Method and device for determining a target distance to a vehicle in front during partially automated or automated driving using a driver assistance system in a hands-off domain

Abstract

The invention relates to a method for determining a target distance (20) to a vehicle ahead during partially automated or automated driving using a driver assistance system (51) in a hands-off domain, wherein at least one currently existing state category (10, 10-x) is obtained and / or acquired, and wherein the target distance (20) is determined taking into account a frequency distribution (15) for the target distance (20) corresponding to the at least one currently existing state category (10, 10-x), wherein the target distance (20) is selected based on a predetermined quantile value (16) of the frequency distribution (15). Furthermore, the invention relates to a device (1) for determining a target distance (20) to a vehicle ahead during partially automated or automated driving using a driver assistance system (51) in a hands-off domain.

Description

[0001] The invention relates to a method and a device for determining a target distance to a vehicle in front during semi-automated or automated driving using a driver assistance system in a hands-off domain.

[0002] Currently, there are Level 2 driver assistance systems that allow a driver to take their hands off the steering wheel in defined scenarios (or domains); the driver assistance system then takes over driving the vehicle independently. Such a scenario or domain could be, for example, a highway without significant curves, because it can be assumed that there are no people on the road and the system does not need to navigate tight bends. In this domain (Operational Design Domain, ODD), the driver is therefore allowed to take their hands off the steering wheel; however, when the vehicle leaves this domain, the driver must put their hands back on the steering wheel.

[0003] The described driver assistance system considers not only lateral vehicle control but also longitudinal vehicle control and interaction with objects. In previous implementations, longitudinal dynamics control is defined by the developers (expert model) to implement a desired system behavior dictated by the system requirements. Longitudinal dynamics control is primarily determined by the desired (target) distance and the vehicle dynamics. Distance categories are selected, for example, in the form of time intervals that are adjustable in increments (e.g., levels 1-5, where 1: very small time interval, 5: very large time interval, etc.). However, the optimal vehicle behavior from a developer's perspective may not correspond to the expectations of an average driver and therefore does not take into account the environment or current scenario in which the vehicle is located.However, this can play an important role in driver acceptance, as well as in a (subconscious) assessment of the system dynamics and, consequently, the perception of comfort. For example, a greater distance may be perceived as more comfortable in heavy rain and on a wet surface than on a dry surface. The multitude of possible influencing factors, however, is currently difficult to represent.

[0004] DE 10 2020 211 539 A1 describes a method and a driver assistance system for a single-track or multi-track motor vehicle, in which an object detection sensor in front of the vehicle detects objects ahead and the vehicle's speed is regulated by a cruise control system such that, upon detection of an object ahead, a predetermined distance between the vehicle and the object ahead is established. While following the object ahead, the driver can change the distance between the vehicle and the object ahead by operating the accelerator pedal or a rotary throttle. When the driver assistance system is deactivated, at least one or more vehicle systems are monitored, and if one or more of these systems is operated, the system infers the driver's level of distraction and automatically activates the driver assistance system depending on the level of distraction.

[0005] DE 10 2018 201 306 A1 describes a method for controlling the distance of a vehicle to a vehicle driving ahead, wherein in a limiting step the distance is limited to a preselected target distance value, and in a setting step the target distance value is set using a change in the accelerator pedal angle.

[0006] The invention is based on the objective of providing a method and a device for determining a target distance to a vehicle in front during semi-automated or automated driving using a driver assistance system in a hands-off domain.

[0007] The problem is solved according to the invention by a method with the features of claim 1 and a device with the features of claim 8. Advantageous embodiments of the invention are set forth in the dependent claims.

[0008] In particular, a method for determining a target distance to a vehicle in front during partially automated or automated driving using a driver assistance system in a hands-off domain is provided, wherein at least one currently existing state category is maintained and / or recorded, and wherein the target distance is determined taking into account a frequency distribution for the target distance corresponding to the at least one currently existing state category, wherein the target distance is chosen based on a given quantile value of the frequency distribution.

[0009] Furthermore, in particular a device for determining a target distance to a vehicle in front during partially automated or automated driving using a driver assistance system in a hands-off domain is provided, comprising a control unit, wherein the control unit is configured to obtain and / or detect at least one currently existing state category, and to determine the target distance taking into account a frequency distribution for the target distance corresponding to the at least one currently existing state category, and to select the target distance based on a predetermined quantile value of the frequency distribution.

