DE102018214420A1 - Driver assistance system - Google Patents

Abstract

Method for detecting travel noise of rail vehicles with the following steps: detecting (St1) a travel noise of a rail vehicle by means of a microphone which can be operated on a road vehicle; Determining (St2) a reaction to the detected driving noise by means of a computing unit which can be operated on a road vehicle.

Description

FIELD OF THE INVENTION

The present invention relates to a method for detecting travel noise from rail vehicles and a training system for a vehicle control system for detecting travel noise from rail vehicles.

TECHNICAL BACKGROUND

The DE102013004271A1 shows a driver assistance system by means of a whistle tone detection for the detection of rail vehicles.

SUMMARY OF THE INVENTION

Against this background, the invention is based on the object of specifying a more reliable driver assistance system for warning of rail vehicles.

According to the invention, this object is achieved by a method for detecting travel noises of rail vehicles with the features of claim 1 and / or by a training system for a vehicle control for detecting travel noises by rail vehicles with the features of claim 9.

• - ein Verfahren zum Detektieren von Fahrtgeräuschen von Schienenfahrzeugen mit den folgenden Schritten: Detektieren eines Fahrtgeräuschs eines Schienenfahrzeugs mittels eines Mikrophons, welches an einem Straßenfahrzeug betreibbar ist; Ermitteln einer Reaktion auf das detektierte Fahrtgeräusch mittels einer Recheneinheit, welche an einem Straßenfahrzeug betreibbar ist; sowie
• - ein Trainingssystem für eine Fahrzeugsteuerung zum Detektieren von Fahrtgeräuschen von Schienenfahrzeugen, mit wenigstens einer Schnittstelle, um Trainingsdaten, welche jeweils ein Audiosignal und ein Soll-Reaktionssignal aufweisen, zu erhalten, einer Auswerteeinheit, welche ein künstliches neuronales Netzwerk bildet und eingerichtet ist, das künstliche neuronale Netzwerk mit Trainingsdaten vorwärtszuspeisen, um Ist-Reaktionssignale zu ermitteln, und eine veränderte Topologie des künstlichen Neuronalen Netzwerks, insbesondere Gewichte, durch Rückwärtsspeisen der Soll-Reaktionssignale in dem künstlichen neuronalen Netzwerk zu ermitteln, wobei die Topologie eingerichtet ist, in der Fahrzeugsteuerung zum Detektieren von Fahrtgeräuschen von Schienenfahrzeugen gespeichert zu werden.

Accordingly, it is provided:

• - A method for detecting travel noise of rail vehicles with the following steps: detecting a travel noise of a rail vehicle using a microphone that can be operated on a road vehicle; Determining a reaction to the detected driving noise by means of a computing unit which can be operated on a road vehicle; such as
• - A training system for a vehicle control for detecting travel noises of rail vehicles, with at least one interface to receive training data, each of which has an audio signal and a target reaction signal, an evaluation unit, which forms and is set up an artificial neural network, the artificial Feeding the neural network forward with training data in order to determine actual reaction signals, and determining a changed topology of the artificial neural network, in particular weights, by feeding back the desired reaction signals in the artificial neural network, the topology being set up in the vehicle controller for detection to be saved from driving noises of rail vehicles.

Motor vehicles in the sense of this patent application are motor-driven land vehicles. Rail vehicles are also included.

A road vehicle is a land vehicle that is primarily intended for use on roads. It is characteristic of all road vehicles that they can maintain or change their direction of travel on the traffic surface with the help of static friction and do not need any tracks for this. For example, motor vehicles are road vehicles.

Rail vehicles are vehicles of trains that run or are guided on one or more rails. Vehicle and rail are a coordinated system, usually referred to as a wheel-rail system. For example, trains are rail vehicles.

Driving noises arise due to the driving of a vehicle. These include e.g. Tire-road noise or wheel-rail noise, drive noise and aerodynamic noise. Wheel-rail noises are contact noises due to the wheel coming into contact with a rail.

Rolling noises and curve noises are wheel-rail noises from rail vehicles.

