Method for providing an operating framework specification for an autonomous vehicle, computer program and data carrier
Patent Information
- Application Number
- DE102023209736
- Authority / Receiving Office
- DE · DE
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2023-10-05
- Publication Date
- 2025-04-10
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
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Abstract
Description
Regardless of the grammatical sex of a certain term, individuals with male, female or other sex identity are included.The invention relates to a method for providing an operating frame specification for an autonomous vehicle, which is designed in particular as a locomotive or train, according to patent claim 1.Autonomous systems, in particular vehicles, are intended to be driven or operate autonomously within a system context defined for them, the so-called operational domain (OD). For example, autonomous driving is intended for specific route sections. For example, if the autonomous vehicle is a keyless transport system, a particular area in a factory may be intended for autonomous operation. In order to be able to provide the intended operating domain or the operating frame to the vehicle or to describe it for the vehicle, an operating design domain (ODD)) which specifies a representation model of the real world or of the OD is developed.The ODD typically includes various parts such as scene, environmental conditions, and dynamic elements. Since the OD or the operating frame or operating domain is generally complex, engineers must abstract from the real world, but at the same time describe the ODD or operating frame specification as exactly as possible, since it specifies, among other things, the principles for quality assurance and testing of the autonomous vehicle.The manual definition of the ODD or its attributes is laborious, since it is not clear which attributes have what relevance and how an abstraction level has to look like.It is therefore an object of the present invention to provide a method, a computer program and a data carrier, by means of which an operating framework specification for an autonomous vehicle can be created in a manner that is as automated as possible and, in particular, attributes are automatically weighted.This object is achieved according to the invention by a method, a computer program and an electronically readable data carrier of the independent patent claims. Advantageous embodiments and refinements are shown in the dependent patent claims, the description and in the drawing.A first aspect relates to a method for providing an operating design domain (ODD) for an autonomous vehicle, in particular a rail vehicle, such as a train. The method according to the invention comprises the following steps:In a first step, a basic model is provided which has attribute values which comprise at least one environment or scene, environment data and at least one dynamic element, such as persons in the environment or scene, and characterize the operating frame specification.In a second step, at least one further attribute value is added to the basic model from a supplementary data record which describes an exceptional situation, such as an accident, for example, for a vehicle type, that is to say rail vehicle, of the vehicle, for example.In a third step, at least one point value is derived for the at least one further attribute value and / or for at least one of the attribute values on the basis of a frequency and / or significance in the supplementary dataset.In a fourth step, an operating frame specification is now provided which is adapted on the basis of the at least one point value and which has thus been derived from the provided basic model which has at least partially characterized the operating frame specification.In other words, in the first step of the method, a first or provisional version of the operating frame or of the ODD would be provided as a basic model, which was created, for example, in the classic manner by experts, as is customary in the prior art. In this case, a set A of attributes or attribute values a is defined, which describe the environment, environmental data and dynamic elements. For example, the environment can be described on the basis of its structure, for example forest or wies, and / or the type of rails. The environmental data specify, for example, weather conditions and lighting conditions within which the autonomous vehicle is intended to be operated. Dynamic elements include, for example, persons with which the surrounding environment is to be expected and which can thus step on the rails.In the second step, supplementary data, which are provided, for example, in the case of the railway, by the railway agency of the European Union (ERA), are now provided.In particular, the exceptional situation can be an accident. Railway accidents are detected by the railway agency, so that relevant attributes for the ODD of the autonomous system or autonomous vehicle can be derived on the basis of existing information about railway accidents. Thus, in the second step of the method, in particular the set A of the attributes is refined to A' extended by new attributes or attribute values a'.Railway accident data contain, for example, information about the exact location at which the accident has occurred, the exact date and time of the accident, so that, for example, exact weather data for the time of the accident can also be called up with this information, either from the supplementary data themselves or on the basis of the information contained there from a weather database or a weather service. Information about particular conditions, such as construction work, and information about the severity of the accident can thus likewise be contained in the supplementary data record and added to the basic model as the at least one further attribute or the at least one further attribute value a', or the basic model can thus be expanded.On the basis of the information of the supplementary data record, attributes about the scene, for example tracks, fixed infrastructure, such as signals, buildings etc. along the tracks, the environmental conditions, for example weather conditions, such as rain, fog, snow, lighting conditions and the like, and the dynamic elements involved in the accidents, such as adults, construction workers, children, animals etc. can be derived. This results in the refined set A' of attributes for the ODD.In order to now find or characterize relevant attributes, in particular accident information, for the provision of an operating frame