EP4066072A1 - Modulpriorisierungsverfahren, modulpriorisierungsmodul, kraftfahrzeug - Google Patents
Modulpriorisierungsverfahren, modulpriorisierungsmodul, kraftfahrzeugInfo
- Publication number
- EP4066072A1 EP4066072A1 EP20808053.1A EP20808053A EP4066072A1 EP 4066072 A1 EP4066072 A1 EP 4066072A1 EP 20808053 A EP20808053 A EP 20808053A EP 4066072 A1 EP4066072 A1 EP 4066072A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- module
- reliability
- prioritization
- modules
- prioritization method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012913 prioritisation Methods 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 20
- 230000004913 activation Effects 0.000 claims abstract description 12
- 238000012549 training Methods 0.000 claims description 15
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 230000008447 perception Effects 0.000 claims description 13
- 230000011218 segmentation Effects 0.000 claims description 4
- 230000003213 activating effect Effects 0.000 claims description 2
- 238000013473 artificial intelligence Methods 0.000 description 14
- 230000007613 environmental effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000010801 machine learning Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
- B60W50/023—Avoiding failures by using redundant parts
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
- B60W2050/0006—Digital architecture hierarchy
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2637—Vehicle, car, auto, wheelchair
Definitions
- Module prioritization procedure module prioritization module, motor vehicle
- the invention relates to a module prioritization method, a module prioritization module, and a motor vehicle.
- safety systems for example braking systems in a motor vehicle
- safety systems can be redundant so that if one system fails, another is available which fulfills the same tasks.
- one of the redundant systems will function more reliably than the other.
- Such a reliability can depend, for example, on external circumstances, environmental influences and the like. It can therefore be advantageous to activate a first of the redundant systems in certain first environmental influences, while a second remains deactivated and is only available in an emergency or if the first system fails, with other environmental influences it being desirable for the second system to be activated while the first remains deactivated and is only available in the event of an emergency or failure of the second system.
- the decision as to which system should be activated and which should be deactivated can be made by an artificial intelligence.
- US Pat. No. 9,852,475 B1 discloses a method which estimates an accident risk based on reliability. However, a redundant set of modules is not checked here. In addition, the reliability is also not determined based on a learning algorithm.
- a vehicle is known from US Pat. No. 7,386,372 B2 which recognizes by means of object recognition whether a passenger is on a vehicle seat.
- the reliability of a module is not determined here.
- the object of the present invention is to provide a module prioritization method, a module prioritization module, and a motor vehicle which at least partially overcomes the above-mentioned disadvantages.
- module prioritization method according to the invention according to claim 1 by the module prioritization module according to the invention according to claim 12 and by the motor vehicle according to the invention according to claim 14.
- an inventive module prioritization method for a redundant set of modules of a hardware system comprises: determining a reliability of at least one module of the redundant set of modules, the reliability being determined based on a learning algorithm for prioritizing an activation of a module of the redundant set of modules.
- a module prioritization module according to the invention is set up to carry out a module prioritization method according to the first aspect.
- a motor vehicle according to the invention has a module prioritization module according to the second aspect.
- redundant modules for example as a safety measure, can be provided, for example in the context of autonomous driving.
- a decision can be made as to which of the redundant modules should be prioritized (or which should be trusted).
- a redundancy resolution can be carried out for data-driven (intelligent) modules by means of a reliability assessment.
- an indicator for a reliability is determined (for example with a Monte Carlo dropout), but this has the disadvantage that such methods require a great deal of computing effort and are therefore typically time-consuming. Therefore, such methods typically cannot be carried out in real time (e.g. during an autonomous journey).
- some exemplary embodiments relate to a module prioritization method for a redundant set of modules of a hardware system, comprising: determining a reliability of at least one module of the redundant set of modules, the reliability being determined based on a learning algorithm for prioritizing activation of a module of the redundant set of modules.
- the hardware system can comprise any hardware system which is set up to generate and / or evaluate data.
- the hardware system can comprise several modules, for example a sensor, a control unit, an on-board computer, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), a braking system, a safety system, and the like.
- a hardware system according to the invention typically comprises at least two modules which form a set according to the invention, wherein the two modules can replace one another.
- two brake systems can be present in a motor vehicle, so that one brake system is active or activated in the event of failure of the other brake system, whereby driving safety can advantageously be ensured.
