Method and assistance system for determining at least one target parameter for driving through a curve in a road

Abstract

The present invention relates to a method for determining at least one target parameter for driving through a curve in a road (302), wherein profiles of recorded and georeferenced vehicle dynamics data (104) are evaluated to identify a curve section (100) of the road curve (302), wherein the target parameter for at least one point of the curve section (100) is extracted from the vehicle dynamics data (104) recorded for that point.

Description

Field of invention

[0001] The invention relates to a method for determining at least one target parameter for driving through a curve in the road, a corresponding assistance system and a corresponding computer program product. State of the art

[0002] A vehicle may have an assistance system that warns the driver if they approach an upcoming curve at an inappropriate speed.

[0003] DE 10 2014 225 625 A1 describes a method for assisting a driver of a single-track motor vehicle to safely navigate a curve. Disclosure of the invention

[0004] Against this background, the approach presented here introduces a method for determining at least one target parameter for cornering through a road curve, a corresponding assistance system, and a corresponding computer program product according to the independent claims. Advantageous further developments and improvements of the approach presented here result from the description and are described in the dependent claims. Advantages of the invention

[0005] In the approach presented here, at least one curve of a road is identified based on vehicle dynamics data recorded over several journeys through the curve, and at least one value from the vehicle dynamics data is determined as a target variable for at least one relevant point of the curve.

[0006] The approach presented here allows for the differentiation of dynamically interesting curves from uninteresting sections of the track. These curves are identified using real-world driving data and recorded information. Data from various drivers and vehicles is used to identify truly relevant curves for a wide range of driver and vehicle types.

[0007] At at least one point on the curve, a characteristic value is read from the vehicle dynamics data. This value and the coordinates of the point are stored and used to assist a driver before or during a curve, helping them to navigate it safely with an adapted driving style.

[0008] A method is proposed for determining at least one target parameter for a curve driving through a road curve, wherein trajectories of recorded and georeferenced vehicle dynamics data are evaluated to identify a curve section of the road curve, wherein the target parameter for at least one point of the curve section is extracted from the vehicle dynamics data recorded for that point.

[0009] Ideas for embodiments of the present invention can be considered to be based, among other things, on the thoughts and findings described below.

[0010] When a vehicle travels around a curve, a centrifugal force acts on it. This centrifugal force is transferred from the vehicle's wheels to the road. The centrifugal force acts perpendicular to the wheel. The maximum force that can be transmitted at the wheel is physically limited. This force is the resultant force of the centrifugal force and a longitudinal force at the wheel. The longitudinal force arises from braking and accelerating the vehicle. The relationship between centrifugal force, braking or acceleration force, and resultant force is graphically represented by the Kamm circle.

[0011] Even without an acting longitudinal force, the maximum transmissible force limits the vehicle's possible cornering speed. A maximum centrifugal force is reached at the apex of the curve trajectory actually driven by the vehicle. At the apex, the curve trajectory has its smallest radius. The curve trajectory can deviate from the road's path. In particular, the curve trajectory can have a larger radius at the apex than the road's path. At the latest by the apex, the speed must be reduced to at least the maximum possible cornering speed.

[0012] The approach presented here identifies and analyzes curves in data recorded by many different vehicles and drivers along a route. For each detected curve, at least one target parameter is determined from the data. This target parameter is typically a target speed at a vertex of the curve. However, the target parameter can also be a different value for another point on the curve. It is also possible to extract the trend of the target parameter across the curve.

[0013] The target parameter does not correspond to the highest value recorded at that point, but is determined using a majority of the data recorded for that point. In particular, outliers are ignored, for example to compensate for measurement errors.

[0014] Driving curve radii can be extracted from the vehicle dynamics data and filtered to obtain a representative curve radius for the road curve. In other words, the driven curve trajectories can be used. Alternatively, the curve radius can be extracted from a map. The driven curve radii can be averaged to obtain the representative curve radius, removing outliers in the process. The representative curve radius allows conclusions to be drawn about how the curve is driven in practice. For example, a lane width might be used more extensively to drive a larger curve radius than the center of the lane would allow.

[0015] The target parameter can be determined using the representative curve radius. For example, the target parameter can be extracted at the vertex of the representative curve radius.

