COMPUTER-AIDED METHOD AND DEVICE FOR PREDICTING SPEEDS FOR VEHICLES ON THE BASIS OF PROBABILITY

Abstract

The invention relates to a computer-aided method for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation. The computer-aided method comprises establishing a state vector of the drive cycle for a current time interval from a past speed curve, providing an acceleration prediction model, determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and appending the predicted speed value to the past speed curve in order to generate the drive cycle. Furthermore, the invention relates to a device for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation, and means for establishing a state vector of the drive cycle for a current time interval from a past speed curve, means for determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, means for integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and means for appending the predicted speed value to the past speed curve in order to generate the drive cycle.

Claims

1. A computer-aided method for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation, comprising the following work steps: establishing a state vector of the drive cycle for a current time interval from a past speed curve; providing an acceleration prediction model; determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector; integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval; and appending the predicted speed value to the past speed curve in order to generate the drive cycle.

2. The method according to claim 1, wherein the determining of an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector comprises the following work steps: establishing a probability value for a current scenario of an acceleration and a probability value for a current scenario of a deceleration and a probability value for a current scenario of a state of constant speed by means of the acceleration prediction model as a function of the state vector; and randomly selecting an acceleration, deceleration or constant speed scenario for the current time interval based on the probability values for current acceleration, deceleration and constant speed scenarios; and/or establishing a probability distribution of acceleration values of the randomly selected scenario by means of the acceleration prediction model as a function of the state vector; and randomly selecting an acceleration value for the current time interval based on the probability distribution of acceleration values of the randomly selected scenario.

3. The method according to claim 1, wherein the drive cycle is generated by iteratively executing the work steps of the method in the listed order and each predicted speed value being appended to the past speed curve from a previous iteration.

4. The method according to claim 1, wherein multiple predicted speed values are in each case obtained for the same future time intervals based on the past speed curve so that statistical speed distributions are obtained for future time intervals.

5. The method according to claim 1, wherein the state vector for a current time interval comprises at least one of the following components: a current speed value, one or more past speed values, one or more acceleration values of one or more time intervals, one or more acceleration change values of one or more time intervals, a value corresponding to a number of time intervals pursuant to the duration of an ongoing acceleration maneuver, a value corresponding to a number of time intervals pursuant to the duration of an ongoing deceleration maneuver and a value corresponding to a number of time intervals pursuant to the duration of an ongoing constant speed state.

6. The method according to claim 1, wherein the acceleration value determined in consideration of probabilities resulting from the acceleration prediction model and the state vector is based on the duration of an ongoing acceleration maneuver, an ongoing deceleration maneuver or an ongoing state of constant speed.

7. The method according to claim 1, wherein the past speed curve comprises at least one speed value.

8. The method according to claim 1, wherein the current scenario of an acceleration and/or the current scenario of a deceleration and/or the current scenario of a constant speed state each exhibit a probability distribution of acceleration values.

9. The method according to claim 2, wherein an expected value of the probability distribution of acceleration values is set based on the past speed curve.

10. The method according to claim 1, wherein the acceleration prediction model is based on a statistical evaluation of measured driving data of at least one real vehicle, wherein preferably the measured driving data of the at least one real vehicle only comprises a chronological sequence of speed values.

11. A method for driving a vehicle using an adaptive cruise control system, in particular a driver assistance system, particularly for predictive driving functions, wherein the drive cycle of the vehicle driving in front of the vehicle is determined using the method according to claim 1.

12. The method according to claim 11, wherein multiple predicted speed values are obtained for the same future time intervals based on the past speed curve so that statistical speed distributions are obtained for future time intervals, wherein safety conditions for driving the vehicle are derived from the statistical speed distributions.

13. A method for generating a drive cycle for a vehicle which is suitable for use by driver assistance systems, in particular for predictive driving functions, and comprises the work steps of claim 1.

14. A method for analyzing at least one component of a motor vehicle, wherein the at least one component or the motor vehicle is subjected to a real or simulated test operation based on at least one drive cycle determined using a method according to claim 1.

15. The method according to claim 14, wherein the method comprises the following further work steps: checking the compliance of the multiple predicted speed values with at least one boundary condition, in particular Real Driving Emissions guidelines, after a defined number of iterations, wherein the check in particular recurs periodically at the end of each of the specific number of iterations, wherein the specific number of iterations in particular corresponds to a predefined total time interval, preferably of approximately 5 minutes.

