DE102022119179A1 - Method, system and computer program product for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function - Google Patents

Abstract

The invention relates to a method for testing and training an ADAS/ADS system and/or a driving function for at least one defined driving task in at least one scenario (SZi), comprising: - Acquiring sensor data (220) by sensors (210) of at least one sensor device (200), which is connected to at least one moving object (10), when driving on one or more routes; - Generating a plurality of data sets (320) at least from the sensor data (220) from an input module (300); - Identifying and Classifying parameterized scenarios (SZpi) from the data sets (320) by a scenario analysis module (400); - Identifying and classifying metadata (570) from the data sets (320) by a metadata analysis module (500); - Linking and integrating the metadata (570) with the parameterized scenarios (SZpi) from a scenario synthesis module (700) in order to generate further parameterized scenarios (SZpk); - Creating one or more test cases (Ti) ; - Creating a simulation environment (940) using the one or more test cases (Ti);- Carrying out a simulation of the behavior of an ADAS/ADS system or a driving function in the simulation environment (940) for the specified driving task.

Description

The invention relates to a method, a system and a computer program product for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function.

Modern vehicles are equipped with a variety of driving assistance systems or automated driving assistance functions to support the driver while driving and increase his or her safety. Driving assistance systems, for example, support speed and distance control and include lane keeping and lane changing functions. A specific maximum speed can be set, which will not be exceeded as long as the speed limit function is activated. Radar sensors and camera systems are used for distance control, in which a certain distance is set, particularly to a vehicle in front. This allows the distance to vehicles in front, but also to vehicles in the side area, to be monitored. This leads to improved driving comfort and greater safety, especially when driving on the motorway and during overtaking maneuvers.

This trend towards driver assistance systems (Advanced Driver Assistance System, ADAS) and automated driving systems (ADS) in motor vehicles, but also in aircraft or watercraft and other moving objects, requires extensive security strategies, as the responsibility for the Vehicle control is no longer entirely the responsibility of the driver, but active functions are taken over by computer units in the vehicle. Therefore, for objects that move completely or partially autonomously, it must be ensured that these systems have a very low error rate in driving behavior. In order to ensure the safe functionality of an ADAS/ADS system, the detection and classification of other objects and the interpretation of traffic scenarios in the vicinity of a moving object, especially a vehicle, are important prerequisites. This requires targeted testing and training of driver assistance systems and automated driving systems both in extreme and exceptional situations (corner cases) as well as in everyday situations. Such extreme situations arise from a special combination of different factors. Examples of this are infrastructural features such as the type of road, the development on the edge of a road, the quality of the markings but also environmental conditions such as weather conditions, the time of day and the season. The behavior of other road users, the geographical topography and the weather conditions also play a major role.

However, as the performance of ADAS/ADS systems increases, the number of driving scenarios that the ADAS/ADS systems have to handle in road traffic also increases. To ensure safe, comfortable and efficient behavior of an ADAS/ADS system, individual driving functions and the entire system go through a verification, calibration and validation process during vehicle development.

However, when integrating modern ADAS/ADS systems into a vehicle, such verification, calibration and validation also represents a major challenge, as there is a functional specification deficit in driving assistance systems. While a requirements-based test process has been established for conventional systems in the automotive industry, in which test cases are implemented based on test specifications, this is currently missing for ADAS/ADS systems because, in contrast to conventional systems, a significantly larger number of influencing variables have to be taken into account, such as in particular the driving environment, which is recorded using sensors. The set of scenarios that can occur in the vehicle's driving environment and that must be correctly recognized and processed by an ADAS/ADS system is represented by an Operational Design Domain (ODD). This includes both everyday driving scenarios and very rarely occurring corner cases. In order to test and train an ADAS/ADS system for all possible driving scenarios, the entire ODD for an ADAS/ADS system would have to be recorded and documented in a requirements catalog. This is not possible due to the complexity of the driving environment and the resulting large number of driving scenarios. This problem is called functional specification deficiency. It complicates both the calibration and validation process of an ADAS/ADS system and requires alternative approaches to the existing methods.

Due to the challenges in testing ADAS/ADS systems, in addition to the known methods, virtual simulation methods are increasingly being used for testing and training for verification, calibration and validation of ADAS/ADS systems. However, these virtual simulation methods must be designed in such a way that an objective comparability of different ADAS/ADS systems with different functionality possible in terms of performance and safety.

However, the virtually created scenarios typically only represent a fraction of possible scenarios for ADAS/ADS systems, because new traffic scenarios and in particular corner cases arise from driving along real traffic routes, which are not yet available in the number of virtually created scenarios have been taken into account. In addition, the modeling of scenarios and scenes is usually carried out manually or semi-automatically, so the time and cost required to create test environments for virtual testing is very high.

The US 8862346 B2 discloses a system and method for simulating and comparing the performance of a virtual vehicle with a reference vehicle having substantially the same driving cycle and behavior.

The US 9361650 B2 discloses a method for synchronizing vehicle sensor information, whereby a virtual model of an event in which the vehicle was involved is created based on stored sensor data.

The US 10026130 B1 discloses a method and a system for determining a collision risk of a semi-autonomous vehicle.

The US 10789650 B1 discloses a system and a method for reconstructing an accident, wherein sensor data of the accident is provided and a scenario model of the accident is created based on the sensor data. Virtual reconstructions of the accident can be created.

The US 2022 048536 A1 discloses a method and a device for testing a driver assistance system in a test environment, wherein simulation scenarios for validating the driver assistance system are automatically generated based on measurement data. Furthermore, classification and labeling of sensor data, generation of meta-information and parameter variation are disclosed.

The object underlying the invention is to provide options for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function with an increased number of virtual scenarios, so that the verification, Calibration and validation process requires less time and can also be carried out with improved efficiency in order to increase the safety of ADAS/ADS systems and/or a driving function and to save resources and costs.

This object is achieved according to the invention with regard to a method by the features of patent claim 1, with regard to a system by the features of patent claim 9, and with regard to a computer program product by the features of patent claim 15. The further claims relate to preferred embodiments of the invention.

The present invention enables the generation of new virtual traffic scenarios by linking existing scenarios based on real sensor data with metadata, so that the amount of virtual scenarios available for creating a simulation environment for testing and training an ADAS/ ADS system and/or a driving function is enlarged, in particular for verification, calibration and validation purposes. With this expanded set of scenarios, relevant simulation environments can be created in which the safety and functionality of a driving assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for a specific driving task can be estimated with a high level of reliability.

