METHOD FOR GENERATING AN OVERTAKE PROBABILITY COLLECTION, METHOD FOR OPERATION OF A MOTOR VEHICLE CONTROL DEVICE, OVERTAKE PROBABILITY COLLECTION DEVICE AND CONTROL DEVICE

Description

[0001] The invention relates to a method for generating an overtaking probability collection and an overtaking probability collection device with a corresponding overtaking probability collection.

[0002] Document EP 1591754 A1 discloses a system for calculating the risk of overtaking maneuvers. This system analyzes a wide variety of environmental data and uses it to calculate a probability of success, which is a measure of the risk involved in an overtaking maneuver. The amount of data required to calculate this probability necessitates a relatively powerful computing system for processing.

[0003] The fundamental idea here is that in modern hybrid vehicles, overtaking maneuvers are typically supported by the activation of an electric motor or an additional electric torque as a comfort feature. For the operating strategy, it is important to predict these overtaking maneuvers, as this allows the electric motor's torque to be reduced in time before the maneuver begins, ensuring that a sufficiently high torque is available at the start. Furthermore, this prediction prevents the electric motor from overheating during the overtaking maneuver and ensures that the vehicle's electrical energy storage system has sufficient energy for the entire overtaking operation.

[0004] Andreas Wilde's dissertation entitled "A modular functional architecture for adaptive and predictive energy management in hybrid vehicles", Technical University of Munich, 2010, presents a method with which the efficiency of hybrid vehicles can be further increased while maintaining and improving driving performance by automatically selecting the operating strategy parameters depending on the current and future driving situation.

[0005] From DE 10 2014 008 380 A1 a method for predictive control and / or regulation of a vehicle's powertrain is known, wherein, among other things, information on driving behavior on a journey is recorded and stored.

[0006] The WO 2015 / 052577 A1 reveals a lane guidance system that takes overtaking probabilities into account.

[0007] Overtaking maneuvers are typically predicted by evaluating vehicle-internal sensor signals, such as turn signal activation, steering angle, downshifting, distance to the vehicle ahead, and relative speed to the vehicle ahead. These vehicle-internal signals are used to calculate an overtaking probability. The hybrid vehicle's operating strategy is then adjusted based on this probability.

[0008] The object of the present invention is to demonstrate a way in which an overtaking probability collection can be generated more reliably and / or a motor vehicle can be operated more safely.

[0009] The problem is solved by the method and an overtaking probability collection device according to the independent claims.

[0010] The invention is based on the understanding that overtaking maneuvers by motor vehicles are carried out individually. For example, the overtaking habits of one driver of a motor vehicle differ from those of another driver of a different motor vehicle. These overtaking habits can also depend on the driver's situation and traffic density; a driver may be less inclined to overtake on weekends than on weekdays on their way to work, and the driver's tendency to overtake can also depend on traffic density. Furthermore, there will be, for example, sections of road where only a few vehicles overtake, and there will also be sections where a relatively large number of vehicles overtake. This information cannot be captured with high precision solely from map data.Maps typically do not show buildings or visibility obstructed by fields of corn or trees. In this case, this information is gathered by monitoring a large number of vehicles and added to a database of overtaking probabilities. The overtaking probability database is thus generated empirically. This allows for a more individualized and precise determination of the overtaking probability.

[0011] The respective driving path is recorded. This means, for example, that the driving trajectory (trajectory relative to the road, trajectory relative to the overtaken vehicle, and / or steering angle over time) of each vehicle is recorded. However, the driving path can also be recorded, for example, by identifying the vehicles based solely on point-in-time positions and determining when a vehicle moves in front of (or alongside) another vehicle. Vehicles can be identified, for instance, by their identification numbers, particularly those of a vehicle's mobile communication unit or a driver's. The driving path can also be recorded using remote sensing data, such as aerial or satellite images. In this case, the vehicles can be extracted from the images or image sequences.For example, vehicles can also be equipped with satellite navigation receivers and transmit their position to a receiving unit. The route can then be determined based on several positions.

