WO2022251890A1 - Verfahren und system zum testen eines fahrerassistenzsystems für ein fahrzeug - Google Patents
Verfahren und system zum testen eines fahrerassistenzsystems für ein fahrzeug Download PDFInfo
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- WO2022251890A1 WO2022251890A1 PCT/AT2022/060183 AT2022060183W WO2022251890A1 WO 2022251890 A1 WO2022251890 A1 WO 2022251890A1 AT 2022060183 W AT2022060183 W AT 2022060183W WO 2022251890 A1 WO2022251890 A1 WO 2022251890A1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3696—Methods or tools to render software testable
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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
- 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
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G06N3/00—Computing arrangements based on biological models
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- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
Definitions
- the invention relates to a computer-implemented method for testing a driver assistance system of a vehicle in a simulated environment, a scenario model being provided which specifies input parameters which are necessary for defining a specific scenario and their respective relationship to the other parameters of the scenario model and which indicates those of the input parameters that are changeable to adapt the particular scenario and their possible parameter values.
- the invention further relates to a corresponding system.
- driver assistance systems Advanced Driver Assistance Systems - ADAS
- autonomous Driving - AD autonomous driving
- Driver assistance systems make an important contribution to increasing active traffic safety and serve to increase driving comfort.
- ABS anti-lock braking system
- ESP electronic stability program
- Driver assistance systems that are already being used to increase active road safety include a parking assistant, an adaptive cruise control system, also known as adaptive cruise control (ACC), which adaptively regulates a desired speed selected by the driver based on the distance from the vehicle in front.
- ACC stop-and-go systems which in addition to the ACC causes the vehicle to continue driving automatically in traffic jams or when vehicles are stationary
- lane keeping or lane assist systems which automatically keep the vehicle in its lane hold
- pre-crash systems which in the event of the possibility of a collision, for example, a Prepare or initiate braking to take the kinetic energy out of the vehicle and, if necessary, initiate other measures if a collision is unavoidable.
- driver assistance systems both increase safety in traffic by warning the driver in critical situations and initiate independent intervention to avoid or reduce accidents, for example by activating an emergency braking function.
- driving comfort is increased by functions such as automatic parking, automatic lane keeping and automatic distance control.
- the safety and comfort gain of a driver assistance system is only perceived positively by the vehicle occupants if the support from the driver assistance system is safe, reliable and - as far as possible - comfortable.
- each driver assistance system depending on its function, must manage scenarios that occur in traffic with maximum safety for the vehicle and without endangering other vehicles or other road users.
- the respective degree of automation of vehicles is divided into so-called automation levels 1 to 5 (see, for example, the SAE J3016 standard).
- the present invention relates in particular to vehicles with driver assistance systems of automation level 3 to 5, which is generally considered to be autonomous driving.
- the challenges for testing such systems are manifold. In particular, a balance must be found between the test effort and the test coverage.
- the main task when testing ADAS/AD functions is to demonstrate that the function of the driver assistance system is guaranteed in all conceivable situations, especially in critical driving situations. Such critical driving situations have a certain degree of danger, since no reaction or an incorrect reaction of the respective driver assistance system can lead to an accident.
- the testing of driver assistance systems therefore requires a large number of driving situations that can arise in different scenarios to be taken into account.
- the range of possible scenarios is generally spanned by many dimensions (e.g. different road properties, behavior of other road users, weather conditions, etc.). From this almost infinite and multidimensional parameter space, it is particularly relevant for testing driver assistance systems to extract such parameter constellations for critical scenarios that can lead to unusual or dangerous driving situations.
- driver assistance systems in particular driver assistance systems for autonomous driving
- scenarios that cover the entire multidimensional parameter space.
- the scenarios tested should be representative of the entire multidimensional parameter space.