[0016] Extrema of the curves can be identified. A cluster of extrema can be identified as a curve section. This cluster can be identified as a curve section if the extrema are closer together than a threshold value. Extrema with excessively large variances can be discarded. The extrema of different drivers differ slightly from one another. However, in an actual curve, all drivers will follow at least similar trajectories.

[0017] The positions of the extrema can be averaged, and an averaged position can be determined as the apex of the road curve. The target parameter, at least for the apex, can be extracted from the vehicle dynamics data. The apex derived from the vehicle dynamics data may differ from the apex of the road surface. By evaluating the vehicle dynamics data, the target parameter can be extracted at the apex actually driven, which is relevant to the driver.

[0018] At least speed profiles can be used as vehicle dynamics data. At the very least, the speed of the vehicles can be evaluated as a profile.

[0019] Alternatively or additionally, lean angle profiles can be used as vehicle dynamics data. For single-track vehicles, such as motorcycles, a lean angle profile can provide precise information about important points in a curve. Motorcycles, in particular, corner differently than multi-track vehicles.

[0020] At least one target velocity at that point can be extracted as a target parameter.

[0021] Speeds recorded at a given point can be filtered to obtain the target speed. For example, predefined percentiles of speeds can be used to remove outliers. Alternatively or additionally, the speeds can be averaged.

[0022] The method is preferably computer-implemented and can be implemented, for example, in software or hardware, or in a hybrid form of software and hardware, for example, in an assistance system.

[0023] Furthermore, the approach presented here creates an assistance system, whereby the assistance system is trained to carry out, control or implement the steps of a variant of the procedure presented here in appropriate facilities.

[0024] The assistance system can be an electrical device with at least one processing unit for processing signals or data, at least one storage unit for storing signals or data, and at least one interface and / or a communication interface for reading or outputting data embedded in a communication protocol. The processing unit can be, for example, a signal processor, a so-called system ASIC, or a microcontroller for processing sensor signals and outputting data signals depending on the sensor signals. The storage unit can be, for example, flash memory, an EPROM, or a magnetic storage device. The interface can be configured as a sensor interface for reading sensor signals from a sensor and / or as an actuator interface for outputting data signals and / or control signals to an actuator.The communication interface can be configured to read or output data wirelessly and / or via a wired connection. The interfaces can also be software modules, such as those found on a microcontroller alongside other software modules.

[0025] Also advantageous is a computer program product or computer program with program code that can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and is used to carry out, implement and / or control the steps of the method according to one of the embodiments described above, in particular if the program product or program is executed on a computer or device.

[0026] It should be noted that some of the possible features and advantages of the invention are described herein with reference to different embodiments. A person skilled in the art will recognize that the features of the control unit and the method can be suitably combined, adapted, or exchanged to arrive at further embodiments of the invention. Brief description of the drawings

[0027] Embodiments of the invention are described below with reference to the accompanying drawings, whereby neither the drawings nor the description are to be interpreted as limiting the invention. Fig. Figure 1 shows a representation of the identification of curve regions in velocity profiles using a method according to an implementation example; Fig. 2 shows a representation of a distinction between a curved area and an area without a curve using a method according to an exemplary embodiment; Fig. Figure 3 shows a representation of a vehicle in front of a curve; Fig. Figure 4 shows a flowchart for determining a warning level before a curve section; Fig. Figure 5 shows examples of how to determine different warning levels before curve sections; Fig. Figure 6 shows a representation of a target speed and an actual speed profile in a curve section; and Fig. Figure 7 shows a representation of a target velocity profile and of possible velocity profiles after a vertex of a curve section.

[0028] The figures are schematic only and not to scale. Identical reference symbols denote identical or equivalent features. Embodiments of the invention

[0029] Fig. Figure 1 shows a representation of the identification of curve sections 100 in speed profiles 102 using a method according to an exemplary embodiment. The speed profiles 102 represent recorded georeferenced vehicle dynamics data 104 from journeys with different vehicles and different drivers on the same route 106. The speed profiles 102 are shown in a diagram where the route 106 is plotted on its abscissa and the speed 108 on its ordinate. The vehicle dynamics data 104 can alternatively or additionally include other recorded measured values, such as acceleration, lean angle, or steering angle.

[0030] Route 106 contains several dynamically relevant cornering sections (100) and sections without such cornering in between. A selection of these cornering sections (100) is highlighted by markings.

[0031] Within the curve ranges 100, the driving dynamics data 104 of all vehicles and all drivers are similar. Within the curve ranges 100, the driving dynamics data 104 show low variation. Between these ranges, the driving dynamics data 104 of the different vehicles and different drivers show high variation.