- Such redundancy can typically be provided in safety modules (for example a motor vehicle), but the present invention is not limited to safety modules.
- a redundant set of distance sensors for example lidar, radar), cameras (for example stereo cameras) and the like can also be provided in order to enable (partially) autonomous driving of a motor vehicle.
- comfort modules for example infotainment modules, heating and air conditioning control modules
- vehicle interior monitoring modules can also be redundant.
- the present invention can always be used when there is or can be a redundant set of modules, in particular also in a technical environment that does not concern motor vehicles, such as medical technology, medical robotics, aviation, shipping, rail transport, space travel, and the like.
- a module prioritization method can include determining a reliability of at least one module of the redundant set of modules.
- the reliability can be an indicator for an accuracy or for a reliability of the at least one module.
- the at least one module depending on environmental influences (for example temperature, time of day, air pressure, and the like) produces a different data output than would be the case with a reference measurement.
- a measured value for example an oil pressure
- a measured value can only be imprecise (ie an amount of a difference between an oil pressure measured by the at least one module and an actual oil pressure is above a threshold value or outside a tolerance range), while the measured value is at other values
- Environmental influences is reliable (ie an amount of a difference between an oil pressure measured by the at least one module and an actual oil pressure is below a threshold value or within a tolerance range).
- the reliability can be determined for at least one module of the redundant set of modules. However, the reliability can also be determined for more than one to all modules of the redundant set of modules.
- the reliability is determined based on a learning algorithm.
- the learning algorithm is implemented by means of an artificial intelligence, which can be provided on the at least one module, on another module of the redundant set of modules, or on a module prioritization module specially provided for this purpose.
- the module prioritization module can have an artificial intelligence which determines a reliability of a perception module which, for example, carries out object recognition based on sensor data and the like.
- an artificial intelligence can also be implemented in the perception module, the reliability of which can be determined, for example, with a Monte Carlo droput (see below).
- the learning algorithm can be applied by an artificial intelligence (Kl) which, for example, uses methods based on machine learning, deep learning, explicit feature, and the like, such as pattern recognition, edge detection, a histogram-based method, pattern matching (Pattern Matching, Color Match, and the like.
- Kl artificial intelligence
- the learning algorithm comprises machine learning.
- the learning algorithm can be based on at least one of a scale invariant feature transform (SIFT), a gray level co-occurrence matrix (GLCM), and the like.
- SIFT scale invariant feature transform
- GLCM gray level co-occurrence matrix
- the machine learning can also be based on a classification method, such as at least one of random forest, support vector machine, neural network, Bayesian network, and the like, such deep learning methods, for example at least one of autoencoder, generative adversarial network, weak supervised learning, boot strapping, and the like.
- a classification method such as at least one of random forest, support vector machine, neural network, Bayesian network, and the like, such deep learning methods, for example at least one of autoencoder, generative adversarial network, weak supervised learning, boot strapping, and the like.
- machine learning can also be based on data clustering methods, such as, for example, density-based spatial clustering of applications with noise (DBSCAN), and the like.
- DBSCAN density-based spatial clustering of applications with noise
- the supervised learning can also be based on a regression algorithm, a perceptron, a Bayesian classification, a naive Bayesian classification, a closest-neighbor classification, an artificial neural network, and the like.
- the reliability can be determined in a training session, so that computing power can advantageously be saved when the module prioritization method according to the invention is used.
- the certain reliability can be stored, for example, in a data memory, determined by the artificial intelligence, with advantageously no further training being necessary, or with an existing training being able to be continued, whereby the determination of the reliability can advantageously be improved.
- the specific reliability can then be determined, that is to say, for example, can be retrieved from the data memory, from the artificial intelligence, and the like, so that, based on the reliability for the at least one module, a decision can be made about whether the module is activated or remains activated), or whether another module of the redundant set of modules is activated (or remains activated), wherein a reliability can also be determined for the other module.
- the present invention is not limited to such a case. For example, it can be predefined that a first module should in no case be activated when the reliability is below a predefined threshold value, so that a second module is automatically activated (or remains).
- prioritization is to be understood as meaning that activation of a first module can be preferred to activation of a second module.
- the reliability can be determined again (for example in the event of a change in environmental influences), so that activation of the second module can then be preferred to activation of the first module.
- the learning algorithm is based on a Monte Carlo dropout.