- a first aspect of the invention is a computer-implemented method for testing a driver assistance system of a vehicle in a simulated environment, having the following work steps:
- a second aspect of the invention relates to a system for testing a driver assistance system of a vehicle in a simulated environment, having:
- Storage means for providing a scenario model which specifies input parameters which are necessary for the definition of a certain scenario and their respective relationship to the other parameters of the scenario model and which of the input parameters which are changeable for adapting the certain scenario and their indicates possible parameter values; Means for selecting combinations of parameter values, each of which characterizes a scenario, on the basis of the scenario model using an algorithm for combinatorial testing, wherein when selecting the combinations for an input parameter under consideration, only interactions with a predefined maximum number of other input parameters are taken into account;
- Means for detecting a driving behavior of the at least one driver assistance system to be tested in the simulated environment Means for detecting a driving behavior of the at least one driver assistance system to be tested in the simulated environment.
- An environment of the vehicle within the meaning of the invention is preferably formed at least by objects that are relevant for the vehicle guidance by the driver assistance system.
- an area surrounding the vehicle includes scenery and dynamic elements.
- the scenery preferably includes all stationary elements.
- a scenario within the meaning of the invention is preferably formed from a chronological sequence of, in particular, static scenes.
- the scenes indicate, for example, the spatial arrangement of the at least one other object relative to the ego object, e.g. B. the constellation of road users.
- a scenario can contain a driving situation in which a driver assistance system at least partially controls the vehicle, which is called the ego vehicle and is equipped with the vehicle assistance system, e.g. B. performs at least one vehicle function of the ego vehicle auto nom.
- a driving situation within the meaning of the invention preferably describes the circumstances that are to be taken into account for the selection of suitable behavior patterns of the driver assistance system at a specific point in time.
- a driving situation is therefore preferably subjective in that it represents the view of the ego vehicle. It preferably further comprises relevant conditions, possibilities and Factors influencing actions.
- a driving situation is more preferably derived from the scene by an information selection process based on transients, e.g. B. emission-specific as well as permanent goals and values.
- a driving behavior within the meaning of the invention is preferably a behavior of the driver assistance system through action and reaction in the surroundings of the vehicle.
- a means within the meaning of the invention can be configured as hardware and/or software and in particular a processing device, in particular a microprocessor device (CPU) and/or a or have several programs or program modules.
- the CPU can be designed to process commands that are implemented as a program stored in a memory system, to detect input signals from a data bus and/or to emit output signals to a data bus.
- a storage system can have one or more, in particular different, storage media, in particular optical, magnetic, solid and/or non-volatile media.
- the program may be arranged to embody or be capable of performing the methods described herein such that the CPU can perform the steps of such methods.
- the invention is based on the idea of delimiting or restricting the parameter space, in which parameter constellations for scenarios are possible, to the most relevant areas.
- the method of combinatorial testing is used for this, in which an algorithm is used to generate relevant combinations of the input parameters.
- the goal of combinatorial testing algorithms is to generate as much information as possible with as few tests as possible.
- Empirical results show that at most sixfold interactions need to be taken into account in order to cause all errors in a driver assistance system to be tested. This takes into account the fact that not all parameters are relevant in order to cause errors in the driver assistance system to be tested.
- the method according to the invention preferably runs fully automatically.
- the scenario model is based on an ontology which specifies parameters with which scenarios can be characterized and relationships or interactions between the various parameters.
- An ontology within the meaning of the invention is preferably a formally ordered representation of a set of parameters and the relationship or interaction existing between them in a traffic scenario.
- the predefined maximum number of other input parameters depends on the input parameter considered in each case when the interaction is taken into account.
- combinatorial testing generally takes into account all possible combinations of parameters or parameter values of a certain degree (combinatorial strength).
- the number of interactions considered preferably depends on the criticality of the input parameter considered in each case.
- the criticality is the relevance of a parameter in relation to a possible accident risk in relation to the driver assistance system.
- hard and soft boundary conditions are set when selecting combinations of parameter values taken into account, where the hard boundary conditions exclude combinations of parameter values that are not physically possible and where the soft boundary conditions contradict combinations of parameter values that are physically possible but when the vehicle maneuvers in the simulated environment.
- the scenario model has a format that can be processed by the algorithm.
- Such a format can be, for example, OWL, a so-called Ontology Web Language.
- the criticality of the considered input parameter or a parameter value of the considered input parameter is determined with the following work steps:
- a quality of the scenario as a function of a predefined criterion in relation to a driving situation that has arisen, in particular a criticality of the driving situation that has arisen;
- the method also has the following work step:
- the varying is carried out by crossing and/or mutation, with parameter values of a part of the input parameters being replaced by parameter values of another scenario or a parameter value of at least one input parameter being replaced by a new value.
