DE102022109342A1 - Method and device for determining a perceptibility measure of a vehicle - Google Patents

Abstract

A method (300) and device (101) for determining information relating to the perceptibility of a vehicle (100) in a section (202) of a traffic route network are described. The method (300) includes determining (301) a perceptibility measure for the perceptibility of the vehicle (100) in the section (202) of the traffic network by a second road user (200) based on map data for the section (202) of the traffic network. The method (300) further includes providing (302) information relating to the determined perceptibility measure for the section (202) of the traffic network, in particular in connection with the map data for the section (202) of the traffic network.

Description

The invention relates to a method and a corresponding device for determining a perceptibility measure of a vehicle. Such a perceptibility measure can, for example, relate to human perception and be characteristic of how well the vehicle can be perceived, in particular heard and/or seen.

A vehicle may have one or more driver assistance systems that support the driver of the vehicle when driving the vehicle.

This document deals with the technical task of increasing the comfort, sovereignty and/or safety of a vehicle and/or another road user. In particular, the technical task includes determining, in particular predicting, and/or changing (sensibly) a perceptibility measure of a vehicle (for perception by one or more other road users).

The task is solved by each of the independent claims. Advantageous embodiments are described, among other things, in the dependent claims. It should be noted that additional features of a patent claim dependent on an independent patent claim can form a separate invention independent of the combination of all the features of the independent patent claim, without the features of the independent patent claim or only in combination with a subset of the features of the independent patent claim can be made the subject of an independent claim, a division application or a subsequent application. This applies equally to technical teachings described in the description, which may constitute an invention independent of the features of the independent patent claims.

According to one aspect, a method for determining information relating to the perceptibility of a (motor) vehicle in at least a section of a traffic route network (in particular a roadway network) is described. The traffic route network can include a variety of traffic routes (e.g. roadways, cycle paths, pedestrian paths, etc.), some of which are connected to one another via junctions (intersections, crossings, roundabouts, entrances, exits, etc.). The topology and/or the nature of the traffic network can be described using map data (e.g. for a navigation system).

The method includes determining a perceptibility measure for the perceptibility of the vehicle in the section of the traffic network by a second road user (e.g. by another vehicle, by a pedestrian, by a cyclist, etc.) based on the map data for the section of the traffic network. The map data can be map data that only or predominantly describes static features (or independent of a specific traffic situation) of the (respective) section of the transport network.

The second road user can (initially) be a possible, probable and/or initially hypothetical road user. Several second road users can be taken into account. One or more second road users can initially be represented by corresponding data.

There can be several perceptibility measures for a (every) second road user, in particular for several (e.g. possible, probable and/or hypothetical) arrangements (e.g. to the section of the traffic route network and/or to the vehicle) and/or movement data of the second road user and/or (Perceptual) boundary conditions (e.g. for several visibility distances, disturbing visual and/or acoustic backgrounds, dimensions of the distractions of the second road user) are determined.

Preferably, the properties of human perception, in particular one or more psycho-optical and/or psychoacoustic effects or dependencies, and/or properties of perception by technical means of the second road user, in particular properties of object recognition, machine vision (e.g. with the known or assumed weaknesses or strengths) must be taken into account. For example, the measure of perceptibility can also take into account a perception that deviates from the average or a norm. This can be done when determining a perceptibility measure for the perceptibility of the vehicle in the section of the traffic network.

The vehicle itself (the perception of which is at issue) can also be represented (initially, as part of determining a perceptibility measure for the perceptibility of the vehicle in the section of the traffic network by a second road user) by data on its position, movement, condition, etc .

The perceptibility measure for the perceptibility of the vehicle in the section of the traffic network can be determined using a computer simulation of virtual journeys of vehicles (e.g. the vehicle and/or one or more second road users) within a (e.g. dynamic) computer model (e.g. taking into account the data of the traffic network). be determined. The computer simulation or computer model can relate to the section of the transport network. It can be simulated how well or how poorly a vehicle in the section of the traffic network can be perceived by another road user (e.g. visible and/or audible and/or recognizable as such). As a result of the computer simulation, one or more perceptibility measures can be determined (e.g. for several, in particular for a large number of virtual rides or variants).

A perceptibility measure can be determined for a large number (eg more than 10, 10 ² , 10 ³ , 10 ⁴ , ...) of variants that initially only exist virtually. In particular, the perceptibility measure can characterize the perception of the vehicle, in particular an ego vehicle, for one or more other road users (eg from the perception perspective of the one or more road users). First of all, it can be about second road users who may, are or will be present (at the respective positions, in certain arrangements, etc.).

To put it simply, the perceptibility measure can refer to one or more possible, probable and/or hypothetical variants. These can (each) be based on the assumption that the vehicle and/or one or more second road users are present (e.g. at a specific position, a specific movement (e.g. with one or more specific movement parameters), and/or under specific perception conditions). or (e.g. at a certain time interval) will be present.

Particularly preferably, a perceptibility measure is (first) determined, which is abstracted from a concrete (e.g. current and/or arising at the moment) perception situation and/or from the one or more second road users. This results in a decisive advantage in that a perceptibility measure can be determined for a (later) specific case, especially in advance. This can be done before a (specific, real) second road user can be recognized (in the specific, real) traffic situation.

The perceptibility measure for the perceptibility of the vehicle in the section of the traffic network can be based on the perception by a human (e.g. hearing and/or seeing) and/or on the perception (in the sense of detection, measurability, recognizability) by technical means, e.g Sensor (such as a camera, a radar sensor, a lidar sensor, etc.) of the second road user. In each case, acoustic and/or optical perceptibility or technical measurability, detectability, etc. can be considered. The perceptibility measure can (in all variants) also include or be a recognizability measure, in particular recognizability as a vehicle. For example, the perceptibility measure of the vehicle can indicate how well the vehicle is perceived (e.g. heard and/or seen) (e.g. by a second road user) and/or as a vehicle (e.g. as a car, truck, two-wheeler, in particular motorcycle, van, etc .) is recognizable.

Recognizability, in particular a correct assignment to the correct class or category of the object, is particularly important for perception by sensors of other road users. The information about whether a technical system of another road user (correctly) perceives the ego vehicle, in particular recognizes it, can be used in the ego vehicle or for the ego vehicle.

For example, the perceptibility measure can characterize how well, safely and/or with what degree of probability the vehicle is perceived by the (human) perception (in particular through the perception channels) of the driver of the other road user and/or with a technical device of the other road user (e.g. with a Driver assistance system of the second road user) can be recognized. Of course, the perceptibility measure can be expressed as a (complementary) measure of insufficient perceptibility.