[0032] In particular, the speed profiles 102 in the curve sections 100 exhibit local minima. The locations or positions of the minima are closely clustered along the track 106. The minima are grouped around the actual apexes 110 of the curve sections 100. The minima show low dispersion in the curve section 100. Before the apex 110, the drivers decelerated to varying degrees from different initial speeds. After the apex 110, the drivers accelerated out of the curve to varying degrees.

[0033] At least one point per curve section 100, a target parameter is extracted from the vehicle dynamics data 104.

[0034] Here, at the vertices 110, the speeds driven by the various drivers and different vehicles are extracted from the speed profiles 102, and a target speed for the respective vertex 110 is determined from these speeds as the target parameter. Outliers at both high and low ends are ignored by filtering the speeds using statistical methods. For example, only a speed interval is used in which 90 percent of the driven speeds lie. The remaining 10 percent are ignored. This also allows vehicle dynamics data 104 with faulty georeferencing to be ignored.

[0035] Within this speed range, different target speeds can then be defined for different driving styles. For example, an upper target speed at the upper limit of the speed range can be selected for a highly dynamic driving style, while a lower target speed near the lower limit of the speed range can be selected for a less dynamic, relaxed driving style.

[0036] Fig. Figure 2 shows a representation of a distinction between a curve region 100 and a region without a curve using a method according to an exemplary embodiment. Here, for example, two consecutive regions are shown. A density of minima of the velocity profiles is shown for both regions. Fig. Figure 1 is shown in a diagram where the distance 106 is plotted on its abscissa and the density on its ordinate. The density is the inverse of the dispersion. In the curve region 100, the density is higher than a threshold value of 200. Therefore, this region is recognized as curve region 100. In the other region, the density is also elevated, but lower than the threshold value and therefore does not represent a relevant curve region.

[0037] Fig. Figure 3 shows a representation of a vehicle 300 approaching a curve 100. The vehicle 300 is a motorcycle. The vehicle 300 is traveling on a road towards a curve 302. The curve 302 is defined by the curve 100. At a point approximately in the middle of the curve 100, the apex 110 is marked as the target point 304. A target speed 306 is stored for the target point 304 in a database of the vehicle or in a remote database.

[0038] In addition to the representation of curve section 100, a representation of a target speed profile 308 through curve section 100 is shown. The target speed profile 308 is shown in a diagram where the distance 106 is plotted on its abscissa and the speed 108 on its ordinate. The target speed profile 308 has a minimum at the target point 304. The minimum corresponds to the target speed 306.

[0039] Fig. Figure 4 shows a flowchart for determining a warning level 400 for a vehicle before a curve. The warning level 400 is determined using a target speed 306 at the apex of the curve, the vehicle's current speed 108, and its distance 404 from the apex. The warning level 400 is determined based on the required target deceleration 406 to reach the target speed 306 at the apex. This target deceleration 406 is compared to a threshold value 408, and the appropriate warning level 400 is determined.

[0040] In one embodiment, the target delay 406 is compared with a single threshold value 408, and the warning level 400 is issued if the target delay 406 is greater than the threshold value 408. If the target delay 406 is less than the threshold value 408, no warning level 400 is issued.

[0041] In one embodiment, the target delay 406 is compared with several different threshold values ​​408. If the target delay 406 is greater than the lowest threshold value 408 but less than the next higher threshold value 408, a lowest warning level 400 is issued. If the target delay 406 is greater than the next higher threshold value 408, a next higher warning level 400 is issued.

[0042] In one embodiment, the target deceleration 406 is further determined taking into account a reaction time 410. This results in a greater target deceleration 406, since the vehicle continues without deceleration or with increased deceleration during the reaction time 410, and the driver can only react to a warning issued at the beginning of the reaction time 410 after this time has elapsed. By considering the reaction time 410, less distance to the target point is available for the required speed reduction, which is why the target deceleration 406 is determined to be higher.

[0043] In one embodiment, the vehicle's current acceleration 412 is taken into account when determining the warning level 400. If the vehicle is already decelerating or braking during the determination of the warning level 400, the negative acceleration 412 is subtracted from the target deceleration 406, and the warning level 400 is determined using the reduced value. However, if the vehicle continues to accelerate with a positive acceleration 412, this acceleration 412 is added to the target deceleration.

