Method, device and computer program for a vehicle

Abstract

The invention relates to a method, a device and a computer program for a vehicle, more specifically to a method, a device and a computer program for determining positions of other vehicles based on received collective perception messages. The method comprises receiving (110) one or more collective perception messages. Each collective perception message comprises information about an absolute position of a vehicle generating the collective perception message and relative positions of one or more further vehicles perceived by the vehicle generating the collective perception message. The method comprises placing (120) the respective vehicle generating the collective perception message and the one or more further vehicles perceived by the vehicle generating the collective perception message on a map using the positions received in the one or more collective perception messages. The method comprises correlating (130) the positions of the vehicles on the map with a course of a road on which the respective vehicle is travelling and correcting (140) the respective positions based on the correlation between the course of the road and the positions of the vehicles.

Description

Technical Field

[0001] The present invention relates to methods, apparatus and computer programs for vehicles, and more particularly to methods, apparatus and computer programs for determining the location of other vehicles based on received collectively sensed messages. Background Technology

[0002] Direct communication between mobile devices (also known as device-to-device (D2D), vehicle-to-vehicle (V2V), or car-to-car (C2C) communication) is a feature of the development of next-generation mobile communication systems. By enabling direct communication between vehicles, message exchange can be achieved with low latency. These messages can then be used to share information among road users.

[0003] One type of message exchanged among vehicles is the "Collective Perception Message" (CPM), standardized by the European Telecommunications Standards Institute (ETSI). These messages contain an abstract representation of the environment in which the vehicle generates the CPM. This type of message can be used by a vehicle to perceive other vehicles that are currently outside the reach of its sensors. Within the CPM, other objects, such as other vehicles, are generally referenced relative to the location of the vehicle generating the CPM. In other words, the CPM may contain information about the vehicle's position in absolute coordinates and information about objects detected by the vehicle's sensors relative to them. These objects can be other road users, especially other vehicles. Information about pedestrians can also be sent in the form of CPM objects. This topic has been addressed in various research projects and is an important application for increasing traffic safety, for example, for roadside sensor systems permanently installed at intersections. Additionally, information related to static objects can also be sent as CPM objects (e.g., road engineering fences).

[0004] There may be expectations for providing improved concepts for determining the location of other vehicles based on received collectively perceived messages. Summary of the Invention

[0005] This expectation is addressed through the subject matter of the independent claims.

[0006] The embodiments of this disclosure are based on the finding that while the relative positions of other vehicles referenced in a CPM may be reasonably accurate relative to the vehicle generating the CPM, the absolute position of the vehicle generating the CPM, as determined by a satellite positioning system, can lead to inaccuracies in determining the vehicle's position. For example, the absolute position and heading of the vehicle generating the CPM may both have low accuracy, causing the positions of other vehicles defined relative to the position of the vehicle generating the CPM to deviate slightly in the perception of the vehicle receiving the CPM. By correlating the positions of the vehicle generating the CPM and the positions of the vehicles being referenced in the CPM with the road route and correcting the positions accordingly, the embodiments attempt to overcome this limitation.

[0007] An embodiment provides a method for a vehicle. The method includes receiving one or more collective perception messages. Each collective perception message includes information about the absolute position of the vehicle that generated the collective perception message and the relative positions of one or more other vehicles being perceived by the vehicle that generated the collective perception message. The method includes using the positions received in the one or more collective perception messages to place the corresponding vehicle that generated the collective perception message and the one or more other vehicles being perceived by the vehicle that generated the collective perception message on a map. The method includes correlating the positions of the vehicles on the map with the routes of roads on which the corresponding vehicles are traveling. The method includes correcting the corresponding positions based on the correlation between the road routes and the vehicle positions. The corresponding positions can be corrected with high accuracy, for example, by co-translating and / or rotating the corresponding vehicles relative to the map, using the positions of the vehicles that generated the CPM and the positions of the vehicles referenced in the CPM.

[0008] In some embodiments, the location of a vehicle may not be considered (only) a discrete location, but rather a trajectory of the vehicle traveling on a road. Accordingly, the method may include tracking the location of a vehicle, such as in one or more collectively sensed messages, to determine the vehicle's trajectory. The vehicle's trajectory may be correlated with the route of the road on which the vehicle is traveling. The vehicle's corresponding location may be corrected based on the correlation between the road route and the vehicle's trajectory. This can further increase accuracy while reducing the workload required to correlate locations with road routes over time.

[0009] For example, correlating the trajectory of a vehicle with the route of a road may include determining a confidence region around each location, and for each trajectory, determining a confidence region around the trajectory based on the confidence region around the location. The corresponding location on the map may be corrected using the confidence region around the trajectory, for example, by limiting the correlation to the confidence region. For example, the confidence region around the trajectory may be thought of as a “confidence tube” around the trajectory, which may shrink over time and thus become more accurate.

