Longitudinal control of a motor vehicle using a driver assistance system

Abstract

The invention relates to a driver assistance system (3) for the longitudinal control of a vehicle (2). Swarm data (S) for a current or future road segment (11a, 11b, 11c) are acquired by the driver assistance system (2), and at least two different speed values ​​(v1, v2) are determined from the swarm data (S). The speed values ​​(v1, v2) result from predetermined parameters (K1, K2) of a statistical distribution (V) of the swarm speed (v) of a vehicle swarm for the road segment (11a, 11b, 11c). According to a selection criterion, one of the at least two speed values ​​(v1, v2) is selected depending on an environmental condition (U) and / or a vehicle value (F) of the vehicle (2) and subsequently used for longitudinal control.

Description

[0001] The invention relates to a method for operating a driver assistance system for longitudinal control of a vehicle. The invention also relates to a corresponding driver assistance system for longitudinal control of the vehicle and a vehicle with a corresponding driver assistance system. Finally, the invention relates to a system comprising a vehicle and a backend server, wherein the vehicle can retrieve data from the backend server for use by the driver assistance system for longitudinal control.

[0002] There are vehicles in which driver assistance systems are used to automatically guide or steer the vehicle. The driver assistance system takes over certain vehicle functions so that the associated driving maneuvers are carried out automatically, preferably without driver intervention. Such driver assistance systems can be divided into longitudinal control or longitudinal guidance and lateral control or lateral guidance systems. Longitudinal control driver assistance systems intervene in the longitudinal control of the vehicle, for example, cruise control. Longitudinal control maneuvers include, for example, acceleration or braking. Lateral control driver assistance systems intervene in lateral control, for example, steering the vehicle. Lateral control maneuvers include, for example, steering maneuvers such as changing lanes, overtaking, or turning.In vehicles, these driver assistance systems can be used alone or in combination.

[0003] To initiate the appropriate driving maneuvers, the driver assistance system observes the vehicle's surroundings, in particular, monitoring road traffic. The respective driving maneuver is implemented in response to this observation. Road traffic can be observed using on-board sensors, such as a camera. Additionally or alternatively, it is conceivable to perform environmental observation externally and provide the vehicle with the resulting observation data. Such observation data can, for example, be swarm data collected by a swarm of multiple swarm vehicles. Various possibilities for using such swarm data to operate a driver assistance system are known from the prior art.

[0004] For example, WO 2022 / 207045 A1 discloses a method for warning a following vehicle of an obstacle on a defined road section. For this purpose, various swarm trajectories with different average vehicle speeds are determined, and a speed difference is calculated. If the speed difference is greater than a threshold value, this is interpreted as an indication of an obstacle on the road, and a warning signal is provided to a following vehicle.

[0005] DE 10 2021 201 063 A1 discloses a method for operating a driver assistance system of a motor vehicle. For a future road section, an average speed of motor vehicles that have already traveled through the road section is calculated from swarm data. The current vehicle speed of a motor vehicle is then compared with the average speed, and a driving maneuver for the motor vehicle is suggested, for example. A driving maneuver can consist of a lane change, for example.

[0006] US 2019 / 0 295 412 A1 discloses a method for controlling vehicle maneuvers based on a speed distribution of vehicle speeds in a vehicle network. Statistical values ​​for the vehicle speed are determined from the speed distribution in order to implement the vehicle maneuver.

[0007] DE 10 2019 217 429 A1 describes a method for supporting the longitudinal control of a vehicle using metadata and a method for making such metadata available. The metadata is generated based on an evaluation of swarm data, with the swarm data originating from a large number of vehicles that have traveled the same section of road. The metadata is transmitted to the vehicle to support longitudinal control when traveling the same section of road.

[0008] Longitudinal control systems are also known from US 2011 / 0 301 802 A1 and US 2020 / 0 050 209 A1, in which speed data of several preceding vehicles in a swarm are statistically evaluated and taken into account when determining a target speed of the ego vehicle.

[0009] It is the object of the present invention to operate a driver assistance system for longitudinal control in such a way that the usability of swarm data is better adapted to the respective driving section that a vehicle is currently traveling through.

[0010] The problem is solved by the subject matter of the independent patent claims. Advantageous developments of the invention are disclosed by the dependent patent claims, the description, and the figures.

[0011] The invention is based on the finding that the swarm data can be better adapted to the respective road section if, in addition to the information from the swarm data, environmental conditions and / or vehicle values ​​are taken into account. This makes it possible to select a situation-adapted vehicle speed for the vehicle to drive through the road section using longitudinal control from the swarm data. For example, weather conditions or the course of the road can be taken into account, to name just a few examples. Swarm data alone no longer needs to be used for longitudinal control. Instead, additional data from the environment or from the vehicle itself can be used, allowing more targeted speed information to be extracted from the swarm data.

[0012] To this end, the invention proposes a method for operating a driver assistance system for longitudinal control of a vehicle. In the method, swarm data of a vehicle swarm comprising at least two swarm vehicles is acquired from map data for a current or future road section. At least two different speed values ​​are determined, i.e., extracted or acquired, from the swarm data. This means that the speed values ​​differ from one another. For example, one speed value can be higher or lower than the other speed value. The speed values ​​are each specified from a predetermined characteristic of a statistical distribution of a swarm speed of the vehicle swarm for the current road section.This means that the speed values ​​can be calculated or determined from the parameters or key figures of the swarm speed distribution specified for the current road section. Each parameter thus specifies a speed value.

