OPTIMIZING A PREDICTIVE DYNAMIC BENDING LIGHT FUNCTION OF A LIGHTING SYSTEM

Abstract

Automatically optimizing a predictive dynamic bending light function of a vehicle lighting system comprises configuring a bending light control unit for controlling the lighting system with initial bending light control parameter values as control parameter values to be used, configuring a classification unit for automatically classifying a performance of the bending light control unit into a desired and at least one further performance class, depending on output values of the control unit, configuring a control parameter optimization unit for ascertaining updated control parameter values depending on input values of the control unit and classifications of the performance assigned to them, as well as acquiring driving trajectory parameter values as the input values during a journey, ascertaining output values of the control unit, automatically classifying the performance depending on the ascertained output values, ascertaining updated control parameter values and adapting the control parameter values to be used to the updated control parameter values, wherein the ascertainment of updated control parameter values comprises the application of a genetic algorithm with which a frequency of a classification of the performance into the desired performance class is increased.

Claims

1-15. (canceled)

16. A programmable apparatus comprising a processor and a memory, comprising programming stored in the memory such that the processor is programmed to: control a lighting system of a vehicle with initial bending light control parameter values, wherein the lighting system includes a headlamp apparatus with an illumination direction that is changeable in response to control signals from the processor; provide classifications of a performance of the lighting system into a target performance class and at least one non-target performance class, based on output values when the lighting system is controlled with the initial bending light control parameter values; and ascertain updated bending light control parameter values based on driving trajectory parameter input values to the lighting system, and ones of the classifications of the performance of the lighting system that are assigned to the driving trajectory parameter input values; wherein ascertaining the updated bending light control parameter values includes applying a genetic algorithm with which a frequency of the classification of the performance of the lighting system into the target performance class is increased.

17. The apparatus of claim 16, wherein ascertaining the updated bending light control parameter values takes place during at least one journey.

18. The apparatus of claim 17, wherein the at least one journey is simulated.

19. The apparatus of claim 16, wherein ascertaining the updated bending light control parameter values includes performing a metamodeling of providing the classifications based on current bending light control parameter values and previously stored bending light control parameter values.

20. The apparatus of claim 19, wherein the genetic algorithm is globally optimized with a non-dominated genetic sorting algorithm, based on models generated with the metamodeling.

21. The apparatus of claim 16, wherein the classifications are output from a trained artificial neural network.

22. The apparatus of claim 21, wherein the artificial neural network is trained in a training phase that includes inputting at least a vehicle speed, a steering angle, and an alignment of headlamps.

23. The apparatus of claim 21, wherein the trained artificial neural network includes respective machine learning models for each of the target performance class and at least one non-target performance class.

24. The apparatus of claim 16, wherein the target class corresponds to a correct reaction of the lighting system, and the at least one non-target class comprises a first and a second non-target class, wherein the first non-target class relates to a delayed reaction of the lighting system, and the second non-target class relates to an unstable reaction of the lighting system.

25. The apparatus of claim 16, wherein the classifications are assigned respective trust levels.

26. A method, comprising: controlling a lighting system of a vehicle with initial bending light control parameter values, wherein the lighting system includes a headlamp apparatus with an illumination direction that is changeable in response to control signals from a processor; providing classifications of a performance of the lighting system into a target performance class and at least one non-target performance class, based on output values when the lighting system is controlled with the initial bending light control parameter values; and ascertaining updated bending light control parameter values based on driving trajectory parameter input values to the lighting system, and ones of the classifications of the performance of the lighting system that are assigned to the driving trajectory parameter input values; wherein ascertaining the updated bending light control parameter values includes applying a genetic algorithm with which a frequency of the classification of the performance of the lighting system into the target performance class is increased.

27. The method of claim 26, wherein ascertaining the updated bending light control parameter values takes place during at least one journey.

28. The apparatus of claim 27, wherein the at least one journey is simulated.

29. The method of claim 26, wherein ascertaining the updated bending light control parameter values includes performing a metamodeling of providing the classifications based on current bending light control parameter values and previously stored bending light control parameter values.

30. The apparatus of claim 29, wherein the genetic algorithm is globally optimized with a non-dominated genetic sorting algorithm, based on models generated with the metamodeling.

31. The method of claim 26, wherein the classifications are output from a trained artificial neural network.

32. The apparatus of claim 31, wherein the artificial neural network is trained in a training phase that includes inputting at least a vehicle speed, a steering angle, and an alignment of headlamps.

