A computer-implemented method for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data is presented. The computer-implemented method includes receiving data from the multi-modal sensors and the knowledge data and extracting feature maps from the multi-modal sensors and the knowledge data by using a traffic participant (TS) extractor to generate a first set of data, using a static objects extractor to generate a second set of data, and using an additional information extractor. The computer-implemented method further includes training the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data, the method comprising: receiving data from the multi-modal sensors and the knowledge data; extracting feature maps from the multi-modal sensors and the knowledge data by: using a traffic participant (TS) extractor to generate a first set of data; using a static objects extractor to generate a second set of data; and using an additional information extractor; and training the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle. 2. The method of claim 1, wherein the feature maps are represented as two-dimensional pixel maps. 3. The method of claim 1, wherein the first set of data is at least position data and velocity data of traffic participants (TPs). 4. The method of claim 1, wherein the TP extractor generates a portion of the first set of data with oriented filled rectangles, an orientation defined by a velocity vector. 5. The method of claim 1, wherein the TP extractor generates a portion of the first set of data by using a normalized relative velocity amplitudes as fill values for rectangles. 6. The method of claim 1, wherein the static objects extractor uses a global positioning system (GPS) sensor to generate absolute location information related to an object on a map. 7. The method of claim 1, wherein the additional information extractor generates data represented in additional pixel maps by setting an entire pixel map of the additional pixel maps to a single value. 8. The method of claim 1, wherein the training data is labeled semi-automatically by defining a set of constraints on sensory variables for each label and encoding each label into a set of rules. 9. The method of claim 8, wherein the training data is further labeled by using the multi-modal sensors for which all rules are verified and assigned to a corresponding label. 10. A system for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data, the system comprising: a memory; and a processor in communication with the memory, wherein the processor is configured to: receive data from the multi-modal sensors and the knowledge data; extract feature maps from the multi-modal sensors and the knowledge data by: using a traffic participant (TS) extractor to generate a first set of data; using a static objects extractor to generate a second set of data; and using an additional information extractor; and train the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle. 11. The system of claim 10, wherein the feature maps are represented as two-dimensional pixel maps. 12. The system of claim 10, wherein the first set of data is at least position data and velocity data of traffic participants (TPs). 13. The system of claim 10, wherein the TP extractor generates a portion of the first set of data with oriented filled rectangles, an orientation defined by a velocity vector. 14. The system of claim 10, wherein the TP extractor generates a portion of the first set of data by using a normalized relative velocity amplitudes as fill values for rectangles. 15. The system of claim 10, wherein the static objects extractor uses a global positioning system (GPS) sensor to generate absolute location information related to an object on a map. 16. The system of claim 10, wherein the additional information extractor generates data represented in additional pixel maps by setting an entire pixel map of the additional pixel maps to a single value. 17. The system of claim 10, wherein the training data is labeled semi-automatically by defining a set of constraints on sensory variables for each label and encoding each label into a set of rules. 18. The system of claim 17, wherein the training data is further labeled by using the multi-modal sensors for which all rules are verified and assigned to a corresponding label. 19. A non-transitory computer-readable storage medium comprising a computer-readable program for training a deep neural network to recognize traffic scenes (TSs) from multi-modal sensors and knowledge data, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of: receiving data from the multi-modal sensors and the knowledge data; extracting feature maps from the multi-modal sensors and the knowledge data by: using a traffic participant (TS) extractor to generate a first set of data; using a static objects extractor to generate a second set of data; and using an additional information extractor; and training the deep neural network, with training data, to recognize the TSs from a viewpoint of a vehicle. 20. The non-transitory computer-readable storage medium of claim 19, wherein the training data is labeled semi-automatically by defining a set of constraints on sensory variables for each label and encoding each label into a set of rules; and wherein the training data is further labeled by using the multi-modal sensors for which all rules are verified and assigned to a corresponding label.