[0013] The method also detects at least one environmental condition for the respective (i.e., current or future) road section and / or at least one vehicle value of the vehicle. Finally, according to a selection criterion, one of the at least two speed values ​​is selected depending on the respective environmental condition and / or the respective vehicle value and used for longitudinal control. The selected speed value can be used as a control parameter for setting a target speed or target acceleration for the vehicle's speed control. In other words, a selection is made between the two or more speed values, of which exactly one, namely the one selected according to the selection criterion, is then used or employed as a control parameter for the longitudinal control. When the vehicle is driving, its speed (or acceleration) can thus be increased or reduced.This means that the road section can be driven through or passed at increased or reduced speed.

[0014] The described method offers the advantage that the control system does not need to be adapted to the average driving style of a swarm of vehicles. In particular, individual adjustment based on driving situations or the needs of the respective driver can be implemented for the selection of the swarm speed as the vehicle speed. This allows the use of the swarm data to be better adapted to the respective driving section or driving situation.

[0015] The current road section refers to a section the vehicle is currently driving through or passing. This means that longitudinal control can intervene in the current driving situation. The future road section refers to a section the vehicle will drive through or pass in the future. The future road section can be determined, for example, based on the vehicle's current position and the destination it is pursuing. This means that longitudinal control can be set or adjusted for an upcoming driving situation.

[0016] A driver assistance system for longitudinal control (longitudinal control driver assistance system) can be understood as an electronic vehicle guidance system. The driver assistance system is designed to guide the vehicle, preferably fully automatically or fully autonomously, in particular without requiring driver intervention in the control system. When the driver assistance system is used, the vehicle automatically performs all required functions, in particular driving maneuvers, the observation and detection of road traffic, and appropriate reactions thereto. In a longitudinal control system, the driver maneuvers particularly relate to vehicle movements in the longitudinal direction, i.e., in particular, in the direction of travel of the vehicle. These include, for example, braking and / or acceleration maneuvers.

[0017] To initiate or execute the respective driving maneuver, the driver assistance system can control one or more of the vehicle's actuators. For longitudinal guidance, for example, a respective brake actuator and / or a drive motor of the vehicle can be controlled. The driver assistance system can be fully automated or partially automated. The degree of automation can be implemented, in particular, by classification according to the SAE J3016 standard.

[0018] The driver assistance system can retrieve or extract the swarm data for the relevant road section from the map data. The road section can be a geographically limited area of ​​a roadway. The area can be limited, for example, to a few square meters, especially 1 to 10 m. 2, be limited. Alternatively, the road section may, for example, refer to an exact geographical position. The geographical position may be specified by geographical coordinates.

[0019] The map data can be in the form of a high-resolution (HD) map, for example. This means that the map data provides a digital representation of the road environment for the respective roadway section. The map data can be in the form of a roadway network with a plurality of (linked or connected) roadway sections. For each roadway section, the map data includes environmental data containing information about at least partially permanent objects or components in the environment. These objects can include, for example, roads, road markings, buildings, traffic infrastructure such as traffic lights, and / or parked vehicles.

[0020] In addition, the map data can contain vehicle data or driving data. Driving data contains information about vehicles that are driving through or have driven on the roadway sections of the road network. Vehicle data can include, for example, a trajectory or swarm trajectory. The trajectory can be understood as the curve or path along which the respective vehicle was guided or has traveled along the roadway section. In addition to the direction of travel and the route, the trajectory can also contain the vehicle speed.

[0021] The environmental data and / or vehicle data can be collected or recorded by the vehicles. Such vehicles are referred to as swarm vehicles. Several swarm vehicles form a vehicle swarm or a vehicle fleet. The swarm vehicles can transmit the collected data to a suitable collection point, such as a backend server. There, it can be compiled and stored as a dataset, forming, for example, the HD map. Because the data is provided by the vehicle swarm, it is also referred to as swarm data.

[0022] The total or total swarm speeds provided by the swarm vehicles for each section can be stored in the backend server in the form of a statistical distribution. Each road section can thus be assigned a distribution function or curve of the swarm speeds. The distribution function provides a model of the swarm speeds of all swarm vehicles. For example, the distribution function can be used to display how many swarm vehicles traveled through the respective section and at what speed. In summary, the HD map thus includes, for example, the swarm data including a model of the swarm speeds.

[0023] Preferably, the swarm data stored in the backend server is continuously updated. This means that the swarm speed distribution, especially the swarm speeds contained therein, can be adjusted. The swarm data is updated, for example, when additional swarm vehicles pass the respective road section and / or certain swarm speed values ​​are rejected as implausible.

[0024] For longitudinal control, the vehicle can retrieve and use the swarm data from the backend server. In this case, only the speed values ​​for specific swarm speeds are taken into account from the swarm data. To select the speed values, the respective parameter is applied to the swarm speed distribution. The parameter is preferably a statistical parameter. Percentiles, for example, are suitable parameters. To determine the speed values, predetermined percentiles can be defined, and the swarm speed values ​​corresponding to the percentiles can be read from the swarm speed distribution.