specification, the available information should be called up according to specific parameters, such as the geolocation and / or the planned ODD and / or on the basis of specific parameters which are defined in the system requirements, for example whether or not a rodless train is to travel at night.Further sources, such as historical information about the day of the accident, can be collected for the supplementary data record; such additional sources can also comprise official analyses of the accident, for example. Advantageously, these sources should be present in computer-readable form so that they can be processed in a simple manner, for example by a control unit or an electronic computing device which at least partially carries out the method, for example by means of a suitable voice analysis.In the third step, on the basis of the supplementary data record, i.e. the information available in the railway accident data, a score is assigned to each attribute of the set A', i.e. in particular to the at least one further attribute value a' or at least one of the attribute values a of the basic model, or the attribute is provided with a score. This score depends on the number or frequency of the exceptional situations, for example on the number of accidents, in the context of which the corresponding attribute or the corresponding attribute value is mentioned. Additionally or alternatively, the severity or the significance in the data record is weighted accordingly. This score or the score can thus be interpreted, for example, as a risk for an attribute value or the at least one further attribute value possibly leading to an accident or the exceptional situation. Frequency and severity are information which can be extracted or derived from the supplementary data record.It can thus be seen that, on account of the provision of the operating frame specification in the fourth step, corresponding attributes or attribute values or the at least one further attribute value having a particularly high point number or a high point value should be taken into account in particular in order, for example, to derive test scenarios from the operating frame specification or ODD.The accuracy of the score could be improved by estimating the current frequency of an event, for example by performing trend analysis for the data of the accident events over the years. Moreover, if it is known that a certain class of events is underpredicted in accident reports, since this class is not subject to reporting, for example, and therefore only a certain percentage of the actual events of this type are reported, this can be used to determine the at least one point value by means of a corresponding supplement.The method according to the invention has the advantage that automatic or at least semi-automatic generation of the operating frame specification or ODD is possible, in particular for the railway sector, at least for specific route sections which contain relevant attributes. Furthermore, the significance of the ODD attributes or attribute values can be quantified on the basis of information about accidents that is subject to messages.The presented method will lead to better results compared to conventionally created ODDs, since more complete models of the attributes or attribute set are used in particular. The method furthermore provides an aid in the derivation of test cases on the basis of important influencing factors, for corresponding exceptional situations or accidents. The higher the risk assessment or the point value of an attribute, the more test cases can be carried out or can be derived accordingly. Furthermore, if the supplementary data record is accident examination data, the operating frame specification or ODD of automated driving systems or driver assistance systems of the autonomous vehicle can be continuously adapted and improved.In addition, the method can be used in a particularly flexible manner, for example in the case of keyless transport systems, railway traffic, but also for automobiles and aircraft.In an advantageous embodiment of the invention, the supplementary data record is called up via an interface, which can be provided, for example, by a communication device, from a central electronic computing device, which is designed in particular as a register database or accident register. In other words, the system which carries out the method, for example the electronic computing device, has at least one interface or an interface which can be formed, for example, by a communication device, such as a modem, via which the supplementary data record can be called up and thus captured, for example, in a memory area. In this case, the central electronic computing device, which thus provides the supplementary data record, can represent, for example, a server which is connected to a communication network, such as the Internet, and itself has an interface or a communication device. This results in the advantage that the method can access, for example, particularly current supplementary datasets provided by corresponding locations, so that, for example, a corresponding driver assistance system of the autonomous vehicle can be updated particularly easily.In a further advantageous embodiment of the invention, the derivation of the at least one point value is additionally carried out on the basis of a time value. The time value can characterize a time-ray in particular and thus assess the results on the basis of the past being behind. Thus, it can be the result, for example, that accident events, which each have a specific attribute value, become less frequently or become less relevant over the course of the years, since, for example, better or different protection mechanisms or a modification or removal of the corresponding infrastructure has taken place. The point value and thus the weighting of the associated attribute value can then be adapted or advantageously taken into account. This results in the advantage that the operating frame specification can be created particularly precisely.In a further advantageous embodiment of the invention, a classification for the exceptional situation takes place for the derivation on the basis of the significance. In other words, it is determined how usual or extreme the exceptional situation is. If the exceptional situation is, for example, an accident, the severity of the accident can be classified. If persons are involved in the accident, for example, the classification can be carried out on the basis of the severity of the damage to the person. Thus, the classification can comprise, for example, the