Rolling noises are generated when the wheels roll on the rail track. The material structure, wear and corrosion of certain surface and structural irregularities of the rail and wheel surfaces cause vertical excitation of the wheel sets and rails when they roll, which stimulate them to vibrate, which are emitted as sound into the surrounding air.

Curve noises such as squeaking curves occur when driving on narrow curves and points, when the wheels cannot roll without slippage due to the radius difference between the two rails. In very narrow track arches there is also a flange start.

Rail impacts (area of abutting rail ends), frogs (elements in switch construction), flat spots (signs of wear on wheels of rail vehicles) as well as deposits (material that has been sanded down and often applied in front of a flat spot or material applied elsewhere) lead to further impulsive noises, which are assigned to the wheel-rail system.

In the case of modern traction vehicles, there are also noises caused by the Slight slippage when accelerating and (electric) braking on slippery rails occur.

The contact between the wheel and the rail causes the tread lugs and the carcass to vibrate and emit airborne sound. In addition, air in the tire flap is displaced in the inlet and sucked in again in the outlet. This creates aerodynamic noises (so-called air pumping).

Drive noises arise from the operation of the engine, transmission and / or drive train of a vehicle as well as their ancillary units and attachments. In electric drives, they are caused by electromagnetic forces, in the bearings and by switching processes. Drive noise enters the vehicle interior and outside as airborne and structure-borne noise.

At higher speeds, noise-producing air vortices form on the outer shell of a vehicle.

A microphone is a sound transducer that converts airborne sound as sound pressure fluctuations into corresponding electrical voltage changes as a microphone signal.

A camera is a photographic apparatus that can record static or moving images on a photographic film or electronically on a magnetic video tape or digital storage medium or transmit them via an interface.

Warning signals are perceptible to the driver of a vehicle and increase or focus their attention. Warning signals can be optical, acoustic or haptic signals.

A direction of travel of a vehicle can be specified by means of a cardinal direction, eg north. A direction of travel can also be specified in relation to a street, for example on the B30 towards Friedrichshafen.

A crossing between a rail and a road is also called a level crossing. A level crossing can be restricted or unrestricted. Restricted level crossings can be restricted to several or one lane. Lowered multi-lane barriers are road blocks. At single-lane level crossings, a vehicle can avoid a lowered barrier and thus pass it, disregarding the traffic rules. Light signal systems for emitting a warning signal are often provided at unrestricted level crossings.

An interface is a device between at least two functional units on which an exchange of logical quantities, e.g. Data, or physical quantities, e.g. electrical signals, either unidirectional or bidirectional. The exchange can be analog or digital. The exchange can also be wired or wireless.

A controller is an electronic module for control or regulation. Controls are used in the automotive sector in all conceivable electronic areas, as well as for the control of machines, systems and other technical processes.

A reaction to a traveling noise of a rail vehicle is, for example, a warning signal to a driver or a brake signal for braking the vehicle. Means for executing the signals are reaction units.

A computing unit is a device that processes incoming information and outputs a result resulting from this processing. Electronic circuits such as central processor units or graphics processors are computing units.

Computer program products typically include a sequence of instructions that cause the hardware, when the program is loaded, to perform a specific procedure that leads to a specific result.

An artificial neural network (KNN, English artificial neural network - ANN ) is in particular a network of networked artificial neurons simulated in a computer program. The artificial neurons are typically arranged on different layers. The artificial neural network usually comprises an input layer and an output layer, the neuron output of which is the only one of the artificial neural network that is visible. Layers lying between the input layer and the output layer are typically referred to as hidden layers. Typically, an architecture or topology of an artificial neural network is initially initiated and then trained in a training phase for a specific task or for several tasks in one training phase.

The training of the artificial neural network typically includes a change in the weight of a connection between two artificial neurons of the artificial neural network. The weight contains information on the strength of considering an input Neuron. Training of the artificial neural network can also include developing new connections between artificial neurons, deleting existing connections between artificial neurons, adjusting threshold values of the artificial neurons and / or adding or deleting artificial neurons.

