Method for determining a maximum coefficient of friction

Abstract

Method (V) for determining a maximum coefficient of friction (fb) between a road surface (3) and at least one tire (2b) of an ego vehicle (1b), wherein: - a reference vehicle (1a) determines its maximum coefficient of friction (fa) between the road surface (3) and at least one of its tires (3a) at a position; - the reference vehicle (1a) normalizes its maximum coefficient of friction (fa) using its tire dimension data (4a) and its tire condition data (5a), thereby correcting the maximum coefficient of friction (fa) of the reference vehicle (1a) by removing the tire condition data (5a) and the tire dimension data (4a); - the reference vehicle (1a) sends the normalized maximum coefficient of friction (fan) to the ego vehicle (1b); - the ego vehicle (1b) uses the normalized maximum coefficient of friction (fan) to determine the maximum coefficient of friction (fan) of its Tire dimension data (4b) and using its tire condition data (5b),its maximum coefficient of friction (fb) between the road surface (3) and its at least one tire (2b).

Description

The present invention relates to a method for determining a maximum coefficient of friction between a roadway and at least one tire of an ego vehicle having the features according to claim 1, to a control unit for a vehicle having the features according to claim 6 and to a computer program product having the features according to claim 7.At present, a vehicle which determines a maximum coefficient of friction between the roadway and its tires can only use it itself. Namely, the maximum friction coefficient is specific to each vehicle and depends on various factors.From DE 101 56 823 A1 an apparatus and a method are known for determining a maximum road friction coefficient for each wheel, regardless of whether the wheel is in a predetermined traction slip state and whether the wheel is a driven wheel.DE 10 2012 217 772 A1 discloses a method for determining the maximum coefficient of frictional engagement between a vehicle tire and a road surface, wherein at least one value tuple is determined, comprising at least one instantaneous coefficient of frictional engagement and an instantaneous slip and / or at least one instantaneous coefficient of frictional engagement and an instantaneous slip angle.DE 10 2016 221 975 A1 discloses a method for determining a relative change in the coefficient of friction between a vehicle tire of a motor vehicle and a road surface. In this case, a setpoint coefficient of friction is established and a setpoint speed change of the motor vehicle is determined on the basis of the established setpoint coefficient of friction.Proceeding from the prior art, the object of the present invention is to propose an alternative and improved method for determining a maximum coefficient of friction of an ego vehicle. The method is intended to provide a result for the maximum coefficient of friction with less effort than the methods known from the prior art.Based on the aforementioned object, the present invention proposes a method for determining a maximum coefficient of friction between a roadway and at least one tire of an ego vehicle according to claim 1, a control unit for a vehicle according to claim 6 and a computer program product according to claim 7. Further advantageous embodiments and developments emerge from the dependent claims.In a method for determining a maximum coefficient of friction between a roadway and at least one tire of an ego vehicle, a reference vehicle first determines its maximum coefficient of friction between the roadway and at least one tire of the reference vehicle at a position. The reference vehicle then normalizes its maximum coefficient of friction using its tire dimension data and its tire condition data.Thereby, the maximum friction coefficient of the reference vehicle is corrected by the tire state data and the tire dimension data.The reference vehicle then sends the normalized maximum friction coefficient to the ego vehicle. The ego vehicle then determines, by means of the normalized maximum friction coefficient, by means of its tire dimension data and by means of its tire state data, its maximum friction coefficient between the roadway and its at least one tire.Both the reference vehicle and the ego vehicle are land vehicles, for example passenger cars or NWs. The reference vehicle is moving on the roadway in time before the ego vehicle. The reference vehicle passes a certain roadway region in time before the ego vehicle. The time interval between the reference vehicle and the ego vehicle can be short, i.e. a few seconds or minutes, as well as long, i.e. several hours or days. The roadway is the underlying surface on which the vehicles are moving.The reference vehicle determines its maximum coefficient of friction between the roadway and at least one of its tires during travel on the roadway. For this purpose, the reference vehicle has at least one sensor system in order to determine precisely this maximum coefficient of friction. Various methods are known from the prior art for determining a maximum coefficient of friction by