Patent Description:
Direct communication between mobile devices, also referred to as device-to-device (D2D), vehicle-to-vehicle (V2V), or car-to-car communication (C2C), has been a feature under development of newer generations of mobile communication systems. By enabling direct communication between vehicles, message exchange can be enabled at low latencies. These messages can be used to share information among road participants. For example, vehicles can share certain parameters and environmental information to improve individual environmental models and mutual awareness. In automated or autonomous driving vehicles need to continuously monitor and develop information on their environment and surroundings. Message exchange with other vehicles and traffic infrastructure can contribute to the development of such an environmental model.

Document <CIT> describes a concept for monitoring remote vehicles relative to a host vehicle through generating a sensor object data map in response to sensed objects, generating a vehicle-to-vehicle object data map, merging both maps, and estimating a relative position of the remote vehicles using the merged maps.

Document <CIT> describes an unmanned vehicle for use with a companion unmanned vehicle. The unmanned vehicle includes a location unit that is configured to determine a current position of the unmanned vehicle. The unmanned vehicle includes a path planning unit that generates a planned path. The unmanned vehicle receives a planned path of the companion unmanned vehicle and a current position of the companion unmanned vehicle. The unmanned vehicle includes a position unit that is configured to determine a relative position between the unmanned vehicle and the companion unmanned vehicle based on at least the planned paths and the current positions of the unmanned vehicle and the companion unmanned vehicle. The unmanned vehicle also includes a control unit that is configured to control a movement of the unmanned vehicle based on at least the relative position between the unmanned vehicle and the companion unmanned vehicle.

Document <CIT> discloses a driving assistance device, which comprises another vehicle detection unit, a classification recognition unit, a trajectory acquisition unit, an interference determination unit, and a driving assistance unit. The other vehicle detection unit is configured so that the position of the other vehicle existing in the periphery of the host vehicle is detected. The classification recognition unit is configured so that the traffic classification in which the other vehicle is positioned is recognized. The trajectory acquisition unit is configured so that the host vehicle trajectory representing the future trajectory of the host vehicle is acquired. The interference determination unit is configured so that a determination is made of whether the other vehicle's course representing the course when the other vehicle is travelling in accordance with the traffic classification will interfere with the host vehicle trajectory. The driving assistance unit is configured so that different driving assistance is performed in an interference state representing a case when the other vehicle's course and the host vehicle trajectory interfere, and in a non-interference state representing a case when there is no interference of the other vehicle's course and the host vehicle trajectory.

Known concepts make use of other vehicle's positions, which might not be accurate. An accuracy of the other vehicle's position may be determined by the localization concept used to determine the position. One common positioning method is provided by the global positioning system (GPS), which allows an accuracy of about <NUM>. The remaining uncertainty about a position can also lead to ambiguities, e.g. if multiple vehicles a located close to each other.

There is a demand for an improved concept for a first vehicle to determine a position of a second vehicle.

This demand is addressed by the independent claims attached.

Embodiments are based on the finding that information on trajectories of vehicles can also be communicated via a radio channel and exploited for localization or positioning. For example, a set of trajectories can be orientated with a relative distance to a transmitting node, e.g. a vehicle, and not with absolute coordination. A set of trajectories can be seen as a trajectory area, e.g. a spatial area, which fits in an environmental map. The information on the trajectories can be used to determine the position of a vehicle in the environmental map, e.g. a high definition (HD) map.

Embodiments provide a method for a first vehicle and for estimating a position of a second vehicle at the first vehicle. The method comprises obtaining information on an environmental map of the first vehicle and receiving information on a trajectory of the second vehicle. The method further comprises estimating the position of the second vehicle in the environmental map based on the information on the trajectory. Using the information on the trajectories may enable a higher accuracy of a position estimated for the second vehicle.

For example, the information on the trajectory is received from the second vehicle. If information on the trajectory is communicated between the vehicles, the process of an overall mutual positioning can be efficiently improved.

The information on the trajectory may comprise information on a planned route of the second vehicle and information on a desired trajectory of the second vehicle. Such information may further improve the positioning process at the first vehicle as a plausibility consideration can be applied based on the environmental map.