33. The apparatus of claim 31, wherein the trained artificial neural network includes respective machine learning models for each of the target performance class and at least one non-target performance class.

34. The method of claim 26, wherein the target class corresponds to a correct reaction of the lighting system, and the at least one non-target class comprises a first and a second non-target class, wherein the first non-target class relates to a delayed reaction of the lighting system, and the second non-target class relates to an unstable reaction of the lighting system.

35. The method of claim 26, wherein the classifications are assigned respective trust levels.

Description

BRIEF SUMMARY OF THE DRAWINGS

[0035] Further advantages of the present disclosure can be seen in the detailed description and the drawings. The disclosure is explained in more detail below, also in association with the following description of exemplary embodiments with reference to be accompanying drawings, in which:

[0036] FIG. 1 shows a schematic illustration of an example of a method for automatically optimizing a predictive dynamic bending light function of a lighting system of a vehicle according to one embodiment; and

[0037] FIG. 2 shows a schematic illustration of an example of a vehicle with a lighting system with automatically self-optimizing predictive dynamic bending light function according to a further embodiment.

[0038] It will be understood that other embodiments can be used and that structural or logical changes can be made without deviating from the scope of the present disclosure. It will be understood that the features of the different exemplary embodiments described above and below can be combined with one another except where specifically stated otherwise. The description is therefore not to be understood in a restrictive sense, and the scope of protection is defined by the appended claims.

DETAILED DESCRIPTION

[0039] A schematic illustration of an example of a method 100 for automatically optimizing a predictive dynamic bending light function of a lighting system of a vehicle according to one embodiment of the disclosure is shown in FIG. 1. The method 100 begins in a start state 101 at the beginning of a journey with the vehicle that has the lighting system whose predictive dynamic bending light function is to be optimized. In the illustrated embodiment, however, the performance 102 of a training phase now takes place first, in which an artificial neural network of a classification unit for automatically classifying a performance of a bending light control unit into a desired performance class and at least one further performance class, depending on output values of the bending light control unit, is trained with a machine learning method that evaluates at least values of a vehicle speed of the vehicle, a steering angle of the steering wheel of the vehicle, and an alignment of driving headlamps of the vehicle. In another embodiment, the artificial neural network is already trained. The training can here be carried out with the same vehicle and the same driver, but also with a different vehicle and/or a different driver, or, independently of the vehicle, on the basis of a database of stored test journey datasets.

[0040] Steps now follow of configuring 103 a bending light control unit for controlling the lighting system of the vehicle with initial bending light control parameter values as bending light control parameter values to be used, of configuring 104 a classification unit (connected to the bending light control unit) for automatically classifying a behavior of the bending light control unit into a desired performance class and at least one further performance class, depending on output values of the bending light control unit, and of configuring 105 a control parameter optimization unit for ascertaining updated bending light control parameter values depending on input values of the bending light control unit and classifications of the performance of the bending light control unit assigned to the input values.

[0041] Acquiring 106 driving trajectory parameter values as the input values of the bending light control unit during at least one journey of the vehicle takes place in a further step, followed by steps of ascertaining 107 output values of the bending light control unit and of automatically classifying 108 the performance of the bending light control unit depending on the ascertained output values during the at least one journey. In the illustrated embodiment, this comprises use 114 of the trained artificial neural network as well as, in addition, assigning 115 a trust level to the respectively classified performance.

[0042] The method is now continued with the ascertainment 109 of updated bending light control parameter values. In the embodiment illustrated in FIG. 1, this comprises a performance 112 of a metamodeling of the automatic classification depending on the bending light control parameter values to be used and stored, previous, bending light control parameter values to be used, in order to be able to perform a multidimensional optimization making use of the genetic algorithm, as well as the application 113 of a genetic algorithm with which a frequency of a classification of the performance of the bending light control unit into the desired performance class is increased. The stored previous bending light control parameter values to be used have been ascertained here during previous iterations of the ascertainment of updated bending light control parameter values based on classifications with reference to output values of the bending light control unit from previous time segments of the journey currently being driven and/or during previous journeys of the same or of another vehicle. At least when no previous bending light control parameter values to be used have been ascertained in this way, bending light control parameter values that are, for example, random, or that preferably have been ascertained as suitable, for example using the Latin hypercube sampling method, can be selected from the parameter space as previous bending light control parameter values to be used. No metamodeling is carried out if no previous bending light control parameter values to be used are available.

[0043] In the illustrated embodiment, this preferably comprises performing a global optimization with a non-dominated genetic sorting algorithm, using models generated with the metamodeling. The performance 112 of the metamodeling also comprises first performing a cleaning and preparation of the data for the metamodeling.

