Method and system for identifying a convoy travel situation

Abstract

A method (3a, 3b, 3c) for matching automated longitudinal guidance of a vehicle to a driver behaviour comprises detecting (31), on the basis of one or more predetermined criteria, whether a convoy travel situation exists in which a vehicle, under the influence of control actions by a driver of the vehicle, follows a target object at a following distance intended by the driver. If it is detected (31) that a convoy travel situation exists, advice to the driver can be triggered (32a) in order to suggest the use of an automated driving function to the driver. Alternatively or additionally, an actual following distance measured in the convoy travel situation can be used as part of a learning algorithm for learning (32b) a following distance for automated longitudinal guidance of the vehicle and / or for setting (32c) a following distance as part of automated longitudinal guidance of the vehicle.

Description

[0001] Method and system for detecting a following situation

[0002] The invention relates to a computer-implemented method for detecting a following situation, a processing device and a computer program for executing such a method, as well as a computer-readable storage medium on which such a computer program is stored. Furthermore, the invention relates to a system for detecting a following situation and a vehicle equipped with such a system.

[0003] Many modern motor vehicles are equipped with automated driving functions which can, in particular, enable automated longitudinal guidance of the vehicle. One example of a widely used automated driving function is automatic adaptive cruise control, which is often also referred to as adaptive cruise control or ACC (for “Adaptive Cruise Control”). If a vehicle in front is traveling slower than a target speed set for the vehicle, the following distance to the vehicle in front is regulated to a target following distance using automatic engine and braking interventions. The target following distance can, for example, be a specific interval in seconds, i.e. it can be specified as a specific time interval at which the vehicle equipped with ACC should follow the vehicle in front.

[0004] Since different drivers prefer different following distances, the target following distance is usually adjustable by the driver within certain limits. For example, it may be possible to set the following distance globally in a number of distance levels, with a respective distance-speed characteristic curve stored for each of the distance levels. However, such predefined distance levels only partially reflect individual needs and habits. While they reflect an average value for average drivers, they do not offer the option of accommodating specific driver preferences.

[0005] The course of the preset distance-speed characteristic curves in conventional systems follows a fixed pattern. At low speeds, the distance per second is usually higher. The higher the speed, the lower the corresponding distance becomes. However, this does not necessarily correspond to the driver's desired behavior. For example, a driver may like to follow closely behind in heavy city traffic and choose comparatively large distances on motorways and country roads in order to reach their destination in a relaxed manner. These preferences are not reflected in conventional distance levels, as this does not correspond to the average driver. Due to the limited range of distance levels, individual driver preferences can only be taken into account to a limited extent.

[0006] Furthermore, different driving situations, such as city traffic or highway driving, are not taken into account, requiring the driver to manually switch between the distance settings in different situations. The need for such manual operations can impair the comfort experience and potentially distract the driver from their driving tasks.

[0007] There are already known solutions in the state of the art for learning desired distances from a driver's driving behavior and taking these into account in automatic longitudinal guidance.

[0008] For example, DE 10 2015 016 993 A1 describes a method for determining a driving profile for a driver assistance system of a vehicle, comprising the steps: when the driver assistance system is inactive, recording distance measurement signals from a distance measurement system of the vehicle and driving dynamics measurement signals of the vehicle;

[0009] Storing the recorded distance measurement signals and driving dynamics measurement signals in a memory device; determining a driving behavior from the stored distance measurement signals and driving dynamics measurement signals, wherein a distance and / or temporal distance to a vehicle in front is determined from the distance measurement signals; and storing the determined driving behavior. Furthermore, a driver assistance control method, in particular an ACC control method using the method, and a driver assistance system for implementing the driver assistance control method are proposed.

[0010] Furthermore, it has already been proposed to learn individual driver behavior with regard to following distances using neural networks (see, for example, HUANG, Xiuling; SUN, Jie; SUN, Jian. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks. Transportation research part C: emerging technologies, 2018, 95th vol., pp. 346-362.).

[0011] When learning a following distance during a journey controlled or at least influenced by the driver, the problem arises that not every distance set by the driver to a vehicle in front actually corresponds to the typical desired following distance. Road traffic is dynamic. New vehicles often merge, acceleration occurs, new speed limits appear, or sudden braking is necessary. In all of these situations, the driver deliberately does not maintain a constant following distance from a target object. Rather, the resulting distances can change dynamically and be temporarily greater or smaller than the actual desired following distance.

[0012] It is an object of the present invention to provide a method and a system for detecting a following driving situation and for utilizing this knowledge, in particular in connection with a longitudinally guiding automated driving function.

[0013] The problem is solved by the subject matter of the independent patent claims. Advantageous embodiments are specified in the dependent patent claims.

[0014] A first aspect of the invention relates to a computer-implemented method for detecting a following situation. The method is intended, in particular, to detect scenarios in which a vehicle is in a following situation intended and influenced by a driver of the vehicle.

[0015] According to some embodiments, which are described in more detail below, it is also a method for adapting an automated longitudinal guidance of a vehicle to a driver behavior detected in the following driving situation.

[0016] The vehicle can, in particular, be a motor vehicle. The term "motor vehicle" is understood to mean, in particular, a land vehicle that is propelled by mechanical power without being tied to railway tracks. A motor vehicle in this sense can be, for example, a passenger car, motorcycle, or tractor.

[0017] The vehicle may be equipped with an automated driving function for longitudinal guidance, such as an ACC function or a more comprehensive function that supports, for example, combined longitudinal and lateral guidance.

[0018] In this document, the term "automated driving function" generally refers to a vehicle function that enables automated driving. The term "automated driving" refers to driving with automated longitudinal and / or lateral guidance. This can, for example, involve extended driving on the highway or limited-time driving while parking. The term "automated driving" encompasses automated driving with any degree of automation. Examples of levels of automation include assisted, partially automated, conditionally automated, highly automated, and fully automated driving (each with an increasing degree of automation). The five levels of automation mentioned above correspond to SAE levels 1 to 5 of the SAE J3016 standard (SAE - Society of Automotive Engineering) as of April 30, 2021.In assisted driving (SAE Level 1), the system performs longitudinal or lateral guidance in certain driving situations, with the expectation that the driver will assume all remaining aspects of the dynamic driving tasks. In partially automated driving (SAE Level 2), the system assumes longitudinal and lateral guidance in certain driving situations, whereby the driver must continuously monitor the system, as in assisted driving. In conditionally automated driving (SAE Level 3), the system assumes longitudinal and lateral guidance in certain driving situations without the driver having to continuously monitor the system; however, the driver must be able to assume control of the vehicle within a certain period of time upon request from the system. In highly automated driving (SAE Level 4), the system assumes control of the vehicle in certain driving situations, even if the driver does not respond to a request for intervention, thus eliminating the driver as a fallback.In fully automated driving (SAE Level 5), the system can perform all aspects of the dynamic driving task under any road and environmental conditions that can also be mastered by a human driver.

