EP 3948656 A1 20220209 - PEDESTRIAN PREDICTION BASED ON ATTRIBUTES
Title (en)
PEDESTRIAN PREDICTION BASED ON ATTRIBUTES
Title (de)
AUF ATTRIBUTEN BASIERENDE FUSSGÄNGERVORHERSAGE
Title (fr)
PRÉDICTION DE PIÉTONS À BASE D'ATTRIBUTS
Publication
Application
Priority
- US 201916363541 A 20190325
- US 201916363627 A 20190325
- US 2020024386 W 20200324
Abstract (en)
[origin: WO2020198189A1] Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.
IPC 8 full level
G06K 9/00 (2022.01)
CPC (source: EP)
G06V 20/58 (2022.01); G06V 20/588 (2022.01); G06V 40/10 (2022.01)
Citation (examination)
GUIDOLINI RANIK ET AL: "Handling Pedestrians in Crosswalks Using Deep Neural Networks in the IARA Autonomous Car", 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), IEEE, 8 July 2018 (2018-07-08), pages 1 - 8, XP033419253, DOI: 10.1109/IJCNN.2018.8489397
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
DOCDB simple family (publication)
WO 2020198189 A1 20201001; CN 113632096 A 20211109; EP 3948656 A1 20220209; JP 2022527072 A 20220530
DOCDB simple family (application)
US 2020024386 W 20200324; CN 202080023879 A 20200324; EP 20719542 A 20200324; JP 2021557327 A 20200324