# US 12,603,006 B2 · Crowdsourcing road conditions from abnormal vehicle events

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- Number: US 12,603,006 B2
- Document ID: US12603006B2
- Status: Granted
- Kind: B2
- Application number: 18/523,777
- Filed: 2023-11-29
- Published: 2024-03-21
- Granted: 2026-04-14
- Earliest claimed: 2020-02-07
- Applicants: Lodestar Licensing Group LLC
- Inventors: Junichi Sato
- Web page: https://patentgrep.com/patent/US12603006B2

## CPC

- G08G 1/012: {from other sources than vehicle or roadside beacons, e.g. mobile networks}
- G08G 1/164: {Centralised systems, e.g. external to vehicles}
- 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 1/0141: {for traffic information dissemination}
- G08G 1/091: {Traffic information broadcasting (broadcasting communication H04H)}
- G08G 1/096775: {where the origin of the information is a central station}
- G08G 1/127: to a central station {; Indicators in a central station}
- G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes}
- 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})

## Abstract

A system for crowdsourcing reporting of road conditions from abnormal vehicle events. Abnormal vehicle events (such as sudden braking, sharp turns, evasive actions, pothole impact, etc.) can be detected and reported to a road condition monitoring system (RCMS). The RCMS can identify patterns in reported road conditions to generate advisory information or instructions for vehicles and users of vehicles. For example, suspected obstacles can be identified and used to instruct a driver or a vehicle to slow down gradually to avoid sudden braking and sharp turns. In some examples, a vehicle can have a camera that can upload an image of a suspected obstacle (e.g., a pothole) to allow the positive identification of a road problem. This provides the RCMS with more confidence to take a corrective action, such as an automated call to a road repair service.

## Claims

### Claim 1

A system comprising:
an artificial neural network (ANN); and
at least one processor configured to:
receive image data from a first plurality of vehicles, the image data associated with conditions for roads on which the vehicles are driving;
train the ANN using the image data; and
send respective advisory data based on an output of the ANN to each of a second plurality of vehicles.

### Claim 2 (depends on 1)

The system of claim 1, wherein the image data is collected by cameras of the first plurality of vehicles.

### Claim 3 (depends on 1)

The system of claim 1, wherein the processor is further configured to receive first data from at least one sensor external to the first plurality of vehicles.

### Claim 4 (depends on 1)

The system of claim 1, wherein the processor is further configured to receive first data from a computing device separate from the first plurality of vehicles.

### Claim 5 (depends on 1)

The system of claim 1, wherein the ANN is trained to recognize road conditions.

### Claim 6 (depends on 3)

The system of claim 3, wherein the sensor is a camera.

### Claim 7 (depends on 3)

The system of claim 3, wherein the ANN is trained further using the first data.

### Claim 8 (depends on 1)

The system of claim 1, wherein an output of the ANN is used to evaluate a respective road condition for each of the second plurality of vehicles, and the respective advisory data is based on the evaluation.

### Claim 9

An apparatus comprising:
at least one sensor configured to generate first data in response to movement of a vehicle; and
a controller configured to:
determine second data associated with operation of the vehicle; and
in response to detecting the movement of the vehicle exceeding a threshold, provide the first data and the second data as input to an artificial neural network (ANN).

### Claim 10 (depends on 9)

The apparatus of claim 9, wherein the first data is an image of a road.

### Claim 11 (depends on 9)

The apparatus of claim 9, wherein the first data is a location.

### Claim 12 (depends on 9)

The apparatus of claim 9, wherein the controller is further configured to:
perform, based on output from the ANN, an action by the vehicle.

### Claim 13

A method comprising:
receiving, from a computing device, first data regarding a first vehicle, wherein the computing device is stationary and the first vehicle is moving;
receiving, from the first vehicle, first image data and position data;
training an artificial neural network (ANN) using the first data, first image data, position data, and second image data received from a second vehicle; and
sending advisory data generated by the ANN to the first and second vehicles.

### Claim 14 (depends on 13)

The method of claim 13, further comprising:
determining a road hazard based on an output from the ANN; and
determining that the second vehicle is approaching the road hazard, wherein the advisory data is sent to the second vehicle in response to determining that the second vehicle is approaching the road hazard.

### Claim 15 (depends on 13)

The method of claim 13, wherein the computing device includes a camera used to collect the first data.

### Claim 16 (depends on 9)

The apparatus of claim 9, wherein the sensor is configured in a mobile device.