WO2020015967A1

WO2020015967A1 - Driver assistance system

Info

Publication number: WO2020015967A1
Application number: PCT/EP2019/067002
Authority: WO
Inventors: Andrea Loi; Lucas Hanson; Julian Fieres; Florian Ade; Anja Petrich; Debora Lovison
Original assignee: Zf Friedrichshafen Ag
Priority date: 2018-07-19
Filing date: 2019-06-26
Publication date: 2020-01-23
Also published as: DE102018212038A1

Abstract

The invention relates to a method for detecting faulty couplings between a towing vehicle and a trailer, comprising the following steps: capturing (St1), as a noise signal, noises that occur near a coupling during the travel of a combination of the towing vehicle and the trailer; detecting (St2) noises characteristic of a faulty coupling by means of a microphone of the combination; determining (St3) a reaction to the detected characteristic noise by means of a computing unit of the combination.

Description

Fahrerassistenzsvstem

FIELD OF THE INVENTION

The present invention relates to a method for detecting faulty couplings between a towing vehicle and a trailer, and a training system for a vehicle control system for detecting faulty couplings between a towing vehicle and a trailer.

TECHNICAL BACKGROUND

Faulty couplings between a towing vehicle and a trailer often cause accidents with property damage or personal injury. This is for example from the website http: // www. Traffic portal. de / board / index.php? showtopic = 83542, which was available on the filing date of this patent application.

SUMMARY OF THE INVENTION

Against this background, the invention is based on the object of specifying a method by means of which faulty couplings between a towing vehicle and a trailer can be detected.

According to the invention, this object is achieved by a method for detecting faulty couplings between a towing vehicle and a trailer with the features of claim 1 and / or by a training system for a vehicle control system for detecting faulty couplings between a towing vehicle and a trailer with the features of Claim 10 solved.

Accordingly, it is provided:

- Method for detecting faulty couplings between a towing vehicle and a trailer with the following steps: noise as a noise signal that occurs at a coupling during the journey of a trailer from the towing vehicle and the trailer, in particular when the trailer is unloaded, by means of a microphone of the trailer; Detecting noises characteristic of a faulty coupling; Determining a reaction to the detected characteristic noise by means of a computing unit of the team; such as

a training system for a vehicle control for detecting faulty couplings between a towing vehicle and a trailer, with at least one interface in order to receive training data, each of which has an audio signal and a target reaction signal, an evaluation unit, which an artificial neural network forms and is set up to feed the artificial neural network forward with training data in order to determine actual reaction signals, and a changed topology of the artificial neural network, in particular weights, by feeding back the desired reaction signals in the artificial neural network. To determine plant, the topology is set up to be stored in the vehicle control system for detecting faulty couplings between a train vehicle and a trailer.

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

A towing vehicle is a towing vehicle in a team or trailer or semitrailer. In the case of a tractor with a trailer, the tractor is the towing vehicle. Other examples are trucks in a trailer truck, tractor units in a tractor-trailer, cars in a car team or a bicycle with a bicycle trailer.

A trailer is a non-motorized part of a team. For example, trailers, caravans, car trailers and the like are trailers. In this application, the term coupling refers to the connection of a towing vehicle to a trailer. Couplings that are not completely closed and / or secured are faulty.

Characteristic noises of a faulty coupling are, for example, rattling noises of insufficiently secured bolts or rubbing noises of the coupling parts which arise due to atypical frictional contact of the coupling parts. Vibrations that arise due to insufficiently secured components can also cause characteristic noises.

Characteristic noises of a faulty coupling are influenced, among other things, by the loading condition, for example “unloaded”, “half-loaded”, “fully loaded” and the like. Characteristic noises are also influenced by signs of wear on the coupling parts.

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.

An interface is a device between at least two functional units on which an exchange of logical quantities, for example data, or physical quantities, for example electrical signals, takes place, either only unidirectionally or bidirectionally. 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 characteristic noise of a faulty coupling is, for example, a warning signal to a driver or a brake signal for braking the team. Devices 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.

A gyroscope is a rotating, symmetrical gyroscope that rotates in a movable bearing. The warehouse can e.g. be a gimbal or a frame in the form of a cage. Due to the conservation of angular momentum, the gyro exhibits a high level of persistence with respect to changes in position in space. If the speed of rotation between the gyroscope and the cage is measured, it is called a gyrometer. Gyroscopes are e.g. used as navigation instruments and for active position control.

Computer program products generally comprise a sequence of instructions, by means of which the hardware, when the program is loaded, is caused to carry out a specific procedure which 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 becomes 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 a 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 the consideration of an input of a neuron. The training of the artificial neural network can also include developing new connections between artificial neurons, deleting existing connections between artificial neurons, adapting threshold values of the artificial neurons and / or adding or deleting artificial neurons include.