The perceptibility measure for the section of the traffic route network can, for example, indicate the probability (eg a probability measure) that the vehicle in the section will be perceived by another road user or, alternatively, will not be perceived. This can be indicated, for example, by a quantitative value, in particular by a value between 0 (no perceptibility) and 1 (full perceptibility). Particularly preferably, the perceptibility measure or probability measure can be divided into several (e.g. 4 or more, 8 or more, 16 or more, 32 or more, 64 or more) levels can be determined.

By determining the quantitative (also to be understood as finely graded) measures, a measure can then be initiated. For example, a particularly well-suited measure can be selected and/or a parameter (e.g. intensity and/or time of effect) of the measure can be adjusted.

Preferably, the perceptibility measure can simultaneously take into account the perceptibility by means of human hearing and by means of vision. For example, the perceptibility measure can include or be a probability measure that the vehicle is perceived at least with one of the perception channels (e.g. at least seen or at least heard) or is both overlooked and ignored. Particularly preferably, a perceptibility measure that applies overall to human perception and to the perception using technical means of the second road user is determined.

For example, the perceptibility measure can (also) take into account a disturbance and/or obscuration of the vehicle (e.g. for one or more specific perception perspectives of one or more second road users). A limited perceptibility can be caused, for example, by the spatial arrangement of the section and/or by objects (such as trees and/or buildings) on the section.

Preferably, the information (to be provided or provided) takes into account several perceptibility measures (e.g. corresponding to different variants) in relation to the determined perceptibility measure.

Particularly preferably, the information is determined depending on the determined perceptibility measure, in particular on the basis of several perceptibility measures.

The information (to be provided or provided) in relation to the determined perceptibility measure for the section of the traffic network can be a result of the processing of several perceptibility measures. The plurality of perceptibility measures can be perceptibility measures that have each been determined for different positions and/or arrangements of the one or more second road users in relation to the section of the traffic route network and/or to the vehicle. They can be measures of perceptibility that can then be expected in a specific (e.g. possible, expected) case. The method preferably includes a corresponding processing step.

In a (simplified) embodiment, the information provided can include several (e.g. 4 or more, 8 or more, 16 or more, 32 or more, 64 or more) perceptibility measures that have been determined (each) for different (possible) variants. In other words, the information to be provided or provided can include or be an array or a LUT (= Lock Up Table) of perceptibility measures that would arise in certain cases (e.g. later possible or probable in reality).

Depending on a concrete, real situation, in particular depending on the development of a concrete, real situation with the vehicle, a corresponding (appropriate) perceptibility measure can be read in and taken into account. This can take place before the one or more second road users can be perceived or recognized from the vehicle in a specific case (e.g. by an occupant of the vehicle and/or with a technical device). This makes it possible to effect or adjust (dose, so to speak) measures (including preventative, forward-looking ones).

Particularly preferably, the information provided is determined in relation to the determined perceptibility measure from several perceptibility measures in the form of a mathematical dependency (e.g. mathematical function, one or more parameters of a predetermined mathematical function, characteristic curve, etc.). The information provided can indicate a mathematical dependency in relation to the determined measure of perceptibility, according to which a measure of perceptibility (e.g. specific, characteristic of a concrete situation) can be determined in a (e.g. subsequent, later) situation.

In particular, the information provided (e.g. for the respective section of the traffic network and/or for a specific vehicle) can include or identify a computer-implemented mathematical relationship with which (with those known in the specific situation or with one or more in the specific situation Parameters) a specific perceptibility measure and/or a parameter of a measure to be implemented can be determined.

In other words, the information provided in relation to the determined perceptibility measure can include or be information with which (for example in a concrete situation, which can also occur later) a concrete perceptibility measure and / or a parameter of a appropriate measure can be determined. In this case, several (eg a large number of) (eg initially only possible or probable, in particular future) situations can be handled, in particular by means of comparatively compact information to be provided or provided.

The at least one piece of information relating to the perceptibility measure can be provided directly or indirectly to one or more vehicles (e.g. vehicles that are located near the specific section of the traffic network). As described in this document, this can be done by means of or depending on the digital card and/or by means of a (e.g. a central or centralized) backend.

A large number (e.g. theoretically infinite) of concrete situations can be handled with the information to be provided (e.g. in a user vehicle).

The perceptibility measure for the section of the traffic network can characterize a dependence, in particular a mathematical dependence, on: a parameter (e.g. the size or color) of the vehicle; a parameter (e.g. the height and/or the type and/or a position) of the road user; a parameter relating to lighting conditions and/or weather conditions (e.g. sunny, dark, rainy, foggy, etc.) in the section of the transport network; a parameter relating to the traffic density (e.g. low, medium, high) in the section of the transport network; and/or a parameter relating to the arrangement of the section within the transport network.

The perceptibility measure can thus be determined and provided in a form that is abstracted from a specific case and/or dependent on further specific parameters in order to enable a vehicle to obtain a specific measure of the perceptibility of the vehicle during a specific journey based on the specific situation at hand (e.g. own visibility and/or audibility) in the specific situation at hand (e.g. in one or more variants of the situation that already exist or will be possible or likely).

As part of the method, a probability measure can be determined for the probability that a second road user is present in the section of the traffic network (at all, so to speak) and/or is arranged (e.g. in a certain way to the section of the traffic network and/or to the vehicle). , who could not perceive the vehicle. The perceptibility measure can then be determined in a particularly precise manner depending on the probability measure. The measure of perceptibility (in the sense that the vehicle is actually overlooked, ignored or not recognized as such) can be determined and, in particular, predicted.

Alternatively or additionally, the procedure can be used to determine a risk measure in relation to a possible consequence of the vehicle not being perceived by the second road user (e.g. in relation to the severity of the possible consequence). The perceptibility measure can then be determined in a particularly precise manner depending on the risk measure.

In a corresponding manner, perceptibility measures can be determined for a large number of different sections (i.e. sub-areas, e.g. with a resolution of one or more decimeters, one or more meters, or one or more 10-meter sections) of the traffic network.

The method further includes providing information regarding the determined perceptibility measure for the section of the traffic network or for the perceptibility measures of the plurality of different sections.

Alternatively or additionally, the provision of information in relation to the determined perceptibility measure can (also) include or be a mathematical dependence on the position information (e.g. geoposition). The information provided can have a particularly compact form and/or be suitable for a particularly precise determination of the (specific) perceptibility measure of the vehicle for certain positions.