means of sensors. In addition, the maximum coefficient of friction is location-dependent. That is, the reference vehicle additionally determines position data, for example, by means of a position determination system, and links this to the maximum friction coefficient. This position data is determined, for example, by means of a GPS system. This linking is necessary so that the ego vehicle to which the normalized maximum friction coefficient is transmitted can assign to which section of the roadway this normalized maximum friction coefficient is present.The maximum coefficient of friction that the reference vehicle determines is specific to this same reference vehicle and is dependent on various factors of the reference vehicle, namely on the dimension of the tires, the state of the tires, on a driving situation. The dimension of the tires comprises, for example, a tire width, a tire diameter, optionally also a profile depth and a tire pressure. Of course, the dimension of the tires may include still further information affecting the dimension of the tires. The state of the tires here comprises, for example, a tire temperature, a tire model, an age of the tires, that is to say an odometer reading traveled by these tires and, if appropriate, a wear state associated therewith. Of course, the state of the tires can also comprise further information which influences the state of the tires. The driving situation includes, for example, a current speed and acceleration that the reference vehicle has. Of course, the driving situation can also comprise further information which has an influence on the driving behavior of the respective vehicle. These explanations apply, as it were, also to the ego vehicle.The aforementioned factors are determined by the reference vehicle or also by the ego vehicle by means of vehicle-specific sensor systems or are stored in a memory. For example, the reference vehicle and / or the ego vehicle may include tire pressure sensors, acceleration sensors, rotational speed and speed sensors, or the like. These sensor systems are known from the prior art. The evaluation of the sensor data of these different sensor systems can take place either in the sensor system itself or in a control unit of the reference vehicle or of the ego vehicle. The evaluation is carried out, for example, by means of an algorithm. The data regarding the tire diameter and the tire width, the data regarding the kilometers traveled by the tires or regarding the profile depth can be stored in the memory, which is a component of the control unit of the reference vehicle or of the ego vehicle.The maximum coefficient of friction is also dependent on the road condition. The roadway condition here comprises the surface condition of the roadway, which is dependent on the weather conditions, for example. A distinction is made, for example, between a wet, a dry, an icy, a snowy, a soiled or otherwise impaired roadway. In addition, the material of the roadway also has an influence on the roadway condition, for example, the roadway can be made of asphalt, concrete, pavement, soil or the like. In order to determine these data on the roadway state, the reference vehicle or the ego vehicle has at least one sensor system. For example, this sensor system can have a camera system. In addition, this sensor system may include a precipitation sensor, a temperature sensor, or the like. Such sensor systems are known from the prior art. The evaluation of the sensor data of these different sensor systems can take place either in the sensor system itself or in the control unit. The evaluation is carried out, for example, by means of an algorithm. In addition, the reference vehicle or the ego vehicle can access data from an external database in order to determine the material of the roadway.As a result of the evaluations and the memory queries, the reference vehicle and also the ego vehicle obtain its specific tire dimension data and its specific tire condition data. In addition, they can additionally obtain their specific driving situation data and the roadway state data for their specific roadway section.The reference vehicle normalizes its maximum friction coefficient using its tire dimension data and tire condition data. Thus, the maximum friction coefficient becomes independent of the above data. In other words, the maximum friction coefficient is corrected by the tire state data and the tire dimension data.The normalized maximum friction coefficient is sent from the reference vehicle to the ego vehicle. For this purpose, the reference vehicle has a communication unit. The ego vehicle also has a communication unit. The communication unit is used to communicate with external devices, e.g. with other vehicles, a cloud, with infrastructure systems or the like, i.e. to transmit and receive data and signals. For this purpose, the communication unit can use a radio standard or another type of communication, for example.The ego vehicle receives the normalized maximum friction coefficient determined for a predetermined position. Based on this normalized maximum friction coefficient, the ego vehicle may calculate its specific