An example of an artificial neural network is a flat artificial neural network (shallow neural network), which often contains only 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 nested, 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. With a late Take the KNN run into account Information from a previous run, creating a memory.

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

In this application, training data are data pairs from input data that are to be processed by the KNN, and target result data that are to be determined by the KNN. During the training, the KNN is adjusted on the basis of a comparison of the target result data with the result determined by the KNN, which results in a training effect.

The input data with which the KNN is fed in this application are noises or audio signals which encode noises. The input data can contain contact noises from couplings.

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

The basic idea of the invention is to monitor the noise emissions of couplings between a towing vehicle and a trailer and to evaluate the noise emissions with regard to characteristic noises of a faulty coupling.

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

In particular, it can be provided that noises are recorded while a trailer is traveling when the trailer is in an unloaded state. This means that only the pure operating noises that arise from the trailer and the towing vehicle as a result of the combination driving. This makes it easier to identify a faulty coupling. Furthermore, it can be advantageous if for the detection of characteristic

Noise a loading condition is taken into account, i.e. that before

Detecting the characteristic noise, a loading condition is determined.

Furthermore, it can be advantageous that detected noises with regard to

Noises that occur due to wear and tear on the coupling parts such as rust are filtered out.

According to a preferred development of the invention, the reaction is a warning signal to a driver of the towing vehicle and / or to other road users. The driver of the towing vehicle can thus be informed of a potential danger from an insufficiently secured team. In addition, other road users can be warned of possible existing rear-end collisions due to a detachable coupling, for example by means of a warning light.

According to a preferred development of the invention, the reaction is a brake signal for braking the team. This allows the team to be decelerated in a controlled manner. In this case, it is expedient if the trailer is not braked abruptly, since the trailer can be lost if it is incorrectly coupled to the towing vehicle.

According to a preferred development of the invention, the reaction is a reduced maximum speed of the team or the towing vehicle. A driver can thus be promptly moved to check the coupling of the team. Furthermore, the forces that act on the faulty coupling can thus be reduced. Thus, the risk that a trailer with a faulty coupling is released from the towing vehicle can be reduced.

Furthermore, it can be provided that further sensor data relating to the movement of the team are recorded by means of further sensors, such as a gyroscope or an acceleration sensor, and these sensor data are used for plausibility checking and / or for improving the accuracy of the detected characteristic noises. According to a preferred development of the invention, a coupling of the combination is also evaluated by means of a camera. The detected noise can thus be checked using image data. Furthermore, a cause of an incorrect coupling can thus be illustrated in a manner that is understandable for a driver.

According to a preferred development of the invention, the noise is recorded by means of a large number of microphones, in particular by a microphone array. In this way, the spatial origin of a noise can be precisely localized. A microphone array comprises a large number of microphones, which are arranged in a grid-like manner.

According to a preferred development of the invention, the detected noise is broken down into several components. This means that extraneous noises that do not affect the coupling can be masked out.

It goes without saying that driver assistance systems for a driver of a trailer for carrying out a method according to one of the preceding claims with a plurality of microphones for detecting noises as noise signals which occur during the journey of a trailer from the towing vehicle and the trailer at the coupling , a computing unit for evaluating the detected noise signals with regard to the detection of faulty couplings between a towing vehicle and a trailer and for determining a reaction to a detected characteristic noise, and with a reaction unit for carrying out a determined reaction on the basis of the detected characteristic device are beneficial.

The computer program product according to one embodiment of the invention executes 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 faulty combinations of combinations that represent a potential danger to road traffic. 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.

It is advantageous if the warning signal is designed as a haptic, optical or audio warning signal. Haptic warning signals can, for example, vibrate signals with which objects with which a driver is in contact are acted upon. 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; Reverse feeding of the artificial neural network due to a difference between the actual reaction signal and the target reaction signal.

The computer program product according to one embodiment of the invention executes 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. If the program in question is used on a computer, the computer program product produces an effect, namely machine learning or training a KNN for detecting faulty couplings between a towing vehicle and a trailer.

CONTENTS OF THE DRAWINGS The present invention is explained in more detail below on the basis of the exemplary embodiments specified in the schematic figures of the drawings. It shows:

Figure 1 is a block diagram of an embodiment of the invention;

Figure 2 is a block diagram of an embodiment of the invention;

Figure 3 is a block diagram of an embodiment of the invention.

The accompanying drawings are intended to provide a further understanding of the embodiments of the invention. They illustrate embodiments and, in connection with the description, serve to explain the 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

FIG. 1 shows a block diagram of a training system 10 in accordance with an embodiment of the invention. The training system 10 comprises an interface 12 and an evaluation unit 20 with a KNN 22. The KNN 22 comprises several neurons, which are shown in simplified form in FIG. 1 with 108a-f. The neurons 108a, b form an input layer 102, the neurons 108c, d, e form a hidden layer 104 and the neuron 108f forms an output layer 106.