[0010] Generally, messages exchanged between vehicles may include temporary identifiers that are periodically changed to avoid long-term tracking of vehicles while achieving short-term identification. For example, each collective sensing message may include an identifier of the vehicle that generated the collective sensing message. The identifier may be changed to a newly generated identifier according to a predefined schedule. The method may include determining a match between a location received in a collective sensing message with a newly generated identifier and a previously determined trajectory, and continuing to track the corresponding location and determine the corresponding trajectory using subsequently received collective sensing messages with the newly generated identifier. In other words, if the vehicle's identifier changes, the method may attempt to determine a match between the newly generated identifier and a previously determined trajectory to continue tracking the trajectory.

[0011] In various embodiments, the positions and / or trajectories of vehicles on maps originating from the same collectively sensed information are mutually correlated with road routes. For example, the positions and / or trajectories of vehicles on maps originating from the same collectively sensed information may be translated and / or rotated relative to the map to correlate the respective vehicles with road routes. This reduces the amount of work required for correlation and improves accuracy because the vehicles remain in a static arrangement.

[0012] In various embodiments, the location and / or trajectory of a vehicle on the map may be correlated with the route of a road based on one or more lanes. Alternatively, the location and / or trajectory of a vehicle on the map may be correlated with the route of a road based on one or more traffic rules. Both road lanes and corresponding traffic rules can provide valuable contextual information for correction.

[0013] Generally, the corrected location can be used by a vehicle to identify other vehicles in its vicinity. For example, the method may include determining the location of one or more vehicles in the vicinity of the vehicle via one or more sensors on the vehicle. The method may also include determining the relationship between a vehicle on a map and one or more vehicles in the vicinity of the vehicle based on the corrected location of the vehicle on the map. For example, a vehicle on a map may be matched with one or more vehicles in the vicinity of the vehicle based on its corrected location on the map. By using the corrected location of a vehicle on a map, the determination of the relationship between locations can be facilitated and / or become more accurate.

[0014] In some embodiments, sensors of the vehicle may be used to determine additional information relating to one or more vehicles in the vicinity of the vehicle. This information may be added to previously known information relating to the respective vehicle. In other words, the method may include supplementing information about vehicles on the map based on determined relationships between vehicles on the map and one or more vehicles in the vicinity of the vehicle.

[0015] In some cases, the same mode of transport may be sensed by multiple modes of transport and thus included in multiple collective sense messages. Accordingly, the method can match the modes of transport sensed by the multiple modes of transport on the map. In other words, at least a first collective sense message can be received from a first mode of transport, and a second collective sense message can be received from a second mode of transport. The method may include matching the modes of transport referenced in the first collective sense message and the modes of transport referenced in the second collective sense message based on the location included in the respective collective sense message and / or based on the corrected location of the mode of transport.

[0016] In various embodiments, the corrected location can be used to assist the vehicle in autonomous or semi-autonomous navigation among other vehicles. In other words, the method may include operating the vehicle in an autonomous or semi-autonomous driving mode using the corrected location of the vehicle on a map.

[0017] Embodiments of this disclosure further provide a corresponding computer program having program code, the computer program being used to perform the proposed method when the computer program is executed on a computer, processor, or programmable hardware component.

[0018] Embodiments of this disclosure further provide a corresponding device for a vehicle. The device includes an interface for receiving collectively sensed messages from other vehicles. The device includes a processing module configured to perform the methods described above. Embodiments further provide vehicles including the device and / or vehicles configured to perform the methods. Attached Figure Description

[0019] Other features or aspects will be described by way of example only and with reference to the accompanying drawings, the following non-limiting embodiments using a device or method, or a computer program or computer program product, wherein: Figure 1a and Figure 1b A flowchart illustrating an example of a method for a vehicle; and Figure 1c A block diagram showing an example of equipment used in vehicles. Detailed Implementation

[0020] Various exemplary embodiments will now be described more fully with reference to the accompanying drawings, in which some exemplary embodiments are illustrated. In the drawings, the thickness of lines, layers, and / or regions may be enlarged for clarity. Optional components may be illustrated using polylines, dashed lines, or dashed lines.

[0021] Accordingly, while the exemplary embodiments are capable of various modifications and alternatives, their embodiments are shown as examples in the accompanying drawings and will be described in detail herein. However, it should be understood that there is no intention to limit the exemplary embodiments to the specific forms disclosed; rather, the exemplary embodiments are intended to cover all modifications, equivalents, and alternatives falling within the scope of the invention. Similar numerals throughout the description of the accompanying drawings denote similar or analogous elements.

[0022] As used herein, the term “or” indicates non-exclusivity or unless otherwise indicated (e.g., “or otherwise” or “or in an alternative”). Furthermore, as used herein, unless otherwise indicated, terms used to describe relationships between elements should be interpreted broadly to include direct relationships or the presence of intermediate elements. For example, when an element is referred to as “connected” or “coupled” to another element, the element may be directly connected or coupled to the other element, or there may be intermediate elements. Conversely, when an element is referred to as “directly connected” or “directly coupled” to another element, no intermediate elements are present. Similarly, terms such as “between,” “adjacent,” etc., should be interpreted in a similar manner.