[0010] The method and the device make it possible to determine a target distance (which can also be referred to as the set distance) to a vehicle in front, depending on the current scenario. The current scenario is defined using state categories.

[0011] It is therefore intended that at least one currently existing state category is maintained and / or recorded. This at least one state category defines, in particular, a current scenario, that is, a context in which the vehicle is currently located. For example, the at least one state category includes vehicle speed, weather conditions, etc. The target distance is determined taking into account a frequency distribution for the target distance that corresponds to the at least one currently existing state category. It is intended that the target distance is selected based on a predefined quantile value of the frequency distribution. This allows the selection of the target distance that is optimal for both driver comfort and safety requirements for a given current scenario.The underlying principle is that the frequency distribution already provides a good estimate for a target distance. Based on the frequency distribution, the target distance is chosen from a predefined quantile value. This allows for the selection of a relative value within the frequency distribution, independent of its specific shape. Specifically, the target distance chosen is the one that coincides with the predefined quantile value. This allows for consideration of the respective frequency distribution as well as, for example, an additional safety margin and / or the driver's personal preference. The frequency distribution was determined using statistical methods, primarily based on fleet data. The quantile value can be specified by experts and / or the driver.

[0012] The acquisition of at least one condition category (e.g., weather conditions, traffic density, etc.) can be achieved, for example, through environmental sensing and / or perception using dedicated vehicle sensors. Alternatively or additionally, the at least one condition category (e.g., vehicle speed, etc.) can also be retrieved and / or provided by the vehicle's control system. Furthermore, alternatively or additionally, the at least one condition category (e.g., weather conditions, traffic density, etc.) can also be retrieved and / or provided by a data service, such as a traffic management system.

[0013] It is specifically intended that the target distance, once determined, is transmitted to the driver assistance system and implemented by it. The driver assistance system then regulates the distance, specifically to the target distance. This also provides a method for operating a vehicle.

[0014] A hands-off domain refers in particular to a domain in which semi-automated or automated driving is possible without the driver having their hands on the steering wheel (hands-off state).

[0015] Parts of the device, in particular the control unit, can be configured individually or collectively as a combination of hardware and software, for example, as program code executed on a computing unit, in particular a microcontroller or microprocessor. However, it can also be provided that parts are configured individually or collectively as an application-specific integrated circuit (ASIC) and / or a field-programmable gate array (FPGA) and / or a graphics processing unit (GPU) and / or a digital signal processor (DSP). The control unit can, in particular, comprise at least one computing unit and at least one memory.

[0016] Furthermore, a method for providing frequency distributions corresponding to state categories for a target distance is also provided for a method according to one of the embodiments described in this disclosure, wherein fleet data of a vehicle fleet are collected during manual journeys, wherein the fleet data are classified according to state categories, wherein for each of the state categories an associated manually driven distance to a front vehicle is extracted, and wherein for each state category a frequency distribution is determined and provided based on the extracted distances and a statistical evaluation.

[0017] In one embodiment, the quantile value is specified taking into account at least one current state category. This allows the specified quantile value to be selected depending on the current situation. This enables the target distance to be even better adapted to the current situation, particularly the driver's perceived comfort.

[0018] In one embodiment, the quantile value is determined based on a predetermined difference from a maximum, mean, or median of the frequency distribution. This allows, for example, the specification of a fixed safety margin with respect to quantiles, which is determined based on the frequency distribution present in a given situation. Furthermore, the driver's personal preferences can also be taken into account. In particular, this makes it possible to specify the quantile value relative to the maximum, mean, or median of the frequency distribution.

[0019] In one embodiment, a minimum distance is specified, and if the selected target distance is less than the specified minimum distance, the selected target distance is replaced by the specified minimum distance. This allows a lower limit for the target distance to be defined, which must not be undercut.

[0020] In one embodiment, the difference and / or the minimum distance is specified taking into account the current state category. This allows the difference and / or the minimum distance to be specified depending on the situation. This enables the target distance to be even better adapted to the current situation.

[0021] In one embodiment, the frequency distribution is stored in the form of a characteristic map dependent on the state category. Starting from the at least one currently existing state category, the corresponding frequency distribution is retrieved or generated from this map. This allows for a simple implementation of the method. It may also be provided that for values ​​of the at least one state category for which no frequency distribution exists in the characteristic map, interpolation and / or extrapolation is performed to generate a frequency distribution.