[0012] The driving process can also be recorded by another vehicle, for example using sensor data and / or a camera signal from the other vehicle, particularly a vehicle following the overtaking vehicle. In other words, the overtaking maneuver can also be recorded by the vehicle behind, for example by its camera system or sensors, which are particularly relevant for driver assistance systems and are capable of detecting the lane-keeping or overtaking behavior of the vehicle ahead.

[0013] Determining an overtaking maneuver can also be done, additionally or alternatively, by evaluating sensor data from at least one sensor of the vehicle itself. This evaluation can determine, for example, when the vehicle is overtaking and when it is not. The sensor data can be provided, for example, by the accelerator pedal position, steering angle, turn signal status, and / or environmental information, in particular radar sensor data, laser scanner data, and / or camera data, especially a stereo camera.

[0014] It may also be provided that the sensor data recorded by the vehicle are sent for evaluation to an external device, in particular an overtaking probability collection device, or to at least one other vehicle. However, it is also possible that the evaluation takes place in the vehicle in which the sensor data are recorded, and preferably only the evaluation result is transmitted to the external device.

[0015] The multitude of motor vehicles (non-overtaking vehicles and overtaking vehicles or vehicles whose potential overtaking maneuver is being considered) can, for example, be represented as a vehicle fleet.

[0016] Based on the specific driving sequence, it is determined whether a given vehicle has overtaken another vehicle or road user in that section of the road. This can be determined, for example, by recording the sequence of two vehicles at the beginning of the section and then recording the sequence of the two vehicles again at the end of the section. If the sequence has changed, the vehicle that was initially in second place and is now in first place—meaning it must have overtaken the other vehicle—can be classified as an overtaking vehicle. If the sequence of vehicles in the section of the road has not changed, the vehicle is classified as a non-overtaking vehicle.

[0017] According to the invention, the ratio between the number of overtaking vehicles and the number of non-overtaking vehicles is determined. This ratio is, in particular, a numerical relationship between the number of overtaking vehicles and the number of non-overtaking vehicles, for example, a percentage value. However, the ratio can also be determined based on a weighting value.

[0018] This ratio provides information about how many of the numerous vehicles on this stretch of road overtake at this geographical location. This ratio is then entered into the overtaking probability database as the overtaking probability for this section of road. This entry can be made, for example, as a database entry.

[0019] Preferably, the system provides that when recording each journey, the time of day at which the journey is recorded is captured, and the vehicles are categorized into time-of-day classes based on this time. A time-of-day-class-dependent ratio is then determined for each class and entered into the overtaking probability database for the respective route segment. The time of day is specifically defined as a time of day. However, it can also refer to a specific time of day, a day of the week, a season, or any other time period. The time-of-day-dependent ratio can, for example, capture variations in overtaking behavior at different times of day.For example, during peak traffic hours, such as in the morning and evening when rush hour traffic reaches its peak, overtaking may occur more frequently than at other times of day. This time-of-day-dependent ratio can be entered into the overtaking probability database, either as a supplement to or an alternative to the time-independent ratio.

[0020] Furthermore, it is preferably provided that when recording each journey, a weather condition is recorded for each journey, and the vehicles are classified into weather condition classes based on the weather condition. A weather condition-dependent ratio is then determined for each vehicle segment and entered into the overtaking probability database. The weather condition describes, for example, air temperature, road surface temperature, rain, snow, brightness, fog, or road surface conditions, particularly a wet or icy road. Preferably, there are several weather condition classes into which the recorded vehicles can be classified.The ratio dependent on weather conditions can be entered into the overtaking probability collection either as a supplement or alternative to the ratio without dependence on weather conditions.

[0021] Furthermore, it is preferably provided that when recording each journey, a visibility ratio is recorded for each journey, and the vehicles are classified into visibility ratio classes based on this ratio. A visibility ratio-dependent ratio is then determined for each visibility ratio class and entered into the overtaking probability database for the respective road segment. The visibility ratio describes how far a driver can see into the distance under the current environmental conditions. For example, the presence of fog impairs visibility, which is why a vehicle overtaking in fog is classified into a different visibility ratio class than a vehicle overtaking in clear conditions.Visibility conditions can vary, for example, due to different levels of brightness, rain, snowfall, or other light influences. Visibility can also describe the extent to which a vehicle's environmental sensor is capable of detecting the surrounding area.

