CN116461517A - Method for detecting a change between a driving state and a vehicle stop - Google Patents
Method for detecting a change between a driving state and a vehicle stop Download PDFInfo
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- CN116461517A CN116461517A CN202310049158.7A CN202310049158A CN116461517A CN 116461517 A CN116461517 A CN 116461517A CN 202310049158 A CN202310049158 A CN 202310049158A CN 116461517 A CN116461517 A CN 116461517A
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- 230000008859 change Effects 0.000 title claims abstract description 19
- 238000010801 machine learning Methods 0.000 claims abstract description 50
- 230000007704 transition Effects 0.000 claims abstract description 26
- 238000012549 training Methods 0.000 claims description 19
- 230000009466 transformation Effects 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 12
- 230000001133 acceleration Effects 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 5
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 238000000844 transformation Methods 0.000 description 2
- 239000006096 absorbing agent Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013016 damping Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/181—Preparing for stopping
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/18—Braking system
- B60W2510/182—Brake pressure, e.g. of fluid or between pad and disc
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/22—Suspension systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/28—Wheel speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
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- Mathematical Physics (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
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- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Automation & Control Theory (AREA)
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- Mechanical Engineering (AREA)
- Control Of Vehicle Engines Or Engines For Specific Uses (AREA)
- Regulating Braking Force (AREA)
Abstract
A method for detecting a change between a driving state and a vehicle stop. The invention relates to a method for detecting a change (W) between a driving state and a vehicle stopping M ) Is a method of (2). Here, the method comprises the following steps: determining (A) at least one driving dynamics state variable (S W ) The driving dynamics state variable (S W ) Input (B) into a trained machine learning system, and output (C) about a transition (W) between a driving state and a vehicle parking M ) Is a piece of information of (a).
Description
Technical Field
The invention relates to a method for detecting a change between a driving state and a vehicle stop. Furthermore, the invention relates to a method for training a machine learning system to recognize a transition between a driving state and a vehicle parking.
Background
Various vehicle functions are indicated depending on information whether the vehicle is in a stopped or dynamic running state. The automated parking brake can be closed only when the vehicle is stopped, for example. Vehicle parking is typically determined by a braking system that detects rotation of each wheel via a rotational speed sensor. Once the sensor no longer recognizes wheel rotation or wheel peripheral speed is below a prescribed threshold, a park condition is determined for the vehicle. With this information, the vehicle functions that are permitted to be executed only in parking can be released.
A method and a device for detecting a stop of a vehicle are known from DE 199 63 B4. In the method, parking identification is performed based on the speed of the vehicle or the speed of at least one wheel of the vehicle. In this case, the recognition is carried out on the basis of the braking force during braking of the vehicle and on the basis of two speed thresholds.
A single hardware failure may result in failure of the primary parking identification. Thus, all vehicle functions having a correlation with information about vehicle parking are degraded or only marginally available. If functional capability must be obtained even in the event of such a simple failure, redundant identification of the vehicle parking is required by secondary parking identification, such as an additional wheel speed sensor system.
Disclosure of Invention
The invention is based on the object of specifying a method with which a change between a driving state and a vehicle stop can be determined in a simple and reliable manner.
This object is achieved by a method for detecting a change between a driving state and a vehicle stop, having the features of claim 1. This object is additionally achieved by a method for training a machine learning system to recognize a change between a driving state and a vehicle stop, having the features of claim 4. Preferred embodiments may be gleaned from the dependent claims.
The invention relates to a method for detecting a change between a driving state and a vehicle stop. Here, the method comprises the following steps: determining at least one driving dynamics state variable, inputting the driving dynamics state variable into a trained machine learning system, and outputting information about a transition between a driving state and a vehicle stop.
Upon recognition of a change between the driving state and the parking of the vehicle, it should be determined on the one hand when the vehicle has reached a parking and on the other hand when the vehicle has started from a parking. For the determination, the driving dynamics state variables are determined. The driving dynamics state variable is a value that changes continuously during driving. The driving dynamics state variable changes in particular during a transition between a driving state and a vehicle stop. Such a state variable is advantageously determined via a sensor. Such values may also be calculated from other measured values.