An example of an artificial neural network is a flat artificial neural network (shallow neural network), which often only contains a single hidden layer between the input layer and the output layer and is therefore relatively easy to train. Another example is a deep neural network, which contains several interleaved hidden layers of artificial neurons between the input layer and the output layer. The deep artificial neural network enables improved recognition of patterns and complex relationships

For example, the artificial neural network can be a single or multilayer feedforward network or a recurrent network. Feedforward networks have neurons that are fed exclusively forward, i.e. a neuron is fed exclusively by higher layers.

A recurrent network has bidirectionally connected neurons, ie a neuron is also fed to deeper layers. This means that when the ANN Consider information from an earlier run, creating a memory.

A training system is a computing unit on which a ANN is trained.

Training data in this application are data pairs from input data that are generated by the ANN are to be processed, as well as target result data, which are from the ANN are to be determined. During training, it will ANN based on a comparison of target earnings data with that of ANN determined result adjusted, which results in a training effect.

The input data with which the ANN This application feeds noises or audio signals that encode noises. The input data can contain aerodynamic noises and / or contact noises from rail vehicles.

An audio signal is an electrical signal that carries acoustic information.

The basic idea of the invention is to recognize a rail vehicle based on a driving noise. This is advantageous since the traveling noise of rail vehicles has characteristic components by means of which a large number of rail vehicles can be detected.

This is particularly advantageous compared to a signal tone detection, since this depends on various factors, e.g. may depend on legal regulations in different countries.

With the invention, a driver of a road vehicle can thus also be supported when viewing a level crossing, in that the perception radius for a rail vehicle of the method according to the invention is greater than that of a driver.

Advantageous refinements and developments result from the further subclaims and from the description with reference to the figures of the drawing.

According to a preferred development of the invention, the reaction is a warning signal to a driver of the road vehicle. The driver of the road vehicle can thus be informed of a potential danger from a rail vehicle.

According to a preferred development of the invention, the reaction is a brake signal for braking the road vehicle. A collision of a road vehicle with a rail vehicle can thus be prevented in an emergency situation. This increases the safety of the occupants of both vehicles.

According to a preferred development of the invention, a rail vehicle is also detected by means of a camera which can be operated on the road vehicle. The detected driving noise can thus be checked using image data. Furthermore, a driver of a road vehicle can thus be supported when viewing a track. This is particularly advantageous at level crossings that are difficult to see, since a camera in particular is often in a better position to view a railroad track than a driver.

According to a preferred development of the invention, a position and a direction of travel of the road vehicle are also determined. It is also determined whether there is an intersection between a rail and a road in the direction of travel of the road vehicle. In this way it can be checked whether a potential hazard due to a level crossing is possible at all. Roads and train tracks often run over one longer distance parallel to each other without the road crossing over the train tracks.

According to a preferred development of the invention, it is also determined whether a system for emitting a warning signal and / or a system for installing a road block is provided at the intersection. Thus, for example, the response to the protection at a level crossing determined by the method can be adapted.

It goes without saying that driver assistance systems for a driver of a road vehicle for carrying out a method according to one of the preceding claims with a plurality of microphones for detecting audio signals from the surroundings of the road vehicle, a computing unit for evaluating the recorded audio signals with regard to the detection of travel noise from rail vehicles and for Determining a reaction to a detected driving noise and using a reaction unit to carry out a determined reaction based on the detected driving noise are advantageous.

The computer program product according to one embodiment of the invention carries out the steps of a method according to the preceding description if the computer program product runs on a computer, in particular an in-vehicle computer. When the program in question is used on a computer, the computer program product produces an effect, namely the detection of rail vehicles that pose a potential danger to a road vehicle.

According to a preferred development of the invention, the audio signal has acoustic information on an aerodynamic driving noise of the rail vehicle and / or on a contact noise due to a contact of the rail vehicle with a rail. Rail vehicles can thus be identified particularly reliably on the basis of characteristic driving noises.

According to a preferred development of the invention, the target reaction signal of the training data has a warning signal which is directed to a driver of the road vehicle.

