Method and system for training and updating a classifier

1. A method for training and updating a backend-side classifier, the method comprising:

receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier; and

training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation;

wherein training the backend-side classifier comprises:

comparing classification results for the same classification object received from a plurality of vehicles;

if the classification results differ, applying a consistency check on the classification results to determine an inconsistent or false classification result; and

training the backend-side classifier only using consistent annotations based on the consistency check.

2. The method of claim 1 , further comprising communicating a consistent annotation back to a vehicle in which an inconsistent or false classification result originates.

3. The method of claim 2 , further comprising initiating an update of the vehicle-side classifier of the vehicle in which the inconsistent or false classification result originates based on the consistent annotation.

4. The method of claim 1 , wherein applying a consistency check on the classification results comprises applying a voting scheme for classification results for the same classification object and setting the classification result that has been voted for by the voting scheme as a consistent annotation for the classification object.

5. The method of claim 1 , wherein applying a consistency check on the classification results comprises annotating the classification data by a manual annotator.

6. The method of claim 1 , wherein training the backend-side classifier comprises updating the backend-side classifier.

7. The method of claim 1 , wherein the backend-side classifier and the vehicle-side classifier is a multi-layer classifier having a plurality of layers and wherein the classification data is an output of a specific layer of the plurality of layers.

8. The method of claim 7 , wherein training the backend-side classifier comprises updating a number of layers corresponding to the specific layer of the plurality of layers.

9. A method for training and updating a backend-side classifier, the method comprising:

receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier;

training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation;

determining, in a vehicle, an inconsistent or false classification result;

generating, in the vehicle, an indication indicating that the classification is inconsistent or false;

receiving, in a backend-device, along with the classification data and the classification result the indication that the classification is inconsistent or false;

determining, by the backend-device, a correct classification result with respect to the classification data;

communicating, by the backend-device; the classification data and the correct classification result to the vehicle or another vehicle;

initiating, by the backend-device; a training of the vehicle-side classifier of the vehicle or the other vehicle based on the classification data and the determined correct classification result as annotation.

10. A method for training and updating a backend-side classifier, the method comprising:

receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier;

training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation;

determining, in a vehicle, an inconsistent or false classification result;

generating, in the vehicle, an indication indicating that the classification is inconsistent or false;

receiving, in a backend-device, along with the classification data and the classification result the indication that the classification is inconsistent false;

determining, by the backend-device, a correct classification result with respect to the classification data;

initiating, by the backend-device; a training of the backend-side classifier based on the classification data and the determined correct classification result as annotation.

11. A method for training and updating a backend-side classifier, the method comprising:

receiving, in a backend-device, from at least one vehicle, classification data along with a respective classification result generated by a vehicle-side classifier;

training the backend-side classifier using the classification data and, if available, a corrected respective classification result as annotation;

determining, in a vehicle, an inconsistent or false classification result for a classification object;

generating, in the vehicle, an indication indicating that the classification is inconsistent or false;

receiving, in a backend-device, along with the classification data and the classification result the indication that the classification is inconsistent false;

communicating, by the backend-device, the classification data to a plurality of other vehicles;

initiating, by the backend-device, a classification of the classification data by the vehicle-side classifier of each of the plurality of other vehicles;

receiving, by the backend-device, classification results from the plurality of other vehicles;

applying a voting scheme for the classification results and setting the classification result that the voting scheme has been voted for as a consistent annotation for the classification object;

communicating, by the backend-device, the consistent annotation to the vehicle or another vehicle; and

initiating, by the backend-device; a training of the vehicle-side classifier of the vehicle or another vehicle based on the classification data and the determined consistent annotation for the classification object.

12. A Backend-Device comprising:

a receiver configured to receive classification data along with a respective classification result generated by a vehicle-side classifier of at least one vehicle; and

one or more processors configured to implement a backend-side classifier and configured to train the backend-side classifier using the classification data and a possibly corrected respective classification result as annotation;

wherein the one or more processors are configured to compare classification results for the same classification object received from a plurality of vehicles; and

if the classification results differ, to apply a consistency check on the classification results to determine an inconsistent or false classification result; and to train the backend-side classifier only using consistent annotations based on the consistency check.

13. A system comprising:

a backend-device including a receiver configured to receive classification data along with a respective classification result generated by a vehicle-side classifier of at least one vehicle; and one or more processors configured to implement a backend-side classifier and configured to train the backend-side classifier using the classification data and a possibly corrected respective classification result as annotation; and

one or more vehicles each comprising a vehicle side classifier;

wherein the backend-device is further configured to communicate a consistent annotation back to a vehicle in which an inconsistent or false classification result originates and wherein the backend-device is further configured to initiate an update of the vehicle-side classifier of the vehicle in which the inconsistent or false classification result originates based on the consistent annotation.