Method for determining individualized distance control for a driver of a motor vehicle operated in at least partially automated fashion, computer program product, and electronic computing device
Patent Information
- Application Number
- PCT/EP2026/053776
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2025-03-26
- Filing Date
- 2026-02-12
- Publication Date
- 2026-10-01
Smart Images
Figure EP2026053776_01102026_PF_FP_ABST
Abstract
Description
[0001] Description
[0002] Method for determining an individualized distance control for a driver of a motor vehicle that is at least partially automated, computer program product, and electronic computing device
[0003] The following invention relates to a method for determining an individualized distance control for a driver of a motor vehicle operated at least partially automatically by means of an electronic computing device according to the applicable claim 1. Furthermore, the invention relates to a corresponding computer program product and a corresponding electronic computing device.
[0004] In many production vehicles, the "Adaptive Cruise Control" (ACC) function, particularly in the form of cruise control, is used to regulate the distance to the vehicle in front. The distance to be regulated is often based on the current speed of the vehicle. Additionally, the driver can use corresponding buttons on the steering wheel to adjust the preset distance setting in increments, for example, in three to five levels, thus varying the distance.
[0005] Analysis of the usage patterns of this distance function has shown that various factors can influence distance behavior in real-world driving situations. Since current production solutions do not account for most of these factors when adjusting the distance, the driver can only maintain the distance by adjusting the corresponding control level when cruise control is activated.
[0006] It is already known from the prior art that the distance control can be adapted to additional influencing factors depending on the situation. These factors can include, for example, traffic density, rainfall, the risk of icy conditions, the lane in use, or the road category.
[0007] However, current technology does not take into account driver-specific preferences for the selected distance. A corresponding individualization of the distance control is therefore not known from the prior art, meaning that the distance control remains largely unindividualized. Thus, there is a need in the prior art to provide a corresponding individualization of the desired distance in an adaptive cruise control system. DE 102022 112 377 A1 describes a method for setting a target distance between a motor vehicle and a vehicle ahead, wherein the motor vehicle and the vehicle ahead are traveling in the first lane of a multi-lane roadway. An adaptive cruise control system is activated to set the target distance. A distance between the motor vehicle and the vehicle ahead is determined. Static environmental data relating to the roadway are acquired.Dynamic environmental data is collected, specifically data relating to a second vehicle in a second lane of the multi-lane roadway. The target distance is adjusted based on both the static and dynamic environmental data.
[0008] The object of the present invention is to create a method, a computer program product and an electronic computing device by means of which the disadvantages of the prior art are overcome.
[0009] In particular, the object of the present invention is to provide a method, a computer program product and an electronic computing device by means of which individualized distance control can be implemented in a motor vehicle that is at least partially automated.
[0010] This problem is solved by a method, a computer program product, and an electronic computing device according to the independent claims. Advantageous embodiments are specified in the dependent claims.
[0011] One aspect of the invention relates to a method for determining an individualized distance control for a driver of an at least partially automated motor vehicle using an electronic computing device. A basic map for distance control is specified, wherein the basic map is at least two-dimensional and corresponds to an averaged distance behavior. A deviation in the driver's distance behavior is determined in at least one domain compared to the averaged distance behavior in the same domain of the two-dimensional basic map. The two-dimensional basic map is adapted to an individualized map in the specified domain, and the individualized distance control is determined as a function of the individualized map.Thus, an individualized map can be derived from the given basic map, and the distance control can be carried out based on this individualized map. In particular, it can be provided, for example, that the electronic computing unit then generates corresponding control signals for the distance control depending on the individualized map, so that the specific individual distance requirement is maintained even in at least partially automated operation of the vehicle.
[0012] For at least partially automated operation of the motor vehicle, the vehicle is equipped with corresponding longitudinal and / or lateral acceleration devices. Specifically, the electronic control unit can be configured to generate control signals for the longitudinal acceleration device. This can initiate braking or acceleration of the vehicle, particularly to set the desired following distance. The desired following distance can also be adjusted based on the vehicle's current speed and the road surface. Furthermore, the following distance can be regulated according to a set desired speed.