[0044] Fig. Figure 5 shows two examples for determining different warning levels 400 before curves. For each example, a time-dependent speed profile 102 of a vehicle 300 before a target point 304 of the curve is shown in a diagram, with time plotted on its abscissa and speed 108 on its ordinate. To determine the respective warning level 400, a speed difference 502 between the speed 108 and a target speed 306, as well as a time duration 504 until reaching the target point 304, are evaluated. From the speed difference 502 and the time duration 504, a target deceleration 406 of the vehicle 300 is directly derived. The target deceleration 406 is represented by the slope of a line connecting an instantaneous coordinate of the vehicle 300 in the diagram and a coordinate of the target point 304.The steeper the connecting line, the greater the target delay 406 to reach the target speed 306 in a time period of 504. The greater the target delay 406, the higher the warning level 400 is determined.

[0045] In the first diagram, the vehicle accelerates 300 towards the target point 304, but is still far from the target point 304. This results in a target deceleration 406, which is greater than a first threshold value 408, and a first warning level 400 is determined.

[0046] In the second diagram, vehicle 300 accelerates to a constant speed of 108 and approaches target point 304. However, the travel time 504 to target point 304 is short, and the speed difference 502 is high. This results in a target deceleration 406, which is greater than both the first threshold 408 and a second threshold. A second warning level 400 is therefore triggered.

[0047] Fig. Figure 6 shows a representation of a target speed profile 308 and an actual speed profile 102 in a curve section 100. The profiles are plotted in a diagram where time is plotted on its abscissa and speed 108 on its ordinate. Here, the speed profile 102 remains below the target speed profile 308. At the beginning of the curve section 100, the speed 108 does approach the target speed profile 308, but the vehicle decelerates more strongly than the target deceleration and passes the target point 304 at a lower speed 108 than the target speed 306. After the target point 304, however, the vehicle accelerates out of the curve more strongly than the target acceleration, causing the speed profile 102 and the target speed profile 308 to converge again.Since the speed will exceed the target speed of 308 (108 km / h), the driver of the vehicle will be warned.

[0048] Fig. Figure 7 shows a representation of a target speed profile 308 and of possible speed profiles 102 after a target point 304 of a curve section 100. The target speed profile 308 and the speed profiles 102 are as shown in Fig. 6 plotted in a diagram, which has time on its abscissa and velocity 108 on its ordinate.

[0049] An initial speed profile 102 shows a lower speed 108 at target point 304 than the target speed profile 308. After target point 304, the speed 108 decreases. Therefore, the speed difference between the target speed and the speed 108 is positive, and the acceleration difference between the target acceleration and the vehicle's acceleration is also positive. The initial speed profile 102 is non-critical, and no warning is issued.

[0050] A second velocity profile 102 also shows a lower velocity at target point 304 than the target velocity profile 308. After target point 304, the velocity 108 increases, as shown in Fig.6. The speed difference between the target speed and the speed of 108 is therefore positive. However, the acceleration difference between the target acceleration and the vehicle's acceleration is negative. A warning level is determined here, and the driver is warned if the warning level exceeds a predefined threshold.

[0051] A third speed profile, 102, shows a higher speed of 108 at target point 304 than the target speed profile 308. After target point 304, the speed increases to 108. Therefore, the speed difference between the target speed and the speed of 108 is negative, and the acceleration difference between the target acceleration and the vehicle's acceleration is also negative. This triggers the highest warning level.

[0052] A fourth speed profile, 102, shows a higher speed of 108 at target point 304 than the target speed profile of 308. After target point 304, the speed decreases to 108. The speed difference between the target speed and the speed of 108 is negative. However, the acceleration difference between the target acceleration and the vehicle's acceleration is positive. In this case, either the highest warning level is issued, or a warning level is determined, and the driver is warned if the warning level exceeds a predefined threshold.

[0053] Possible embodiments of the invention are summarized below or presented using slightly different wording.

[0054] A curve and speed identification system for curve speed warning devices for motorized two-wheelers is presented.

[0055] Here, target speeds and warning points for curve speed warning systems of motorized two-wheelers are determined by computer algorithms from map data and / or vehicle dynamics data.