The method may further comprise receiving information on the dimensions of the second vehicle and estimating the position of the second vehicle in the environmental map further based on the information on the dimensions of the second vehicle. Taking the dimensions of the second vehicle into account may further improve the positioning accuracy.

In some embodiments the method may further comprise receiving information on an estimated location of the second vehicle and estimating the position of the second vehicle in the environmental map further based on the information on the estimated location of the second vehicle. The location estimated by the second vehicle together with the information on its trajectory may further contribute to improving the accuracy of the positioning of the second vehicle.

Moreover, the method may include an optional refining of the estimation of the position of the second vehicle based on logical considerations regarding movements or locations of one or more objects in environmental map. Predetermined objects in the environmental map, e.g. buildings, road structure or other road users, may allow further refinement of the position of the second vehicle.

For example, the logical considerations may comprise evaluating against a predetermined street map. A position of the second vehicle can be evaluated against a street map, e.g. a plausibility check can be conducted on whether the speed of the second vehicle and its position match an according lane of the road.

The estimating may be further based on messages communicated with other vehicles or infrastructure in the environment. For example, other road participants may have determined a position for the second vehicle and their position may further improve the accuracy.

The environmental map may be based on a high-density street map, objects detected using sensor data of the first vehicle, and messages with environmental content received from other vehicles in the environment of the first vehicle. The environmental map may hence comprise a plurality of objects, which can be based on information from independent sources, thereby enhancing reliability of the estimated objects' properties.

For example, the estimating comprises determining a confidence area for the second vehicle in the environmental map. Using a confidence area may provide an efficient measure or means to develop an accuracy for the position of the second vehicle, as any further information can influence (ideally improve) the confidence.

In some embodiments the method further comprises refining the confidence area based on an actual road and/or traffic situation in the environment of the first vehicle. Information on an actual scene or situation may be considered to further improve a positioning accuracy.

For example, the receiving comprises receiving the information on a trajectory of the second vehicle in a collective perception message (CPM) or a maneuver coordination message (MCM) from the second vehicle. Using these messages may enable an easy implementation of a communication concept for the information on the trajectories in car-to-car (C2C) or vehicle-to-vehicle (V2V) communication.

Embodiments also provide an apparatus for a first vehicle and for estimating a position of a second vehicle at the first vehicle. The apparatus comprises one or more interfaces configured to communicate messages and a control module, which is configured to control the one or more interfaces. The control module is further configured to perform one of the methods described herein. Another embodiment is a vehicle comprising an embodiment of the apparatus.

Embodiments further provide a computer program having a program code for performing one or more of the above described methods, when the computer program is executed on a computer, processor, or programmable hardware component. A further embodiment is a computer readable storage medium storing instructions which, when executed by a computer, processor, or programmable hardware component, cause the computer to implement one of the methods described herein.

It will be further understood that the terms "comprises", "comprising", "includes" or "including", when used herein, specify the presence of stated features, integers, steps, operations, elements or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components or groups thereof.

<FIG> illustrates a block diagram of an embodiment of a method <NUM> for a first vehicle and for estimating a position of a second vehicle at the first vehicle. The method <NUM> comprises obtaining <NUM> information on an environmental map of the first vehicle. For example, an environmental map may be generated based on sensor data of the first vehicle. Such sensor data may comprise visual or video data, radar data, lidar (light detection and ranging) data, etc. The sensor data may be complemented by data received from other road participants or traffic infrastructure. There are multiple sources for environmental information available at a vehicle. A source are the vehicle's sensors, which sense the environment. Based on the sensor data an environmental map can be determined. An improved environmental map can be generated by merging information from different sources. With the introduction of message exchange between vehicles or traffic participants, the message content can be used to determine an environmental map. The messages from the traffic participants form a source for information on the environment.

As further shown in <FIG> the method <NUM> comprises receiving <NUM> information on a trajectory of the second vehicle. Such information may be received directly from the second vehicle or from another source, e.g. traffic infrastructure or any data server. The method <NUM> further comprises estimating <NUM> the position of the second vehicle in the environmental map based on the information on the trajectory.