The machine learning system has the advantage that it can determine statements about a change between the driving state and the vehicle parking from values which are only influenced by such a change. The determination of the transformation is thereby simplified, since the machine learning system is not specified to a specific value. So that all values affected only by the transformation can be used. Such a machine learning system can thus be used variably.
In a preferred embodiment of the invention, the driving dynamics state variables include the acceleration of the vehicle body in the longitudinal direction, the acceleration of the vehicle body about the vehicle transverse axis, the state of compression of the individual wheels (einfeldizumand), the brake pressure in the master brake cylinder, the brake pressure in the wheel brakes of the individual wheels and the wheel speed. Each of these parameters changes when changing between the driving state and the vehicle stopping. So that the transformations can be identified from these values by the machine learning system.
Advantageously, these driving dynamics state variables are determined by means of existing sensors. In other words, these values of these sensors are already required for other vehicle functions and thus have been installed. No additional sensors have to be installed to identify the transformations so that this method can be performed economically. For example, level sensors for measuring compression have been used for dynamic headlamp range adjustment.
The object on which the invention is based is additionally achieved by a method for training a machine learning system to recognize a change between a driving state and a vehicle parking. In a first step, training data comprising at least one driving dynamics state variable representing a transition between a driving state and a vehicle stop are input into a machine learning system.
From these training data, the machine learning system determines a transition between a driving state and a vehicle parking. In particular, the point in time of this transformation is determined. The determined transition of the machine learning system is then compared with the actual point in time of the transition between the driving state and the vehicle parking. The actual transformation is a value determined, for example, by a wheel speed sensor. In other words, it is checked whether the transformed time point determined by the machine learning system coincides with the actual time point of the transformation. In a next step, the determined deviation is evaluated using a cost function.
In a further step, parameters characterizing the behavior of the model are changed, with the aim of improving the evaluation by the cost function in the further processing of the training data by the machine learning system. This improves the determination of the transformation at each learning step. If the determined reliability factor is greater than a predetermined value, the determination of the transformation using the machine learning system is released. Thus, the determination of the transformation using the machine learning system is only used when sufficient accuracy is achieved. Thereby improving the safety of such vehicles. The determination of the transformation by means of a machine learning system is a simple and economical possibility here. This applies in particular to the determination of the driving dynamics state variables by means of already existing sensors.
In a further preferred embodiment of the invention, data concerning the identified transition between the driving state and the vehicle parking is provided via the primary identification unit. The primary identification unit is here a device which usually detects the transformation. The recognition of the transformation by the machine learning system thus constitutes a redundant system for the primary recognition unit.
Training data need not be generated prior to delivery of the vehicle due to the primary identification unit data. Thus, training data is generated in each vehicle itself. Thus, the use of machine learning systems in motor vehicles is economically possible.
The machine learning system is preferably further trained during operation by means of the determined driving dynamics state variables. Thus, after the reliability factor is sufficient, training is not terminated. Due to wear, various factors may change during the life of the vehicle. For example, the damping effect of a shock absorber decreases over time. The load state may also change during different driving periods. The machine learning system is thus constantly adapted to the current event, so that the accuracy of the transition between the driving state and the vehicle parking can be determined with high accuracy. Continuously checking the reliability factor by constant training makes it possible to quickly recognize inactivity of the machine learning system. In this way, the reliability of the recognition of the transition between the driving state and the vehicle parking is additionally increased.
In an advantageous further development, the recognition of the change between the driving state and the vehicle parking is deactivated by means of the machine learning system if the training has been interrupted for a predetermined time during operation. If the recognition of the parking is carried out only by the machine learning system after the primary recognition unit is defective, the training of the system remains non-taking place. However, due to lack of training, machine learning systems become inaccurate due to wear of the vehicle. In order not to provide the vehicle system with erroneous values for the transformation, the identification is completely deactivated after a predetermined time. Additionally, other fault messages may be output for the driver. Thereby improving the safety of the vehicle.
Advantageously, before the input of the driving dynamics state variables, a characteristic variable is additionally determined from the driving dynamics state variables, which is likewise input into the machine learning system. The characteristic variables are derived from the driving dynamics state variables and provide different recognitions from the various driving dynamics state variables. Such characteristic variables may include, for example, average values, gradients in the rising/falling direction, frequencies or decay times.
