Intelligent control of active aerodynamic devices

Abstract

A method of operating a vehicle system of a vehicle includes determining, by means of the vehicle system, an impending driving situation of the vehicle based at least in part on sensor data obtained by the vehicle system, and operating, by means of the vehicle system, an impending driving situation of the vehicle. An expected behavior of a driver of the vehicle relative to the impending driving condition is determined based at least in part on the impending driving condition using a trained algorithm trained based at least in part on a previous behavior of the driver and a previous driving condition. The method further comprises determining, by means of the vehicle system, a target setting of at least one active aerodynamic device of the vehicle based at least in part on the impending driving situation and an expected behavior of the driver, and controlling the at least one active aerodynamic device by means of the vehicle system according to the determined target setting of the at least one active aerodynamic device.

Description

Technical Field

[0001] The present application relates to a method of operating a vehicle system of a vehicle associated with one or more active aerodynamic devices of the vehicle. The present application also relates to a computer program product, a vehicle system and a vehicle. Background Art

[0002] Aerodynamic devices for vehicles, such as spoilers, diffusers, etc., are suitable for improving the performance of the vehicle under specific conditions. For example, when the vehicle is moving at high speeds, aerodynamic devices of the above type can enhance the downforce of the vehicle, thereby allowing improved traction and maneuverability of the vehicle. However, these advantages come at the expense of increased drag, which leads to increased energy consumption and, therefore, makes the operation of the vehicle less economical. Moreover, in many typical driving situations, the advantages brought about by rigid aerodynamic devices may not be needed or desired.

[0003] Active aerodynamic devices are known. They allow the aerodynamic device to be adjusted, for example, according to driver selections (such as a selected driving mode) or detected vehicle conditions (such as a detected vehicle speed, etc.). For this purpose, active aerodynamic devices, such as active rear spoilers, active rear diffusers, etc., usually comprise one or more controllable actuators that allow the position of the aerodynamic device to be automatically changed.

[0004] In KR101632148B1, it has been proposed to automatically adjust the position of the active rear spoiler according to route information obtained by means of a navigation function of the vehicle.

[0005] There is a need to improve the operation of vehicle systems associated with one or more active aerodynamic devices of a vehicle. Summary of the invention

[0006] Therefore, a method, a computer program product, a vehicle system, and a vehicle are provided.

[0007] According to a first aspect, a method of operating a vehicle system of a vehicle is provided. The method comprises determining, by means of the vehicle system, an upcoming driving situation of the vehicle based at least in part on sensor data obtained by the vehicle system. The method further comprises determining, by means of the vehicle system, an expected behavior of a driver of the vehicle with respect to the upcoming driving situation based at least in part on the upcoming driving situation using a trained algorithm, the trained algorithm being trained at least in part on previous behavior of the driver and previous driving situations. The method further comprises determining, by means of the vehicle system, a target setting of at least one active aerodynamic device of the vehicle based at least in part on the upcoming driving situation and the expected behavior of the driver, and controlling, by means of the vehicle system, the at least one active aerodynamic device according to the determined target setting of the at least one active aerodynamic device.

[0008] Predictive control of the active aerodynamic devices is facilitated by determining an upcoming driving situation of the vehicle and an expected behavior of the driver using a trained algorithm, wherein the algorithm has been trained based on previous behavior of the driver and previous driving situations. In this way, an improved adjustment of the active aerodynamic devices to the upcoming driving situation is achieved, in particular because an adjustment time of at least one active aerodynamic device for adopting a position according to a control signal can be partially or completely compensated.

[0009] Furthermore, an improved control of the active aerodynamic device is achieved according to the driver's personal preferences and / or habits with respect to the upcoming driving situation. In particular, the driver's personal preferences and / or habits can be derived from the driver's previous behavior in previous driving situations and can serve as the basis for the trained algorithm. The driver's convenience can thus be improved.

[0010] Furthermore, an improved control of the active aerodynamic devices is achieved, since the vehicle state of the vehicle in an upcoming driving situation can be predicted more accurately. This applies when the vehicle state in the upcoming driving situation depends on the driver's behavior before and / or during the upcoming driving situation. Thus, the adjustment of at least one active aerodynamic device can be implemented in a predictive manner depending on the most likely vehicle state of the vehicle during the upcoming driving situation. Thus, the effectiveness of the active aerodynamic devices can be increased.

