# US 12,110,033 B2 · Methods and systems to optimize vehicle event processes

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- Number: US 12,110,033 B2
- Document ID: US12110033B2
- Status: Granted
- Kind: B2
- Application number: 17/444,013
- Filed: 2021-07-29
- Published: 2023-02-02
- Granted: 2024-10-08
- Earliest claimed: 2021-07-29
- Assignees: GM Global Technology Operations LLC
- Applicants: GM Global Technology Operations LLC
- Inventors: Andrew P Rose; Donald K Grimm; Fan Bai
- Web page: https://patentgrep.com/patent/US12110033B2

## CPC

- B60W 50/0205: {Diagnosing or detecting failures; Failure detection models}
- B60W 40/08: related to drivers or passengers
- G06F 16/9035: Filtering based on additional data, e.g. user or group profiles
- G06N 20/00: Machine learning
- G06Q 40/08: Insurance
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G 1/0133: {for classifying traffic situation}
- G08G 1/0141: {for traffic information dissemination}
- G08G 1/0145: {for active traffic flow control}
- G08G 1/205: {Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental}
- B60W 2050/021: {Means for detecting failure or malfunction}
- B60W 2050/0215: {Sensor drifts or sensor failures}
- B60W 2540/221: Physiology, e.g. weight, heartbeat, health or special needs
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- G08G 1/164: {Centralised systems, e.g. external to vehicles}
- G08G 1/167: {Driving aids for lane monitoring, lane changing, e.g. blind spot detection}
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE

## Abstract

In various embodiments, methods and systems are provided for processing data pertaining to a vehicle event for a vehicle. In accordance with an exemplary embodiment, vehicle sensor data is obtained from one or more vehicle sensors pertaining to the vehicle event. Also in an exemplary embodiment, an assessment of the vehicle event is determined via a processor, including as to a fault or a severity, or both associated with the vehicle event, based on the vehicle sensor data.

## Claims

### Claim 1

A method for processing data pertaining to a vehicle event for a vehicle, the method including:
obtaining vehicle sensor data from one or more vehicle sensors pertaining to the vehicle event;
obtaining, from a computer memory, an event database of prior vehicle events;
determining, via a processor, an assessment of the vehicle event, including a fault or a severity, or both associated with the vehicle event, based on the vehicle sensor data, wherein the step of determining the assessment of the vehicle event comprises:
generating, via the processor, a damage assessment model for the vehicle resulting from the vehicle event, using the sensor data;
generating, via the processor, a probability model, based on the damage assessment model and the event database, that assesses probabilities to different possible outcomes for the vehicle as a result of the vehicle event; and
determining, via the processor based on the probability model, service requirements for the vehicle, including for parts and labor for repair of the vehicle, resulting from the vehicle event as determined using the probability model; and
automatically taking a vehicle control action, via the processor based on the assessment of the vehicle event and the service requirements using the probability model.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
generating the database of prior vehicle events, via one or more computer processors, via machine learning.

### Claim 3 (depends on 1)

The method of claim 1, wherein:
the step of obtaining the vehicle sensor data comprises obtaining the vehicle sensor data from the one or more vehicle sensors that are built into the vehicle; and
the step of determining the assessment comprises determining, via the processor, the assessment using the vehicle sensor data from the one or more vehicle sensors that are built into the vehicle.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
notifying, via instructions provided by the processor, an insurance provider of the vehicle event along with the assessment of the vehicle event.

### Claim 5 (depends on 1)

The method of claim 1, wherein:
the step of obtaining the vehicle sensor data comprises:
obtaining first sensor data from the one or more vehicle sensors before the vehicle event;
obtaining second sensor data from the one or more vehicle sensors during the vehicle event; and
obtaining third sensor data from the one or more vehicle sensors after the vehicle event; and
the step of determining the assessment of the vehicle event comprises determining, via the processor, the assessment of the vehicle event using each of the first sensor data, second sensor data, and third sensor data.

### Claim 6 (depends on 1)

The method of claim 1, wherein the step of determining the assessment of the vehicle event comprises determining, via the processor, a cause of fault of the vehicle event, based on the vehicle sensor data.

### Claim 7 (depends on 6)

The method of claim 6, wherein:
the step of obtaining the vehicle sensor data includes obtaining at least some of the vehicle sensor data before the vehicle event; and
the step of determining the cause of fault of the vehicle event is made by the processor based on a driving behavior of a driver of the vehicle, based on the at least some of the vehicle sensor data obtained prior to the vehicle event.

### Claim 8 (depends on 6)

The method of claim 6, wherein the cause of the vehicle event is determined by the processor based on an attentiveness of a driver of the vehicle as reflected in the sensor data.