The information can preferably be provided in connection with the map data for the traffic route network. For example, sections of the information to be provided can be assigned to respective sections of the map of the traffic route network (in particular also to be understood as added or linked directly or indirectly). For example, these can each be used as an additional attribute for the individual sections of the map (e.g. in a grid of one or more 10 meter sections).

The information provided in relation to the determined perceptibility measure can be read by a (any) user vehicle (also: the real ego vehicle in the real situation), for example when driving on the section of the traffic network from the corresponding one Section of the digital map or depending on a corresponding assignment information (e.g. link) can be read. This information can be used to increase the comfort and safety of road users in the traffic network (e.g. the vehicle and/or another road user).

The vehicle is preferably a vehicle with a (particularly) quiet drive or low operating noise, in particular an at least partially electrically driven vehicle, e.g. electric vehicle, hybrid vehicle or vehicle with a traction energy storage device, which can be driven at least predominantly electrically, at least in sections. On the one hand, such vehicles are advantageous due to the low noise pollution in the surrounding area, but they carry the risk of not being noticed sufficiently (e.g. too late or not by all road users). Therefore, the application of the method to vehicles with low operating noise, especially with quiet drives, is particularly advantageous.

Furthermore, the method can also take into account a parameter of the operating noise of the vehicle (in a specific variant or during the specific journey). For example, whether the vehicle (actually) runs electrically.

According to a further aspect, a method for operating a (motor) vehicle in at least a section of a traffic route network is described. The method includes, as part of a journey of the vehicle, reading in information relating to a perceptibility measure for the section of the traffic network. The information can be read in connection with map data for the section of the transport network. The perceptibility measure can indicate the perceptibility of a vehicle in the section of the traffic network by another road user, and/or identify a dependency, in particular a mathematical relationship, with which a perceptibility measure of the vehicle can be determined.

As already explained above, the information relating to the perceptibility measure can be provided in a form that is abstracted from a specific case and/or dependent on further specific parameters (preferably in the form of a mathematical relationship already described), e.g. as a function of a parameter (e.g. the size or color) of the vehicle; a parameter (e.g. the height and/or type or position) of the road user; a parameter relating to lighting conditions and/or weather conditions (e.g. sunny, dark, rainy, foggy, etc.) in the section of the transport network; a parameter relating to the traffic density (e.g. low, medium, high) in the section of the transport network; and/or a parameter relating to the arrangement of the section within the transport network

The method can further include determining, based on the information read, a specific measure of the perceptibility of the vehicle in the section of the traffic network. For this purpose, values for one or more of the above-mentioned parameters can be determined (e.g. one or more parameters relating to the vehicle, and/or one or more parameters relating to the lighting conditions and/or the weather conditions). The values of one or more parameters can be combined with the information read in to determine the specific measure of the vehicle's perceptibility. The specific measure of perceptibility can indicate the probability (in the current constellation and/or situation) that the vehicle in the section will be perceived by another road user or, alternatively, will not be perceived.

As part of the method, a probability measure can be determined for the probability that another road user is present in the section of the traffic network (at all, so to speak) and/or is arranged, in particular in a specific way, in the section of the traffic network that does not have the vehicle could perceive.

The specific measure of perceptibility can then be determined in a particularly precise manner as a probability measure or depending on the probability measure (and possibly taken into account later).

Alternatively or additionally, the procedure can be used to determine a risk measure in relation to a possible consequence (in particular consequence) of the vehicle not being perceived by the other (the possible second) road user (e.g. in relation to the severity of the possible consequence) . The specific measure of perceptibility can then be determined in a particularly precise manner depending on the risk measure.

Furthermore, the method can include effecting at least one measure depending on the information provided in relation to perceptibility. The measure can depend (eg in a predetermined dependency) on the information provided in relation on the perceptibility measure, the specific measure of perceptibility and/or the mathematical connection.

For example, a measure can be implemented (only or primarily) if the specific measure of perceptibility and/or the perceptibility measure (in particular a probability of sufficient perceptibility) is smaller than a (first) specific threshold value. Alternatively or additionally, the (e.g. otherwise active or to be effected) measure can be reduced or shortened to a lower level or the effecting of a measure can be dispensed with if the specific perceptibility measure is greater than a (second) specific threshold value. The effect of the measure can therefore also include or be a reduction or adjustment of a measure (which would otherwise be carried out) or switching off the measure (for the section of the transport network). As a result, energy consumption and/or disruption to the vehicle's occupants, other road users or bystanders can be reduced (at least statistically).

The at least one measure can include: a measure in relation to the driver of the vehicle, in particular in relation to the issuance of information (e.g. the issuance of a warning) to the driver of the vehicle, for example in relation to the section of the traffic network ahead; a measure relating to a function, in particular a function for automated longitudinal and/or lateral guidance, of the vehicle (e.g. an automatic reduction in driving speed); and/or an action in relation to a possible other road user who may not be aware of the vehicle in the section of the road network (e.g. sending a car-to-car message and/or causing a light signal and/or a horn signal).

Alternatively or additionally, the at least one measure can include: activating a lighting system of the vehicle; causing (acoustic and/or visual) signaling in one direction to a possible other road user who may not be aware of the vehicle in the section of the traffic network; controlling a driver assistance system of the vehicle; and/or effecting a safety measure to protect an occupant of the vehicle (such as pre-tensioning a seat belt).

Alternatively or additionally, the at least one measure can include: aligning one or more sensors (such as a camera or a radar sensor) of the vehicle for travel in the section of the traffic network; selecting one or more sensors of the vehicle for travel in the section of the traffic network; and/or setting and/or adapting processing of sensor data from one or more sensors of the vehicle for travel in the section of the traffic network.

The method preferably includes reading in information relating to a perceptibility measure for a large number of different sections of the traffic network, which are arranged, for example, on a (planned) route of the vehicle.

The method may further include identifying at least one section from the plurality of different sections where another road user could be present (e.g. arranged in a certain way) or is arranged. This can be determined based on the sensor data from one or more environmental sensors of the vehicle.

Furthermore, the method can include determining the specific measure of the perceptibility of the vehicle in the identified section of the traffic network and/or effecting a measure in relation to the identified section and/or in relation to one or more second road users, depending on the information provided.

The level of perceptibility can also be determined for second road users who have not been, not yet or cannot be reliably recognized (e.g. with sensors on the vehicle and/or with the data received from the vehicle). In other words, the procedure also enables expedient handling of situations or cases that have not yet occurred (but, for example, could occur or are expected to occur with a certain degree of probability). For example, the user vehicle can be provided with information that indicates the probability with which a cyclist, who could be coming from a street with insufficient visibility, could miss and/or ignore the vehicle or not identify it as a vehicle; and/or information can be provided to effect an appropriate action.