- crossing or mutation the entire parameter space can be searched particularly reliably for possible parameter combinations with high criticality.
- parameters of the scenario are selected from the following group, depending on the type of driver assistance system to be tested:
- Speed in particular an initial speed, of a road user; a direction of movement, in particular a trajectory, of a road user; lighting conditions; Weather; road surface; Temperature; number and position of static and/or dynamic objects; state and appearance of static and/or dynamic objects; a speed and a direction of movement, in particular a trajectory, of the dynamic objects; condition of signaling installations, in particular light signaling installations; traffic signs, number of lanes; acceleration or deceleration of road users or objects; Signs of soiling and/or aging of the road surface, geographic orientation of the traffic situation.
- FIG. 1 shows a diagram of the probability of occurrence of scenarios depending on their criticality
- FIG. 2 shows a block diagram of an exemplary embodiment of a method for testing a driver assistance system of a vehicle in a simulated environment
- FIG. 3a shows a first example of a simulated scenario of a first criticality
- FIG. 3b shows a second example of a simulated virtual scenario with a second criticality
- FIG. 4 shows an exemplary embodiment of a system for testing a driver assistance system of a vehicle in a simulated environment
- Figure 5 shows an embodiment of a means for operating a driver assistance system to be tested in a simulated environment.
- Figure 1 shows the probability of occurrence of scenarios depending on the criticality of the scenarios. The probability of occurrence is the probability with which scenarios will occur in real road traffic.
- FIGS. 2 to 3b A method for testing a driver assistance system of a vehicle in a simulated environment, with which as many highly critical scenarios as possible are covered, is described below with reference to FIGS. 2 to 3b:
- a scenario model based on an ontology is preferably provided.
- This scenario model preferably specifies input parameters that are necessary to define a specific scenario and their respective relationship to the other parameters of the scenario model. More preferably, the scenario model indicates those of the input parameters that can be changed to adapt the specific scenario, and also their possible parameter values.
- the relationship between the parameters of the scenario model and the strength of their respective interaction is preferably specified by the ontology.
- the ontology is preferably set up by describing the environment of the system. This forms the basis for creating scenarios and represents a knowledge base in relation to all elements used to build a scenario can become.
- the ontology is formed from two groups of elements: dynamic elements, such as people, moving objects, etc., and static objects, such as fixed elements, trees, road signs, etc.
- the ontologies are based on concepts that describe the relationships between individual elements constrain, using a formal syntax to represent the structure and inheritance between the concepts. In detail, the individual work steps in the construction of an ontology can look like this:
- a static part of the ontology is built. This refers to the static part of the concept and its elements including relationships.
- the static part consists of the environment, road infrastructure and conditions.
- the environmental part consists of the solid elements, such as the subsoil, houses, etc.
- the road infrastructure part includes road sections, solid objects surrounding the road like buildings, trees, road signs.
- the static part can also contain sub-concepts such as tracks.
- Road markings can be part of a road section concept, for example.
- Another step is the construction of the dynamic part.
- a road segment has parameters such as course angle, radius, grade, length, etc.
- the road marking has parameters such as color, type, style, elevation, width, etc.
- the dynamic part is set up.
- the main concepts are the ego vehicle, other vehicles, and other moving objects such as pedestrians or animals.
- the moving objects in turn have sub-concepts, which in turn define properties and behavior.
- relevant parameters are defined for each concept of the dynamic part and for each domain of the parameter.
- the Parameters are, for example, acceleration, initial speed, initial position, speed when changing lanes, etc..
- the parameters for each concept for dynamic parts and domains of parameters are defined. These are, for example, acceleration, initial speed, initial position, lane changing speed, etc..
- a second work step 102 of the method 100 combinations of parameter values, which each represent a scenario, are selected on the basis of the scenario model using an algorithm for combinatorial testing and taking into account boundary conditions.
- a single interaction with other input parameters is taken into account.
- these combinations of parameter values are changed using an evolutionary algorithm, the change taking into account a cost function that favors interactions and parameter combinations that have not yet been taken into account, in which critical scenarios are to be expected.
