DE102019213951A1 - Method for minimizing coverage of a field of view of a sensor of a vehicle - Google Patents

Abstract

The invention relates to a method (100) for minimizing coverage of a field of view of a sensor (302) of a vehicle (200), wherein the coverage of the sensor (302) can be minimized by adapting the movement path of the vehicle (200).

Description

The present invention relates to a method for minimizing coverage of a field of view of a sensor of a vehicle, a computer program, a computer system and a vehicle.

Video-based sensors can hardly be compensated for or replaced by other sensors. Only camera systems are used today to record and recognize objects such as traffic signs. Cameras have become indispensable in vehicles. Much environmental data is already stored locally or in a cloud, but there are always new traffic situations that cannot be accessed locally or in the cloud or that are difficult to save, such as a pedestrian crossing a street. In addition, some traffic situations cannot be called up because the information is not accessible to a vehicle due to network overload or a lack of connectivity.

Disclosure of the invention

With embodiments of the invention, an improved method for minimizing coverage of a field of view of a sensor of a vehicle can be provided in an advantageous manner.

The invention is defined in the independent claims. Advantageous developments of the invention emerge from the dependent claims and the following description.

• - Ermitteln eines ersten Sichtfelds des Sensors zu einem ersten Zeitpunkt,
• - Ermitteln eines zweiten Sichtfelds des Sensors zu einem zweiten Zeitpunkt,
• - Detektieren einer ersten Abdeckung des ersten Sichtfelds,
• - Detektieren einer zweiten Abdeckung des zweiten Sichtfelds,
• - Berechnen eines Verdeckungsbereichs, welcher durch die erste Abdeckung im ersten Sichtfeld des Sensors und auch durch die zweite Abdeckung des zweiten Sichtfelds des Sensors verdeckt ist,
• - Ermitteln einer Größe des Verdeckungsbereichs,
• - Vergleichen der Größe des Verdeckungsbereichs mit einem vorbestimmten Schwellwert der Größe des Verdeckungsbereichs,
• - Berechnen einer Anpassung eines Bewegungspfads des Fahrzeuges, wenn die Größe des Verdeckungsbereichs den vorbestimmten Schwellwert erreicht und/oder überschreitet,
• - Anpassen des berechneten Bewegungspfads, wenn durch die Anpassung des Bewegungspfads die Größe des Verdeckungsbereichs reduziert wird.

A first aspect relates to a method for minimizing coverage of a field of view of a sensor of a vehicle, which has the following steps:

• - determining a first field of view of the sensor at a first point in time,
• - Determination of a second field of view of the sensor at a second point in time,
• - Detecting a first coverage of the first field of view,
• - Detecting a second coverage of the second field of view,
• - Calculating a concealment area which is covered by the first cover in the first field of view of the sensor and also by the second cover of the second field of view of the sensor,
• - determining a size of the concealment area,
• - comparing the size of the concealment area with a predetermined threshold value of the size of the concealment area,
• - Calculating an adaptation of a movement path of the vehicle if the size of the concealment area reaches and / or exceeds the predetermined threshold value,
• - Adjustment of the calculated movement path if the size of the obscuration area is reduced by adjusting the movement path.

The advantage of this embodiment is that objects, such as traffic signs, which cannot be represented by means of a high-precision map or foreseen by means of an online connection, can be detected by a camera in a vehicle. Furthermore, for example, moving traffic signs, such as a traffic sign which is located on a trailer and follows a truck, can be detected in such a way that the size of the obscuration of the sensor is reduced. Thus, the degree of automation of a vehicle can also be increased, since the method can increase the degree of automation due to the improved detection of the surroundings.

According to the first aspect, a first field of view of the sensor is ascertained, calculated and / or detected at a first point in time. The point in time for determining the first field of view can be selected as a function of a movement path of the vehicle, which can be known to the sensor. The field of view of the sensor is a detection area of the sensor, which is formed by the sensor. In other words, a first field of view data element can be received which is representative of a first field of view at a first point in time. Furthermore, a second field of view of the sensor is determined, calculated and / or detected at a second point in time. Furthermore, a second field of view data element can be provided which is representative of a second field of view. In addition, a first covering, covering and / or covering of the sensor of the first field of view is detected, calculated, estimated and / or ascertained. In other words, a first coverage data element is obtained or provided which is representative of a first coverage of the sensor at a first point in time. Furthermore, a second coverage of the second field of view is detected, calculated and / or ascertained. Furthermore, a second coverage data element can be provided and / or determined, which is representative of the coverage of the second field of view at a second point in time. From the first cover in the first field of view of the sensor and through the second cover of the In the second field of view of the sensor, a concealment area is calculated, calculated and / or ascertained. The occlusion area is a combination of the covers and the fields of view. In other words, the concealment area is an area which is covered by the first cover in the first field of view of the sensor and also by the second cover of the second field of view of the sensor, cannot be resolved and / or cannot be recognized and is therefore darkened for the sensor and / or is not visible. Furthermore, a size of the concealment area is determined, calculated and / or queried. Furthermore, the size of the coverage area of a geographic resolution can be described or by the size of the coverage or the determined and / or calculated coverage area. In addition, the size of the concealment area is compared, checked and / or cross-checked with a predetermined threshold value, limit value, maximum and / or minimum of the size of the concealment area. In addition, an adaptation of a movement path of the vehicle is calculated and / or determined when the size of the concealment area reaches and / or exceeds the predetermined threshold value. Furthermore, the calculated movement path is adapted, modified and / or newly determined if the size of the concealment area is reduced, minimized and / or reduced by adapting the movement path. The adaptation of the movement path can be acceleration, braking and / or a lane change of the vehicle. Alternatively or in addition, a chassis setting, in particular an electric chassis, of the vehicle can be adapted to the calculated movement path. Here, the first field of view can be covered by a first cover and the second field of view can be covered by a second cover, which overlap at one point and can thus form a concealment area. Furthermore, the size of the concealment area can be determined. If the size of the occlusion area is very small, it can be assumed that no important information is contained in the occlusion area. If the size of the concealment area is greater than or equal to the predetermined threshold value for the size of the concealment area, this can indicate that important information can be recognized in the concealment area. Various scenarios are thus checked or calculated, which can lead to reducing the size of the concealment area and thus being able to recognize further information within the concealment area.