[0044] After the ascertainment 109 of updated bending light control parameter values, an adaptation 110 of the bending light control parameter values to be used to the updated bending light control parameter values then takes place, for example during the at least one journey.

[0045] In a step of checking 111 whether the optimization of the predictive dynamic bending light function should end, a check is carried out as to whether an associated termination criterion is satisfied. The criterion is, for example, satisfied if the bending light control unit, or the lighting system, or the vehicle is deactivated. It can also be provided that optimization is ended after the journey has ended, while it is also possible to continue the optimization over a plurality of journeys. It can also be provided that the optimization is ended if the respective, newly ascertained, updated bending light control parameter values (within a tolerance range, if relevant) no longer deviate from the bending light control parameter values to be used that are valid up to that point. It can also be provided that a final test journey ends the optimization, wherein the control of the lighting controller is subjected to a quality check, for example by an expert. Depending on the embodiment, the termination criterion can, for example, be one of those mentioned previously, or a combination of more than one.

[0046] If the checking 111 shows that the optimization should not end (shown in FIG. 1 with a “−”), the method is continued with the acquisition 106 of further driving trajectory parameter values. If the checking 111 shows that the optimization should end (shown in FIG. 1 with a “+”), the method proceeds to an end state 116.

[0047] A schematic illustration of an example of a vehicle 300 with a lighting system 200 with automatically self-optimizing predictive dynamic bending light function according to a further embodiment of the disclosure is shown in FIG. 2. In addition to the lighting system 200, the vehicle 300 in particular comprises a vehicle bus, a CAN bus 301 in the illustrated embodiment, via which the driving trajectory parameter values 302 can be received. This involves values from sensor signals, each recorded with suitable sensors, such as the profile of the steering angle of the steering wheel of the vehicle, the vehicle speed, the yaw rate, the engaged gear and/or the state of the trafficator, said values characterizing the driving trajectory (including the operating state of the vehicle during the journey). Video recordings of a front camera 303 which, in the illustrated embodiment, has a video analysis module, i.e. a programmable apparatus that is configured to ascertain the road course from the video recordings, supply further driving trajectory parameter values, either again via the CAN bus 301 of the vehicle 300 or, as shown in FIG. 1, directly to a driving trajectory parameter acquisition apparatus 220 of the lighting system 200. The driving trajectory parameter acquisition apparatus 220 comprises, in particular, an interface in order to receive the driving trajectory parameter values 302 from the CAN bus 301.

[0048] The illustrated lighting system 200 of the vehicle has an automatic self-optimizing predictive dynamic bending light function. This means that a driving headlamp apparatus 201 is designed to change an illumination direction 206, 208, i.e. a lighting direction of the driving headlamp apparatus 201, in response to control signals 213 of a bending light control unit 210. In the embodiment shown in FIG. 1, the driving headlamp apparatus 201 has a rotatably mounted right-hand driving headlamp 202 connected to a first drive motor 203, and a rotatably mounted left-hand driving headlamp 204 connected to a second drive motor 205, wherein the drive motors 203, 205 receive the control signals 213 and are designed to turn the right-hand driving headlamp 202 and the left-hand driving headlamp 204 depending on the control signals 213 each through an angle, in order to improve the illumination of the road by the first light cone 207 with the first illumination direction 206 of the right-hand driving headlamp 202 and the second light cone 209 with the second illumination direction 208 of the left-hand driving headlamp 204 when the vehicle 300 travels through a bend. In a further exemplary embodiment (not illustrated), the driving headlamps 202, 204 are not necessarily mounted rotatably and pivotable using drive motors 203, 205, but rather comprise a matrix system consisting of a plurality of LEDs and a controller in order to change the illumination direction 206, 208 through appropriate activation and deactivation of LEDs in controlled segments of the matrix system.

[0049] The lighting system in addition comprises the bending light control unit 210 that is designed to transmit the control signals 213 for controlling the illumination direction 206, 208 of the right-hand and of the left hand driving headlamps 202, 204 to the first and second drive motor 203, 205 to the driving headlamp apparatus 201. The bending light control unit 210 is connected for this purpose to the driving headlamp apparatus 201 and initialized with initial bending light control parameter values 211 as bending light control parameter values to be used. The initial bending light control parameter values can be ascertained in a training phase, or can already be ascertained in advance and available in a memory 212 in order to be loaded by the bending light control unit 210.