[0011] The method may further comprise determining a current vehicle state of the vehicle by means of the vehicle system. At least one of determining the upcoming driving situation, determining the expected behavior of the driver and / or determining a target setting of the at least one active aerodynamic device may also be implemented at least in part based on the current vehicle state of the vehicle. The current vehicle state of the vehicle may be at least in part defined by the current behavior of the driver, in particular one or more current control input actions provided by the driver to the vehicle system, such as a current steering input and / or a current acceleration / deceleration input. Additionally or as an alternative, the current vehicle state of the vehicle may be at least in part defined by the current configuration, in particular the current position, of at least one active aerodynamic device.

[0012] The current vehicle state of the vehicle may partially indicate an upcoming driving situation, a possible upcoming behavior of the driver and / or a range of achievable configurations of the at least one active aerodynamic device. Thus, taking into account the current vehicle state of the vehicle enables a more accurate, more reliable and / or more efficient determination of an upcoming driving situation, an expected behavior of the driver and / or a target setting of the at least one active aerodynamic device.

[0013] The current vehicle state may include a selectable driving mode of the vehicle. The driving mode may be selected from a group of driving modes including at least one of economy, comfort, sport and / or racing. The selected driving mode may indicate one or more current preferences of the driver and / or one or more functions of the vehicle, which are enabled or disabled depending on the selected driving mode. Therefore, taking into account the driving mode of the vehicle also enables a more accurate, reliable and / or efficient determination of an upcoming driving situation, the driver's expected behavior and / or a target setting of at least one active aerodynamic device.

[0014] The method may further comprise: detecting the driver's behavior with respect to an upcoming driving situation by means of the vehicle system, and updating the trained algorithm based on the detected driver's behavior by means of the vehicle system. In this way, a continuous training of the trained algorithm is achieved. Thus, a more accurate, reliable and / or efficient determination of an upcoming driving situation, the driver's expected behavior and / or a target setting of at least one active aerodynamic device is facilitated.

[0015] The trained algorithm can be stored by means of an onboard device, in particular by means of a storage device of an onboard control unit of a vehicle system. By means of the vehicle system, determining the driver's expected behavior with respect to the upcoming driving situation using the trained algorithm can be implemented by means of the onboard device, in particular by means of a processor of the onboard control unit. In this way, a completely vehicle-based implementation of the method is achieved, which dispenses with a permanent connection of the vehicle to a communication network when implementing the method.

[0016] The method may also include receiving updated algorithm data for the trained algorithm via a communication module of the vehicle system, and updating the trained algorithm according to the received updated algorithm data by means of the onboard device. The updated algorithm data may relate to at least one of: one or more updated types of driving situations, or updated classification data for analyzing the obtained sensor data with respect to one or more driving situations.

[0017] The term "active aerodynamic device" as used herein refers to any type of active movable structure that affects the aerodynamic properties of a vehicle, wherein moving the active movable structure changes the effect of the active movable structure on the aerodynamic properties of the vehicle. The at least one active aerodynamic device may in particular comprise at least one of an active rear spoiler, an active grille shutter, an active chassis front device, an active chassis height and / or an active rear diffuser of the vehicle.

[0018] The target setting of the at least one active aerodynamic device may correspond to a target position of the at least one active aerodynamic device. Controlling the at least one active aerodynamic device according to the determined target setting may include adjusting a variable position of the at least one active aerodynamic device according to the target setting.

[0019] The method may be implemented repeatedly with respect to at least one of the determining step and the controlling step. In this way, in consecutive cycles of implementing the method, the driver's behavior in response to the control of the at least one active aerodynamic device during a first cycle may be taken into account when determining an upcoming driving situation, the driver's intended behavior and / or a target setting of the at least one active aerodynamic device during one or more subsequent cycles. This also facilitates implementing the method as a "closed loop", wherein the at least one active aerodynamic device is controlled according to the driver's intended behavior and the driver's intended behavior depends on the previous control of the at least one active aerodynamic device.