<FIG> illustrates block diagrams of embodiments of an apparatus <NUM> for a vehicle <NUM> and a vehicle <NUM>. The apparatus <NUM> for a first vehicle <NUM> and for estimating a position of a second vehicle at the first vehicle <NUM> comprises one or more interfaces <NUM>, which are configured to communicate message, e.g. with the second vehicle. The apparatus <NUM> further comprises a control module <NUM>, which is coupled to the one or more interfaces <NUM>, and which is configured to control the one or more interfaces <NUM>. The control module <NUM> is further configured to perform one of the methods <NUM> described herein. Another embodiment is a vehicle <NUM> comprising an embodiment of the apparatus <NUM> (shown in broken lines in <FIG> as being optional from the perspective of the apparatus <NUM>).

In embodiments, the one or more interfaces <NUM> may correspond to any means for obtaining, receiving, transmitting or providing analog or digital signals or information, e.g. any connector, contact, pin, register, input port, output port, conductor, lane, etc. which allows providing or obtaining a signal or information. An interface may be wireless or wireline and it may be configured to communicate, i.e. transmit or receive signals, information with further internal or external components. The one or more interfaces <NUM> may comprise further components to enable according communication, e.g. in a mobile communication system, such 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 diplexers, one or more filters or filter circuitry, one or more converters, one or more mixers, accordingly adapted radio frequency components, etc. The one or more interfaces <NUM> may be coupled to one or more antennas, which may correspond to any transmit and/or receive antennas, such as horn antennas, dipole antennas, patch antennas, sector antennas etc. The antennas may be arranged in a defined geometrical setting, such as a uniform array, a linear array, a circular array, a triangular array, a uniform field antenna, a field array, combinations thereof, etc. In some examples the one or more interfaces <NUM> may 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, acknowledgement packets/messages, etc..

As shown in <FIG> the one or more interfaces <NUM> are coupled to the respective control module <NUM> at the apparatuses <NUM>. In embodiments the control module <NUM> may be implemented using one or more processing units, one or more processing devices, any means for processing, such as a processor, a computer or a programmable hardware component being operable with accordingly adapted software. In other words, the described functions of the control module <NUM> may as well be implemented in software, which is then executed on one or more programmable hardware components. Such hardware components may comprise a general purpose processor, a Digital Signal Processor (DSP), a micro-controller, etc..

In embodiments, communication, i.e. transmission, reception or both, may take place among vehicles directly and/or between mobile transceivers/vehicles and a network component/entity (infrastructure or mobile transceiver, e.g. a base station, a network server, a backend server, etc.). Such communication may make use of a mobile communication system. Such communication may be carried out directly, e.g. by means of device-to-device (D2D) communication, which may also comprise vehicle-to-vehicle (V2V) or car-to-car (C2C) communication in case of vehicles, and which may be carried out using the specifications of a mobile communication system.

In embodiments the one or more interfaces <NUM> can be configured to wirelessly communicate in the mobile communication system. For example, direct cellular vehicle-to-anything (C-V2X), where V2X includes at least V2V, V2-Infrastructure (V2I), V2-Pedestrian (V2P), etc., transmission according to 3GPP Release <NUM> onward can be managed by infrastructure (so-called mode <NUM> in LTE) or run in a UE (so-called mode <NUM> in LTE).

User equipment (UEs)/vehicles may communicate directly with each other, i.e. without involving any base station transceiver, which is also referred to as Device-to-Device (D2D) communication. An example of D2D is direct communication between vehicles, also referred to as Vehicle-to-Vehicle communication (V2V), car-to-car, dedicated short range communication (DSRC), respectively. Technologies enabling such D2D-communication include <NUM>. 11p and beyond, 3GPP (Third Generation Partnership Project) system (<NUM> (<NUM>th Generation), <NUM> (<NUM>th Generation), NR (New Radio) and beyond), etc. For example, vehicles exchange certain messages, for example Cooperative Awareness Messages (CAM) or Decentralized Environment Notification Messages (DENM), etc. The content of such messages may enable recipients to become aware of their environment and determine the first environmental map.