[0025] To detect the environmental condition(s), the vehicle includes, for example, an environmental detection device. The environmental detection device can, for example, have one or more vehicle sensors for environmental observation or detection. The sensor can be a camera, a radar, and / or an ultrasonic sensor. Of course, other types of sensors can also be used additionally or alternatively. The respective sensor can detect a representation or image of the environment and transmit this, for example in the form of sensor data, to the driver assistance system for evaluation. The driver assistance system can evaluate or process the sensor data, which can also be referred to as environmental data. To this end, the driver assistance system can, for example, implement a suitable algorithm, such as an object recognition algorithm or image analysis algorithm, and apply it to the sensor data.As a result, this algorithm returns the respective environmental condition. The environmental condition can be an environmental state or an object state of an object in the environment. An example of an environmental condition is a road class. Further examples of environmental conditions are described in more detail later.

[0026] To record the vehicle value, the vehicle can comprise at least one or more vehicle sensors. The respective vehicle sensor can measure or record the respective vehicle value(s). The vehicle value can, for example, indicate a vehicle condition or a vehicle setting. An example of a vehicle value is a friction coefficient, which indicates the friction between the vehicle tires and the road surface. Further examples of vehicle values ​​are described in more detail later. The vehicle sensor can transmit or transfer the respective vehicle value to the driver assistance system for further processing.

[0027] The driver assistance system records or receives the respective ambient conditions and the respective vehicle value and uses them to select or use exactly one speed value from the speed values ​​available in the swarm data. The selection is made according to the selection criterion. To do this, the driver assistance system applies the selection criterion to the speed values. This means, for example, that the speed values ​​are checked and one of them is selected while the others are discarded. The selected speed value is used for longitudinal control.

[0028] The described method is preferably repeated for an entire route while the vehicle is traveling. Additionally or alternatively, the method can be repeated for each section at least as long as the longitudinal control for the vehicle is active during the journey.

[0029] The described method is preferably implemented as a computer-implemented method. Unless otherwise stated, all steps of the method can thus be performed by a data processing device of the driver assistance system. In particular, the data processing device is configured or adapted to perform the steps of the method. For this purpose, the data processing device can, for example, store a computer program or program code containing instructions that, when executed by the data processing device, cause the data processing device to execute the computer-implemented method.

[0030] The invention includes embodiments which provide additional advantages.

[0031] According to one embodiment, the 50th and 60th percentiles of the statistical distribution of the swarm speed are provided as the respective characteristic for the at least two speed values. For example, if more than two speed values ​​are available for selection, additional percentiles, for example the 40th, 70th, 55th, or 65th percentile of the swarm speed distribution, can be used in addition to the 50th and 60th percentiles.

[0032] Alternatively, a mean and a predetermined deviation from the mean can be used as parameters. Alternatively, a minimum or maximum value of a swarm velocity dispersion measure can be used as a parameter. Suitable dispersion measures include, for example, the standard deviation or variance, a range, or a quartile range of the swarm velocities in the swarm velocity distribution.

[0033] According to one embodiment, a road class or road category and / or a road surface and / or an ambient temperature and / or a weather condition and / or a time of day for the current road section is taken into account as the respective environmental condition. This means that the condition of the vehicle's surroundings can be used for the evaluation or selection of the speed values.

[0034] The road class can, for example, be used to distinguish between the type or category of road to which the road section is assigned. A road class can, for example, mean a motorway, federal highway, country road, city road, a road in a residential area, a field track, or another type of road class. Each road class is assigned different specifications for driving behavior or road conditions. By taking the road classes into account, for example, the speed selection can be based on a particularly narrow or wide roadway or on a road surface that is easy or difficult to drive on. Preferably, for narrow roadways and / or poor road surfaces (e.g. country roads, city roads, field tracks, residential areas), the reduced speed value is selected, for example, according to the 50th percentile. For wide roadways and / or good road surfaces (e.g.For example, on motorways and federal highways, the higher speed value can be selected according to the 60th percentile.

[0035] The road surface can, for example, indicate a curvature or curve value for the respective road section. The curvature value indicates, for example, whether the road section is straight or has one or more curves. Thus, the geometric shape of the road section can be taken into account for the speed assessment. For a curved road, the reduced speed value can be selected based on the 50th percentile, for example, while for straight roads, the higher speed value can be selected based on the 60th percentile, for example.

[0036] The ambient temperature, for example, can be used to estimate the vehicle's grip on the ground. Particularly at low temperatures below 4°C, grip may be reduced. This can, under certain conditions, cause the vehicle to swerve or slide. At low temperatures (e.g., below 4°C), the reduced speed value can be selected based on the 50th percentile, for example, while at higher temperatures (e.g., 4°C or more), the higher speed value can be selected based on the 60th percentile, for example. To detect the temperature, the ambient detection device can comprise a temperature sensor, for example.