[0056] The target speeds and warning points for curve speed warning systems for motorized two-wheelers are determined with sufficient reliability by computer algorithms using map data and / or vehicle dynamics data, enabling reliable warnings to be issued to the rider at relevant points in the curve. These serve as the fundamental database for curve speed warning systems. If curve radii are determined using vehicle dynamics, they reflect the radii actually driven (influenced by line choice), whereas the geometric road radii in map data typically correspond to the radius of the center line.

[0057] First, areas are determined that are to be considered as a curve.

[0058] From a sufficiently large number of recordings of driving dynamics data from real journeys on the same stretches of road, containing the speed v and the lean angle α, the radius can be determined using the relationship R=v2g∗tan α

[0059] To be determined. Possibilities for determining a curve region from this are, in the first step... a) to calculate the mean radius or other statistical percentile of all recorded journeys or b) to find local minima of the radius of each individual trip and to determine a density distribution of local radius minima.

[0060] A hysteresis analysis is then performed for both variants. When considering the density of local minima, a lower and upper threshold can be exceeded. If the lower threshold is again undershot, a curve is formed between the sections where the lower threshold is reached.

[0061] The evaluation using statistically determined radii (mean radius or other percentiles) is the reverse of this. The intersection with a higher radius threshold marks the curve region, provided that a lower threshold is crossed in between.

[0062] Alternatively, the curvature of the road can be considered (=1 / radius).

[0063] Data sources can include, for example, proprietary maps with road curve radii or freely usable maps from which curve radii are determined using geometric methods based on the road course.

[0064] The determination of curve ranges is based on hysteresis considerations, as described above.

[0065] When curve radii are determined by driving dynamics, they reflect the radii actually driven (influenced by line choice), while the geometric road radii in map material usually correspond to the radius of the center line.

[0066] On identified curve regions, the speed profiles and, if applicable, the vehicle dynamics radii of real-world driving data are analyzed and statistically evaluated. For a warning function, higher speed percentiles are suitable, e.g., the 95th percentile. The point with the statistically lowest speed and / or the point with the smallest static radius are suitable reference points for a curve region that a speed warning system can use as a warning point.

[0067] As an extension, the warning system can also use several warning reference points of a curve area, up to a quasi-continuous specification of a target speed through the entire curve area.

[0068] If the measurement data set is labelled with further attributes, e.g. motorcycle type, weather, day / night, a corresponding sub-data set can be used in the statistical assignment of the speed percentiles to make adjustments to the individual motorcycle and the external conditions.

[0069] Finally, it should be noted that terms such as "comprising," "encompassing," etc., do not exclude other elements or steps, and terms such as "a" or "an" do not exclude a plurality. Reference numerals in the claims are not to be considered as limitations. QUOTES INCLUDED IN THE DESCRIPTION

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

[0000] DE 10 2014 225 625 A1

[0003]

Claims

[1] Method for determining at least one target parameter for a curve driving through a road curve (302), wherein profiles of recorded and georeferenced vehicle dynamics data (104) are evaluated to identify a curve section (100) of the road curve (302), wherein the target parameter for at least one point of the curve section (100) is extracted from the vehicle dynamics data (104) recorded for that point. [2] Method according to claim 1, wherein driven curve radii are extracted and filtered from the vehicle dynamics data (104) to obtain a representative curve radius of the road curve (302). [3] Method according to claim 2, wherein the target parameter is determined using the representative curve radius. [4] Method according to one of the preceding claims, wherein extrema of the curves are sought, a cluster of extrema being identified as a curve region (100). [5] Method according to claim 4, wherein positions of the extrema are averaged and an averaged position is determined as the vertex (110) of the road curve (302), wherein the target parameter is extracted from the vehicle dynamics data (104) at least for the vertex (110). [6] Method according to one of the preceding claims, wherein at least speed profiles (102) are used as the vehicle dynamics data (104). [7] Method according to one of the preceding claims, wherein at least lean angle profiles are used as the vehicle dynamics data (104). [8] Method according to one of the preceding claims, wherein at least one target velocity (306) is extracted as the target parameter at the point. [9] Method according to claim 8, wherein speeds driven at the point (108) are filtered to obtain the target speed (306). [10] Assistance system, wherein the assistance system is configured to execute, implement and / or control the method according to one of the preceding claims in appropriate facilities. [11] Computer program product configured to instruct a processor, when the computer program product is executed, to execute, implement and / or control the method according to any one of claims 1 to 9. [12] Machine-readable storage medium on which the computer program product according to claim 11 is stored.

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