Generation of test data having consistent initial conditions for stimulating a control unit to be tested
A method for processing raw data recorded from the real world with the aid of a sensor into test data for stimulating a control unit, including: providing recorded raw data from the real world; ascertaining real objects detected by the sensor from the raw data, and generating route data sets, which each describe a scene including images of these real objects at consecutive points in time. providing a library of synthetic objects; assigning synthetic objects to detected images of the real objects, replacing the detected images with synthetic objects; supplementing temporally consecutive route data sets with supplementary data sets before the first route data set; and generating test data by converting the route data sets supplemented by the supplementary data sets into raw data that would have been recorded in the real world by the sensor during the introductory period and the recording period.
1. A method for processing raw data recorded from the real world with the aid of a sensor into test data for stimulating a control unit to be tested, the method comprising:
providing recorded raw data from the real world, which were recorded along a recording route from at least one portion of the surroundings of the recording route with the aid of a sensor for a recording period, and which include temporally consecutive data value sets which resulted from real objects detected by the sensor;
ascertaining the real objects detected by the sensor from the temporally consecutive data value sets and generating temporally consecutive route data sets, which each describe a scene, including images of the real objects at consecutive points in time;
providing a library of synthetic objects;
ascertaining an absolute velocity of the sensor along the recording route at a point in time zero of the recording period and in an event that the absolute velocity of the sensor along the recording route at the point in time zero is other than zero at the point in time zero of the recording period, supplementing the temporally consecutive route data sets with temporally consecutive supplementary data sets before a first route data set in such a way that the sensor has an absolute velocity of zero at a point in time zero of an introductory period, and the temporally consecutive supplementary data sets include synthetic objects which demonstrate a quasi-continuous temporal sequence of movements between temporally directly consecutive supplementary data sets, the sequence of movements quasi-continuously resulting in the images of the real objects of the first route data set at the point in time zero of the recording period; and
generating the test data for stimulating the control unit to be tested by converting the supplementary data sets into raw data which would have been recorded in the real world with the aid of the sensor during the introductory period if the sensor had detected the synthetic objects in the temporally consecutive supplementary data sets as real objects; and
supplementing the recorded raw data with the raw data obtained by converting the supplementary data sets before the first route data set at the point in time zero of the recording period.
2. The method according to claim 1 , wherein the route data sets, which each describe a scene including images of the real objects at consecutive points in time, are formed by temporally consecutive frames, which each contain a virtual representation of the scene.
3. The method according to claim 1 , wherein the synthetic objects, which show, between temporally directly consecutive supplementary data sets, a quasi-continuous temporal sequence of movements, which quasi-continuously lead to the images of the real objects of the first route data set at point in time zero of the recording period, are selected in such a way that the synthetic objects are seen as being in a predetermined range of similarities with the images of the real objects to which they lead within the scope of an assessment with the aid of a predetermined metric or within the scope of an assessment by a correspondingly trained artificial neural network.
4. The method according to claim 3 , wherein a synthetic object is seen as being within the predetermined range of similarities with an image of a real object, if raw data which was recorded from a real object with the aid of the sensor corresponds to the synthetic object within a predetermined range of similarity, the raw data having been recorded by the sensor from the real object to whose image the synthetic object leads.
5. The method according to claim 1 , wherein the sensor includes a radar sensor, a LIDAR sensor, a camera, and/or an ultrasonic sensor.
6. The method according to claim 1 , further comprising:
testing the control unit using the test data; and
repeating the method steps of claim 1 if the control unit demonstrates, during application of the test data, that it does not accept the test data as raw data recorded in the real world.
7. The method according to claim 6 , wherein, during the repetition of the method steps of claim 1 , images of virtual objects are made more similar to the real objects corresponding to the images of the virtual objects.
8. The method according to claim 6 , wherein, during the repetition of the method steps of claim 1 , images of virtual objects are inserted which were previously not used, due to too small a size of the real objects corresponding to the virtual objects.
9. A nonvolatile, computer-readable memory medium having commands stored thereon which, when executed on a processor, effectuate a method according to claim 1 .
10. A non-volatile, computer-readable memory medium having test data stored thereon, which were obtained according to claim 1 .