[0037] The weather condition can, for example, be a weather condition in the area surrounding the vehicle. A distinction can be made between snow, rain and / or sunshine, for example. The weather condition can also have a negative impact on the vehicle's roadholding. Furthermore, the weather condition can have a negative impact on the visibility or field of vision of the environment detection device and / or the driver. For example, the sun can dazzle the driver or the driver assistance system depending on the position of the sun, or snow and rain can reduce the detectability of objects in the surroundings. In the event of rain or snowfall or a dazzling position of the sun, the reduced speed value can be selected according to the 50th percentile, for example; otherwise, the higher speed value can be selected according to the 60th percentile, for example.To detect the weather condition, the environment detection device may comprise, for example, a weather sensor, for example a rain sensor or a brightness sensor.

[0038] The time of day refers specifically to ambient brightness. For example, a distinction should be made between day and night. The time of day can negatively impact the visibility of the environment detection device and / or the driver. At night, the reduced speed value can be selected based on the 50th percentile, for example; during the day, the higher speed value can be selected based on the 60th percentile, for example. To determine the time of day, a brightness value in the environment can be detected. For this purpose, the environment detection device can comprise a brightness sensor, for example.

[0039] According to one embodiment, a vehicle driving profile and / or a grip of the vehicle to the driving surface for the respective road section is taken into account as the respective vehicle value.

[0040] The vehicle driving profile can, for example, be a driving mode. The driver can set the driving mode themselves, for example, or it can be set by the driver assistance system itself based on the driving behavior. A distinction can be made between an Eco mode, a Comfort mode, and a Sport mode. Depending on the vehicle driving profile, different vehicle settings are specified for the vehicle, such as the suspension of the shock absorbers or a maximum acceleration or maximum speed. The selected speed value can therefore be tailored to the driver's desired driving behavior. For example, the speed value for the 50th

[0041] The 60th percentile can be selected if the driving mode is set to Eco or Comfort mode. Alternatively, the speed value for the 60th percentile can be selected if the driving mode is set to Sport mode.

[0042] To determine the vehicle's grip on the road surface, a coefficient of friction between the vehicle tires and the road surface can be determined. For this purpose, a tire sensor on the tire, which is assigned to an ESP control unit, can be used, for example. Using the tire sensor, for example, propulsion during acceleration or braking of the vehicle can be measured, and this can determine the coefficient of friction, i.e. the grip on the road surface. Preferably, for example, the 50th percentile is selected for the speed value if the coefficient of friction exceeds a predetermined threshold. The threshold can, for example, be a value at which an increased risk of slipping on the road surface can be assumed (low grip). Alternatively, the 60th percentile is used for the speed value, for example, if the coefficient of friction exceeds the threshold (high grip).

[0043] According to the selection criterion, the respectively recorded environmental condition and / or the respectively recorded vehicle value is assigned to one of at least two predefined categories. Each of the at least two categories is assigned exactly one of the at least two speed values ​​to be used for longitudinal control. Depending on the assigned category, the respective speed value assigned or assigned to the category is then selected. In other words, the recorded conditions or values ​​are categorized. One category, for example, includes all environmental conditions and / or vehicle values ​​for which the speed value is to be applied, for example according to the 60th percentile. Another category, for example, includes all environmental conditions and / or vehicle values ​​for which the speed value is to be applied, for example according to the 50th percentile.Categorization can be implemented, for example, using an assignment table or look-up table. This makes selecting speed values ​​particularly easy.

[0044] According to one embodiment, the selected speed value is output to the driver as a suggestion. The selected speed value is only used for longitudinal control if a driver input is detected that contains or indicates confirmation of the selected speed value. This means that longitudinal control is only switched to the selected speed value if the driver confirms the speed value through the input. Otherwise, a predefined default value, for example, always the 50th percentile, is selected or used as the speed value.

[0045] For outputting information, the vehicle may include a suitable output device, such as a display or a screen. Preferably, the output device may include an input interface with which the driver input can be recorded. The display may thus be configured as a touchscreen. Alternatively, an input device separate from the output device may be provided on the motor vehicle for recording the input. This may, for example, be a push-button switch or a rotary switch.

[0046] As an alternative to driver selection, the respective speed value can be selected automatically by the driver assistance system. This means that the driver assistance system decides independently or automatically based on the selection criteria which speed values ​​are used for longitudinal control.

[0047] According to one embodiment, a difference between the at least two speed values ​​is determined. The one of the at least two speed values ​​with the higher numerical value is used or selected for longitudinal control only if the difference falls below a predetermined limit.

[0048] This allows a plausibility check of the speed values ​​for longitudinal control to be performed. A high difference may indicate a high dispersion of the speed values ​​in the swarm speed distribution. In this case, no relevant or significant statement can be made about reasonable speeds for the section. This can happen, for example, if only a few swarm vehicles have already driven through the road section.

[0049] If the plausibility check shows that the selected speed value is rejected, for example, the other of the at least two speed values ​​or a lowest of the speed values ​​or another specified standard value can be used for the longitudinal control.

[0050] For use cases or application situations that may arise during the method and which are not explicitly described here, it may be provided that, in accordance with the method, an error message and / or a request to enter user feedback is issued and / or a default setting and / or a predetermined initial state is set.