It is advantageous if the warning signal is designed as a haptic, optical or audio warning signal. Haptic warning signals can, for example, vibrate the objects with which a driver is in contact. For example, a steering wheel or an area of a driver's seat can be subjected to a vibration signal. Alternatively or additionally, the warning signal can be designed as an optical signal which is shown on a display, for example on a head-up display. As an alternative or in addition, audio warning signals, i.e. sounds, are also conceivable.

It goes without saying that a method for training an artificial neural network of a driver assistance system is advantageous with the following steps: providing at least one pair of an audio signal and a target reaction signal; Feeding the artificial neural network forward with the at least one audio signal; Determining an actual reaction signal based on the forward feeding; Backfeeding the artificial neural network due to a difference between the actual response signal and the target response signal.

The computer program product according to one embodiment of the invention carries out the steps of a method according to the preceding description if the computer program product runs on a computer, in particular an in-vehicle computer. When the program in question is used on a computer, the computer program product produces an effect, namely machine learning or training ANN for the detection of driving noises from rail vehicles.

list of figures

• 1 ein Blockschaltbild einer Ausführungsform der Erfindung;
• 2 ein Blockschaltbild einer Ausführungsform der Erfindung;
• 3 ein Blockschaltbild einer Ausführungsform der Erfindung.

The present invention is explained in more detail below with reference to the exemplary embodiments given in the schematic figures of the drawings. It shows:

• 1 a block diagram of an embodiment of the invention;
• 2 a block diagram of an embodiment of the invention;
• 3 a block diagram of an embodiment of the invention.

The accompanying drawings are intended to provide further understanding of the embodiments of the invention. They illustrate embodiments and, in conjunction with the description, serve to explain principles and concepts of the invention. Other embodiments and many of the advantages mentioned result from the drawings. The elements of the drawings are not necessarily shown to scale with respect to one another.

In the figures of the drawings, elements, features and components that are the same, have the same function and have the same effect — unless stated otherwise — are each provided with the same reference symbols.

DESCRIPTION OF EMBODIMENTS

1 shows a block diagram of a training system 10 according to an embodiment of the invention. The training system 10 includes an interface 12 and an evaluation unit 20 with a ANN 22 , The ANN 22 includes several neurons that are in 1 simplified with 108a-f are shown. The neurons form 108a . b an input layer 102 who have favourited Neurons 108c . d . e form a hidden layer 104 and the neuron 108f forms an output layer 106 ,

The neurons 108a . b the input layer 102 are over the interface 12 with an audio signal 16 forward fed. The audio signal 16 is in the neurons 108a . b the input layer weighted with initial weights. It can be provided that the audio signal 16 is broken down into several signal components and the signal components are weighted. It can be provided 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 that to the neurons 108c . d . e the next lower layer, i.e. the hidden layer 104 , are passed as input values. It can be provided that the hidden layer 104 has several layers.

Similar to the input layer 102 are going to the neurons 108c , d, e weighted input values passed to the hidden layer and one or more functions are applied to the weighted input values. The evaluation of the functions, which is applied to the weighted input values, form the output values of the neurons 108c . d . e. These output values are sent to the neurons of the output layer 106 passed as input values. In 1 are the neurons of the output layer 106 exemplary as a neuron 108f shown. The neuron 108f calculated from the input values by the neurons 108c , d, e of the hidden layer 104 were passed, weighting the input values and using one or more functions on the weighted input values an output value. An actual reaction signal can be derived from this output value 24 derived. This flow is also called a feed forward ANN designated.

In a next step, the actual reaction signal 24 with the target reaction signal 18 which of the evaluation unit 20 over the interface 12 was passed, compared.

The next step is the topology of the individual layers 102 . 104 . 106 of ANN 22 adjusted so that the ANN 22 for the given audio signal 16 the target reaction signal 18 calculated. Adapting the topology 26 may include changing weights, adding connections between neurons, removing connections between neurons, and / or changing functions that are applied to weighted input values. This sequence is also called a reverse feed ANN designated.

