US 11,940,803 B2
Method, apparatus and computer storage medium for training trajectory planning model
Teng Zhang, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD., Beijing (CN)
Filed by BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Mar. 29, 2021, as Appl. No. 17/216,208.
Claims priority of application No. 202010623703.5 (CN), filed on Jun. 30, 2020.
Prior Publication US 2021/0216077 A1, Jul. 15, 2021
Int. Cl. G05D 1/02 (2020.01); B60W 60/00 (2020.01); G05D 1/00 (2006.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G05D 1/0221 (2013.01) [B60W 60/001 (2020.02); G05D 1/0246 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); B60W 2420/42 (2013.01); B60W 2554/20 (2020.02); B60W 2554/402 (2020.02); B60W 2554/4029 (2020.02); B60W 2555/60 (2020.02)] 18 Claims
OG exemplary drawing
 
1. A method for training a trajectory planning model, comprising:
obtaining an image of a physical environment in which a vehicle is located via at least one sensor of the vehicle, wherein the obtained image comprises a plurality of objects surrounding the vehicle;
obtaining a feature chart from the trajectory planning model based on the image, the feature chart indicating a plurality of initial trajectory points of the vehicle in the image;
identifying the image to determine in the image a first area associated with a road object in the plurality of objects and a second area associated with a non-road object in the plurality of objects, wherein identifying the image comprises: obtaining a depth image via a trained depth estimation network, wherein each pixel value of the depth image is a distance between a point in the image of the physical environment and the vehicle, and obtaining the first area associated with the road object by multiplying the depth image with a matrix array having a first value corresponding to the first area and a second value corresponding to the second area;
determining planning trajectory points based on a positional relationship of the plurality of initial trajectory points with respect to the first area and the second area; and
training the trajectory planning model based on the planning trajectory points and actual trajectory points of the vehicle.