[0013] In particular, the invention assumes that a domain-specific (ODD - Operational Design Domain) distance control system is present. This system regulates, for example, a domain-specific distance to the vehicle ahead, depending on various influencing factors. These influencing factors can include, for example, speed, traffic density, the lane or number of lanes in use, rainfall amount, time of day, risk of slipperiness, road category (e.g., urban, rural, or motorway), and the type of vehicle ahead (e.g., truck or car).
[0014] As input for an individualized solution, a domain-specific adaptation of the learned baseline data must therefore be made. Each driver has different preferences regarding how a particular influencing factor, or a combination of influencing factors, affects their preferred following distance. For example, one driver might always prefer a very large following distance in the rain, regardless of the domain. In contrast, another driver might only prefer a large following distance in certain rainy situations, such as on the highway in heavy traffic, but otherwise only slightly increase the following distance in the rain. It is therefore necessary that an individualized function of driving behavior is analyzed and reacted to in a domain-specific manner. As a starting point for the individualization, a baseline characteristic map, at least two-dimensional, and especially n-dimensional, is used to represent an average following distance behavior learned from fleet data.Each combination of influencing factors in this basic characteristic curve can be assigned a corresponding distance. This individualized distance can then be used to adjust the spacing within the different domains.
[0015] The invention thus solves the problem of personalization. The electronic computing unit learns the driver's individual following distance behavior and adjusts the ACC control accordingly. This results in a comfortable driving experience, as the driver can set the desired safety distance. By adapting to different driving environments, such as highways or city traffic, the electronic computing unit can consider various driving situations and adjust the following distance behavior to the respective context.
[0016] According to an advantageous implementation, a subdomain of the domain exhibiting the differing following distance behavior is identified, and the base map is further adapted to the individualized map depending on this specific subdomain. In particular, a corresponding following distance is assigned to each combination of influencing factors in the base map. One problem with this approach is that, given the high dimensionality of the influencing factors and the resulting combinatorics, the weighting factors in certain domains can only be adjusted very rarely, as the specific scenarios occur infrequently. Consequently, the electronic computing device would learn and adapt to the driver very slowly. This challenge becomes particularly clear in the following example: If a driver generally maintains a greater following distance than previously planned in rainy conditions, the adjustment must be made individually for each scenario (domain) involving rain, for example, rain and highway driving.The scene (domain) "Rain and Town" would not be adjusted. The solution to this problem is, for example, to adjust not only the specific domain but also domains that encompass a subdomain of the occurring influencing factors. For instance, the domain "Night, Rain, and Highway" could be defined. In addition to the aforementioned domain, the subdomains in which, for example, "Night, Rain, and Highway" occur are also adjusted, such as "Night, Rain, and Highway," "Day, Rain, and Highway," and "Day, Rain, and Country Road." Since the influence of each individual influencing factor on the driver's perception is unknown, the electronic computing device must be successively adapted to the driver across multiple domains.For example, it can be provided that the greater the overlap of the corresponding domains, the greater the adjustment can be, but if only one influencing factor matches, only a small adjustment will be made.
[0017] It is further advantageous to determine the differing distance behavior during manual operation of the vehicle and / or during semi-automated operation. In particular, there are thus two approaches under which driver interaction can be used to individually adjust the function. In both cases, it is recognized that the vehicle is in a steady, homogeneous following mode. The first approach involves analyzing the driver's distance behavior during manual operation, which specifically means that ACC (Adaptive Cruise Control) is deactivated. For individualization, the deviation between the distance manually selected by the driver in that situation and an optimal distance calculated by the electronic computing unit for the domain is used.The second approach is to analyze the driver's following distance behavior in at least partially assisted mode, i.e., when ACC is activated. This allows for the determination of corresponding distance deviations in both manual and partially automated ferry operation, enabling the creation of a highly individualized characteristic map.
