# US 10,121,367 B2 · Vehicle lane map estimation

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- Number: US 10,121,367 B2
- Document ID: US10121367B2
- Status: Granted
- Kind: B2
- Application number: 15/142,183
- Filed: 2016-04-29
- Published: 2017-11-02
- Granted: 2018-11-06
- Earliest claimed: 2016-04-29
- Assignees: Ford Global Technologies, LLC
- Applicants: Ford Global Technologies, LLC
- Inventors: Hassene Jammoussi; Pankaj Kumar
- Web page: https://patentgrep.com/patent/US10121367B2

## CPC

- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- B60W 30/09: Taking automatic action to avoid collision, e.g. braking and steering
- G01C 21/30: Map- or contour-matching
- G01C 21/3602: {Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera}
- G06T 11/65: on geographic maps
- G08G 1/0125: {Traffic data processing}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G 1/0141: {for traffic information dissemination}
- G08G 1/096716: {where the received information does not generate an automatic action on the vehicle control}
- G08G 1/096725: {where the received information generates an automatic action on the vehicle control}
- G08G 1/096775: {where the origin of the information is a central station}
- 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})
- 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)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A computer can receive, from a vehicle sensor, data about a plurality of second vehicles, define two or more vehicle clusters based on location data of second vehicles, each cluster including two or more of the second vehicles determined to be traveling in a same lane, identify two or more lane boundaries according to clusters, and use lane boundaries to generate a lane map.

## Claims

### Claim 1

A method, comprising:
receiving data, in a first vehicle from each of a plurality of second vehicles, including: a respective second vehicle geolocation and a second vehicle lateral distance from a lane boundary;
defining two or more vehicle clusters based on the received data, each cluster including two or more second vehicles determined to be traveling in a same lane;
using the defined two or more vehicle clusters, to identify two or more lane boundaries based at least in part on the received respective second vehicle lateral distance from the lane boundary; and
using the identified two or more lane boundaries to generate a lane map.

### Claim 2 (depends on 1)

The method of claim 1, wherein defining two or more vehicle clusters further includes:
determining a lane width based on distances of the second vehicles from one another; and
assigning, to a cluster, second vehicles determined to be traveling in a same lane based on the distances of the second vehicles from one another.

### Claim 3 (depends on 2)

The method of claim 2, wherein defining two or more vehicle clusters further includes determining a lane curvature.

### Claim 4 (depends on 3)

The method of claim 3, wherein determining the lane curvature includes fitting a curve to a plurality of travelled locations of a second vehicle.

### Claim 5 (depends on 3)

The method of claim 3, wherein determining the lane curvature is at least based on a statistical forecast of the lane curvature.

### Claim 6 (depends on 3)

The method of claim 3, further comprising receiving a yaw angle of the first vehicle, wherein determining the lane curvature is based at least in part on the yaw angle of the first vehicle.

### Claim 7 (depends on 2)

The method of claim 2, further comprising:
receiving a geolocation of the first vehicle; and
determining a vehicle cluster that includes the first vehicle.

### Claim 8 (depends on 2)

The method of claim 2, further comprising:
generating an updated lane map based on updated vehicle clusters; and
broadcasting the updated lane map to a stationary server.

### Claim 9 (depends on 8)

The method of claim 8, wherein generating the updated lane map includes at least one of changing a lane width and changing a number of lanes.

### Claim 10 (depends on 8)

The method of claim 8, wherein defining the vehicle clusters is further based on traffic data.

### Claim 11 (depends on 8)

The method of claim 8, wherein defining the vehicle clusters is further based on road construction data.

### Claim 12

A system comprising a computer including a processor and a memory, the memory storing instructions such that the processor is programmed to:
receive, from a vehicle sensor, data about a plurality of second vehicles;
define two or more vehicle clusters based on location data, each cluster including two or more of the second vehicles determined to be traveling in a same lane;
identify two or more lane boundaries, based at least in part on a received respective second vehicle lateral distance from a lane boundary, according to the defined two or more vehicle clusters; and
using the identified two or more lane boundaries to generate a lane map.

### Claim 13 (depends on 12)

The system of claim 12, wherein defining two or more vehicle clusters further includes:
determining a lane width based on distances of the second vehicles from one another; and
assigning, to a cluster, second vehicles determined to be traveling in a same lane based on the distances of the second vehicles from one another.

### Claim 14 (depends on 12)

The system of claim 12, wherein defining two or more vehicle clusters further includes determining a lane curvature.

### Claim 15 (depends on 14)

The system of claim 14, wherein determining the lane curvature includes fitting a curve to a plurality of travelled locations of a second vehicle.

### Claim 16 (depends on 14)

The system of claim 14, wherein determining the lane curvature is at least based on a statistical forecast of the lane curvature.

### Claim 17 (depends on 14)

The system of claim 14, further comprising a yaw rate sensor operable to determine a yaw angle of a vehicle, wherein determining the lane curvature is based at least in part on the yaw angle of the vehicle.