Vehicle, driver assistance system and method for controlling a vehicle on hills or dips

Abstract

Method for controlling a vehicle (100), wherein predetermined route data, in particular from a digital map, are analyzed (202) in an analysis with regard to a gradient of incline and decline, and wherein in the analysis, a hilltop lying ahead is recognized on the basis of an incline in the route data that is greater than a first threshold value, and a subsequent incline in the route data that is less than a second threshold value, and a subsequent decline in the route data that is greater than a third threshold value, and / or wherein in the analysis, a depression lying ahead is recognized on the basis of a decline in the route data that is greater than a first threshold value, and a subsequent decline in the route data that is less than a second threshold value, and a subsequent incline in the route data that is greater than a third threshold value,and wherein the vehicle (100) is controlled (206) to a functional reaction which is determined (204) depending on a result of the analysis, , wherein the preceding crest is recognized in the analysis (202) if the gradient in the route data for at least a first route is greater than the first threshold value, and / or if the subsequent gradient in the route data for at least a second route is smaller than the second threshold value, and / or if the subsequent gradient in the route data for at least a third route is greater than the third threshold value, wherein the preceding depression is recognized in the analysis (202) if the gradient in the route data is greater than the first threshold value for at least a first route, and / or if the subsequent gradient in the route data is smaller than the second threshold value for at least a second route, and / or if the subsequent gradient in the route data is greater than the third threshold value for at least a third route, and wherein in the analysis a category of the preceding crest or the preceding depression is determined depending on different first thresholds, depending on different second thresholds and / or depending on different third thresholds (202), wherein the functional response is determined depending on the category (204).

Description

[0001] The invention relates to a vehicle, a driver assistance system and a method for controlling a vehicle on hills or dips.

[0002] DE 10 2009 045 321 A1 already relates to a method for controlling a vehicle, in which, in accordance with the invention, predetermined route data are analyzed in an analysis with regard to a gradient of uphill and downhill, and wherein in the analysis a hillock lying ahead is recognized on the basis of an uphill gradient in the route data and a subsequent downhill gradient in the route data, and / or wherein in the analysis a dip lying ahead is recognized on the basis of a downhill gradient in the route data that is greater than a first threshold value and a subsequent downhill gradient in the route data that is smaller than a second threshold value and a subsequent uphill gradient that is greater than a third threshold value, and wherein the vehicle is controlled to a functional reaction that is determined depending on a result of the analysis.

[0003] DE 199 47 408 C2, DE 10 2004 006 133 B4 and DE 10 2018 219 604 A1 disclose further methods for controlling a vehicle on hills or valleys.

[0004] The object of the invention is to provide an improved method for controlling a vehicle on hills and valleys.

[0005] The invention is defined in claim 1.

[0006] The method for controlling a vehicle at crests or dips according to independent claim 1 provides that predetermined route data, in particular from a digital map, are analyzed in an analysis with regard to a gradient of uphill and downhill, and wherein in the analysis, a crest lying ahead is recognized on the basis of an uphill gradient in the route data that is greater than a first threshold value, and a subsequent uphill gradient in the route data that is less than a second threshold value, and a subsequent downhill gradient in the route data that is greater than a third threshold value, and / or wherein in the analysis, a dip lying ahead is recognized on the basis of a downhill gradient in the route data that is greater than a first threshold value, and a subsequent downhill gradient in the route data that is less than a second threshold value, and a subsequent uphill gradient that is greater than a third threshold value,and wherein the vehicle is controlled to a functional reaction that is determined depending on a result of the analysis. The reaction eliminates functional limitations, e.g., comfort and safety limitations, of assisted and highly automated driving in a frequently occurring driving situation. In the analysis, the upcoming crest is detected if the gradient is greater than the first threshold for at least a first stretch, and / or if the subsequent gradient in the route data is smaller than the second threshold for at least a second stretch, and / or if the subsequent gradient in the route data is greater than the third threshold for at least a third stretch. This ensures that these situations are safely mastered. In the analysis, the upcoming depression is detected if the gradient is greater than the first threshold for at least a first stretch, and / or if the subsequent gradient in the route data,for at least a second stretch is smaller than the second threshold, and / or if the subsequent gradient in the stretch data is greater than the third threshold for at least a third stretch. This ensures that these situations are reliably managed. In the analysis, a category of the upcoming crest or valley is determined based on various first thresholds, depending on various second thresholds, and / or depending on various third thresholds, whereby the functional response is determined depending on the category. Different strict thresholds can be used to distinguish between sharp crests, medium crests, and moderate crests. Different strict thresholds can be used to distinguish between sharp crests, medium crests, and moderate crests. The functional response can be adapted to these.

