US 12,216,475 B2
Autonomous vehicle object classifier method and system
Jiachen Li, Albany, CA (US); Haiming Gang, San Jose, CA (US); Hengbo Ma, Albany, CA (US); and Chiho Choi, San Jose, CA (US)
Assigned to Honda Motor Co., Ltd., Tokyo (JP)
Filed by Honda Motor Co., Ltd., Tokyo (JP)
Filed on Aug. 25, 2021, as Appl. No. 17/411,894.
Claims priority of provisional application 63/216,202, filed on Jun. 29, 2021.
Prior Publication US 2022/0413507 A1, Dec. 29, 2022
Int. Cl. G05D 1/00 (2024.01); B60W 50/08 (2020.01); B60W 60/00 (2020.01); G01S 17/89 (2020.01); G01S 17/931 (2020.01); G06F 18/2433 (2023.01); G06F 18/25 (2023.01); G06V 10/44 (2022.01); G06V 10/46 (2022.01); G06V 10/80 (2022.01); G06V 20/58 (2022.01)
CPC G05D 1/0221 (2013.01) [B60W 50/08 (2013.01); B60W 60/0027 (2020.02); G01S 17/89 (2013.01); G06F 18/2433 (2023.01); G06V 10/462 (2022.01); G06V 20/58 (2022.01); G01S 17/931 (2020.01); G06F 18/253 (2023.01); G06V 10/454 (2022.01); G06V 10/806 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A system for object identification, comprising:
a feature extractor:
extracting a first set of visual features from a first image of a scene detected by a first sensor;
extracting a second set of visual features from a second image of the scene detected by a second sensor of a different sensor type than the first sensor;
concatenating the first set of visual features, the second set of visual features, and a set of bounding box information associated with the first image and the second image;
determining a number of object features associated with a corresponding number of objects other than an ego-vehicle from the scene and a global feature for the scene; and
receiving ego-vehicle feature information associated with the ego-vehicle; and
an object classifier:
receiving the number of object features, the global feature, and the ego-vehicle feature information;
generating relational features with respect to relationships between each of the number of objects from the scene, the relational features including a mutual influence between each of the number of objects from the scene;
classifying each of the number of objects from the scene as a binary classification of relevant or non-relevant to a behavior of the ego-vehicle, based on the number of object features, the relational features, the global feature, the ego-vehicle feature information, and an intention of the ego-vehicle; and
labeling each of the number of objects from the scene as relevant or non-relevant to the behavior of the ego-vehicle,
wherein the object classifier is trained utilizing semi-supervised learning including a labeled dataset and an unlabeled dataset, wherein the unlabeled dataset is annotated with pseudo labels generated from classifying each of the number of objects, the pseudo labels indicating each of the number of objects from the scene as relevant or non-relevant to the behavior of the ego-vehicle.