Method for predictively estimating transmission conditions for a communication between two communication partners, device, vehicle and computer program for carrying out the method steps of the method

Abstract

The invention relates to a method for predictively estimating transmission conditions for communication between two communication partners (B, A) moving along a road (FB), wherein the communication partners (B, A) themselves each estimate the current transmission conditions. The method is characterized in that the communication partners (B, A) use the estimated transmission conditions of a plurality of communication partners (C, D) travelling ahead in order to be able to better estimate the transmission conditions at a future point in time. In a variant, they themselves also estimate the transmission conditions for a future position based on sensor-based acquisition of the environment and fuse the estimation results obtained thereby with the results they obtain from the vehicles (C, D) travelling ahead.

Description

Technical Field

[0001] The invention relates to a method for predictively estimating transmission conditions for communication between two communication partners, a corresponding device for carrying out the method steps of the method, a vehicle and a computer program. In particular, the method can be used when predicting transmission conditions for a planned direct vehicle communication. Background Art

[0002] For the context of vehicles equipped with wireless communication modules that communicate directly with each other on public roads, for cooperative or autonomous driving scenarios, very high reliability is very important. The technology for vehicle-to-vehicle (V2V) direct communication has been developed and is continuing to develop. As an example, direct vehicle communication via WLAN is mentioned. As an example, decentralized solutions are developed according to the WLAN standard IEEE 802.11p for V2V communication. According to this technology, point-to-point (ad-hoc) radio networks are designed for communication between vehicles (communication in the "ad-hoc domain").

[0003] However, vehicle communication is also possible in the field of mobile communication networks. The term "mobile communication network" here refers to a provider-based mobile communication network, in other words a centralized and managed mobile network. Another term for a mobile communication network is a mobile communication system, both terms are synonymous in this article. In this technology, the network is divided into multiple mobile cells, each of which is assigned a base station that serves the mobile cell. Individual participants log in to the base station. Participants are assigned specific communication resources, on which they can exchange data with the base station. Direct vehicle-to-vehicle communication has been possible since the fourth generation of mobile communication. Here, vehicles communicate directly with each other. However, the base station is responsible for managing the communication resources for vehicle-to-vehicle communication. This is the area in which communication takes place in the so-called "infrastructure domain". In the long-term evolution (LTE) technology, this scheme is called LTE V (for vehicles); in the 5G initiative, this scheme is called device-to-device communication (D2D). This is also the field of vehicle communication that the present invention is concerned with.

[0004] Typical communication scenarios for vehicle-to-vehicle communication are safety scenarios, traffic efficiency and infotainment scenarios. The following example scenarios are called in the field of safety: "Cooperative Forward Collision Warning", "Pre-Crash-Erkennung / Warnung", "High-Density Platooning". In these areas, vehicles exchange information with each other, such as position, direction of movement and speed, as well as parameters such as size and weight. Other information that is important for transmission is, for example, intention information, such as "intention to overtake", "vehicle turning left / right", etc., which is meaningful for cooperative driving. Here, sensor data is often transmitted. If there is a danger and the driver does not react, the vehicle may automatically slow down, thereby avoiding an accident or at least minimizing the consequences of an unavoidable accident. Messages are also exchanged in the field of "platooning", which involves driving in a convoy. Usually, the entire convoy is controlled by a vehicle configured as a guide vehicle. For example, feedback of information about intentional braking operations is planned from front to back to avoid a collision.

[0005] In addition, there is a large amount of communication for infotainment purposes, of which Internet access is the most important. Internet access is important for a large number of multimedia services.

[0006] Currently, the following mobile communication technologies can be used: UMTS, HSPA, LTE and the upcoming 5G standard based on 3GPP. For direct V2V communication, LTE V and 5G D2D are mentioned.

[0007] The listed communication technologies show that time-critical data transmission is particularly important for safety-critical communications. In contrast, in time-critical safety scenarios, there are very high requirements in terms of functional safety. These usually prohibit the use of continuous, time-critical data transmission-based technologies.

[0008] There are some approaches that try to improve the stability of time-critical data transmission. One approach involves the concept of so-called "sensor aware predictive communication". This approach is based on a detailed understanding of the environment in which the planned communication takes place. In particular, it is important to know whether the communication will be affected by static or dynamic signal scattering. Here, the environment is acquired using on-board environmental acquisition sensors.