• - Anpassen des vorbestimmten Schwellwerts der Größe des Verdeckungsbereichs an einen Grad der Automatisierung des Fahrzeuges.

According to one embodiment, the method also has the step:

• - Adaptation of the predetermined threshold value of the size of the concealment area to a degree of automation of the vehicle.

In other words, the predetermined threshold value of the size of the concealment area can be adapted, adapted and / or set to a degree of automation of the vehicle. The automation of the vehicle can be a level of automation of the vehicle and / or a level and / or degree of an autonomously driving vehicle. The advantage of this embodiment can be that resources can be saved by correctly adapting the degree of automation of the vehicle if a high degree of automation is not required.

• - Anpassen einer Auflösung und/oder einer Reichweite des ersten Sichtfelds des Sensors und/oder des zweiten Sichtfelds des Sensors, basierend auf dem Grad der Automatisierung des Fahrzeuges.

According to one embodiment, the method also has the step:

• - Adapting a resolution and / or a range of the first field of view of the sensor and / or of the second field of view of the sensor, based on the degree of automation of the vehicle.

In other words, a resolution of the accuracy and / or a range of the first field of view sensor or of the second field of view sensor can be adapted, adapted and / or configured based on the degree or level of automation of the vehicle. For example, the range of the sensor can be reduced if there is a low degree of automation in the vehicle. On the other hand, the resolution, for example the camera resolution of the sensor, can be increased if there is a high degree of automation. This embodiment can have the advantage that the longevity of the sensor system can be improved, since it does not have to run permanently.

• - Prüfen, ob der berechnete Bewegungspfad innerhalb eines zulässigen Bereichs des Bewegungspfads ist,
• - Ausgeben eines Fehlersignals, wenn der berechnete Bewegungspfad die zumindest eine Begrenzung des Bewegungspfads überschreitet, und/oder
• - Aufheben des Grads der Automatisierung des Fahrzeuges, basierend auf dem Fehlersignal.

According to one embodiment, the method further comprises the steps:

• - Check whether the calculated movement path is within a permissible range of the movement path,
• - Outputting an error signal if the calculated movement path exceeds the at least one limit of the movement path, and / or
• - Override the degree of automation of the vehicle based on the error signal.

In other words, the calculated movement path is checked and / or determined within a permissible range of the movement path. Furthermore, an error signal can be output, calculated and / or provided if the calculated movement path exceeds, crosses and / or violates the at least one limitation of the movement path. Furthermore, the degree of automation of the vehicle, based on the Error signal, reduced, minimized, canceled, resolved and / or terminated. The permissible area can be, for example, a catalog of guidelines, such as the road traffic regulations, which restrict the freedom of movement of the vehicle. Furthermore, the permissible area can be the surroundings of the vehicle, such as, for example, the specification of a lane delimitation by a roadway. In an exemplary embodiment, a movement path is calculated which requires a speed of 140 hm / h. However, the maximum permitted speed in this section of the route is 130 km / h. An error signal is thus output because the calculated movement path of 140 km / h exceeds the limitation of the movement path of 130 km / h. The degree of automation of the vehicle can thus be reduced so that the driver can choose the speed himself and / or the automation of the vehicle can be canceled so that the driver has complete control over the vehicle.

According to one embodiment, the adaptation of the movement path of the vehicle is a change in the speed of the vehicle and / or a lane change of the vehicle. In other words, the movement path of the vehicle can be adapted by changing the speed of the vehicle and / or changing lanes. The change in the speed of the vehicle can be braking, accelerating and / or maintaining speed. A lane change can be a change in the lane of the vehicle, for example one, two or a large number of lanes.

• - Erkennen zumindest eines Objekts, einer Position des Objekts und/oder einer Art des Objekts im ersten Sichtfeld des Sensors und/oder im zweiten Sichtfeld des Sensors, wobei das zumindest eine Objekt zumindest einen Teil der ersten Abdeckung im ersten Sichtfeld und/oder zumindest einen Teil der zweiten Abdeckung im zweiten Sichtfeld ausbildet,
• - Ermitteln des Verdeckungsbereichs, zudem basierend auf der erkannten Position und/oder der Art des Objekts im ersten Sichtfeld des Sensors und/oder im zweiten Sichtfeld des Sensors.

According to one embodiment, the method can have the steps:

• - Detection of at least one object, a position of the object and / or a type of object in the first field of view of the sensor and / or in the second field of view of the sensor, the at least one object at least part of the first cover in the first field of view and / or at least one Forms part of the second cover in the second field of view,
• - Determination of the concealment area, also based on the detected position and / or the type of object in the first field of view of the sensor and / or in the second field of view of the sensor.

In other words, at least one object can be recognized, detected and / or ascertained. Furthermore, a position of the object and / or a type of object in the first field of view of the sensor and / or in the second field of view of the sensor can be recognized, detected and / or ascertained. A position of the object can be, for example, a geographical position in relation to the vehicle. Furthermore, a type of object can be determined, which can be, for example, another vehicle, a construction site, a road maintenance vehicle and / or any other type of obstacle in the vicinity. Furthermore, the cover in the first field of view and / or in the second field of view can be partially formed by the object which was recognized. Furthermore, the cover can also consist of a combination of a cover and the recognized object. Furthermore, the concealment area can also be ascertained, calculated and / or determined based on the recognized position and / or the type of object in the first field of view of the sensor and / or in the second field of view of the sensor. For example, the object can be a low-loader truck which, although it partially creates a cover, also has areas in which, for example, a traffic sign can be recognized.