[0019] In one step of the method, one or more predetermined criteria are used to determine whether a following situation exists in which the vehicle, under the influence of the driver's control actions, follows a target object (in particular, a vehicle in front) at a following distance intended by the driver. It is not necessary to determine with absolute certainty that the vehicle is following the target object at a following distance intended by the driver. Rather, the objective is to determine whether certain criteria, explained below as examples, are met, which experience indicates that this is likely the case.

[0020] The aim is to detect when the driver has (probably) intentionally set a certain following distance to the target object. As a prerequisite for this, it can first be checked, for example, whether "manual" longitudinal guidance (e.g. carried out conventionally by actuating an accelerator and / or brake pedal) is currently in effect or whether no longitudinally guiding automated driving function, such as ACC, is activated. In this context, an activated speed limiter should only be considered an activated longitudinally guiding driving function if it is currently limiting the vehicle's speed, because at speeds below the limit set by the speed limiter, longitudinal guidance is carried out manually by the driver.

[0021] If it is determined that such a following situation exists, it can be assumed with a certain degree of probability that the actual following distance reflects the driving behavior desired by the driver and is therefore particularly meaningful for the purposes of learning the individual desired following distance.

[0022] Accordingly, according to one embodiment variant, in a further step of the method, an actual following distance recorded in the following situation is used within the framework of a learning algorithm to learn a following distance for automated longitudinal guidance.

[0023] The learning algorithm can be based on adapting the distance control system to the individual driving behavior by learning specific distance characteristics. The goal is to save the driver's preferences and adjust the set distance to their needs. The distance control system should therefore be adapted to the driver's personal driving profile in order to make it as comfortable as possible for the driver.

[0024] Specifically, this can be achieved by automatically adapting a stored distance-speed characteristic curve, which is used in the context of automated longitudinal guidance to adjust the distance to the vehicle in front depending on the speed, to the driver's driving behavior observed in the detected following situation.

[0025] The distance-speed characteristic curve can be designed in such a way that it assigns a target following distance to a number of speed values, which are referred to as support point speeds in the context of this description. The target following distance can, for example, be a time interval (hereinafter sometimes also referred to as a second interval), which specifies the time offset with which the vehicle should follow the vehicle in front. If the second interval is, for example, 1.5 seconds, the longitudinal guidance of the vehicle is regulated in such a way that the vehicle reaches a considered point (i.e. a longitudinal position along the shared lane of the vehicle and the vehicle in front) 1.5 seconds after the vehicle in front. Alternatively, the target following distance can also be specified as a real distance, i.e. with a unit of length.

[0026] The characteristic curve can be adapted in defined learning phases during a following journey controlled or at least influenced by the driver. In other words, a prerequisite for a learning phase can be that a following journey situation has been detected, as described with reference to the previous method step. If this prerequisite is met, a learning phase can take place and the recorded distance-speed characteristic curve can be adapted within the framework of the learning algorithm depending on the actual following distance recorded in the detected following journey situation. In particular, it can be provided within the framework of a learning algorithm that no learning phase takes place if no following journey situation determined according to the above-mentioned method step currently exists.

[0027] It can also be provided that, once it has been determined that all other conditions for assuming a following situation are met, a predetermined debounce time is initially waited for before a learning phase begins, i.e., before the learning algorithm actually becomes active. This can further increase the probability that an actual following distance processed within the framework of the learning algorithm actually corresponds to a stable desired following distance. The debounce time can, for example, be in the range of 1 s to 10 s, such as 3 s.

[0028] For example, to adapt the distance-speed characteristic curve, a method can be carried out which is the subject of the applicant's patent application filed with the German Patent and Trademark Office on January 15, 2024, entitled "Method for learning a following distance and system for automated longitudinal guidance of a vehicle." The content of that patent application is hereby incorporated into the present patent application. The method for detecting a following situation described in the present patent application can advantageously be used to define one or more learning phases for the method for learning a following distance according to the other patent application mentioned above. In particular, a condition for initiating a learning phase can be that a following situation is detected.

[0029] According to one embodiment, the driver is informed by means of a suitable output device, such as a visual display or an acoustic output device, when a learning phase of a learning algorithm for learning a following distance is active. For example, the driver can additionally be informed about the "direction" in which a learned following distance is currently being adjusted, i.e., whether their current driving behavior leads to an increase or decrease of a previously stored target following distance (e.g., for the current speed).

[0030] The knowledge gained in the process step explained above that a following situation exists can also be used advantageously beyond a learning algorithm for the following distance.

[0031] Thus, according to a further embodiment variant, which can be implemented alternatively or in addition to the embodiment variant described above relating to a learning algorithm, it is provided that a notification is triggered to the driver when it is determined that a following driving situation exists.

[0032] The condition “if it is determined that a following driving situation exists” is not necessarily to be understood as a sufficient condition for triggering the notification. In other words, according to some embodiments, the notification does not have to be triggered every time a following driving situation is detected in the above-mentioned method step. Rather, it can (but does not have to) be provided that further conditions must be met for the notification to be triggered. According to some embodiments, the detection of a method situation according to the above-mentioned method step can be treated as a necessary condition for triggering the notification. In general, the statement that the notification is triggered when it is determined that a following driving situation exists should be understood to mean that the notification is issued depending on the detection of a following driving situation, i.e., taking this circumstance into account.

[0033] The notification can serve to suggest the use of an automated driving function to the driver in a following situation. If a following situation is detected, a longitudinally controlled automated driving function, such as ACC, can relieve the driver of the task of maintaining an appropriate following distance. Proactively offering functions can increase driver comfort because they may activate the automated longitudinal guidance more frequently and specifically in suitable driving scenarios.

[0034] The notification can be communicated to the driver via a suitable output device, for example, visually, acoustically, and / or haptically. It is also conceivable that a human-machine interface is used for this purpose, which is configured to detect a driver input for activating the automated driving function in response to the notification, such as by touching an input field on a touchscreen displayed along with the notification.

[0035] According to a further embodiment, which can be implemented alternatively or in addition to either of the two aforementioned embodiments (concerning the use of the actual following distance for a learning algorithm or for providing a driver notification), the actual following distance detected in the following driving situation is used to set a following distance within the framework of automated longitudinal guidance of the vehicle. Thus, beyond influencing a following distance learned by means of a learning algorithm, a (possibly more direct) functional reaction of a longitudinally guiding automated driving function to an actual following distance detected in a recognized following situation can be provided.