The method makes it possible, for example, to increase the comfort and/or traffic safety of a vehicle in an efficient and reliable manner. In particular, the procedure can help to solve problems related to inadequate perception (e.g. audibility and/or Detectability) of electric vehicles to reduce or solve.

According to a further aspect, a computer program, in particular a computer program product comprising a computer program, is described, wherein the computer program is designed to carry out at least one of the methods described in this document when it is executed on at least one computing unit. There can be several computer programs or a computer program product comprising several computer programs, which can be run, for example, on different computing units of the device or on several devices in order to carry out (if necessary together, in an information technology connection) at least one of the methods described in this document.

The computer program product can be designed as an update of a previous computer program, which includes the parts of the computer program or the corresponding program code, for example as part of a functional expansion, for example as part of a so-called “remote software update”. The computer program product includes in particular a medium readable by the data processing device, on which the program code is stored, or at least an encrypted file.

According to a further aspect, a device for determining information relating to the perceptibility of a vehicle in a section of a traffic route network is described. The device is set up, based on map data for the section of the traffic network, to determine a perceptibility measure for the perceptibility of the vehicle in the section of the traffic network by a second and / or other road user. The device is also set up to provide information relating to the determined perceptibility measure for the section of the traffic network, in particular in connection with the map data for the section of the traffic network.

According to a further aspect, a device for operating a vehicle in a section of a traffic route network is described. The device is set up to read in information relating to a perceptibility measure for the section of the traffic network as part of a journey of the vehicle, in particular in connection with map data for the section of the traffic network. The perceptibility measure can indicate the perceptibility of a vehicle in the section of the traffic network by another road user. The device is also set up to determine a specific measure of the perceptibility of the vehicle in the section of the traffic route network based on the information read in, and to effect at least one measure depending on the specific measure of perceptibility.

According to a further aspect, a (road) motor vehicle (in particular a passenger car or a truck or a bus or a motorcycle) is described which comprises at least one of the devices described in this document.

According to a further aspect, a vehicle-external unit, such as a server, is described, which includes at least one of the devices described in this document.

It should be noted that the methods, devices and systems described in this document can be used both alone and in combination with other methods, devices and systems described in this document. Furthermore, any aspects of the methods, devices and systems described in this document can be combined with one another in a variety of ways. In particular, the features of the claims can be combined with one another in a variety of ways. Furthermore, features listed in brackets are to be understood as optional features.

• 1 beispielhafte Komponenten eines Fahrzeugs;
• 2 einen Abschnitt eines Verkehrswegenetzes mit einem beispielhaften Knotenpunkt zwischen unterschiedlichen Verkehrswegen in dem Verkehrswegenetz;
• 3a ein Ablaufdiagramm eines beispielhaften Verfahrens zur Ermittlung eines Wahrnehmbarkeitsmaßes für einen Abschnitt eines Verkehrswegenetzes; und
• 3b ein Ablaufdiagramm eines beispielhaften Verfahrens zur Ermittlung von Information in Bezug auf eine Wahrnehmbarkeit eines Fahrzeugs in einem Abschnitt eines Verkehrswegenetzes, zur Erhöhung des Komforts und/oder der Sicherheit eines Verkehrsteilnehmers in dem Verkehrswegenetz.

In the following, the invention is described in more detail using exemplary embodiments without restricting the generality. Show it

• 1 exemplary components of a vehicle;
• 2 a section of a traffic route network with an exemplary junction between different traffic routes in the traffic route network;
• 3a a flowchart of an exemplary method for determining a perceptibility measure for a section of a traffic route network; and
• 3b a flowchart of an exemplary method for determining information relating to the perceptibility of a vehicle in a section of a traffic network, to increase the comfort and / or safety of a road user in the traffic network.

As explained at the beginning, the present document is concerned with determining, in particular predicting, a perceptibility measure of a vehicle in a traffic route network, in particular in a road network. In particular, these include also increasing the comfort, sovereignty and/or safety of a vehicle and/or another road user based on the perceptibility measure. This can be done depending on the information provided.

In this context shows 1 an exemplary system 120 with a vehicle 100 and a vehicle-external unit 110, such as a backend server. The vehicle 100 includes a communication unit 104, which is set up to communicate with the vehicle-external unit 110 via a (possibly wireless) communication connection 111 (e.g. WLAN, 3G, 4G or 5G), for example to transmit data from the vehicle 100 to the Vehicle-external unit 110 and/or from the vehicle-external unit 110 to the vehicle 100.

The vehicle 100 may include one or more environment sensors 102 that are set up to capture environment data (i.e. sensor data) relating to the environment of the vehicle 100. Examples of surrounding sensors 102 are a radar sensor, a lidar sensor, an ultrasonic sensor, a camera, etc. A (control) device 101 of the vehicle 100 can be set up to evaluate the surrounding data, for example to detect an object in the surroundings of the vehicle 100.

The vehicle 100 can include one or more longitudinal and/or transverse guidance actuators 103 (e.g. a drive motor, a braking device and/or a steering device), which are set up to guide the vehicle 100 longitudinally and/or transversely at least partially or completely automatically. The device 101 can be set up to operate the one or more longitudinal and/or transverse guidance actuators 103 depending on the environmental data, e.g. to at least partially or completely automatically guide the vehicle 100 longitudinally and/or transversely and/or to provide a driver assistance function.

The vehicle-external unit 110 and/or the (control) device 101 can be set up to analyze digital map data in relation to a traffic route network traveled by the vehicle 100. The map data can, for example, show the course of one or more traffic routes (in particular streets and/or carriageways). Furthermore, junctions between different traffic routes can be displayed. Based on the map data, for example, a navigation system of the vehicle 100 can plan a route through the traffic network from a starting point to a destination point. The driver of the vehicle 100 can be guided along the planned route by the navigation system.

2 shows an exemplary traffic situation 200 in which a (first) vehicle 100 approaches a junction 205 on a first traffic route 201. A second vehicle 200 is also traveling towards junction 205 on a different traffic route. It can happen that the first vehicle 100 is not perceived by the second vehicle 200, for example because there is an object 206 at the junction 205, which blocks the driver of the second vehicle 100's view of the first traffic route 201.