[0022] Furthermore, it is preferably intended that the respective driving paths of the numerous motor vehicles are recorded on several different road segments at various geographical locations, and that a majority of the conditions for the different road segments are determined, and an overtaking probability map is generated with the majority of the conditions at the various geographical locations. The overtaking probability map allows the majority of the conditions to be visualized as overtaking probabilities. Furthermore, at least one attribute from at least one geographic information system can be added to the overtaking probabilities via the overtaking probability map. For example, a road attribute can be added to the overtaking probability map.The road attribute can then describe, for example, the road category assigned to the road segment or its current structural condition. For instance, the road segment might have a damaged surface or be under construction or repair. The overtaking probability map provides a general overtaking probability. This means that if a vehicle moves onto a road segment shown on the overtaking probability map, the map can display the overtaking probability for that vehicle at its current position. The vehicle's location on the map can be determined, for example, using a GNSS receiver (GNSS - global navigation satellite system).

[0023] It is also possible to compare an individual driver's overtaking behavior with the overtaking probabilities in the overtaking probability database, and to assign the driver to an overtaking probability class (especially one independent of location) based on a driving class-specific driving style value that describes the driver's risk tolerance during an overtaking maneuver. For example, a driver can be assigned a driving style value by comparing their current driving or overtaking behavior with the overtaking probabilities in the database. If the driver is traveling on the section of road at a specific geographical location, it can be determined how the driver behaves in relation to the vehicles in the overtaking probability database.If this is then compared, particularly across several different sections of the route, the driver can be assigned to a location-independent overtaking probability class. This overtaking probability class indicates, for example, whether the driver is risk-taking (i.e., eager to overtake) or safety-conscious (i.e., less inclined to overtake). There can be multiple overtaking probability classes, each associated with a different driving style. The driving style score describes the driver's risk tolerance during the overtaking maneuver. This score can be implemented, for example, as a weighting factor. The overtaking probability can then be calculated using the driving style score, for instance, arithmetically. An advantage of the driving style score is that the vehicle's current position is no longer required for its application.The driving style score can, for example, classify the driver as safety-conscious, and the vehicle can then be operated accordingly for future overtaking maneuvers. The vehicle's position is only considered when determining individual overtaking behavior, specifically for comparison with the overtaking probability database. The driving style score can therefore be used regardless of location, i.e., without knowing the vehicle's position.

[0024] The following describes an unclaimed method for operating a motor vehicle's control unit. In contrast to the preceding method, which concerns the collection of overtaking probabilities, the method described below complementarily involves using this collected data to support an overtaking maneuver. In this method, an overtaking probability map is read from the motor vehicle. The overtaking probability map is generated from a collection of overtaking probabilities containing at least one recorded overtaking probability, specifically according to the method described above. The overtaking probability, in turn, is determined based on the ratio between overtaking vehicles and non-overtaking vehicles, also specifically according to the method described above.Depending on the overtaking probability map, a control signal is issued by the motor vehicle.

[0025] The overtaking probability map can be read from an internal vehicle memory where the map is stored. Alternatively, it can be read from an external server where the map is stored. In this case, the vehicle is preferably wirelessly connected to the external server.

[0026] The control signal can then, for example, be used to issue a message to the driver of the motor vehicle, or it can be used to intervene in at least one component of the motor vehicle's drivetrain.

[0027] The following describes a further, unclaimed method for operating a control device of a motor vehicle, in which a driving style value, i.e. a numerical value or another symbol characterizing the driving style, is read from the motor vehicle, which is generated on the basis of an overtaking probability collection with at least one recorded overtaking probability, wherein the overtaking probability is determined on the basis of a ratio between overtaking vehicles and non-overtaking vehicles, in order to output the control signal of the motor vehicle depending on the driving style value.