An environmental model may be a digital model of the environment of the vehicle, which can be based on sensor data or on exchanged messages. For example, a vehicle can be equipped with multiple sensors, such as visual/optical (camera), radar, ultrasonic, lidar (light detection and ranging) etc. A vehicle may model its surroundings using this sensor data. At least in some embodiments such a model may be based on known static data, e.g. as map data comprising a course of one or more roads, intersections, traffic infrastructure (lights, signs, crossings, etc.), buildings, etc. Such a basic layer for the environmental model may be complemented by dynamic or moving objects detected through sensor data. Such a sensor data-based environmental model may form the basis for the second environmental map.

An environmental map may comprise static and dynamic objects in the environment of the vehicle/traffic entity along at least a part of the vehicle's trajectory. Such a part of the trajectory may be, for example, the part the vehicle is planning to travel in the next <NUM>, <NUM> minute, <NUM> minutes, <NUM> minutes, etc. A dynamic object is one that is not permanently static/fixed such as other road participants, pedestrians, vehicles, but also semi-static objects such as components of a moving construction side, traffic signs for road or lane narrowing, etc. For example, such dynamic objects may be other vehicles, pedestrians, bicycles, road participants, etc. When determining the environmental model not all objects in the model may be determined with the same confidence. There are objects for which a higher certainty can be achieved than for others. For example, if multiple sensors can identify or confirm a certain object its presence and/or state of movement can potentially be determined with a higher confidence compared to a case in which only data from a single sensor is indicative of an object. Similar considerations apply with respect to a message-based map. If there is an object in the environment multiple traffic participants report on, a higher confidence results as compared to the case in which only a single road participant reports on the object.

For example, vehicles may share their trajectories and other parameters such as current speed, acceleration, etc., in order to enable cooperative driving. The following trajectory formats may be distinguished:.

In embodiments the information on the trajectory may be received from the second vehicle. For example, one of the above messages may be used, e.g. the receiving comprises receiving the information on the trajectory of the second vehicle in a collective perception message or in a maneuver coordination message from the second vehicle.

According to the above point III the information on the trajectory may comprise information on a planned route of the second vehicle and information on a desired trajectory of the second vehicle. In this case, a trajectory depicts the planned or desired future path mostly expressed as a spatial-temporal description of maneuvers, e.g. <NUM> sec. Note, that V2X messages might not be received in case of parked vehicles. When they start their journey, there might not be any information about their driving history that can be used to estimate more exact information about their position and heading. In such situation only planned and desired trajectories from MCM may be used for this purpose.

<FIG> shows an example of trajectories from different vehicles on which further details can be found in <NPL>. <FIG> illustrates two traffic scenes at intersections with different trajectories.

Note, that all possible trajectories of the vehicle are shown but not all of these trajectories are shared via radio link. In fact, only one trajectory (planed trajectory) and optional a second so called desired trajectory for maneuver negotiation is sent within the communication message as for e.g. maneuver coordination message (MCM). It is conceivable that not only one planed trajectory is sent as an example but a description of an area of possible trajectories. Furthermore, vehicles will be equipped with high definition (HD) maps, which are highly accurate.

<FIG> illustrates an example for positioning and association problems in embodiments. <FIG> shows two scenarios, a scenario with two vehicles V1 and V2 on the left and another scenario with three vehicles V1, V2, and V3 on the right. First, focus is on the inaccuracy of the absolute positioning of a vehicle itself, e.g. using a GPS signal. Vehicle <NUM> (V1) receives a message with the position of vehicle <NUM> (V2) which is inaccurate due to the uncertainty of the GPS signal. In this embodiment the method <NUM> comprises receiving information on an estimated location of the second vehicle and estimating the position of the second vehicle in the environmental map further based on the information on the estimated location of the second vehicle. Because of the inaccuracy V1 is not sure where V2 is exactly located. This is illustrated on the left-hand side of <FIG>, where the inaccuracy is +/- <NUM>. Second, if V1 receives a message from two cars driving close by, i.e. V2 and V3, ambiguities may result, as shown on the right-hand side of <FIG>. V1 has difficulties to associate each message to the corresponding vehicle. Is V3 the left or the right vehicle?.

