Proximate vehicle localization and identification

Patent metadata

Field	Value
Publication number	US-11222219-B2
Application number	US-201916550616-A
Country	US
Kind code	B2
Filing date	Aug 26, 2019
Priority date	Apr 15, 2019
Publication date	Jan 11, 2022
Grant date	Jan 11, 2022

Abstract

Disclosed embodiments pertain to a method for determining position information of a target vehicle relative to an ego vehicle. The method may comprise: obtaining, by at least one image sensor, first images of one or more target vehicles and classifying at least one target vehicle from the one or more target vehicles based on the one or more first images. Further, vehicle characteristics corresponding to the least one target vehicle may be obtained based on the classification of the least one target vehicle. Position information of the at least one target vehicle relative to the ego vehicle may be determined based on the vehicle characteristics.

First claim

What is claimed is:

1. An ego vehicle capable of determining one or more position information of at least one target vehicle relative to the ego vehicle, the ego vehicle comprising:
at least one image sensor,
a memory, and
at least one processor communicatively coupled to the memory and the at least one image sensor and configured to:
obtain, using the at least one image sensor, one or more first images of one or more target vehicles;
classify the at least one target vehicle from the one or more target vehicles based on vehicle features identified in the one or more first images;
determine one or more target vehicle attributes associated with the at least one target vehicle based on the one or more first images and classification of the at least one target vehicle, the one or more target vehicle attributes comprising at least one of a temporary attribute, a semi-permanent attribute, and symmetric attributes;
obtain one or more vehicle characteristics corresponding to the least one target vehicle based on the classification of the least one target vehicle;
determine the one or more position information of the at least one target vehicle relative to the ego vehicle based on the one or more vehicle characteristics; and
track the at least one target vehicle in captured images based at least partially on the one or more target vehicle attributes associated with the at least one target vehicle.

2. The ego vehicle of claim 1 , wherein the one or more position information of the at least one target vehicle relative to the ego vehicle comprises at least one of:
a corresponding range of the at least one target vehicle from the ego vehicle, or
a corresponding orientation of the at least one target vehicle relative to the ego vehicle, or
a corresponding position of the at least one target vehicle relative to the ego vehicle.

3. The ego vehicle of claim 1 , wherein to classify the at least one target vehicle, the at least one processor is configured to:
determine at least one of a vehicle make or a vehicle model corresponding to the at least one target vehicle.

4. The ego vehicle of claim 3 , wherein to obtain the one or more vehicle characteristics corresponding to the least one target vehicle, the at least one processor is configured to:
obtain, based on the corresponding vehicle make and the corresponding vehicle model of the at least one target vehicle, at least one of:
one or more corresponding dimensional parameters of the at least one target vehicle; or
one or more corresponding 3-Dimensional (3D) models of the at least one target vehicle; or
one or more corresponding form factors of the at least one target vehicle; or
any combination thereof.

5. The ego vehicle of claim 1 , wherein to classify the at least one target vehicle, the at least one processor is configured to:
determine, for the at least one target vehicle, one or more corresponding matching second images of vehicles, wherein each of the one or more second images of vehicles is associated with a corresponding vehicle classification; and
classify the at least one target vehicle based on the one or more corresponding matching second images.

6. The ego vehicle of claim 5 , wherein the one or more corresponding second images of vehicles are obtained from one or more of:
a vehicle database coupled to the ego vehicle, or
the at least one target vehicle; or
a server coupled to a Vehicle to Everything (V2X) network associated with the ego vehicle; or
a cloud-based service coupled to the V2X network associated with the ego vehicle; or
any combination thereof.

7. The ego vehicle of claim 1 , wherein the at least one processor is further configured to:
display one or more corresponding representations of the at least one target vehicle, based, at least in part, on the one or more position information of the at least one target vehicle, wherein the one or more corresponding representations of the at least one target vehicle are obtained based on the one or more vehicle characteristics corresponding to the at least one target vehicle.

8. The ego vehicle of claim 7 , wherein to display the one or more corresponding representations of the at least one target vehicle, the at least one processor is configured to:
display at least one of: one or more corresponding 3-Dimensional (3D) models of the at least one target vehicle; or one or more corresponding form factors of the at least one target vehicle; or any combination thereof.

9. The ego vehicle of claim 1 , wherein the at least one target vehicle is classified based on at least one corresponding partially occluded image of the at least one target vehicle comprised in the one or more first images.

10. The ego vehicle of claim 9 , wherein the at least one processor is further configured to:
display at least one of: one or more corresponding 3-Dimensional (3D) models of the at least one target vehicle; or one or more corresponding form factors of the at least one target vehicle, based, at least in part, on the one or more position information of the at least one target vehicle, wherein the one or more corresponding 3D models and the one or more corresponding form factors are obtained based on the one or more vehicle characteristics.

11. The ego vehicle of claim 1 , wherein the one or more position information of the at least one target vehicle is determined based on a comparison of the one or more vehicle characteristics corresponding to the at least one target vehicle with corresponding features of the at least one target vehicle in the one or more first images.

12. The ego vehicle of claim 1 , wherein the semi-permanent attribute comprises a semi-permanent attribute over a single driving session; wherein the one or more target vehicle attributes further comprises a semi-permanent attribute over multiple driving sessions, and a permanent attribute.

13. The ego vehicle of claim 1 , wherein the one or more target vehicle attributes comprise one or more of physical attributes, behavioral attributes, positional attributes, and visual communication attributes.

14. The ego vehicle of claim 1 , wherein the ego vehicle further comprises one or more of at least one radar sensor, at least one lidar sensor, at least one sound sensor, and at least one transceiver, wherein the at least one processor communicatively coupled to the one or more of at least one radar sensor, at least one lidar sensor, at least one sound sensor, and the at least one transceiver, wherein the at least one processor is further configured to:
obtain data related to the one or more target vehicles using the one or more of at least one radar sensor, at least one lidar sensor, at least one sound sensor, and at least one transceiver;
wherein the at least one processor is further configured to classify the at least one target vehicle further based on the data related to the one or more target vehicles.

15. The ego vehicle of claim 14 , wherein the one or more target vehicle attributes further comprise one or more of auditory attributes, and radio frequency characteristics.

16. The ego vehicle of claim 14 , wherein the data related to the one or more target vehicles obtained with the at least one transceiver comprises one or more of a vehicle classification, vehicle characteristics, vehicle identification information, and vehicle behavioral attributes.

17. The ego vehicle of claim 1 , wherein the at least one processor is further configured to:
track the at least one target vehicle based on at least one corresponding partially occluded image of the at least one target vehicle by associating

Assignees

• Qualcomm Inc

Inventors

• Lund Benjamin
• Blow Anthony
• Park Edwin Chongwoo

Classifications

• G06V2201/08

  Detecting or categorising vehicles · CPC title

• G06V20/584 Primary

  of vehicle lights or traffic lights · CPC title

• H04W4/80

  Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication · CPC title

• H04W4/023

  using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds · CPC title

• H04W4/027

  using movement velocity, acceleration information · CPC title