Based on the input data, the machine learning system determines data related to the evaluation and weights the data accordingly. The machine learning system becomes easy to determine the relevant data by increasing the amount of data. Thereby improving the accuracy of the determination of the transition between the running state and the vehicle stop. Additionally, the time before the machine learning system has sufficient reliability is shortened.
In a further advantageous embodiment, the driving dynamics state variables, which characterize the transition between the driving state and the vehicle parking, are transmitted to a cloud in which the machine learning system is trained. Thus, training does not occur directly in the vehicle. As a result, the computing power implemented in the vehicle can be significantly reduced, whereby drive energy can be saved and the effective distance can be increased. In addition, it is possible to adapt only minimally the determined calculation models of the same type of vehicle in the cloud, so that the result is improved and sufficient reliability is achieved more quickly. The parameters for calculating the transformation determined in the cloud are then transmitted to the vehicle.
In addition, a control unit of a motor vehicle is described, with which the method according to the invention is implemented. Such a control unit may have a plurality of control devices. The primary recognition unit and the machine learning system may be arranged on the control device. Also, these functions may be arranged on different control devices. The advantages mentioned for the method are achieved with such a control unit.
The machine learning system is advantageously arranged on an already existing control device. Thus, no separate control device is required for the machine learning system. This recognition of the change between the driving state and the parking of the vehicle can thus be provided simply and economically.
The above-described method may in particular be computer-implemented and thus embodied in software, for example. The invention is thus also directed to a computer program having machine-readable instructions which, when executed on one or more computers, cause the one or more computers to perform the described methods.
The invention also relates to a machine-readable data carrier and/or a downloaded product having a computer program. The downloaded product is a digital product that is transmittable via the data network, i.e. that can be downloaded by a user of the data network, which digital product can be sold for immediate downloading, for example in an online store.
Drawings
Embodiments of the invention are illustrated in the accompanying drawings and described in more detail in the following description. Wherein:
figure 1 shows an embodiment of a method for identifying a transition between a driving state and a vehicle parking,
FIG. 2 illustrates an embodiment of a method for training a machine learning system.
Detailed Description
Fig. 1 shows a method for detecting a change W between a driving state and a vehicle parking M Embodiments of the method of (a). Thus, not only the parking of the vehicle but also the start of the vehicle is determined with this method. In a first step a, a driving dynamics state variable S is determined from, for example, a sensor W . In this case, the driving dynamics state variable S W May be acceleration in the longitudinal and transverse directions of the vehicle. For example, when changing W between parking and driving conditions M When the characteristic vehicle body vibration occurs.
In a subsequent step B, the driving dynamics state variable S W Is input into a trained machine learning system. The machine learning system is based on the driving dynamics state variables S W Calculating a transition W between a driving state and a vehicle parking M Is a time point of (2). The corresponding information is output in a subsequent step C.
FIG. 2 illustrates an embodiment of a method for training a machine learning system. In a first step F, driving dynamics state variables S of the vehicle are recorded during driving W . In this embodiment, in particular, the wheel speed n of the wheel speed sensor is determined here R Acceleration of the vehicle body in the longitudinal direction and acceleration of the vehicle body around the lateral axis of the vehicle. Here, the wheel speed n of the wheel speed sensor R Provided by the primary identification unit. In step G, the primary identification unit calculates the actual transition W between the driving state and the vehicle parking T 。
In step H, all driving dynamics state variables S W Additionally provided to a computing unit, which is specific to the driving dynamics state variables S W Determining characteristic parameter K G Such as average, decay time. The computing unit may be arranged either in the vehicle or in the cloud. In a further step I, the driving dynamics state variables S W And the characteristic variable KG is input into the machine learning system. In a next step J, the machine learning system calculates a transition W between the driving state and the vehicle parking M 。
In a subsequent step K, the transformation W to be identified by the machine learning system M Is calculated by the primary identification unit T Is compared with the time points of the (c). In step L, the bias is evaluated using a cost function, and in a subsequent step M, parameters of the machine learning model are changed. And (3) subsequent inspection: reliability factor Z F Whether a predetermined value X for reliability is exceeded. If this should be the case, the pair transformation W is released in step N M Is a function of the identification of the device. Transform W M The identification of (2) then represents a redundant system to the primary identification unit.