• - Berechnen eines Bewegungspfads des erkannten Objekts, basierend auf einer ersten Position des erkannten Objekts im ersten Sichtfeld des Sensors und basierend auf einer zweiten Position des erkannten Objekts im zweiten Sichtfeld des Sensors,
• - Ermitteln des Verdeckungsbereichs, basierend auf dem berechneten Bewegungspfad des erkannten Objekts.

According to one embodiment, the method further comprises:

• Calculating a movement path of the recognized object based on a first position of the recognized object in the first field of view of the sensor and based on a second position of the recognized object in the second field of view of the sensor,
• - Determination of the concealment area based on the calculated movement path of the recognized object.

In other words, the movement path of the recognized object can be calculated, ascertained or determined based on a first position of the recognized object in the first field of view of the sensor and based on a second position of the recognized object in the second field of view of the sensor. Alternatively or additionally, a linear movement path can be calculated based on the first position of the detected object in the first field of view of the sensor and based on a second position of the detected object in the second field of view of the sensor. For example, the movement path can be determined based on a position x, y of the recognized object at a first point in time and a further position of the recognized object at position x ', y' at a second point in time by calculating the difference between the two coordinates and the two points in time is formed. The concealment area can thus be ascertained, calculated and / or determined based on the calculated movement path of the recognized object. As shown by way of example, this can take place by means of a comparative analysis between the first position of the recognized object and the second position of the recognized object.

• - Ermitteln eines dritten Sichtfelds des Sensors zu einem dritten Zeitpunkt,
• - Erkennen des zumindest einen Objekts und einer dritten Position des Objekts im dritten Sichtfeld des Sensors,
• - Ermitteln einer dynamischen Bewegung im berechneten Bewegungspfad des erkannten Objekts, basierend auf der ersten Position, der zweiten Position und der dritten Position des Objekts.

According to one embodiment, the driving further comprises the steps:

• - Determination of a third field of view of the sensor at a third point in time,
• - Detecting the at least one object and a third position of the object in the third field of view of the sensor,
• Determining a dynamic movement in the calculated movement path of the recognized object, based on the first position, the second position and the third position of the object.

In other words, a third field of view of the sensor can be ascertained, determined and / or detected at a third point in time. The at least one object can be recognized, ascertained and / or determined at a third position of the object in the third field of view of the sensor. Furthermore, a dynamic movement in the calculated movement path of the recognized object can be ascertained, calculated and / or determined based on the first, second and third positions of the object. The dynamic movement can be an acceleration or braking process or any other dynamic driving change of the vehicle. For example, an object can be detected in a first position at a first point in time, a second position at a second point in time and a third position at a third point in time, which is determined by the positions x, y, x ', y' and x ″, y '' can be described at the respective point in time. The dynamic movement of the vehicle can be determined through a comparative analysis of the three positions.

• - Abrufen eines Signals, welches zumindest teilweise den ermittelten Verdeckungsbereich beschreibt,
• - Reduzieren der Größe des Verdeckungsbereichs, basierend auf dem abgerufenen Signal.

According to one embodiment, the method further comprises the steps:

• - Retrieval of a signal which at least partially describes the detected coverage area,
• - Reducing the size of the occlusion area based on the retrieved signal.

In other words, a signal or a signal data element which is representative of a signal which at least partially describes the determined occlusion area can be called up, downloaded and / or provided. Furthermore, the size of the concealment area can be reduced, minimized and / or dissolved based on the retrieved signal or the signal data element. For example, the signal is information that can be called up from a high-resolution GPS map that is linked to a speed limit. Thus, for example, the size of the concealment area can be reduced, since the maximum speed in the route section could already be determined by means of the signal.

• - Priorisieren des ersten Teilbereichs oder des zweiten Teilbereichs des Verdeckungsbereichs,
• - Anpassen des berechneten Bewegungspfads basierend auf dem priorisierenden Teilbereich der Abdeckung des Sichtfelds des Sensors.

In one embodiment, the concealment area has at least a first partial area and a second partial area. The method also has the following steps:

• - Prioritizing the first sub-area or the second sub-area of the concealment area,
• Adaptation of the calculated movement path based on the prioritizing sub-area of the coverage of the field of view of the sensor.

In other words, the concealment area can have at least a first partial area and a second partial area. This can be caused in particular by a large number of vehicles which are in the field of vision of the sensor.

Furthermore, the first sub-area or the second sub-area of the concealment area can be prioritized, determined and / or increased in importance. Furthermore, the size of the first sub-area and / or a size of the second sub-area can be calculated. Furthermore, the size of the first sub-area and / or the second sub-area can be compared against a predetermined threshold value. In the event that the size of the first sub-area or the second sub-area reaches and / or exceeds the predetermined limit value, the first and / or the second sub-area can be neglected. This can be particularly helpful when detecting street signs, since if the size of the first and / or the second partial area is smaller than, for example, 10 cm, there can be no street sign in this area. Furthermore, the calculated movement path of the vehicle can be adapted, adapted and matched based on the prioritized sub-area of the coverage of the field of view of the sensor. For example, a part of a road sign was detected in the first sub-area of the concealment area, so that the movement path is adapted in such a way that the road sign is completely visible to the sensor of the vehicle.