[0028] The driving style value, in turn, describes a driver's risk tolerance during an overtaking maneuver. This driving style value can be read, for example, from an internal vehicle memory or from an external device connected to the vehicle's wheel.

[0029] The control signal can in turn provide the driver with a hint, or it can be used to control a component of the vehicle's drivetrain depending on the control signal.

[0030] In particular, the motor vehicle can be operated at least semi-autonomously, depending on the control signal.

[0031] Preferably, the control signal specifies a target state of charge and / or a target temperature of a main energy storage device and / or a target drive torque of a vehicle's drive unit. For example, the control signal can specify the target state of charge or at least a target state of charge range, ensuring sufficient energy is available for an upcoming overtaking maneuver. This allows the energy storage device to be recharged, for instance, by an internal combustion engine so that it can provide sufficient energy for the electric motor during the overtaking process, or the vehicle can be operated more economically before the overtaking maneuver. The control signal can also be used to specify the target temperature of the main energy storage device.For example, the vehicle can be prepared for an upcoming overtaking maneuver identified based on the overtaking probability map and / or driving style data. Since overtaking typically draws up to the maximum available power, at least temporarily, it is necessary for the main energy storage unit to be at a temperature below the target temperature or a temperature limit before the overtaking maneuver, so that the energy for the overtaking maneuver can be provided as desired. Additionally or alternatively, the target drive torque of the drive unit and the control signal can also be specified, particularly when an overtaking maneuver is identified as imminent based on the overtaking probability map and / or driving style data.

[0032] The vehicle is specifically designed as a hybrid vehicle with an electric motor and an internal combustion engine. The energy storage system is primarily intended for the electric motor. The drive unit is preferably designed as an electric motor. The control signal allows the vehicle to be prepared for an upcoming overtaking maneuver.

[0033] Furthermore, it can be provided that a target vehicle ahead is overtaken by the motor vehicle depending on the control signal. The motor vehicle can also be controlled by the control signal in such a way that an overtaking maneuver depends at least partially on the control signal. For example, the motor vehicle can be operated at least semi-autonomously, meaning that intervention in an acceleration or deceleration device of the motor vehicle can be carried out by means of the control signal.

[0034] When overtaking, the steering angle and / or distance to the target vehicle and / or the activity of the vehicle's turn signal indicator can be taken into account. By evaluating further vehicle-internal signals such as the steering angle, distance, and turn signal indicator activity, the overtaking maneuver, which is carried out depending on the overtaking probability map and / or the driving style score, can be performed more safely. The overtaking probability can be adjusted based on the steering angle and / or distance and / or turn signal indicator activity, and therefore predicted more accurately for the specific current situation.

[0035] The invention also relates to an overtaking probability collection device with an overtaking probability collection generated according to the inventive method. The overtaking probability collection device is, in particular, designed as a vehicle-external server. The server can, for example, consist of several distributed server units or be a single, central unit.

[0036] The overtaking probability collection device has, in particular, an interface with which at least one motor vehicle can establish a wireless coupling or connection and via which at least part of the data of the overtaking probability collection can be transmitted.

[0037] The overtaking probability collection is specifically designed as a database.

[0038] Furthermore, a control device for at least preparing an overtaking maneuver of a motor vehicle with a main energy storage unit and a drive unit is possible, which has an interface that is set up to be controlled with a control signal of a method according to the invention for operating a control device.

[0039] The motor vehicle is preferably designed as a hybrid vehicle with an electric motor and an internal combustion engine.

[0040] Advantageous embodiments of the methods according to the invention are to be regarded as advantageous embodiments of the overtaking probability collection device or the control device.

[0041] Further features of the invention will become apparent from the claims, the figures, and the description of the figures. The features and combinations of features mentioned above in the description, as well as the features and combinations of features mentioned below in the description of the figures and / or shown in the figures alone, can be used not only in the combinations specified, but also in other combinations or on their own, without departing from the scope of the invention.

[0042] Exemplary embodiments of the invention are explained in more detail below with reference to schematic drawings.