[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a,” “one,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” or “includes,” as used herein, specify the presence of the stated feature, integer, step, operation, element, or component, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof.

[0024] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the example embodiments pertain. It will be further understood that terms (e.g., those defined in common dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant field, and will not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0025] Figure 1a and Figure 1b A flowchart illustrating an example of a method for a vehicle 100 is provided. The method includes receiving 110 one or more collective perception messages (CPM). Each CPM includes information about the absolute position of the vehicle generating the CPM and the relative positions of one or more other vehicles being perceived by the vehicle generating the CPM. The method includes using the positions received in the one or more CPM messages to place 120 the corresponding vehicle generating the CPM and the one or more other vehicles being perceived by the vehicle generating the CPM on a map. The method includes correlating 130 the positions of the vehicles on the map with the routes of roads on which the corresponding vehicles are traveling. The method includes correcting 140 the corresponding positions based on the correlation between the road routes and the vehicle positions.

[0026] Figure 1c A block diagram illustrating an example of a corresponding device 10 for a vehicle is shown. Device 10 includes an interface 12 for receiving collectively sensed messages from other vehicles 200. The device includes a processing module 14 configured to perform, for example, in conjunction with interface 12. Figure 1a and / or Figure 1b The processing module 14 is coupled to the interface 12 and optionally to one or more sensing sensors (not shown) of the vehicle. Figure 1c The vehicle 100, including device 10, is further shown.

[0027] The following description involves Figure 1a and / or Figure 1b The method and corresponding equipment 10 or vehicle 100.

[0028] Various embodiments of this disclosure relate to methods, apparatus, and computer programs for vehicles (e.g., so-called "connected" vehicles, i.e., vehicles including an interface 12 for communicating with other vehicles in their vicinity). In the context of this application, such vehicles may be referred to as V2X vehicles, i.e., vehicles equipped for V2X (vehicle-to-everything) communication. These vehicles can communicate directly with each other, i.e., without involving any base transceiver station, which is also referred to as vehicle-to-vehicle (V2V) or device-to-device (D2D) communication. Technologies enabling such D2D / V2V or V2X communication include 802.11p and above, 3GPP (3rd Generation Partnership Project) systems (4G, 5G, NR, and above), etc. For example, vehicles exchange certain messages, such as CPM, Cooperative Awareness Messages (CAM), or Distributed Environment Notification Messages (DENM). The content of these messages enables the receiver to know its environment and determine a map of the vehicle's environment. For example, vehicle 100 can also be represented as self-aware vehicle, because the perspective of the proposed concept is focused on this vehicle.

[0029] Various embodiments of this disclosure relate to the concept of associating CPM objects with an environmental model of a self-aware vehicle by means of prior knowledge. Generally, a CPM includes an abstract representation of the environment of the vehicle that generates the CPM, as perceived by the vehicle that generates the CPM. Within the CPM, other objects, such as other vehicles, are generally referenced relative to the position of the vehicle that generates the CPM. In other words, a CPM may include the position of the transmitting vehicle (i.e., the vehicle that generates the CPM) in absolute coordinates and information about objects detected by the vehicle's sensors relative to them. These objects can be other road users, especially other vehicles. Information about pedestrians can also be transmitted in the form of CPM objects. The proposed concept can be limited to vehicles other than CPM objects. Accordingly, each collective perception message includes information about the absolute position (i.e., in absolute coordinates) of the vehicle that generates the collective perception message and the relative position (i.e., in coordinates relative to the vehicle that generates the CPM) of one or more other vehicles being perceived by the vehicle that generates the collective perception message (using the vehicle's perception sensors).

[0030] Assigning CPM transmitting vehicles (i.e., the vehicles that generate CPM messages) and the CPM objects they detect (and reference in the CPM) to objects detected by the receiving vehicles themselves is often difficult and often impossible. This is because CPM transmitting vehicles typically cannot determine their own attitude (position and orientation) with sufficient accuracy. Information about the CPM objects they detect is correspondingly inaccurate. In addition to inaccuracies in sensor measurement and calibration, errors in estimating the vehicle's orientation (heading) also have an impact. Angular errors have a particularly large impact when detecting distant objects.

[0031] In contrast to some other concepts, the proposed concept may focus not only on the association of vehicles that send V2X messages (CAM, DENM, CPM), but also on the association of CPM objects (i.e., vehicles that send messages in CPM and are detected by CPM vehicles using their sensors).