maximum friction coefficient by using its specific tire state data and tire dimension data. The ego vehicle therefore does not have to determine its maximum friction coefficient completely by itself by sensor, but can rely on the information of the reference vehicle. This results in a time saving and a cost saving. The computational effort and evaluation effort in the ego vehicle is reduced. This also makes it possible to save energy.In addition, the reference vehicle can normalize its maximum friction coefficient by means of its driving situation data. The ego vehicle can then additionally determine its maximum friction coefficient by means of its driving situation data. If the maximum friction coefficient of the reference vehicle is additionally corrected by the driving situation data, the value of the maximum friction coefficient becomes more accurate than without this additional cleaning.In addition, the reference vehicle may normalize its maximum friction coefficient using road condition data. The ego vehicle can then additionally determine its maximum friction coefficient using the roadway state data. If the maximum friction coefficient of the reference vehicle is additionally corrected by the road condition data, the value of the maximum friction coefficient becomes more accurate than without this additional cleaning. Standardization by means of the roadway state data may be necessary, for example, when the reference vehicle passes a roadway section far in front of the ego vehicle, e.g., at a distance of several hours. This is because the roadway condition may change due to changed weather conditions.The reference vehicle transmits the normalized maximum friction coefficient to the ego vehicle in one variant by means of vehicle-to-vehicle communication. Vehicle-to-vehicle communication is also referred to as C2C communication. This communication is advantageous if the reference vehicle is travelling on the roadway directly or indirectly in front of the ego vehicle. For example, the ego vehicle may travel in a platoon behind the reference vehicle or the reference vehicle may arrive at the ego vehicle in an opposite lane.In another variant, the reference vehicle sends the normalized maximum friction coefficient to the ego vehicle via a cloud. That is, the normalized maximum friction coefficient is stored in the cloud and may be retrieved from the ego vehicle as needed. This has the advantage that the reference vehicle can have determined and sent the normalized maximum friction coefficient at a greater time interval before the ego vehicle passes the respective roadway section. This is particularly advantageous for roadway areas that are less frequent.As the reference vehicle moves on the roadway, it continuously determines and normalizes its maximum coefficients of friction for all roadway segments it passes. Thus, a map of the distance traveled may be created with respect to the normalized maximum friction coefficients that may be sent to the ego vehicle.A control unit for a vehicle has a communication unit or can be connected to a communication unit. The control unit can be connected to at least one sensor system of the vehicle. The control unit comprises means for performing the method already described in the previous claims. The communication unit serves to allow the vehicle, i.e. the reference vehicle or the ego vehicle, to communicate with external systems. This has already been described. The control unit is suitable for both the ego vehicle and the reference vehicle. The control unit can have the communication unit itself, i.e. the communication unit can be integrated into the control unit. Alternatively, the control unit may be connected to the communication unit including the vehicle. The connection is such that data and signal exchange can take place.The control unit may be connected to at least one sensor system of the vehicle when the control unit is used in the vehicle. The sensor system can be one of the sensor systems already explained.The control unit comprises means for carrying out the method which has already been described. These means may be in the form of a computer program product, for example, which is described below.A computer program product comprises program code means for performing the method already described in the previous description. The computer program product can be embodied on a physical data carrier, for example, or in a downloadable data stream, for example.Various exemplary embodiments and details of the invention are described in more detail with reference to the figures explained below. The following are shown: FIG. 1 shows a schematic illustration of a method for determining a maximum friction coefficient according to one exemplary embodiment, FIG. 2 shows a schematic illustration of two vehicles each having a control unit according to an exemplary embodiment.FIG. 1 shows a schematic illustration of a method V for determining a maximum friction coefficient fb according to one exemplary embodiment. First, the maximum friction coefficient fa of a reference vehicle 1 aat a specific position of the reference vehicle 1 aon the road is