In embodiments one or more trajectories are also received via the radio channel. The set of trajectories are usually orientated with relative distance to the transmitting node, e.g. V2, and not with absolute coordination. The set of trajectories can be seen as trajectory area. A spatial area which fits in a high definition (HD) map. However, the absolute position of the vehicle may also be shared via the radio channel. V1 is trying to find a reasonable location of V2 in its own HD map. The assumption is, that V2 only shares drivable and reasonable trajectories. Therefore, V1 uses the trajectory set of V2 and obtains possible absolute position/s for V2.

The environmental map may be based on a high-density street map, objects detected using sensor data of the first vehicle, and messages with environmental content received from other vehicles in the environment of the first vehicle.

The idea of fitting V2X messages to a map is can be summarized as follows:
Objects detected by vehicle sensors or fixed sensors and V2X objects can be associated. The assignment of V2X messages or the sending vehicles to the vehicles detected with the environment perception of a vehicle or a stationary sensor unit is usually very difficult and often even impossible. The reason is that V2X vehicles which determine/estimate their ego position with GNSS (global navigation satellite system) very often send their ego pose so inaccurately that an association does not succeed.

In embodiments the following steps may be carried out:.

V2X vehicles send status messages cyclically, in the European standard the so-called Cooperative Awareness Messages (CAM) or in the US standard the Basic Safety Messages (BSM). These contain the transmission positions estimated by the localization system and an indication of their accuracy, which is displayed as an ellipse (confidence ellipse). This is a distribution of the probability with which the true position lies within the ellipse. Furthermore, the history of the last sent positions is given, the path history (limited to a maximum distance of e.g. <NUM> or <NUM> positions). The transmission rate of the CAM/BSM depends on the driving dynamics and is between <NUM> and <NUM>. The path history is transmitted at <NUM>. For privacy reasons, V2X messages contain a pseudonym, which is changed cyclically, e.g. after <NUM> minutes. In this case, the old path history is deleted and the creation of a new path history is started. Event-messages (Decentralized Environmental Notification Message, DENM) also contain a history of the last sent items. It can be assumed that new, future V2X messages will also send position information.

Ego-localization of vehicles is typically done by means of GNSS systems (e.g. GPS) using vehicle odometry. Here the accuracy is in the meter range and can increase to several 10meters in urban environments if the view of the satellites is lost (urban canyons). For this reason, automatically moving vehicles normally (additionally) use other principles for their ego-localization, such as landmark based localization, which achieves accuracy in the decimeter range. It can be assumed that, at least for the first generations of V2X vehicles, GNSS-based ego-localization will be used essentially.

CAM/BSM also contain other information such as vehicle speed and direction of movement, the status of the indicators and the vehicle class. Emergency vehicles also send information when they are driving with special rights of way or when they are securing a danger zone.

In embodiments, information about the vehicle location (ego-localization), the localization accuracy, the direction of movement, the path history, the vehicle dynamics and additional information such as the vehicle class and emergency vehicle with special right of way may be processed.

Often several sensor systems are used to detect objects in the vicinity of a vehicle, e.g. camera, lidar and radar. During the determination of the objects, the information of the individual sensor systems is merged. This can be done at the object level (high-level fusion), whereby the objects are first determined individually by the sensor systems and then a fusion takes place. However, a fusion can also take place at the sensor data level (low-level fusion). Here the sensor data is first fused and then the objects are determined.

In some embodiments, the estimating <NUM> is further based on messages communicated with other vehicles or infrastructure in the environment.

At least in some embodiments, a kind of high-level fusion is to take place, in which the V2X vehicles are assigned to the detected vehicles. For examples, embodiments may assume that the recipient vehicle has the following minimum equipment.

In some embodiments, the estimating <NUM> comprises determining a confidence area for the second vehicle in the environmental map. The method <NUM> may further comprise refining the confidence area based on an actual road and/or traffic situation in the environment of the first vehicle.

The following steps may be taken to improve association and for creating a V2X environment map:.