[0007] Preferably, a raw value of the gradient is determined from the route data, in particular by deriving the route data, and the gradient is determined as a function of the raw value of the gradient filtered with a low-pass filter.

[0008] Preferably, a raw value of the gradient is determined from the route data, in particular by deriving the route data, and the gradient is determined as a function of the raw value of the gradient filtered with a low-pass filter.

[0009] Preferably, the gradients are verified using on-board sensors. This makes the process robust.

[0010] Preferably, signals from an inclination sensor or acceleration information with which an inclination is modelled are used in the vehicle to check whether the course of the incline or decline characterised by the signals lies within a tolerance range to the course of the incline or decline from the route data.

[0011] As a functional response, for example, in the case of a predictive cruise control system, the speed of the vehicle is reduced; in the case of steering assistance, a mechanism for stabilizing estimated lane markings or for limiting steering torques is activated; in the case of hands-free systems, a driver takeover is initiated in good time beforehand; in the case of a highly automated system, a handover to a driver of the vehicle is initiated in good time beforehand; and / or as the functional response to a detected crest in the case of a sensor-based longitudinal control system, a loss of the target object due to the crest is compensated by a temporary bridging; high beam control is adjusted to avoid possible dazzling of oncoming traffic, and / or a warning of the crest and / or information is issued to the driver, particularly without a controlling driver assistance system.that at a sufficiently high speed of the vehicle, control over the vehicle could be lost and / or as the functional reaction to a detected dip, a driver of the vehicle is alerted that, due to a ground clearance, a collision of a front spoiler of the vehicle in the dip is imminent, and / or a driver is offered an automatic lifting of a front end or that the automatic lifting of the front end is carried out.

[0012] The driver assistance system for controlling a vehicle on hills or dips or the vehicle comprising this driver assistance system is designed to carry out the method and has corresponding advantages.

[0013] Further advantageous embodiments are evident from the following description and the drawing. The drawing shows: Fig. 1 a vehicle with a driver assistance system, Fig. 2 a flowchart with steps in a method for controlling the vehicle.

[0014] In Fig. 1 schematically shows a vehicle 100 with a driver assistance system 102. The driver assistance system 102 is designed to control the vehicle 100 when traveling over hills or dips. In the example, the vehicle 100 includes an inclination sensor system 104 and an instrument cluster 106. The inclination sensor system 104 is designed to detect an inclination of the vehicle 100. In the example, the instrument cluster 106 includes a graphical and / or acoustic interface for outputting information. The driver assistance system 102 is connected to the inclination sensor system 104 for transmitting signals and to the instrument cluster 106 for transmitting information via a communication connection 108.

[0015] The driver assistance system 102 is designed to carry out a method for controlling the vehicle 100, as described below.

[0016] The method includes a step 202.

[0017] In step 202, given route data are analyzed in an analysis with regard to a gradient of incline and decline.

[0018] In the example, the route data comes from a digital map.

[0019] In the analysis, an upcoming crest is detected based on an incline in the route data that is greater than a first threshold for crest detection, a subsequent incline in the route data that is less than a second threshold for crest detection, and a subsequent decline in the route data that is greater than a third threshold for crest detection.