determined. This maximum friction coefficient fa is present between at least one tire of the reference vehicle 1 aand the roadway. This is done by methods known from the prior art. For this purpose, the reference vehicle 1 aincludes a control unit and at least one sensor system, which is shown in more detail in FIG. 2.The maximum friction coefficient fa of the reference vehicle 1 ais entered by tire dimension data 4 aof the reference vehicle 1 athat are specific to the reference vehicle 1 a, tire state data 5 aof the reference vehicle 1 a, and driving situation data 6 aof the reference vehicle 1 a. In addition, road condition data 7 enters the maximum friction coefficient fa of the reference vehicle 1 a. This maximum friction coefficient fa is thus specific to the reference vehicle 1 aand specific to the position at which the reference vehicle 1 ais located on a roadway. This maximum friction coefficient fa is associated with the position data of the reference vehicle 1 a, so that the assignment of the maximum friction coefficient fa to a specific road section is ensured.The maximum friction coefficient fa of the reference vehicle 1 ais normalized in a next step of the method V by calculating the tire dimension data 4 aof the reference vehicle 1 a, tire state data 5 aof the reference vehicle 1 a, and driving situation data 6 aof the reference vehicle 1 aand also the roadway state data 7 from the maximum friction coefficient fa. That is, the maximum friction coefficient fa of the reference vehicle 1a is corrected by the above data. As a result, the normalized maximum friction coefficient fan is obtained. This calculation is carried out by a control unit of the reference vehicle 1 a, which can use an algorithm for this purpose. The calculated-out data are determined by the reference vehicle 1 asensory or are stored in a database in a memory of the control unit.The normalized maximum friction coefficient fan is then forwarded from the reference vehicle 1 ato an ego vehicle 1 b. This is done by means of a communication unit of the reference vehicle 1a, which is shown in more detail in FIG. 2. The ego vehicle 1 b receives the normalized maximum friction coefficient fan, which is linked to position data by means of its communication unit. The normalized maximum friction coefficient fancan be sent, for example, directly by means of C2C communication or via a cloud.Subsequently, the ego vehicle 1 bdetermines its maximum friction coefficient fbfor the same position at which the reference vehicle was previously located. This maximum friction coefficient fb is present between at least one tire of the ego vehicle 1 band the roadway. For this purpose, a control unit of the ego vehicle 1 buses tire dimension data 4 bof the ego vehicle 1 b, tire state data 5 bof the ego vehicle 1 b, and driving situation data 6 bof the ego vehicle 1 band also the roadway state data 7. The calculation is preferably carried out by means of an algorithm. The data mentioned above are determined by the ego vehicle 1 bsensory or are stored in a memory of the control unit of the ego vehicle 1 b.The determination of the maximum friction coefficient fb for the ego vehicle 1 bis carried out with less effort than the conventional determination of a maximum friction coefficient, because the ego vehicle 1 bdoes not itself have to carry out all measurements in order to determine or estimate its maximum friction coefficient fb. Instead, measurements of the reference vehicle 1 acan be used. In addition, time can be saved by means of the method just shown. Because of the reduced complexity with regard to the determination, energy can also be saved.FIG. 2 shows a schematic illustration of two vehicles 1 a, 1 b, each having a control unit 9 according to an exemplary embodiment. Both vehicles 1 a, 1 bmove along the roadway 3 in the same direction of travel 11.The vehicle 1 a, which is arranged at the front in the direction of travel 11, is the reference vehicle 1 a. The vehicle 1 b, which is arranged at the rear in the direction of travel 11, is the ego vehicle 1 b. The ego vehicle 1 blocally and temporally follows the reference vehicle 1 aon the roadway 3.Each of these vehicles 1 a, 1 bhas a control unit 9 which has means for carrying out the method from FIG. 1. The control unit also has a memory, which is not shown here. In this memory, tire dimension data of the respective vehicles 1 a, 1 bare stored.In addition, each vehicle 1 a, 1 bhas at least one sensor system 12 which is connected to the control unit 9 so that data and signal exchange can take place. For a better overview, only one sensor system 12 per vehicle 1 a, 1 bis shown. The sensor systems 12 serve, for example, to monitor the environment of the respective vehicle 1 a, 1 b(indicated by the arrows) and / or to determine tire dimension data of the respective vehicles 1 a, 1 band / or to determine driving situation data of the respective vehicles 1 a, 1 band / or to determine roadway state data of the roadway 3 and / or to determine position data of the respective vehicles 1 a, 1 b. By means of the data of the sensor systems 12 and optionally by means of the data from the memory of