[0036] For example, if an automated driving function with automated longitudinal guidance of the vehicle is activated after the following situation has been determined, a following distance to a target object can be set depending on the actual following distance detected in the following situation (at least temporarily), deviating from a target following distance that is normally intended for the automated driving function at the relevant speed according to a stored distance-speed characteristic curve. The following distance can be set within the framework of the automated driving function, for example, in such a way that it gradually approaches the target following distance. This can be particularly useful in a situation in which the actual following distance detected in the following situation deviates significantly from the target following distance according to the characteristic curve.For example, a driver may initially deliberately drive with a relatively large following distance from a vehicle in front without ACC activated. If the driver then activates ACC, it may be advantageous for driver comfort to adjust the longitudinal control so that the following distance is not abruptly reduced to the target following distance by the ACC function, but rather that the following distance maintained by ACC only gradually approaches the target following distance. However, for this to work, it is crucial that the system has previously recognized the following situation in which the driver deliberately set the larger following distance.

[0037] With regard to the predetermined criteria for determining whether a following situation exists, several possible embodiments are described below which can be combined with one another. As explained in more detail below, these criteria can relate, for example, to driving dynamics variables (such as acceleration or speed) of the host vehicle and / or variables that (also) relate to the target object (such as a speed difference between the vehicle in front and the host vehicle). Such variables can be provided, for example, by a data bus system of the vehicle (possibly in conjunction with environmental sensors). Furthermore, special driving situations, such as approaching a traffic light or intersection, can be taken into account and, in particular, evaluated as an indicator that a following situation does not exist.

[0038] In general, the criteria are based on the idea that preferably only scenarios of stable following (e.g. with a comparatively constant following distance) in which it can be assumed that the driver has deliberately set his desired following distance and his primary goal is not other traffic conditions should be recognized as following scenarios within the meaning of the present method. Such situations can be considered meaningful for the purposes of learning the driver's desired following distance, for example, so it is sensible to carry out a learning phase when such a following situation is recognized. In contrast, dynamic traffic situations or situations in which a selected distance deviates implausibly from previously learned behavior should preferably be disregarded, i.e. not be recognized as following situations within the meaning of the present method.For example, learning phases of a learning algorithm for the following distance can then be specifically scheduled outside of such less meaningful driving situations.

[0039] According to one embodiment, as a (e.g. necessary and / or sufficient) condition for determining that a following driving situation exists, it is checked whether a detected actual following distance is within a predetermined value range.

[0040] For example, an actual following distance can be viewed in the form of a second interval. One condition for determining a following situation can be that the second interval is not less than a predetermined minimum value, which can be less than or equal to 0.5 s, for example. Alternatively or additionally, a condition for determining a following situation can be that the second interval is not greater than a predetermined maximum value, where the maximum value can be selected to be greater than or equal to 3 s, for example. The idea behind this is that the second interval to the vehicle in front must be within realistic ranges for the following movement intended by the driver. Second intervals that are, for example, less than 0.5 seconds and more than 3 seconds, can under certain circumstances be viewed as unrealistic and may indicate a special situation that deviates from a typical, intentional following movement.

[0041] According to a further embodiment, which can be combined with the one described above, a (e.g., necessary and / or sufficient) condition for determining that a following situation exists is whether an acceleration of the vehicle and / or an acceleration of the target object is within a predetermined value range. The value range can, for example, be a comparatively small interval around 0, such as the interval from -0.5 m / s. 2 up to +0.5 m / s 2 In other words, a condition for determining a following situation can be that the acceleration has a predetermined maximum value, for example 0.5 m / s 2By checking whether the vehicle acceleration is close to 0, it is possible to consider only stationary following situations. This is based on the idea that in dynamic situations, controlling the distance is often not the primary control objective, so a recorded actual distance would be of little use.

[0042] Another criterion, which can be considered alternatively or in addition to the aforementioned criteria, concerns a speed difference relative to the target object. A prerequisite for classifying a driving situation as a following situation may be that this speed difference must not be too large, so that the actual following distance remains relatively constant. According to one embodiment, a (e.g., necessary and / or sufficient) condition for determining that a following situation exists can be to check whether a difference between a speed of the target object and a speed of the vehicle (or vice versa) is smaller in magnitude than a predetermined maximum speed difference value.Alternatively, as a prerequisite for determining that a following situation exists, it can be checked whether a difference between a speed of the target object and a speed of the vehicle is smaller than a predetermined maximum speed difference value or equal to the maximum speed difference value.

[0043] It is also within the scope of the invention that the determination of whether a following situation exists can be made depending on whether a special driving situation has been detected. In particular, it can be provided that it is determined that no following situation exists (or at least that it is not determined that a following situation exists) if a special driving situation exists. In other words, certain driving situations can be specifically excluded so that they are not taken into account, for example, for the purposes of learning a following distance.

[0044] The special driving situation may, for example, concern a road course, a traffic routing (particularly ahead) or a traffic routing infrastructure (particularly ahead), as explained below using specific examples.

[0045] According to one possible design variant, the special driving situation involves the vehicle approaching a traffic light (especially a red or yellow one). When approaching traffic lights, constant driving phases cannot generally be assumed. Depending on the driver type, driving behavior varies between coasting and braking shortly before the traffic light. The driver's primary goal is no longer the desired distance from the vehicle in front, but the traffic light.

[0046] In a further embodiment, the special driving situation includes the vehicle approaching an intersection, in particular an intersection where the vehicle must give way to other vehicles potentially approaching.

[0047] Studies of real-world driving have shown that drivers often drop back slightly before intersections – as well as before traffic lights – so that the distance to the vehicle in front can, at least temporarily, increase beyond the desired distance that the driver would otherwise set when following. Furthermore, the following distance can also be shortened or increased by the driver of the vehicle in front braking or accelerating before the intersection or traffic light, for example, to take advantage of a yellow light.

[0048] It is also possible that the special driving situation relates to an imminent turn by the vehicle. In the case of imminent (approaching) turns, which can be detected, for example, based on a selected navigation route and / or based on the detected activation of a direction indicator (in particular of the driver's own vehicle, but optionally also of a vehicle in front), it can be assumed that the driver will reduce the following distance due to a necessary imminent braking, so that the resulting following distance can no longer be assumed to be representative of a normal desired following distance. Information about the special driving situations mentioned above as examples, i.e. in particular about the approach of a traffic light, an intersection and / or a turn, can be obtained, for example, from a digital map in conjunction with a vehicle position, which, for example,may originate from a global navigation satellite system. Additionally or alternatively, such driving situations can also be provided in a conventional manner using the vehicle's environmental sensor system, which may in particular comprise one or more cameras.