[0043] They show: Fig. 1 is a schematic representation of an embodiment of a method according to the invention for generating an overtaking probability collection; Fig. 2 is a schematic representation of the method in which an overtaking probability map is generated based on the overtaking probability collection and / or a driver of a motor vehicle is assigned to an overtaking probability class; and Fig. 3 is a schematic representation of an embodiment of a method for operating a control device by which a control signal is output depending on the overtaking probability map and / or the driving style value.

[0044] In the figures, identical or functionally equivalent elements are given the same reference symbols.

[0045] Fig. 1 Figure 1 illustrates an exemplary procedure for generating an overtaking probability database 1. The overtaking probability database 1 is specifically designed as a database, for example, a relational database, and, according to the embodiment, is stored in an overtaking probability database 2. The overtaking probability database 2 is, for example, designed as a server. The overtaking probability database 2 can be, for example, centralized or distributed, so that it has different subunits, which are located, for example, at different geographical locations. The overtaking probability database 2 can also be referred to as the backend.

[0046] A section of road 3 is shown at a geographical position 4. Section 3 is shown at a first time 5 and at a second time 6. According to the exemplary embodiment, the first time 5 differs from the second time 6. Section 3 is, in particular, any road segment which, for example, as shown in the exemplary embodiment, has a first lane 7 and a second lane 8. The first lane 7 and the second lane 8 can be designed for opposite or parallel traffic.

[0047] In section 3 of the route at time 5, a first motor vehicle 9 is shown. Furthermore, in section 3 of the route at time 5, a first target vehicle 10 is shown. The first target vehicle 10 is positioned in front of the first motor vehicle 9 and, according to the embodiment, is moving in the same direction as the motor vehicle 9.

[0048] A first driving profile 11 is recorded in the first vehicle 9. This first driving profile 11 can, for example, be a trajectory of the first vehicle 9 or simply a positional relationship or sequence between the first vehicle 9 and the first target vehicle 10. Based on the first driving profile 11, it can be determined whether the first vehicle 9 overtakes the first target vehicle 10. On the track segment 3 at the second time 6, a second vehicle 12 is traveling. The second vehicle 12 can be different from or identical to the first vehicle 9. Preferably, however, the second vehicle 12 is different from the first vehicle 9. Analogous to the first driving profile 11, the second vehicle 12 has a second driving profile 13. Based on the second driving profile 13, it is recognized that the second vehicle 12 overtakes a second target vehicle 14.

[0049] The first vehicle (9) and the second vehicle (12) constitute a multiple vehicle. However, multiple journey paths of other vehicles on route segment 3 can also be recorded.

[0050] Based on the respective driving sequence 11, 13, the motor vehicles 9, 12 are then assigned either to a class of overtaking vehicles 15 or to a class of non-overtaking vehicles 16. According to the embodiment of Fig. 1 For example, the first motor vehicle 9 is assigned to class 16 (non-overtaking vehicles), while the second motor vehicle 12 is assigned to class 16 (overtaking vehicles). The assignment to the respective classes 15 and 16 is made, in particular, in the overtaking probability collection unit 2, preferably after the driving profiles 11 and 13 have been transmitted to the overtaking probability collection unit 2.

[0051] The assignment to the respective class 15 can also be made in the respective motor vehicle 9, 12 itself, so that only the respective class 15, 16 is transferred to the overtaking probability collection device 2.

[0052] In particular, the motor vehicles 9, 12 can be coupled with the overtaking probability collection device 2 in order to transmit the respective driving profile 11, 13 or the respective class 15, 16.

[0053] However, information about overtaking vehicles 15 and non-overtaking vehicles 16 can also be determined, for example, using mobile phone data or other characteristics that can be traced to an overtaking maneuver between motor vehicles. It is therefore possible that the motor vehicles 9, 12 are no longer connected to the overtaking probability collection device 2, since the information about the overtaking status of the motor vehicles 9, 12 is obtained through external monitoring, for example, by remote sensing methods.