• - Erkennen zumindest eines Teils eines Informationsträgers, insbesondere an einem Randverdeckungsbereich im ersten Teilbereich und/oder zweiten Teilbereich,
• - Priorisieren des ersten Teilbereichs oder des zweiten Teilbereichs basierend darauf, in welchem Teilbereich sich der zumindest teilweise erkannte Informationsträger befindet.

According to one embodiment, you know Prioritizing the first sub-area or the second sub-area the steps to:

• - Detection of at least a part of an information carrier, in particular at an edge concealment area in the first sub-area and / or second sub-area,
• Prioritizing the first sub-area or the second sub-area based on the sub-area in which the at least partially recognized information carrier is located.

In other words, a part of an information carrier, in particular at an edge of the concealment area, can be recognized, determined and / or identified in the first sub-area and / or in the second sub-area. Furthermore, the first sub-area or the second sub-area can be prioritized, preferred and / or given a high level of importance based on the sub-area in which the at least partially recognized information carrier is located. For example, part of an information carrier, such as a road traffic sign, is recognized in the first sub-area. The first sub-area is thus prioritized in order to make the concealment area visible to the sensor or the vehicle, in particular at the position where the part of the information carrier or the road sign was recognized.

• - Ermitteln einer Verdeckungsbereichsrate des ersten Sichtfelds des Sensors und/oder des zweiten Sichtfelds des Sensors über einen vorbestimmten Zeitraum,
• - Vergleichen der ermittelten Verdeckungsbereichsrate mit einem vorbestimmten Grenzwert der Verdeckungsbereichsrate,
• - Anpassen des berechneten Bewegungspfads, wenn der vorbestimmte Grenzwert der Verdeckungsbereichsrate erreicht und oder überschritten wird.

According to one embodiment, the method has the steps:

• - Determination of a coverage area rate of the first field of view of the sensor and / or of the second field of view of the sensor over a predetermined period of time,
• - comparing the determined coverage area rate with a predetermined limit value of the coverage area rate,
• Adaptation of the calculated movement path if the predetermined limit value of the occlusion area rate is reached and / or exceeded.

In other words, a coverage area rate of the first field of view of the sensor and / or of the second field of view of the sensor can be ascertained, calculated and / or detected over a predetermined period of time. A coverage area rate is the ratio between a first coverage in the first field of view and / or a second coverage of the second field of view of the sensor over a predetermined period of time. In other words, the coverage area rate is a degree of coverage of the sensor over a predetermined period of time. Furthermore, the concealment area rate can be compared with a predetermined limit value of the concealment area rate, contrasted and / or a comparative analysis can be carried out. For example, the predetermined limit value of the coverage area rate describes a value of the degree of coverage of the field of view of the sensor, from which safe driving in a highly automated vehicle may no longer be possible. Furthermore, the calculated movement path can be adapted, optimized and / or changed if the predetermined limit value of the occlusion area rate is reached or exceeded. For example, the predetermined limit value of the occlusion area rate has a maximum value of 10 percent. However, the coverage of the field of view of the sensor is 12 percent over the predetermined period of time. The calculated movement path can thus be adapted so that the degree of coverage falls below the predetermined limit value of 10 percent.

For example, the sensor of the vehicle can record and log the surroundings or the field of view formed by the sensor (e.g. the right edge of the road) in order to then be able to adapt the driving behavior in the case of covered areas to the covered and / or unrecognizable areas of the field of view yet to be captured. In doing so, the method can calculate which vision problems will arise in the upcoming course of the journey. The driving behavior of the vehicle can thus be optimized or controlled and / or regulated with a view to improving the recognition of the surroundings. In the ideal case, this can result in complete recognition of the surroundings (e.g. complete recognition of the right edge of the lane). Driving on motorways can represent a field of application for highly automated driving. The safety of the occupants can be increased by means of the method. In addition, the trustworthiness of automated driving can be increased and an expanded range of solutions can be provided in borderline situations. For example, the method can contribute to increasing the quality of the execution of software and / or camera-based applications (e.g. automated driving), in particular without additional financial outlay. The method can improve the reliability of automated driving because it is not necessary for there to be an online connection. The use of the vehicle's sensors and the algorithms used can thus be improved. Using the method, "invisible" objects can be made visible or. get into the field of view of the camera. The method can first define one (or also several) areas which are to be observed or in which objects are to be searched for and recognized. In the following, the example for a camera or for an optical sensor of the vehicle is described as an example. Using the example of a German federal motorway, this could be, for example, the left or right edge of the lane as well as any areas above the lane (sign gantries). Depending on the automation level (Society of Automotive Engineers (SAE) level L1, L2, L3 L4 or L5), the value of the surrounding areas can also vary. Depending on the driving situation (e.g. SAE degree of automation L1-L5, current attention span of the driver, Speed of the vehicle, required reaction time, connectivity status, Car2Car communication coverage and / or the like) can be assessed how much of the respective surrounding area should be clearly recognized. 0% can mean that no environmental information (signs, notices, etc.) has to be recognized because the vehicle fully trusts the sensors (internal + external) (or the information would be sufficient because the driver is currently in control of the vehicle or the vehicle is under remote control on defined routes). 100% can mean that every environment and traffic information must be seen (there can of course also be further intermediate ranges between 0 and 100%).

On the basis of the classification, the selected surrounding area can then be continuously scanned or observed. During the scan, it is determined how high the coverage of the field of vision or the field of vision (camera recording) IS, WILL BE and / or WAS. IST can mean that it can be determined how the current field of view is covered by the cameras. IST can correspond to a snapshot or a live image from the camera. IST can also provide feedback as to what percentage of the field of vision should be clearly recognized (which therefore also includes traffic information).