[0049] According to a further embodiment, which can be combined with those described above, as a prerequisite for determining that a following situation exists, it is checked whether an actual following distance detected at a speed of the vehicle does not deviate by more than a predetermined amount from a following distance provided for this speed according to a stored distance-speed characteristic curve.

[0050] For example, it can be checked whether the actual following distance, when entered into a distance-speed diagram for the corresponding speed, lies within a predetermined tolerance band around the stored (possibly already learned over a certain period of time) distance-speed characteristic curve.

[0051] The tolerance band, or more generally, the predetermined value, can depend on the consistency of the previously stored driving behavior, which is expressed in the stored distance-speed characteristic curve. The consistency of the previously stored driving behavior can be quantified in the form of a plausibility value. The plausibility value can be calculated, for example, by incrementing an integral during learning phases. The longer the learning phases are close to the previously learned characteristic curve, the higher the plausibility.

[0052] Depending on such a plausibility value, a range can be defined around the stored value within which learning is still possible. This range is larger the smaller the plausibility value is. Ranges outside this range are excluded because they are too far removed from normal driver behavior and could therefore indicate an exceptional situation.

[0053] The invention is based on the idea that a learned following distance can be considered more reliable the longer the actual distance was close to the distance in question during learning. For example, a longer learning phase (or several long learning phases in total) may have shown that a driver normally maintains a second interval in the range between 1.3 s and 1.4 s at a certain speed. If a considerable increase in the actual following distance (i.e. a second interval of significantly more than 1.4 s) is then detected in a situation, this may be accidental following, i.e. a situation in which a vehicle in front is traveling relatively far ahead of the driver's own vehicle at roughly the same speed, without the driver of the driver's own vehicle consciously following this vehicle in front or setting a desired following distance.Such outliers can be detected by comparing the actual following distance with the previously learned characteristic curve, taking the plausibility value into account, and can be distinguished from (real) following situations. This can prevent, for example, further learning of the characteristic curve from leading to a deterioration of the characteristic curve due to an outlier in the actual following distance.

[0054] In accordance with what has been described above, in one embodiment, the predetermined measure is set as a function of a plausibility value, wherein the plausibility value has been increased or decreased at several previous points in time as a function of a difference between an actual following distance detected at the respective point in time and a target following distance applicable at the respective point in time according to the distance-speed characteristic curve, compared to the last considered point in time. In particular, it can be provided that the predetermined measure is set smaller, the larger the plausibility value is (and vice versa). This can be realized, for example, by the predetermined measure (ie, for example,the width of the tolerance band around the characteristic curve, as described above) is determined by a mathematical expression in which at least one term is proportional to the inverse of the plausibility value or in which the plausibility value is subtracted from a constant.

[0055] The plausibility value, which is explained in more detail below in the detailed description, can be used to determine how reliable the currently learned distance is. Based on this, the learning of incorrect distances that are far removed from the previously learned distance can be suppressed.

[0056] The plausibility value can be calculated for the entire characteristic curve. However, it is also possible to perform the calculation separately for individual or all interpolation points of the characteristic curve to make the distinction more precise. In addition to the above-described use of the plausibility value to determine whether a following situation exists, the plausibility value can also be used in another way within a learning algorithm to learn a following distance. For example, the patent application "Method for learning a following distance and system for automated longitudinal guidance of a vehicle" dated January 15, 2024, mentioned above, describes how, within such a learning algorithm, a plausibility factor can be defined as a function of the plausibility value when determining a new target following distance. Such that the plausibility factor decreases with an increasing plausibility value and, conversely, increases with a decreasing plausibility value.If the plausibility value is very high, the adaptation of the characteristic curve to the respective actual following interval is reduced or slowed down. If the plausibility value is low, however, more significant adjustments of the characteristic curve to the current actual values ​​can be made, so that the learned characteristic curve changes more quickly over several calculation steps.

[0057] According to one embodiment of the method proposed here, a change in the target object is detected. Then, as a (e.g. necessary and / or sufficient) prerequisite for determining that a following situation exists, it is checked whether a predetermined minimum period of time has elapsed since the detected change in the target object. For example, the change in the target object can be caused by another vehicle cutting in between the own vehicle and a previous vehicle in front (the previous target object), thereby becoming the new target object. Experience shows that, due to the dynamic nature of the situation, the driver of the own vehicle needs a certain amount of time to adjust his desired distance to the new target object again. Therefore, it can be useful to wait a certain minimum period of time before (possibly, ie(if other potential requirements are also met) a following situation is detected again. The minimum duration can be, for example, in the range of 2 to 12 seconds.

[0058] According to a further development, the minimum duration can be variably set depending on a variable characterizing the dynamics of the situation, for example specifically depending on a speed of the vehicle and / or the new target object and / or depending on whether the vehicle is in an inner-city or an extra-urban environment. The time lag (i.e. the minimum duration) after the change of target object can thus be longer, the more dynamic the situation is. A second aspect of the invention is a (data) processing device which has at least one processor and is configured to carry out the method according to the third aspect of the invention by means of the at least one processor. According to some embodiments, this can also be a spatially distributed processing device (for example across a plurality of spaced-apart processors or microcontrollers).The processing device can, for example, be a control unit (or a part thereof), such as a control unit that controls the automated longitudinal guidance of the vehicle. However, embodiments are also possible in which the processing device is arranged outside the vehicle. It is conceivable, for example, that the processing device is part of a backend server to which the vehicle is connected via a wireless communication link.

[0059] A third aspect of the invention is a system for detecting a following driving situation.

[0060] The system comprises a processing device according to the second aspect of the invention or is or can be connected via data technology to such a processing device—e.g., one located on board the vehicle or in a backend. Thus, the system can utilize the method for detecting a following situation according to the first aspect of the invention. In this respect, the above and following explanations of the method according to the invention and its possible embodiments can be understood analogously to the system according to the invention, and vice versa.

[0061] If the processing device determines that a following driving situation exists, the system is designed to issue a message to the driver via an output device in order to suggest the use of an automated driving function.

[0062] Alternatively or additionally, the system can be configured to carry out automated longitudinal guidance of the vehicle in such a way that a following distance to a target object is set as a function of an actual following distance detected in the detected following driving situation.

[0063] For example, it can be provided that the system or a control module (e.g. a control unit) of the system uses a distance-speed characteristic curve learned or updated by means of the method as a basis for distance control. It is also within the scope of the invention that the system can be set up, when an automated driving function with automated longitudinal guidance of the vehicle is activated after the following situation has been determined, to set a following distance to a target object depending on an actual following distance detected in the following situation (at least temporarily), deviating from a target following distance which, according to a stored distance-speed characteristic curve, normally applies to the automated driving function at the relevant speed.