[0020] In one example, the analysis detects the upcoming crest if the gradient for at least a first section is greater than the first threshold for crest detection.

[0021] In one example, the analysis detects the upcoming hilltop if the subsequent gradient in the route data is smaller than the second threshold for hilltop detection for at least a second route.

[0022] In one example, the analysis detects the upcoming crest if the subsequent gradient in the route data is greater than the third threshold for crest detection for at least a third route.

[0023] Preferably, the crest is detected if, based on the route data, a course of the route is detected that meets these conditions for crest detection.

[0024] In the analysis, a depression ahead is detected based on a gradient in the route data that is greater than a first threshold for depression detection, followed by a gradient in the route data that is smaller than a second threshold for depression detection, and followed by an gradient that is greater than a third threshold for depression detection.

[0025] In one example, the analysis detects the upcoming depression if the gradient for at least a first section is greater than the first threshold for depression detection.

[0026] In one example, the analysis detects the upcoming depression if the subsequent gradient in the route data is smaller than the second threshold for depression detection for at least a second route.

[0027] In one example, the analysis detects the upcoming dip if the subsequent gradient in the route data is greater than the third threshold for dip detection for at least a third route.

[0028] Preferably, the depression is detected when, based on the route data, a course of the route is detected that meets these conditions of depression detection.

[0029] The analysis determines a category of the upcoming crest or valley.

[0030] A category of the upcoming hilltop is determined depending on various initial threshold values for hilltop detection.

[0031] A category of the upcoming hilltop is determined depending on various second threshold values for hilltop detection.

[0032] A category of the upcoming hilltop is determined, for example, depending on various third threshold values for hilltop detection.

[0033] For at least two of the three thresholds or for all three thresholds, different thresholds for different categories are provided for the peak detection.

[0034] For example, three different threshold values are specified. This determines one of three possible categories of peaks for each peak. More or fewer different threshold values and categories can also be specified for the peaks.

[0035] For example, it is provided that a first category of hilltop is recognized for an incline that is greater than a smallest of the first threshold values for hilltop detection and smaller than the other of the first threshold values for hilltop detection. For example, it is provided that the first category of hilltop is recognized for a subsequent incline that is smaller than a smallest of the second threshold values for hilltop detection and smaller than the other of the second threshold values for hilltop detection. For example, it is provided that the first category of hilltop is recognized for a decline that is greater than a smallest of the third threshold values for hilltop detection and smaller than the other third threshold values for hilltop detection.

[0036] For example, it is provided that a second category of crest is recognized for an uphill gradient that lies between two first threshold values for crest detection. For example, it is provided that the second category of crest is recognized for a subsequent uphill gradient that lies between two second threshold values for crest detection. For example, it is provided that the second category of crest is recognized for a subsequent downhill gradient that lies between two third threshold values for crest detection.

[0037] For example, it is provided that a third category of hilltop is recognized for an incline that is greater than a largest of the first threshold values for hilltop detection. For example, it is provided that the third category of hilltop is recognized for a subsequent incline that is greater than a largest of the second threshold values for hilltop detection. For example, it is provided that the third category of hilltop is recognized for a decline that is greater than a largest of the third threshold values for hilltop detection.

[0038] For example, a category of the sink ahead is determined depending on different initial thresholds for sink detection.

[0039] For example, a category of the sink ahead is determined depending on different second thresholds for sink detection.

[0040] For example, a category of the sink ahead is determined depending on different third thresholds for sink detection.

[0041] For example, three different thresholds are specified. This determines one of three possible categories of depressions for each peak. More or fewer different thresholds and categories can also be specified for the depressions.

[0042] Preferably, different thresholds for different categories are provided for at least two of the three thresholds or for all three thresholds for sink detection.

