Method for spatial characterization of at least one vehicle image

1 . A method for a spatial characterization of at least one vehicle image of image information, wherein the image information encompasses a vehicle image of an external vehicle and an environment image of an environment of the external vehicle, comprising:

determining a bounding box for the vehicle image, wherein the bounding box delimits the vehicle image from the environment image,

determining a splitting line for the bounding box, wherein the splitting line partitions the vehicle image into at least a first portion and a second portion different from the first portion, wherein the first portion of the vehicle image includes a first side of the external vehicle, and the second portion of the vehicle image includes a second side of the external vehicle different from the first side the external vehicle, and

determining the spatial characterization based on the bounding box and the splitting line,

wherein the determining of the bounding box and the splitting line is based on machine learning techniques,

wherein the spatial characterization is determined based on information indicating whether the second portion is situated right or left of the splitting line, and

wherein the information indicating whether the second portion is situated right or left of the splitting line is a binary parameter having a first value indicating that the second portion is situated right of the splitting line and a second value indicating that the second portion is situated left of the splitting line.

2 . The method according to claim 1 , wherein the spatial characterization is performed as a three-dimensional reconstruction of the external vehicle from the vehicle image.

3 . The method according to claim 1 , wherein the bounding box is designed to separate the vehicle image from the environment image in order to fully mask the external vehicle.

4 . The method according to claim 1 , wherein the first portion of the vehicle image includes a front or a rear view of the external vehicle and the second portion of the vehicle image includes a side view of the external vehicle.

5 . The method according to claim 1 , wherein the splitting line is configured as a vertical line in relation to a ground surface on which the external vehicle is standing.

6 . The method according to claim 1 , wherein a classification of the external vehicle depicted by the vehicle image is performed, wherein a classification result of the classification is used for determining the spatial characterization.

7 . The method according to claim 6 , wherein the classification result is used to determine a ratio between width and length of the external vehicle.

8 . The method according to claim 1 , wherein a three-dimensional back projection of the external vehicle from the vehicle image is carried out for determining the spatial characterization.

9 . The method according to claim 1 , wherein determining the bounding box and the splitting line includes:

applying the machine learning techniques, in a form of at least one neural network, with the image information as input for the neural network,

using at least one result from an application of the at least one neural network as the bounding box and as the splitting line,

wherein the at least one result includes the information indicating whether the second portion is situated right or left of the splitting line.

10 . The method according to claim 9 , wherein the result comprises information about a position of the splitting line in relation to the bounding box.

11 . The method according to claim 9 , wherein the result comprises information as to a position of a side of the external vehicle relative to the splitting line.

12 . The method according to claim 1 , wherein the image information is recorded by a vehicle camera of another vehicle before determining the bounding box and the splitting line.

13 . The method according to claim 12 , wherein recording of the image information is performed during a drive to monitor the environment of the other vehicle in order to subsequently detect the external vehicle in the environment based on the bounding box and the splitting line.

14 . The method according to claim 12 , wherein the spatial characterization is performed in order to determine an orientation and/or a position of the external vehicle in relation to the other vehicle.

15 . A non-transitory computer-readable medium having stored thereon instructions which, when executed by a processing device, cause the processing device to perform a method for a spatial characterization of at least one vehicle image of image information, wherein the image information encompasses a vehicle image of an external vehicle and an environment image of an environment of the external vehicle, the method comprising:

determining a bounding box for the vehicle image, wherein the bounding box delimits the vehicle image from the environment image,

determining a splitting line for the bounding box, wherein the splitting line partitions the vehicle image into at least a first portion and a second portion different from the first portion, wherein the first portion of the vehicle image includes a first side of the external vehicle, and the second portion of the vehicle image includes a second side of the external vehicle different from the first side the external vehicle, and

determining the spatial characterization based on the bounding box and the splitting line,

wherein the determining of the bounding box and the splitting line is based on machine learning techniques,

wherein the spatial characterization is determined based on information indicating whether the second portion is situated right or left of the splitting line, and

wherein the information indicating whether the second portion is situated right or left of the splitting line is a binary parameter having a first value indicating that the second portion is situated right of the splitting line and a second value indicating that the second portion is situated left of the splitting line.