[0020] Furthermore, the imminence of the upcoming driving situation (i.e. the period from the moment the upcoming driving situation has been determined until the moment the upcoming driving situation begins) may correspond to a period allowing at least two cycles of the method to be implemented. Thus, the upcoming driving situation determined during the first cycle, the expected behavior of the driver and / or the target setting of the at least one active aerodynamic device may be taken into account when determining the upcoming driving situation, the expected behavior of the driver and / or the target setting of the at least one active aerodynamic device during one or more subsequent cycles before the upcoming driving situation determined during the first cycle has begun and / or before the at least one active aerodynamic device has adopted a configuration according to the control during the first cycle. This allows recursive measurement, thereby achieving improved smoothness, improved consistency and / or readjustment between one or more consecutive target settings of the at least one active aerodynamic device.

[0021] The sensor data may be obtained from at least one sensor of the vehicle system, the at least one sensor comprising at least one of a position sensor, a camera, a lidar sensor, a radar sensor and / or an ultrasonic sensor.

[0022] Any of the upcoming driving situation and the previous driving situation may be associated with one or more of: road trajectory, road conditions, crosswind conditions, time of day, weather conditions, traffic conditions, environment, in particular terrain environment, vehicle status, in particular any of the various possible settings of the at least one active aerodynamic device and / or driver status.

[0023] Any of the driver's expected behavior and previous behavior may include at least one of the driver's activities and / or preferences.

[0024] Determining the expected behavior of the driver based at least in part on the upcoming driving situation may include determining the expected behavior of the driver based at least in part on any of various possible settings of the at least one active aerodynamic device.

[0025] Determining the target setting of the at least one active aerodynamic device may also be performed based on stored adjustment times of the at least one active aerodynamic device for adopting a position in dependence on a received control signal.

[0026] According to another aspect, a computer program product is provided. The computer program product comprises portions of program code which, when executed by a processor of a vehicle system of a vehicle, configure the vehicle system to implement the presently provided method.

[0027] According to another aspect, a vehicle system is provided, comprising a processor and a storage device operatively coupled to the processor, wherein the storage device comprises portions of program code, which when executed by the processor causes the vehicle system to implement the presently provided method.

[0028] According to another aspect, a vehicle is provided. The vehicle comprises a vehicle system as currently provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Further details, advantages and purposes of the present invention will become apparent from the accompanying drawings and detailed description. In the accompanying drawings:

[0030] Figure 1 A vehicle having a vehicle system according to an example;

[0031] Figure 2 A flowchart of a method of operating a vehicle system according to an example;

[0032] Figure 3 A control unit for a vehicle according to an example;

[0033] Figure 4 A flow chart of a method of operating a vehicle system according to another example. DETAILED DESCRIPTION

[0034] Figure 1 A vehicle 100 is schematically and exemplarily shown. The vehicle 100 includes a vehicle system 110. The vehicle system 110 includes a control unit 120, the control unit 120 including a processor 122 and a data storage device 124 operatively connected to the processor 122. The vehicle system 110 also includes one or more sensors 130, 132, such as a front camera 130 and a position sensor 132. The vehicle system 110 also includes at least one active aerodynamic device 140, such as an active rear spoiler.

[0035] In the example shown, the vehicle system 110 further comprises a communication unit 150 adapted to receive communication signals, such as radio signals, via a communication network to which the vehicle system 110 is connected. The sensors 130, 132, the active aerodynamic device 140 and the communication unit 150 are operatively connected to the control unit 120.

[0036] The sensors 130, 132 are adapted to detect one or more conditions indicative of an upcoming driving situation of the vehicle 100. In the example shown, the front camera 130 is adapted to detect objects located in front of the vehicle 100, including the trajectory or route of the road S on which the vehicle 100 is moving, road conditions, environment E, etc. Figure 1100 is shown by a dashed line originating from the front camera 130. In addition, a position sensor 132, which may be a GPS module of the vehicle system 110, detects the position of the vehicle 100. In conjunction with map information stored, for example, in a data storage device 124 of a navigation application associated with the vehicle system 110, the detected position of the vehicle 100 indicates the trajectory of the road ahead of the vehicle 100. Additional conditions indicating an upcoming driving situation, such as terrain objects in the environment E, local weather, etc., may also be included in the map information.

[0037] Based on the sensor data received from the sensors 130, 132, the control unit 120 determines an upcoming driving situation of the vehicle 100. By means of the control unit 120, the upcoming driving situation is determined with respect to its potential relevance to a possible behavior of a driver D of the vehicle 100 with respect to the upcoming driving situation. The upcoming driving situation is further determined with respect to an advantageous control of the active aerodynamic device 140 with respect to both the upcoming driving situation and the expected (i.e., possible) behavior of the driver D. For example, the control unit 120 determines the upcoming driving situation with respect to a straight or curved trajectory of the road S in front of the vehicle 100, the slipperiness of the road S, lighting conditions, etc., so that these conditions are indicative of a possible driving behavior of the driver D and a corresponding advantageous configuration of the active aerodynamic device 140.