- a simulated environment is created based on the combination of parameter values.
- the driver assistance system to be tested is operated in the simulated environment.
- the driving behavior of the at least one driver assistance system to be tested is recorded in the simulated environment.
- a criticality of the scenario is determined as a function of a predefined criterion in relation to the driver assistance system, in particular a system error or a collision or imminent collision.
- an assessment of the criticality of the input parameter value under consideration or of the parameter value is established or increased, depending on the criticality of the scenario.
- an eighth work step 108 the criticality of the scenario is compared with a threshold value.
- combinations of parameter values are changed by means of an evolutionary algorithm, with the change taking into account a cost function, which favors interactions and parameter combinations that have not yet been taken into account, in which critical scenarios are to be expected.
- Crossing or mutating, for example, can be considered as evolutionary algorithms, in which case parameter values of a part of the parameters are replaced by parameter values of another scenario or a parameter value of at least one parameter is replaced by a new value.
- the sub-routine of steps 102 to 109 is repeated until a defined coverage with respect to the parameter space of the considered input parameter is achieved.
- the subroutine is preferably repeated for each input parameter.
- the method 100 can be used to analyze the parameter space for the input parameter considered in each case; in particular, the criticality of the input parameters or parameter range of the input parameter can be identified.
- a tenth step 110 of the method 100 combinations of parameter values, each of which represents a scenario, are selected on the basis of the scenario model using an algorithm for combinatorial testing.
- Soft and hard boundary conditions are preferably taken into account here.
- the hard boundary conditions exclude combinations of parameter values that are not physically possible.
- parameter values are preferably excluded by the boundary conditions which are physically possible, but contradict a maneuver of the vehicle in the simulated environment.
- Examples of hard and soft boundary conditions result from FIG. 4.
- the trajectory of the ego vehicle 1 cannot pass through the construction site.
- the vehicle 1 cannot be located in the simulation at a location where other vehicles of the simulated traffic are located.
- a soft boundary condition in Fig. 4 is that the ego Vehicle, when it wants to get from the starting position to the end position, does not drive in a completely different direction. In FIG. 4, therefore, a soft boundary condition is that the ego vehicle drives in the direction of the target position end and not in the opposite direction, which could also be taken from the point of view of hard boundary conditions.
- FIG. 3a shows a first scenario 3, in which a pedestrian 6 crosses a street.
- a motorcycle 4 approaches the pedestrian 6 on the lane facing the pedestrian 6 .
- other vehicles 5b, 5c, 5d are parked, through which the pedestrian 6 is not or only poorly visible to the motorcycle 4.
- another vehicle 5a drives on fleas of pedestrian 6.
- the ego vehicle 1 whose longitudinal and lateral control is carried out by a driver assistance system 2 , approaches behind the other vehicle 5 a . Whether the motorcyclist 4 is visible to the ego vehicle 1 or its driver assistance system 2 is improbable according to the constellation according to FIG. 3a.
- the other vehicles 5a, 5b, 5c, 5d, the pedestrian 6 and the motorcyclist 4 and the lanes form a simulated environment for the ego vehicle 1 or for the driver assistance system 2, which ds ego vehicle 1 controls.
- Parameters of this scenario 3 are, for example, the number of other vehicles, their position, the number of other road users, their position and speed, the nature of the ground, the markings on the road, etc.
- the ego vehicle 1 or its driver assistance system 2 for overtaking the other vehicle 5a participating in traffic.
- the motorcycle 4 reduces its speed, as a result of which the ego vehicle 1 can pass by the other vehicle 5a.
- the input parameter for the speed of the motorcycle 4 is changed in such a way that it does not reduce its speed but continues to drive at the same speed. Therefore, as shown in Fig. 3b, it comes along high probability of collision between the ego vehicle 1 and the motorcycle 4 when the ego vehicle 1 performs the overtaking operation with respect to the other vehicle 5a.
- a scenario with high criticality is generated by changing the input parameter speed of the motorcycle 4 or its speed curve. Consequently, the speed of the motorcycle 4 has a high relevance with regard to the criticality and the speed of the motorcycle 4 assumed in FIG. 3b or its speed curve has a high criticality.