[0043] For example, it is provided that a first category of depression is detected for a downhill gradient that is greater than a smallest of the first threshold values for depression detection and smaller than the other of the first threshold values for depression detection. For example, it is provided that the first category of depression is detected for a subsequent uphill gradient that is smaller than a smallest of the second threshold values for depression detection and smaller than the other of the second threshold values for depression detection. For example, it is provided that the first category of depression is detected for a downhill gradient that is greater than a smallest of the third threshold values for depression detection and smaller than the other third threshold values for depression detection.

[0044] For example, a second category of depression is detected for a downhill slope that lies between two first thresholds for depression detection. For example, the second category of depression is detected for a subsequent downhill slope that lies between two second thresholds for depression detection. For example, the second category of depression is detected for a subsequent uphill slope that lies between two third thresholds for depression detection.

[0045] For example, a third category of depression is identified for a gradient that is greater than the largest of the first thresholds for depression detection. For example, a third category of depression is identified for a subsequent gradient that is greater than the largest of the second thresholds for depression detection. For example, a third category of depression is identified for an incline that is greater than the largest of the third thresholds for depression detection.

[0046] It may be stipulated that all the conditions for a category must be met in order to select a category, or that individual conditions can be met selectively in order to select a corresponding category.

[0047] In one embodiment, a raw value of the gradient is determined from the route data, in particular by deriving the route data.

[0048] In one embodiment, the slope is determined depending on the raw value of the slope filtered with a low-pass filter.

[0049] In one embodiment, a raw value of the gradient is determined from the route data, in particular by deriving the route data.

[0050] In one embodiment, the gradient is determined depending on the raw value of the gradient filtered with a low-pass filter.

[0051] In one embodiment, the gradient of the uphill and downhill slope is checked for plausibility using in-vehicle sensors. For example, signals from the inclination sensor system 104 or acceleration information used to model an inclination are used in the vehicle 100 to check whether the gradient of the uphill or downhill slope characterized by the signals lies within a tolerance range relative to the gradient of the uphill or downhill slope from the route data. If the tolerance range is exceeded, the analysis results in neither a crest nor a depression being detected, for example.

[0052] A step 204 is then executed.

[0053] In step 204, a functional response is determined depending on a result of the analysis.

[0054] A step 206 is then executed.

[0055] In step 206, the vehicle 100 is controlled to perform the functional response. If different categories are provided, the functional response is determined depending on the detected category.

[0056] As a functional reaction, for example, in the case of a predictive adaptive cruise control, the speed of the vehicle is reduced by 100.

[0057] As a functional response, for example, in the case of steering assistance, a mechanism is activated to stabilize estimated lane markings or to limit steering torques.

[0058] As a functional reaction, for example, with a hands-free system, driver takeover is initiated in good time beforehand.

[0059] As the functional reaction, for example, in a highly automated system, a handover to a driver of the vehicle 100 is initiated in good time beforehand.

[0060] As the functional reaction to a detected hill, for example in a sensor-based longitudinal control system, a loss of the target object due to the hill is compensated by a temporary bridging.

[0061] As a functional reaction to a detected hilltop, for example, high beam control is adjusted to avoid possible dazzling of oncoming traffic.

[0062] As a functional reaction to a detected hilltop, for example, a warning about the hilltop is issued.

[0063] As a functional reaction to a detected hilltop, for example, information is output to the driver, particularly without the controlling driver assistance system 104, e.g., on the instrument cluster 106 in the vehicle 100. The information includes, for example, an indication that control of the vehicle 100 could be lost at a correspondingly high speed of the vehicle 100.

[0064] As a functional reaction to a detected depression, the driver of the vehicle 100 is informed, for example, on the instrument cluster 106, that there is a risk of a collision of a front spoiler of the vehicle 100 in the depression due to ground clearance.

[0065] As a functional reaction to a detected dip, the driver is offered, for example, an automatic lifting of the front of the vehicle.

[0066] As a functional reaction to a detected dip, for example, the front of the vehicle is automatically raised.