The (control) device 101 of the first vehicle 100 can be set up to determine map data in relation to the first traffic route 201. The map data for a large number of different sections 202 of the first traffic route 201 can each display a probability measure 203 that the first vehicle 100 will be perceived, in particular seen and/or, by another road user 200 in the respective section 202 (e.g. with a certain probability measure). or heard, or alternatively (e.g. with a certain probability measure) not perceived. An appropriate measure can then be implemented while the first vehicle 100 is traveling, depending on the determined probability measure 203. The measure can be used to increase the comfort, sovereignty and/or safety of the first vehicle 100 and/or the one or more second road users 200.

For example, an intelligent navigation system or an intelligent vision and lighting system can be provided, which is designed to warn in advance in road traffic where, from where and possibly when a road user 100 would not be sufficiently perceptible, in particular recognizable, by another road user 200 , e.g. with a probability measure that exceeds a threshold value, is ignored, overlooked and/or not recognized as a vehicle (particularly if there is a risk of an accident).

This can also be provided for those road locations and/or situations that are (also physically) recognizable as dangerous for the individual road user 100 only retrospectively and/or from the perspective of the other road user 200. This can also be provided for road locations and/or driving situations where no sensors (e.g. including data processing) of the second (other) road user 200 would be sufficient to perceive the first road user 100.

All features described in this document can be attributed to at least two alternatives or Design variants that can be used in addition to one another can be obtained. In a first embodiment variant, the determination 301 of a perceptibility measure for the perceptibility of the vehicle 100 in the section 202 of the traffic route network is carried out by a second road user 200 on the basis of map data for the section 202 of the traffic route network, in particular at least partially detached from a specific situation. This can also be done offline and/or in a unit 110 external to the vehicle. The advantage of this variant is, in particular, that a (comparatively complex) determination of a large number of perceptibility measures (e.g. each for a large number of possible cases) can be carried out bundled or for several user vehicles).

For this purpose, information (which can later be used in vehicles) can be generated in advance, in particular by means of a (computer) simulation, which can be provided in map data, for example. The information can be generated offline (e.g. in a server, data center, etc.). The information can be assigned to different sections 202 of a digital map. A map layer can therefore be created and provided for “being perceived, especially for being overlooked/overheard/not recognized as a vehicle”.

The information obtained is preferably designed in such a way that the information can be used in a subsequent real situation in order to determine a concrete measure of perceptibility (e.g. a concrete measure of “being seen and/or heard”). A user vehicle 100 can read the information (e.g. in real driving) for the respective section 202 of the map and, if necessary, carry out a measure based on this.

When determining the information for a section 202, overlooking by another road user 200 and/or insufficient detectability (“overlooking”) by a sensor system can be taken into account.

In a second embodiment variant, the determination 301 of a perceptibility measure for the perceptibility of the vehicle 100 in the section 202 of the traffic route network is carried out by a second road user 200 on the basis of map data for the section 202 of the traffic route network, at least partially in relation to a possible or probable, e.g. predicted, situation . This can be done at least partially with means of the vehicle 100 and/or at least partially with means of the vehicle-external unit 110. This can be done depending on a specific need, for example depending on one or more parameters of the position of the vehicle 100 and/or one or more parameters of the (e.g. current and/or expected) situation. The said information can be provided within the vehicle 100.

A distinction can be made between a user vehicle (also ego vehicle) 100 and a (other) road user 200 (who overlooks or could overlook the user vehicle 100). The measures described in this document can also be carried out if no other road user 200 is actually present or recognized.

3a shows a flowchart of a (possibly computer-implemented) method 300 for determining information regarding the perceptibility of a (motor) vehicle 100 in at least one section 202 of a traffic route network, in particular in at least one section 202 of a traffic route 201 (e.g. a roadway ) of a traffic network (e.g. a road and/or road network). At least part of the method 300 can be carried out by a unit 110 external to the vehicle (such as a server).

The method 300 includes determining 301 a perceptibility measure 203 for the perceptibility of the vehicle 100 in the section 202 of the traffic network by a second road user 200 based on map data for the section 202 of the traffic network. The perceptibility measure 203 can be determined for a variety of different sections 202. The perceptibility measure 203 can, for example, have a value (e.g. between 0 and 1) that indicates how well and/or how likely the vehicle 100 will be perceived by another road user 200 in the respective section 202. The value 0 can, for example, indicate a probability of 0% for perceptibility. On the other hand, the value 1 can, for example, indicate a 100% probability of perceptibility. The perceptibility measure can be determined using a (computer) simulation based on the map data.

The method 300 further includes providing 302 information relating to the determined perceptibility measure 203 for the section 202 of the traffic network (ie in relation to one or more sections 202). The information can include one or more perceptibility measures 203 and/or be determined depending on several perceptibility measures and/or identify a mathematical relationship that is used to determine a specific perceptibility measure of the vehicle (in a specific situation tion and depending on the parameters of the situation).

The information can be provided in connection with the map data for the section 202 of the transport network (e.g. as an additional attribute in the map data for the respective section 202). This can be done in advance.

3b shows a flowchart of a (possibly computer-implemented) method 310 for operating a (motor) vehicle 100 in at least one section 202 of a traffic route network. The method 310 can be carried out by a (control) device 101 of the vehicle 100 as part of a journey of the vehicle 100.

The method 310 includes reading 311 information relating to a perceptibility measure 203 for the section 202 of the traffic network. The information can be provided and/or read in connection with map data for section 202 of the traffic network.

The perceptibility measure 203 can indicate the perceptibility of a vehicle 100 in the section 202 of the traffic route network by another (initially possible, potential) road user 200.

The perceptibility measure 203 may have been determined based on a simulation and/or based on the map data. The information relating to the perceptibility measure 203 can, for example, indicate the probability that the vehicle 100 will be perceived (or, conversely, not perceived) by another road user 200 in the section 202.

The method 310 further includes determining 312, based on the information read, a specific measure of the perceptibility of the vehicle 100 in the section 202 of the traffic network. For this purpose, the read perceptibility measure 203 for the section 202 can be combined with specific information relating to the current situation in the section 202, such as the lighting situation, the traffic density, the weather situation, the season, etc.

Furthermore, the method 310 includes effecting 313 at least one measure depending on the specific measure of perceptibility. In this way, the comfort and/or safety of the vehicle 100 can be increased.

A method 300 for recognizing or predicting the road sections 202 and/or situations in which a vehicle 100 cannot be adequately perceived (e.g. overlooked, ignored and/or not recognized as such) is thus described. The method 300 can include reading in card data, in particular offline (in advance on a server).

The map data can correspond to a classic digital map and/or include photorealistic, three-dimensional and/or perspective-displayable data (e.g. photos).