• - Erfassen von Sensordaten durch Sensoren zumindest einer Sensoreinrichtung, die mit zumindest einem sich bewegenden Objekt verbunden ist, beim Befahren einer oder mehrerer Fahrstrecken, wobei die Sensoren die Sensordaten von der Umgebung des sich bewegenden Objekts aufnehmen;
• - Generieren einer Mehrzahl von Datensätzen zumindest aus den Sensordaten von einem Eingabemodul;
• - Identifizieren und Klassifizieren von parametrisierten Szenarien aus den Datensätzen von einem Szenarien-Analysemodul;
• - Identifizieren und Klassifizieren von Metadaten aus den Datensätzen von einem Metadaten-Analysemodul;
• - Verknüpfen und Integrieren der Metadaten mit den parametrisierten Szenarien von einem Szenarien-Synthesemodul, um weitere parametrisierte Szenarien zu generieren und damit die Menge von parametrisierten Szenarien zu vergrößern;
• - Erstellen von einem oder mehreren Testfällen durch Auswählen von parametrisierten Szenarien aus der erweiterten Menge und von konkreten Szenarioparametern von einem Testagenten mittels einer Teststrategie für die Fahraufgabe;
• - Erstellen einer Simulationsumgebung mittels des einen oder mehrerer Testfälle von einem Simulationsmodul;
• - Durchführen einer Simulation des Verhaltens eines ADAS/ADS-Systems oder einer Fahrfunktion in der Simulationsumgebung für die festgelegte Fahraufgabe.

According to a first aspect, the invention provides a method for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for at least one defined driving task in at least one scenario. A scenario represents a traffic event in a temporal sequence and is defined by a selection of scenario parameters and associated scenario parameter values, whereby in a parameterized scenario the scenario parameters and associated scenario parameter values are freely selectable, and in a specific scenario the specific scenario parameters and associated specific scenario parameter values are fixed . The procedure includes the following steps:

• - Acquiring sensor data by sensors of at least one sensor device, which is connected to at least one moving object, when driving on one or more routes, the sensors recording the sensor data from the surroundings of the moving object;
• - Generating a plurality of data sets at least from the sensor data from an input module;
• - Identifying and classifying parameterized scenarios from the datasets from a scenario analysis module;
• - Identifying and classifying metadata from the datasets by a metadata analysis module;
• - Linking and integrating the metadata with the parameterized scenarios from a scenario synthesis module to generate further parameterized scenarios and thus increase the set of parameterized scenarios;
• - Creating one or more test cases by selecting parameterized scenarios from the expanded set and concrete scenario parameters from a test agent using a test strategy for the driving task;
• - Creating a simulation environment using one or more test cases from a simulation module;
• - Carrying out a simulation of the behavior of an ADAS/ADS system or a driving function in the simulation environment for the specified driving task.

In a further development, it is provided that the scenario analysis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms, and that the metadata analysis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the scenario synthesis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the test agent comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the simulation module includes a software application that uses calculation methods and/or artificial intelligence algorithms.

In an advantageous embodiment it is provided that the calculation methods and/or algorithms of artificial intelligence are used as mean values, minimum and maximum values, lookup tables, models for expected values, linear regression methods, Gaussian processes, fast Fourier transformations, integral and differential calculations, Markov Methods, probability methods such as Monte Carlo methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, feedback neural networks, and / or folded neural networks are formed.

In a further embodiment it is provided that a parameter is a physical quantity, a chemical quantity, a torque, a speed, a voltage, a current, an acceleration, a speed, a braking value, a direction, an angle, a radius, a location , a number, a moving object such as a motor vehicle, a person or a cyclist, an immovable object such as a building or a tree, a road configuration such as a highway, a street sign, a traffic light, a tunnel, a roundabout, a turning lane, a traffic volume , represents a topographical structure such as a slope, a time, a temperature, a precipitation value, a weather and/or a season.

In particular, the sensors are radar systems, LIDAR systems for optical distance and speed measurement, image-recording 2D/3D cameras in the visible, IR and/or UV range, GPS systems, acceleration sensors, speed sensors, capacitive sensors, inductive sensors, voltage sensors , torque sensors, speed sensors, precipitation sensors and / or temperature sensors.

In an advantageous embodiment it is provided that the metadata is an index value for the criticality of a scenario, an identification of a road type such as a highway, a country road or a street in a locality such as a city or a village, a specific intersection arrangement, a specific Road sign, a specific bridge or tunnel construction, a time of day such as morning or afternoon, a specific level of solar radiation, a specific weather situation such as a light rain shower or a heavy thunderstorm, a country-specific marking, and/or country-specific properties of the traffic signs.

Advantageously, the scenario analysis module, the metadata analysis module, the scenario synthesis module, the test agent and/or the simulation module are integrated in a cloud computing infrastructure.

In particular, the sensor device and/or the input module is/are equipped with mobile radio modules of the 5G or 6G mobile radio standard for sending and receiving data.

According to a second aspect, the invention provides a system for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for a specified driving task ready for at least one scenario. A scenario represents a traffic event in a temporal sequence and is defined by a selection of parameters and associated parameter values, and in a parameterized scenario the parameters and associated parameter values are freely selectable, and in a specific scenario the parameters and associated parameter values are fixed. The system includes a sensor device, an input module, a scenario analysis module, a metadata analysis module, a scenario synthesis module, a test case module with a test agent and a simulation module. The input module is designed to record sensor data by sensors of at least one sensor device, which is connected to at least one moving object, when driving on one or more routes, the sensors recording the sensor data from the surroundings of the moving object, and a plurality of data sets at least to generate from the sensor data. The scenario analysis module is designed to identify and classify parameterized scenarios from the data sets from a scenario analysis module. The metadata analysis module is designed to identify and classify metadata from the data sets. The scenario synthesis module is designed to link and integrate the metadata with the parameterized scenarios in order to generate further parameterized scenarios and thus increase the amount of parameterized scenarios. The test agent is trained to create one or more test cases by selecting parameterized scenarios from the expanded set and concrete scenario parameters using a test strategy for the driving task. The simulation module is designed to create a simulation environment using one or more test cases and to carry out a simulation of the behavior of an ADAS/ADS system or a driving function in the simulation environment for the specified driving task.

In an advantageous embodiment, it is provided that the scenario analysis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms, and that the metadata analysis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the scenario synthesis module comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the test agent comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and that the simulation module includes a software application that uses calculation methods and/or artificial intelligence algorithms.

In a further embodiment it is provided that the calculation methods and/or algorithms of artificial intelligence are used as mean values, minimum and maximum values, lookup tables, models for expected values, linear regression methods, Gaussian processes, fast Fourier transformations, integral and differential calculations, Markov Methods, probability methods such as Monte Carlo methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, feedback neural networks, and / or folded neural networks are formed.