- a Monte Carlo dropout can be used for an artificial neural network during training.
- impressions are presented to the neural network at each iteration.
- an impression can be understood as a measured value or an output date (or output data) of a sensor, an output of a control device, and the like.
- some neurons of the neural network are switched off pseudo-randomly with each iteration.
- the neural network can, for example, determine a measured value of a sensor in each iteration based on the output data.
- the determined value can be different for each iteration.
- a mean value over all measured values can be formed, a mean deviation of the mean value (for example Gaussian error, standard deviation, half-width, and the like) being a measure of the reliability.
- a reliability label can be generated which, for example, can be used by a further artificial intelligence as an input to determine the reliability in a more efficient way than with a Monte Carlo dropout.
- a Monte Carlo dropout does not have to be carried out every time, but rather only the generated reliability labels are taken into account, whereby computing power can advantageously be saved.
- the Monte Carlo dropout can be carried out with a high intensity (in a statistically relevant number of repetitions), since a high latency can be accepted in a training session.
- This also advantageously results in a high sampling rate with which the Monte Carlo dropout can be carried out than when it is started up by a user (for example in road traffic).
- the redundant set of modules includes at least one of the controller, sensor, processor, and security system as described herein.
- the module prioritization method further comprises: selecting a module of the redundant set of modules based on the determined reliability; and activating the selected module.
- the module can be selected by the further artificial intelligence.
- the selected module does not have to correspond to the at least one module in every case.
- the reliability can be determined for at least one module, while, however, a further module is selected and activated without the reliability having been determined.
- the reliability is further determined based on a monitored training of the at least one module, as described herein.
- the module prioritization method further comprises: using an input of the monitored training to determine the reliability of the at least one module, as described herein.
- a module prioritization module can use input from a perception module (as described herein).
- An activation of at least one intermediate layer of the perception module for determining the reliability can also serve as an input in a module prioritization module.
- a combination of an input and at least one intermediate layer of the perception module can also be considered.
- the module prioritization method further comprises: using an output of the monitored training to determine the reliability of the at least one module, as described herein (for example using multiple reliability labels).
- the learning algorithm is carried out with a neural network, as described herein.
- the neural network comprises a convolution network.
- reliability is determined for at least one of object recognition, semantic segmentation, free space recognition, and depth estimation.
- a predetermined object a pattern, a shape, and the like can be recognized based on the methods described above (for example, pattern matching).
- a classification can take place for each pixel or for a group of pixels of a camera.
- free space detection the absence of an object or a distance, an area, a volume, and the like, between a first object (for example a vehicle) and a second object can be detected.
- Such a distance can also be determined in the case of a depth estimate.
- the reliability further comprises a pixel-precise reliability and / or a bounding box value.
- the pixel-precise reliability can comprise an output value of a pixel or a group of pixels for a semantic segmentation.
- a bounding box value can include a threshold value which enables a statement to be made about the extent to which a recognized object deviates from a predetermined object (or pattern, and the like) for object recognition, free space recognition, and / or depth estimation.
- Some exemplary embodiments relate to a module prioritization module which is set up to carry out a module prioritization method according to the invention.
- the module prioritization module can be a control unit, a computer, a CPU, a GPU, an FPGA (field-programmable gate array), which has an artificial intelligence, as described herein, or which is set up to execute an algorithm or a method which or which is based on an artificial intelligence training.
- a control unit a computer, a CPU, a GPU, an FPGA (field-programmable gate array), which has an artificial intelligence, as described herein, or which is set up to execute an algorithm or a method which or which is based on an artificial intelligence training.
- the module prioritization module is also set up to determine the reliability based on a reliability label of a perception module, as described herein.
- the perception module can be a first artificial intelligence which determines a reliability based on a Monte Carlo dropout, as described herein.
- Some exemplary embodiments relate to a motor vehicle which has a module prioritization module according to the invention, as described herein.
- the motor vehicle can refer to any vehicle operated by an engine (e.g. internal combustion engine, electric machine, etc.), such as an automobile, a motorcycle, a truck, a bus, agricultural or forestry tractors, and the like, where, As described above, the present invention is not intended to be limited to an automobile.
- an engine e.g. internal combustion engine, electric machine, etc.
- the motor vehicle further comprises a perception module for generating a reliability label for the module prioritization module, as described herein.