### Claim 9 (depends on 8)

The method of claim 8, wherein the cause of the vehicle event is determined by the processor based also on a curvature of a roadway on which the vehicle is travelling, as reflected in the sensor data.

### Claim 10 (depends on 1)

The method of claim 1, wherein the step of determining the assessment of the vehicle event comprises determining, via the processor, the assessment of damage to the vehicle, based on the vehicle sensor data, wherein the step of obtaining the vehicle sensor data includes obtaining at least some of the vehicle sensor data during the vehicle event; and the step of determining the assessment of damage to the vehicle is made by the processor based on an impact to the vehicle, including a severity and a location of the impact, based on the at least some of the vehicle sensor data obtained during the vehicle event.

### Claim 11 (depends on 1)

The method of claim 1, wherein the step of determining the assessment of the vehicle event comprises determining, via the processor, an assessment of injury to one or more passengers of the vehicle, based on the vehicle sensor data.

### Claim 12 (depends on 11)

The method of claim 11, wherein:
the step of obtaining the vehicle sensor data includes obtaining at least some of the vehicle sensor data during the vehicle event; and
the step of determining the assessment of injury is made by the processor based on an impact to the vehicle, including a severity and a location of the impact, based on the at least some of the vehicle sensor data obtained during the vehicle event.

### Claim 13 (depends on 1)

The method of claim 1, further comprising:
determining, via the processor, an estimated cost for repairing the vehicle as a result of the vehicle event, based on the parts and labor required for the repairing of the vehicle and respective costs associated therewith.

### Claim 14 (depends on 13)

The method of claim 13, further comprising:
providing, via instructions provided by the processor, to a third party, a parts manifest that includes the parts required for the repairing of the vehicle.

### Claim 15 (depends on 13)

The method of claim 13, further comprising:
securing, via instructions provided by the processor, a service appointment for the repairing of the vehicle using the parts manifest.

### Claim 16 (depends on 15)

The method of claim 15, further comprising:
securing, via instructions provided by the processor, a rental car for a user of the vehicle based on the service appointment.

### Claim 17 (depends on 1)

The method of claim 1, wherein the probability model comprises one or more probability frequency diagrams, functions, or tables.

### Claim 18

A system for processing data pertaining to a vehicle event for a vehicle, the system comprising:
one or more vehicle sensors configured to generate vehicle sensor data pertaining to the vehicle event; and
a processor that is coupled to the one or more vehicle sensors and configured to at least facilitate:
obtaining, from a computer memory, an event database of prior vehicle events generated via machine learning;
determining an assessment of the vehicle event, including a fault or a severity, or both associated with the vehicle event, based on the vehicle sensor data, including by:
generating a damage assessment model for the vehicle resulting from the vehicle event, using the sensor data;
generating a probability model, based on the damage assessment model and the event database, that assesses probabilities to different possible outcomes for the vehicle as a result of the vehicle event; and
determining, based on the probability model, service requirements for the vehicle, including for parts and labor for repair of the vehicle, resulting from the vehicle event as determined using the probability model; and
automatically taking a vehicle control action, based on the assessment of the vehicle event and the service requirements using the probability model.

### Claim 19 (depends on 18)

The system of claim 18, wherein:
the one or more vehicle sensors are configured to generate:
first sensor data before the vehicle event;
second sensor data during the vehicle event; and
third sensor data after the vehicle event;
the processor is configured to at least facilitate determining the assessment of the vehicle event using each of the first sensor data, second sensor data, and third sensor data; and
the processor is configured to at least facilitate determining a cause of fault, a damage to the vehicle, and an injury to one or more passengers of the vehicle from the vehicle event, based on each of the first sensor data, second sensor data, and third sensor data.

### Claim 20

A vehicle comprising:
a body;
one or more vehicle sensors that are built into the body and configured to generate vehicle sensor data pertaining to a vehicle event; and
a processor that is coupled to the one or more vehicle sensors and configured to at least facilitate:
obtaining, from a computer memory, an event database of prior vehicle events generated via machine learning;
determining an assessment of the vehicle event, including a fault or a severity, or both associated with the vehicle event, based on the vehicle sensor data, including by:
generating a damage assessment model for the vehicle resulting from the vehicle event, using the sensor data;
generating a probability model, based on the damage assessment model and the event database, that assesses probabilities to different possible outcomes for the vehicle as a result of the vehicle event; and
determining, based on the probability model, service requirements for the vehicle, including for parts and labor for repair of the vehicle, resulting from the vehicle event as determined using the probability model; and
automatically taking a vehicle control action, based on the assessment of the vehicle event and the service requirements using the probability model.