[0050] The bending light control unit 210 is moreover connected to a classification unit 214 implemented as an AI unit, that is configured, for example programmed, for automatically classifying a performance of the bending light control unit 210 into a desired performance class and at least one further performance class, depending on output values 215 of the bending light control unit 210. Depending on the format of the control signals 213, the output values 215 of the bending light control unit 210 can be identical to the control signals 213, or can have a different format which, however, characterizes the control signals (for example rotation angle figures instead of activation durations of the drive motors).

[0051] The lighting system 200 also comprises a control parameter optimization unit 216 connected to the bending light control unit 210 and the classification unit 214, for ascertaining updated bending light control parameter values 217 depending on input values 218 of the bending light control unit 210 and classifications 219 of the performance of the bending light control unit 210 into the respective performance classes assigned to the input values 218. The driving trajectory parameter acquisition apparatus 220 is also configured to acquire driving trajectory parameter values 302. The driving trajectory parameter acquisition apparatus 220 comprises an interface to the CAN bus 301 of the vehicle 300, via which, and also, if relevant, directly, driving trajectory parameter values 302 are received from a front camera 303 of the vehicle 300, and are transmitted as input values of the bending light control unit 210 while the vehicle is travelling to said driving trajectory parameter acquisition apparatus, and also (directly or indirectly) to the control parameter optimization unit 216.

[0052] It is provided in one embodiment that the bending light control unit 210, the classification unit 214 and the control parameter optimization unit 216 are implemented as circuits and/or with the aid of programmable logic circuits. In a further embodiment it is provided that the lighting system 200 comprises a programmable apparatus that comprises at least one processor and a memory, for example the memory 220, while the bending light control unit 210, the classification unit 214 and the control parameter optimization unit 216 are (in whole or in part) modules that can be executed by the processor, and the memory contains code segments, i.e. program code, which, when loaded by the processor, cause it to execute the modules and configure the lighting system 200 to perform the method 100 for automatically optimizing a predictive dynamic bending light function described in connection with FIG. 1.

[0053] It will be understood that although method steps are described in accordance with a certain ordered sequence, they could in part be carried out in a sequence other than that described here. It will also be obvious that certain steps may be carried out simultaneously or in sequence, once or a plurality of times, that other steps may be added, or that certain steps described here may be omitted. In other words, the present descriptions are provided for the purposes of clarifying particular embodiments, and should not be understood as a restriction of the disclosed object.

[0054] Although the invention has been more closely illustrated and described in more detail through the preferred exemplary embodiments, the invention is not restricted by the disclosed examples, and other variations can be derived from this by the expert without going beyond the scope of protection of the invention. The invention should therefore not be restricted to individual embodiments, but only by the appended claims.

LIST OF REFERENCE SIGNS

[0055] 100 Method [0056] 101 Start [0057] 102 Performing a training phase [0058] 103 Configuring a bending light control unit [0059] 104 Configuring a classification unit [0060] 105 Configuring a control parameter optimization unit [0061] 106 Acquiring driving trajectory parameter values [0062] 107 Ascertaining output values of the bending light control unit [0063] 108 Automatic classification [0064] 109 Ascertaining updated bending light control parameter values [0065] 110 Adapting the bending light control parameter values to be used [0066] 111 Checking whether the optimization should end [0067] 112 Performing a metamodeling [0068] 113 Application of a genetic algorithm [0069] 114 Using a trained artificial neural network [0070] 115 Assigning a trust level [0071] 116 End [0072] 200 Lighting system [0073] 201 Driving headlamp apparatus [0074] 202 Rotatably mounted right-hand driving headlamp [0075] 203 First drive motor for turning the right-hand driving headlamp [0076] 204 Rotatably mounted left-hand driving headlamp [0077] 205 Second drive motor for turning the left-hand driving headlamp [0078] 206 First illumination direction [0079] 207 First light cone [0080] 208 Second illumination direction [0081] 209 Second light cone [0082] 210 Bending light control unit [0083] 211 Initial bending light control parameter values [0084] 212 Memory [0085] 213 Control signals [0086] 214 Classification unit [0087] 215 Output values of the bending light control unit [0088] 216 Control parameter optimization unit [0089] 217 Updated bending light control parameter values [0090] 218 Input values of the bending light control unit [0091] 219 Classifications [0092] 220 Driving trajectory parameter acquisition apparatus [0093] 221 Bending light control parameter values to be used [0094] 300 Vehicle [0095] 301 CAN bus [0096] 302 Driving trajectory parameter values [0097] 303 Front camera with video analysis module