[0009] Another approach involves the concept of so-called Agile Quality of Service Adaptation (AQoSA). This concept involves a cycle in which an application presents its QoS requirements regarding communication reliability to the communication system, and the communication system responds and informs the application how it estimates the QoS conditions for the desired time period. The application then has time to adjust its settings to the notified QoS conditions. The planned safety-critical operations can then be executed with appropriate settings. Then, the loop is run for each of the other stages. Although the AQoSA concept is primarily used for ordinary cellular mobile communications (uplink, downlink), it can also be used for sidelink communications. However, the communication partners (vehicles) communicate with each other here. Then, QoS requirements are not made to the base station, but directly to one or more vehicles. This technology is advantageous if the QoS conditions can be estimated reliably enough at a prediction level on the order of one second or more.

[0010] In newer mobile communication standards (3GPP Release 12 and higher, i.e. LTE-V and the upcoming fifth generation mobile communication 5G), the cellular concept is weakened to enable direct vehicle-to-vehicle communication (sidelink communication).

[0011] In mobile communications, resource management is a very important aspect for implementing efficient multipath schemes. As soon as periodically repeated data must be transmitted, it is more efficient to allocate transmission resources for transmitting this data and to allocate the transmission resources to the sending station. In today's mobile communication standards, this task is associated with a management unit, which is also known as the term "scheduling". The management unit is usually arranged in the base station of the mobile communication cell. In the LTE mobile communication system, the base station is called an evolved Node Basis, or eNodeB for short. This so-called "scheduling" of the mobile operator determines which frequency resources can be used for direct communication and when.

[0012] The "scheduling" is usually a software component in the base station and informs each participant at which point in time and on which frequency of the transmission frame it can send specific data. The main task of the "scheduling" is to distribute the transmission resources fairly to the different participants. Thus, conflicts in both transmission directions from the participants (uplink) and to the participants (downlink) are avoided and the traffic is adjusted so that more efficient access to a large number of users is possible.

[0013] However, the transmission conditions can fluctuate greatly from time to time, and this is all the more so when car-to-car communication is a highly dynamic process. Vehicles are in motion and the environment is constantly changing. The distances between the communication partners are constantly changing. Traffic density plays an important role and is also constantly changing. It can therefore happen that from one moment to the next, two vehicles that were previously able to communicate in a directly visible connection are now being communicated with by one or more vehicles. The transmission conditions are therefore constantly changing. The aim is to better estimate the transmission conditions in advance for safety-critical applications.

[0014] There are already different methods for immediate channel estimation. As examples, the methods "least square estimation (LSE)" and "minimum mean square estimation (MMSE)" are mentioned. As further examples, methods based on channel prediction (CP) are mentioned. In CP-based methods, the channel prediction is usually based on a simple extrapolation from current and past estimates to the future. Some methods also take into account the properties of the channel statistics in order to make the prediction more reliable. Another problem is that the accuracy when using, for example, spline interpolation calculation methods depends to a large extent on the dynamics of the environmental conditions.

[0015] There are also newer sensor-based channel estimation methods that also collect environmental conditions and draw conclusions from them about the presence of static or dynamic reflections in order to better predict channel conditions. With this method, more accurate channel estimates can be achieved and relatively short-term channel predictions can be achieved. However, the accuracy of the prediction is linked to the accuracy of the environmental collection with the sensors used and to the system's ability to project the positions of correctly collected objects into the future.

[0016] The two methods mentioned above are based on in situ measurements, but they do not allow the future transmission conditions of two continuously moving communication partners to be estimated with high accuracy.

[0017] DE 10 2015 214 968 A1 discloses that, in order to better estimate the channel quality in car-to-car communication, environmental influences are also taken into account. For example, reflections may occur at larger buildings, which lead to multipath reception. The speed of the mobile participant and the motion vector are determined from this. The position of the mobile participant is then estimated for a future point in time t1. Using an environmental model, this can achieve an increased accuracy of estimating the channel quality at point in time t1.

[0018] EP 2 789 139 B1 discloses a method for multi-hop forwarding of data packets in a vehicle ad hoc network. Each node knows both its own coordinates and the geographical coordinates of the destination. The coordinates of the communication partner that is only one hop away are obtained from the periodically sent cooperation awareness message (CAM). Summary of the invention

[0019] The technical problem to be solved by the present invention is to find a solution to this problem, which allows for better estimation of transmission conditions in advance in safety-critical applications in the field of vehicle-to-vehicle communication. The solution should primarily meet safety requirements.

[0020] The above-mentioned technical problem is solved by a method for predictively estimating transmission conditions for communication between two communication partners according to claim 1 , a device for carrying out the method steps of the method according to claim 10 , a vehicle according to claim 11 and a computer program according to claim 12 .

[0021] The dependent claims contain advantageous developments and improvements of the invention based on the subsequent description of these measures.