[0032] The proposed concept may include one or more of the following characteristics: - Receive 110 CPM messages - For example, store the received CPM messages in a table to create a history. - Place the CPM delivery vehicle and its CPM object on the map (i.e., the V2X environment map). - Improve lane and location allocation based on logical connections (e.g., by associating CPM-sending vehicles and CPM objects with road routes 130, and adjusting the locations on the map accordingly 140). - Advanced Fusion 150 consists of vehicles detected by self-aware vehicles and corresponding CPM objects from the V2X environment map.

[0033] In order to use the proposed method, the receiving vehicle (i.e., vehicle 100) may include one or more of the following devices: - A V2X receiver unit (e.g., interface 12), which enables reception of CPM, CAM, and DENM. - Digital maps with sufficient accuracy (such as high-definition maps) (representing road routes) - Systems used for self-location (e.g., GPS receivers) - Sensor systems used for object detection, such as cameras, LiDAR, radar, etc. - Computational units (e.g., processing modules) are used to compute the environment model and to associate V2X vehicles and CPM objects.

[0034] Take one or more of the following measures to enforce the association: A V2X environment map can be created. In the context of this application, the V2X environment map, also known as a map, places the corresponding vehicles on it. In some embodiments, the location of the vehicles referenced in the CPM message can be stored in a V2X environment table, and the V2X environment table can be used to generate the map. Based on the received CPM message, the location of the sending vehicle (with its confidence ellipse / region) and the vehicle's heading can be stored in the V2X environment table. Furthermore, CPM objects can be stored in this table along with the specified confidence level they are assigned in the CPM (e.g., in the x, y, and z directions relative to the CPM vehicle's coordinate system, hereinafter referred to as confidence distance). The sorting criteria for the table can be the sender pseudonym (i.e., the identifier contained in the CPM message) and the transmission time. The V2X environment table can be updated periodically. It can be limited to a predefined time period, such as the last 30 seconds. Like a circular buffer, outdated information can be replaced by new information and can be stored in other locations as needed. The contents of the V2X environment table can be input into a digital map. In other words, the method includes using the location received in one or more collective perception messages to place the corresponding vehicle that generated the collective perception message, along with one or more other vehicles perceived by the vehicle that generated the collective perception message, on a map. For example, the location received in one or more collective perception messages for each CPM may include the absolute location of the vehicle that generated the CPM and one or more locations for one or more other vehicles relative to the absolute location of the vehicle that generated the CPM. For example, the (V2X environment) map may be a static map including a series of one or more roads, intersections, traffic infrastructure (lights, signs, intersections, etc.), buildings, etc.

[0035] (V2X environment) Maps may include static and dynamic objects in the environment of a vehicle along at least a portion of its trajectory. Such a portion of the trajectory could be, for example, the part of the vehicle that is scheduled to travel over the next 30 seconds, 1 minute, 5 minutes, 10 minutes, etc. Dynamic objects are objects that are not permanently static / fixed (e.g., other road users, pedestrians, vehicles) and are also semi-static objects (e.g., components on moving construction sides, traffic signs indicating road or lane narrowing, etc.). For example, such dynamic objects could be other vehicles, pedestrians, bicycles, road users, etc. When determining an environment model, not all objects in the model can be determined with the same confidence level. There are objects for which a higher degree of certainty can be achieved than for other objects. For example, if multiple CPMs or sensors can identify or confirm an object, its presence and / or movement status can potentially be determined with a higher confidence level compared to a situation where only data from a single CPM or sensor indicates the object.

[0036] For CPM-transmitting vehicles, the trajectory of their travel path and the confidence tube (i.e., the confidence region around the trajectory) can be derived from their position and / or confidence ellipse. In other words, the method may include tracking 122 the position of a vehicle, as received in one or more collectively sensed messages, to determine 124 the trajectory of the vehicle. For example, the trajectory of the vehicle may be derived from the individual position of the vehicle over time. The method may include determining a confidence region around the position for each position. For example, the CPM may include information about the confidence level of the absolute position of the vehicle that generated the CPM, which can be used to generate the confidence region around the position. For the path traveled by each CPM object, the confidence distances in the x, y, and z directions included in the CPM may result in a trajectory with a confidence tube (i.e., the confidence region around the trajectory). If the confidence level changes during the observation period, the width of the confidence tube can change. The method may include determining the confidence region around the trajectory (e.g., by combining the confidence regions around the position) based on the confidence region around the position of the trajectory for each trajectory. These can later be used to correct locations on a map, for example, by limiting the correlation to a confidence region around the location or trajectory. In other words, a confidence region around a trajectory can be used to correct the corresponding location on the map. Each confidence tube can contain a trajectory generated at the location sent with the CPM. The trajectories of the perceived CPM objects can be derived from or composed of their individual locations. They can be supplemented by confidence tubes. If an individual CPM message is not received, the missing location can be estimated or interpolated, for example, by averaging from neighboring values. The location of the V2X vehicle sending the message can be specified in the CPM in absolute coordinates. The values ​​of the CPM objects (trajectory, confidence tube) can be relative to them. Outliers can be discarded, and a best fit between the trajectory and confidence tube (relative to the road lane) can be performed if no message is received.