[0064] For example, the system can adjust the following distance within the framework of the automated driving function so that it gradually approaches the target following distance. This can be particularly useful in a situation in which the actual following distance detected during following deviates significantly from the target following distance according to the characteristic curve. For example, a driver can initially drive with a comparatively large following distance to a vehicle in front without ACC activated. If the driver then activates ACC, the system can adjust the longitudinal control so that the following distance is not immediately shortened to the target following distance, but rather that the following distance maintained by ACC gradually approaches the target following distance.

[0065] A fourth aspect of the invention is a vehicle, in particular a motor vehicle, with a system according to the third aspect of the invention.

[0066] A fifth aspect of the invention is a computer program comprising instructions which, when executed by a processing device (such as, for example, a processing device according to the second aspect of the invention), cause the processing device to execute a method according to the first aspect of the invention. The computer program can be divided into several separate subprograms, each of which can be executed by different, possibly spatially separated, processing devices (such as, for example, by several separate processors). A processing device according to the second aspect of the invention can thus be configured, in particular programmed, to execute a computer program according to the fifth aspect of the invention.

[0067] A sixth aspect of the invention is a computer-readable storage medium comprising instructions that, when executed by a (possibly distributed) processing device, cause the device to perform a method according to the first aspect of the invention. In other words, a computer program according to the fifth aspect of the invention can be stored on the computer-readable storage medium.

[0068] The invention will now be explained in more detail using exemplary embodiments and with reference to the accompanying drawings.

[0069] Fig. 1 illustrates an example and schematically a system for detecting a following driving situation.

[0070] Fig. 2A-C each illustrate exemplary and schematic steps of a method for detecting a following driving situation.

[0071] Fig. 3A illustrates an example of a distance-speed characteristic curve including an upper and a lower limit characteristic curve.

[0072] Fig. 3B illustrates an example of a distance-speed characteristic curve including a tolerance band that depends on a plausibility value.

[0073] In the following, the system 1 shown schematically in Fig. 1 is explained with reference to the steps 31, 32a, 32b, 32c of embodiments 3a, 3b, 3c of a method for detecting a following driving situation, illustrated in Figs. 2A-C.

[0074] The system 1 comprises a distance sensor system 12 for detecting the actual following distance of the vehicle from a vehicle in front (the vehicle in front). For example, the distance sensor system 12 can comprise one or more radar sensors. Using a radar sensor, in addition to the following distance, a relative speed between the vehicle and the vehicle in front can also be determined.

[0075] Furthermore, the system 1 comprises a speed sensor system 13 for detecting an actual speed of the vehicle. The speed sensor system 13 can comprise, for example, one or more wheel speed sensors, one or more acceleration sensors (from whose measured values ​​an actual speed can be calculated) and / or a receiver for a global satellite navigation system (wherein an actual speed can be determined based on a change in the vehicle's position detected by the global satellite navigation system). A (data) processing device 10 of the system 1 is signal-connected to the distance sensor system 12 and the speed sensor system 13 and is configured to receive a distance signal A comprising the actual following distance from the distance sensor system 12 and a speed signal G comprising the actual speed from the speed sensor system 13.The processing device 10 also receives a relative speed signal V from the distance sensor 12, from which, in combination with the speed signal G, a speed of the vehicle in front can be determined.

[0076] In addition, the processing device 10 is signal-connected to a data memory 11 of the system 1 and is configured to receive characteristic curve information K from it (such as speed values ​​and the target following distance values ​​assigned thereto) according to a distance-speed characteristic curve (hereinafter also referred to as characteristic curve) stored therein.

[0077] The processing device 10 is further configured to determine updated characteristic curve information K' depending on a detected driving behavior (in particular depending on detected actual distances and actual speeds) and to output this to the data memory 11 for storage, so that an updated characteristic curve is stored in the data memory 11 as a result.

[0078] The data memory 11 is further connected via signaling to a control unit 14, which is configured to control the automated longitudinal guidance of the vehicle. The control unit 14 can, for example, read characteristic curve information K' from the data memory 11 according to the updated characteristic curve and, depending on the updated characteristic curve information K', generate control signals S for controlling the automated longitudinal guidance, in particular to adjust a target following distance from a vehicle ahead according to the updated characteristic curve information K'.

[0079] In the exemplary embodiment shown in Fig. 1, the processing device 10, which is configured to detect a following situation, is a processing device separate from the control unit 14, which controls the automated longitudinal guidance of the vehicle. The control unit 14 is connected to the processing device 10 via signals and receives the distance and speed information A, G, V from it as the basis for the longitudinal control of the vehicle. However, embodiments are also possible in which one and the same processing device detects the following situation and then controls the automated longitudinal guidance depending on a characteristic curve learned during the following situation and / or depending on an actual following distance detected in the following situation. In other words, with reference to Fig. 1, the processing device 10 could be identical to the control unit 14 or be a part of it (or vice versa).

[0080] The control unit 14 can output the control signals S to a longitudinal guidance actuator 15, in particular to a braking system and / or to a drive module of the vehicle. The longitudinal guidance actuator 15 can control the automated longitudinal guidance depending on the control signal S and, in particular, regulate a desired following distance from a vehicle in front. In Fig. 1, the longitudinal guidance actuator 15 is shown in dashed lines, since it can basically be a generic longitudinal guidance actuator known per se in the prior art, which need not be understood as part of the system 1 according to the invention.

[0081] The processing device 10 of the system 1 can be configured to adapt a distance-speed characteristic curve as a function of an actual following distance within the framework of a learning algorithm for learning a following distance for automated longitudinal guidance of the vehicle during a learning phase.

[0082] Fig. 3A shows an example of such a (distance-velocity) characteristic curve in the form of a diagram in which a second interval d is plotted against a speed v. The actual characteristic curve is shown in the diagram in the form of small circles connected by a solid line ("base" in the diagram legend). The data points represented as small circles each assign a second interval to a support point speed (hereinafter also referred to as "support point"). The characteristic curve between the support points results from a (here linear) interpolation.

[0083] In the example characteristic curve shown in Fig. 3A, five sampling points are used. The larger the number of sampling points, the greater the customizability of the characteristic curve. Therefore, the use of significantly more than five sampling points would generally be sensible, provided that the available memory allows this in a specific application. Due to the interpolation that takes place anyway between the individual data points, it is not necessary to select steps of 1 km / h or even finer steps between the sampling points to achieve a meaningful result. The most common speed limits such as 30, 50, 80, 100 and 120 km / h, as well as some higher speeds, e.g. 140, 160 or even 180 km / h, are sufficient to have a noticeable effect for the driver. If one adds to this the speed limits of the system (i.e.especially the automated driving function), for example, you get 10 support points to be saved with 10 corresponding distance values ​​to be saved.