[0022] In order to solve this problem, it is proposed to estimate the future channel conditions by using the channel estimation results of the communication partner traveling ahead, observe the traffic flow and obtain a prediction about the future channel conditions. Here, the channel estimation results from the communication partner can be used. However, the channel estimation results from multiple communication partners can also be used.

[0023] The proposed method for predictively estimating transmission conditions for communication between two communication partners moving along a road comprises that the communication partners each estimate the current transmission conditions themselves and use the estimated transmission conditions of the communication partner traveling ahead to estimate the transmission conditions at a future point in time. This makes it possible to increase the accuracy of the predictive estimation of the transmission conditions. The method can be used in particular in situations where there is a relatively uniform traffic flow or in situations where planned car-to-car communication is used. In the case of a uniform traffic flow, it usually occurs that vehicles travel at similar distances. A prominent example of the use of planned car-to-car communication is the so-called "platooning" application.

[0024] The method can be improved and expanded by various measures. In one variant, at least one of the following communication partners performs a step of sensor-based acquisition of the environment and determines whether similar framework conditions for direct vehicle communication exist between the communication partners traveling ahead. In one variant, it is considered whether the communication partner traveling ahead is moving at a similar distance to the two following communication partners. This is meaningful because the transmission conditions vary greatly depending on the distance between the communication partners.

[0025] The method is advantageous if, if it has been determined that the communication partner travelling ahead at least meets the distance condition, one of the communication partners requests in a request message at least one of the communication partners travelling ahead to report the current transmission conditions estimated on his side to the communication partner behind. This allows the measurement results of the communication partner travelling ahead to be selected in a targeted manner.

[0026] In another variant, one of the communication partners driving ahead at least periodically reports the estimated current transmission conditions to the communication partner behind. This can be done via a broadcast transmission mode. Here, the communication partner behind can obtain the report without sending a request. This saves the corresponding management overhead of sending a request message and responding to it. However, it is more difficult to select the appropriate report from a large number of reports and will cause more data traffic for vehicle direct communication.

[0027] In an extended embodiment, the communication partner determines at least the distance to the communication partner traveling ahead after the sensor-based acquisition and calculates therefrom the time at which the transmission conditions are predicted, thereby ensuring that the following vehicle will be in the same / similar position at this time and that similar transmission conditions will exist since at least the static surrounding objects will be in the same / similar position.

[0028] In a further development of the method, one of the communication partners determines possible signal scattering problems after sensor-based detection of the environment and estimates future transmission conditions taking into account signal backscatter. This is particularly advantageous because the vehicle-to-vehicle communication itself can be affected by such signal reflections. Multipath reception can occur and signals can be eliminated.

[0029] To further increase the accuracy, it is very advantageous if the communication partner fuses its own estimated transmission conditions with the transmission conditions reported by the communication partner travelling ahead. This is the use of a data fusion technique which is suitable for increasing the accuracy by superimposing information from different independent sources.

[0030] It is further advantageous here that the communication partner itself estimates the transmission conditions several times in succession and checks before the fusion step whether the future transmission conditions estimated several times by itself converge and also checks whether the communication conditions reported several times in succession by the communication partner traveling ahead converge, and if in both cases the estimation results converge, then the fusion step is performed. This allows a preliminary test that provides information whether the computationally intensive data fusion step contributes to improving the prediction accuracy. If the measurement series does not converge, the data fusion can be omitted.

[0031] For a device for carrying out the method steps of the method, it is advantageous according to the proposal that the device is equipped with at least one processor device which is designed to carry out the corresponding method steps. The device is designed so that it can carry out at least the necessary method steps from the perspective of the communication partner. In a further variant, the device is designed so that it can carry out the steps of the communication partner of the vehicle traveling ahead and the vehicle behind.

[0032] A further embodiment of the invention is a vehicle having a corresponding device for carrying out the method.

[0033] Furthermore, the proposal can also be implemented as a computer program which has a program code for processing in a processor device and in this case executes the method steps of the claimed method. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Exemplary embodiments of the invention are shown in the drawings and are explained in more detail below with reference to the drawings.

[0035] In the attached figure:

[0036] Figure 1 The principle of vehicle-to-vehicle communication between two vehicles moving sequentially on a road and communicating with each other via a sidelink channel of a mobile communication system is shown;

[0037] Figure 2 A typical block diagram of the automotive electronics of a vehicle is shown;

[0038] Figure 3 A diagram illustrating a process of estimating future transmission conditions for planned vehicle-to-vehicle communications is shown; and

[0039] Figure 4 A block diagram showing functionality for estimating future transmission conditions for vehicle-to-vehicle communications. DETAILED DESCRIPTION

[0040] This specification illustrates the principle of disclosure according to the present invention. Therefore, it should be understood that those skilled in the art will be able to conceive of different arrangements that, although not explicitly described herein, embody the principle of disclosure according to the present invention and are also protected within the scope of disclosure of the present invention.

