the raw sensor data, the processed sensor data, and the supplemental sensor data to determine that a window should be classified as a braking event.

12. The method of claim 8 , further comprising:
applying, by the braking event classification device, the classification machine learning algorithm to determine a probability that the raw sensor data associated with the window is a braking event; and
determining, by the braking event classification device, that the probability is greater than a probability threshold.

13. The method of claim 8 , further comprising:
transmitting, by the braking event notification device, the notificat

Assignees

• Allstate Insurance Co

Inventors

• Snyder Jared S
• Ferguson Dana

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• B60T17/22 Primary

  Devices for monitoring or checking brake systems; Signal devices · CPC title

• G06Q40/08

  Insurance · CPC title

• G06N99/005

  Physics · mapped topic

• B60Q1/44

  for indicating braking action {or preparation for braking, e.g. by detection of the foot approaching the brake pedal} · CPC title