In particular, it is provided that a parameter is a physical quantity, a chemical quantity, a torque, a speed, a voltage, a current, an acceleration, a speed, a braking value, a direction, an angle, a radius, a location, a number , a moving object such as a motor vehicle, a person or a cyclist, an immovable object such as a building or a tree, a road configuration such as a highway, a street sign, a traffic light, a tunnel, a roundabout, a turning lane, a traffic volume, a topographic Structure such as a slope, a time, a temperature, a precipitation value, a weather and / or a season; and that the metadata is an index value of the criticality of a scenario, an identification of a road type such as a highway, a country road or a street in a locality such as a city or a village, a specific intersection arrangement, a specific road sign, a specific bridge or tunnel construction, a time of day such as morning or afternoon, a specific level of solar radiation, a specific weather situation such as a light rain shower or a heavy thunderstorm, country-specific markings and/or country-specific characteristics of the traffic signs.

Advantageously, the sensors are radar systems, LIDAR systems for optical distance and speed measurement, image-recording 2D/3D cameras in the visible, IR and/or UV range, GPS systems, acceleration sensors, speed sensors, capacitive sensors, inductive sensors, voltage sensors , torque sensors, speed sensors, precipitation sensors and / or temperature sensors.

In particular, the scenario analysis module, the metadata analysis module, the scenario synthesis module, the test agent and/or the simulation module are integrated in a cloud computing infrastructure.

Advantageously, the sensor device and/or the input module is/are equipped with mobile radio modules of the 5G or 6G mobile radio standard for sending and receiving data.

According to a third aspect, the invention provides a computer program product comprising executable program code configured to, when executed, carry out the method according to the first aspect.

The invention is explained in more detail below using exemplary embodiments shown in the drawing.

• 1 ein Blockdiagramm zur Erläuterung eines Ausführungsbeispiels eines erfindungsgemäßen Systems;
• 2 ein Flussdiagramm zur Erläuterung der einzelnen Verfahrensschritte eines erfindungsgemäßen Verfahrens;
• 3 ein Blockdiagramm eines Computerprogrammprodukt gemäß einer Ausführungsform des dritten Aspekts der Erfindung.

This shows:

• 1 a block diagram to explain an exemplary embodiment of a system according to the invention;
• 2 a flowchart to explain the individual method steps of a method according to the invention;
• 3 a block diagram of a computer program product according to an embodiment of the third aspect of the invention.

Additional features, aspects and advantages of the invention or its embodiments are explained in the following description in conjunction with the claims.

Simulated traffic scenarios that are created through programming are increasingly being used to test, train and validate driver assistance systems (ADAS) and automated driving systems (ADS). In the context of the invention, a scenario is a traffic event in a temporal sequence. An example of a scenario is driving on a highway bridge, turning in a turning lane, driving through a tunnel, turning into a roundabout or stopping in front of a pedestrian crossing. In addition, specific visibility conditions, for example due to twilight or high levels of sunlight, as well as environmental conditions such as the weather and season, traffic volume and certain geographical topographical conditions can influence a scenario. It is well known that road conditions look different in heavy rain and slippery conditions than on a sunny summer day. For example, an overtaking operation can be described as a scenario in which a first vehicle is initially behind another vehicle, then changes lanes to the other lane and increases speed in order to overtake the other vehicle. Such a scenario is also known as a cut-in scenario.

In order to be able to use driver assistance systems (ADAS) and automated driving systems (ADS) in a motor vehicle, they must be tested, trained, verified, calibrated and validated for reliable use. The purpose of calibration is to adapt the functions to the respective vehicle type and to the desired behavior of a driving function without changing the software code. For this purpose, the calibration parameters are modified and made available to the ADAS/ADS system in a data set. The aim of the validation is to comprehensively test the data set obtained during calibration, to demonstrate the reliability and robustness of a driver assistance system (ADAS) or an automated driving system (ADS) throughout the entire ODD and then to grant approval.

1 shows a system 100 according to the invention for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for a simulated verification, calibration and/or validation of an ADAS/ADS system and/or a driving function. The system 100 according to the invention includes one or more moving objects 10, at least one sensor device 200, an input module 300, a scenario analysis module 400, a metadata analysis module 500, a scenario synthesis module 700, a test case module 800 and a simulation module 900, each can be provided with a processor and/or a storage unit.

In connection with the invention, a “module” can be understood to mean, for example, a processor and/or a processor unit and/or a memory unit for storing program instructions. The processor is specifically set up to execute the program instructions in order to implement or realize the method according to the invention or a step of the method according to the invention. In particular, a module can be integrated into a cloud computing infrastructure 250.

In connection with the invention, a “processor” can be understood to mean, for example, a machine or an electronic circuit. A processor can in particular be a main processor (Central Processing Unit, CPU), a microprocessor or a microcontroller, for example an application-specific integrated circuit or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc . A processor can also be understood as a virtualized processor, a virtual machine or a soft CPU. It can be, for example also be a programmable processor which is equipped with configuration steps for carrying out the said method according to the invention or is configured with configuration steps in such a way that the programmable processor has the inventive features of the method, the system, the modules, or other aspects and/or partial aspects of the invention realized. In particular, the processor can contain highly parallel computing units and powerful graphics modules.

In the context of the invention, a “memory unit” or a “memory module” can be understood to mean, for example, a volatile memory in the form of a random access memory (RAM) or a permanent memory such as a hard drive or a data carrier or, for example, a removable memory module become. The storage module can also be a cloud-based storage solution.

“Database” means both a storage algorithm and the hardware in the form of a storage unit. In particular, the databases can be designed as part of a cloud computing infrastructure.

The moving object 10 is in particular a motor vehicle, an autonomous motor vehicle, an agricultural vehicle such as a combine harvester, a robot in production or in service and care facilities, or a watercraft or a flying object such as an air taxi. The moving object 10 can also be designed as a motorcycle or an electric bicycle. The moving object 10 can also be referred to as an ego vehicle within the scope of the invention.

The sensor device 200 is in particular connected to the moving object 10 and includes sensors 210 that record sensor data 220 from the surroundings of the object 10 such as road markings, vehicles, people, crash barriers, traffic signs, etc. and transmit it to the input module 300.

In connection with the invention, sensor data 220 means both raw data and already processed data from the measurement results of the sensors 220 and, if necessary, other data sources. In particular, it can be provided that the input module 300 is connected to a database 270 in which further data is contained. Furthermore, the input module 300 can have a user interface 350 or be connected to a user interface 350. The user interface 350 is designed for the input and generation of data in the form of text messages and/or voice messages and/or images and graphics, in particular by a user. In particular, a keyboard, a microphone, a camera and/or a display designed as a touchscreen are provided for entering the data.