[0041] An element defined in a claim (which is represented as a means for performing a specific function) should include any type of implementation of its function, such as including a) a combination of circuit elements that perform the function, or b) any form of software (including firmware, microcode, etc.) combined with appropriate circuits for executing the software to perform the function.

[0042] Figure 1 The system architecture for realizing vehicle-to-vehicle communication through a sidelink transmission channel provided by a mobile communication system is shown. Reference numeral 200 denotes a base station eNodeB (evolved Node Basis) of an LTE mobile communication service provider.

[0043] Figure 1 The base station 200 in the embodiment is located near a main road on which vehicles travel. Two vehicles A and B are shown here, which come one after the other at a certain distance on the lane of the road. Passenger cars are shown, but any other vehicle can also be used. Examples of other vehicles are: buses, commercial vehicles, especially trucks, motorcycles, agricultural machinery, construction machinery, rail vehicles, including robots and drones, etc. In general, the present invention can be used in land vehicles, rail vehicles, water vehicles and aircraft.

[0044] exist Figure 1 In the figure, both direct car-to-car communication via sidelink channels and conventional mobile radio communication in uplink and downlink directions are shown, which takes place via a mobile radio base station 200 and further via the communication network of a mobile operator 300, which is referred to as Evolved Packet Core (EPC) in LTE. In addition, a connection to the Internet 400 is also possible via routing technology, so that communication with an external central processing unit 410 that can be reached in the Internet is also possible.

[0045] In LTE terminology, a mobile terminal device corresponds to a user equipment UE (user equipment), which enables a user to access network services, wherein the user equipment is connected to the UTRAN or E-UTRAN via a radio interface. Usually, such user equipment corresponds to a smartphone. There are also user equipment installed in vehicles. For this purpose, Figure 1 As shown in FIG. 1 , the vehicle is equipped with an on-board connection unit 160 (OCU). The on-board connection unit 31 corresponds to an LTE communication module, using which the vehicle can receive mobile data and send mobile data.

[0046] The base stations are connected to the EPC 300 via a so-called S1 interface. The different interfaces of the LTE network architecture are standardized. In particular, reference is made to the different LTE specifications that are publicly available.

[0047] This mobile radio technology is standardized and reference is made here to the corresponding specifications of the mobile radio standards. As a modern example of a mobile radio standard reference is made to the 3GPP initiative and the LTE (Long Term Evolution) standard. Currently, many relevant ETSI specifications are provided in Release 14. As an example, the following examples are cited from Release 13: ETSI TS 136 213 V13.0.0 (2016-05); it is the further developed Universal Terrestrial Radio Access (E-UTRA); physical layer (3GPP TS 36.213 Version 13.0.0 Version 13).

[0048] LTE stands for high transmission rate and short response time. The transmission rate can be increased in LTE through better modulation methods, more flexible frequency use and larger channel bandwidth. According to the specification, LTE currently has a transmission rate of more than 300MBit / s in the downlink and more than 75MBit / s in the uplink in each 20MHz frequency band mathematically, and has a smaller overhead.

[0049] Figure 2 A block diagram of a motor vehicle electronic device is schematically shown and some subsystems or applications of an infotainment system are shown by way of example. The infotainment system comprises, among other components: a touch-sensitive display unit 20, a computing device 40, an input unit 50 and a memory 60. The display unit 20 comprises both a display surface for displaying variable graphical information and an operating surface (touch-sensitive layer) arranged on the display surface for inputting commands by a user.

[0050] The display unit 20 is connected to the computing device 40 via a data line 70. The data line can be designed according to the LVDS (Low Voltage Differential Signaling) standard. The display unit 20 receives control data for controlling the display surface of the touch screen 20 from the computing device 40 via the data line 70. The control data of the input command is also transmitted from the touch screen 20 to the computing device 40 via the data line 70. Reference numeral 50 denotes an input unit. Associated with this are the operating elements already mentioned, such as buttons, rotary regulators, slider regulators or rotary push regulators, with which the operator can make inputs through menu navigation. Input is usually understood as selecting a selected menu option, as well as changing parameters, turning functions on or off, etc.

[0051] The memory device 60 is connected to the computing device 40 via a data line 80. A pictogram catalogue and / or a symbol catalogue is stored in the memory 60 together with the pictograms and / or symbols for possible insertion of additional information.