The method 300 further includes determining one or more perceptibility measures 203 of a (only virtual) vehicle 100 for a large number of relevant road sections 202, e.g. sections 202 of lanes 201, intersections 205, curves, etc. The perceptibility measure 203 can be used for different positions and /or angles can be determined in an area. In particular, the perceptibility measure 203 can be dependent on the position, the direction and/or the angle from which a vehicle 100 in the respective section 202 is perceived (or not perceived) by another road user 200.

Furthermore, the method 300 includes providing information based on the perceptibility factors. The information can be assigned to a digital map for use by one or more user vehicles 100.

The above-mentioned steps of the method 300 can be carried out in particular offline and/or as part of a computer simulation. The perceptibility measure 203 (or the corresponding perception) can relate to an auditory and/or visual perception. For example, a low perceptibility measure 203 may indicate poor visibility or audibility of the ego vehicle 100 and/or an increased risk of being overlooked or overheard.

The at least one perceptibility measure 203 can characterize a mathematical relationship that is a function of the perceptibility (of a vehicle 100 with certain parameters) depending on several further parameters (e.g. the possible other road user 200, his arrangement within the road arrangement and / or relative to the ego Vehicle 100, a boundary condition, a disturbance parameter, etc.).

The information provided can be a locally valid function that (e.g. when using the case-specific parameters) is used to determine the case-specific, concrete measure of perceptibility.

Furthermore, a method 310 is described, which includes, for example, reading provided information, in particular from a digital map while a user vehicle 100 is driving, in particular with regard to a current or predicted position of the user vehicle 100.

The method 310 can further include determining a specific measure of the perceptibility of the vehicle 100, in particular for certain positions and/or angles, depending on the information provided, in particular with regard to the current or predicted position of the vehicle 100. Based on this, then at least one measure must be carried out.

The above steps can be carried out for specific user vehicles 100. The information about the perceptibility measure 203 of the user vehicle 100 can be determined and taken into account in relation to a specific road location (currently or in the near future) and/or driving situation. The at least one measure can (if necessary) be taken with foresight.

As part of the described methods 300, 310, the information provided can identify a mathematical dependency according to which the perceptibility measure 203 of the user vehicle 100 can be perceived by at least a potential road user 200, for example depends on: a parameter of the user vehicle 100; and/or a parameter of the road user 200; and/or a parameter of the driving situation; and/or a parameter of the roadway arrangement; and/or a visibility parameter.

In doing so or as a result, the perceptibility measure 203 can identify a mathematical dependency that can be applied to several situations that are possible in reality, which is applicable in particular to different user vehicles 100, different road users 200 and/or several variants or conditions that are not known a priori.

In doing so or thereby, comparatively universal information suitable for different cases can be determined and provided. This can be comparatively compact (taking into account the comparatively broad applicability for a large number of cases). This makes it relatively easy to assign it to (e.g. link to) a corresponding section 202 of a digital map.

The information provided can, for example, identify a mathematical relationship specific to a specific position and/or a pattern of the roadway arrangement, which (e.g. in the case of the user vehicle 100 in a specific situation) leads to determining the case-specific, concrete perceptibility measure. For example, one or more of the parameters (mentioned above) are determined or predicted in a user vehicle 100 (e.g. using sensors 102) and processed using the information provided (e.g. inserted into a mathematical function characterized by the mathematical function provided).

The information to be provided can be determined by means of a simulation, in particular by means of a traffic simulation and/or by means of a simulation of the driving operation of a large number of virtual road users, for example within a virtual environment that simulates a real environment.

This can be determined for a mathematical model or for an assumption of an ego vehicle 100 and/or observer. Particularly preferably (possibly only) a virtual vehicle model, e.g. a simulation representing a vehicle, can be used.

Data that relate to a typical vehicle and/or to a specific vehicle or a vehicle of a specific type can be taken into account.

For example, the resulting perceptibility measure 203 may relate to the perception by a pedestrian; by a cyclist; by a car driver; and/or by a truck driver. For example, the resulting perceptibility measure 203 may relate to the perception of a pedestrian; a car, in particular of a certain size, form factor, color, and/or type; and/or a truck, particularly of a particular size, form factor, color, and/or type.

The simulation can be a probability-based simulation. For example, this can be carried out using a probability-based simulation or a so-called Monte Carlo simulation or can include part of a Monte Carlo simulation.

The perceptibility measure 203 of the user vehicle 100 by a road user 200 may refer to: a human, particularly the driver of a vehicle 200, which the ego vehicle 100 potentially overlooks; and/or a technical device of a vehicle 200, which potentially does not recognize the ego vehicle 100, does not recognize it early or incorrectly.

In other words, a first perceptibility measure can relate to a perception by a human, in particular a (at least assumed) road user 200 (who may overlook the user vehicle). On the other hand, a second perceptibility measure for a technical device, in particular for one or more sensors or sensor-based systems of a (at least potential) road user 200, can be determined, in particular predicted.

The at least one measure can be carried out: for the driver of the user vehicle 100 and can in particular include driver information; in relation to a functionality of the user vehicle 100; and/or for the road user 200, who will at least presumably and/or not adequately perceive the user vehicle 100 according to the prediction.

Particularly preferably, a (appropriate, best-fitting) measure is selected (e.g. from several measures of different types) and/or a parameter of a measure is determined or adjusted, e.g. relating to the spatial orientation, intensity, and/or duration of the measure.

The specific level of perceptibility of the vehicle can be increased to an appropriate value (e.g. predetermined or related to the specific driving situation) by one or more (possible, expected, not yet recognized or certain, e.g. already recognized by means of the vehicle) second road users.

For example, a probability measure of insufficient awareness of a possible safe situation can be less than 10%; 5%; 1%; etc. are reduced, and/or the probability of insufficient awareness of a possible dangerous situation to less than 1%; 0.5%; 0.1%; 0.01%; 0.001%, etc. can be reduced,

• • Betätigung eines Lichtsystems des Nutzer-Fahrzeugs 100;
• • Signalisierung in Richtung und/oder mit einem Winkel, aus welchen das Nutzer-Fahrzeug 100 zumindest voraussichtlich und/oder gemäß der Prädiktion nicht hinreichend wahrnehmbar ist;
• • Ansteuern eines Fahrerassistenzsystems des Nutzer-Fahrzeugs 100; und/oder
• • Ansteuern einer Funktionalität der aktiven Sicherheit, insbesondere Vorbefüllen des Bremssystems, Betätigung eines Gurtstraffer, etc.