The sensors 210 of the sensor device 200 can in particular be one or more radar systems with one or more radar sensors, one or more LIDAR systems (Light Detection and Ranging) for optical distance and speed measurement, one or more image-recording 2D/3D cameras in the visible Area, but also in the IR and UV area, and/or GPS systems. Furthermore, acceleration sensors, speed sensors, capacitive sensors, inductive sensors, voltage sensors, torque sensors, speed sensors, precipitation sensors and/or temperature sensors, etc. can be provided.

In particular, the 2D/3D image-recording camera is designed as an RGB camera in the visible range with the primary colors blue, green and red. However, a UV camera in the ultraviolet range and/or an IR camera in the infrared range can also be provided. The cameras, which differ in their recording spectrum, can therefore model different lighting conditions in the recording area. Furthermore, it is envisaged that a 3D camera is designed as a stereo camera.

The recording frequency of the sensor device 200 is designed in particular for fast speeds of the moving object 10 and can record sensor data 220 with a high image recording frequency. Furthermore, the sensor device 200 can be equipped with a microphone for detecting acoustic signals. This allows rolling noise from tires or engine noise to be recorded.

In addition, it can be provided that the sensor device 200 automatically starts the image recording process when there is a significant change in area in the recording area of the sensor device 200, for example when a clear change in a traffic situation is recognizable. This enables a selective data collection process and only relevant sensor data 220 is processed by the input module 300. This allows computing capacity to be used more efficiently.

In particular, it is intended to use a weatherproof action camera as the camera type for one or more cameras, which is particularly suitable for Outside area of the object 10 can be arranged. An action camera has wide-angle fisheye lenses, making it possible to achieve a visible radius of approximately 180°. This allows the road ahead to be comprehensively mapped. Action cameras can usually record videos in Full HD (1,920 x 1,080 pixels), but action cameras can also be used in Ultra HD or 4K (at least 3,840 x 2,160 pixels), which results in a significant increase in image quality. The image capture rate is typically 60 frames per second in 4K and up to 240 per second in Full HD. An integrated image stabilizer can also be provided. In addition, action cameras are often equipped with an integrated microphone. Differential signal processing methods can also be used to specifically block out background noise.

The attachment position of a camera on the object 10 determines which recording area can be recorded by the camera. In particular, it can be provided that the recording areas of two or more cameras overlap in order to generate a panoramic display, for example by means of subsequent image processing. This allows the spatial surroundings of a moving object 10 to be comprehensively recorded.

Radar sensors can be used for longer distances of up to 250 meters and have the advantage of being independent of weather and lighting conditions. The performance of a radar depends on many factors such as the selected hardware components, software processing and the radar echo. For example, radar accuracy is less precise with a lower signal-to-noise ratio than with a high signal-to-noise ratio. In addition, the installation position is crucial for the high performance of a radar sensor, as effects such as multipath propagation and distortion caused by covers affect the detection accuracy.

In addition to image-recording cameras and radar sensors, LIDAR sensors represent an important type of sensor for perceiving the environment for moving objects 10. As with cameras and radar sensors, the environment can be recorded and distances to other surrounding objects can be measured. In particular, 3D LIDAR sensors can record detailed information about an environmental object through a high sampling rate. Compared to radar sensors, LIDAR sensors are characterized by a higher spatial and depth resolution. When it comes to LIDAR sensors, a distinction is made between a mechanical scanning LIDAR with mechanically rotating components for scanning a laser beam and an SSL LIDAR (Solid State Lidar) without moving components. An SLL LIDAR system typically consists of a laser source or laser diode, optical elements such as lenses and diffusers, beam control elements, photodetectors and signal processing units. The recording range of SLL-LIDAR is smaller, but the cost is lower and the reliability is higher.

Furthermore, a GPS connection is advantageously provided in order to determine the geographical location of the object 10 and to assign it to the recorded sensor data 220.

In particular, it can be provided that the sensor data 220 from several moving objects 10, in particular a fleet of objects 10, are transmitted to the input module 300. However, it can also be provided that the sensor data 220 is first transmitted to the cloud computing infrastructure 250 and from there to the input module 300. In particular, it can be provided that the input module 300 is integrated into the cloud computing infrastructure 250. This ensures fast calculations, as cloud-based solutions offer the advantage of high and therefore fast computing performance.

The sensor data 220 of the surroundings of the object 10 recorded by the sensor device 200 are passed on to the input module 300 or initially to the cloud computing infrastructure 250 using data connections. In particular, a wireless data connection is provided, which can be designed, for example, as a mobile radio connection and/or a near-field data connection such as ^Bluetooth® , Ethernet, NFC (near field communication) or Wi-Fi®. For the communication of the sensor device 200 with the input module 300 or the cloud computing infrastructure 250, a 5G mobile phone connection or 6G mobile phone connection is used in particular, since data transmission can take place in real time in this way. For this purpose, the sensor device 200 is equipped with the corresponding mobile radio modules.

5G is the fifth generation mobile communications standard and, compared to the 4G mobile communications standard, is characterized by higher data rates of up to 10 Gbit/sec, the use of higher frequency ranges such as 2100, 2600 or 3600 megahertz, increased frequency capacity and thus increased data throughput and real-time data transmission, as up to a million devices per square kilometer can be addressed simultaneously. The latency times range from a few milliseconds to less than 1 ms, so that real-time transmission of data and calculation results is possible. Therefore, the sensor data 220 recorded by the sensor device 200 can be sent in real time to the input module 300 or the cloud computing infrastructure 250.

By integrating the input module 300 in a cloud computing infrastructure 250 in conjunction with a 5G mobile phone connection, processing of the sensor data 220 recorded by the sensor device 200 can be ensured in real time. In order to protect the connection to the cloud computing infrastructure 250 using a mobile radio connection, cryptographic encryption methods are provided in particular.

Sensor data 220 are further processed in the input module 300 in order to generate manageable data sets 320 from the raw data of the sensor data 220 for further processing. For example, filtering, time synchronization, changing the data formats (e.g. MPEG, JPEG, FP16) or data fusion of the sensor data 220 can be carried out. In addition, the data retrieved from the database 270 and the data entered using the user interface 350 can be integrated into the respective data sets 320 by the input module 300.

The data sets 320 are now passed on to both the scenario analysis module 400 and the metadata analysis module 500. In the scenario analysis module 400, scenarios SZ _i are derived from the data sets 320 using a software application 450. An example of a scenario SZ _i is an overtaking operation in which a first vehicle is initially behind another vehicle, then changes lane to the other lane and increases the speed in order to overtake the other vehicle. Such a scenario is also known as a cut-in scenario. Other examples of scenarios include turning onto another street, waiting at a traffic light, entering a construction site, etc.