[0037] In many cases, trajectories may not be clearly assigned to lanes on a map. Therefore, in a subsequent step, corrections can be made using logical relationships, such as ontology or rules, and trajectories and confidence tubes can be moved. The method includes associating the position of a vehicle on a map with the route of a road, where the vehicle is traveling. For example, associating the position of a vehicle on a map with the route of a road may include determining how the vehicle best fits the route of the road without violating constraints and / or according to logical relationships (e.g., not traveling between lanes or alongside the road or violating traffic rules). Accordingly (i.e., based on the correlation between the route of the road and the position of the vehicle), the corresponding position is corrected. For example, the correlation between the route of the road and the position of the vehicle may involve the position of the vehicle relative to the route of the road that best fits the constraints and / or logical relationships. The corrected trajectory / position values ​​and / or confidence tubes may be stored in a second table. The table with the original values ​​may be retained so that it can be used later for, for example, control purposes or additional corrections. The probability of the correctness of each correction may be estimated and stored, which can then be processed during the association process.

[0038] In cases where location data of the transmitting vehicle (i.e., the vehicle generating CPM) jumps due to measurement errors, such as those caused by its self-localization (e.g., possibly using a GPS system), segment-by-segment corrections to the trajectory and confidence tube can be performed. The trajectory and confidence tube of its CPM object can be corrected accordingly.

[0039] For the correction of CPM vehicles and CPM objects, one or more of the following logical relationships can be used: For example, correction may take into account drivable and impassable areas (shown) on the map. For instance, vehicles may be unable to pass through buildings or travel alongside bridges. In other words, the location of a vehicle is correlated with the route of a road. The location and / or trajectory of a vehicle on the map may be correlated with the route of a road based on one or more lanes. Alternatively, correction may take into account logical relationships when CPM objects are moved as a group. CPM sends vehicles and their CPM objects to form a group, which can be inserted into the map as a whole by translation and rotation. In other words, the location and / or trajectory of vehicles on maps originating from the same collective sense message may be mutually correlated with the route of a road. In other words, during correction, the locations and / or trajectories of vehicles on maps originating from the same collective sense message may retain their relative positions during correlation. For example, the locations and / or trajectories of vehicles on maps originating from the same collective sense message may be translated and / or rotated relative to the map to correlate the respective vehicles with the route of a road. Alternatively, corrections may take into account the logical relationships arising from the shape of the trajectory, such as the radius or distance to a salient point (e.g., the start of a curve). Alternatively, corrections may take into account effective traffic controls. These can be used for plausibility checks and to determine the probability of the correction's correctness. In other words, the position and / or trajectory of a vehicle on the map are based on one or more traffic rules on the road and their relation to the road's route.

[0040] As previously noted, when relating location to road routes, not only location but also the vehicle's trajectory can be considered. In other words, the vehicle's trajectory can be correlated with the road route 130, and the corresponding vehicle is traveling on said road. 140 The vehicle's corresponding position can be corrected based on the correlation between the road route and the vehicle's trajectory.

[0041] In addition to the V2X environment map, another map or model can be generated, which can be represented as an object environment map. The creation of the object environment map is discussed below. In parallel with the steps above for creating the V2X environment map, the vehicle can continuously detect objects in its environment using its sensors. The method may include determining the location of one or more vehicles near the vehicle via one or more sensors of the vehicle. For example, the vehicle may include visual / optical (capturing device), radar, ultrasonic, lidar (light detection and ranging), or other sensors. The object environment map may be a digital model of the vehicle's environment, based on the vehicle's sensor data. The vehicle can use this sensor data to model its surrounding environment.

[0042] The generation of the object environment map can accommodate measurement inaccuracies in object detection and inaccuracies in self-localization. The location of each object can be input into the object environment table and / or the object environment map. For each vehicle, the history of observed travel distances (trajectory, location over time) can also be stored. Self-trajectories and self-confidence parameters can also be included in the object environment map. The object environment table or object environment map can be updated periodically.

[0043] The following discusses the matching of objects from an object map and a V2X environment map. For example, a vehicle in the object environment map can be compared with a vehicle in the corresponding area from the V2X environment map. Because the distance of V2X communication is typically greater than the sensor range, the environment map may typically only include a subset of the V2X environment map. The trajectories of objects detected by sensors and the trajectories of V2X objects (CAM, DENM, CPM transport vehicles, and CPM objects) can be compared. In other words, the method may include determining the relationship between a vehicle on the map and one or more vehicles near the vehicle based on the corrected location of the vehicle on the map. For example, a vehicle on the map can be matched with one or more vehicles near the vehicle based on the corrected location of the vehicle on the map. Statistical methods can be used in this regard. Among other things, spatial routes and velocity profiles can be considered. From a defined threshold (e.g., a correlation metric), assignment can be made, i.e., a V2X transmitter or CPM object can be assigned to a detected vehicle. The assignment may also take into account information contained in the V2X message, such as the vehicle class (truck, car). The assignment may also take into account the probability of trajectory correction for V2X vehicles. Information acquired via sensors can be used to enhance the information contained in the V2X environment map. Accordingly, the method may include, for example, supplementing information about vehicles on the map based on determined relationships between vehicles on the map and one or more vehicles nearby, by improving the accuracy of the location and / or trajectory of the respective vehicles.