[0022] In one embodiment, the quantile value and / or the difference and / or the minimum distance are stored in the form of a characteristic map dependent on the state category. From this map, the corresponding quantile value and / or the corresponding difference and / or the corresponding minimum distance are retrieved or generated based on the at least one currently existing state category. This allows for a simple implementation of the method. It may also be provided that for values ​​of the at least one state category for which no quantile value and / or difference and / or minimum distance is present in the characteristic map, interpolation and / or extrapolation is performed to provide a corresponding value.

[0023] In one embodiment, the at least one state category comprises at least one of the following: speed, traffic density, occupied lane, number of lanes, rainfall amount, time of day, road surface condition (e.g., dry, wet, or icy, etc.), road category (e.g., urban, rural, and / or motorway, etc.), vehicle type of a preceding vehicle (e.g., sedan, van, or truck, etc.), selected driver profile (e.g., sporty, normal, or eco-friendly, etc.), and / or driver type (e.g., experienced, inexperienced, old, young, etc.). This list is not exhaustive. Combinations of the state categories or of values ​​and / or characteristics of the state categories define, in particular, a scenario in which the vehicle finds itself.

[0024] Further features for the design of the device result from the description of embodiments of the method. The advantages of the device are the same in each case as in the embodiments of the method.

[0025] Furthermore, a vehicle is created comprising a driver assistance system equipped for at least partially automated longitudinal guidance with controlled distance to a vehicle in front, and a device according to one of the described embodiments, wherein the driver assistance system controls the distance to the target distance determined by means of the device.

[0026] The invention is explained in more detail below with reference to preferred embodiments and the figures. These show: Fig. 1 a schematic representation of an embodiment of the device for determining a target distance to a vehicle in front during semi-automated or automated driving using a driver assistance system in a hands-off domain; Fig. 2 a schematic representation to illustrate the invention; Fig. 3a to 3c schematic representations to illustrate embodiments of the method and the device; Fig. 4a to 4c are schematic representations to illustrate a further embodiment of the method and the device; Fig. 5 a schematic flowchart of embodiments of the method.

[0027] The Fig. Figure 1 shows a schematic representation of an embodiment of the device 1 for determining a target distance 20 to a vehicle ahead during partially automated or automated driving using a driver assistance system 51 in a hands-off domain. The device 1 is, in particular, arranged in a vehicle 50. The vehicle 50 comprises the driver assistance system 51, which can perform at least longitudinal vehicle guidance in a hands-off domain. The method is described in more detail below with reference to the device 1.

[0028] The device 1 comprises a control unit 2. The control unit 2 comprises a computing unit 2-1 and a memory 2-2. The computing unit 2-1 is configured to perform the necessary calculations for carrying out process steps and can access data and program code stored in the memory 2-2 for this purpose.

[0029] The control unit 2 is configured to obtain and / or detect at least one currently existing state category 10 and to determine the target distance 20 taking into account a frequency distribution 15 corresponding to the at least one currently existing state category 10 for the target distance 20, and to select the target distance 20 based on a predetermined quantile value 16 of the frequency distribution 15. The determined target distance 20 is supplied to the driver assistance system 51, which sets the target distance 20 by steering and / or regulating.

[0030] The at least one currently existing condition category 10 (e.g., a road type) can be detected and / or provided, for example, by means of a sensor 52 of the vehicle 50. Furthermore, the at least one currently existing condition category 10 (e.g., a vehicle speed) can also be queried and / or provided by a vehicle control unit 53.

[0031] The Fig. Figure 2 shows a schematic representation to illustrate the invention. Fig. Figure 2 shows frequency distributions 15 for two different scenarios, which can be expressed using the state category "weather". The frequency distributions 15 were determined primarily from fleet data and represent values ​​used by drivers during manual driving. A frequency 21 (ordinate, y-axis) is plotted against the target distance 20 (abscissa, x-axis). The left frequency distribution 15 occurs, for example, in sunny weather for a vehicle fleet, while the right frequency distribution 15 occurs in rainy weather (with otherwise identical conditions and state categories). It can be seen that the frequency distributions 15 are shifted relative to each other and each exhibits a maximum 25 at a different target distance 20. This can be explained by reduced visibility and drivers' perceived safety, as drivers typically maintain a greater distance from the vehicle in front during rainy weather.