[0052] Other components of the infotainment system, camera 150, radio 140, navigation device 130, telephone 120 and instrument cluster 110 are connected to the device for operating the infotainment system via data bus 100. As data bus 100, a high-speed solution of the CAN bus according to ISO standard 11898-2 can be considered. Alternatively, for example, a bus system based on Ethernet technology, such as IEEE802.03cg, can also be considered. A bus system that transmits data via optical fiber can also be used. As an example, MOST (Media Oriented System Transport) bus or D2B bus (Domestic Digital Bus) bus are listed. For internal and external wireless communication, the vehicle 10 is equipped with the communication module 160 mentioned above. The communication module can be designed for mobile radio communication, for example according to the LTE (Long Term Evolution) standard. Similarly, the communication module can be designed for WLAN (Wireless LAN) communication, which is used to communicate with the equipment of the occupants in the vehicle or for vehicle-to-vehicle communication via WLAN-p.

[0053] The communication bus 100 of the infotainment system is connected to the gateway 30. Other parts of the automotive electronic equipment are also connected to the gateway. On the one hand, the communication bus 104 of the powertrain is connected, which is usually implemented in the form of a CAN bus. As an example, the control devices of the powertrain are listed and shown: the engine control device 172, the ESP control device 174 and the transmission control device 176. In addition, the communication bus 102 for the driver assistance system is connected, which can be designed in the form of a FlexRay bus. Two driver assistance systems are shown here: a driver assistance system 182 for automatic distance control ACC (Adaptive Cruise Control), a driver assistance system DCC (Dynamic Chassis Control) for adaptive chassis control 184, and a LIDAR (Light Detection and Ranging) sensor 186. In addition, the communication bus 106 is also connected to the gateway 30. The communication bus connects the gateway 30 to the on-board diagnostic interface 190. The task of the gateway 30 is to perform format conversion for the different communication systems 100, 102, 104, 106 so that data can be exchanged with each other. In the embodiment shown, the driver assistance system DCC uses a highly accurate map of the environment for the driving task. In a first embodiment, the environment map can be pre-stored in the memory of the driver assistance system DCC. For this purpose, the environment map is usually loaded via the communication module 160, forwarded by the gateway 30 and written to the memory of the driver assistance system 184. In another variant, only a part of the environment map is loaded and written to the memory of the driver assistance system. This can achieve a smaller memory in the driver assistance system 184 and reduce costs.

[0054] Vehicles A and B are equipped with two environment sensor video cameras 150 and a LIDAR sensor 186. Usually, even a plurality of cameras 150 (front camera, rear camera, left camera, right camera) are installed in the vehicle 10. Therefore, an all-around view of the vehicle 10 can be generated by image processing. The LIDAR sensor 186 is usually installed in the front area of ​​the vehicle 10 and collects the environment in the driving direction of the vehicles A and B. In addition, ultrasonic sensors and RADAR sensors (radar sensors) can also be used.

[0055] The above-mentioned environmental sensors that can detect the vehicle environment are used at different distances and for different application purposes. The following descriptions of effective distances and uses apply roughly:

[0056] Stereo camera, effective range 500m, for collecting 3D maps for automatic emergency braking assistance, lane change assistance; for traffic sign recognition and adaptive cruise control.

[0057] Camera, effective range 100m, for collecting 3D maps for autonomous emergency braking assistance, lane change assistance; for traffic sign recognition, adaptive cruise control, forward collision warning, automatic lighting control and parking assistance.

[0058] Ultrasonic sensor, effective distance <10m, parking assistance.

[0059] Radar sensors with an effective range of 20cm to 100m are used for automatic emergency brake assist, automatic speed control, adaptive cruise control, blind spot assist, and cross-traffic alert.

[0060] LIDAR sensor, effective range 100m, used to collect 3D maps for automatic emergency braking assistance.

[0061] Figure 3 A diagram is shown which illustrates the process of estimating future transmission conditions for a planned car-to-car communication. There are various application scenarios in which such a planned car-to-car communication can be used. A prominent example is the so-called "platooning" application scenario.

[0062] More and more logistics companies are looking for technological solutions to save fuel and improve traffic safety in their truck operations. The travel of trucks (or vehicles in general) in a platoon offers the possibility of reducing fuel consumption by reducing the distance between vehicles traveling together in a platoon. There is a direct correlation between the distance between platoon vehicles and the reduction of fuel consumption and costs.