The minimum measure can include one or more entries from the following list,

• • Actuation of a lighting system of the user vehicle 100;
• • Signaling in the direction and/or at an angle from which the user vehicle 100 is at least presumably and/or not sufficiently perceptible according to the prediction;
• • Controlling a driver assistance system of the user vehicle 100; and or
• • Controlling active safety functionality, in particular pre-filling the brake system, actuation of a belt tensioner, etc.

• • ein Lichtsignal in eine bestimmte Raumrichtung;
• • ein Tonsignal in eine bestimmte Raumrichtung;
• • ein Anzeigen einer zweidimensionalen oder dreidimensionalen Grafik und/oder eines fotorealistischen oder eines fotorealistisch verwandelten Bildes;
• • ein Anzeigen einer beweglichen Darstellung, beispielsweise einer Animation oder einer Bildsequenz; und/oder
• • ein Veranlassen einer Darstellung eines Abschnitts einer virtuellen Realität für den zumindest einen Verkehrsteilnehmer 200.

For example, the signaling to the outside can include the following forms and/or types of signaling:

• • a light signal in a certain spatial direction;
• • a sound signal in a specific spatial direction;
• • displaying a two-dimensional or three-dimensional graphic and/or a photorealistic or photorealistically transformed image;
• • displaying a moving representation, such as an animation or an image sequence; and or
• • causing a section of a virtual reality to be displayed for the at least one road user 200.

The signaling from the user vehicle 100 to the outside can indicate a specific, e.g. planned, action of the user vehicle 100 itself and/or a recommended or to be avoided action for the (e.g. potentially existing) road user 200 in the vicinity of the user vehicle Represent 100.

In other words, the signaling can identify the intention of the user vehicle 100, for example a light projection onto the roadway, which informs the (potentially existing) road user 200 about an intention of the driver or a driver assistance system of the user vehicle 100.

Alternatively or additionally, the signaling may include concrete information, which, for example, relates to certain parts of the room and/or directions and/or suggests the actions to the road user 200 that the road user 200 should carry out (e.g. to avoid an undesirable situation).

The signaling can be intended for a specific road user 200, for example for a crossing vehicle, a vehicle to be overtaken, a pedestrian, a cyclist, a road user who plays a specific role within the traffic situation, etc.

• • ein Ausrichten eines oder mehrerer Sensoren 102 des Nutzer-Fahrzeugs 100; und/oder
• • die Auswahl der Sensordaten eines oder mehrerer Sensoren 102 des Nutzer-Fahrzeugs 100 die, insbesondere besonders intensiv bzw. auf eine spezielle Weise, verarbeitet werden; und/oder
• • das Steuern der Verarbeitung der Sensordaten eines oder mehrerer Sensoren 102 des Nutzer-Fahrzeugs 100.

The at least one measure can include (possibly depending on the determined, specific perceptibility measure of the user vehicle 100):

• • aligning one or more sensors 102 of the user vehicle 100; and or
• • the selection of the sensor data of one or more sensors 102 of the user vehicle 100, which are processed, in particular particularly intensively or in a special way; and or
• • Controlling the processing of the sensor data of one or more sensors 102 of the user vehicle 100.

For example, the “attention” of the sensors 102 or the data processing of the user vehicle 100 and/or the driver of the user vehicle 100 can be directed particularly or primarily to the parts of space, angles and/or positions from which the user vehicle 100 can be overlooked.

The at least one measure can include generating driver information, depending on the specific perceptibility measure of the user vehicle 100, in relation to a specific road location and/or driving situation for the driver of the vehicle 100, in particular current or to be traveled in the near future.

The at least one measure, in particular the driver information, can include displaying geometric conditions that indicate possible overlooking, in particular: a position at which the vehicle 100 can be overlooked; a part of the room (such as an exit) from which the ego vehicle 100 can be overlooked; and/or an angle from which the vehicle 100 can be overlooked.

A method 310 for recognizing or predicting road sections and/or situations in which a user vehicle 100 can be overlooked by a road user 200 is thus described. The method 310 may include reading in information determined by means of a simulation, characterizing a perceptibility of the vehicle 100 in different positions and/or from different angles, while a vehicle 100 is driving, in particular in relation to a current or predicted position of the vehicle 100.

The term “section 202 of the transport network” can mean a specific section (e.g. characterized by its unique geoposition) (e.g. of one or more decimeters, one or more meters, one or more 10-meter sections, etc.) or a specific one Pattern, in particular a geometric pattern, corresponding section can be understood. The procedure, for example a simulation already described, does not necessarily have to be repeated individually for every new intersection of identical construction. For the very different, less standardized sections (e.g. typical for old towns), the determination of the perceptibility measures (e.g. by means of simulation) is preferably carried out using the data specific to the respective section.

In particular, the map data used can be representative of 2.5-dimensional or three-dimensional geometric data. A possible disruption or concealment of the perception of the vehicle can advantageously be taken into account.

The method 310 can further include determining at which positions and/or angles (relevant, e.g. dangerous or endangering) road users 200 are located or at least could be located with a certain probability.

Furthermore, the method 310 may include determining a specific measure of the perceptibility of the vehicle 100 (for the selected positions and/or angles) depending on the map information read and the determined position. Additionally, method 310 may include taking an action.

The information determined by means of a simulation can be read in in connection with driving on a section 202 of the map information, e.g. a map layer for a current or predicted (real) position of the (real) vehicle 100.

Furthermore, one or more corresponding devices 101, 110 to the methods 300, 310 described in this document are described.

Furthermore, one or more computer programs for the methods 300, 310 described in this document are described

The at least one perceptibility measure can relate to a perception by a person through seeing and/or hearing. A low level of perceptibility can indicate insufficient seeing and/or hearing of the ego vehicle 100 and/or a probable overlooking and/or overhearing.

The perceptibility measure may take into account a probability measure, characterizing a probability that insufficient perception actually exists. In a simplified case, the probability measure can characterize a presence of a perceiver (eg another road user 200) in the relevant spatial area and/or time interval (to which the perceptibility measure relates).

For example, if the ego vehicle 100 is actually hardly noticeable by a road user 200 from a relatively dark alley, but (according to map information or map statistics) a road user 200 only very rarely leaves the dark alley, a relatively low probability measure can be assumed. In this case, driver information, in particular a strong warning, may be dispensed with.