In the context of the present invention, a distinction is made between a parameterized scenario SZp and a concrete scenario SZc. A parameterized scenario SZp can also be referred to as a logical scenario. Any parameterized scenario SZp _i is defined by various scenario parameters P ₁ , P ₂ , ..., P _n from a set of possible scenario parameters P _i and associated scenario parameter values PV ₁ , PV ₂ , ..., PV _n from a set of possible ones Scenario parameter values PV _i are defined, with scenario parameter values PV _i defining the value range of a scenario parameter P _i . A scenario parameter P _i represents, for example, a physical quantity, a chemical quantity, a torque, a speed, a voltage, a current, an acceleration, a speed, a braking value, a direction, an angle, a radius, a location, a number, a moving object such as a motor vehicle, a person or a cyclist, an immovable object such as a building or a tree, a road configuration such as a highway, a street sign, a traffic light, a tunnel, a roundabout, a turning lane, a traffic volume, a topographical structure such as a gradient, a time, a temperature, a precipitation value, a weather and/or a season. Scenario parameters P _i thus characterize properties and features of a scenario within the scope of the present invention. An example of a scenario parameter P _i of a scenario SZp _i is the speed of an ego vehicle and the value range of the associated scenario parameter value PV _i can cover the range from 100 km/h to 180 km/h. For another scenario SZp _k, the value range of the scenario parameter value PV _i can extend from 40 km/h to 70 km/h.

A parameterized scenario SZp comprises a temporal sequence of time intervals Δt ₁ , Δt ₂ ., ..., Δt _n , in each of which different scenes and events take place. A parameterized scenario SZp begins with a starting scene and then develops further through occurring events, from which new subsequent scenes emerge over time. The starting scene is thus changed by one or more events. An event can be a reaction actively triggered by a road user, such as an acceleration, or an event that recurs cyclically, such as the switching of a traffic light. The start scene and the individual subsequent scenes therefore only cover a small time interval Δt or a snapshot, while a parameterized scenario SZp covers a longer period of time. A possible illustration of a scenario SZp is a graph representation in which the events are represented as edges and the individual scenes as nodes of a graph.

In a parameterized or logical scenario SZp, both the scenario parameters P _i and the associated scenario parameter values PV _i are not all fixed. A scenario SZ is referred to as a concrete scenario SZc, in which the concrete scenario parameters Pc _i and the associated concrete scenario parameter values PVc _i or value ranges of the concrete scenario parameters values PVc _i are fixed. Both scenarios, both the parameterized scenario SZp and the concrete scenario SZc, are scenarios that are written in particular in a machine-readable code or script.

The software application 450 creates 320 parameterized scenarios SZp _i from the data sets. Using the software application 450, a suitable parameterized scenario SZp _i can be derived from one or more data sets 320 for a specific geographical location. The software application 450 uses in particular calculation methods and/or artificial intelligence algorithms to identify and classify the parameterized scenarios SZp _i . The calculation methods and/or algorithms of artificial intelligence can be mean values, minimum and maximum values, lookup tables, models for expected values, linear regression methods, Gaussian processes, fast Fourier transformations, integral and differential calculations, Markov methods, probability methods such as Monte Carlo methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, feedback neural networks, and/or convolutional neural networks. In particular, the artificial intelligence algorithms can be designed as encoders and decoders with neural networks and/or at least one reinforcement learning agent (LV).

A neural network consists of neurons arranged in several layers and connected to each other in different ways. A neuron is able to receive information from outside or from another neuron at its input, evaluate the information in a certain way and forward it in a modified form to another neuron at the neuron output or output it as the end result. Hidden neurons are located between the input neurons and output neurons. Depending on the network type, there may be multiple layers of hidden neurons. They ensure the forwarding and processing of the information. Output neurons ultimately deliver a result and output it to the outside world. Neural networks can be trained through unsupervised or supervised learning.

The different arrangement and connection of the neurons creates different types of neural networks, such as in particular a feedforward neural network (FNN), a feedback network (recurrent neural network, RNN) or a folded neural network (convolutional Neural Network, CNN). A convolutional neural network has multiple convolution layers and is very suitable for machine learning and applications in the field of pattern recognition and image recognition. Since a large part of the data recorded by the sensors is available as images, convolutional neural networks (CNN) are used in particular.

The Convolutional Neural Network (CNN) is particularly well suited for machine learning and artificial intelligence (AI) applications in the field of image and speech recognition because it has multiple convolution layers. The way a convolutional neural network works is to a certain extent modeled on biological processes and the structure is comparable to the visual cortex of the brain. Conventional neural networks consist of fully or partially connected neurons in several levels and these structures reach their limits when processing images, as there would have to be a number of inputs corresponding to the number of pixels. The convolutional neural network is made up of different layers and is basically a partially locally linked feedforward neural network. The individual layers of the CNN are the convolutional layer, the pooling layer and the fully connected layer. The Convolutional Neural Network (CNN) is therefore suitable for machine learning and artificial intelligence applications with large amounts of input data, such as in image recognition. The network works reliably and is insensitive to distortions or other optical changes. The CNN can process images captured under different lighting conditions and from different perspectives. It still recognizes the typical features of an image. Since the CNN includes several local partially connected layers, it has a significantly lower storage space requirement than fully connected neural networks, as the convolutional layers significantly reduce the storage requirements. This also shortens the training time of a CNN, especially when using modern graphics processors.

The scenarios SZp _i identified and classified by the software application 450 are passed on directly to the scenario synthesis module 700 or they are stored in a database, in particular in the cloud computing infrastructure 250, from which they are used for further processing by the scenario synthesis module 700 can be accessed. In particular, both the scenario analysis module 400 and the scenario synthesis module 700 are in the cloud computing infrastructure Structure 250 integrated. This also applies to the metadata analysis module 500.

In the metadata analysis module 500, the data sets 320 are converted into metadata 570 and classified (labeled). For this purpose, the metadata analysis module 500 uses a software application 550, which also uses various calculation methods and/or artificial intelligence algorithms such as, in particular, deep learning with, for example, at least one folded neural network (CNN) and/or at least one reinforcement learning agent (LV ) can use. The metadata 570 is, for example, an index value for the criticality of a scenario, an identification of a road type such as a highway, a country road or a street in a locality such as a city or a village, a specific intersection arrangement, a specific street sign, a specific bridge - or tunnel construction, a time of day such as morning or afternoon, a specific solar radiation value, a specific weather situation such as a light rain shower or a heavy thunderstorm, a country-specific identifier such as Germany or Switzerland, country-specific properties of the traffic signs, etc. The classified metadata 570 are then forwarded to the scenario synthesis module 700 or they are stored in a database, in particular in the cloud computing infrastructure 250, from which they can be retrieved for further processing by the scenario synthesis module 700.