16. The method according to claim 15, wherein the method comprises the following further work steps: correcting the probability value for a current acceleration scenario and/or the probability value for a current deceleration scenario and/or the probability value for a current constant speed state scenario for the current time interval based on the check; and/or correcting the acceleration value for the current time interval based on the check.

17. A computer program product having instructions which, when executed by a computer, prompts it to execute the steps of a method according to claim 1.

18. A computer-readable medium on which a computer program product according to claim 17 is stored.

19. A device for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation, comprising: means for establishing a state vector of the drive cycle for a current time interval from a past speed curve; means for providing an acceleration prediction model; means for determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector; means for integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval; and means for appending the predicted speed value to the past speed curve in order to generate the drive cycle.

Description

[0046] Further features and advantages derive from the following description in conjunction with the figures. The figures show at least partly schematically:

[0047] FIG. 1 a preferential exemplary embodiment of an inventive computer-aided method for generating a drive cycle for a vehicle, wherein the method is suitable for simulating a real driving operation;

[0048] FIG. 2 a preferential exemplary embodiment of an inventive computer-aided method for generating an RDE-compliant drive cycle for a vehicle; and

[0049] FIG. 3 a preferential exemplary embodiment of a device for generating a drive cycle for a vehicle which is suitable for simulating a real driving operation.

[0050] FIG. 1 shows a preferential exemplary embodiment of a computer-aided method 100 according to the invention for generating a drive cycle for a vehicle, wherein the method 100 is suitable for simulating a real driving operation.

[0051] Step 101 of the method 100 has past data being provided. The past data represents past speed data or a past speed curve and comprises or consists of speed values which are respectively associated with consecutive defined time intervals. The defined time intervals can be constant time intervals or can vary in length. The past data can comprise or consist of a single speed value associated with a single time interval. This single speed value can also be zero. The most recent speed value of the past speed curve is assigned to a current time interval.

[0052] In step 102, the state vector x.sub.t is established for the current time interval from the past speed data or speed curve respectively. The state vector x.sub.t has as components the current speed value v.sub.t of the current time interval t, the acceleration value a.sub.t−1 of the time interval t−1 immediately prior to the current time interval t, a value s.sub.a,t corresponding to the number of time intervals in which an acceleration maneuver occurs immediately prior to the current time interval, a value s.sub.e,t corresponding to the number of time intervals in which a deceleration maneuver occurs immediately prior to the current time interval, as well as a value s.sub.k,t corresponding to the number of time intervals in which a state of constant speed was maintained immediately prior to the current time interval.

[0053] The s.sub.j,t designation used in FIG. 1 refers to the three values of s.sub.a,t, s.sub.e,t and s.sub.k,t, wherein index j can either assume a for an acceleration maneuver, e for a deceleration maneuver or k for a state of constant speed. The state vector can have further components or other components corresponding to speed values, acceleration values, changes in acceleration values, or a number of time intervals.

[0054] In step 103, a probability value p(x.sub.t) for a current acceleration scenario and a probability value q(x.sub.t) for a current deceleration scenario are established from the state vector by means of an acceleration prediction model. The probability value y for a constant speed state then results preferably from the following relationship y=1−p(x.sub.t)−q(x.sub.t). The acceleration prediction model is based preferably on a statistical evaluation of measured driving data of a real vehicle, wherein the measured driving data consists exclusively of a chronological sequence of speed values associated with chronologically consecutive time intervals.

[0055] In step 104, based on the probability values p(x.sub.t), q(x.sub.t) and 1−p(x.sub.t)−q(x.sub.t) established in step 103, a random selection of one of three scenarios then follows in the sense of a statistical random sampling; i.e. an acceleration scenario, a deceleration scenario or the scenario of the state of constant speed.

[0056] A probability distribution of acceleration values is established for the randomly selected scenario by way of the acceleration prediction model as a function of the state vector, preferably a continuous probability distribution is modeled thereto. Further preferably, each possible acceleration value within the randomly selected scenario can be assigned a probability.

[0057] In step 105, a random selection in the sense of a statistical random sampling of any random acceleration value a.sub.t is made for the current time interval t from the established probability distribution of the randomly selected scenario.

[0058] In step 106, the randomly selected acceleration value a.sub.t is integrated over the current time interval tin order to obtain a next predicted speed value v.sub.t+1 for a next time interval t+1 in the future.

