DATA MEMORY, COMPUTER UNIT AND METHOD FOR EXECUTING A FUNCTION OF A VEHICLE

Abstract

A data memory, a computing unit and a method for performing a function of a vehicle are described, wherein values of an item of information relating to predefined local areas of a digital map are taken into account when the function is performed, the values of the information being stored in the form of an expected value and a distribution of the values around the expected value.

Claims

1-17. (canceled)

18. A data memory for a computing unit of a vehicle comprising: multiple values of at least one item of information for each of predefined local areas of a digital map stored in the data memory; wherein the values of the information are taken into account by the computing unit when a function of the vehicle is performed; and wherein the values are stored in the form of an expected value and a distribution of the values around the expected value.

19. The data memory as claimed in claim 18, wherein the distribution of the values is a normal distribution with a standard deviation.

20. The data memory as claimed in claim 18, wherein the expected value provided is at least one of an expected value range and the distribution provided is a distribution range.

21. The data memory as claimed in claim 18, wherein the information is a parameter of a movement of a vehicle on a road, and wherein the values of the information are values of the parameter.

22. The data memory as claimed in claim 21, wherein the parameter is at least one of a velocity, an acceleration and an energy consumption of at least one vehicle.

23. The data memory as claimed in claim 18, wherein the information is a parameter of a traffic controller, in particular a traffic light.

24. The data memory as claimed in claim 23, wherein the parameter is at least one of: a duration of a red phase, a duration of a green phase and a duration of an amber phase of the traffic controller.

25. A computing unit for a vehicle for, with instructions for: receiving multiple values of information from a data memory, wherein the multiple values are of at least one item of information for each of predefined local areas of a digital map stored in the data memory, and wherein the multiple values are stored in the form of an expected value and a distribution of values around the expected value; and executing a function of the vehicle using the multiple values of information.

26. The computing unit as claimed in claim 25, wherein the function is a control function of the vehicle.

27. The computing unit as claimed in claim 26, wherein the control function is at least one of a longitudinal guidance and a transverse guidance of the vehicle.

28. The computing unit as claimed in claim 25, wherein the computing unit carries out short-distance planning with a linear combination of the expected values and distributions of the values of the information.

29. The computing unit as claimed in claim 25, wherein the computing unit carries out long-distance planning with a Markov chain of the expected values and distributions of the values of the information.

30. A method for performing a function of a vehicle comprising: using multiple values of an item of information relating to predefined local areas of a digital map, and wherein the multiple values of the information are an expected value and a distribution of values around the expected value.

31. The method as claimed in claim 30, wherein the distribution of values is a normal distribution with a standard deviation.

32. The method as claimed in claim 30, wherein at least one of the expected value provided is an expected value range and the distribution provided is a distribution range.

33. The method as claimed in claim 30, wherein the information is a parameter of a movement of a vehicle on a road, and wherein the values of the information are values of the parameter.

34. The method as claimed in claim 30, wherein the parameter is at least one of: a velocity of at least one vehicle, an acceleration of at least one vehicle, an energy consumption of at least one vehicle, and a parameter of a traffic controller.

35. The method as claimed in claim 30, wherein the parameter is of the traffic controller and is at least one of: a duration of a red phase, a duration of a green phase and a duration of an amber phase of a traffic controller.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0024] The invention is explained in more detail below with reference to the figures, in which:

[0025] FIG. 1 shows a schematic depiction of a vehicle on a road in front of a traffic light;

[0026] FIG. 2 shows a schematic depiction of a digital map with predefined local areas;

[0027] FIG. 3 shows a schematic depiction of a normal distribution of a value with a standard deviation;

[0028] FIG. 4 shows a table containing classes of expected values;

[0029] FIG. 5 shows a schematic program sequence for carrying out a method in which a function of a vehicle is performed on the basis of values of an item of information; and

[0030] FIG. 6 shows a schematic depiction of part of a digital map containing routes with local points at which information is stored.

DETAILED DESCRIPTION

[0031] FIG. 1 shows a schematic depiction of a vehicle 1 travelling on a road 2 in the direction of a traffic light 3. The road 2 is divided into fictitious road sections 4, 5, 6. The vehicle 1 is located on the first road section 4. The second road section 5 is located in front of the vehicle 1. A third road section 6 is provided between the second road section 5 and the traffic light 3.

