System and method for adverse vehicle event determination

• US 10,453,150 B2
• Filed: 06/18/2018
• Issued: 10/22/2019
• Est. Priority Date: 06/16/2017
• Status: Active Grant

• Current Assignee: Nauto, Inc.
• Original Assignee: Nauto, Inc.
• Inventors: Thompson, Alex, Sankaran, Shobana, Hornbaker, Charles, Heck, Stefan
• Primary Examiner(s): Anderson, Mike
• Application Number:
  US16/011,013
• Publication Number:
  US 20180365772A1
• Time in Patent Office: 491 Days
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A method for determining an adverse vehicle event, including: sampling sensor data an onboard vehicle system coupled to an ego-vehicle; at the onboard vehicle system, extracting a set of event parameters from the sensor data, wherein the vehicle event data is associated with a vehicle event occurring within the time interval; computing a loss probability based on the set of event parameters in response to the loss probability exceeding a threshold probability, transforming the set of event parameters into insurance claim data; and automatically transmitting the insurance claim data to an endpoint, wherein the endpoint is determined based on the participant identifier.

Claims

1. A method for automatically determining insurance claim data, comprising:

sampling interior image data over a time interval using an interior-facing camera of an onboard vehicle system rigidly coupled to an ego-vehicle;

contemporaneously sampling exterior image data over the time interval using an exterior-facing camera of the onboard vehicle system;

at a processor of the onboard vehicle system, extracting vehicle event data from the interior image data and the exterior image data, wherein the vehicle event data is associated with a vehicle event occurring within the time interval, wherein extracting the vehicle event data comprises extracting a set of event parameters, including extracting;

a first event parameter comprising a plurality of participant identifiers, wherein each event participant identifier is associated with insurance policy data corresponding to a participant in the vehicle event;

a second event parameter comprising a peak acceleration of the ego-vehicle during the time interval, wherein extracting the peak acceleration of the ego vehicle comprises;

determining a first velocity of each object depicted in a first frame of the exterior image data;

determining a second velocity of each object depicted in a second frame of the exterior image data, wherein the first and second velocity are defined in three dimensions relative to the ego-vehicle; and

calculating the peak acceleration of the ego-vehicle based on a comparison between the first velocity of each object and the second velocity of each object; and

a third event parameter comprising a peak attitudinal shift of the ego-vehicle during the time interval, wherein extracting the peak attitudinal shift of the ego-vehicle comprises;

determining a first position of each object depicted in the first frame of the exterior image data at a first time point within the time interval, wherein the first position is defined in two dimensions relative to a boundary of the frame;

determining a second position of each object depicted in the second frame of the exterior image data at a second time point within the time interval subsequent to the first time point, wherein the second position is defined in two dimensions relative to the boundary of the frame; and

calculating the peak attitudinal shift of the ego-vehicle based on a comparison between the first position of each object and the second position of each object;

calculating a loss probability based on the peak acceleration and the peak attitudinal shift;

in response to the loss probability exceeding a threshold probability;

determining a fault status for each of the plurality of participant identifiers, and

transforming the set of event parameters and the fault status for each of the plurality of participant identifiers into insurance claim data;

prompting a user corresponding to one participant identifier to provide a transmission instruction at a mobile device associated with the user; and

in response to receiving the transmission instruction, automatically transmitting the insurance claim data to an endpoint at a remote computing system.

2. The method of claim 1, wherein the onboard vehicle system further comprises:

an accelerometer, and further comprising;

sampling an accelerometer signal from the accelerometer at a time point prior o the time interval, and

sampling the interior image data and the exterior image data in response to the accelerometer signal exceeding a threshold value.

3. The method of claim 2, wherein, at the time point, the ego-vehicle is parked.

4. The method of claim 1, wherein calculating the loss probability based on the peak acceleration and the peak attitudinal shift comprises calculating the loss probability using an adverse vehicle event model, and

wherein the adverse vehicle event model is trained based on a corpus of event parameters extracted from a population of ego-vehicles and corresponding insurance claim data associated with the population of ego-vehicles.

5. The method of claim 1, further comprising:

training a claims adjustment module based on vehicle event data extracted from interior image data and exterior image data sampled by one or more onboard vehicle systems of a population of ego-vehicles, and

wherein transforming the set of event parameters and the fault status for each of the plurality of participant identifiers into insurance claim data is performed using the claim adjustment module.