classes slightly injured, severely injured, life-threateningly injured and with death sequence. This results in the advantage that attribute values can be provided with associated point values for the method in a particularly advantageous manner, which point values are provided by the test scenarios that are particularly advantageous for persons involved, in order thus to enable particularly safe operation in the future in autonomous vehicles.In a further advantageous embodiment of the invention, a method of machine learning is used for extracting the at least one further attribute value and / or for deriving, and thus in particular for determining the significance of the at least one point value, for the associated attribute value or at least one further attribute value. In other words, the derivation of the point value or the extraction of the at least one further attribute value is carried out by means of or with the aid of artificial intelligence, such as a self-learning algorithm. A self-learning algorithm or the method of machine learning can be, in particular, a large language model (LLM) or a generative adventarial network (GAN) or a generating generic network, which represents an algorithm for unsupervised learning. For example, an advantage of the GAN is that only a small amount of training data or a generally small amount of data need be provided, as a result of which data can be used for which, for example, no target values exist. Thus, for example, by means of unsupervised learning, it is possible to derive new structures from data, which may be advantageous in the attribution or the ascertainment of the further attribute values or relevant attribute values.In a further advantageous embodiment of the invention, the at least one further attribute value specifies and / or supplements the environment, the environmental data and / or the dynamic elements. In other words, at least one of the three pillars of the basic model: environment, environment data and dynamic elements is supplemented. Thus, for example, the dynamic elements in the basic model can have only included passengers, and these can now be supplemented by construction operators. Environmental data can specify weather data, for example, so that, if only fine weather data are available up to now, rain events can now also be detected. This results in the advantage that the operating frame specification can be adapted particularly advantageously, so that precise operating conditions can be established for the autonomous vehicle, for example.In a further advantageous embodiment of the invention, a test scenario for the vehicle is specified on the basis of the adjusted operating frame specification and / or the vehicle is operated on the basis of the adjusted operating frame specification and / or in the test scenario. In other words, on the basis of the adjusted operating frame specification provided by the method, a test scenario for an operation-in particular test operation-of the autonomous vehicle is determined, in which test scenario the vehicle can be tested, for example, in order thus to adjust the driver assistance system that operates the vehicle autonomously. This results in the advantage that the autonomous vehicle can be operated particularly advantageously on the basis of the method.In a further advantageous embodiment of the method, an accident statistic relating to the vehicle type of the vehicle is used as the supplementary data set, and the at least one further attribute value characterizes an accident as an exceptional situation. In other words, as already mentioned, a supplementary data record is used which describes an accident as an exceptional situation, wherein in particular it is possible to resort to accident statistics which are well managed and extensive, as managed for people cars, for example, so that, for example, the frequency, but also the significance, for the respective attribute values can be determined particularly precisely. Thus, it is particularly advantageous to derive boundary conditions in which the autonomous vehicle can be operated as accident-free as possible or is to be operated without accident.In a further advantageous embodiment of the invention, the vehicle type of the autonomous vehicle is a train or, in particular, a locomotive, a sensorless transport vehicle, in particular for a sensorless transport system, for example in a production plant, a motor vehicle, in particular a passenger car, or an aircraft, in particular an aircraft. In other words, the method can, at least in the presence of a reliable supplementary data record, represent a vehicle of any desired vehicle type. This results in the advantage that the method can be used particularly flexibly and thus advantageously for a wide range of vehicles and thus traffic areas.A second aspect of the invention comprises a computer program. The computer program can be loaded, for example, in a memory of an electronic computing device which carries out at least part of the method and comprises program means for carrying out the steps of the method when the computer program is executed in the electronic computing device or a control device.Advantages and advantageous embodiments of the first aspect of the invention are to be regarded as advantages and advantageous embodiments of the second aspect of the invention and vice versa.A third aspect of the invention relates to an electronically readable data carrier. The electronically readable data carrier comprises electronically readable control information stored thereon, which comprises at least one computer program as just presented and are configured such that, when the data carrier is used in an electronic computing device, they can execute a method according to the first aspect of the invention presented here.Advantages and advantageous configurations of the third aspect of the invention are to be regarded as advantages and advantageous configurations of both the second and the first aspect of the invention and vice versa.For use cases or application situations which can arise in the method and which are not explicitly described here, provision can be made for an error message and / or a request for inputting a user feedback to be output and / or for a default setting and / or a predetermined initial state to be set according to the method.Regardless of the grammatical sex of a certain term, individuals with male, female or other sex identity are included.It shows: FIG. 1 is a schematic flow diagram for a method for providing an operating frame specification for an autonomous vehicle; FIG. 2 shows a schematic