As outlined above CPM objects may be associated into the environment model of the ego-vehicle with the help of prior knowledge. V2X message CPM (Collective Perception Message), which is currently being standardized at ETSI (European Telecommunications Standards Institute), contains the position of the sending vehicle in absolute coordinates and information on objects detected by the vehicle sensors relative to them. These objects are mainly other road users, especially other vehicles. Information on pedestrians could also be sent in the form of CPM objects. This topic is dealt with in various research projects and is important for increasing traffic safety, e.g. it is an important application for permanently installed roadside sensor systems at intersections. In addition, information about static objects could also be sent as CPM objects, such as construction site barriers.

The assignment of CPM or of CPM sending vehicles and the CPM objects detected by them to the objects detected by the receiving vehicles themselves is usually very difficult and often even impossible. The reason is that CPM-sending vehicles often cannot determine their ego-pose (location and orientation) with sufficient accuracy. The information on the CPM objects they detect is correspondingly inaccurate. In addition to measurement and calibration inaccuracies of the sensors, errors in the estimation of their vehicle orientation (heading) also have an effect here. Angular errors have a particularly large effect when detecting distant objects.

Embodiments might not only focus on the association of the vehicles sending the V2X messages (CAM, DENM, CPM), but may also deal with the association of the CPM objects, i.e. the vehicles sent in the CPM and detected by the CPM vehicles with their sensors.

In further embodiments the method <NUM> may comprise.

In some embodiments it is assumed that the recipient vehicle has the following minimum equipment:.

The following steps may be performed for association:.

Embodiments may use the above procedures complement them by using planned and desired trajectories from MCM. That means embodiments use the shared planed and desired trajectories of other vehicles. Furthermore, it might be possible to predict a trajectory of vehicles based on the CAM message, which however is not accurate as the shared trajectory through a MCM message.

Embodiments may use an approach for advanced positioning, which might be combined with the above as follows:.

<FIG> shows a simplified example of the trajectory HD map fitting in an embodiment. The procedure is carried out from left to right. V1 receives from V2 the relative position of the trajectories (shown on the very left in step <NUM> in <FIG>). It also receives the vehicle dimensions of V2 (step <NUM> in <FIG>, second from the left). V1 has an accurate HD map in which it tries to fit the information obtained in steps <NUM> and <NUM> (shown in step <NUM>, third from the left). The output is an obtained possible set of absolute positions of V2 in the map of V1 (shown on the right of <FIG>).

As already mentioned, in embodiments the respective methods may be implemented as computer programs or codes, which can be executed on a respective hardware. Hence, another embodiment is a computer program having a program code for performing at least one of the above methods, when the computer program is executed on a computer, a processor, or a programmable hardware component. A further embodiment is a (non-transitory) computer readable storage medium storing instructions which, when executed by a computer, processor, or programmable hardware component, cause the computer to implement one of the methods described herein.

The description and drawings merely illustrate the principles of the invention.

Furthermore, the following claims are hereby incorporated into the detailed description, where each claim may stand on its own as a separate embodiment. While each claim may stand on its own as a separate embodiment, it is to be noted that - although a dependent claim may refer in the claims to a specific combination with one or more other claims - other embodiments may also include a combination of the dependent claim with the subject matter of each other dependent claim. Such combinations are proposed herein unless it is stated that a specific combination is not intended. Furthermore, it is intended to include also features of a claim to any other independent claim even if this claim is not directly made dependent to the independent claim.

Claim 1:
A method (<NUM>) for a first vehicle (<NUM>; V1) and for estimating a position of a second vehicle (V2) at the first vehicle (<NUM>; V1), wherein the method is carried out in a control module of the first vehicle, the method (<NUM>) comprising
obtaining (<NUM>) information on an environmental map of the first vehicle (<NUM>, V1) that is generated based on cyclically updating a V2X environment table;
receiving (<NUM>) information on a trajectory of the second vehicle (V2), wherein the information is received via a maneuver coordination message;
estimating (<NUM>) the position of the second vehicle (V2) in the environmental map based on fitting the information on the trajectory in the environmental map;
receiving dimensions of the second vehicle via at least one of a cooperative awareness message, a collective perception message, and the maneuver coordination message; and
estimating the position of the second vehicle in the environmental map further based on the information on the dimensions of the second vehicle by fitting the trajectory into its environmental map while considering the dimensions of the second vehicle.