- the number of combinations to be tested can be significantly reduced compared to fully factorial testing. For example, if only 21 parameters of a scenario are taken into account, the number of tests can be reduced from 1.7 trillion tests in the full factorial case to just 1,028,297 tests if interactions with other parameters, i.e. six-fold interactions among all parameters, are taken into account or even to 125 test cases if only double interactions, ie the parameter under consideration and one further other parameter are taken into account. Depending on the desired coverage, such a reduced number of interactions taken into account can contribute significantly to a reduced computational effort. The inventors have found that in most cases the consideration of triple interaction, ie the parameter under consideration with two other parameters in each case, is sufficient to adequately depict reality in general.
- Such an approach therefore has a mixed combinatorial strength, which is selected depending on the input parameters considered.
- those parameters which have a high criticality with regard to scenarios are preferably tested with a higher strength, ie with more interactions with other parameters, than parameters with a low criticality.
- the speed of the motorcycle 4 has a significantly higher criticality than z.
- the relevance or criticality of the individual input parameters can be determined here, as described in relation to the subroutine.
- a simulated environment 3 is generated in an eleventh work step 111, as is shown, for example, in FIGS. 3a and 3b and has already been described in relation to these.
- the driver assistance system 2 to be tested is operated in the simulated environment according to the respective scenario.
- the ego vehicle 1 in FIGS. 3a and 3b which has the driver assistance system 2 to be tested, is overtaking. This overtaking maneuver is part of the scenario.
- the driving behavior of the driver assistance system 2 to be tested in the simulated environment 3 is recorded.
- the driver assistance system 2 Based on the recorded driving behavior of the driver assistance system 2 to be tested, it can be determined whether the driver assistance system has reacted or acted adequately with regard to aspects such as active and passive safety and comfort of the occupants.
- FIG. 5 An exemplary embodiment of a system 20 for testing a driver assistance system of a vehicle in a simulated environment is shown in FIG. 5 .
- Such a system preferably has storage means 11 for providing a scenario model which specifies input parameters which are necessary for the definition of a certain scenario and their respective relationship to the other parameters of the scenario model and which indicates those of the input parameters which are changeable for adapting the certain scenario are, and specifies their possible parameter values.
- such a system 10 preferably comprises means 12 for selecting combinations of parameter values, each of which characterizes a scenario, on the basis of the scenario model using an algorithm for combinatorial testing, wherein when selecting the combination for an input parameter under consideration, only interactions with a predefined maximum number of other input parameters are taken into account.
- such a system 10 preferably comprises means 13 for generating a simulated environment 3 on the basis of the selected combination of parameter values.
- the means 13 for generating a simulated environment 3 preferably have a device 13-1 which is set up to generate a virtual environment 3 of the vehicle 1 on the basis of the selected combination of parameter values.
- An interface 13 - 2 is set up in order to simulate or emulate the virtual environment on the driver assistance system 2 .
- Such an interface 13-2 can be a screen, for example, if the driver assistance system 2 has an optical camera.
- the sensor of driver assistance system 2 is a radar sensor, which emits a signal S.
- This signal S is detected by the interface 13-2, which is designed as a radar antenna.
- the means 13-1 for generating the simulated environment 3 calculate a response signal S' on the basis of the detected signal S and the simulated environment 3, which in turn is output to the radar of the driver assistance system 2 via the radar antennas 13-2.
- the driver assistance system 2 can be tested in this way. Additional means 14 are provided for this in order to operate the driver assistance system 2 to be tested in the simulated environment 3 according to the respective scenario.
- Such means 13 can be interfaces or other devices in order to give the driver assistance system 2 commands, based on which this guides the vehicle.
- the system 10 preferably has means 15 for detecting a driving behavior of the at least one driver assistance system 2 to be tested in the simulated environment 3 .
- the driver assistance system is operated in a real environment or in the vehicle 1 on a test bench, such means 15 can be speed sensors and sensors for determining the lateral guidance of the vehicle.
- the driver assistance system 2 is operated in a virtual environment or only the software of the driver assistance system is tested (flardware-in-the-loop, software-in-the-loop), the means 15 can also be embodied as data interfaces.
- the aforementioned means are preferably formed by a data processing system.