illustration of a basic model of the operating frame specification; and FIG. 3 shows a schematic illustration of an operating frame specification adapted on the basis of the method.FIG. 1 shows a schematic flow diagram for a method for providing an operating frame specification BV or in the English operational design domain (ODD), for an autonomous vehicle or a vehicle, which can be operated at least partially autonomously, for example by means of a driver assistance system. In this case, the vehicle can be a rail vehicle or a railway, in particular in the exemplary embodiment shown.The presented method comprises steps S 1 to S 4:In the first step S 1, a basic model GM shown in FIG. 2 is provided, which has attribute values a, which comprise at least one environment SZ, environment data UD and at least one dynamic element DE and characterize or at least partially describe the operating frame specification BV or ODD.In the second step S2, at least one further attribute value a' is added to the basic model GM from a supplementary data record which describes an exceptional situation, such as an accident, for the vehicle type of the vehicle.In the third step S3, at least one point value is derived for the at least one further attribute value a' and / or for at least one of the attribute values a on the basis of a frequency and / or significance in the supplementary dataset.Finally, in the fourth step S 4, an operating frame specification ABV or ODD is provided which is adapted on the basis of the at least one point value.The method presented is intended to be used to present a computer program which comprises program means which are suitable for carrying out steps S 1 to S 4 of the method, for example, in an electronic computing device. In addition, an electronically readable data carrier is also to be presented, which can provide corresponding program means when reading into the electronic computing device.In the present case, the vehicle type of the autonomous vehicle is intended to be a train or a railway. Alternatively, the vehicle type of the autonomous vehicle could represent a sensorless transport vehicle for a sensorless transport system, an automobile, in particular a passenger car or an aircraft, such as an aircraft.In the basic model GM shown in FIG. 2, the scene or the environment SZ is further divided into a basic structure BS into a drivable surface or track structure GS and a special structure SS. The environmental data UD can be composed of a lighting BL and the weather WE. The at least one dynamic element DE rewrites persons PE in particular. In this case, the persons PE are composed, for example, of the two attribute values a children and adults.The lighting BL comprises, for example, daylight lighting and an unoiled sky as the respective attribute or attribute value a. The weather WE can comprise, for example, an external temperature, a wind intensity, a rainfall and / or a snowfall as the respective attribute value a. The track structure GS describes, for example, a geometry or particular track configurations, such as, for example, points, as the respective attribute value a. The basic structure BS describes, for example, buildings and / or vegetation as attribute values. The special structures SS can be, for example, bridges, control values or the like, which are each stored as an attribute value in the basic model GM.The supplementary data record, the at least one further attribute value a' of which has flowed into the extended model in FIG. 3, describes, for example, an accident statistic relating to the vehicle type of the vehicle, and thus, for example, an official statistic, named ERADIS, which is managed by the ERA and describes railway accidents, the at least one further attribute value a is a value which characterizes an accident as an exceptional situation.The method for providing the adapted ODD or the operating frame specification ABV can be supplemented in particular by a further step, not shown in FIG. 1, in which a test scenario for the vehicle is to be derived on the basis of the adapted operating frame specification ABV and / or the vehicle or the at least one driver assistance system of the autonomous vehicle is to be operated on the basis of or with the adapted operating frame specification ABV and / or the test scenario determined therefrom.The electronic computing device can have an interface in order to be able to retrieve the supplementary data record or the interface can be provided by means of a communication device. In this case, the retrieval can be carried out in particular by a central electronic computing device which comprises or has a register database, for example, or is designed as this database. The central electronic computing device may be provided by the ERA, for example.FIG. 3 shows at least one further attribute value a' which is added or added to the basic model GM. For this purpose, accident data are retrieved, for example, from the central electronic computing device and relevant data relating to a corresponding train section on which the autonomous vehicle or the leadless train is intended to travel are added to the basic model GM.Based on the accident data, information about the route section on which the accident occurred, the date and time of the accident and thus information about the illumination at this time, the persons and trains involved in the accident, and also information about the weather at the time of the accident can be obtained as well as historical weather data. This data can be derived, for example, from the available, in particular computer-readable, information, so that, for example, the at least one further attribute value can be extracted from the supplementary data, for example, by means of a method of machine learning. Such a method can be, in particular, a large language model or a generating generic network (GAN). The method of machine learning can furthermore be used for deriving the at least one point value if corresponding data or the at least one further attribute value a' are extracted from the available information or are stored with a corresponding format in the supplementary data record.For example, the data of the dataset includes the exact locations, the exact date, additional affected persons such as workers, and that the weather has dealt with temperatures below 0° and light rain.Thus, the supplemented basic model - seen in Fig. 3 - comprises three new attribute values a'. In addition to cloudless sky, mild rainfall, workers and temperatures below 0°.Other attributes such as location were already present. Thus, generally