BEING WILL: can be a prognosis from the ACTUAL and the prognosticated recognition of the field of vision for a certain period of time, or a field of vision at a later point in time. The period of time can include a time axis and is defined by a duration of x seconds (e.g. the view for the next 10 seconds, or the view until a certain point on the route is reached). The prognosis can include how your own vehicle and the rest of the traffic, including the surrounding area, will change (or move) over time. The method can determine their trajectory in relation to the concealed field of vision for all movable as well as for all immovable objects which block the field of view of the sensor.

WAR: can describe past reference positions which have already been passed and which can no longer be recognized. WAR can state how much of a certain section of the route has not been covered and what can therefore no longer be detected by its own local sensors or could not be detected.

For example, the method can increase the safety of a vehicle; especially without additional financial expense. The earlier detection of hidden objects means that the described method can react much more quickly to the environment. The advantage of identifying and recognizing non-visible objects, in particular for the field of view of the sensor, is an improvement in the environment model. As a result, trajectory planning and route guidance can be improved and dangerous situations can be better avoided.

In road traffic it is usually not possible to always have a highly accurate map with all traffic rules up-to-date. The method described requires significantly lower communication requirements with the backend or other vehicles and can be implemented with existing implementations. The method can thus increase the level of security without this necessarily being coupled with higher manufacturing costs for the hardware used. This method can in particular be implemented in the form of software that can be distributed by participants in the network as an update or upgrade to existing software or firmware and, in this respect, represent an independent product.

The method can begin with loading the environment data (e.g. map data) and the movement path of the own vehicle. With this data, the process can continuously calculate the maximum visibility that can be achieved - in this case, visibility means that traffic information (traffic lights, signs, construction sites) can be recognized up to a certain distance. The result of this step can be a vector from a distance, coordinates, a time and / or a resolution. The current path of movement of the vehicle can be used to continuously determine how much time elapses until the viewing horizon (i.e. the current maximum visibility from the current position) is reached.

The method can then suggest that data on the degree of automation, the driver status and, for example, also the speed be included in order to determine the required visibility. This does not have to correspond to the maximum visibility, but the further you can look ahead (from the current position), the more autonomously the vehicle can drive, the faster the vehicle can drive and / or the less the driver is required as a mandatory monitor. The theoretical field of view can be continuously determined for this visual range. This can mean that the procedure determines the theoretical and necessary viewing area of the cameras (or other sensors). In this viewing area, objects can be identified that restrict the vehicle's field of vision for optimal visibility. These can be vehicles that stand between your own vehicle and traffic information. For this purpose, the method can propose to predict the motion vectors for these objects. This can be a stationary object (construction site) and / or a moving object (vehicle).

On the basis of this data, the method can propose to determine the real field of vision up to the required visual range. The method can determine which sub-areas of the theoretical point of view are sufficiently recognized, insufficiently recognized and not recognized.

The theoretical mapping of a field of vision at time t can be compared with the resulting real field of vision or within the scope of the necessary visual range. The scan can determine exactly which area, e.g. based on GPS and time data) was not seen and can also calculate how large this area is. The question may arise whether there can be a sign in this area. If, for example, only a field of vision of 10 cm directly on the edge of the road was not recognized, then there cannot be a standardized road sign. If the area is too small, the procedure can ignore the area that is too small. The scan and the interpretation of the field of vision can mark the non-visible area in the ACTUAL and in the ARE BECOMING AREA. The method can now determine whether there are gaps in view that cannot be made visible at all or only with a strong influence on the current trajectory. These areas of view can then be excluded from the detection attempt, but can remain marked. Now, optionally, the cloud (backend, or other vehicles, databases) can use the geoposition and the time to try to determine the area. This can also happen when the local data is generally available, since, as described, construction site vehicles, mowing vehicles, accidents, etc. are highly dynamic events that are not stored in the local data.

The process can then try to close the gaps in view. First of all, it can be assessed which gap should be made "visible" first. One factor here can be that the area immediately disappears from the field of view of the sensors, as well as that a traffic notice was partially seen. The method can now determine how the trajectory of the vehicle is adapted for each viewing gap so that the viewing gaps can be closed; always taking into account the higher-level existing trajectory.

The method can now determine which changes are necessary in order to recognize the respective viewing areas. The priority of the viewing areas can be determined as well as the intervention in the possibly existing trajectory or the driving mode. The method can also check whether the driving behavior (driving strategy, trajectory) fits the required coverage area. So if almost 100% coverage of the visual area is required (driver is asleep, no Car2x, no connectivity, etc.) and the real coverage (i.e. the areas that were actually seen) is less than necessary, then the procedure can suggest feedback to the To give driving strategy. This can be an adjustment of the speed, the tolerance of the acceleration or a lane change. For example, the specification can only be to use the lane that is closest to the field of vision.

Another aspect of the invention is a computer program which, when executed on a processor, instructs the processor to carry out steps of the method as described above and below. The computer program can be stored, for example, in a memory of the computer system or in a computer-readable medium.

Another aspect of the invention relates to a computer system which has at least one processor and / or a computer-readable medium,
wherein the processor is set up to carry out the method, as described above and below, and / or
wherein the computer-readable medium is set up to store the computer program, as described above and below.

The computer readable medium can be a storage medium, such as e.g. B. a USB stick, a CD, a DVD, a data storage device, a server, a hard drive or other medium on which a computer program can be stored as described above. The computer system can be a PC, a server, a server structure and / or a cloud computing structure.

Another aspect of the invention relates to a vehicle which has a sensor unit for monitoring the surroundings of the vehicle, the sensor unit having at least one sensor. The sensor is set up to form at least one field of view which the sensor monitors. The sensor unit is set up to carry out steps of the method as described above and below. Furthermore, the sensor can be a camera, a radar, a lidar and / or any other type of sensor which is suitable for forming a field of view and for detecting a cover.