[0018] One preferred design feature provides that overriding the semi-automated ferry operation is defined as a deviation in the distance behavior. Specifically, this allows for individualization by enabling the driver to override the electronic control unit, i.e., by manually accelerating or braking during at least partially automated ferry operation. This allows for a highly customized driving map.
[0019] Furthermore, it has proven advantageous to determine an overlap factor between the domain and another domain for the distance behavior, and to adapt the base map to the individualized map in the other domain depending on this overlap factor. In particular, this iteratively adjusts all at least similar scenarios / domains. To calculate the new domain distance, which can subsequently also be referred to as a dimension, the last distance value of the domain, especially initialized with a base parameter, is used. The deviation from the last individualized distance is calculated for the currently occurring scenario. The distance difference is weighted using the domain overlap. The greater the domain overlap, the higher the weighting in the calculation of the new domain distance.Ideally, for example, the domain overiapp is 1 if the domain currently being adapted corresponds to the current scenario (noberiapp is identical to noimensionen).
[0020] It has also proven advantageous to perform the adaptation of the characteristic map within a driving cycle and / or a predefined period. For example, the parameter adjustment can thus be performed once per detected subsequent trip and / or operating cycle and / or for longer manual subsequent trips at fixed time intervals. This enables the electronic control unit to perform the corresponding adaptation at different time intervals or during different subsequent trips.
[0021] It is also advantageous to specify a weighting factor for the adaptation. For example, the overlap factor and a corresponding distance difference can be extended by the appropriate weighting factor, also known as attenuation factors, to control how quickly the electronic computing unit adapts the basic characteristic map to the deviating distance values. Thus, outliers in the deviating distance behavior can lead to reduced adaptation of the characteristic map. This results in a more robust adaptation of the characteristic map and therefore a more robust algorithm for providing the individualized distance control.
[0022] Furthermore, it has proven advantageous to perform further adaptation of an already adapted domain in the individualized characteristic map depending on a quality criterion for the adapted domain. Thus, the quality criterion for the degree of individualization of a specific domain can be used as an extension. If a specific scenario or domain has been trained with a sufficiently large number of events, the electronic computing device can, for example, prevent this from being "unlearned" again by the influence of similar scenarios. This can be implemented, for example, by a counter that triggers when the distance difference in the current scenario is less than a defined threshold, indicating that the domain is essentially well individualized. If the threshold is exceeded, the counter is decremented.Once the counter reaches a certain value, similar domains can no longer influence the current scenario.
[0023] Furthermore, a minimum value for the domain overlap can be useful for the entire process; for example, if less than 50 percent of the domains match, learning can be excluded. Other options include learning a weighting matrix of the domains instead of a new distance field, which extends the basic parameterization. Expert-based weighting of specific influencing factors is also possible to control their impact during cross-domain learning. Similarly, instead of learning a distance, a time gap, especially a distance normalized to speed, can be individualized.Furthermore, it may also be provided that the driver can be identified, for example by camera or key, and on the basis of this the individualized characteristic map can be retrieved from a storage device and then individualized during a journey by the driver.
[0024] The presented method is, in particular, a computer-implemented method. Therefore, a further aspect of the invention relates to a computer program product with program code means which, when the program code means are executed by the electronic computing device, cause it to carry out a method according to the preceding aspect.
[0025] Furthermore, the invention therefore also relates to a computer-readable memory module with at least the computer program product according to the preceding aspect.
[0026] A further aspect of the invention relates to an electronic computing device for determining an individualized distance control for a driver of a motor vehicle that is at least partially automated, wherein the electronic computing device is configured to carry out a method according to the preceding aspect. In particular, the method is carried out by means of the electronic computing device.
[0027] Furthermore, the invention also relates to a motor vehicle with at least the electronic computing device. For this purpose, the motor vehicle is at least partially automated and, for example, has at least one adaptive cruise control (ACC).
[0028] Advantageous embodiments of the process are considered to be advantageous embodiments of the computer program product, the computer-readable storage module, the electronic computing device, and the motor vehicle. The electronic computing device and the motor vehicle possess tangible features to enable the execution of the corresponding process steps. An electronic computing unit / computer can generally be defined as a data processing device that contains at least one processing circuit. This computing unit is capable of processing data to perform various arithmetic operations. Such operations can also include indexed accesses to a data structure, such as a load profile table (LUT).