[0084] However, according to an advantageous embodiment, the individual second intervals may not be learned completely freely; rather, certain restrictions can be provided within which they must move. In the diagram in Fig. 3A, this is illustrated by an example of a lower and an upper limit characteristic curve (see legend: "Limit"). The learned characteristic curve must lie between the upper and lower limit characteristic curves.

[0085] The upper limit can be determined, for example, by the performance of the sensor technology. A classic radar sensor has a range of around 150 meters, with more advanced components up to 300 meters. If objects are further away, the target object can be lost, which can lead to longitudinal control behavior that the driver finds uncomfortable. If the distant control targets are repeatedly lost, this results in accelerations and braking that are incomprehensible to the driver. For example, in a following vehicle where the target object is traveling at 190 km / h, a following distance corresponding to the maximum visibility of the radar of 150 meters would correspond to a second interval of 2.84 s. To avoid incorrect braking or other undesirable effects, an additional buffer is preferably provided. Therefore, in the embodiment shown in Fig.3A assumes a maximum interval of 2.5 seconds, which represents the upper limit of the characteristic curve. Depending on the sensor performance, this can be further increased or decreased.

[0086] For the lower limit, legal requirements and conditions from standards can be taken into account as boundary conditions. For example, a distance resulting from the learned characteristic curve should not be less than the distance resulting from a characteristic curve that corresponds to the lowest distance level that can be set by the driver. If an ACC function has three distance levels, for example, the smallest distance level should form the lower limit. The lower limit characteristic curve in Fig. 3A corresponds to a limit defined in this way. In a system with three selectable distance levels, a characteristic curve stored for the middle distance level can be used as the starting point for learning the characteristic curve. This characteristic curve can serve as a known reference value for the driver, who may have already driven previous models of the same vehicle make.Learning in both positive and negative directions (with regard to the following distance) is quickly possible from this basis, so that a positive effect for the driver can be felt promptly.

[0087] As explained above, certain driving situations or time periods during a journey can be identified as suitable for learning the characteristic curve. This can particularly apply to a relatively constant following situation with regard to a following distance, which is controlled by the driver.

[0088] Each of Fig. 2A-C shows, in the form of a schematic block diagram, steps 31, 32a, 32b, 32c of a respective embodiment variant 3a, 3b, 3c of a method for detecting a following driving situation.

[0089] In a method step 31, which is common to all embodiment variants 3a, 3b, 3c, it is determined on the basis of one or more predetermined criteria whether a following driving situation exists in which the vehicle, under the influence of operating actions of the driver, follows a target object (in particular a vehicle in front) at a following distance probably intended by the driver.

[0090] Processing device 10 can execute method step 31, for example, within the framework of a following-detection algorithm, in particular depending on the signals A, G, V supplied to it. A number of criteria can be used to determine whether a following-detection situation exists.

[0091] For example, on the basis of the distance signal A, as a prerequisite for determining 31 that a following driving situation exists, it can be checked whether a detected actual following distance lies within a predetermined value range.

[0092] Alternatively or additionally, as a condition for determining 31 that a following situation exists, it can be checked whether an acceleration of the vehicle and / or an acceleration of the target object is within a predetermined value range. To determine the acceleration of the vehicle or the target object, the speed signal G or the relative speed signal V (from which, in conjunction with the speed signal G, a speed of the target object can be determined) can be derived over time.

[0093] Another possible criterion can also directly relate to a difference between the speed of the target object and the speed of the vehicle provided by the relative speed signal V. For example, as a prerequisite for determining 31 that a following situation exists, it can be checked whether this difference is smaller (or less than or equal to) a predetermined maximum speed difference value.

[0094] The determination 31 whether a following driving situation exists can also be made depending on whether a special driving situation has been recognized.

[0095] A special driving situation in this sense may involve the vehicle approaching a traffic light or intersection, or an impending turn (e.g., detected due to the activation of a turn signal or active navigation route guidance). In such situations, it can be assumed that the driver is guiding the vehicle longitudinally primarily with the preceding event in mind, so the resulting actual following distance cannot be assumed to be representative of a normal desired following distance. Accordingly, it may be provided that no following situation is detected in the special driving situations mentioned.

[0096] Alternatively or in addition to the aforementioned criteria, as a condition for determining 31 that a following driving situation exists, it can be checked whether an actual following distance detected at a speed of the vehicle does not deviate by more than a predetermined amount from a following distance provided for this speed according to a stored distance-speed characteristic curve.

[0097] For example, as illustrated schematically in Fig. 3B by way of example, a tolerance band T can be placed around the stored characteristic curve (which may have already been learned over a certain period of time). The tolerance band T can have different widths for different speeds, whereby the width of the tolerance band does not have to be symmetrical (with regard to deviations upwards and downwards along the distance axis). For example, in Fig. 3B a width B of the tolerance band T is shown, which indicates a maximum permissible positive deviation of the actual following distance at a speed of 50 m / s compared to the previously stored characteristic curve. If the recorded actual following distance at 50 m / s is so large that it exceeds the second interval provided according to the previously stored characteristic curve by more than the predetermined amount (i.e. the width B), no following situation is detected.

[0098] The tolerance band T, or more generally the predetermined dimension (such as the width B in Fig. 3B as well as corresponding widths of the tolerance band T for distance deviations downwards or distance deviations upwards and / or downwards for other speeds), may depend on a consistency of the already stored driving behavior, which is expressed in the stored distance-speed characteristic curve.

[0099] The longer the driver is in the vehicle, the more information is already known about the driver's intention. Based on this, the plausibility or credibility of the previously learned value can be determined. If the current actual following distance deviates too significantly from the previously learned value, it can be concluded that a true following situation does not exist, and (further) learning of the characteristic curve based on the current actual following distance can be prevented, for example.

[0100] In particular, such a plausibility check, taking into account the significance of the previously learned characteristic curve, can be used to identify apparent or random following situations and subsequently not treat them as actual following situations. This could, for example, concern a scenario in which the driver is driving in an inner-city area at 50 km / h in accordance with the speed limit. Further away, another vehicle is driving and is also obeying the speed limit. Neither of them deviates significantly from this and therefore selects the same desired speed. However, the driver of the learning vehicle does not deliberately follow the vehicle in front at a certain distance; rather, the first priority here is to comply with the speed limit. Another example scenario is following behind a tractor which loses part of its load.The driver does not want to come into contact with these vehicles and therefore maintains a significantly greater distance. Since in both situations both vehicles are constantly following one another, the accelerations are minimal, and the speeds of the vehicles do not differ from each other, these situations could initially appear to be relevant following situations based on some of the criteria mentioned above. In both example scenarios, however, the currently recorded actual following distance deviates implausibly, namely by more than a predetermined amount, from the driver's desired distance, as recorded in other situations. Due to this deviation, the situations can be identified as non-following situations.