[0063] The challenge is to minimize the distance between vehicles in a platoon. This requires a distance control with an accurate and reliable reference for measuring the relative distance between two vehicles traveling one behind the other in a platoon. Here, a number of vehicles, or even many vehicles, are controlled in a coordinated manner with the help of a technical control system so that they can travel one after the other at a very small distance without affecting traffic safety. Especially in the case of trucks, this leads to a significant reduction in fuel consumption and a significant reduction in greenhouse gases. However, this application is particularly safety-critical. It must always be ensured that the vehicles in the platoon can stop. The braking process is coordinated so that all vehicles brake at the same time to avoid problems with cargo sliding or even collisions. Vehicle-to-vehicle communication is used for this purpose. Therefore, it is required that the vehicles can exchange safety-related data with high reliability. It is therefore proposed that the vehicle periodically estimates whether the transmission conditions are still sufficient for the planned application for a specific time period, such as a few seconds.

[0064] This is also important for other applications. For example, the examples of map updates or software updates are given as examples of further applications. For these applications, it is already clear when they are started that they will take a long time. It is also important to obtain an estimate of the transmission conditions in advance.

[0065] exist Figure 3 In the figure, the reference symbol FB indicates a road with two lanes in one direction. On road FB, two vehicles B and A are traveling one after the other in the right lane at a typical distance, for example 25 m. Vehicle-to-vehicle communication V2VBA is performed between the two vehicles for a cooperative driving process. This application can again refer to the example of a platoon, in which the vehicles cooperate with each other so that they travel one after the other with the smallest possible distance. Vehicles B and A exchange the following information with each other, at which speed they should travel respectively to reduce the distance. Here, the data exchange is carried out over a longer period of time, so it is necessary to estimate the transmission conditions in advance.

[0066] To this end, the vehicle B traveling ahead captures the environment in front of it. This can be done either with the video camera 150 or with the LIDAR sensor 186 or with a combination of both sensors. Both sensors provide image data, which are then analyzed using corresponding object recognition algorithms. These algorithms are assumed to be known to those skilled in the art. Figure 3 As shown in , the analysis shows that vehicles C and D are traveling ahead, and large buildings G1 and G2 are located on the left and right sides of the road in the driving section ahead. The collection of vehicles C and D traveling ahead and buildings G1 and G2 located ahead is Figure 3 Indicated by the reference symbol SEN. Vehicle B can also determine at what distance vehicles D and C are traveling one after the other. This can also be estimated by image processing, wherein known vehicle dimensions are set with respect to the determined distance between them. Mathematically, the ray theorem can be used as the basis for the calculation. On the other hand, the distance can also be measured by the vehicles C, D traveling in front and reported to the vehicle B behind via vehicle-to-vehicle communication. In the case of high traffic density, it usually occurs automatically that the vehicles in the convoy travel one after the other at a relatively similar distance. The transmission conditions in vehicle-to-vehicle communication depend to a large extent on the distance at which the vehicles communicating directly with each other travel one after the other. The actual transmission conditions are determined for each vehicle direct communication. For this purpose, there is a so-called "CSI report" in LTE.

[0067] CSI (Channel Status Information) stands for Channel Status Information. As the name implies, it is an indicator of the quality of the transmission channel at a specific time. CSI information basically consists of three main parts:

[0068] CQI (Channel Quality Indicator)

[0069] PMI (Precoding Matrix Index)

[0070] RI (Rank Indicator)

[0071] Not all of these metrics are measured for every CSI report. Depending on the situation and the configuration from the network, the participant stations perform different combinations of measurements. However, this is known and will not be described in detail here.

[0072] Now, if two vehicles C and D are traveling ahead of each other at a similar distance to the two vehicles B and A, then according to the method proposed here, CSI information is used for their vehicle-to-vehicle communication to improve the prediction of the transmission conditions of the two communicating vehicles B and A. The vehicle-to-vehicle communication between vehicles C and D is Figure 3 This is done as follows.

[0073] First, vehicle B determines the distance to the vehicle C traveling ahead. For this purpose, the same technique can be used as for determining the distance between vehicles C and D. Vehicle B knows its own driving speed. Then, based on the determined distance, the time t required to travel the distance S at its own speed v is determined according to the known formula S=v*t. This time t then corresponds to the predicted time point at which the transmission conditions will be predicted. However, it is not possible to simply take the measured values ​​from the vehicles C, D traveling ahead and use them as its own estimate. It is also necessary to use its own prediction of the future transmission conditions derived from the acquisition of the environment in a sensor-based manner. In principle, it is known how to make this estimate as accurately as possible. This method is largely based on environmental acquisition. The objects located ahead are determined. Here, these are two static buildings G1 and G2 and mobile objects C, D, E. Vehicle B now calculates in advance where it is located at the determined time point and whether it will be affected by signal reflections caused by the radiation emitted by itself or by the radiation from the vehicles around it. Here, the movement trajectory of the vehicles is used to estimate where they will be located in the environment of vehicle B later. For vehicle E, it is determined that it stops at the desired time point. Figure 3 The surrounding objects are divided into static objects and dynamic objects. The dynamic objects are then vehicles A, B, E. The static objects are buildings G1 and G2. Therefore, environmental modeling is performed. It is then calculated whether the risk of signal interference originates at least from the self-radiation of the signal of the vehicle-to-vehicle communication caused by reflections scattered back by the identified objects in the environment. If so, the transmission conditions are estimated accordingly more poorly. Possible reflections in Figure 3 At high frequencies in the 5 GHz range used for direct vehicle communication, rectilinear propagation can be assumed and a simple mirror law can be applied to calculate the reflection direction.