The at least one perceptibility measure can take into account a risk measure for the consequences of a possible oversight, in particular include a risk measure or be standardized to a risk measure. For example, if the ego vehicle 100 is actually difficult to perceive from a specific position and/or direction, but (according to map information or map statistics) road users 200 drive out of the specific position and/or direction comparatively slowly (at least statistically), a relatively there is a low level of risk. In this case, driver information, in particular a strong warning, can be dispensed with.

The at least one perceptibility measure can relate to a detection and/or recognition by a sensor or a sensor-based system, in particular of a (at least assumed) road user 200 (who may not detect and/or recognize the ego vehicle 100 or not appropriately).

During a real, specific journey of the (user) vehicle 100 (in a specific situation), the information relating to the determined perceptibility measure for the section 202 of the traffic network can provide information about how the vehicle 100 is viewed by one or more other road users 200 , in particular subjectively, is perceived and/or which measure or measure should be implemented with which parameter in order to expediently change the specific measure of perceptibility.

Through the measures described in this document, new functionalities can be provided in a vehicle 100. Furthermore, sovereignty and/or safety when driving can be increased. If necessary, the costs for sensors 102 can be reduced.

The present invention is not limited to the exemplary embodiments shown. In particular, it should be noted that the description and the figures are only intended to illustrate the principle of the proposed methods, devices and systems by way of example.

Claims (14)

Method (300) for determining information relating to the perceptibility of a vehicle (100) in a section (202) of a traffic route network; wherein the method (300) comprises, - Determining (301) a perceptibility measure for the perceptibility of the vehicle (100) in the section (202) of the traffic network by a second road user (200) based on map data for the section (202) of the traffic network; and - Providing (302) information relating to the determined level of perceptibility for the section (202) of the traffic network, in particular in connection with the map data for the section (202) of the traffic network. Procedure (300) according to Claim 1 , wherein the perceptibility measure for the perceptibility of the vehicle (100) in the section (202) of the traffic network based on a computer simulation of virtual trips, in particular of the vehicle (100) and / or one or more second road users (200), within a computer model, in particular for section (202) of the traffic network. Method (300) according to one of the preceding claims, wherein the perceptibility measure for the perceptibility of the vehicle (100) in the section (202) of the traffic route network relates to, - the perception by a human; and or - the perception by a technical means, in particular comprising a sensor, of the second road user (200). Method (300) according to one of the preceding claims, wherein the method (300) comprises - determining a probability measure for a probability that a second road user (200) is present in the section (202) of the traffic route network, in particular in a certain type and is arranged in a manner or will be present who could not perceive the vehicle (100); and - Determination of the perceptibility measure depending on the probability measure. Method (310) for operating a vehicle (100) in a section (202) of a traffic route network; wherein the method (310), as part of a journey of the vehicle (100), comprises - reading in (311) information relating to a perceptibility measure for the section (202) of the traffic network, in particular in connection with map data for the section (202) of the traffic network; wherein the perceptibility measure indicates the perceptibility of a vehicle (100) in the section (202) of the traffic route network by another road user (200); - Determining (312), based on the information read, a specific measure of the perceptibility of the vehicle (100) in the section (202) of the traffic network; and - Effect (313) at least one measure depending on the specific measure of perceptibility. Procedure (310) according to Claim 5 , wherein the method (310) comprises - determining a risk measure in relation to a possible consequence of the vehicle (100) not being perceived by the other road user (200); and - effecting an action depending on the level of risk. Procedure (310) according to Claim 5 or 6 , wherein the at least one measure is or comprises - a measure in relation to a driver of the vehicle (100), in particular in relation to the output of information to the driver of the vehicle (100), for example in relation to the section (202) ahead ) of the transport network; - a measure relating to a function, in particular a function for automated longitudinal and/or lateral guidance, of the vehicle (100); and/or - a measure in relation to a possible other road user (200) who may not notice the vehicle (100) in the section (202) of the traffic route network. Method (310) according to one of Claims 5 until 7 , wherein the at least one measure comprises - actuating a lighting system of the vehicle (100); - causing signaling in one direction to a possible other road user (200) who may not perceive the vehicle (100) in the section (202) of the traffic route network; - Controlling a driver assistance system of the vehicle (100); and/or - effecting a safety measure to protect an occupant of the vehicle (100). Method (310) according to one of Claims 5 until 8th , wherein the at least one measure comprises - aligning one or more sensors (102) of the vehicle (100) for travel in the section (202) of the traffic network; - Selecting and/or prioritizing data from one or more sensors (102) of the vehicle (100) for travel in the section (202) of the traffic network; and/or - adapting processing of sensor data from one or more sensors (102) of the vehicle (100) for travel in the section (202) of the traffic network. Method (310) according to one of Claims 5 until 9 , wherein the method (310) comprises - reading in information relating to a perceptibility measure for a plurality of different sections (202) of the traffic network, which are arranged in particular on a route of the vehicle (100); - Identifying at least one section (202) from the plurality of different sections (202) at which another road user (200) could be or is arranged; and - determining (312) the specific measure of the perceptibility of the vehicle (100) in the identified section (202) of the traffic network. Method (300, 310) according to one of the preceding claims, wherein the information in relation to the perceptibility measure for the section (202) of the traffic route network characterizes a dependency, in particular a mathematical dependency, of, - a parameter of the vehicle (100); - a parameter of the road user (200); - a parameter relating to lighting conditions in the section (202) of the traffic network; - a parameter relating to a traffic density in the section (202) of the traffic route network; and or - A parameter relating to an arrangement of the section (202) within the traffic network. Computer program, in particular a computer program product comprising a computer program, wherein the computer program is designed, when executed on at least one computing unit, to carry out one or more steps of a method (300, 310) according to at least one of the preceding claims. Device (110) for determining information relating to the perceptibility of a vehicle (100) in a section (202) of a traffic route network; wherein the device (110) is set up - based on map data for the section (202) of the traffic network, to determine a perceptibility measure for the perceptibility of the vehicle (100) in the section (202) of the traffic network by a second road user (200). ; and - to provide information regarding the determined perceptibility measure for the section (202) of the traffic network, in particular in connection with the map data for the section (202) of the traffic network. Device (101) for operating a vehicle (100) in a section (202) of a traffic route network; wherein the device (101) is set up as part of a journey of the vehicle (100), - reading information relating to a perceptibility measure for the section (202) of the transport network, in particular in connection with map data for the section (202) of the transport network; wherein the perceptibility measure indicates the perceptibility of a vehicle (100) in the section (202) of the traffic route network by another road user (200); - based on the information read, to determine a specific measure of the perceptibility of the vehicle (100) in the section (202) of the traffic network; and - to bring about at least one measure depending on the specific measure of perceptibility.

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