[0029] The computing unit can consist of a combination of hardware and software components, including computers, microcontrollers, integrated circuits, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), systems-on-a-chip (SoCs), processors such as microprocessors, central processing units (CPUs), graphics processing units (GPUs), and digital signal processors (DSPs). The computing unit can also comprise a physical or virtual cluster of computers or other units of the aforementioned type.
[0030] In various embodiments, the processing unit has one or more hardware and / or software interfaces as well as one or more memory units. A memory unit can be implemented as volatile data storage, such as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, such as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), magnetoresistive random access memory (MRAM), or phase-change random access memory (PCRAM).
[0031] For use cases or application situations that may arise during the procedure and are not explicitly described here, it may be provided that, according to the procedure, an error message and / or a request for user feedback is issued and / or a default setting and / or a predetermined initial state is set.
[0032] The invention also includes combinations of the features of the described embodiments.
[0033] Exemplary embodiments of the invention are described below. Figure 1 shows a schematic top view of an embodiment of a motor vehicle with an embodiment of an electronic computing device;
[0034] Fig. 2 shows a schematic block diagram according to one embodiment of a basic characteristic curve; and
[0035] Fig. 3 shows a schematic block diagram according to one embodiment of an individualized characteristic map.
[0036] The embodiments described below are preferred embodiments of the invention. In these embodiments, the described components each represent individual features of the invention that can be considered independently of one another. Each of these features further develops the invention independently and can therefore be considered part of the invention individually or in a combination other than that shown. Furthermore, the described embodiments can also be supplemented by other features of the invention already described.
[0037] In the figures, functionally identical elements are each provided with the same reference symbols.
[0038] Fig. 1 shows a schematic top view of an embodiment of a motor vehicle 1. The motor vehicle 1 is, in particular, at least partially automated. For this purpose, the motor vehicle 1 has at least one assistance system 2. The assistance system 2 is designed, for example, to regulate a distance 3 to a vehicle 4 driving ahead. In particular, the assistance system 2 can, for example, be designed in the form of a so-called ACC (Adaptive Cruise Control).
[0039] For this purpose, the assistance system 2 can, for example, act on at least one longitudinal acceleration device 5. In particular, at least partially automated acceleration and braking of the vehicle 1 can thus be carried out via the longitudinal acceleration device 5. Furthermore, a lateral acceleration device can also be controlled, for example, via the assistance system 2. For example, the vehicle 1 can be configured for at least one operation at Level 2 according to SAE standards. The vehicle 1 also has an electronic computing device 6. The electronic computing device 6 can be specifically configured for the downstream process or be configured as part of the assistance system 2.
[0040] The electronic computing device is specifically designed to determine an individualized distance control for a driver of the at least partially automated motor vehicle 1.
[0041] Fig. 2 shows a schematic block diagram according to one embodiment of a characteristic map. In particular, a so-called basic characteristic map 7 is shown, wherein in the present embodiment the basic characteristic map 7 has at least two dimensions. In the present embodiment, the two dimensions are, for example, roads 8, 9, 10 and rain 11, 12, 13. In particular, Fig. 2 shows, for example, the domain country road 8, the domain motorway 9, and the domain local road 10. Furthermore, the domain no rain 11, the domain light rain 12, and the domain heavy rain 13 are shown. In this two-dimensional basic characteristic map 7, averaged distance values 14 are shown, wherein the averaged distance values 14 were learned from fleet data.