[0051] According to one aspect, the invention also relates to a driver assistance system for longitudinal control of a vehicle. The driver assistance system is designed to acquire swarm data of a vehicle swarm of at least two vehicles from map data for a current and / or future road section. Furthermore, the driver assistance system is designed to determine at least two different speed values ​​from the swarm data, wherein the speed values ​​each indicate a predetermined characteristic of a statistical distribution of the swarm speed of a vehicle swarm for the current road section. The driver assistance system is also designed to acquire at least one environmental condition for the current road section or at least one vehicle value of the vehicle.According to a selection criterion, the driver assistance system is designed to select one of the at least two speed values ​​depending on the respective ambient conditions among the respective vehicle value and to use it for the longitudinal control.

[0052] The driver assistance system preferably carries out the method according to the invention. For this purpose, the driver assistance system can have a data processing device or control device. The control device can have a processor device that is configured to carry out an embodiment of the method according to the invention. For this purpose, the processor device can have at least one microprocessor and / or at least one microcontroller and / or at least one FPGA (Field Programmable Gate Array) and / or at least one DSP (Digital Signal Processor). In particular, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit) or an NPU (Neural Processing Unit) can be used as the microprocessor. Furthermore, the processor device can have program code that is configured to carry out the embodiment of the method according to the invention when executed by the processor device.The program code can be stored in a data memory of the processor device. The processor device can, for example, be based on at least one circuit board and / or on at least one SoC (System on Chip).

[0053] According to one aspect, the invention also relates to a vehicle with a driver assistance system for longitudinal control, as described above by way of example. The vehicle is preferably designed as a motor vehicle, in particular as a car. It is preferably a passenger car, a truck, a passenger bus, or a motorcycle.

[0054] According to one aspect, the invention also relates to a system comprising a vehicle as described above and a vehicle-external backend server. The backend server is configured to store the map data containing the swarm data with the at least two speed values ​​for the respective road section. The vehicle is configured to retrieve or download the map data from the backend server. For this purpose, the backend server and the vehicle can be connected to one another via a communication link. The vehicle and the backend server can each have suitable communication modules for this purpose, such as radio modules, in particular mobile radio modules.

[0055] Preferably, as described above, the backend server collects the environmental data and vehicle data of the swarm vehicles, and in particular, processes the swarm data and / or map data. In particular, the backend server defines a mathematical model for determining the parameters or specifies the parameters for determining the speed values. Speed ​​values ​​are thus preferably calculated in the backend server and provided to the vehicle as part of the swarm data.

[0056] The backend server can, for example, be an internet-based backend service. The backend server can be configured as a cloud server or an edge cloud server.

[0057] The invention also includes further developments of the driver assistance system according to the invention, the vehicle according to the invention, and / or the system according to the invention that have features as already described in connection with the further developments of the method according to the invention. For this reason, the corresponding further developments of the driver assistance system according to the invention, the vehicle according to the invention, and / or the system according to the invention are not described again here.

[0058] The invention also encompasses combinations of the features of the described embodiments. The invention therefore also encompasses implementations that each comprise a combination of the features of several of the described embodiments, unless the embodiments are described as mutually exclusive.

[0059] Exemplary embodiments of the invention are described below. Shown are: Fig. 1 a schematic representation of a system with a vehicle and a backend server; Fig. 2 a schematic diagram of a distribution function that can be stored in the backend server in the form of map data and Fig. 3 a schematic process flow diagram for a method for operating a driver assistance system of the vehicle.

[0060] The exemplary embodiments explained below are preferred embodiments of the invention. In the exemplary embodiments, the described components of the embodiments each represent individual features of the invention that can be considered independently of one another, each of which also develops the invention independently of one another. Therefore, the disclosure is intended to encompass combinations of the features of the embodiments other than those shown. Furthermore, the described embodiments can also be supplemented by further features of the invention already described.

[0061] In the figures, the same reference symbols designate elements with the same function.

[0062] Fig. 1 shows a schematic representation of a system 1 with a vehicle 2 and a vehicle-external backend server 6. Fig. Figure 1 shows vehicle 2 from a bird's-eye or top-down view while driving on a roadway 10. Roadway 10 is, for example, a lane on a country road. Roadway 10 is divided into individual roadway sections 11a, 11b, and 11c. Exactly three such roadway sections are shown here. Fig. Figure 1 shows vehicle 2, which is currently located in the first lane section 11a (current lane section). Lane sections 11b and 11c lie in front of vehicle 2 on a route s in the direction of travel F. Vehicle 2 will pass or drive through these lane sections 11b, 11c as it continues its journey, i.e., in the future (future lane sections).

[0063] The Fig. The vehicle 2 shown in Figure 1 is intended to be guided or operated automatically along the route s. For this purpose, the vehicle 2 comprises a driver assistance system 3. The driver assistance system 3 is preferably designed as a longitudinally guiding or longitudinally regulating driver assistance system. For this purpose, the driver assistance system can implement a longitudinal control module. The longitudinal control module can be implemented, for example, as a predictive ACC (ACC: Adaptive Cruise Control). By means of the ACC, in particular, a speed of the vehicle 2 can be automatically adjusted. For this purpose, for example, a drive and / or a brake of the vehicle 2 can be controlled in order to carry out braking or acceleration maneuvers.

[0064] In addition to longitudinal control, the driver assistance system 3 can, for example, implement lateral guidance or lateral control. For this purpose, the driver assistance system can implement a lateral control module, such as a so-called Travel Assist. With Travel Assist, the steering of the vehicle 2, i.e., movement transverse to the direction of travel F, can be controlled automatically.