[0044] CPM objects can also be CAM (Collaborative Awareness Message) or DENM (Distributed Environment Notification Message) sending vehicles or other CPM sending vehicles. It is also possible that a vehicle has been identified as a CPM object by several CPM sending vehicles. For example, at least a first collective awareness message can be received from a first vehicle, and a second collective awareness message can be received from a second vehicle. In various embodiments, the task may therefore be to resolve these ambiguities and determine a match between vehicles. In other words, the method may include matching the vehicle referenced in the first collective awareness message with the vehicle referenced in the second collective awareness message based on the location included in the respective collective awareness message and / or based on the corrected location of the vehicle.

[0045] In various embodiments, the corrected map entries can be used for this purpose. In the first step, the affected V2X vehicles or CPM objects can be identified. For example, measures of spatial proximity or overlap of trajectories and confidence tubes can be used for this purpose. Next, criteria that allow for a reasonable check of whether it is the same object can be considered. Appropriate criteria are, for example, comparing speed-time curves (when deceleration begins simultaneously) or angle-time curves for changes in vehicle orientation (heading). Furthermore, information contained in the V2X messages (such as vehicle class (truck, car)) can be considered. Statistical methods can be used here. Assignment can be made from thresholds that will be defined (e.g., relevance measures).

[0046] Ambiguity can be eliminated from the map. Deleted objects can be stored in another table to allow for later review and correction. For example, objects retained on the map can be marked as having been detected multiple times. In some embodiments, multiple detections of individual objects can occur over a longer time period, for example, because this arises from traffic flows and vehicles actually traveling parallel to each other. In this case, the rationality check can be simplified and performed, for example, only at defined intervals, i.e., as periodic confirmation. When duplicates are deleted, an assessment can be made regarding which object was deleted and which was retained. For this purpose, an assessment can be made regarding which information might be more accurate. Less accurate information can be deleted. In some embodiments, during this step, the objects are corrected, and only the corrected values ​​remain on the map.

[0047] Generally, for example, each collective sensing message includes an identifier of the vehicle that generated the collective sensing message. The identifier can be changed to a newly generated identifier according to a predefined schedule (the identifier can be static or based on the number of nearby vehicles). When the pseudonym is changed (by generating a new identifier), the V2X vehicle disappears, and a new vehicle is created. Therefore, a plausibility check can be performed to ensure that the two vehicles are identical. The method may include determining a match between the location received in the collective sensing message with the newly generated identifier and a previously determined trajectory. For example, a match can be found if the location received in the collective sensing message with the newly generated identifier extends the previously determined trajectory. The method may include using subsequently received collective sensing messages with newly generated identifiers to continue tracking the corresponding location and determining the corresponding trajectory. For example, it might be possible if the same sensed object is traveling using the same sensor set and the old pseudonym of the new pseudonym, and the road route does not allow for the re-evaluation of the new vehicle, then the same vehicle is involved.

[0048] The generated V2X environment can be used to operate the vehicle. In other words, the method may include operating the vehicle in an autonomous or semi-autonomous driving mode using the corrected location of the vehicle on a map. For example, a map including the corrected location can be used by the vehicle to determine the location, (projected) trajectory, and / or distance of other vehicles near the vehicle, which can be used to plot the vehicle's route on the road.

[0049] In embodiments, interface 12 can be used with any component applied to acquire, receive, transmit, or provide analog or digital signals or information, such as any connector, contact, pin, register, input port, output port, conductor, channel, etc., which allow the provision or acquisition of signals or information. The interface can be wireless or wired, and it can be configured to exchange (i.e., transmit or receive signals) information with other internal or external components. Interface 12 may include additional components for implementing corresponding communications, such as in mobile communication system 400. These components may include transceiver (transmitter and / or receiver) components, such as one or more low-noise amplifiers (LNAs), one or more power amplifiers (PAs), one or more duplexers, one or more dual-signalers, one or more filters or filter circuitry systems, one or more converters, one or more mixers, correspondingly adapted RF components, etc. Interface 12 may be coupled to one or more antennas, which may correspond to any transmit and / or receive antenna, such as horn antennas, dipole antennas, patch antennas, sector antennas, etc. Antennas can be arranged in a defined geometry, such as uniform arrays, linear arrays, circular arrays, triangular arrays, uniform field antennas, field arrays, and combinations thereof. In some examples, interface 12 can serve the purpose of transmitting or receiving, or both transmitting and receiving, information such as information related to capabilities, control information, payload information, application requirements, trigger indications, requests, messages, data packets, acknowledgment packets / messages, etc.

