Systems and methods for radar localization in autonomous vehicles

Patent metadata

Field	Value
Publication number	US-2018024239-A1
Application number	US-201715714738-A
Country	US
Kind code	A1
Filing date	Sep 25, 2017
Priority date	Sep 25, 2017
Publication date	Jan 25, 2018
Grant date	—

Abstract

Systems and method are provided for controlling a vehicle. In one embodiment, a localization method includes receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes, and constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes. The method further includes defining, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location, and determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features.

First claim

What is claimed is:

1 . A localization method comprising:
receiving sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; constructing a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; defining, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; determining, with a processor, a feature correlation between the first set of features and a second, previously determined set of features; and estimating a position of the vehicle based on the feature correlation.

2 . The method of claim 1 , wherein the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity.

3 . The method of claim 1 , wherein the sensor data includes at least radar data.

4 . The method of claim 1 , wherein the first set of features includes a histogram of one of the corresponding attributes.

5 . The method of claim 4 , wherein the first set of features is a convex hull of the histogram.

6 . The method of claim 1 , wherein the first set of features includes a summary statistic of one of the corresponding attributes.

7 . The method of claim 6 , wherein the summary statistic is a mean value.

8 . The method of claim 6 , wherein the summary statistic is a measure of variance.

9 . The method of claim 1 , further including classifying each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, or a static-nonmoveable object, and determining the feature correlation based only on the sensor data groups associated with static-nonmoveable objects.

10 . A system for controlling a vehicle, comprising:
a feature determination module, including a processor, configured to: receive sensor data relating to an environment of a vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; construct a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; and define, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; and a feature correlation module configured to determine, with a processor, a feature correlation between the first set of features and a second, previously determined set of features.

11 . The system of claim 10 , wherein:
the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and the sensor data is at least one of radar data and lidar data.

12 . The system of claim 10 , wherein the first set of features includes a histogram of one of the corresponding attributes.

13 . The system of claim 10 , wherein the first set of features includes a summary statistic of one of the corresponding attributes.

14 . The system of claim 13 , wherein the summary statistic is a mean value.

15 . The system of claim 13 , wherein the summary statistic is a measure of variance.

16 . The system of claim 10 , wherein the feature determination module classifies each of the sensor data groups as being associated with one of a dynamic object, a static-moveable object, or a static-nonmoveable object, and the feature correlation module determines the feature correlation based only on the sensor data groups associated with static-nonmoveable objects.

17 . An autonomous vehicle, comprising:
at least one sensor that provides sensor data relating to an environment of the autonomous vehicle, the sensor data including a plurality of sensor returns associated with objects in the environment, each of the sensor returns having a plurality of corresponding attributes; and a controller that, by a processor: receives the sensor data; constructs a first plurality of sensor data groups, each including a self-consistent subset of the plurality of sensor returns based on their corresponding attributes; defines, for each of the first plurality of sensor data groups, a first set of features, wherein each feature is based on at least one of the corresponding attributes and each has an associated feature location; determines, with a processor, a feature correlation between the first set of features and a second, previously determined set of features; and estimates a position of the vehicle based on the feature correlation.

18 . The autonomous vehicle of claim 17 . wherein the first set of features includes at least one of a histogram or a summary statistic of one of the corresponding attributes.

19 . The autonomous vehicle of claim 18 , summay statistic is a mean value.

20 . The autonomous vehicle of claim 17 , wherein
the plurality of corresponding attributes includes at least one of Doppler shift, return power, and neighborhood similarity; and the sensor data includes radar data.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Branson Elliot

Classifications

• B60R16/0231 Primary

  Circuits relating to the driving or the functioning of the vehicle (monitoring tyres B60C23/00; indicating overspeed B60K31/00; for dash boards B60K37/00, B60Q3/10; for indicating emergencies B60Q1/52; brake control systems B60T; registering or indicating the working of vehicles G07C5/00; measuring distance G01C, e.g. combinations of speed and distance G01C23/00; engine indicators G01L; measuring speed or acceleration G01P) · CPC title

• G01S7/4808

  Evaluating distance, position or velocity data · CPC title

• G01S17/931

  of land vehicles · CPC title

• G01S13/931 Primary

  of land vehicles · CPC title

• G01S17/58

  Velocity or trajectory determination systems; Sense-of-movement determination systems · CPC title