speaking, if an attribute a' is not already part of the set A of all attribute values a, it is added to A, resulting in an extended set A' of attribute values a'. In this case, it is possible to distinguish between new subcategories, such as workers, or new attribute values for existing subcategories, such as a new temperature range for the external temperature. The ODD can thus be automatically extended by data which are associated with a potential risk for the accident or for the exceptional situation.On the basis of the now available supplemented model shown in FIG. 2, an in particular quantitative point value or a so-called risk score is calculated in step S3 for each of the new attributes from the set of attributes a' of the ODD space. For this purpose, all accidents in this case or exceptional situations should be determined which are associated with a specific attribute a' in A'.For example, a frequency for the occurrence of an accident may be determined in conjunction with a'. In addition, a severity of the accident or the significance of the accident should be determined on the basis of the information contained in the railway accident database, a quantitative number can likewise be derived from these.Thus, advantageously, for the derivation on the basis of the significance, a classification for the exceptional situation or the accident takes place. An example of such a classification may be the severity of the accident. It comprises, for example, a qualitative classification of severity, for example marginal or critical, and a corresponding quantitative value. The qualitative value should be assigned to the railway accident data manually. The quantitative value can be determined automatically, for example from the following table, which is kept ready accordingly.no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no no0Marginal10Heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy heavy100critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical critical10.000In a Dis Trod10.000.000In the present example, the severity may be defined as critical, for example, if a worker has been seriously injured in the accident. Therefore, a value of 10,000 is set for the severity level and thus for significance and a value of 2.10^ -6 events per year is set for the frequency, for example. Thus, for example, by multiplying the frequency by the significance, the at least one point value can be determined, which thus results in 0.01 in the example.The at least one further attribute value a' is annotated with this value. This is done for all attributes in set a'. If no point value can be determined for an attribute value a or a' or no risk value is determined, since the attribute is not associated with any accident data present, the corresponding point value can be set to zero.In particular, steps S 2 and S 3 of the method can be repeated as many times as desired, so that the ODD can be checked separately after each iteration. Further, the order of steps S 1 is arbitrary as compared with S 2 and S 3. For example, an ODD description can first be created on the basis of accident data and the basic model GM is then supplemented.The presented method has the advantage that an operating frame for an autonomous vehicle can be specified in a particularly advantageous manner. This further results in the advantage that the method can be operated particularly advantageously or safely in the autonomous operation predefined for it.
Claims
Method for providing an operating frame specification (BV) for an autonomous vehicle, having the steps: - providing a basic model (GM) which has attribute values (a) which comprise at least one environment (SZ), environment data (DU) and a dynamic element (DE) and characterize the operating frame specification (BV); (S1) - adding at least one further attribute value (a') to the basic model (GM) from a supplementary dataset which describes an exceptional situation for a vehicle type of the vehicle; (S2) - deriving at least one point value for the at least one further attribute value (a') and / or for at least one of the attribute values (a) on the basis of a frequency and / or significance in the supplementary dataset; (S3) and - providing an operating frame specification (ABV) adapted on the basis of the at least one point value. (S4)Method according to Claim 1, characterized in that the supplementary data record is called up via an interface from a central electronic computing device which is in particular designed as a register database.Method according to Claim 1 or 2, characterized in that the derivation of the at least one point value is additionally carried out on the basis of a time value.Method according to one of the preceding claims, characterized in that a classification for the exceptional situation takes place for the derivation on the basis of the significance.Method according to one of the preceding claims, characterized in that a method of machine learning is used for extracting the at least one further attribute value (a') and / or for deriving the at least one point value.Method according to one of the preceding claims, characterized in that the at least one further attribute value (a') supplements and / or specifies the environment (SZ), the environmental data (UD) and / or the dynamic element (DE).Method according to one of the preceding claims, characterized in that a test scenario for the vehicle is specified on the basis of the adjusted operating frame specification (ABV) and / or the vehicle is operated on the basis of the adjusted operating frame specification (ABV) and / or in the test scenario.Method according to one of the preceding claims, characterized in that an accident statistic relating to the vehicle type of the vehicle is used as the supplementary data record and the at least one further attribute value (a') characterizes an accident as an exceptional situation.Method according to one of the preceding claims, characterized in that the vehicle type of the autonomous vehicle is a train, a keyless transport vehicle, a motor vehicle or an aircraft.A computer program directly loadable into a memory of an electronic computing device, comprising program means for performing the steps of the method of any of claims 1 to 9 when said computer program is executed.Electronically readable data carrier having electronically readable control information stored thereon, which comprise at least one computer program according to claim 10 and are configured such that, when the data carrier is used in an electronic computing device, they carry out a method according to one of claims 1 to 9.