- FIG. 1 shows an exemplary embodiment of a module prioritization method according to the invention in a block diagram
- FIG. 2 shows a further exemplary embodiment of a module prioritization method according to the invention in a block diagram
- Fig. 3 shows an embodiment of a motor vehicle according to the invention in a block diagram.
- FIG. 1 An embodiment of a module prioritization method 1 according to the invention is shown in FIG. 1 in a block diagram.
- a reliability of at least one module of a redundant set of modules of a hardware system is determined, the reliability being determined based on a learning algorithm for prioritizing activation of a module of the redundant set of modules, as described herein.
- FIG. 11 Another exemplary embodiment of a module prioritization method 10 according to the invention is shown in a block diagram in FIG. 11, a reliability of at least one module of a redundant set of modules of a hardware system is determined, the reliability being determined based on a learning algorithm for prioritizing activation of a module of the redundant set of modules, as described herein.
- a module of the redundant set of modules is selected based on the determined reliability, as described herein.
- FIG 3 shows an exemplary embodiment of a motor vehicle 20 according to the invention.
- the motor vehicle 20 has a redundant set of emergency braking systems 21.
- the motor vehicle 20 has a perception module 22 which is set up to determine the reliability of the hazard braking systems of the set of hazard braking systems 21 with the aid of a Monte Carlo dropout.
- the perception module 22 is set up to generate a reliability label and to transmit this to a module prioritization module 23 comprised by the motor vehicle 20, which, on the basis of the label, learns to prioritize an emergency braking system.
- the module prioritization module 23 is also set up to transmit an activation command to a controller 24, which then activates one of the emergency braking systems.
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102019218614.9A DE102019218614A1 (de) | 2019-11-29 | 2019-11-29 | Modulpriorisierungsverfahren, Modulpriorisierungsmodul, Kraftfahrzeug |
PCT/EP2020/082163 WO2021104904A1 (de) | 2019-11-29 | 2020-11-13 | Modulpriorisierungsverfahren, modulpriorisierungsmodul, kraftfahrzeug |
Publications (1)
Publication Number | Publication Date |
---|---|
EP4066072A1 true EP4066072A1 (de) | 2022-10-05 |
Family
ID=73455688
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP20808053.1A Pending EP4066072A1 (de) | 2019-11-29 | 2020-11-13 | Modulpriorisierungsverfahren, modulpriorisierungsmodul, kraftfahrzeug |
Country Status (3)
Country | Link |
---|---|
EP (1) | EP4066072A1 (de) |
DE (1) | DE102019218614A1 (de) |
WO (1) | WO2021104904A1 (de) |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5084878A (en) * | 1988-10-24 | 1992-01-28 | Hitachi, Ltd. | Fault tolerant system employing majority voting |
US7386372B2 (en) * | 1995-06-07 | 2008-06-10 | Automotive Technologies International, Inc. | Apparatus and method for determining presence of objects in a vehicle |
US6820213B1 (en) * | 2000-04-13 | 2004-11-16 | Stratus Technologies Bermuda, Ltd. | Fault-tolerant computer system with voter delay buffer |
US7266532B2 (en) * | 2001-06-01 | 2007-09-04 | The General Hospital Corporation | Reconfigurable autonomous device networks |
US10185998B1 (en) * | 2014-05-20 | 2019-01-22 | State Farm Mutual Automobile Insurance Company | Accident fault determination for autonomous vehicles |
US9978013B2 (en) * | 2014-07-16 | 2018-05-22 | Deep Learning Analytics, LLC | Systems and methods for recognizing objects in radar imagery |
DE102017210955A1 (de) * | 2017-06-28 | 2019-01-17 | Volkswagen Aktiengesellschaft | Verfahren, vorrichtung und computerlesbares speichermedium mit instruktionen zum auflösen einer redundanz von zwei oder mehr redundanten modulen |
-
2019
- 2019-11-29 DE DE102019218614.9A patent/DE102019218614A1/de active Pending
-
2020
- 2020-11-13 EP EP20808053.1A patent/EP4066072A1/de active Pending
- 2020-11-13 WO PCT/EP2020/082163 patent/WO2021104904A1/de unknown
Also Published As
Publication number | Publication date |
---|---|
DE102019218614A1 (de) | 2021-06-02 |
WO2021104904A1 (de) | 2021-06-03 |
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