[0101] For example, the predetermined value (such as the width B of the tolerance band T in Fig. 3B) can be set as a function of a plausibility value k, which has been increased or decreased at several previous points in time as a function of a difference between an actual following distance detected at the respective point in time and a target following distance applicable at the respective point in time according to the distance-speed characteristic curve, compared to the last considered point in time. In particular, it can be provided that the predetermined value is set smaller, the larger the plausibility value k is (and vice versa). This can, for example,be realized by determining the width B of the tolerance band by a mathematical expression in which at least one term is inversely proportional to the plausibility value k or in which the plausibility value k is subtracted from a constant, so that the term decreases linearly with increasing plausibility value k.

[0102] The plausibility value k can, for example, be defined as a value between 0 and 1 with a fixed resolution, where 0 indicates that no learning has taken place and 1 represents maximum plausibility. The plausibility value k should not only depend on how long the learning took, but also on the extent to which the actual intervals during learning correspond to the time gaps already learned. The plausibility value k can increase if the current intervals are very similar to the time gaps already learned, or decrease if new current intervals deviate significantly from what has been learned so far. The plausibility value k therefore does not merely represent a learning duration, but can rather be interpreted as a statement about the reliability of the currently stored characteristic curve.

[0103] The increment or decrement of the plausibility value k can be determined depending on a deviation between a recorded actual following distance d istand a characteristic curve for the speed v learned up to this point ist applicable target following distance d geiernt be determined. If this difference is small during an active learning phase, the plausibility value k is increased more significantly. If the difference is large, the plausibility value k is increased by a smaller value or even decreased. If the geiernt then due to the learning algorithm to d ist then k increases again.

[0104] In addition, a range of values ​​around d geiernt The value in which the plausibility k increases or decreases can be determined by specifying a fixed distance value d a where the plausibility k should decrease if the actual following distance d ist by more than the distance value d a from the previously learned following distance d geiernt deviates.

[0105] Algorithmically, a calculation of an updated plausibility value k' based on a previous plausibility value k (e.g. determined in a previous calculation cycle) can be realized as follows: k ! = k + a d - d a - \d ist - d gelernt \)

[0106] The rear part describes a function which depends on the difference between the learned distance d geiernt and the current actual distance d ist This function has its zero exactly at the set distance value d a By choosing the factor a d You can specify how quickly the value of k should actually change. As mentioned above, the plausibility value k can be limited, for example, to the value range between 0 and 1, to always allow for further learning.

[0107] Preferably, the plausibility value k is stored persistently over several learning phases (and possibly also over several trips of the driver) and continuously increased or decreased accordingly.

[0108] However, it should be noted that the driver's distance preferences are not the same on every trip. Emotions and stress can influence the current perception of distance. A plausibility k that may have been at its maximum on the last trip may not necessarily be high on the next trip.

[0109] Therefore, according to one embodiment, the stored plausibility value k is limited. This will be illustrated by an example scenario: During the last trip, the driver largely stayed within the same value range close to a following interval of 2 seconds, so that the plausibility value k is close to 1 at the end of the trip, which rules out larger distance deviations. If the driver now wants to drive a smaller interval of approximately 1.3 seconds during a new trip, this lies outside the limit range and cannot be learned. This problem can be solved by reducing the plausibility value k to, for example, 0.7 after each trip end (or at the start of a new trip). In the example scenario, the current interval in seconds would then be back within the range that enables learning.This solution gives the driver greater flexibility at the start of the journey, allowing them to respond to changing distance requirements. However, the limitation or resetting of the plausibility value k after the end of the journey (or at the start of a new journey) should be set at a level that prevents significantly incorrect scenarios from being learned.

[0110] Overall, the plausibility value k can be used to determine how reliable the currently learned distance is. Based on this, for example, the learning of incorrect distances that are far from the previously learned distance can be suppressed.

[0111] The plausibility value k can be calculated for the entire characteristic curve. However, it is also possible to perform the calculation separately for individual or all support points to make the distinction more precise.

[0112] If a change of the target object is detected, a condition for determining 31 that a following travel situation exists can be checked whether a predetermined minimum period of time has passed since the detected change of the target object.

[0113] For example, a change in target object can be triggered when another vehicle cuts in between the driver's own vehicle and a vehicle in front (the previous target object), thereby becoming the new target object. Experience shows that, due to the dynamic nature of the situation, the driver of the driver's own vehicle needs a certain amount of time to adjust their desired distance to the new target object. Therefore, it may be advisable to wait a certain minimum period of time before a following situation is detected again. This minimum period can, for example, be in the range of 2 to 12 seconds.

[0114] The minimum duration can be variably adjusted depending on a variable that characterizes the dynamics of the situation, such as, for example, the speed of the vehicle and / or the new target object and / or whether the vehicle is in an urban or rural environment. The time lag (i.e., the minimum duration) after the target object change can thus be longer, the more dynamic the situation.

[0115] The system 1 comprises an output device 16 and is configured, when the processing device 10 determines that a following situation exists, to output a notification to the driver via the output device 16 in order to suggest the use of an automated driving function with automated longitudinal guidance (such as ACC). At the method level, this corresponds to embodiment variant 3a according to Fig. 2A, in which the processing device 10 generates control signals H for the output device 16 in a step 32a in order to trigger a corresponding notification. In Fig. 1, the output device 16 is shown exemplary and schematically as a loudspeaker, since the notification can be output, for example, in acoustic form (e.g., as a voice output). Alternatively or additionally, the output device can also, for example, comprise means for optical and / or haptic notification output, such asa display, a steering wheel vibration device, a seat vibration device or the like.

[0116] In embodiment 2b of the method illustrated in Fig. 2B, after the determination 31 of a following situation, in a further step 32b, an actual following distance detected in the following situation is used within the framework of a learning algorithm to learn a following distance for automated longitudinal guidance of the vehicle. For example, upon determination 31 that the following situation exists, a learning phase of the learning algorithm can be started. The learning phase can continue as long as it is determined (e.g., in each subsequent calculation cycle) that the following situation still exists.