All disclosures which are described above and below in relation to one aspect of the invention apply equally to all further aspects of the invention.

Exemplary embodiments are described below with reference to the accompanying figures.

Figure list

• 1 shows a schematic flow diagram which describes the method according to an embodiment.
• 2 shows a schematic flow diagram which describes the method according to an embodiment.
• 3 shows a vehicle according to an embodiment.
• 4th shows a vehicle according to an embodiment.
• 5 shows a vehicle according to an embodiment.
• 6th shows a vehicle according to an embodiment.
• 7th shows a vehicle according to an embodiment.
• 8th shows a vehicle according to an embodiment.
• 9 Figure 3 shows a system according to an embodiment.

Detailed description of embodiments

The figures are only schematic and not true to scale. Identical, identically acting or similar elements with the same reference symbols can be used in the figures.

• - Ermitteln S1 eines ersten Sichtfelds 102 des Sensors zu einem ersten Zeitpunkt,
• - Ermitteln S2 eines zweiten Sichtfelds 104 des Sensors 302 zu einem zweiten Zeitpunkt,
• - Detektieren S3 einer ersten Abdeckung 106 des ersten Sichtfelds 102,
• - Detektieren S4 einer zweiten Abdeckung 108 des zweiten Sichtfelds 104,
• - Berechnen S5 eines Verdeckungsbereichs 110, welcher durch die erste Abdeckung 106 im ersten Sichtfeld 102 des Sensors 302 und auch durch die zweite Abdeckung 108 des zweiten Sichtfelds 104 des Sensors 302 verdeckt ist,
• - Ermitteln S6 einer Größe eines Verdeckungsbereich 110,
• - Vergleichen S7 der Größe des Verdeckungsbereichs 110 mit einem vorbestimmten Schwellwert der Größe des Verdeckungsbereichs 110,
• - Berechnen einer Anpassung S8 eines Bewegungspfads des Fahrzeuges 200, wenn die Größe des Verdeckungsbereichs 110 den vorbestimmten Schwellwert erreicht und/oder überschreitet,
• - Anpassen S9 des berechneten Bewegungspfads, wenn durch die Anpassung des Bewegungspfads des Fahrzeuges 200 die Größe des Verdeckungsbereichs 110 reduziert wird.

1 shows a schematic flow diagram to illustrate a method 100 to minimize coverage of a field of view of a sensor 302 of a vehicle 200 , comprising the steps:

• - Determine S1 a first field of view 102 of the sensor at a first point in time,
• - Determine S2 a second field of view 104 of the sensor 302 at a second point in time,
• - Detect S3 a first cover 106 of the first field of view 102 ,
• - Detect S4 a second cover 108 of the second field of view 104 ,
• - To calculate S5 an occlusion area 110 which through the first cover 106 in the first field of view 102 of the sensor 302 and also through the second cover 108 of the second field of view 104 of the sensor 302 is covered,
• - Determine S6 a size of an occlusion area 110 ,
• - To compare S7 the size of the occlusion area 110 with a predetermined threshold value of the size of the occlusion area 110 ,
• - Compute an adjustment S8 a movement path of the vehicle 200 when the size of the occlusion area 110 reaches and / or exceeds the predetermined threshold value,
• - To adjust S9 of the calculated path of movement, if by adapting the path of movement of the vehicle 200 the size of the occlusion area 110 is reduced.

This embodiment can develop the advantage that areas which are covered by a cover can be made visible to a vehicle and thus information which would have supposedly been covered by the cover can be made visible. In this way, for example, confidence in an automated vehicle can be increased.

By way of example, a vehicle has 200 a sensor unit 300 on which at least one sensor 302 having. The sensor 302 is set up to form a field of view so that the sensor 302 at least part of the surroundings of the vehicle 200 can monitor. The sensor can 302 a first field of vision 102 at a first point in time and a second field of view 104 determine and / or save at a second point in time. Furthermore, a first cover 106 and a second cover 108 in the first and second fields of view, respectively 102 , 104 can be detected, the cover for example a vehicle that is in the vicinity of the vehicle 200 is located, can be. From the first field of vision 102 and the second field of view 104 respectively the first cover 106 and the second cover 108 can be a concealment area 110 calculated which one by the first coverage 106 in the first field of view 102 of the sensor 302 and also through the second cover 108 of the second field of view 104 of the sensor 302 is covered. Thus is the occlusion area 110 an overlap of the first cover 106 and the second cover 108 . Subsequently, a size of the occlusion area 110 be determined to decide whether the occlusion area 110 has a size which could be relevant to the evaluation of the environment. This can be done by increasing the size of the occlusion area 110 with a predetermined threshold value of the size of the occlusion area 110 can be compared. Should be the size of the occlusion area 110 Reaching and / or exceeding the predetermined threshold value can be adapted to a movement path of the vehicle 200 be calculated. In addition, the movement path can be adjusted if the adjustment of the path results in the Size of the occlusion area 110 is reduced. In particular, a control signal can be sent to a vehicle 200 be transmitted when the size of the occlusion area 110 can be reduced by changing the path of movement of the vehicle 200 is changed.