[0032] The vehicle 1 has a computing unit 7 and a data memory 8. The computing unit 7 and the data memory 8 are connected to one another for data interchange. Furthermore, the vehicle 1 has a locating device 9, for example in the form of a GPS system. In addition, the vehicle has an output device 10 for outputting information to a driver. Furthermore, the vehicle 1 has a controller 11 for controlling a control function of the vehicle. The control function can include, for example, longitudinal guidance of the vehicle, that is to say positive or negative acceleration. In addition, the control function can include transverse guidance of the vehicle, that is to say a steering function of the vehicle. The output device 10 can be provided in order to output information about road sections or road sections lying ahead.

[0033] Depending on the chosen embodiment, the locating device 9 can also include route planning. In addition, the route planning can also be performed by the computing unit 7.

[0034] Furthermore, there can be provision for an external data memory 12 that stores values of information relating to local areas of a digital map. Both the external data memory 12 and the data memory 8 of the vehicle 1 store values of information in the form of an expected value and a distribution of the values around the expected value. For example, the distribution of the values is in the form of a normal distribution with a standard deviation. In addition, the external data memory 12 and the data memory 8 can store the values of the information as value ranges and/or the distribution as distribution range, in particular as variance.

[0035] The information can be a parameter of a movement of a vehicle on a road or a parameter of an operating function of a vehicle, for example an energy consumption. The values of the information are values of the parameter of the movement or of the operating state of the vehicle. For example, the parameter can be a velocity, an acceleration and/or an energy consumption of at least one, in particular multiple, vehicles. A multiplicity of values are stored for each parameter.

[0036] In the exemplary embodiment depicted, each road section can have stored for it a multiplicity of velocities at which vehicles travel or have travelled on these road sections according to previous measurements. In addition, each road section can have stored for it values for accelerations of vehicles that vehicles perform or have performed on these road sections. The accelerations of the vehicles were measured beforehand. In addition, each of the road sections 4, 5, 6 can have stored for them values for energy consumptions of vehicles that vehicles have or have had on the road sections according to measurements performed. In addition, each road section can have stored for it values for durations of a red phase, a green phase and/or an amber phase that vehicles experience or have experienced, according to measurements performed, on the road sections.

[0037] As already stated, the values of the parameters for predefined local areas of a digital map, which correspond to local areas of a real map, are not stored as individual values of the information, but rather the values of an item of information are stored in the form of an expected value and in the form of a distribution of the values around the expected value. This saves storage capacity both in the data memory 8 and in the external data memory 12. In addition, transmission capacity and time are saved when the data are read and/or when the data are written from and/or to the data memory 8 or from and/or to the external data memory 12.

[0038] FIG. 2 shows a schematic depiction of a section of a digital map 13 in which the road 2 is shown. The digital map 13 is presented to the driver e.g. via an appropriate display in the vehicle. In addition, the road sections 4, 5, 6 are depicted as dashed boxes in the section 13. Furthermore, the traffic light 3 is also depicted. In addition, the vehicle 1 is depicted schematically as a box. The traffic light 3 is arranged at a junction 14 at which the road 2 crosses another road 15. The position of the vehicle 1 is recorded in the real world using the locating device 9. The real position of the vehicle 1 is assigned by the computing unit 7 to a fictitious position of the vehicle 1 on the digital map 13. The data for the digital map 13 are stored, for example, in the data memory 8 or in a further data memory, not depicted. The digital roads are divided into road sections 4, 5, 6, each road section representing a predefined local area. Instead of a road section, a local area can also comprise only one point on a road or a larger area of the map containing multiple roads. Values of information are stored for the local areas of the digital map in accordance with the data format described.

[0039] The computing unit 7 can take into account the information relating to the local areas of the digital map for performing a function of the vehicle. For example, the function can be the output of driver information via the output device 10. In addition, the performance of the function can include longitudinal guidance or transverse guidance of the vehicle. Furthermore, the performance of a function of the vehicle can include route planning for the vehicle.

[0040] Depending on the chosen embodiment, the values of the information can be received either directly from the data memory 8 and/or at least partially or completely from the external data memory 12. For this purpose, the external data memory has a transmission device. In addition, the vehicle has at least one receiving device, in particular a transmitting/receiving device.