[0074] Finally, vehicles A and B can use this information to estimate whether their planned transmission will be interfered with by reflections of their own radiation at the expected time. Figure 3 In the position shown in , it can be concluded in the analysis that their reflections will no longer interfere with the own radiation. Thus, based on the sensor-based acquisition of the environment and the own analysis, an estimate of the future transmission conditions is obtained.

[0075] Further details on the techniques for sensor-based transmission condition prediction are provided by Roman Alieiev, Jiri Blumenstein, Roman Thorsten Hehn, Andreas Kwoczek and Thomas Kürner in 2017 in the open paper “Sensor-Based Predictive Communication for Highly Dynamic Multi-Hop Vehicular Networks” at the 25th European Signal Processing Conference (EUSIPCO).

[0076] However, it is proposed to additionally use the determined transmission conditions of a vehicle travelling ahead (which has already assumed the future position in advance) in order to further improve the accuracy of the estimation of the transmission conditions.

[0077] Figure 4 The block diagram in shows the interaction of the different components of the OCU unit (On-Board Connectivity Unit) 160, but these components are usually implemented as different program parts of suitable software or a combination of software and hardware. The OCU unit 160 is equipped with a correspondingly powerful computer unit.

[0078] Reference numeral 1610 denotes a prediction unit, which makes a prediction about the transmission condition based on its own sensor information. The working mode of the prediction unit has been explained in detail above.

[0079] Reference numeral 1620 denotes a communication unit responsible for communicating with the vehicles C, D traveling ahead. To this end, the communication unit 1620 obtains information about the identities of the communication partners C, D traveling ahead from the prediction unit 1610, which information is important for predicting the transmission conditions. The communication unit 1620 then forms a request message for vehicle D, which is sent to vehicle D via vehicle-to-vehicle communication via the sending / receiving unit 1640. Then, as already described above, vehicle D sends its CSI report on the communication with vehicle C. Vehicle D can send the report once, multiple times, or simply only periodically, without the need for a prior request. In a variant in which vehicle D periodically sends a CSI report, even the sending of a request message by vehicle B can be omitted.

[0080] After the information is present in vehicle B, the two independent information about the prediction of the transmission conditions are fused in the data fusion unit 1630. For this purpose, known data fusion techniques are used. As a result, the accuracy of the prediction of the transmission conditions for the planned communication between vehicles A and B is significantly improved again.

[0081] In a variant, before the step of performing the data fusion in the fusion unit, it is first checked whether the individual results of the transmission conditions estimated several times in succession by the prediction unit 1610 have converged. The same operation is performed for the transmission conditions transmitted several times in succession by the vehicle D. Then, data fusion is performed only when the measurement series have converged in both cases.

[0082] In any case, it is understandable why data fusion will lead to higher accuracy. The prediction made by the prediction unit 1610 also already informs the influence of vehicle E predicted at the prediction time point. This does not exist in the actual measurement of vehicle D. However, the actual relationship is measured for this and errors in environment modeling and motion estimation are avoided.

[0083] All examples and conditional statements mentioned herein should not be understood as limitations on such specifically mentioned embodiments. For example, those skilled in the art will recognize that the block diagrams shown herein represent conceptual diagrams of exemplary circuit arrangements. In a similar manner, it can also be recognized that the flowcharts, state transition diagrams, pseudocodes, etc. shown represent different variations for illustrating processes, which can be stored in computer-readable media and can therefore be executed by a computer or processor.

[0084] It should be understood that the proposed method and related devices can be implemented in different forms of hardware, software, firmware, special processors or combinations thereof. Special processors may include application-specific integrated circuits (ASICs), reduced instruction set computers (RISCs) and / or field programmable gate arrays (FPGAs). Preferably, the proposed method and device are implemented as a combination of hardware and software. Preferably, the software is installed as an application on a program storage device. Typically, this is a machine based on a computer platform having hardware, such as one or more central processing units (CPUs), random access memories (RAMs) and one or more input / output (I / O) interfaces. In addition, an operating system is typically installed on the computer platform. The different processes and functions described herein may be part of an application or may be part of an operating system.