[0032] The Fig. 3a, Fig. 3b and Fig. Figure 3c shows schematic diagrams to illustrate embodiments of the method and the apparatus. As shown in the Fig. 2 each a frequency distribution 15, where the Fig. 3a shows, for example, a frequency distribution 15 for the target distance 20 on the left lane of a motorway (assuming right-hand traffic), which Fig. 3b a frequency distribution 15 on the right lane of the motorway and the Fig. 3c is a frequency distribution 15 on a single-lane country road in each direction. For illustrative purposes, let us assume that the 75th percentile is chosen as the quantile value. Fig. 3a, Fig. 3b and Fig. Figures 3c should each have the same scale on the abscissa (x-axis). It can be seen that different frequency distributions 15 exist for each of the three different situations shown. The respective maximum 25 lies at different values ​​of the target distance 20. Due to the different frequency distributions 15, in particular due to the different shapes, the respective 75th percentile lies at different values ​​of the target distance 20. Accordingly, according to the disclosed method, a different target distance 20 is chosen for each of the three situations, namely the one that coincides with the 75th percentile.

[0033] It may be provided that the quantile value 16, taking into account at least one current state category 10 ( Fig. 1) is specified. In the in the Fig. 3a, Fig. 3b and Fig. In the examples shown in 3c, the 75th percentile would not be relevant in all cases, but rather other values, which are specified taking into account the current state category 10.

[0034] The Fig. 4a, Fig. 4b and Fig. Figure 4c shows schematic diagrams to illustrate a further embodiment. The diagrams are basically the same as in the Fig. 3a, Fig. 3b and Fig. 3c, in particular, the respective frequency distributions 15 are the same. In this embodiment, it is provided that the quantile value 16 is determined starting from a predetermined difference 27 to the maximum 25 of the frequency distribution 15. The difference 27 can be specified as a quantile difference or as a distance difference. Alternatively, the quantile value 16 can also be determined starting from a predetermined difference 27 to the mean or the median.

[0035] It may be stipulated that a minimum distance of 26 ( Fig. 3a, Fig. 3b, Fig. 3c) is specified, whereby in the case where the selected target distance 20 is smaller than the specified minimum distance 26, the selected target distance 20 is replaced by the specified minimum distance 26. This is also shown schematically in the Fig. 3a, Fig. 3b and Fig. Figure 3c illustrates this, assuming for simplicity that the specified minimum distance 26 is independent of the present state category 10. In the examples shown, the quantile value 16 would be in the Fig. 3a is smaller than the specified minimum distance of 26, so the minimum distance of 16 is used as the target distance of 20 instead. In the case of the Fig. 3b and Fig. In 3c, however, this is not the case, so the target distance 20, which coincides with the quantile value 16, is used.

[0036] However, it may also be provided that the difference 27 and / or the minimum distance 26 is specified taking into account the existing condition category 10.

[0037] It may be provided that the frequency distribution 15 is in the form of a characteristic map 17 dependent on the state category 10 ( Fig. 1) is stored, from which, starting from the at least one currently existing state category 10, the corresponding frequency distribution 15 is retrieved or generated.

[0038] It may be provided that the quantile value 16 and / or the difference 27 and / or the minimum distance 26 is stored in the form of a characteristic map 18 dependent on the state category 10, from which, starting from the at least one currently existing state category 10, the corresponding quantile value 16 and / or the corresponding difference 27 and / or the corresponding minimum distance 26 is retrieved or generated.

[0039] It may be stipulated that at least one condition category 10 includes at least one of the following: a speed 10-1, a traffic density 10-2, a lane in use 10-3, a number of lanes 10-4, a rainfall amount 10-5, a time of day 10-6 (e.g., day, night), a road surface condition 10-7, a road category 10-8, a vehicle type 10-9 of a preceding vehicle, a selected driver profile 10-10, and / or a driver type 10-11. In particular, a current scenario is defined by several condition categories 10 or respective values ​​and / or characteristics of these condition categories 10.

[0040] The Fig. Figure 5 shows a schematic flowchart of embodiments of the method for determining a target distance to a vehicle in front during semi-automated or automated driving using a driver assistance system in a hands-off domain.

[0041] The procedure is initiated, for example, when it is determined that the vehicle is in a hands-off domain.

[0042] In process step 100, at least one currently existing state category is obtained and / or recorded.

