# US 2018/0022348 A1 · Methods and systems for determining lane health from an autonomous vehicle

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2018/0022348 A1
- Document ID: US20180022348A1
- Status: Published application
- Kind: A1
- Application number: 15/706,197
- Filed: 2017-09-15
- Published: 2018-01-25
- Earliest claimed: 2017-09-15
- Assignees: GM Global Technology Operations LLC
- Applicants: GM Global Technology Operations LLC
- Inventors: Kevin Chu; Shiva Ghose
- Web page: https://patentgrep.com/patent/US20180022348A1

## CPC

- B60W 40/06: Road conditions
- B60W 60/00253: {Taxi operations}
- G01C 21/28: with correlation of data from several navigational instruments
- G01C 21/3415: {Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents}
- G05D 1/0274: {using mapping information stored in a memory device (navigation using map-matching G01C21/30)}
- G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road}
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/096725: {where the received information generates an automatic action on the vehicle control}
- G08G 1/096741: {where the source of the transmitted information selects which information to transmit to each vehicle}
- G08G 1/096791: {where the origin of the information is another vehicle}
- G08G 1/161: {Decentralised systems, e.g. inter-vehicle communication}
- G08G 1/165: {for passive traffic, e.g. including static obstacles, trees}
- G08G 1/167: {Driving aids for lane monitoring, lane changing, e.g. blind spot detection}
- B60W 2420/403: Image sensing, e.g. optical camera
- B60W 2420/408: Radar; Laser, e.g. lidar
- B60W 2420/54: Audio sensitive means, e.g. ultrasound
- B60W 2552/00: Input parameters relating to infrastructure
- B60W 2552/05: Type of road, e.g. motorways, local streets, paved or unpaved roads
- B60W 2552/35: Road bumpiness, e.g. potholes
- B60W 2552/53: Road markings, e.g. lane marker or crosswalk
- B60W 2556/35: Data fusion
- B60W 2556/50: of positioning data, e.g. GPS [Global Positioning System] data
- G06F 18/24143: {Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]}
- 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
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)
- G05D: SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- 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})
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Systems and method are provided for controlling an autonomous vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data associated with an environment of a first autonomous vehicle; determining, by a processor, lane health information based on the sensor data; and selectively controlling, by a processor, a second autonomous vehicle based on the lane health information.

## Claims

### Claim 1

A method of controlling an autonomous vehicle, comprising:
receiving, by a processor, sensor data associated with an environment of a first autonomous vehicle;
determining, by a processor, lane health information based on the sensor data; and
selectively controlling, by a processor, a second autonomous vehicle based on the lane health information.

### Claim 2 (depends on 1)

The method of claim 1, wherein the sensor data is obtained by at least one of a lidar, a radar, and a camera that senses an environment of the autonomous vehicle.

### Claim 3 (depends on 1)

The method of claim 1, further comprising processing the sensor data to determine map data associated with the environment, and wherein the determining the lane health is based on the map data associated with the environment.

### Claim 4 (depends on 1)

The method of claim 1, further comprising processing the sensor data to determine observation data associated with the vehicle, wherein the observation data includes at least one of vehicle data, road data, and activity data.

### Claim 5 (depends on 1)

The method of claim 1, further comprising processing the sensor data to determine object data associated with the environment, and wherein the determining the lane health is based on the object data associated with the environment.

### Claim 6 (depends on 5)

The method of claim 5, wherein the object data includes at least one of object speed data, object acceleration data, and object classification data.

### Claim 7 (depends on 1)

The method of claim 1, further comprising processing the sensor data to determine vehicle location data in relation to the environment, and wherein the determining the lane health is based on the vehicle location data in relation to the environment.

### Claim 8 (depends on 1)

The method of claim 1, further comprising processing the lane health information from the first vehicle with lane health information from at least one other vehicle to determine an overall lane health, and wherein the selectively controlling is based on the overall lane health.

### Claim 9 (depends on 1)

The method of claim 1, wherein the lane health information indicates the health of a lane of a road.

### Claim 10 (depends on 1)

The method of claim 1, further comprising at least one of determining if multiple riders can ride together, determining when to dispatch an autonomous vehicle, determining which autonomous vehicle to dispatch from a plurality of autonomous vehicles, and determining a route including which lane of the route for the autonomous vehicle, and wherein the selectively controlling is based on the determining.

### Claim 11

A system for controlling an autonomous vehicle, comprising:
a first non-transitory module that, by a processor, receives sensor data associated with an environment of a first autonomous vehicle;
a second non-transitory module that, by a processor, determines lane health information based on the sensor data; and
a third non-transitory module that, by a processor, selectively controls a second autonomous vehicle based on the lane health information.

### Claim 12 (depends on 11)

The system of claim 11, wherein the sensor data is obtained by at least one of a lidar, a radar, and a camera that senses an environment of the autonomous vehicle.

### Claim 13 (depends on 11)

The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine map data associated with the environment, and wherein the second non-transitory module determines the lane health based on the map data associated with the environment.

### Claim 14 (depends on 11)

The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine observation data associated with the vehicle, wherein the observation data includes at least one of vehicle data, road data, and activity data.

### Claim 15 (depends on 11)

The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine object data associated with the environment, and wherein the second non-transitory module determines the lane health based on the object data associated with the environment.

### Claim 16 (depends on 15)

The system of claim 15, wherein the object data includes at least one of object speed data, object acceleration data, and object classification data.

### Claim 17 (depends on 11)

The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine vehicle location data in relation to the environment, and wherein the third non-transitory module determines the lane health based on the vehicle location data in relation to the environment.

### Claim 18 (depends on 11)

The system of claim 11, further comprising a fourth module that, by a processor, processes the lane health information from the first vehicle with lane health information from at least one other vehicle to determine an overall lane health, and wherein the third non-transitory module selectively controls based on the overall lane health.

### Claim 19 (depends on 11)

The system of claim 11, further comprising a fourth module that, by a processor, determines at least one of if multiple riders can ride together, determining when to dispatch an autonomous vehicle, which autonomous vehicle to dispatch from a plurality of autonomous vehicles, and a route including which lane of the route for the autonomous vehicle, and wherein the third non-transitory module selectively controls based on the determining.

### Claim 20

An autonomous vehicle, comprising:
at least one of a camera, a lidar, and a radar that senses an environment of the autonomous vehicle and that generates sensor data; and
a controller that, by a processor, receives the sensor data, determines lane health information based on the sensor data, and communicates the lane health information to a remote location for selectively controlling a second autonomous vehicle based on the lane health information.