- the means 14 for operating the driver assistance system 2 to be tested in an environment 3 of the vehicle 1 can, however, also be formed by a test bench, in particular a test bench for a driver assistance system 2 or a vehicle 1 .
- the means 15 for detecting a driving behavior of the at least one driver assistance to be tested in the simulated environment 3 can in any case be partially formed by sensors.
- A, B range of scenarios 1 vehicle 2 driver assistance system
- 5a, 5b, 5c, 5d further vehicles 6 pedestrians 11 means for simulating 12 means for operating a driver assistance system
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DE112022001411.3T DE112022001411A5 (de) | 2021-06-02 | 2022-06-01 | Verfahren und System zum Testen eines Fahrerassistenzsystems für ein Fahrzeug |
CN202280037709.1A CN117413257A (zh) | 2021-06-02 | 2022-06-01 | 用于测试车辆用司机辅助系统的方法和系统 |
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ATA50449/2021A AT524932B1 (de) | 2021-06-02 | 2021-06-02 | Verfahren und System zum Testen eines Fahrerassistenzsystems für ein Fahrzeug |
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CN116597690A (zh) * | 2023-07-18 | 2023-08-15 | 山东高速信息集团有限公司 | 智能网联汽车的高速公路测试场景生成方法、设备及介质 |
Family Cites Families (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE102018128890A1 (de) * | 2018-11-16 | 2020-05-20 | Bayerische Motoren Werke Aktiengesellschaft | Verfahren und Testvorrichtung zum Testen eines Fahrassistenzsystems für ein Kraftfahrzeug |
DE102019209538A1 (de) * | 2019-06-28 | 2020-12-31 | Robert Bosch Gmbh | Verfahren und Vorrichtung zum Prüfen eines Systems, zur Auswahl realer Tests und zum Testen von Systemen mit Komponenten maschinellen Lernens |
DE102019124018A1 (de) * | 2019-09-06 | 2021-03-11 | Iav Gmbh Ingenieurgesellschaft Auto Und Verkehr | Verfahren zum Optimieren von Tests von Regelsystemen für automatisierte Fahrdynamiksysteme |
DE102019126195A1 (de) * | 2019-09-27 | 2021-04-01 | Bayerische Motoren Werke Aktiengesellschaft | Verfahren zur effizienten, simulativen Applikation automatisierter Fahrfunktionen |
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2021
- 2021-06-02 AT ATA50449/2021A patent/AT524932B1/de active
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2022
- 2022-06-01 CN CN202280037709.1A patent/CN117413257A/zh active Pending
- 2022-06-01 WO PCT/AT2022/060183 patent/WO2022251890A1/de active Application Filing
- 2022-06-01 DE DE112022001411.3T patent/DE112022001411A5/de active Pending
Non-Patent Citations (3)
Title |
---|
D. R. KUHN ET AL.: "Practical Combinatorial Testing", NIST SPECIAL PUBLICATION |
FELBINGER HERMANN ET AL: "Comparing two systematic approaches for testing automated driving functions", 2019 IEEE INTERNATIONAL CONFERENCE ON CONNECTED VEHICLES AND EXPO (ICCVE), IEEE, 4 November 2019 (2019-11-04), pages 1 - 6, XP033692838, DOI: 10.1109/ICCVE45908.2019.8965209 * |
RIEDMAIER STEFAN ET AL: "Survey on Scenario-Based Safety Assessment of Automated Vehicles", IEEE ACCESS, IEEE, USA, vol. 8, 8 May 2020 (2020-05-08), pages 87456 - 87477, XP011789435, DOI: 10.1109/ACCESS.2020.2993730 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN116597690A (zh) * | 2023-07-18 | 2023-08-15 | 山东高速信息集团有限公司 | 智能网联汽车的高速公路测试场景生成方法、设备及介质 |
CN116597690B (zh) * | 2023-07-18 | 2023-10-20 | 山东高速信息集团有限公司 | 智能网联汽车的高速公路测试场景生成方法、设备及介质 |
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Publication number | Publication date |
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AT524932B1 (de) | 2022-11-15 |
AT524932A4 (de) | 2022-11-15 |
CN117413257A (zh) | 2024-01-16 |
DE112022001411A5 (de) | 2024-01-18 |
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