[0038] In addition to or in lieu of the front camera 130 and the position sensor 132, other sensor types are used in other examples to determine an upcoming driving situation. Suitable other sensor types include one or more cameras other than the front camera, a lidar sensor, a radar sensor, and / or an ultrasonic sensor of the vehicle system 110, etc. In addition, in addition to or in lieu of the foregoing examples, in some examples, an upcoming driving situation is determined based on time of day, traffic conditions, the environment E, the vehicle state (including any of the various possible configurations of the active aerodynamic device 140), the driver state of the driver D (such as the detected fatigue level of the driver D), etc.

[0039] When the control unit 120 has determined an upcoming driving situation, the control unit 120 determines the expected behavior of the driver D with respect to the upcoming driving situation based at least in part on the determined upcoming driving situation. To this end, a trained algorithm is used, which has been trained based on the previous behavior of the driver D in other (e.g., similar) driving situations. In some examples, the trained algorithm is obtained by implementing a machine learning process on collected data related to the previous behavior of the driver D and the previous driving situation.

[0040] The use of a trained algorithm enables improved prediction of the expected behavior of driver D relative to an upcoming driving situation. For example, different drivers exhibit different reaction patterns, have different preferences and / or different abilities, including different confidence levels, when operating vehicle 100 relative to an upcoming driving situation. The use of a trained algorithm helps to take into account the individual behavior patterns of drivers when determining the expected behavior of driver D.

[0041] Based on the determined upcoming driving situation and the expected behavior of the driver D, the control unit 120 determines a target setting for the active aerodynamic device 140. In some examples, the target setting for the active aerodynamic device 140 is determined such that a trade-off between the steering performance, comfort, and economical energy usage of the vehicle 100 is optimized according to the specific needs corresponding to the upcoming driving situation and / or the personal preferences of the driver D, which typically correspond to different configurations of the active aerodynamic device 140.

[0042] When the control unit 120 has determined the target setting for the active aerodynamic device 140, the control unit 120 controls the active aerodynamic device 140 according to the target setting. For example, the control unit 120 generates a control signal based on the determined target setting, and outputs the control signal to one or more actuators (not shown) of the active aerodynamic device 140, which are configured to adjust the configuration, such as the position, of the active aerodynamic device 140.

[0043] exist Figure 1 In the example of , the active aerodynamic device 140 includes an active rear spoiler. In other examples, the one or more active aerodynamic devices 140 of the vehicle 100 additionally or alternatively include at least one of active grille shutters, active front chassis devices, active chassis height, and / or active rear diffuser.

[0044] In some examples, the vehicle system 110 detects the actual behavior of the driver D relative to the upcoming driving situation, for example, when the upcoming situation begins. The control unit 120 then compares the detected actual behavior of the driver D with the expected behavior of the driver D that has been previously determined relative to the upcoming driving situation. By analyzing the detected behavior of the driver D in this manner, the accuracy and / or reliability of the trained algorithm can be determined. In addition, the trained algorithm can be continuously trained based on this analysis.

[0045] In some examples, the trained algorithm is stored by means of the storage device 124 of the control unit 120. In these examples, determining the expected behavior of the driver D may be performed by the control unit 120 in a completely vehicle-based manner. Thus, a connection of the vehicle system 110 to one or more servers via a communication network (e.g., by means of the communication unit 150) for implementing the method may be dispensed with.

[0046] In addition, where the trained algorithm is stored on the vehicle 100, in some examples, the control unit 120 receives updated algorithm data for the trained algorithm through the communication module 150, and updates the trained algorithm according to the received updated algorithm data. In some examples, the updated algorithm data relates to one or more updated driving situation types, to updated classification data for analyzing the obtained sensor data with respect to any of a variety of possible upcoming driving situations, and the like.