2 shows a schematic flow diagram of an extended method 101 according to one embodiment. The procedure can include the steps S1 Determining a first field of view 102 , S2 Finding a second field of view 104 , S3 Detecting a first cover 106 , S4 Detecting a second cover 108 , S5 Calculate an occlusion area 110 , S6 Determine the size of the occlusion area 110 , S7 Compare the size of the occlusion area 100 , S8 Calculating an adjustment of a movement path, S9 Adjust the calculated movement path. Furthermore, the method can include the step S10 Adjusting the predetermined threshold. In addition, the method can include the step S11 Adjusting a resolution have. In addition, the method can include the steps S12 Checking the calculated movement path, S13 Outputting an error signal and / or S14 Reduce the level of automation. Furthermore, the method can include the step S15 Detect at least one object and S16 Determine the occlusion area 110 exhibit. In addition, the method can include the step S17 Calculate a movement path and S18 Determine the occlusion area 110 include. In addition, the method can include the steps S19 Determining a third field of view, S20 Detecting the at least one object as well S21 Detect dynamic motion exhibit. Furthermore, the method can the S22 steps S22 Get a signal and S23 Reduce the size of the occlusion area 110 include. Furthermore, the method can have the steps S24 Prioritize as well S25 Customize. In addition, the method can have the steps S26 Recognize at least part of an information carrier as well S27 Prioritize. In addition, the method can comprise the steps S28 Determining an occlusion area rate, S29 Compare the determined occlusion area rate and S30 Adjusting the calculated movement path.

3 shows a schematic plan view of a vehicle 200 which is a field of view 102 trains. The field of view 102 has a cover 106 on. Furthermore, the vehicle is 200 ' the vehicle 200 at another point in time. The vehicle points 200 ' a second field of vision 104 on. The second field of vision 104 has a second cover 108 on. The intersection between the first cover 106 and the second cover 108 forms the concealment area 110 .

4th shows a schematic plan view of a vehicle 200 . The vehicle 200 has a first field of view 102 on. An object can be in the first field of view 250 can be detected. In addition, the vehicle is 200 ' the vehicle 200 at a further, in particular a second, point in time. The vehicle 200 ' shows the field of view 104 where the object is at a second point in time 250 ' . In addition, the vehicle is 200 '' the vehicle 200 at a third point in time. The vehicle 200 '' forms a third field of vision 112 out. In the third field of vision 112 becomes the object at a third point in time 250 " recognized. From the movement of the vehicle 200 , 200 ' and 200 '' a dynamic movement of the vehicle can be determined. Furthermore, by means of the movement of the object 250 at a first, second and third point in time, 250 , 250 ' and 250 '' to detect a dynamic movement of the object. The two movement paths can be used to minimize the occlusion area 110 be used.

5 shows a schematic top view of a vehicle at a first point in time. The vehicle 200 has a first field of view 102 on. The vehicle 200 at a second point in time 200 ' forms a second field of vision 104 out. Both in the first field of view 102 as well as in the second field of view 104 there is an object 250 . The object 250 forms a first cover 106 and a second cover 108 , such as in 1 and 4th shown from which is in the occlusion area 110 overlap.

6th shows a schematic plan view of a vehicle 200 at a first point in time. The vehicle 200 forms a first field of view 102 out. The object 250 , which is in the first field of vision 102 is located, forms a first cover 106 out. The vehicle 200 ' is the vehicle 200 at a second point in time. The vehicle 200 ' forms a second field of vision 104 out. In the second field of vision 104 is the object at a second point in time 250 ' . Through the object 250 ' a second cover 108 is invoked. The vehicle 200 '' is the vehicle 200 at a third point in time. The vehicle 200 '' forms a third field of vision 112 out. In the third field of vision 112 is the object 250 " to be seen at a third time. The object at a third point in time 250 " forms another cover. The further cover and the second cover 108 form a concealment area 110 . 6th clearly shows how the movement path of a vehicle can be changed to include the area of obscuration 110 to minimize. Because the object 250 performs a dynamic movement between the three points in time, the dynamic movement of the object can 250 by changing the vehicle's path of movement 200 can be anticipated and thus the size of the occlusion area 110 can be further reduced.

7th shows a vehicle 200 . The vehicle 200 forms a first field of view 102 out. The first field of vision 102 has a concealment area 110 on. The concealment area 110 has a first part 120 and a second part 122 on. In the event that in the first part 120 or in the second part 122 an information carrier is detected, the path of movement of the vehicle 200 adjusted accordingly to the occlusion area 110 to minimize.

8th shows a vehicle 200 , which is a sensor unit 300 having. The sensor unit 300 has a sensor 302 which is set up to form a field of view.

9 shows a computer system 400 which at least one processor 402 , a computer readable medium 404 and an interface 406 having. the interface 406 can be set up to receive information from the sensor unit 300 to receive and control signals to the vehicle 200 to pass on.

In addition, it should be pointed out that “comprising” and “having” do not exclude any other elements and the indefinite articles “a” or “a” do not exclude a multiplicity. It should also be pointed out that features that have been described with reference to one of the above exemplary embodiments can also be used in combination with other features of other exemplary embodiments described above. Reference signs in the claims are not to be regarded as a restriction.

List of reference symbols

100 -

Procedure

101 -

extended procedure

102 -

first viewing area

104 -

second viewing area

106 -

first cover

108 -

second cover

110 -

Concealment area

112 -

third viewing area

120 -

first part

122 -

second part

200 -

vehicle

200 '-

Vehicle at a second time

200 '' -

Vehicle at a third point in time

250 -

object

250 '-

Object at a second point in time

250 '' -

Object at a third point in time

300 -

Sensor unit

302 -

sensor

400 -

Computer system

402 -

processor

404 -

Computer readable medium

406 -

interface

S1 -

Determining a first field of view

S2 -

Finding a second field of view

S3 -

Detecting a first cover

S4 -

Detecting a second cover

S5 -

Calculate an occlusion area

S6 -

Determining a size of the occlusion area

S7 -

Compare the size of the occlusion area

S8 -

Compute an adjustment of a movement path

S9 -

Adjusting the calculated movement path

S10 -

Adjusting the predetermined threshold

S11 -

Adjusting a resolution

S12 -

Check

S13 -

Output of an error signal

S14 -

Reduce and / or cancel

S15 -

Recognize at least one object

S16 -

Determine the occlusion area

S17 -

Calculate a movement path

S18 -

Determine the occlusion area

S19 -

Finding a third field of view

S20 -

Recognizing the at least one object

S21 -

Detect dynamic movement

S22 -

Get a signal

S23 -

Reduce the size of the occlusion area

S24 -

Prioritize

S25 -

Adjusting the calculated movement path

S26 -

Detect

S27 -

Prioritize

S28 -

Determine

S29 -

to compare

S30 -

Adjusting the calculated movement path

Claims (15)