[0042] For example, it is shown that a fleet of motor vehicles 1, 4 on country road 8 and in no rain 11 chooses a distance of 25 meters. Furthermore, it is shown that in no rain 11 on motorway 9, a distance of 30 meters is chosen. It is also shown that on a local road 10 and in no rain 11, a distance of 20 meters is chosen. Furthermore, it is shown that in light rain 12 and country road 8, a distance of 35 meters is chosen. Furthermore, it is shown that in light rain 12 and motorway 9, a distance of 33 meters is chosen. Furthermore, it is shown that in light rain 12 and local road 1025 meters is chosen. Furthermore, it is shown that in heavy rain 13 and country road 8, a distance of 40 meters is chosen. Furthermore, it is shown that in heavy rain 13 and motorway 9, a distance of 42 meters is chosen. Furthermore, Fig. 2 shows that in case of heavy rain, a distance of 28 meters is chosen between road 13 and local road 10.This is of course to be regarded as purely exemplary and by no means exhaustive for the invention.
[0043] Fig. 3 shows another schematic block diagram according to an embodiment of an individualized characteristic map 15. For this purpose, domains 8, 9, 10, 11, 12, and 13 from Fig. 2 are listed as follows. Instead of the averaged distance behavior or the averaged distance values 14, individualized distances 16 can now be set. The figures show, in particular, the method for determining the individualized distance control for the driver of the at least partially automated motor vehicle 1. The basic characteristic map 7 for the distance control is specified, wherein the basic characteristic map 7 is at least two-dimensional and corresponds to an averaged distance behavior. The process then involves determining a different distance behavior of the driver in at least one domain 8, 9, 10, 11, 12, 13 compared to the average distance behavior in the same domain 8, 9, 10, 11, 12, 13 of the two-dimensional basic characteristic map 7.The two-dimensional basic characteristic map 7 in the specified domain 8, 9, 10, 11, 12, 13 is adapted to the individualized characteristic map 15, and the individualized distance control is determined as a function of the individualized characteristic map 15.
[0044] It may also be provided that a subdomain with a different distance behavior is determined and the basic characteristic map 7 is additionally adapted to the individualized characteristic map 15 depending on the determined subdomain. The different distance behavior can be determined during manual ferry operation of the vehicle 1 and / or during semi-automated ferry operation of the vehicle 1. In particular, overriding of the semi-automated ferry operation can be determined as a different distance behavior.
[0045] Furthermore, it may be provided that an overlap factor is determined between domain 8, 9, 10, 11, 12, 13 and another domain 8, 9, 10, 11, 12, 13 for the distance behavior, depending on a certain overlap factor, the basic characteristic map 7 is additionally adapted to the individualized characteristic map 15 in the further domain 8, 9, 10, 11, 12, 13.
[0046] Furthermore, it can also be stipulated that the adaptation of the characteristic map 7, 15 is carried out in a driving cycle and / or within a specified period. A weighting factor for the adaptation can also be specified. Additionally, a further adaptation of an already adapted domain 8, 9, 10, 11, 12, 13 in the individualized characteristic map 15 can be carried out depending on a quality criterion for the adapted domain 8, 9, 10, 11, 12, 13.
[0047] In particular, the figures show that, for example, a domain-specific (ODD - Operation Design Domain) distance control system is in place. This system regulates a domain-specific distance 3 to the vehicle ahead 4, depending on various influencing factors. These influencing factors can include, for example: speed, traffic density, lane or number of lanes in use, amount of rainfall, time of day (e.g., day or night), risk of slipperiness, road category (e.g., urban, rural, or motorway 9), and the type of vehicle ahead 4 (e.g., truck or car).
[0048] For example, a distance 3 or a time gap can be learned from fleet data for each domain 8, 9, 10, 11, 12, 13. This is shown in particular by the averaged distance value 14.
[0049] For an individualized solution, the learned baseline data must be adapted to specific domains. Each driver has different preferences regarding how a particular influencing factor, or combination of influencing factors, affects their preferred following distance (distance 3). For example, one driver might always prefer a very large following distance in the rain, regardless of the domain (8, 9, 10, 11, 12, 13). In contrast, another driver might only prefer a large following distance in certain rainy situations, such as on the motorway (9) in heavy traffic, but otherwise only slightly increase the following distance (distance 3) in the rain (11, 12, 13). Therefore, it is necessary to analyze and respond to an individualized function of driving behavior in a domain-specific manner.
