A computer-implemented method for generating a virtual vehicle environment for testing highly automated driving functions of a motor vehicle using pre-captured video image data, radar data, and/or a lidar point cloud of a real vehicle environment, comprising the steps of: receiving (S1A, S1B) the pre-captured video image data, radar data, and/or the lidar point cloud of the real vehicle environment including a plurality of real objects (12a, 12b, 12c, 12d, 12e) identified in advance (S1A) or subsequently (S1B) by applying at least one first machine learning algorithm; generating a first feature vector representing a respective real object (12a, 12b, 12c, 12d, 12e) by applying a second machine learning algorithm to the respective real object (12a, 12b, 12c, 12d, 12e) and storing the first feature vector; providing a plurality of stored second feature vectors representing synthetically generated objects (14a, 14b, 14c, 14d, 14e); identifying a second feature vector having a greatest similarity measure to the first feature vector; selecting (S6A) the identified second feature vector and calling a stored synthetic object (14a, 14b, 14c, 14d, 14e) associated with the second feature vector and corresponding to the real object (12a, 12b, 12c, 12d, 12e) or procedurally generating (S6B) the synthetic object (14a, 14b, 14c, 14d, 14e) corresponding to the real object (12a, 12b, 12c, 12d, 12e) as a function of the identified similarity measure; and integrating the synthetic object (14a, 14b, 14c, 14d, 14e) into a specified virtual vehicle environment, the virtual vehicle environment being a computer-generated representation of the real vehicle environment captured by sensors, wherein if the similarity measure of the identified second feature vector to the first feature vector is greater than or equal to a specified threshold value, then the identified second feature vector is selected and the stored synthetic object (14a, 14b, 14c, 14d, 14e) associated with the second feature vector and corresponding to the real object (12a, 12b, 12c, 12d, 12e) is called (S6A), and wherein if the similarity measure of the identified second feature vector to the first feature vector is less than a specified threshold value, then the synthetic object (14a, 14b, 14c, 14d, 14e) is procedurally generated (S6B). receiving (S1A, S1B) the pre-captured video image data, radar data, and/or the lidar point cloud of the real vehicle environment including a plurality of real objects (12a, 12b, 12c, 12d, 12e) identified in advance (S1A) or subsequently (S1B) by applying at least one first machine learning algorithm; generating a first feature vector representing a respective real object (12a, 12b, 12c, 12d, 12e) by applying a second machine learning algorithm to the respective real object (12a, 12b, 12c, 12d, 12e) and storing the first feature vector; providing a plurality of stored second feature vectors representing synthetically generated objects (14a, 14b, 14c, 14d, 14e); identifying a second feature vector having a greatest similarity measure to the first feature vector; selecting (S6A) the identified second feature vector and calling a stored synthetic object (14a, 14b, 14c, 14d, 14e) associated with the second feature vector and corresponding to the real object (12a, 12b, 12c, 12d, 12e) or procedurally generating (S6B) the synthetic object (14a, 14b, 14c, 14d, 14e) corresponding to the real object (12a, 12b, 12c, 12d, 12e) as a function of the identified similarity measure; and integrating the synthetic object (14a, 14b, 14c, 14d, 14e) into a specified virtual vehicle environment, the virtual vehicle environment being a computer-generated representation of the real vehicle environment captured by sensors, wherein if the similarity measure of the identified second feature vector to the first feature vector is greater than or equal to a specified threshold value, then the identified second feature vector is selected and the stored synthetic object (14a, 14b, 14c, 14d, 14e) associated with the second feature vector and corresponding to the real object (12a, 12b, 12c, 12d, 12e) is called (S6A), and wherein if the similarity measure of the identified second feature vector to the first feature vector is less than a specified threshold value, then the synthetic object (14a, 14b, 14c, 14d, 14e) is procedurally generated (S6B).