Method (100) for minimizing coverage of a field of view of a sensor (302) of a vehicle (200), comprising the steps: - determining (S1) a first field of view (102) of the sensor (302) at a first point in time, - determining (S2) a second field of view (104) of the sensor (302) at a second point in time, - detecting (S3) a first cover (106) of the first field of view (102), - detecting (S4) a second cover (108) of the second field of view (104), - calculating (S5) a concealment area (110) which is covered by the first cover (106) in the first field of view (102) of the sensor and also by the second cover (108) of the second field of view (104) of the sensor, - determining (S6) a size of the concealment area (110), - comparing (S7) the size of the concealment area (110) with a predetermined threshold value of the size of the concealment area (110), Calculating (S8) an adaptation of a movement path of the vehicle (200) if the size of the concealment area (110) reaches and / or exceeds the predetermined threshold value, Adaptation (S9) of the calculated movement path of the vehicle (200) if the size of the concealment area (110) is reduced by adapting the movement path of the vehicle (200). Procedure according to Claim 1 wherein the method further comprises the step of: adapting (S10) the predetermined threshold value of the size of the concealment area (110) to a degree of automation of the vehicle (200). Procedure according to Claim 2 wherein the method further comprises the step of: -adapting (S11) a resolution and / or a range of the first field of view (102) of the sensor (302) and / or of the second field of view (104) of the sensor (302) based on the degree the automation of the vehicle (200). Method according to one of the Claims 2 to 3 The method further comprising the steps of: checking (S12) whether the calculated movement path is within a permissible range of the movement path, outputting (S13) an error signal if the calculated movement path exceeds the at least one limit of the movement path, -reducing (S14 ) and / or overriding the degree of automation of the vehicle (200) based on the error signal. Method according to one of the preceding claims, wherein the adaptation of the movement path of the vehicle (200) is a change in the speed of the vehicle (200) and / or a lane change of the vehicle (200). Method according to one of the preceding claims, the method comprising the steps of: - Detecting (S15) at least one object (250), a position of the object (250), and / or a type of object (250) in the first field of view (102) of the sensor (302) and / or in the second field of view (104) of the sensor (302), wherein the at least one object (250) forms at least part of the first cover (106) in the first field of view and / or at least a part of the second cover (108) in the second field of view (104), - Determination (S16) of the concealment area (110) also based on the detected position and / or the type of object (250) in the first field of view (102) of the sensor (302) and / or in the second field of view (104) of the sensor (302) ). Procedure according to Claim 6 wherein the method further comprises the steps of: calculating (S17) a movement path of the recognized object (250) based on a first position of the recognized object (250) in the first field of view (102) of the sensor and based on a second position of the recognized object (250 ') in the second field of view (104) of the sensor, determining (S18) the concealment area (110) based on the calculated movement path of the detected object (250). Method according to a Claims 6 to 7th wherein the method further comprises the steps of: -determining (S19) a third field of view (112) of the sensor at a third point in time, -recognition (S20) of the at least one object (250) and a third position of the object (250 ″) in the third field of view (112) of the sensor, determining (S21) a dynamic movement in the calculated movement path of the detected object (250) based on the first position, the second position and the third position of the object (250, 250 ', 250'' ). Method according to one of the preceding claims, wherein the method further comprises the steps of: retrieving (S22) a signal which at least partially describes the determined concealment area (110), -reducing (S23) the size of the concealment area (110) based on the retrieved Signal. Method according to one of the preceding claims, wherein the concealment area (110) has at least a first sub-area (120) and a second sub-area (122), the method further comprising the steps: - prioritizing (S24) the first sub-area (120) or the second sub-area (122) of the concealment area (110), Adapting (S25) the calculated movement path based on the prioritized sub-area of the coverage of the field of view of the sensor. Procedure according to Claim 10 , the prioritization of the first sub-area or the second sub-area comprising the steps: -recognition (S26) of at least part of an information carrier, in particular at an edge of the concealment area, in the first sub-area (120) and / or second sub-area (122), -prioritising (S27) of the first sub-area (120) or of the second sub-area (122) based on the sub-area in which the at least partially recognized information carrier is located. Method according to one of the preceding claims, the method comprising the steps of: Determining (S28) an occlusion area rate of the first field of view of the sensor and / or of the second field of view of the sensor over a predetermined period of time, -Comparing (S29) the determined occlusion area rate with a predetermined limit value of the occlusion area rate, - Adapting (S30) the calculated movement path if the predetermined limit value of the occlusion area rate is reached and / or exceeded. Computer program which, when executed on a processor, instructs the processor to carry out steps of the method (100) according to one of the Claims 1 to 12th perform. Computer system (400) which has at least one processor (402) and / or a computer-readable medium (404), the processor (402) being set up for the method (100) according to one of the Claims 1 to 12th perform, and / or wherein the computer-readable medium (404) is set up for the computer program according to Claim 13 save. Vehicle (200), having a sensor unit (300) for monitoring the surroundings of the vehicle (200), the sensor unit (300) having at least one sensor (302) which is set up to form at least one field of view which the sensor monitors, with the sensor unit (300) is set up for this purpose according to one of the steps of the method (100) Claims 1 to 12th to execute.

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