The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 24, 2024

Filed:

Feb. 01, 2022
Applicant:

Aptiv Technologies Ag, Schaffhausen, CH;

Inventors:

Lukas Hahn, Wuppertal, DE;

Maximilian Schaefer, Wuppertal, DE;

Kun Zhao, Düsseldorf, DE;

Frederik Lenard Hasecke, Solingen, DE;

Yvonne Schnickmann, Wuppertal, DE;

Andre Paus, Wuppertal, DE;

Assignee:

Aptiv Technologies AG, Schaffhausen, CH;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 50/00 (2006.01); G01C 21/16 (2006.01); G01C 21/34 (2006.01); G01S 13/58 (2006.01); G01S 13/86 (2006.01); G01S 19/49 (2010.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06V 20/58 (2022.01); G08G 1/16 (2006.01);
U.S. Cl.
CPC ...
B60W 60/0027 (2020.02); B60W 50/0097 (2013.01); G06N 3/08 (2013.01); B60W 2554/4029 (2020.02);
Abstract

The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.


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