[0050] like Figure 1c As shown, interface 12 is coupled to a corresponding processing module 14 at device 10. In embodiments, processing module 14 may be implemented using one or more processing units, one or more processing devices, any component for processing (such as a processor), a computer, or a programmable hardware component that can operate with correspondingly adapted software. In other words, the functionality of processing module 14 may also be implemented in software, which then executes on one or more programmable hardware components. Such hardware components may include general-purpose processors, digital signal processors (DSPs), microcontrollers, etc.

[0051] In this embodiment, communication (i.e., transmission, reception, or both) may occur directly within the vehicle and / or between the mobile transceiver / vehicle and network components / entities (infrastructure or mobile transceiver, such as base stations, network servers, back-end servers, etc.). Such communication may utilize a mobile communication system. This communication may be performed directly, for example, by means of device-to-device (D2D) communication, which in the case of a vehicle may also include vehicle-to-vehicle (V2V) or car-to-car (C2C) communication, and may be executed using the specifications of a mobile communication system.

[0052] In an embodiment, interface 12 can be configured to communicate wirelessly in a mobile communication system. For example, direct cellular vehicle-to-everything (C-V2X) transmission (where V2X includes at least V2V, V2 infrastructure (V2I), V2 pedestrian (V2P), etc.) according to 3GPP release 14 or later can be managed by the infrastructure (so-called mode 3 in LTE) or run in the UE (so-called mode 4 in LTE).

[0053] Examples of the proposed concept may include recording the tracks of CPM-deployed vehicles and detected objects described in the CPM. The proposed concept may include assigning locations / tracks on a map (V2X environment map) and improving lane and location allocation based on the logical relationship between the CPM-deployed vehicles and the detected objects described in the CPM. Examples may include recording the tracks of objects observed using vehicle sensors and assigning these tracks on another map (object environment map). The proposed concept can provide high-level fusion of detected vehicles and corresponding objects from the V2X environment map, as well as an evaluation of track correlation.

[0054] As already mentioned, in embodiments, the corresponding methods may be implemented as computer programs or code capable of execution on corresponding hardware. Therefore, another embodiment is a computer program having program code for performing at least one of the methods described above when executed on a computer, processor, or programmable hardware component. A further embodiment is a computer-readable storage medium storing instructions that, when executed by a computer, processor, or programmable hardware component, cause the computer to implement one of the methods described herein.

[0055] Those skilled in the art will readily recognize that the steps of the various methods described above can be performed by a programmed computer, for example, to determine or calculate the location of time slots. In this document, some embodiments are also contemplated to cover program storage devices (e.g., digital data storage media) that are machine- or computer-readable and encoded with machine-executable or computer-executable programs of instructions that perform some or all of the steps of the methods described herein. The program storage device may be, for example, a digital memory, a magnetic storage medium (such as disks and tapes, hard disk drives), or an optically readable digital data storage medium. Embodiments are also contemplated to cover: a computer programmed to perform the steps of the methods described herein; or a (Field-Programmable Logic Array) ((F)PLA) or (Field-Programmable Gate Array) ((F)PGA) programmed to perform the steps of the methods described above.

[0056] This description and accompanying drawings illustrate only the principles of the invention. It will therefore be appreciated that those skilled in the art will be able to design various arrangements, which, while not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. Furthermore, it is generally understood that all examples described herein are for educational purposes only, to aid the reader in understanding the principles of the invention and the concepts contributed by inventor(s) to promote the art, and are not intended to be limited to such specific narrative examples and conditions. Moreover, all statements herein describing the principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to include their equivalents.

[0057] When provided by a processor, functionality can be provided by a single dedicated processor, a single shared processor, or multiple individual processors (some of which may be shared). Furthermore, the explicit use of the terms "processor" or "controller" should not be construed as exclusively referring to hardware capable of executing software, but may imply, including but not limited to, digital signal processor (DSP) hardware, network processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), read-only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage devices. Other conventional or custom hardware may also be included. Their functionality can be performed through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually; the specific technology is optional for the implementer, as understood more specifically from the context.

[0058] Those skilled in the art will appreciate that any block diagram herein represents a conceptual view of an illustrative circuit system embodying the principles of the invention. Similarly, it will be appreciated that any flowchart, schematic diagram, state transition diagram, pseudocode, etc., represents various processes that can be represented substantially in a computer-readable medium and therefore executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0059] Furthermore, the following claims are thus incorporated into the detailed description, wherein each claim may stand alone as a separate example. While each claim may stand alone as a separate example, it should be noted that—although a dependent claim may refer in the claims to a particular combination of one or more other claims—other examples may also include combinations of the subject matter of that dependent claim with the subject matter of each of the other dependent claims. Such combinations are presented herein unless it is stated that a particular combination is not expected. Furthermore, it is expected to include the features of any other independent claim, even if that claim is not directly dependent on that independent claim.