[0065] In order to implement the respective assistance function (lateral control, longitudinal control), the driver assistance system 3 uses, for example, swarm data D. A swarm, in this case in particular a large number of vehicles, which are also called swarm vehicles, which together form a vehicle swarm. The swarm vehicles record environmental data in an environment, in particular while driving, as well as their own vehicle data for each road section they drive through. This data can be collected and compiled, for example, in the form of map data K. The map data K can be in the form of a high-resolution map (HD map), for example. The HD map provides a digital representation of the environment and the vehicle characteristics of each swarm vehicle for each road section that one of the swarm vehicles has passed through and driven on.

[0066] Each vehicle that is part of the vehicle swarm can access this HD map, in particular the swarm data it contains, for the respective road section 11a, 11b, 11c that it is currently and / or will be driving through in the future.

[0067] In the system according to Fig. 1, the map data K can be stored in the backend server 6. The backend server 6 is, for example, a cloud server or edge cloud server that can establish or enter a communication connection with the vehicle 2. The swarm data D can be transmitted from the backend server 6 to the vehicle 2 via the communication connection, for example, a radio connection, for implementing the respective assistance function.

[0068] As part of the vehicle data, the swarm data can contain, for example, a swarm trajectory and a swarm speed v. Travel Assist can use the swarm trajectory 12 from the swarm data S to implement the steering function. The swarm trajectory can, for example, be used to improve the stability and availability of the function. The ACC can use the swarm speed from the swarm data for cruise control. This allows longitudinal control to be made more human-like and comfortable.

[0069] For each road section, there are usually a large number of swarm vehicles providing their respective speeds in the swarm data S. For longitudinal control, it is necessary to select a suitable one of the swarm speeds v. In previous driver assistance systems 3, only the 50th percentile from the distribution of the swarm speed for each road section 11a, 11b, 11c is transmitted to the vehicle 2 as the driving speed in the swarm data. Although this enables longitudinal control according to the average driving style of the vehicle swarm, individual adaptation based on a driving situation or the needs of the respective driver is not possible.

[0070] Therefore, the present driver assistance system 3 utilizes an option to expand the transmitted swarm data by an additional percentile as a downward-compatible protocol adaptation and data-saving addition to the current implementation. The processing and selection of the two transmitted percentiles for the swarm speed for individual control of the driving speed for vehicle 2 is based on the driving situation and depends on the driver's request. The percentile forms a statistical parameter K1, K2, which can be applied to the set Ma, Mb, Mc of the swarm speeds v present in the swarm data S in order to determine speed values ​​v1, v2 for the driving speed of vehicle 2 for the respective road section 11a, 11b, 11c.

[0071] In order to take the driving situation and / or the driver's wishes into account, the vehicle records one or more environmental conditions U for the respective road section 11a, 11b, 11c and, additionally or alternatively, one or more vehicle values ​​F. Examples of an environmental condition U include, for example, a road class and / or an ambient temperature. Other environmental conditions U can be, for example, a road surface, a weather condition, and / or a time of day. Examples of the vehicle value given here include, for example, a vehicle driving profile P and a coefficient of friction R. The coefficient of friction R describes, in particular, the grip of the vehicle 2 on the driving surface of the roadway 10.

[0072] To detect the surroundings, the vehicle comprises an environment detection device with one or more sensors. In the present case, such a sensor is shown as, for example, an environment camera 4, which is designed as a front camera and records or records the surroundings in the direction of travel F in front of the vehicle 2. The environment camera 4 can transfer or transmit the resulting image data to the driver assistance system 3 for evaluation. The driver assistance system 3 can, for example, apply an image analysis algorithm to the image data and thus, for example, extract the respective environmental conditions from the image data. Another sensor of the environment detection device can be, for example, a temperature sensor, with which the ambient temperature T can be detected. Alternatively, it is possible, for example, for the vehicle 2 to retrieve or receive the ambient temperature from a weather service.

[0073] For detecting or recording the respective vehicle value, the vehicle 2 comprises a sensor device 5. The sensor device can comprise one or more sensors. A sensor can, for example, be a tire sensor. With the tire sensor, propulsion can be measured, for example, when braking or accelerating the vehicle 2. The measurement result can, for example, be output to an ESP control unit of the sensor device 5, which can use this to determine the coefficient of friction R for the friction and thus the adhesion to the ground. Another sensor of the sensor device 5 can, for example, be a driving profile sensor, with which the vehicle driving profile P can be determined. The vehicle driving profile P can, for example, be set by the driver of the vehicle 2 themselves. In particular, in the present case, a distinction can be made between three driving profiles or driving modes, namely an Eco mode, a Comfort mode, and a Sport mode.The modes differ, for example, in how aggressively certain driving maneuvers are executed and in their respective fuel consumption. Different suspension settings can also be provided for each mode.

[0074] In the embodiment according to Fig. Figure 1 now shows that a driving speed with a first speed value v1 is to be selected for road sections 11a and 11b. The speed value v1 can, for example, be determined from the 60th percentile of the set or distribution in Ma, Mb of the swarm speeds v for road sections 11a and 11b.