[0054] A ratio 17 is determined between the overtaking vehicles 15 and the non-overtaking vehicles 16. This ratio 17 can, for example, be expressed as a percentage. Furthermore, this ratio 17 is entered into the overtaking probability database 1 as an overtaking probability 18 for the section 3. Preferably, the overtaking probability database 1 is generated by the initial entry of the overtaking probability 18.

[0055] Additionally or alternatively, when the first journey 11 is recorded, a first time of day 19 is recorded, and when the second journey 13 is recorded, a second time of day 20 is recorded. The times of day 19 and 20 can be, for example, specific times or periods of the day. The first time of day 19 and the second time of day 20 can be the same or different. According to the exemplary embodiment, the first time of day 19 and the second time of day 20 are different, so the first time of day 19 is classified into a first time-of-day class 21, and the second time of day 20 is classified into a second time-of-day class 22. Thus, the first vehicle 9 is also classified into the first time-of-day class 21, and the second vehicle 12 is classified into the second time-of-day class 22. Based on the time-of-day classes 21 and 22, a time-of-day-class-dependent ratio 23 can then be determined.The time-of-day-class-dependent ratio 23 can be determined as the overtaking probability 18 or as a further overtaking probability, which is entered in the overtaking probability collection 1 alongside the overtaking probability 18. In particular, the overtaking probability 18 or the time-of-day-class-dependent overtaking probability can then be determined for the different times of day 19, 20.

[0056] Furthermore, additionally or alternatively, when recording the first journey 11, a first weather condition 24 is also recorded. Similarly, when recording the second journey 13, a second weather condition 25 is recorded. The recorded journeys 11 and 13, and the recorded vehicles 9 and 12, are then classified into weather condition classes based on weather conditions 24 and 25. For example, the first vehicle 9 with the first weather condition 24 can be classified into a first weather condition class 26, and the second vehicle 12, which is recorded with the second weather condition 25, can be classified into a second weather condition class 27. Based on weather condition classes 26 and 27, a weather-class-dependent ratio 28 can then be determined, analogous to ratio 17.

[0057] Analogous to the time-of-day class-dependent ratio 23, the weather-condition class-dependent ratio 28 can also be entered as the overtaking probability 18 in the overtaking probability collection, or it can be entered in the overtaking probability collection in addition to the overtaking probability 18.

[0058] The weather conditions, for example, determine the degree of rainfall, the degree of sunshine, or the outside temperature status.

[0059] In addition or alternatively, a visibility ratio is also recorded when recording the respective journey path 11, 13. For example, a first visibility ratio 29 is recorded for the first journey path 11, and a second visibility ratio 30 is recorded for the second journey path 13. The visibility ratios 29, 30 are then classified into at least a first visibility class 31 or a second visibility class 32 for the time of day 19, 20 and the weather conditions 24, 25. Furthermore, a visibility class-dependent ratio 33 is then determined, particularly in each of the visibility class 31, 32. The visibility class-dependent ratio 33 is determined analogously to the time-of-day-dependent ratio 23 or the weather-condition-dependent ratio 28.

[0060] The visibility conditions 29, 30 describe how far a driver of the respective motor vehicle 9, 12 can see the traffic situation in front of him or in the direction of travel, or the surrounding area in the direction of travel, and / or how far he can recognize objects, especially obstacles, within it. The respective visibility conditions 29, 30 can also describe, for example, how far an environmental detection sensor of the respective motor vehicle 9, 12 can detect obstacles in the surrounding area in the direction of travel. A radar sensor, for example, is weather-sensitive in a different way than a camera in the visible spectral range.

[0061] Using the overtaking probability collection 1, the overtaking probability 18 can finally be numerically determined for section 3. This means it can be stated how likely it is that a motor vehicle passing through section 3 will perform an overtaking maneuver. The overtaking probability 18 can be specified in particular for the time-of-day classes 21 and 22, the weather conditions classes 26 and 27, and the visibility conditions classes 31 and 32.

