DETECTION OF HAZARD SOUNDS

Abstract

A training system (10) for a vehicle control unit for detecting hazard sounds, in particular accident sounds, that has at least one interface (12) for inputting training data (15) containing an audio signal (16) and a target reaction signal (18) in each case, an evaluation unit (20) that forms an artificial neural network (22) and is configured for forward propagation of the artificial neural network (22) with training data (14) in order to calculate an actual reaction signal (24), and calculating weightings through backward propagation of the target reaction signal (18) in the artificial neural network (22), wherein the weightings are configured to be stored in the vehicle control unit for detecting accident sounds.

Claims

1. A training system for a vehicle control unit for detecting hazard sounds, in particular accident sounds, that has at least one interface, for inputting training data containing an audio signal and a target reaction signal in each case, an evaluation unit forming an artificial neural network, configured for forward propagation of the artificial neural network with training data in order to calculate actual reaction signals, and calculating a modified topology of the artificial neural network, in particular weightings, through backward propagation of the target reaction signals in the artificial neural network, wherein the topology is configured to be stored in the vehicle control unit for detecting hazard sounds.

2. The training system according to claim 1, which comprises at least one microphone, in particular numerous directional microphones, wherein the microphone is configured to pick up sounds corresponding to a driving situation.

3. The training system according to claim 1, wherein the audio signal contains information regarding a braking sound of a vehicle and/or a collision of a vehicle with another object.

4. The training system according to claim 1, wherein a target reaction signal of the training data contains a warning signal directed toward a driver.

5. The training system according to claim 4, wherein the warning signal is a haptic, visual, or audio warning signal.

6. The training system according to claim 4, wherein the target reaction signal contains two warning signals, in particular a first warning signal directed toward the driver of an ego-vehicle, and a second warning signal directed toward the driver of a second vehicle.

7. The training system according to claim 6, wherein the second warning signal is a visual warning signal.

8. A process for training an artificial neural network of a vehicle control unit, which has the following steps: provision (S1) of at least one pair of signals, comprising an audio signal and a target reaction signal; forward propagation (S2) of the artificial neural network with the at least one audio signal; calculating (S3) an actual reaction signal based on the forward propagation (S2); backward propagation (S4) of the artificial neural network based on a difference between the actual reaction signal and the target reaction signal.

9. A vehicle control unit for detecting hazard sounds in driving situations, in particular accident sounds, comprising at least one microphone, preferably a directional microphone, for picking up driving situation sounds, and an evaluation unit, configured for forward propagation of an artificial neural network with the vehicle situation sounds that has been trained in accordance with the process according to claim 8, in order to assign the driving situation sounds to a reaction signal.

10. A vehicle with a vehicle control unit according to claim 9, wherein the vehicle has at least one means for outputting a warning signal, wherein the means comprises, in particular, a display screen, a projector that projects a visual signal on a windshield and/or rear window, a vibrator for vibrating a steering wheel, and/or a loudspeaker.

11. A computer program that contains program code for executing the process according to claim 8.

12. The training system according to claim 2, wherein the audio signal contains information regarding a braking sound of a vehicle and/or a collision of a vehicle with another object.

13. The training system according to claim 2, wherein a target reaction signal of the training data contains a warning signal directed toward a driver.

14. The training system according to claim 3, wherein a target reaction signal of the training data contains a warning signal directed toward a driver.

15. The training system according to claim 5, wherein the target reaction signal contains two warning signals, in particular a first warning signal directed toward the driver of an ego-vehicle, and a second warning signal directed toward the driver of a second vehicle.

Description

CONTENTS OF THE DRAWINGS

[0053] The present invention shall be explained in greater detail below based on the schematic figures in the drawings. Therein:

[0054] FIG. 1 shows a block diagram of an embodiment of the invention;

[0055] FIG. 2 shows a block diagram of an embodiment of the invention.

[0056] The drawings are intended to further explain the embodiments of the invention. They illustrate embodiments, and serve to explain the principles and concepts of the invention in conjunction with the description. Other embodiments and many of the specified advantages can be derived from the drawings. The elements of the drawings are not necessarily drawn to scale.

[0057] If not otherwise specified, elements that are identical, functionally identical, or that have the same effect are indicated by the same reference symbols in the figures.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0058] FIG. 1 shows a block diagram of a training system 10 according to one exemplary embodiment of the invention. The training system 10 comprises an interface 12 and an evaluation unit 20 with an ANN 22. The ANN 22 comprises numerous neurons, indicated in a simplified manner by 108a-f. Neurons 108a, b form an input layer 102, neurons 108c, d, e form a hidden layer 104, and neuron 108f forms an output layer 106.

[0059] Neurons 108a, b of the input layer 102 are forward propagated with the audio signal 16 via the interface 12. The audio signal 16 is weighted in the neurons 108a, b of the input layer with initial weightings. It may be the case thereby that the audio signal 16 is divided into numerous signal components, and the signal components are weighted. It may also be the case that one or more functions are applied to the weighted input data. The evaluation of the function forms the output value of a neuron 108a, b, which are output to the neurons 108c, d, e of the underlying layer, thus the hidden layer 104, as input values. The hidden layer 104 may contain numerous layers.

[0060] As with the input layer 102, the input values that are output to the neurons 108c, d, e of the hidden layer are weighted and one or more functions are applied to the weighted input values. The evaluation of the functions applied to the weighted input values forms the output values of the neurons 108c, d, e. These output values are input to the neurons of the output layer 106 as input values. In FIG. 1, the neurons of the output layer 106 are shown as a neuron 108f, by way of example. The neuron 108f calculates an output value from the input values that are input by the neurons 108c, d, e of the hidden layer 104 by weighting the input values and using one or more functions on the weighted input values. An actual reaction signal 24 can be derived from this output value. This sequence is also referred to as forward propagation of an ANN.

[0061] In a next step, the actual reaction signal 24 is compared with the target reaction signal 18, output to the evaluation unit 20 via the interface 12.

[0062] In the next step, the topology of the individual layers 102, 104, 106 of the ANN 22 is modified such that the ANN 22 calculates the target reaction signal 18 for the output audio signal 16. The adaptation of the topology 26 can comprise a modification of the weighting, the addition of connections between neurons, the removal of connections between neurons, and/or the modification of functions applied to the weighted input values. This sequence is also referred to as backward propagation of an ANN.

[0063] FIG. 2 shows a block diagram of a process for training an ANN according to an embodiment of the invention. The process comprises steps S1-S4.

[0064] A pair of signals comprising an audio signal 16 and a target reaction signal 18 are provided in step S1.

[0065] The ANN 22 is forward propagated with the audio signal 16 in step S2.

[0066] In step S3, an actual reaction signal 24 is calculated on the basis of the forward propagation in S2.

[0067] The artificial neural network 22 is backward propagated in step S4, based on the difference between the actual reaction signal 24 and the target reaction signal 18. A modified topology 26 of the ANN, in particular the weighting, is calculated thereby, in order to improve the calculation of actual reaction signals based on the forward propagation.

REFERENCE SYMBOLS

[0068] 10 training system [0069] 12 interface [0070] 14 training data [0071] 16 audio signal [0072] 18 target reaction signal [0073] 20 evaluation unit [0074] 22 artificial neural network [0075] 24 actual reaction signal [0076] 26 topology [0077] 102 input layer [0078] 104 hidden layer [0079] 106 output layer

[0080] 108a-f neurons

[0081] S1-S4 process steps