[0085] The present disclosure is not limited to the embodiments described herein. There is still room for different adjustments and modifications, which those skilled in the art will also consider as belonging to the present disclosure based on their professional knowledge.

[0086] Reference numerals list

[0087] 20 Touch-sensitive display unit

[0088] 30 Gateway

[0089] 40 computing units

[0090] 50 Input Units

[0091] 60 storage units

[0092] 70 Data line to display unit

[0093] 80 Data lines of storage units

[0094] 90 Data line to input unit

[0095] 100 First data bus

[0096] 102 Second data bus

[0097] 104 Third data bus

[0098] 106 Fourth data bus

[0099] 110 Instrument cluster

[0100] 120 Phone

[0101] 130 Navigation equipment

[0102] 140 Radio

[0103] 150 Camera

[0104] 160 Communication module

[0105] 172 Engine control equipment

[0106] 174 ESP control equipment

[0107] 176 Transmission control equipment

[0108] 182 Spacing control equipment

[0109] 184 Chassis control equipment

[0110] 186 LIDAR Sensor

[0111] 190 On-board diagnostic interface

[0112] 200 Mobile radio base stations

[0113] 300 Evolved Packet Core

[0114] 400 Internet

[0115] 410 Backend Central Computer

[0116] 1610 Prediction Unit

[0117] 1620 CSI communication unit

[0118] 1630 Fusion Unit

[0119] 1640 Send / Receive Unit

[0120] AE Vehicle

[0121] G1, G2 Buildings

[0122] FB Road

[0123] V2VBA Car-to-Car Communication

[0124] V2VCD Vehicle-to-Vehicle Communication

[0125] CSI CSI communication

[0126] RFL signal reflection

[0127] SEN Object recognition via sensor acquisition

Claims

1. A method for predictively estimating transmission conditions for communication between two communication partners (B, A) moving along a road (FB), wherein the two communication partners (B, A) themselves each estimate the current transmission conditions, characterized in that The two communication partners (B, A) receive and use the estimated transmission conditions of a plurality of communication partners (C, D) travelling ahead to estimate the transmission conditions at a future point in time.

2. The method according to claim 1, wherein: At least one of the two communication partners (B, A) behind performs a step of acquiring the environment in a sensor-based manner, wherein the at least one communication partner at least determines, after acquiring the environment in a sensor-based manner, whether there are framework conditions for direct vehicle communication between the communication partner (C, D) traveling in front that are similar to the framework conditions for direct vehicle communication between the two communication partners (B, A).

3. The method according to claim 2, wherein: After the sensor-based acquisition of the surroundings, the at least one communication partner determines at least whether the communication partner (C, D) travelling ahead is moving ahead at a similar distance to the two communication partners behind.

4. The method according to claim 3, wherein: If it has been determined that the communication partner (C, D) traveling ahead is moving at least at a similar distance to the two communication partners (B, A) behind, the at least one communication partner requests at least one of the communication partners (C, D) traveling ahead in a request message to report the current transmission conditions estimated on his side to the two communication partners (B, A) behind.

5. The method according to claim 1 or 2, wherein: One of the communication partners (C, D) travelling ahead at least periodically reports estimated current transmission conditions to the two communication partners (B, A) following behind.

6. The method according to claim 2 or 3, wherein: After detecting the surroundings by means of sensors, the at least one communication partner determines at least the distance to the communication partner (C, D) travelling ahead and calculates therefrom the time at which the transmission condition is predicted.

7. The method according to claim 2 or 3, wherein: After detecting the environment using sensors, the at least one communication partner determines a possible signal scattering (RFL) problem and estimates future transmission conditions taking the signal scattering (RFL) into account.

8. The method according to claim 7, wherein: The at least one communication partner combines its own estimated transmission conditions with the transmission conditions reported by the preceding communication partner (C, D) to obtain a more accurate estimate of future transmission conditions.

9. The method according to claim 8, wherein: The at least one communication partner estimates the transmission conditions multiple times in succession and checks before the fusion step whether the future transmission conditions estimated multiple times by itself converge, and checks whether the communication conditions reported multiple times in succession by the communication partner (C, D) traveling ahead converge. If the estimation results converge in both cases, the fusion step is performed.

10. A device for executing the method steps of the method according to any of the preceding claims, wherein the device is equipped with at least one processor device, which is designed to execute one or more of the method steps of the method according to any of the preceding claims.

11. A vehicle, characterized in that: The vehicle has a device according to claim 10 .

12. A computer program product having a program code which, when processed in a processor device, executes the method steps according to any one of claims 1 to 9.

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