[0062] In particular, the respective driving path 11, 13 is recorded at several different track sections at various geographical locations. From the resulting plurality of ratios of overtaking vehicles 15 to non-overtaking vehicles 16, it is possible to determine for the different track sections – as in the embodiment of Fig. 2 shown - an overtaking probability map 34 is generated.

[0063] The overtaking probability map 34 is available as a collection in which various overtaking probabilities 18 are entered on different sections of the route 3 depending on their geographical position.

[0064] When generating the overtaking probability map 34, at least one attribute 35 from a geographic information system 36 can be added to the overtaking probability map 34. Attribute 35 can, for example, be a road category describing route segment 3. However, attribute 35 can also contain a variety of other information about the environment through which route segment 3 passes. For example, the attribute can describe road conditions, buildings, or vegetation. Attribute 35 also includes, for example, the number of existing lanes.

[0065] The overtaking probability map 34 can then be provided to another motor vehicle, for example, by the overtaking probability collection device 2.

[0066] Fig. 2 However, it also shows how an individual overtaking behavior 37 of a driver 38 of a third vehicle 39 is recorded and compared with the overtaking probabilities 18. The third vehicle 39 can be identical or different to the first vehicle 9 or the second vehicle 12. Based on the comparison of the overtaking behavior 37 with the overtaking probability 18, the driver 38 is assigned to a location-independent overtaking probability class 40. Preferably, there are several overtaking probability classes 40. Each of the overtaking probability classes 40 has a class-specific driving style value 41. The driving style value 41 describes the risk tolerance of the driver 38 during an overtaking maneuver 42. The driving style value 41 can, for example, be specified as a weighting factor.

[0067] According to the embodiment of Fig. 2 The driver 38 is assigned either to a first overtaking probability class 43 or a second overtaking probability class 44. According to the exemplary embodiment, the first overtaking probability class 43 has a first driving style value 45 and the second overtaking probability class 44 has a second driving style value 46.

[0068] Fig. 3 Figure 47 shows a fourth motor vehicle on a roadway 48. A third target vehicle 49 is driving in front of the fourth motor vehicle 47.

[0069] The fourth motor vehicle 47 has a main energy storage unit 50 and a drive unit 51. According to the exemplary embodiment, the fourth motor vehicle 47 is designed as a hybrid vehicle and the drive unit 51 is designed as an electric motor which is supplied with energy by the main energy storage unit 50.

[0070] Furthermore, the fourth motor vehicle 47 has a control unit 52. The control unit 52 is configured, for example, as an overtaking preparation device or as an overtaking assistance device. The control unit 52 has an interface 53 which can be controlled by a control signal 54.

[0071] According to one embodiment, when the control unit 52 is operated, the overtaking probability map 34 is read and the control signal 54 is output depending on the overtaking probability map 34. The control signal 54 can then, for example, be used to issue a message to the fourth vehicle 47 or to intervene in the control of the fourth vehicle 47.

[0072] Additionally or alternatively, the control unit 52 can be operated by reading the driving style value 41 and outputting the control signal 54 depending on the driving style value 41.

[0073] The control signal, which is output depending on the overtaking probability map 34 or the driving style value 31, can then, for example, specify a target state of charge 55 and / or a target temperature 56 of the main energy storage 50 and / or a target drive torque 57 of the drive unit 51.

[0074] For example, depending on the overtaking probability map and / or the driving style value, it can be recognized that another driver 58 of the fourth vehicle 47 is likely to initiate an overtaking maneuver 59. If the probability of this assumption is high based on the overtaking probability map (i.e., if the fourth vehicle 47 is traveling on track segment 3 with geoposition 4) and / or the driving style value 41 (i.e., if the other driver 58 has an above-average inclination to overtake, regardless of location), then the control signal 54 is output to prepare the overtaking maneuver 59. For preparing the overtaking maneuver, for example, it is useful to ensure that sufficient energy is available from the main energy storage unit 50 and that the main energy storage unit 50 has a current temperature that allows energy to be drawn for the overtaking maneuver to the intended extent.The control signal 54 can, for example, also be used to engage a lower gear in preparation for the overtaking maneuver 59. Alternatively, the target drive torque 57 of the drive unit 51 can be specified, so that the drive unit 51 is prepared for the overtaking maneuver 59.