The scenario synthesis module 700 has a software application 750, which expands the set of available scenarios by linking the metadata 570 with the already generated scenarios SZp _i . An example of a scenario SZp _i represents an overtaking maneuver, a so-called cut-in scenario. However, the scenario parameters P _i of the scenario SZp _i primarily relate to the speed of the ego vehicle 10, the speed of the vehicle driven by the ego vehicle 10. Vehicle 10 overtaken target vehicle, the distance between the two vehicles, a necessary acceleration to carry out the overtaking maneuver, etc. The further framework conditions of the overtaking maneuver, such as an overtaking maneuver on a highway, are in the scenario SZp _i generated from the real sensor data 220 taken as given and represent an integral part of the scenario SZp _i . By linking such a scenario SZp _i with further metadata 570, such as a country road instead of a motorway, further scenarios SZp _k can be generated, thereby increasing the amount of available parameterized virtual ones Scenarios Szp _i ; Szp _k is increased. The extended set of parameterized virtual scenarios Szp _i created in this way; Szp _k is stored in a scenario database 770, which is arranged in particular in the cloud computing infrastructure 250.

For the creation of test cases T _i that are of interest for the verification, calibration and/or validation of ADAS/ADS systems, a test agent 850 of the test case module 800 selects in the scenario database 770 from the expanded set of parameterized virtual scenarios Szp _i ; Szp _k suitable parameterized scenarios Szp _i ; Szp _k and concrete scenario parameters Pc _i ; Pc _k off. In addition, further parameters, such as calibration parameters, can be selected from other databases by the test agent 850. The created test cases T _i are stored in a test case database 870. These relevant test cases T _i created by the test agent 850 cover the relevant traffic situations defined in the “Operational Design Domain” (ODD) that must be controlled by an ADAS/ADS system. A test case T _i is therefore a relevant concrete scenario SZp _i through the definition of concrete scenario parameters Pc _i and possibly further parameters. In particular, the test agent 850 creates a plurality of test cases T _i for one or more driving tasks using a software application. The respective driving task is formulated before starting a simulation, for example by an expert such as an engineer. However, it can also be provided that a list of driving tasks is generated by a software application. An example driving task is changing lanes on a highway.

For the selection and design of the test cases T _i, a test strategy is provided in particular, which specifies how the test agent 850 creates the test cases T _i . Various calculation methods and algorithms, in particular artificial intelligence algorithms, can be used to determine the test strategy.

The test strategy of the test agent 850 thus decides on the configuration of the test cases T _i by selecting suitable parameterized scenarios from the expanded set of scenarios Szp _i ; Szp _k and concrete scenario parameters Pc _i ; Pc _k and, if necessary, other parameters. The test strategy used is dictated by the purpose of the simulation, i.e. what knowledge should be gained about the behavior of an ADAS/ADS system or a specific driving function when carrying out a specified driving task through a simulation. For example, for the virtual validation of an ADAS/ADS system, various parameterized scenarios SZp _i and combinations of concrete scenario parameters can be created using one combinatorial process can be created. In addition, an iterative strategy based on mathematical optimizers can be implemented.

In the test case database 870, additional text messages, video sequences and/or audio sequences, etc. can be stored for the test cases T _i with further information that may be relevant for testing the test cases T _i in a simulation environment 940.

To create the simulation environment 940 for carrying out a virtual test and training of an ADAS/ADS system, the test agent 850 passes a test case T _i or several test cases T _i to the simulation module 900. In addition, the simulation module 900 can provide additional information from another database 970 retrieve information that is important for creating the simulation environment 940. This database 970 can in turn be integrated into the cloud computing infrastructure 250. In particular, further historical data in the form of images, graphics, time series, parameters, etc. can be stored in the database 970. For example, target variables and target values can be stored in the database 970, which define a safety standard for the ADAS/ADS system, which is determined when simulating the behavior of the ADAS/ADS system in the respective test case T _i or the test cases T _i simulation environment must be adhered to.

In addition, other data sources or databases can be used to create the simulation environment 940. This includes, in particular, databases that contain data about the road network with road specifications such as lanes and bridges, the road infrastructure such as the road surface, the peripheral development, the road layout, etc. and are made available by authorities.

In addition, data on traffic figures such as the real hourly traffic volume at a specific traffic location are of interest for certain types of scenarios, such as a traffic jam. Another source of data is aerial photography, for example from Google Maps. Mapillary can also be used for street images. Mapillary collects geo-tagged user-generated street images captured by dashcams and smartphones. These images are available under an open source license. Since the weather conditions are also important for a simulation environment 940, weather data represents another data source. Weather data includes historical weather measurements and future weather forecasts.

The calculation and storage of the geographical coordinates of the detected objects is preferably carried out in the EPSG 25832 coordinate system (Universal Transverse Mercator (UTM) Zone 32N). This system is also used by authorities in Germany. The lateral and longitudinal positions are presented in meters. In addition, global reference systems such as the “World Geodetic System 1984 (WGS 84)”, which is also used in GPS (Global Positioning System) receivers, can be used. For example, the entire map content from Germany can be imported.

The simulation module 900 has a software application 950 for creating the simulation environment 940 from one or more test cases T _i , the test cases T _i according to the invention being based on a re-simulation of the parameterized scenarios SZp _i derived from real sensor data 220 by a variation using metadata. A defined driving task for an ADAS/ADS system and/or a specific driving function can be virtually tested or simulated in the respective simulation environment 940 determined by one or more test cases T _i . The simulated test results or training results can now be transferred, for example, to an evaluation module that uses different metrics, in particular in the form of performance indicators (KPIs), to evaluate the simulated test results or training results with regard to the performance and functionality of one or more functions or the overall performance of an ADAS/ADS system or a driving function. In addition, the quality of the simulation process carried out can be assessed, particularly in the form of simulation quality criteria (SQCs). The evaluation module can create evaluation results from these evaluations.

KPIs are used to describe the performance of an ADAS/ADS system under test, with different KPIs being set for different evaluation categories such as comfort, safety, naturalness of driving and efficiency. Additionally, additional KPIs can be implemented to verify the correct functionality of the ADAS/ADS system under test. An example of a KPI is the evaluation of a minimum distance to another vehicle or an average acceleration in a deceleration scenario. Based on an evaluation metric, the results for a simulated test case T _i can be represented by numerical values or Boolean values.