The neurons 108a, b of the input layer 102 are fed forward with an audio signal 16 via the interface 12. The audio signal 16 is weighted in the neurons 108a, b of the input layer with initial weights. It can be provided be 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, which are transferred to the neurons 108c, d, e of the next lower layer, that is to say the hidden layer 104, as input values. It can be provided that the hidden layer 104 has a plurality of layers.

Similar to input layer 102, the input values passed to 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 that is applied to the weighted input values form the output values of the neurons 108c, d, e. These output values are transferred to the neurons of the output layer 106 as input values. In FIG. 1, the neurons of the output layer 106 are exemplified as a neuron 108f. Neuron 108f calculates an output value from the input values that were transferred from neurons 108c, d, e to hidden layer 104, weighting the input values and applying one or more functions to the weighted input values. An actual reaction signal 24 can be derived from this output value. This process is also known as the forward feed of a KNN.

In a next step, the actual reaction signal 24 is compared with the target reaction signal 18, which was transmitted to the evaluation unit 20 via the interface 12.

In the next step, the topology of the individual layers 102, 104, 106 of the KNN 22 is adapted in such a way that the KNN 22 calculates the target reaction signal 18 for the transferred audio signal 16. The adaptation of the topology 26 can include changing the weights, adding connections between neurons, removing connections between neurons and / or changing functions that are applied to weighted input values. This process is also known as the reverse feed of a KNN. FIG. 2 shows a block diagram of a method for training a KNN according to an embodiment of the invention. The method comprises steps S1-S4.

In step S1, a signal pair consisting of an audio signal 16 and a desired reaction signal 18 is provided.

In step S2, the KNN 22 is fed forward with the audio signal 16.

In step S3, an actual reaction signal 24 is determined on the basis of the forward feed S2.

In step S4, the artificial neural network 22 is fed backwards due to a difference between the actual reaction signal 24 and the target reaction signal 18. In this case, a changed topology 26 of the KNN, in particular weights, is ascertained in order to improve the determination of actual reaction signals on the basis of the forward feeding.

FIG. 3 shows a block diagram of a method for detecting faulty couplings between a towing vehicle and a trailer. In step St1, a noise that occurs at the coupling during the journey of a combination of the towing vehicle and the trailer is detected as a noise signal by means of a microphone. In step St2, a noise characteristic of a faulty coupling is detected.

In step St3, a reaction to the detected characteristic noise is determined by means of a computing unit of the team. Bezuaszeichen

10 training system

12 interface

14 training data

16 audio signal

18 target reaction signal

20 evaluation unit

22 artificial neural network

24 Actual reaction signal

26 topology

102 input layer

104 hidden layer

106 output layer

108a-f neurons

S1 -S4 process steps

St1 - St3 procedural steps

Claims

claims

1. A method for detecting faulty couplings between a towing vehicle and a trailer with the following steps:

- Detection (St1) of noises as a noise signal, which occur during the journey of a trailer from the towing vehicle and the trailer at a coupling, by means of a microphone of the trailer;

- Detecting (St2) noises characteristic of a faulty coupling;

- Determining (St3) a reaction to the detected characteristic noise using a computing unit of the team.

2. The method according to claim 1, wherein the reaction is a warning signal to a driver of the team and / or to other road users.

3. The method according to any one of the preceding claims, wherein the reaction is a brake signal for braking the team.

4. The method according to any one of the preceding claims, wherein the reaction is a reduced maximum speed of the team.

5. The method according to any one of the preceding claims, wherein the coupling of the combination is further evaluated by means of a camera.

6. The method according to any one of the preceding claims, wherein the characteristic noise with a plurality of microphones, in particular from a microphone array, is detected.

7. The method according to any one of the preceding claims, wherein the noise signal is broken down into components and / or filtered.

8. Driver assistance system for a driver of a team to carry out a method according to one of the preceding claims a multiplicity of microphones for detecting noises as a noise signal, which occur during the travel of a combination of the towing vehicle and the trailer on a coupling,

- A computing unit for evaluating the detected noise signals with regard to the detection of noise characteristic of a faulty coupling and with

a reaction unit for carrying out a determined reaction on the basis of the characteristic noise detected.

9. Computer program product with program code means to carry out a method according to any one of claims 1-7.

10. Training system (10) for a vehicle control system for detecting noises characteristic of a faulty coupling, 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) forward with training data (14) in order 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 to be stored in the vehicle controller for detecting noises characteristic of a faulty coupling.

11. Training system according to claim 10, wherein a target reaction signal (18) of the training data (14) has a warning signal which is directed to a driver.

12. A method for training an artificial neural network (22) of a driver assistance system according to claim 8, comprising the following steps: - Providing (S1) at least one pair of an audio signal and a target 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 reaction signal (24) and the target reaction signal (18).

13. Computer program product with program code means in order to carry out the method according to claim 12.