[0060] It should be further noted that the methods disclosed in this specification or in the claims can be implemented by means of a device having a component for each of the corresponding steps of performing these methods.

[0061] Reference Symbol List 10 Equipment 12 interfaces 14 Processing Module 100 means of transportation 110 receives one or more collective perception messages. 120 Place the vehicle on the map 122 Track the location of vehicles 124. Determine the trajectory of a vehicle. 126. Match confirmed. 130. Linking the location of vehicles with road routes. 140. Correct the corresponding position. 150. Determine the location of vehicles near the aforementioned vehicle. 160. Establishing a Relationship 162 Supplementary Information 170 Matching vehicles 180 Operating vehicles 200 (one or more) means of transport.

Claims

1. A method for a vehicle (100), the method comprising: Receive (110) one or more collective perception messages, wherein each collective perception message includes information about the absolute position of the vehicle (200) that generated the collective perception message and the relative position of one or more other vehicles that are being perceived by the vehicle that generated the collective perception message, wherein the one or more other vehicles are different from the vehicle that received the collective perception message and the vehicle that generated the collective perception message. Using the location received in the one or more collective sensing messages, the corresponding vehicle that generated the collective sensing message and the one or more other vehicles that are being sensed by the vehicle that generated the collective sensing message are placed (120) on the map; and The location of the vehicle on the map is correlated with the route of the road (130), the corresponding vehicle is traveling on the road, and the corresponding location is corrected (140) based on the correlation between the route of the road and the location of the vehicle.

2. The method according to claim 1, wherein, The method includes tracking (122) the location of the vehicle received in one or more collectively sensed messages to determine (124) the trajectory of the vehicle, wherein the trajectory of the vehicle is correlated with the route of the road (130), the corresponding vehicle is traveling on the road, and wherein the corresponding location of the vehicle is corrected based on the correlation between the route of the road and the trajectory of the vehicle (140).

3. The method according to claim 2, wherein, Linking the trajectory of the vehicle to the route of the road (130) includes determining a confidence region around each location, and determining a confidence region around each trajectory based on the confidence region around the location of the trajectory, wherein the confidence region around the trajectory is used to correct the corresponding location on the map.

4. The method according to any one of claims 2 or 3, wherein, Each collective sensing message includes an identifier of the vehicle that generated the collective sensing message, the identifier being changed to a newly generated identifier according to a predefined schedule, wherein the method includes determining (126) a match between a location received in a collective sensing message with the newly generated identifier and a previously determined trajectory, and using subsequently received collective sensing messages with the newly generated identifier to continue tracking (122) the corresponding location and determining the trajectory.

5. The method according to any one of claims 1 to 3, wherein, The location and / or trajectory of the vehicle on the map, which originates from the same collective perception message, are mutually correlated with the route of the road.

6. The method according to claim 5, wherein, The positions and / or trajectories of the vehicles on the map, which originate from the same collective perception message, are translated and / or rotated together relative to the map to correlate the respective vehicles with the routes of the roads.

7. The method according to any one of claims 1 to 3, wherein, The location and / or trajectory of the vehicle on the map are correlated with the route of the road based on one or more lanes of the road. And / or the location and / or trajectory of the vehicle on the map are related to the route of the road based on one or more traffic rules on the road.

8. The method according to any one of claims 1 to 3, comprising determining (150) the location of one or more vehicles near the vehicle via one or more sensors of the vehicle, and determining (160) the relationship between the vehicle on the map and the one or more vehicles near the vehicle based on the corrected location of the vehicle on the map.

9. The method according to claim 8, wherein, The vehicle on the map is matched with one or more vehicles nearby based on the corrected position of the vehicle on the map.

10. The method according to claim 8, comprising supplementing (162) information about the vehicle on the map based on a determined relationship between the vehicle on the map and one or more vehicles nearby.

11. The method according to any one of claims 1 to 3, wherein, The method includes receiving (110) at least a first collective perception message from a first vehicle and receiving a second collective perception message from a second vehicle, the method comprising matching (170) the vehicle referenced in the first collective perception message and the vehicle referenced in the second collective perception message based on the location included in the received collective perception message and / or based on the corrected location of the vehicle.

12. The method according to any one of claims 1 to 3, comprising operating (180) the vehicle in an autonomous or semi-autonomous driving mode using the corrected position of the vehicle on the map.

13. A computer program product having program code, the computer program product being configured to perform the method of any one of claims 1 to 12 when the program code is executed on a computer, processor or programmable hardware component.

14. A device (10) for a vehicle (100), said device comprising: Interface (12), which is used to receive collective perception messages from other vehicles (200); as well as Processing module (14) is configured to perform the method according to any one of claims 1-12.

15. A means of transport (100) comprising the device (10) according to claim 14.

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