the control unit 9, the reference vehicle 1 adetermines its maximum friction coefficient fa, which is present between the roadway 3 and the tires 2 aof the reference vehicle 1 a.The reference vehicle 1 anormalizes its maximum friction coefficient fa using the method described in FIG. 1. The normalized maximum friction coefficient fanis sent to a cloud 8 by means of the communication unit 10. The reference vehicle 1 athus communicates with the cloud 8.The ego vehicle 1 balso communicates with the cloud 8 and receives from it the normalized maximum friction coefficient fan by means of its communication unit 10. This transmission and reception is carried out by means of the communication units 10 of the reference vehicle 1 aand of the ego vehicle 1 b.By means of its control unit 9, by means of the normalized maximum friction coefficient fan, by means of its data from the sensor systems 12 and, if appropriate, by means of the data from the memory of the control unit 9, the ego vehicle 1 bcalculates its maximum friction coefficient fb for the above-mentioned same position. This maximum friction coefficient fb is present between the roadway 3 and the tires 2 bof the ego vehicle.The examples shown here are chosen only by way of example. The ego vehicle does not have to follow the reference vehicle spatially. It is sufficient if the ego vehicle passes the same roadway section as the reference vehicle. The ego vehicle can therefore also pass this roadway section at a distance of several minutes or hours. It is also possible for the ego vehicle to travel on the roadway in a direction opposite to the reference vehicle. In addition, it is possible for the respective communication unit to be integrated into the corresponding control unit. In addition, it may be sufficient if only the tire dimension data of the reference vehicle and the tire state data of the reference vehicle are used to obtain the normalized maximum friction coefficient.Reference numerals denote reference numerals1 areference vehicle 1 bego vehicle 2 a tire of reference vehicle 2 b tire of ego vehicle 3 roadway 4 a tire dimension data of reference vehicle 4 b tire dimension data of ego vehicle 5 a tire state data of reference vehicle 5 b tire state data of ego vehicle 6 a driving situation data of reference vehicle 6 b driving situation data of ego vehicle 7 roadway state data 8 cloud 9 control unit 10 communication unit 11 driving direction 12 sensor system fa maximum friction coefficient of reference vehicle fb maximum friction coefficient of ego vehicle fan normalized maximum friction coefficient of reference vehicle v Method

Claims

Method (V) for determining a maximum friction coefficient (fb) between a roadway (3) and at least one tire (2b) of an ego vehicle (1b), wherein - a reference vehicle (1a) determines its maximum friction coefficient (fa) between the roadway (3) and at least one of its tires (3a) at a position, - the reference vehicle (1a) normalizes its maximum friction coefficient (fa) by means of its tire dimension data (4a) and by means of its tire state data (5a), whereby the maximum friction coefficient (fa) of the reference vehicle (1a) is corrected by the tire state data (5a) and by the tire dimension data (4a), the reference vehicle (1a) sends the normalized maximum friction coefficient (fan) to the ego vehicle (1b), the ego vehicle (1b) determines by means of the normalized maximum friction coefficient (fan), by means of its tire dimension data (4b) and by means of its tire state data (5b), its maximum friction coefficient (fb) between the roadway (3) and its at least one tire (2b).Method (V) according to Claim 1, wherein the reference vehicle (1a) additionally normalizes its maximum friction coefficient (fa) by means of its driving situation data (6a), and the ego vehicle (1b) additionally determines its maximum friction coefficient (fb) by means of its driving situation data (6b).Method (V) according to one of the preceding claims, wherein the reference vehicle (1a) normalizes its maximum friction coefficient (fa) additionally by means of roadway state data (7) and the ego vehicle (1b) determines its maximum friction coefficient (fb) additionally by means of the roadway state data (7).Method (V) according to one of the preceding claims, wherein the reference vehicle (1a) sends the normalized maximum friction coefficient (fan) to the ego vehicle (1b) by means of vehicle-to-vehicle communication.Method (V) according to one of the preceding claims, wherein the reference vehicle (1a) sends the normalized maximum friction coefficient (fan) to the ego vehicle (1b) via a cloud (8).Control unit (9) for a vehicle (1a, 1b), wherein the control unit (9) has a communication unit (10) or can be connected to a communication unit (10), wherein the control unit (9) can be connected to at least one sensor system (12) of the vehicle (1a, 1b), and wherein the control unit (9) has means for carrying out the method (V) according to one of the preceding claims.Computer program product comprising program code means for performing the method according to any of claims 1 to 5.

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