[0041] FIG. 3 shows a schematic depiction of a curve for a normal distribution of the values of an item of information.

[0042] The normal distribution is described by the following formula:

[00001]$f\left(x,\mu ,{\sigma }^2\right)=\left(\frac{1}{\sigma \sqrt{2\pi }}\right)e^{-\frac{1}{2}{\left(\frac{x-\mu }{\sigma }\right)}^2}$

[0043] The expected value p defines the point at which the normal distribution has its maximum. The variance σ.sup.2 defines the standard deviation through the root of the variance σ. The depicted normal distribution has the value 1 for the root of the variance σ. Experiments have shown that the average of a large number of observed values for information from vehicles is approximately normally distributed and follows the central limit theorem. The values of the information can therefore be assumed to be normally distributed without great loss of accuracy and can be stored in the form of a normal distribution E (x) and σ.sup.2.

[0044] In FIG. 3, the values w of the information are plotted along the horizontal axis. The number A of values is plotted along the vertical axis. A further reduction in the volume of data, in particular a volume of data to be transmitted, can be achieved by using classes of values.

[0045] FIG. 4 shows a schematic depiction of a table, with, in the top two rows, expected values from the value 0 to the value 200 being divided into expected value ranges from 0 to 10, from 10 to 20, from 20 to 30, from 30 to 40, from 50 to 80, from 80 to 100, from 100 to 130, from 130 to 160 and from 160 to 200, that is to say into ten classes of expected values from 0 to 9. In addition, in the bottom two rows, the values for the variance of the distribution of the expectation ranges have also been divided into four value ranges, i.e. into four classes. In the example depicted, the values for the variance Var are divided into the value ranges 0 to 5, 5 to 10, 10 to 20 and 20 to 50. This results in the variance classes 0, 1, 2 and 3. If, for example, the expected value 136.765 and the variance 4.76 are stored or transmitted with this scheme, then class 8 for the expected value and class 0 for the variance are stored or transmitted instead.

[0046] FIG. 5 shows a schematic depiction of a simple program sequence for carrying out a method for performing a function of a vehicle. At program point 500, the computing unit 7 uses the locating device 9 to ascertain the real position of the vehicle 1 on a road 2. At a next program point 510, the computing unit 7 searches a digital map for a position in the digital map that corresponds to the real position of the vehicle. At a next program point 520, the computing unit 7 checks whether values of information are stored for the area in which the vehicle is located in the digital map or for the areas of the digital map in the direction of travel ahead of the vehicle. The values of the information can be stored both in the data memory 8 of the vehicle 1 and in the external data memory 12. In addition, the data memory can store an instruction that indicates what information the computing unit should take into account for what function and in particular in what way.

[0047] At a next program point 530, the computing unit performs a function of the vehicle taking into account the values of the information from the digital map.

[0048] The function can include the planning of a route from the current position to a predefined destination. In addition, the function can include acceleration or braking of the vehicle. Furthermore, the function can include a steering function of the vehicle. For example, the function can include a reduction in energy consumption on a specified road section. In addition, the function can involve passing through a traffic light 3 with the shortest possible waiting time. In addition, the function can involve outputting values of the information to the driver.

[0049] The proposed type of storage of the values of the information saves storage space and transmission capacity between the computing unit and the data memory 8 or the external data memory 12.

[0050] Due to the type of storage used, further information about the distribution of the values of the information can also be ascertained by forming quantiles and probabilities on the basis of the normal distribution. The methods described are suitable for computing units that are in the form of engine control units for vehicles.

[0051] In addition, the values of the information can be used by the computing unit for route planning. The computing unit can plan a route from a starting point to a destination, taking into account the values of the information on the digital map. For example, the computing unit can search for a route from a starting point to a destination according to a predefined criterion. The criterion can be, for example, the route with the lowest energy consumption, the shortest route or the fastest route.

[0052] FIG. 6 shows a schematic depiction of a partial section of a digital map containing roads, which are depicted as lines. From a starting point 20 to a destination 21, three different routes 31, 32, 33 are depicted. Along the routes 31, 32, 33 there is provision on the roads in the digital map for local points 41, 42, 43, 44, 45, 46 at which values of information are stored in the form of an expected value and a distribution of the values of the information. Instead of the local points there could also be provision on the digital map for local areas to which values of information in the form of an expected value and a distribution of the values of the information are assigned. A local area can cover e.g. a predefined distance of between half a meter and several meters or up to 100 meters.