[0075] The fourth motor vehicle 47 can be different from or identical to the third motor vehicle 39, the first motor vehicle 9, or the second motor vehicle 12. Thus, the overtaking probability map 34 and / or the driving style value 41 can be applied by motor vehicle 47, and at the same time, the overtaking probability 18 can be determined or adjusted.

[0076] The control signal 54 is therefore output depending on an expected overtaking probability. The expected overtaking probability is determined, for example, using at least one machine learning or statistical method. By incorporating the overtaking probability 18 into the preparation or execution of overtaking maneuvers 59, the motor vehicle 47 is operated more safely.

[0077] The willingness to overtake in certain situations depends on individual driving style. By calculating driver-specific overtaking probabilities, it is possible to adapt overtaking preparation measures and / or the overtaking maneuver itself to the individual driving behavior of the other driver.

[0078] Another advantage of the overtaking probability collection system 1 is the increased predictive range or forecast horizon. With the commonly used in-vehicle signals, the overtaking probability can only be determined shortly before the overtaking maneuver, which is too short-term for adjusting the operating strategy, e.g., increasing the state of charge of a traction battery. The server-based approach provided by the overtaking probability collection system 2 allows the locations to be determined several kilometers in advance, enabling the components in the powertrain to be controlled in a timely manner.

Claims

1. Computer-implemented method for producing an overtaking probability collection (1), having the following steps: - recording a respective driving characteristic (11, 13) from a multiplicity of motor vehicles (9, 12) passing through at least one route section (3) at a geographical position (4); characterized by the steps of: - assigning the respective motor vehicles (9, 12) to a class of overtaking vehicles (15) or to a class of non-overtaking vehicles (16) on the basis of the respective driving characteristic (11, 13); - determining a ratio (17) between a number of the overtaking vehicles (15) and a number of the non-overtaking vehicles (16); and - entering the ratio (17) into the overtaking probability collection (1) as an overtaking probability (18) for the route section (3) at the geographical position (4).

2. Method according to Claim 1, wherein the recording of the respective driving characteristic (11, 13) involves recording a respective time of day (19, 20) for which the respective driving characteristic (11, 13) is recorded, and the motor vehicles (9, 12) are categorized into time-of-day classes (21, 22) on the basis of the time of day (19, 20) and a respective time-of-day-class-dependent ratio (23) is determined and the respective time-of-day-class-dependent ratio (23) is entered into the overtaking probability collection (1) for the route section (3).

3. Method according to Claim 1 or 2, wherein the recording of the respective driving characteristic (11, 13) involves recording a respective weather condition (24, 25) for which the respective driving characteristic (11, 13) is recorded, and the motor vehicles (9, 12) are categorized into weather-condition classes (26, 27) on the basis of the weather condition (24, 25) and a respective weather-condition-class-dependent ratio (28) is determined and the respective weather-condition-class-dependent ratio (28) is entered into the overtaking probability collection (1) for the route section (3).

4. Method according to one of the preceding claims, wherein the recording of the respective driving characteristic (11, 13) involves recording a respective visibility (29, 30) for which the respective driving characteristic (11, 13) is recorded, and the motor vehicles (9, 12) are categorized into visibility classes (31, 32) on the basis of the visibility (29, 30) and a respective visibility-class-dependent ratio (33) is determined and the respective visibility-class-dependent ratio (33) is entered into the overtaking probability collection (1) for the route section (3).

5. Method according to one of the preceding claims, wherein the respective driving characteristic (11, 13) of the multiplicity of motor vehicles (9, 12) is recorded on multiple different route sections (3) at different geographical positions (4) and a plurality of the ratios (17) are determined for the different route sections (3) and an overtaking probability map (34) is produced using the plurality of ratios (17) at the different geographical positions (4).

6. Overtaking probability collecting device (2) having an overtaking probability collection (1) produced in accordance with a method according to one of Claims 1 to 5.