[0050] There are two main approaches to the conditions under which driver interaction can be used to individually adjust the function. In both cases, it is recognized that the vehicle 1 is in a steady, homogeneous following mode. For example, the analysis of the driver's following distance behavior can be performed in purely manual operation of the vehicle 1. For individualization, the deviation between the distance 3 manually selected by the driver in the given situation and an optimal distance 3 calculated by the electronic computing unit 6 for domains 8, 9, 10, 11, 12, 13 is used. A second approach involves analyzing the driver's following distance behavior in assisted driving mode. For individualization, the driver's overrides of the electronic computing unit 6 are used, i.e., manual acceleration or braking.
[0051] The starting point for individualization is the n-dimensional base map 7, as shown in Fig. 2, for mapping a fleet data-learned average following distance behavior. As already presented, a corresponding following distance 3 can be assigned to each combination of influencing factors. One problem here is that, given the high dimensionality of the influencing factors and thus the high number of combinatorics, the weighting factors in certain domains 8, 9, 10, 11, 12, 13 can only be adjusted rarely, since the specific scenarios occur infrequently. Consequently, the electronic computing device 6 would learn and adapt to the driver only very slowly. This challenge becomes clear in the following example: If a driver generally maintains a greater following distance 3 than previously planned in rain 11, 12, 13, the adjustment must be made individually for each scenario involving rain 11, 12, 13, for example, rain + highway. The scenario rain + town would not be adjusted.
[0052] One solution to this problem is to adapt not only the specific domain 8, 9, 10, 11, 12, 13, but also domains 8, 9, 10, 11, 12, 13 that comprise a subset of the influencing factors that have occurred.
[0053] For example, the domain "night + rain + highway" can be captured. In addition to the aforementioned domains 8, 9, 10, 11, 12, 13, the subdomains in which "night", "rain" 11, 12, 13 and "highway" 9 occur are also partially adjusted, for example, "night and no rain" 11 and highway 9, "day and rain" 12 and highway 9, and "day and rain" 12 and country road 8.
[0054] Since the influence of each individual factor on the driver's perception is unknown, the electronic computing unit 6 must be successively adapted to the driver across domains. The greater the overlap of the scenarios, the greater the adaptation can be; if, for example, only one influencing factor matches, only a small adaptation is made.
[0055] As an example, a potentially differing driver request can be identified in a specific scenario. For instance, the current scenario can be assumed to be rain + highway + daytime + heavy traffic. Based on this base domain, an individualized distance parameter for all, especially similar, domains 8, 9, 10, 11, 12, 13 can be calculated using, for example, the following formula. This calculation utilizes a base parameterization, specifically according to the base map 7:
[0056] dneu, domain —dalt, domain + Adszenario X (p X A.where dalt, domain corresponds to the last distance value to the individualized domain 8, 9, 10, 11, 12, 13, Adszenario corresponds to the distance difference (dcurrent scenario - dalt, current scenario), <p entspricht einem Domänenüberiapp, was wiederum durch die Formel:
[0057] noverlap / nD dimensions XA< XA< scenario
[0058] is determined with A <p als Abschwächungsfaktor des Domänenüberlapps, A als Abschwächungsfaktor, und A<p Szenario als Gütekriterium für die Individualisierung des Szenarios angesehen wird.
[0059] All scenarios, especially similar ones, are iteratively adjusted. To calculate the new distance 3 of domains 8, 9, 10, 11, 12, 13, the last distance value of domains 8, 9, 10, 11, 12, 13, specifically initialized with the basic parameterization, is used. The deviation from the last individualized distance 3 is calculated for the currently occurring scenario. The distance difference is weighted using the domain overlap. The greater the domain overlap, the higher the weighting in the calculation of the new domain distance. Ideally, the domain overlap is 1 if the domain currently being adjusted (8, 9, 10, 11, 12, 13) corresponds to the current scenario, i.e., the domain overlap is identical to the n dimensions.
[0060] The domain overlap and distance difference can be extended with attenuation factors to control how quickly the system adapts to differing distance values. The parameter adjustment can be performed, for example, once per detected following journey or at fixed time intervals for longer manual following journeys.