In 2 the method steps for testing and training an ADAS/ADS system and/or a driving function for a defined driving task are shown in at least one scenario SZ _i .

In a step S10, sensor data 220 are recorded by sensors 210 of at least one sensor device 200, which is connected to at least one moving object 10, when driving on one or more routes, the sensors 210 recording the sensor data 220 from the surroundings of the moving object 10 .

In a step S20, a plurality of data sets 320 are generated at least from the sensor data 220 by an input module 300.

In a step S30, parameterized scenarios SZp _i from the data sets 320 are identified and classified by a scenario analysis module 400.

In a step S40, metadata 570 is identified and classified from the data sets 320 by a metadata analysis module 500.

In a step S50, the metadata 570 is linked and integrated with the parameterized scenarios SZp _i by a scenario synthesis module 700 in order to generate further parameterized scenarios SZp _k and thus the set of parameterized scenarios Szp _i ; SZp _k to increase.

In a step S60, one or more test cases T _i are created by selecting parameterized scenarios SZp _i ; SZp _k from the extended set and from concrete scenario parameters Pc _i ; Pc _k created by a test agent 850 using a test strategy for the driving task.

In a step S70, a simulation environment 940 is created by a simulation module 900 using the one or more test cases T _i .

In a step S80, a simulation of the behavior of an ADAS/ADS system or a driving function is carried out in the simulation environment 940 for the specified driving task.

3 schematically illustrates a computer program product 1000 that includes an executable program code 1050 configured to execute the method according to the first aspect of the present invention.

The present invention allows an optimal amount of virtual parameterized scenarios to be generated by linking existing scenarios based on real sensor data with metadata that is also based on real sensor data. By increasing the amount of virtual scenarios available, improved simulation environments can be created for testing and training an ADAS/ADS system and/or a driving function, particularly for verification, calibration and validation purposes. This allows the safety and functionality of a driving assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for a specific driving task to be estimated more accurately and precisely with a high level of reliability. Overall, resources can be saved because both the actual driving of test routes with standard traffic situations and with specific corner cases can be reduced. For example, rarely occurring traffic situations, such as a vehicle in front driving around a warning beacon in a construction site area that falls into the lane of the ego vehicle, can be simulated and used for functional validation in the virtual simulation environment. In addition, training data for the AI algorithms of the various software applications in the modules can be generated from the test cases, so that the entire simulation system continuously improves itself through self-learning effects.

Reference symbols

10

moving object

100

system

200

Sensor device

210

Sensors

220

Sensor data

250

Cloud computing infrastructure

270

Database

300

Input module

320

record

350

User interface

400

Scenario analysis module

450

Software application

500

Metadata analysis module

550

Software application

570

Metadata

700

Scenario synthesis module

750

Software application

770

Scenario database

800

Test case module

850

Test agent

870

Test case database

900

Simulation module

940

Simulation environment

950

Software application

970

Database

1000

Computer program product

1050

Program code

QUOTES INCLUDED IN THE DESCRIPTION

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

Cited patent literature

• US 8862346 B2 [0008]
• US 9361650 B2 [0009]
• US 10026130 B1 [0010]
• US 10789650 B1 [0011]
• US 2022048536 A1 [0012]

Claims (15)