[0067] If a crest or dip is detected in the analysis, the response of vehicle 100 may include one or more of these functional responses. Otherwise, none of these functional responses are executed in the example.

Claims

[1] Method for controlling a vehicle (100), wherein predetermined route data, in particular from a digital map, are analyzed (202) in an analysis with regard to a gradient of incline and decline, and wherein in the analysis, a hilltop lying ahead is recognized on the basis of an incline in the route data that is greater than a first threshold value, and a subsequent incline in the route data that is less than a second threshold value, and a subsequent decline in the route data that is greater than a third threshold value, and / or wherein in the analysis, a depression lying ahead is recognized on the basis of a decline in the route data that is greater than a first threshold value, and a subsequent decline in the route data that is less than a second threshold value, and a subsequent incline in the route data that is greater than a third threshold value,and wherein the vehicle (100) is controlled (206) to a functional reaction which is determined depending on a result of the analysis (204), wherein the preceding crest is recognized in the analysis (202) if the gradient in the route data for at least a first route is greater than the first threshold value, and / or if the subsequent gradient in the route data for at least a second route is smaller than the second threshold value, and / or if the subsequent gradient in the route data for at least a third route is greater than the third threshold value, wherein the preceding depression is recognized in the analysis (202) if the gradient in the route data is greater than the first threshold value for at least a first route, and / or if the subsequent gradient in the route data is smaller than the second threshold value for at least a second route, and / or if the subsequent gradient in the route data is greater than the third threshold value for at least a third route, and wherein in the analysis a category of the preceding crest or the preceding depression is determined depending on different first thresholds, depending on different second thresholds and / or depending on different third thresholds (202), wherein the functional response is determined depending on the category (204). [2] Method according to claim 1, characterized bythat a raw value of the gradient is determined from the route data, in particular by a derivation of the route data, and the gradient is determined as a function of the raw value of the gradient filtered with a low-pass filter (202). [3] Method according to one of the preceding claims, characterized by that a raw value of the gradient is determined from the route data, in particular by deriving the route data, and the gradient is determined depending on the raw value of the gradient filtered with the low-pass filter (202). [4] Method according to one of the preceding claims, characterized by that the gradient of the incline and the decline is checked for plausibility using on-board sensors (202). [5] Method according to claim 4, characterized bythat signals from an inclination sensor (104) or acceleration information with which an inclination is modelled are used in the vehicle (100) to check (202) whether the course of the incline or the decline which is characterised by the signals lies within a tolerance range to the course of the incline or the decline from the route data. [6] Method according to one of the preceding claims, characterized bythat, as the functional reaction in a predictive cruise control system, a speed of the vehicle (100) is reduced, in the case of steering assistance, a mechanism for stabilizing estimated lane markings or for limiting steering torques is activated, in the case of a hands-free system, a driver takeover is initiated in good time beforehand, in the case of a highly automated system, a handover to a driver of the vehicle (100) is initiated in good time beforehand, and / or as the functional reaction to the detected crest in a sensor-based longitudinal control system, a loss of the target object due to the crest is compensated by a temporary bridging, a high beam control is adjusted in order to avoid possible dazzling of oncoming traffic, and / or a warning of the crest and / or information to the driver is output, in particular without a controlling driver assistance system (102),that at a correspondingly high speed of the vehicle (100), control over the vehicle (100) could be lost and / or as the functional reaction to the detected depression, the driver of the vehicle (100) is informed that, due to a ground clearance, a collision of a front spoiler of the vehicle (100) in the depression is imminent, and / or the driver is offered an automatic lifting of a front end or that the automatic lifting of the front end is carried out., [7] Driver assistance system (102) for controlling a vehicle (100) on hills or dips, characterized by that the driver assistance system (102) is designed to carry out the method according to one of claims 1 to 6. [8] Vehicle (100), characterized by that the vehicle (100) comprises the driver assistance system (102) according to claim 7.

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