The 2 shows a block diagram of a method for training a ANN according to an embodiment of the invention. The process includes the steps S1 -S4.

In the step S1 becomes a pair of signals from an audio signal 16 and a target response signal 18 provided.

In the step S2 will ANN 22 with the audio signal 16 forward fed.

In the step S3 becomes an actual reaction signal 24 due to the forward feed S2 determined.

In the step S4 becomes the artificial neural network 22 due to a difference between the actual reaction signal 24 and the target response signal 18 reverse fed. This changes the topology 26 of the KNN, in particular weights, in order to improve the determination of actual reaction signals due to the forward feeding.

The 3 shows a block diagram of a method for detecting travel noise of rail vehicles. In the step St1 a traveling noise of a rail vehicle is detected by means of a microphone which can be operated on a road vehicle. In the step St 2 a response to the detected driving noise is determined by means of a computing unit which can be operated on a road vehicle.

LIST OF REFERENCE NUMBERS

10

training system

12

interface

14

training data

16

audio signal

18

Target response signal

20

evaluation

22

artificial neural network

24

Is response signal

26

topology

102

input layer

104

hidden layer

106

output layer

108a-f

neurons

S1-S4

steps

St1, St2

steps

QUOTES INCLUDE IN THE DESCRIPTION

This list of documents listed by the applicant has been generated automatically and is only included for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102013004271 A1 [0002]

Claims (13)

Method for detecting travel noise from rail vehicles with the following steps: - Detecting (St1) a driving noise of a rail vehicle by means of a microphone which can be operated on a road vehicle; - Determining (St2) a reaction to the detected driving noise by means of a computing unit which can be operated on a road vehicle. Procedure according to Claim 1 , the reaction being a warning signal to a driver of the road vehicle. Method according to one of the preceding claims, wherein the reaction is a brake signal for braking the road vehicle. Method according to one of the preceding claims, wherein a rail vehicle is further detected by means of a camera which can be operated on the road vehicle. Method according to one of the preceding claims, further comprising determining a position and a direction of travel of the road vehicle and further determining whether there is an intersection between a rail and a road in the direction of travel of the road vehicle. Procedure according to Claim 5 , wherein it is further determined whether a system for emitting a warning signal and / or a system for establishing a road block is provided at the intersection. Driver assistance system for a driver of a road vehicle for performing a method according to one of the preceding claims a large number of microphones for recording audio signals from the surroundings of the road vehicle, - A computing unit for evaluating the recorded audio signals with regard to the detection of travel noises from rail vehicles and for determining a reaction to a detected travel noise and with a reaction unit for carrying out a determined reaction on the basis of the detected driving noise. Computer program product with program code means for a method according to one of the Claims 1 - 6 perform. Training system (10) for a vehicle control system for detecting travel noises from rail vehicles, with - at least one interface (12) in order to receive training data (14), which each have an audio signal (16) and a target reaction signal (18), - an evaluation unit (20), which forms and is set up an artificial neural network (22), feed the artificial neural network (22) with training data (14) to determine actual reaction signals (24), and to determine a changed topology (26) of the artificial neural network (22), in particular weights, by feeding back the desired reaction signals (18) in the artificial neural network (22), the topology (26) being set up in the vehicle controller for Detect driving noises from rail vehicles. Training system according to Claim 9 , wherein the audio signal (16) has acoustic information on an aerodynamic driving noise of the rail vehicle and / or on a contact noise due to a contact of the rail vehicle with a rail. Training system according to one of the preceding Claims 9 or 10 wherein a target reaction signal (18) of the training data (14) has a warning signal which is directed to a driver. Method for training an artificial neural network (22) of a driver assistance system according to Claim 7 with the following steps: - providing (S1) at least one pair of an audio signal and a desired reaction signal (18); - Forward feeding (S2) of the artificial neural network with the at least one audio signal (16); - Determining (S3) an actual reaction signal (24) based on the forward feed (S2); - Reverse feeding (S4) of the artificial neural network due to a difference between the actual response signal (24) and the target response signal (18). Computer program product with program code means to carry out the method according to the Claim 12 perform.

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