[0053] The distribution of the values can be a normal distribution with a standard deviation. The expected value provided can be an expected value range and/or the distribution provided can be a distribution range. The information can be a parameter of a movement of a vehicle or an operating parameter of a vehicle, the values of the information being the values of the parameter. The parameter can be a velocity, an acceleration and/or an energy consumption of at least one vehicle. In addition, the information can be a parameter of a traffic controller, in particular parameters of a traffic light. For example, the parameter can be a duration of a red phase, a duration of a green phase and/or a duration of an amber phase of the traffic controller of the traffic light.

[0054] Depending on the length of the route, in particular depending on the number of local points or local areas that are recorded by advance planning, the term short distance 47 or long distance can be used. In order to take into account the expected values and the distributions of the values of the local points, a linear combination of the expected values and the distributions can be carried out for a short distance 47. A short distance can comprise e.g. 2 to 5 local points or local areas and relate to e.g. a distance in front of a traffic light 3 or in front of a junction, etc. (FIG. 6).

[0055] For n normally distributed random variables

X.sub.i(i=1, . . . ,n) with X.sub.i∝(μ.sub.i;σ.sub.i.sup.2)

the linear combination

[00002]$Y=a_0+a_1{X}_1+a_2{X}_2+\mathrm{.Math.}+a_n{X}_n=a_0+\underset{i=1}{\overset{n}{.Math.}}{a}_i{X}_i$

is also normally distributed with the expected value

[00003]$E\left(Y\right)=a_0+\underset{i=1}{\overset{n}{.Math.}}{a}_{i}E\left(X_i\right)=a_0+\underset{i=1}{\overset{n}{.Math.}}{a}_i{\mu }_i.$

If the X.sub.i (i=1, . . . , n) are stochastically independent, the following is true for the variance

[00004]$\mathrm{Var}\left(Y\right)=\underset{i=\square }{\overset{n}{.Math.}}{a}_i^2.Math.\left(X_i\right)=\underset{i=1}{\overset{n}{.Math.}}{a}_i^2{\sigma }_i^2.$

The variance must be greater than zero, and therefore a.sub.j≠0 must also be true for at least one j∈{1, . . . , n}.

[0056] A long distance can comprise more than 5 local points or local areas. Depending on the application, the short distance can also comprise more or fewer local points or local areas. The distance between two local points or local areas can be between 1 m and 100 m or more in the real world. In the city, the distances between the local points or local areas are shorter than outside a city.

[0057] For a long distance, the expected values and the distributions thereof are taken into account according to a Markov chain.

[0058] Formally, a Markov chain by definition has the following appearance:

P(X.sub.n=s|X.sub.0=x.sub.0,X.sub.1=x.sub.1, . . . ,X.sub.n−1=x.sub.n−1)=P(X.sub.n=s|X.sub.n−1=x.sub.n−1)

X.sub.n is the random variable, while s and x.sub.n is the corresponding value that the random variable assumes or has assumed.

[0059] The transition probability of there being a change from state i to state j is defined as follows:

P(X.sub.n+1=j|X.sub.n=i) for all i,j∈S

[0060] So this is the sequence of values that the random variable X can assume. If these probabilities do not depend on i then the term homogeneous Markov chain is used.

[0061] Depending on the application, the computing unit can carry out advance planning for a short distance and advance planning for a long distance at parallel times. As a result, controls for the vehicle can be carried out for a short distance ahead in accordance with the advance planning for the short distance. Controls for the vehicle for a long distance are carried out by the computing unit in accordance with the advance planning for the long distance. For example, the route from a starting point to a more distant destination can be planned using the long-distance planning. In addition, while the vehicle is travelling, for example when approaching a traffic light, a velocity of the vehicle can be controlled using the short-distance planning.

[0062] The foregoing preferred embodiments have been shown and described for the purposes of illustrating the structural and functional principles of the present invention, as well as illustrating the methods of employing the preferred embodiments and are subject to change without departing from such principles. Therefore, this invention includes all modifications encompassed within the scope of the following claims.