[0117] When a learning phase of a learning algorithm for learning a following distance is active, the driver can be informed of this fact via output device 16 or via another output device (not separately shown in Fig. 1). For example, the driver can additionally be informed about the "direction" in which a learned following distance is currently being adjusted, i.e., whether the driver's current driving behavior leads to an increase or decrease in a previously stored target following distance.

[0118] The learning algorithm can, in particular, comprise adapting a distance-speed characteristic curve of the type shown in Figs. 3A-B depending on the detected actual following distance. As mentioned above, for adapting the distance-speed characteristic curve, a method can be implemented, for example, which is the subject of the applicant's patent application filed with the German Patent and Trademark Office on January 15, 2024, entitled "Method for learning a following distance and system for automated longitudinal guidance of a vehicle."

[0119] As mentioned, when executing the learning algorithm, the processing device 10 can receive characteristic curve information K from the data memory 11 and determine updated characteristic curve information K' depending on the actual following distance detected in the following driving situation. The updated characteristic curve information K' can in turn be stored in the data memory 11 and retrieved by the control unit 14. The control unit 14 can then generate control signals S for the longitudinal guidance actuator system 15 based on the updated characteristic curve information K' in order to regulate a desired following distance from a preceding vehicle according to the updated characteristic curve information K'.

[0120] More generally, i.e. also in a manner other than by adapting a characteristic curve within the framework of a learning algorithm as described above, the system 1 can be configured to carry out an automated longitudinal guidance of the vehicle in such a way that a following distance to a target object is set as a function of an actual following distance detected in the detected following driving situation.

[0121] At the process level, this aspect corresponds to embodiment variant 3c according to Fig. 20, in which the control unit 14 generates control signals S for the longitudinal guide actuator system 15 in a step 32c as a function of the distance signal A.

[0122] If an automated driving function with automated longitudinal guidance of the vehicle is activated after the following driving situation has been detected, the control unit 14 can set the following distance to a target object depending on the actual following distance detected in the following driving situation, at least temporarily deviating from a target following distance that normally applies to the automated driving function at the relevant speed according to a stored distance-speed characteristic curve. For example, the following distance can be set within the framework of the automated driving function in such a way that it gradually approaches the target following distance. If, for example,If a driver initially drives deliberately with a relatively large following distance to a vehicle in front without ACC activated and then activates the ACC, the control unit 14 can adapt the longitudinal control in the interests of driver comfort so that the following distance maintained by the ACC gradually approaches the target following distance.

Claims

Patent claims 1. Computer-implemented method (3a, 3b, 3c) for detecting a following driving situation, comprising the steps: Determining (31), based on one or more predetermined criteria, whether a following driving situation exists in which a vehicle, under the influence of operating actions of a driver of the vehicle, follows a target object at a following distance intended by the driver; - if it is determined (31) that a following situation exists, o triggering (32a) a notification to the driver to suggest the use of an automated driving function; and / or o using an actual following distance recorded in the following situation ■ as part of a learning algorithm for learning (32b) a following distance for automated longitudinal guidance of the vehicle and / or ■ for setting (32c) a following distance within the framework of an automated longitudinal guidance of the vehicle.

2. Method (3a, 3b, 3c) according to claim 1, wherein as a condition for determining (31) that a following driving situation exists, it is checked whether a detected actual following distance is in a predetermined value range 3. Method (3a, 3b, 3c) according to one of the preceding claims, wherein as a condition for determining (31) that a following driving situation exists, it is checked whether an acceleration of the vehicle and / or an acceleration of the target object is in a predetermined value range.

4. Method (3a, 3b, 3c) according to one of the preceding claims, wherein, as a condition for determining (31) that a following travel situation exists, it is checked whether a difference between a speed of the target object and a speed of the vehicle is smaller in magnitude than a predetermined maximum speed difference value and / or wherein, as a condition for determining (31) that a following travel situation exists, it is checked whether a difference between a speed of the target object and a speed of the vehicle is smaller in magnitude than a predetermined maximum speed difference value or equal to the maximum speed difference value.

5. Method (3a, 3b, 3c) according to one of the preceding claims, wherein a change of the target object is detected and wherein, as a prerequisite for determining (31) that a following travel situation exists, it is checked whether a predetermined minimum period has elapsed since the detected change of the target object.

6. Method (3a, 3b, 3c) according to one of the preceding claims, wherein the determination (31) as to whether a following driving situation exists is carried out depending on whether a special driving situation has been detected, wherein the special driving situation comprises the following: the vehicle approaching a traffic light; and / or the vehicle approaching an intersection; and / or an impending turn of the vehicle.

7. Method (3a, 3b, 3c) according to one of the preceding claims, wherein, as a condition for determining (31) that a following driving situation exists, it is checked whether an actual following distance detected at a speed of the vehicle does not deviate by more than a predetermined amount from a following distance provided for this speed according to a stored distance-speed characteristic curve.

8. Method (3a, 3b, 3c) according to claim 7, wherein the predetermined measure is set as a function of a plausibility value, wherein the plausibility value has been increased or decreased at several previous points in time as a function of a difference between an actual following distance detected at the respective point in time and a desired following distance applicable at the respective point in time according to the distance-speed characteristic curve, compared to the last point in time considered.

9. Processing device (10) having one or more processors, wherein the processing device (10) is configured to carry out the method (3a, 3b, 3c) according to one of the preceding claims by means of the processor or by means of the plurality of processors.

10. System (1) for detecting a following driving situation, wherein the system (1) comprises a processing device (10) according to claim 9 or is provided with such a Processing device (10) is connected or connectable for data purposes, and wherein the system (1) is configured, when it is determined (31) that a following driving situation exists, to output a notification to the driver via an output device (16) in order to suggest the use of an automated driving function; and / or to carry out automated longitudinal guidance of the vehicle in such a way that a following distance to a target object is set as a function of an actual following distance detected in the following driving situation.

11. System (1) according to claim 10, wherein the system (1) is configured, when an automated driving function with automated longitudinal guidance of the vehicle is activated after the following driving situation has been determined, to set a following distance to a target object as a function of an actual following distance detected in the following driving situation, deviating from a target following distance applicable according to a stored distance-speed characteristic curve for the automated driving function.

12. System (1) according to claim 11, wherein the system (1) is configured to adjust the following distance within the scope of the automated driving function in such a way that it approaches the target following distance over time.

13. Vehicle with a system (1) according to claim 12.

14. A computer program comprising instructions which, when the computer program is executed by a processing device (10), cause the processing device (10) to carry out a method (3a, 3b, 3c) according to one of claims 1 to 8.

15. A computer-readable storage medium comprising instructions which, when executed by a processing device (10), cause the processing device (10) to carry out a method (3a, 3b, 3c) according to one of claims 1 to 8.

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