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:
Jul. 14, 2026

Filed:

May. 28, 2024
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Maxim Dolgov, Renningen, DE;

Faris Janjos, Stuttgart, DE;

Yinzhe Shen, Karlsruhe, DE;

Assignee:

Robert Bosch GmbH, Stuttgart, DE;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); G06T 7/11 (2017.01); G08G 1/01 (2006.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); G06T 7/11 (2017.01); G08G 1/0112 (2013.01); G08G 1/012 (2013.01); G08G 1/0133 (2013.01); G08G 1/0141 (2013.01);
Abstract

A computer-implemented method is for behavior planning of a participant in a traffic scene. The method includes generating a grid-based scene representation based on aggregated scene-specific information and dividing the grid-based scene representation into multiple tiles each representing a partial area of the traffic scene. The method further includes distributing the scene-specific information over at least two semantic levels of the scene representation. The distribution is retained during the division into tiles in order to form at least one type of sub-tile based on the tiles and the semantic levels. The method further includes mapping the grid-based scene representation to latent features based on the tiles or sub-tiles. The semantic relationships between the tiles or sub-tiles of at least one type are taken into account. The latent features thus generated are used as input for at least one downstream deep learning module for predicting a development of the traffic scene.


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