If the reliability factor Z F The method should not be high enough and restarted. Even if the reliability factor Z F It should be high enough that the method also restarts in this case, as the machine learning system is thereby trained further. Since various factors such as load or wear may vary, the transition W between the running state and the vehicle parking can be more accurately identified M 。
Claims (13)
1. A method for detecting a change (W) between a driving state and a vehicle parking M ) The method comprising the steps of:
-determining (a) at least one driving dynamics state variable (S W )，
-comparing the driving dynamics state variable (S W ) Input (B) into a trained machine learning system, and
-the output (C) is related to the transition (W) between the driving state and the vehicle parking M ) Is a piece of information of (a).
2. The method according to claim 1, characterized in that the driving dynamics state variables (S W ) At least one of the following parameters is specified:
acceleration of the vehicle body in the longitudinal direction,
acceleration of the vehicle body around the transverse axis of the vehicle,
the compressed state of the individual wheels,
the brake pressure in the master brake cylinder,
brake pressure in wheel brakes of individual wheels, and
-wheel speed.
3. Method according to claim 1 or 2, characterized in that existing sensors are used for measuring driving dynamics state variables (S W )。
4. A method for training a machine learning system to identify a transition (W) between a driving state and a vehicle parking M ) The method comprising the steps of:
-inputting training data (I) comprising at least one transition (W) representative of the driving state and the stopping of the vehicle M ) Is a driving dynamics state variable (S) W )，
-determining (J) a transition (W) between a driving state and a vehicle parking by means of the machine learning system M )，
-transforming (W M ) And the actual transition between the running state and the vehicle parking (W T ) The comparison (K) is performed,
evaluating (L) the deviation using a cost function,
changing (M) parameters characterizing the behavior of the model, with the aim of predictively improving the evaluation by means of a cost function when the training data are further processed by means of a machine learning system, and
-if the determined reliability factor (Z F ) Greater than a predetermined value (X), releasing (N) the transformation (W) using the machine learning system M ) Is determined by the above-described method.
5. Method according to claim 4, characterized in that, in relation to the identified transition (W T ) Is provided via the primary identification unit.
6. Method according to claim 4 or 5, characterized in that the machine learning system is operated by means of the determined driving dynamics state variables (S W ) Is further trained.
7. Method according to claim 6, characterized in that the transition between the driving state and the vehicle parking (W M ) Is a function of the identification of the device.
8. Method according to any one of claims 4 to 7, characterized in that, upon input of the driving dynamics state variables (S W ) Previously, the driving dynamics state variable (S W ) Wherein the characteristic parameter (K) is determined G ) The characteristic variable is likewise input to the machine learning system.
9. Method according to any of claims 4 to 8, characterized in that the transition (W M ) Is transmitted to a cloud in which the machine learning system is trained.
10. A motor vehicle control unit for implementing the method according to any one of the preceding claims.
11. A computer program comprising machine-readable instructions which, when executed on one or more computers, cause the one or more computers to perform the method of any one of claims 4 to 9.
12. A machine-readable data carrier and/or a download product with a computer program according to claim 11.
13. A computer equipped with a computer program according to claim 11 and/or with a machine-readable data carrier and/or a downloadable product according to claim 12.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102022200562.7 | 2022-01-19 | ||
| DE102022200562.7A DE102022200562A1 (en) | 2022-01-19 | 2022-01-19 | Method for detecting a change between a driving state and a vehicle standstill |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116461517A true CN116461517A (en) | 2023-07-21 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310049158.7A Pending CN116461517A (en) | 2022-01-19 | 2023-01-17 | Method for detecting a change between a driving state and a vehicle stop |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN116461517A (en) |
| DE (1) | DE102022200562A1 (en) |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE19963750B4 (en) | 1999-12-30 | 2008-06-19 | Robert Bosch Gmbh | Method and device for detecting the stoppage of a vehicle |
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2022
- 2022-01-19 DE DE102022200562.7A patent/DE102022200562A1/en active Pending
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- 2023-01-17 CN CN202310049158.7A patent/CN116461517A/en active Pending
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| Publication number | Publication date |
|---|---|
| DE102022200562A1 (en) | 2023-07-20 |
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