SYSTEMS AND METHODS FOR SENSOR-BASED VEHICLE CRASH PREDICTION, DETECTION, AND RECONSTRUCTION

• US 20170210323A1
• Filed: 01/26/2017
• Published: 07/27/2017
• Est. Priority Date: 01/26/2016
• Status: Active Grant

• Current Assignee: Cambridge Mobile Telematics, Inc.
• Original Assignee: Truemotion, Inc.
• Inventors: Vaisman, Eddie, Shiebler, Dan, Cordova, Brad
• Granted Patent: US 11,220,258 B2
• Time in Patent Office: Days
• CPC Class Codes: B60R 2021/0027 Post collision measures, e.g. notifying emergency services
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/09675 where a selection from the received information takes place in the vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/205 Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

Abstract

Embodiments of the present invention relate to transportation systems. More particularly, embodiments relate to methods and systems of vehicle data collection by a user having a mobile device. In a particular embodiment, vehicle data (also termed herein “driving data” or “data”) is collected, analyzed and transformed, and combinations of collected data and transformed data are used in different ways, including, but not limited to, predicting, detecting, and reconstructing vehicle accidents.

Claims

1. A method of predicting vehicle accidents, the method comprising:

obtaining a plurality of measurements from one or more sensors of a mobile device in a vehicle during a drive;

obtaining contextual data for the plurality of measurements;

generating an accident likelihood metric using at least one of the plurality of measurements or the contextual data, wherein the accident likelihood metric represents a likelihood that the vehicle will be involved in an accident during the drive; and

displaying the accident likelihood metric on the mobile device.

2. The method of claim 1, wherein the contextual data includes at least one of weather data, road quality data, traffic data, unsafe driving path indicators, unsafe driving time indicators or a recommended route.

3. The method of claim 1, wherein the accident likelihood metric is generated using the plurality of measurements and the contextual data.

4. The method of claim 1, wherein the accident likelihood metric is generated further using driver-specific data, wherein the driver-specific data is specific to a driver of the vehicle during the drive.

5. The method of claim 4, wherein the driver-specific data includes at least one of past driving behavior of the driver or demographics of the driver.

6. A method of detecting vehicle accidents, the method comprising:

obtaining a plurality of measurements from a sensor of a mobile device in a vehicle during a drive;

obtaining an accident threshold value for the sensor, wherein the accident threshold value represents a minimum value obtained from the sensor that is indicative of an accident;

identifying a measurement in the plurality of measurements that exceeds the accident threshold value;

determining a fixed duration window corresponding to a first portion of the drive, wherein the first portion of the drive includes a first subset of the plurality of measurements, and wherein the first subset of the plurality of measurements includes the identified measurement;

processing the first subset of the plurality of measurements to determine a variable duration window corresponding to a second portion of the drive within the first portion of the drive, wherein the second portion of the drive includes a second subset of the first subset of the plurality of measurements, and wherein the second subset includes the identified measurement; and

analyzing the second subset to identify an accident in the vehicle during the drive.

7. The method of claim 6, wherein the sensor includes at least one of an accelerometer, a gyroscope, or a magnetometer.

8. The method of claim 6, further comprising, after identifying the accident in the vehicle during the drive:

automatically alerting a third party of the accident.

9. The method of claim 6, further comprising, after identifying the accident in the vehicle during the drive:

pushing a notification relevant to the accident to the mobile device.

10. The method of claim 6, further comprising, after identifying the accident in the vehicle during the drive:

disabling the sensor of the mobile device.

11. A method of reconstructing vehicle accidents, the method comprising:

obtaining a plurality of measurements from a sensor of a mobile device in a vehicle during a drive;

identifying an accident in the vehicle during the drive from the plurality of measurements;

identifying an accident time associated with the accident; and

at least one of;

analyzing the plurality of measurements obtained before the accident time to identify at least one prior event;

analyzing the plurality of measurements obtained during the accident time to identify at least one concurrent event;

or

analyzing the plurality of measurements obtained after the accident time to identify at least one subsequent event.

12. The method of claim 11, wherein identifying the accident in the vehicle includes:

obtaining an accident threshold value for the sensor, wherein the accident threshold value represents a minimum value obtained from the sensor that is indicative of the accident; and

identifying a measurement in the plurality of measurements that exceeds the accident threshold value.

13. The method of claim 12, wherein identifying the accident time associated with the accident includes:

identifying the accident time that is associated with the measurement that exceeds the accident threshold value.

14. The method of claim 13, further comprising:

transmitting at least one of the at least one prior event, the at least one concurrent event, or the at least one subsequent event to a third party.

15. The method of claim 11, further comprising:

determining fault for the accident using at least one of the at least one prior event, the at least one concurrent event, or the at least one subsequent event.