Traffic boundary mapping

Patent metadata

Field	Value
Publication number	US-10891497-B2
Application number	US-201916608516-A
Country	US
Kind code	B2
Filing date	Mar 22, 2019
Priority date	Mar 23, 2018
Publication date	Jan 12, 2021
Grant date	Jan 12, 2021

Abstract

The present disclosure provides devices, systems and methods for mapping of traffic boundaries.

First claim

What is claimed is:

1. A system, comprising
a memory; and
a processor coupled to the memory, wherein the processor is configured to:
receive a first visual data at a first time from a camera coupled to a vehicle;
identify a traffic boundary within the first visual data;
determine a location of the vehicle within a map at the first time;
determine a location of the traffic boundary within the map, based at least in part on the location of the traffic boundary within the first visual data and the location of the vehicle at the first time;
select a first one or more cells of an occupancy grid based at least in part on the determined location of the traffic boundary, wherein a plane of the occupancy grid corresponds to a plane of a road; and
increment a value of the first one or more cells.

2. The system of claim 1 , wherein the traffic boundary is a road boundary.

3. The system of claim 1 , wherein the traffic boundary is an inferred lane boundary, wherein the inferred lane is not indicated by any road markings.

4. The system of claim 3 , wherein the inferred lane boundary corresponds to an offset from a row of parked vehicles.

5. The system of claim 1 , wherein the processor is further configured to:
identify an object within the first visual data;
receive a second visual data at a second time from the camera;
identify the same object within the second visual data;
receive a movement data at the second time, and
determine a location of the object within the map based at least in part on a first location of the object in the first visual data, a second location of the object in second visual data, and the movement data.

6. The system of claim 5 , wherein the movement data comprises a signal from a wheel odometer.

7. The system of claim 1 , wherein the processor is further configured to:
process the first visual data with a neural network to produce a localization data and a traffic boundary type data; wherein the localization data corresponds to the location of the traffic boundary within the first visual data; and wherein the traffic boundary type data corresponds to a traffic boundary class to which the traffic boundary belongs.

8. The system of claim 7 , wherein the class of traffic boundary is one of a visible lane boundary, a road boundary, an intersection marking, or an inferred lane boundary.

9. The system of claim 5 , wherein the processor is further configured to:
process the first visual data with a neural network to produce an object localization data and an object type data; wherein the object localization data corresponds to the location of the object within the first visual data; and wherein the object type data corresponds to an object class to which the object belongs.

10. The system of claim 1 , wherein the processor is further configured to:
select a second one or more cells of the occupancy grid, wherein the second one or more cells substantially surround the first one or more cells; and
decrement a value of the second one or more cells.

11. The system of claim 1 , wherein determining the location of the vehicle within the map comprises determining an estimate of the location of the vehicle based at least in part on:
a Global Navigation Satellite System (GNSS);
an Inertial Measurement Unit (IMU);
a wheel odometer; and
detected objects within the first visual data.

12. The method system of claim 11 , wherein the detected objects comprise the traffic boundary.

13. The system of claim 11 , wherein determining the location of the vehicle within the map comprises:
determining a first position estimate based on an output from the GNSS;
determining a second position estimate based on the IMU and the wheel odometer;
determining a difference between the first position estimate and the second position estimate; and
configuring an error estimate associated with the GNSS based on the difference.

14. The system of claim 1 , wherein the processor is further configured to:
identify a static object within the first visual data;
determine a map location of the static object or the traffic boundary on a road-referenced landmark map;
determine a position estimate of the vehicle;
determine a topographic height of the vehicle based on a topographic map; and
project the determined map location to a position relative to an earth-based anchor point.

Assignees

• Netradyne Inc

Inventors

• Cox Jonathan Albert
• Taranalli Veeresh
• Julian David Jonathan
• Chakravarthy Badugu Naveen
• Campos Michael
• Kahn Adam David
• Venkatachalam Jayaraman Venkata Ramanan
• Yedla Arvind

Classifications

• G01C21/30 Primary

  Map- or contour-matching · CPC title

• G06V10/80

  Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L17/10) · CPC title

• G06V10/774

  Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V20/10

  Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59) · CPC title

• G06V10/82

  using neural networks · CPC title