[0061] An optional extension could be the quality criterion for the degree of individualization of a given scenario. If a specific scenario has been learned with a sufficiently large number of events, it might be possible to prevent this learning from being lost due to the influence of similar scenarios. This can be implemented, for example, with a counter that increments when the difference in the current scenario is less than a defined threshold, indicating that the scenario is well-individualized. When the threshold is exceeded, the counter is decremented. Once the counter reaches a certain value, additional scenarios can no longer influence the current scenario. Furthermore, a minimum value for domain overlap can also be useful. For example, if less than 50 percent of the scenarios match, learning can be prevented.
[0062] Optionally, instead of a distance field, a weighting matrix of domains 8, 9, 10, 11, 12, and 13 can be learned, extending the basic parameterization. Expert-based weighting of specific influencing factors can also be enabled to control their impact during cross-domain learning. Instead of learning a distance of 3, a time gap, especially distance 3 normalized to speed, can be individualized. Reference symbol list
[0063] motor vehicle
[0064] Assistance system
[0065] Distance
[0066] preceding vehicle longitudinal acceleration device
[0067] electronic computing device
[0068] Basic map
[0069] country road
[0070] Highway
[0071] Local road
[0072] no rain
[0073] little rain
[0074] lots of rain
[0075] average distance
[0076] individualized characteristic map
[0077] individualized distance
Claims
Patent claims 1. Method for determining an individualized distance rule for a driver of a motor vehicle operated at least partially automatically (1) using an electronic computing device (6), comprising the steps: Specifying a basic characteristic map (7) for a distance control, wherein the basic characteristic map (7) is at least two-dimensional and wherein the basic characteristic map (7) corresponds to an averaged distance behavior; Determining a different following distance behavior of the driver in at least one domain (8, 9, 10, 11, 12, 13) compared to the average following distance behavior in the same domain (8, 9, 10, 11, 12, 13) of the two-dimensional basic map (7); - Adapting the two-dimensional basic map (7) in the determined domain (8, 9, 10, 11, 12, 13) to an individualized map (15); and Determining the individualized distance control depending on the individualized characteristic map (15).
2. Method according to claim 1, characterized by the fact that a subdomain of the domain (8, 9, 10, 11, 12, 13) with the different spacing behavior is determined and the basic characteristic map (7) is additionally adapted to the individualized characteristic map (15) depending on the determined subdomain.
3. Method according to claim 1 or 2, characterized by the fact that the different distance behavior is determined during manual operation of the motor vehicle (1) and / or during semi-automated ferry operation of the motor vehicle (1).
4. Method according to claim 3, characterized by the fact that An override of the semi-automated ferry operation is determined as a deviation in distance behavior.
5. Method according to any one of the preceding claims, characterized in that an overlap factor is determined between the domain (8, 9, 10, 11, 12, 13) and another domain (8, 9, 10, 11, 12, 13) for the distance behavior and, depending on the determined overlap factor, the basic characteristic map (7) is additionally adapted in the further domain (8, 9, 10, 11, 12, 13) to the individualized characteristic map (15).
6. Method according to any one of the preceding claims, characterized by the fact that the adaptation of the characteristic map (7, 15) is carried out in a driving cycle and / or in a specified period.
7. Method according to any of the preceding claims, characterized by the fact that A weighting factor is specified for the adaptation.
8. Method according to any one of the preceding claims, characterized by the fact that a further adaptation of an already adapted domain (8, 9, 10, 11, 12, 13) in the individualized characteristic field (15) is carried out depending on a quality criterion for the adapted domain (8, 9, 10, 11, 12, 13).
9. Computer program product comprising program code means which cause an electronic computing device (6) to perform a method according to one of claims 1 to 8 when the program code means are executed by the electronic computing device (6).
10. Electronic computing device (6) for determining an individualized distance control for a driver of a motor vehicle (1) that is at least partially automated, wherein the electronic computing device (6) is configured to carry out a method according to one of claims 1 to 8.