Object detection circuitry and object detection method using combined probability data
The present disclosure generally pertains to an object detection circuitry configured to: obtain first feature data which are based on first sensing data of a first sensor; compare the first feature data to a first predetermined feature model being representative of a predefined object, wherein the first predetermined feature model is specific for the first sensor, thereby generating first object probability data; obtain second feature data which are based on second sensing data of a second sensor; compare the second feature data to a second predetermined feature model being representative of the predefined object, wherein the second predetermined feature model is specific for the second sensor, thereby generating second object probability data; and combine the first and the second object probability data, thereby generating combined probability data for detecting the predefined object.
1. An object detection circuitry configured to:
obtain first feature data which are based on first sensing data of a first sensor;
compare the first feature data to a first predetermined feature model being representative of a predefined object O thereby generating first object probability data representing a first probability of an existence of the predefined object O in a first object parameter space, wherein the first predetermined feature model represents a plurality of different postures of the predefined object O and is specific for the first sensor;
obtain second feature data which are based on second sensing data of a second sensor;
compare the second feature data to a second predetermined feature model being representative of the predefined object O thereby generating second object probability data representing a second probability of an existence of the predefined object O in a second object parameter space, wherein the second predetermined feature model represents a plurality of different postures of the predefined object O and is specific for the second sensor;
combine the first and the second object probability data, thereby generating combined probability data for detecting the predefined object O; and
detect the predefined object O using the combined probability data.
2. The object detection circuitry of claim 1 , further configured to:
detect a plurality of maxima of the combined probability data being indicative for at least one position parameter of the predefined object O; and
determine the at least one position parameter of the predefined object O.
3. The object detection circuitry of claim 2 , wherein the at least one position parameter includes at least one of a position, a distance, an angle, or a posture.
4. The object detection circuitry of claim 2 , further configured to:
determine at least one of a correctness or a precision of the detection of the predefined object O.
5. The object detection circuitry of claim 2 , further configured to:
track the detected predefined object O.
6. The object detection circuitry of claim 1 , further configured to:
generate a first feature map based on the first sensing data;
generate a second feature map based on the second sensing data; and
transfer the first and the second feature map into a predefined coordinate system.
7. The object detection circuitry of claim 1 , wherein the first and the second sensor include at least one of a radar sensor, a lidar sensor, a camera, or a time-of-flight sensor.
8. The object detection circuitry of claim 1 , wherein at least one of the first predetermined feature model or the second predetermined feature model is based on a supervised training of an artificial intelligence.
9. The object detection circuitry of claim 1 , wherein the predefined object O is based on a class of predefined objects.
10. The object detection circuitry of claim 1 , further configured to:
iteratively convolve the first feature data with the first predetermined feature model, thereby generating the first object probability data;
iteratively convolve the second feature data with the second predetermined feature model, thereby generating the second object probability data; and
iteratively convolve the first and the second object probability data, thereby generating the combined object probability data.
11. An object detection method comprising:
obtaining first feature data which are based on first sensing data of a first sensor;
comparing the first feature data to a first predetermined feature model being representative of a predefined object O thereby generating first object probability data representing a first probability of an existence of the predefined object O in a first object parameter space, wherein the first predetermined feature model represents a plurality of different postures of the predefined object O and is specific for the first sensor;
obtaining second feature data which are based on second sensing data of a second sensor;
comparing the second feature data to a second predetermined feature model being representative of the predefined object O thereby generating second object probability data representing a second probability of an existence of the predefined object O in a second object parameter space, wherein the second predetermined feature model represents a plurality of different postures of the predefined object O and is specific for the second sensor;
combining the first and the second object probability data, thereby generating combined probability data for detecting the predefined object O; and
detecting the predefined object O using the combined probability data.
12. The object detection method of claim 11 , further comprising:
detecting a plurality of maxima of the combined probability data being indicative for at least one position parameter of the predefined object O; and
determining the at least one position parameter of the predefined object O.
13. The object detection method of claim 12 , wherein the at least one position parameter includes at least one of a position, a distance, an angle, or a posture.
14. The object detection method of claim 12 , further comprising:
determining at least one of a correctness or a precision of the detection of the predefined object O.
15. The object detection method of claim 12 , further comprising:
tracking the detected predefined object O.
16. The object detection method of claim 11 , further comprising:
generating a first feature map based on the first sensing data;
generating a second feature map based on the second sensing data; and
transferring the first and the second feature map into a predefined coordinate system.
17. The object detection method of claim 11 , wherein the first and the second sensor include at least one of a radar sensor, a lidar sensor, a camera, or a time-of-flight sensor.
18. The object detection method of claim 11 , wherein at least one of the first predetermined feature model or the second predetermined feature model is based on a supervised training of an artificial intelligence.
19. The object detection method of claim 11 , wherein the predefined object O is based on a class of predefined objects.
20. The object detection method of claim 11 , further comprising:
iteratively convolving the first feature data with the first predetermined feature model, thereby generating the first object probability data;
iteratively convolving the second feature data with the second predetermined feature model, thereby generating the second object probability data; and
iteratively convolving the first and the second object probability data, thereby generating the combined probability data.