[0075] In the curve contained in road section 11c, the vehicle speed should preferably be reduced to a second speed value v2. For this purpose, the 50th percentile is used as the respective parameter K2 instead of the 60th percentile of the swarm speed distribution.

[0076] Fig. Figure 2 shows an example schematic diagram of a distribution function that specifies the set Ma, Mb, Mc of all swarm speeds v, i.e., the distribution of the swarm speeds v for the respective route section 11a, 11b, 11c. The distribution function is an example model that can be generated in the backend server 6 to determine or set the vehicle speed. The distribution function V can, for example, be contained in the map data K. Fig. Figure 2 shows the distribution function V in a three-dimensional xyz diagram. The x-axis represents the swarm velocity v. The y-axis represents the number n of swarm vehicles. The z-axis indicates the distance s traveled by vehicle 2.

[0077] For each section 11a, 11b, 11c, the diagram now shows the statistical distribution of the swarm speeds of all swarm vehicles. Furthermore, the 50th percentile, here K2, and the 60th percentile, here K1, are plotted as parameters for determining the speed values ​​v1, v2. The 50th percentile forms the median of the respective distribution curve and indicates that 50% of the swarm vehicles drove slower and 50% of the swarm vehicles drove faster than the speed value v2 assigned to the 50th percentile. The 60th percentile indicates that 60% of the swarm vehicles drove slower and 40% of the swarm vehicles drove faster than the assigned speed value v1. Along the section s, the diagram shows Fig. 2 also shows the curve of the speed values ​​v1, v2 as a function of the respective parameter K1, K2. The speed value v2 is reduced compared to the speed value v1.

[0078] Based on Fig. 3, a method can be described in more detail by way of example, which can be carried out for operating the driver assistance system 3, in particular for the longitudinal control. Fig. 3 shows a corresponding process flow diagram 7 with individual process steps that the longitudinal driver assistance system 3 can perform. Various input parameters E are provided to the driver assistance system 3, and the driver assistance system 3 provides, for example, a target speed as output parameter A, which can be transferred to a control loop for the longitudinal control as a control parameter.

[0079] As input parameters E, the driver assistance system 3 detects in particular the first and second speed values ​​v1, v2 as well as the respective ambient condition U and the respective vehicle value F. In the exemplary embodiment Fig. 3, for example, the road class and the route s as well as the ambient temperature T are transferred as environmental conditions U. The vehicle driving profile P and the friction coefficient R are transferred as vehicle values ​​F, for example.

[0080] In the process, the driver assistance system first checks the temperature T and the friction coefficient R. In a step S1, it is checked whether the ambient temperature T is less than 4°C or the friction coefficient is less than a specified friction limit value R GThe limit values ​​for temperature T and friction coefficient R are chosen in such a way that it can be assumed that if the respective limit value is undershot, there will be reduced or impaired grip between the tyres and the road surface.

[0081] If the verification result according to step S1 is positive (T < 4°C or R < R G ), the process continues in step S2. In step S2, the second parameter K2, i.e., the 50th percentile, is used for speed selection. Thus, the second speed value v2 is used as the target speed for longitudinal control.

[0082] In step S3 it is checked whether the temperature T is greater than 4°C or 4°C and at the same time the friction coefficient R is greater than the limit friction coefficient R G If this comparison result is positive (T ≥ 4°C and R > R G ), the method continues in a step S4 or S6.

[0083] In steps S4 and S6, the course of roadway 10 is checked. If, for example, it turns out in step S4 that the roadway is curved, i.e., has one or more bends with a predetermined steepness, it can be assumed that a slower speed is advisable for driving through the respective road section 11a, 11b, 11c. The route can be identified by analyzing the image data. For example, a curvature value of the respective road section 11a, 11b, 11c can be determined and compared with a predetermined limit value. The limit value provides a measure of the steepness of the curve. If the route is too steeply curved, the method continues either in step S5 or in step S7.

[0084] In steps S5 and S7, the vehicle driving profile P, in particular the selected driving mode, must be checked. If step S5 determines that the vehicle driving profile P is set to Comfort or Eco mode, for example, rather than Sport mode, it can be assumed that the driver intends to drive the winding road particularly smoothly. Therefore, starting from step S5, step S2 can be executed again, according to which the parameter K2, i.e. the 50th percentile, is used to determine the speed value. Accordingly, the speed value v2 is selected as the speed value and used as the target speed for longitudinal control.

[0085] If, however, it turns out in step S7 that Sport mode is active as the vehicle driving profile P, it can be assumed that the driver prefers aggressive driving through the winding section of the road. The method then continues with step S8.

[0086] In step S8, the parameter K1, i.e., the 60th percentile, is used to determine the vehicle speed. Accordingly, the first speed value v1 results as the vehicle speed. The first speed value v1 is thus used as the target speed for longitudinal control.

[0087] However, if after step S3 it turns out in step S6 that the route is essentially straight, it can be assumed that no abrupt braking or acceleration maneuvers are necessary to travel through the section of road. The straight section can therefore be traveled without significant changes in speed, without, for example, the vehicle driving profile P having to be taken into account. The method can then continue directly in step S8. As previously described, the parameter K1 and thus the first speed value v1 are again used as the setpoint speed or target speed for the longitudinal control. As an alternative to the setpoint speed, a setpoint acceleration can of course also be output to the longitudinal control as a control parameter.