[0043] In a process step 101, the target distance is determined taking into account a frequency distribution for the target distance corresponding to the at least one currently existing state category, whereby the target distance is chosen based on a given quantile value of the frequency distribution.

[0044] In process step 102, the specified target distance can be transmitted to the driver assistance system.

[0045] In a process step 103, it may be provided that the driver assistance system sets the target distance by regulating a distance to the vehicle in front to the target distance.

[0046] Further embodiments of the method have already been described in more detail above with reference to the device.

[0047] The frequency distributions are or were provided in particular by means of a procedure for providing frequency distributions corresponding to state categories for a target distance, as already described in the general description. Reference symbol list 1 Device 2 Control unit 2-1 Computing Equipment 2-2 storage 10 condition category 10-1 Speed ​​(of the vehicle) 10-2 traffic density 10-3 occupied lane 10-4 Number of tracks 10-5 rainfall 10-6 Daytime 10-7 a road surface condition 10-8 Street category 10-9 Vehicle type 10-10 selected driver profile 10-11 Driver type 15 Frequency distribution 16th quantile value 17 Characteristic map 18 characteristic map 20 target distance 21 Frequency 25 Maximum 26 Minimum distance 27 Difference 50 vehicles 51 Driver assistance systems 52 Sensors 53 Vehicle control 100-103 process steps QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2020 211 539 A1

[0004] DE 10 2018 201 306 A1

[0005]

Claims

[1] Method for determining a target distance (20) to a vehicle ahead during partially automated or automated driving using a driver assistance system (51) in a hands-off domain, where at least one currently existing state category (10,10-x) is preserved and / or recorded, and wherein the target distance (20) is determined taking into account a frequency distribution (15) corresponding to the at least one currently existing state category (10,10-x) for the target distance (20), where the target distance (20) is chosen based on a given quantile value (16) of the frequency distribution (15). [2] Method according to claim 1, characterized by , that the quantile value (16) is specified taking into account at least one current state category (10,10-x). [3] Method according to claim 1 or 2, characterized by, that the quantile value (16) is determined starting from a given difference (27) to a maximum (25), a mean or a median of the frequency distribution (15). [4] Method according to any of the preceding claims, characterized by , that a minimum distance (26) is specified, wherein in the case where the selected target distance (20) is smaller than the specified minimum distance (26), the selected target distance (20) is replaced by the specified minimum distance (26). [5] Method according to claim 3 or 4, characterized by , that the difference (27) and / or the minimum distance (26) is specified taking into account the existing state category (10,10-x). [6] Method according to any of the preceding claims, characterized by, that the frequency distribution (15) is stored in the form of a characteristic map (17) dependent on the state category (10, 10-x), from which the corresponding frequency distribution (15) is retrieved or generated starting from the at least one currently existing state category (10, 10-x). [7] Method according to any of the preceding claims, characterized by , that the quantile value (16) and / or the difference (27) and / or the minimum distance (26) is stored in the form of a characteristic map (18) dependent on the state category (10,10-x), from which, starting from the at least one currently existing state category (10,10-x), the corresponding quantile value (16) and / or the corresponding difference (27) and / or the corresponding minimum distance (26) is retrieved or generated. [8] Device (1) for determining a target distance (20) to a vehicle ahead during semi-automated or automated driving using a driver assistance system (51) in a hands-off domain, comprising: a control device (2), wherein the control device (2) is configured to to obtain and / or record at least one currently existing state category (10, 10-x), and to determine the target distance (20) taking into account a frequency distribution (15) corresponding to the at least one currently existing state category (10, 10-x) for the target distance (10, 10-x), and to choose the target distance (20) starting from a given quantile value (16) of the frequency distribution (15). [9] Vehicle (50) comprising a driver assistance system (51) configured for at least semi-automated longitudinal guidance with controlled distance to a vehicle in front, and a device (1) according to claim 8, wherein the driver assistance system (51) controls the distance to the target distance (20) determined by means of the device (1). [10] Method for providing frequency distributions (15) corresponding to state categories (10,10-x) for a target distance (20) for a method according to one of claims 1 to 7, wherein fleet data of a vehicle fleet are collected during manual journeys, wherein the fleet data are classified according to state categories (10,10-x), wherein for each of the condition categories (10,10-x) a corresponding manually driven distance to a vehicle in front is extracted, and where for each state category (10,10-x) a frequency distribution (15) is determined and provided based on the extracted distances and a statistical evaluation.

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