Method for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for at least one defined driving task in at least one scenario (SZ _i ), wherein a scenario (SZ _i ) represents a traffic situation in a temporal period sequence and is defined by a selection of scenario parameters (P ₁ , P ₂ , ... , P _n ) and associated scenario parameter values (PV ₁ , PV ₂ , ..., PV _n ), and where in a parameterized scenario (SZp _i ) the scenario parameters (P ₁ , P ₂ , ... , P _n ) and associated scenario parameter values (PV ₁ , PV ₂ , ..., PV _n ) are freely selectable, and in the case of a specific scenario (SZc _i ) the specific ones Scenario parameters (Pc ₁ , Pc ₂ , ..., P _cn ) and associated concrete scenario parameter values (PVc ₁ PV _c2 , ..., PVc _n ) are defined, comprising: - Acquiring (S10) of sensor data (220) by sensors ( 210) at least one sensor device (200), which is connected to at least one moving object (10), when driving on one or more routes, the sensors (210) receiving the sensor data (220) from the surroundings of the moving object (10). to record; - Generating (S20) a plurality of data sets (320) at least from the sensor data (220) from an input module (300); - Identifying and classifying (S30) parameterized scenarios (SZp _i ) from the data sets (320) from a scenario analysis module (400); - Identifying and classifying (S40) metadata (570) from the data sets (320) by a metadata analysis module (500); - Linking and integrating (S50) the metadata (570) with the parameterized scenarios (SZp _i ) from a scenario synthesis module (700) in order to generate further parameterized scenarios (SZp _k ) and thus the set of parameterized scenarios (SZp _i ; SZp _k ) to increase; - Creating (S60) one or more test cases (T _i ) by selecting parameterized scenarios (SZp _i ; SZp _k ) from the expanded set and concrete scenario parameters (Pc _i ; Pc _k ) from a test agent (850) using a test strategy for the driving task; - Creating (S70) a simulation environment (940) using one or more test cases (T _i ) from a simulation module (900); - Carrying out (S80) a simulation of the behavior of an ADAS/ADS system or a driving function in the simulation environment (940) for the specified driving task. Procedure according to Claim 1 , wherein the scenario analysis module (400) comprises a software application (450) that uses artificial intelligence calculation methods and/or algorithms, wherein the metadata analysis module (500) comprises a software application (550) that uses artificial intelligence calculation methods and/or algorithms intelligence used; wherein the scenario synthesis module (700) comprises a software application (750) that uses calculation methods and/or artificial intelligence algorithms; wherein the test agent (850) comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and wherein the simulation module (900) comprises a software application that uses calculation methods and/or artificial intelligence algorithms. Procedure according to Claim 2 , where the calculation methods and/or algorithms of artificial intelligence are used as mean values, minimum and maximum values, lookup tables, models for expected values, linear regression methods, Gaussian processes, fast Fourier transformations, integral and differential calculations, Markov methods, probability methods such as Monte Carlo -Methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, feedback neural networks, and / or folded neural networks are formed. Procedure according to one of the Claims 1 until 3 , where a parameter (P _i ) is a physical quantity, a chemical quantity, a torque, a speed, a voltage, a current, an acceleration, a speed, a braking value, a direction, an angle, a radius, a location, a Number, a moving object such as a motor vehicle, a person or a cyclist, an immovable object such as a building or a tree, a road configuration such as a highway, a street sign, a traffic light, a tunnel, a roundabout, a turning lane, a traffic volume, a topographical structure such as a slope, a time, a temperature, a precipitation value, a weather and/or a season. Procedure according to one of the Claims 1 until 4 , whereby the sensors (210) are radar systems, LIDAR systems for optical distance and speed measurement, image-recording 2D/3D cameras in the visible, IR and/or UV range, GPS systems, acceleration sensors, speed sensors, capacitive sensors, inductive Sensors, voltage sensors, torque sensors, speed sensors, precipitation sensors and / or temperature sensors are formed. Procedure according to one of the Claims 1 until 5 , where the metadata (570) is a Index value of the criticality of a scenario, an identification of a road type such as a highway, a country road or a street in a locality such as a city or a village, a specific intersection arrangement, a specific road sign, a specific bridge or tunnel structure, a time of day such as morning or afternoon , a specific solar radiation value, a specific weather situation such as a light rain shower or a heavy thunderstorm, a country-specific label, and/or country-specific properties of the traffic signs. Procedure according to one of the Claims 1 until 6 , wherein the scenario analysis module (400), the metadata analysis module (500), the scenario synthesis module (700), the test agent (850) and / or the simulation module (900) are integrated in a cloud computing infrastructure (250). are. Procedure according to one of the Claims 1 until 7 , wherein the sensor device (200) and/or the input module (300) is/are equipped with mobile radio modules of the 5G or 6G mobile radio standard for sending and receiving data. System (100) for testing and training a driver assistance system (ADAS) and/or an automated driving system (ADS) and/or a driving function for a defined driving task in at least one scenario (SZ _i ), wherein a scenario (SZ _i ) represents a traffic situation in represents a temporal sequence and is defined by a selection of parameters (P ₁ , P ₂ , ..., P _n ) and associated parameter values (PV ₁ , PV ₂ , ..., PV _n ), and in a parameterized scenario (SZp _i ) the parameters (P ₁ , P ₂ , ..., P _n ) and associated parameter values (PV ₁ , PV ₂ , ..., PV _n ) are freely selectable, and in a specific scenario (SZc _i ) the parameters (Pc ₁ , Pc ₂ , ..., P _cn ) and associated parameter values (PVc ₁ , PV _c2 , ..., Pvc _n ) are defined, comprising a sensor device (200), an input module (300). scenario analysis module (400), a metadata analysis module (500), a scenario synthesis module (700), a test case module (800) with a test agent (850) and a simulation module (900); wherein the input module (300) is designed to record sensor data (220) by sensors (210) of at least one sensor device (200), which is connected to at least one moving object (10), when driving on one or more routes, the sensors (210) record the sensor data (220) from the surroundings of the moving object (10), and generate a plurality of data sets (320) at least from the sensor data (220); wherein the scenario analysis module (400) is designed to identify and classify parameterized scenarios (SZp _i ) from the data sets (320) from a scenario analysis module (400); wherein the metadata analysis module (500) is designed to identify and classify metadata (570) from the data sets (320); wherein the scenario synthesis module (700) is designed to link and integrate the metadata (570) with the parameterized scenarios (SZp _i ) in order to generate further parameterized scenarios (SZp _k ) and thus the set of parameterized scenarios (SZp _i ; SZp _k ) to enlarge; wherein the test agent (850) is designed to produce one or more test cases (T _i ) by selecting parameterized scenarios (SZp _i ; SZp _k ) from the expanded set and concrete scenario parameters (Pc _i ; Pc _k ) by means of a test strategy for the create a driving task; wherein the simulation module (900) is designed to create a simulation environment (940) using the one or more test cases (T _i ) and a simulation of the behavior of an ADAS/ADS system or a driving function in the simulation environment (940) for the specified driving task to carry out. System (100) after Claim 9 , wherein the scenario analysis module (400) comprises a software application (450) that uses artificial intelligence calculation methods and/or algorithms, wherein the metadata analysis module (500) comprises a software application (550) that uses artificial intelligence calculation methods and/or algorithms intelligence used; wherein the scenario synthesis module (700) comprises a software application (750) that uses calculation methods and/or artificial intelligence algorithms; wherein the test agent (850) comprises a software application that uses calculation methods and/or artificial intelligence algorithms; and wherein the simulation module (900) comprises a software application that uses calculation methods and/or artificial intelligence algorithms. System (100) after Claim 10 , where the calculation methods and/or algorithms of artificial intelligence are used as mean values, minimum and maximum values, lookup tables, models for expected values, linear regression methods, Gaussian processes, fast Fourier transformations, integral and differential calculations, Markov methods, probability methods such as Monte Carlo -Methods, temporal difference learning, extended Kalman filters, radial basis functions, data fields, convergent neural networks, deep neural networks, feedback neural networks, and / or folded neural networks are formed. System (100) according to one of the Claims 9 until 11 , where a parameter (P _i ) is a physical quantity, a chemical quantity, a torque, a speed, a voltage, a current, an acceleration, a speed, a braking value, a direction, an angle, a radius, a location, a number, a moving object such as a motor vehicle, a person or a cyclist, an immovable object such as a building or a tree, a road configuration such as a highway, a street sign, a traffic light, a tunnel, a roundabout, a turning lane, a traffic volume, a topographical structure such as a slope, a time, a temperature, a precipitation value, a weather and/ or represents a season; and wherein the metadata (570) is an index value of the criticality of a scenario, an identification of a road type such as a highway, a country road or a street in a locality such as a city or a village, a specific intersection arrangement, a specific street sign, a specific bridge or tunnel construction, a time of day such as morning or afternoon, a specific level of solar radiation, a specific weather situation such as a light rain shower or a heavy thunderstorm, country-specific markings and/or country-specific characteristics of the traffic signs. System (100) according to one of the Claims 9 until 12 , whereby the sensors (210) are radar systems, LIDAR systems for optical distance and speed measurement, image-recording 2D/3D cameras in the visible, IR and/or UV range, GPS systems, acceleration sensors, speed sensors, capacitive sensors, inductive Sensors, voltage sensors, torque sensors, speed sensors, precipitation sensors and / or temperature sensors are formed. System (100) according to one of the Claims 9 until 13 , wherein the scenario analysis module (400), the metadata analysis module (500), the scenario synthesis module (700), the test agent (850) and / or the simulation module (900) are integrated in a cloud computing infrastructure (250). are, and wherein the sensor device (200) and/or the input module (300) is/are equipped with mobile radio modules of the 5G or 6G mobile radio standard for sending and receiving data. Computer program product (1000), comprising an executable program code (1050) which is configured so that, when executed, it implements the method according to one of Claims 1 until 8th executes.

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