[0088] In summary, the following architecture can be implemented as an example: First, swarm data S is generated via a fleet of uploaded vehicles. In backend 6, a model of the swarm speed is then generated (distribution function V) and an additional percentile (parameter K1) is generated for the standard 50th percentile (parameter K2) and integrated into the HD map, i.e., the map data K. The additional percentile is transmitted to vehicle 2 together with the standard percentile according to a predetermined transmission protocol. The additional percentile is transmitted in a data-efficient manner and with downward compatibility to the previous transmission protocol for the standard percentile. This is therefore an extension of the current protocol variant. The additionally transmitted percentile is now used in the front radar, i.e., the ACC, for individual longitudinal control.The driver can choose between different driving profiles. The optimal percentile can be applied and used for each road class. For example, the 50th percentile can be used on country roads and the 60th percentile in urban areas. It is also possible, for example, to reduce from the 60th to the 50th percentile if, for example, difficult driving or weather conditions are detected (cornering, snow, or rain). A plausibility check on the speed values ​​v1 and v2 can particularly preferably be carried out in the ACC. If, for example, a large distance (difference between the 50th and 60th percentile) is detected, this can indicate a high scatter of the data at this position, i.e. for this road section 11a, 11b, 11c. In this case, a downgrade of the described function is possible. For example, this means that the standard 50th percentile is used.Percentile is used for the respective route section 11a, 11b, 11c.

[0089] Overall, the embodiments show a swarm speed transmission for longitudinal control and the use of several percentiles for speed determination.

Claims

[1] Method for operating a driver assistance system (3) for longitudinal control of a vehicle (2), wherein - for a current and / or future road section (11a, 11b, 11c), swarm data (S) of a vehicle swarm with at least two swarm vehicles are recorded from map data (K), - at least two different speed values ​​(v1, v2) are determined from the swarm data (S), wherein the speed values ​​(v1, v2) are each specified from a predetermined characteristic value (K1, K2) of a statistical distribution (V) of a swarm speed (v) of the vehicle swarm for the respective road section (11a, 11b, 11c), wherein - at least one environmental condition (U) for the respective road section (11a, 11b, 11c) and / or at least one vehicle value (F) of the vehicle (2) is recorded, and - according to a selection criterion, one of the at least two speed values ​​(v1, v2) is selected depending on the respective ambient condition (U) and / or the respective vehicle value (F) and used for the longitudinal control. [2] Method according to claim 1, wherein the 50th percentile and the 60th percentile of the statistical distribution (V) of the swarm speed (v) are provided as the respective characteristic value (K1, K2) for the at least two speed values ​​(v1, v2). [3] Method according to one of the preceding claims, wherein a road class (A) and / or a roadway profile and / or an ambient temperature (T) and / or a weather condition and / or a time of day for the respective road section (11a, 11b, 11c) is taken into account as the respective ambient condition (U). [4] Method according to one of the preceding claims, wherein a vehicle driving profile (P) and / or a grip of the vehicle to the driving surface for the respective road section (11a, 11b, 11c) is taken into account as the respective vehicle value (F). [5] Method according to one of the preceding claims, wherein according to the selection criterion the respectively detected ambient condition (U) and / or the respectively detected vehicle value (F) are assigned to one of at least two predetermined categories and each of the at least two categories is assigned exactly one of the at least two speed values ​​(v1, v2) which is to be used for longitudinal control. [6] Method according to one of the preceding claims, wherein the selected speed value (v1, v2) is output as a suggestion to a driver, and the selected speed value (v1, v2) is only used for the longitudinal control if a driver input is detected which contains a confirmation of the selected speed value (v1, v2). [7] Method according to one of the preceding claims, wherein a difference between the at least two speed values ​​(v1, v2) is determined, and the one of the at least two speed values ​​(v1, v2) with the higher numerical value is only used for the longitudinal control if the difference falls below a predetermined limit value. [8] Driver assistance system (3) for longitudinal control of a vehicle (2), wherein the driver assistance system (3) is designed: - to collect swarm data (S) of a vehicle swarm with at least two vehicles from map data (K) for a current and / or future road section (11a, 11b, 11c), - to determine at least two different speed values ​​(v1, v2) from the swarm data (S), wherein the speed values ​​(v1, v2) each indicate a predetermined characteristic (K1, K2) of a statistical distribution (V) of a swarm speed (v) of the vehicle swarm for the respective road section (11a, 11b, 11c), - to record at least one environmental condition (U) for the respective road section (11a, 11b, 11c) and / or at least one vehicle value (F) of the vehicle (2), and - to select one of the at least two speed values ​​(v1, v2) according to a selection criterion as a function of the respective ambient condition (U) and / or the respective vehicle value (F) and to use it for the longitudinal control. [9] Vehicle (F) with a driver assistance system (3) according to claim 8. [10] System (1) comprising a vehicle (2) according to claim 9 and a vehicle-external backend server (6), wherein the backend server (6) is designed to store the map data (K) containing the swarm data (S) with the at least two speed values ​​(v1, v2) for the respective road section (11a, 11b, 11c), and the vehicle (2) is designed to retrieve the swarm data (S) from the backend server (6).

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