[0059] In step 107, the new speed value v.sub.t+1 is appended to the past speed curve. The new speed value v.sub.t+1 for time interval t+1 is thereafter treated as the current time interval in a second iteration of the method in step 102. Iteratively running through steps 102 to 107 creates a drive cycle which, due to the nature of the acceleration prediction model, is similar to a drive cycle measured under real conditions.

[0060] FIG. 2 shows a preferential exemplary embodiment of an inventive computer-aided method 200 for generating an RDE-compliant drive cycle for a vehicle.

[0061] Step 201 of method 200 is identical to the above-described step 101 of method 100. Past speed data is provided.

[0062] Step 202 of method 200 includes the above-described steps 102 and 103 of method 100. The state vector x.sub.t is established for the current time interval from the past speed curve. A probability value p(x.sub.t) for a current acceleration scenario and a probability value q(x.sub.t) for a current deceleration scenario are established from the state vector by means of an acceleration prediction model.

[0063] After multiple predicted speed values have been obtained and appended to the past speed curve through the iterative execution of method 100, a checking of the previously predicted; i.e. previously generated, speed values for compliance with the criteria of the RDE guidelines recurs periodically in step 203 of method 200 at the end of a defined number of iterations of method 100. For example, this periodically recurring check can in each case take place after a number of time intervals corresponding to the elapsing of a five-minute time span of the drive cycle, although other spans of time for the periodic check are also possible.

[0064] After the probability values p(x.sub.t) and q(x.sub.t) for a current acceleration scenario and for a current deceleration scenario have been established in step 202, should the checking in step 203 show that the previously predicted speed values appended to the past speed curve do not adhere to the criteria of the RDE guidelines, the established probability values p(x.sub.t) and q(x.sub.t) are corrected accordingly in step 204.

[0065] The current acceleration scenario or deceleration scenario respectively thus receives corrected probability values p′(x.sub.t) and q′(x.sub.t). Should, for example, the checking in step 203 show that the previously predicted speed values and corresponding time intervals do not meet the criteria of the RDE guidelines for the duration of highway driving at increased speed, the probability for an acceleration scenario is increased by the correction and the probability for a deceleration scenario is correspondingly reduced in step 204.

[0066] In step 205, one of the three scenarios is thereafter randomly selected; i.e. an acceleration scenario, a deceleration scenario or a scenario of the state of constant speed based on the probability values p′(x.sub.t), q′(x.sub.t) and 1−p′(x.sub.t)−q′(x.sub.t) corrected via step 204 and a random selection of any random acceleration value a.sub.t is made from the established probability distribution of the randomly selected scenario as described in the context of method 100.

[0067] Alternatively or additionally to the correction in step 204, the randomly selected acceleration value can be corrected in step 206 pursuant to the step 203 check, whereby corrected acceleration value a′.sub.t is generated.

[0068] In step 207, the corrected acceleration value a′.sub.t is integrated over the current time interval t in order to obtain a next predicted speed value v.sub.t+1 for a next time interval t+1 in the future.

[0069] In step 208, the new speed value v.sub.t+1 is appended to the past speed curve. The new speed value v.sub.t+1 for time interval t+1 is thereafter treated as the current time interval in the next iteration of the method 200 in step 202. Iteratively running through steps 202 to 208 creates a drive cycle which, due to the nature of the acceleration prediction model, is similar to a drive cycle measured under real conditions and is in compliance with the RDE guidelines.

[0070] FIG. 3 shows a preferential exemplary embodiment of a device 300 for generating a drive cycle for a vehicle which is suitable for simulating a real driving operation. The device for generating the drive cycle for a vehicle comprises means 301 for establishing a state vector of the drive cycle for a current time interval from a past speed curve. Furthermore, the device for generating the drive cycle comprises means 302 for providing an acceleration prediction model, means 303 for determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, means 304 for integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and means 305 for appending the predicted speed value to the past speed curve in order to generate the drive cycle.

[0071] It should be noted that the exemplary embodiments are only examples not intended to limit the scope of protection, application and configuration in any way. Rather, the foregoing description is to provide the person skilled in the art with a guideline for implementing at least one exemplary embodiment, whereby various modifications can be made, particularly as regards the function and arrangement of the described components, without departing from the scope of protection resulting from the claims and equivalent combinations of features.

LIST OF REFERENCE NUMERALS

[0072] 300 device for generating a drive cycle for a vehicle [0073] 301 means for establishing a state vector of the drive cycle [0074] 302 means for providing an acceleration prediction model [0075] 303 means for determining an acceleration value [0076] 304 means for integrating the determined acceleration value [0077] 305 means for appending the predicted speed value to the past speed curve