Method for Uncertainty Estimation in Object Detection Models

1 . A computer-implemented method for evaluating a prediction quality of a model usable for detecting objects, the method comprising:

inputting, into the model, a set of data samples, wherein each data sample includes a scene representation including an object;

outputting, by the model, a set of predictions, wherein the set of predictions include, for each data sample of the set of data samples, a predicted feature of the object in the scene representation and a predicted uncertainty associated with the predicted feature;

estimating an uncertainty estimation quality of the model based on the set of predictions; and

determining, based on the uncertainty estimation quality, whether a further training of the model to improve the uncertainty estimation quality is required.

2 . The method of claim 1 wherein estimating the uncertainty estimation quality includes generating an uncertainty distribution by, for each of the predicted feature of the set of predictions, scaling a difference between the predicted feature of the object, and a corresponding ground truth.

3 . The method of claim 2 wherein estimating the uncertainty estimation quality includes determining the uncertainty estimation quality by calculating a statistical property of the uncertainty distribution.

4 . The method of claim 2 wherein scaling includes determining a post-processed predicted uncertainty based on the predicted uncertainty and dividing the difference by the post-processed predicted uncertainty.

5 . The method of claim 3 wherein scaling includes determining a post-processed predicted uncertainty based on the predicted uncertainty and dividing the difference by the post-processed predicted uncertainty.

6 . The method of claim 1 wherein conducting the further training of the model comprises:

determining a first loss associated with a first loss weight using a regression loss function;

determining a second loss associated with a second loss weight using an uncertainty loss function; and

combining the first loss and the second loss according to the associated first and second loss weights.

7 . The method of claim 6 wherein:

training the model includes, based on at least one of a first loss and a second loss, adapting a value of at least one of a first weight and a second weight; and

the value of the first weight and the value of the second weight is between an upper weight limit value and a lower weight limit value.

8 . The method of claim 7 further comprising:

setting, prior to training, the value of the first loss weight to the upper weight limit value and the value of the second loss weight to the lower weight limit value,

wherein adapting includes:

determining that the first loss is smaller than or equal to a detection quality threshold; and

setting the value of the second weight to the upper weight limit value.

9 . The method of claim 7 further comprising:

setting, prior to training, the value of the first loss weight to the upper weight limit value and the value of the second loss weight to the lower weight limit value,

wherein adapting includes:

determining that the first loss is smaller than or equal to a detection quality threshold; and

increasing the value of the second weight by a preset value.

10 . The method of claim 1 wherein the scene representation is generated based on at least one of radar data, image data, and Light Detection and Ranging (LiDAR) data.

11 . The method of claim 1 wherein the predicted feature of the object in the scene representation indicates bounding box information associated with the object, including at least one of a position of the object, a size of the object, a speed of the object, and a rotation of the object.

12 . A computer-implemented method for detecting objects in a vicinity of a vehicle, the method comprising:

the method of claim 1 ;

receiving a scene representation; and

generating a predicted feature of an object within the scene representation and a predicted uncertainty associated with the predicted feature of the object within the scene representation,

wherein the generating is based on the model.

13 . A non-transitory computer-readable medium comprising instructions that include:

inputting, into a model usable for detecting objects, a set of data samples, wherein each data sample includes a scene representation including an object;

outputting, by the model, a set of predictions, wherein the set of predictions include, for each data sample of the set of data samples, a predicted feature of the object in the scene representation and a predicted uncertainty associated with the predicted feature;

estimating an uncertainty estimation quality of the model based on the set of predictions; and

determining, based on the uncertainty estimation quality, whether a further training of the model to improve the uncertainty estimation quality is required.

14 . The non-transitory computer-readable medium of claim 13 further comprising the model.

15 . The non-transitory computer-readable medium of claim 14 wherein the model includes:

an object detection head configured to output a predicted feature of an object within scene representation; and

an uncertainty head configured to output a predicted uncertainty associated with the predicted feature of the object within the scene representation.

16 . An apparatus comprising memory and a set of processors operatively coupled to the memory and configured to execute instructions stored by the memory, wherein the instructions include:

inputting, into a model usable for detecting objects, a set of data samples, wherein each data sample includes a scene representation including an object;

outputting, by the model, a set of predictions, wherein the set of predictions include, for each data sample of the set of data samples, a predicted feature of the object in the scene representation and a predicted uncertainty associated with the predicted feature;

estimating an uncertainty estimation quality of the model based on the set of predictions; and

determining, based on the uncertainty estimation quality, whether a further training of the model to improve the uncertainty estimation quality is required.

17 . A vehicle comprising the apparatus of claim 16 .

18 . The vehicle of claim 17 wherein the instructions include:

receiving a scene representation; and

generating a predicted feature of an object within the scene representation and a predicted uncertainty associated with the predicted feature of the object within the scene representation,

wherein the generating is based on the model.