[0047] In some examples, the control unit 120 determines a current vehicle state of the vehicle 100. The current vehicle state of the vehicle 100 includes a current configuration of the active aerodynamic device 140. The current configuration of the active aerodynamic device 140 can limit the range of possible configurations of the active aerodynamic device 140, which can be achieved by controlling the active aerodynamic device 140 until the impending driving situation begins. The range of achievable configurations can be limited due to the adjustment time required to change from one configuration of the active aerodynamic device 140 to another configuration. In addition, in some examples, the current vehicle state includes the current behavior of the driver D relative to the current control input operation, such as the current steering input and / or the current acceleration / deceleration input provided by the driver D to the vehicle system 110.

[0048] In some examples, the current vehicle state additionally or alternatively includes a selectable driving mode of the vehicle 100. The driver D of the vehicle 100 can select from a group of driving modes including economy, comfort, sport and / or racing. The selected driving mode indicates the current preference or need of the driver D regarding the overall performance of the vehicle 100. This can be advantageously considered when determining the target setting of the active aerodynamic device 140. In addition, the selected driving mode can affect the functionality of the vehicle system 110, because some functions can be automatically enabled or disabled, enhanced or weakened, etc. according to the selected driving mode. This can be advantageously considered when determining an upcoming driving situation.

[0049] Therefore, in some examples, the control unit 120 determines at least one of an upcoming driving situation, an expected behavior of the driver D, and / or a target setting of the active aerodynamic device 140 based additionally on the current vehicle state of the vehicle 100. Taking into account the selected driving mode allows a more accurate determination of the possible or expected behavior, including preferences, of the driver D with respect to the upcoming driving situation. In addition, the selected driving mode indicates the priorities or preferences of the driver D regarding the trade-off between steering performance, comfort, and economy, based on which the target setting can be determined.

[0050] In some examples, the method is implemented repeatedly, such as continuously and / or at intervals. In examples where the method is implemented continuously or at relatively short intervals, for example, when determining the current vehicle state, the driver D's reaction to the adjustment of the active aerodynamic device 140 due to the previous cycle of implementing the method can be determined, and this reaction can be taken into account when determining the expected behavior of the driver D during the subsequent cycle of implementing the method. In this way, the method is implemented in some examples in a way that creates a "closed loop", including: controlling the active aerodynamic device 140 based on the expected behavior of the driver D, and then determining the expected behavior of the driver D based on the previous control of the active aerodynamic device 140, and so on.

[0051] Figure 2 200 is a flow chart showing a method 200 for operating a vehicle system of a vehicle. As described above, for example, the method 200 may be performed by Figure 1 The vehicle system 110 shown in FIG. 1 is implemented.

[0052] In particular, the method 200 includes step 210 of determining an upcoming driving situation of the vehicle based at least in part on sensor data obtained by the vehicle system. The method 200 also includes step 220 of determining, by means of the vehicle system, an expected behavior of a driver of the vehicle with respect to the upcoming driving situation using a trained algorithm. The trained algorithm has been trained based at least in part on previous behavior of the driver and previous driving situations.

[0053] The method 200 further comprises a step 230 of determining, by means of the vehicle system, a target setting of at least one active aerodynamic device of the vehicle based at least in part on an upcoming driving situation and an expected behavior of the driver. Furthermore, the method 200 comprises a step 240 of controlling, by means of the vehicle system, at least one active aerodynamic device according to the determined target setting of the at least one active aerodynamic device.

[0054] In addition, if Figure 2As indicated by the dashed arrow in the figure, the method 200 is repeatedly implemented in some examples. In some examples, the repeated implementation of the method 200 includes the dynamic (especially continuous) implementation of the method 200 or the implementation of the method 200 at predefined intervals.

[0055] Figure 3 Schematically and exemplarily, a control unit 300 of a vehicle system for a vehicle is shown. The control unit 300 comprises a processor 310 operatively connected to a memory device 320. Furthermore, the control unit 300 is configured to receive and / or output data signals via one or more interfaces, such as Figure 3 As shown schematically in FIG.

[0056] The memory device 320 stores program codes that can be executed by the processor 310. The control unit 300 can be implemented by an on-board control unit, such as Figure 1 In other examples, the control unit 300 is at least partially implemented at one or more network nodes that perform network communication with the vehicle system to receive data from the vehicle system and / or output and transmit generated data to the vehicle system through the communication network.

[0057] The memory device 320 stores program codes which, when executed by the processor 310, configure the control unit 300 to implement the above-mentioned Figure 1 and Figure 2 The operation of any example described.

[0058] In particular, the memory device 320 stores program code that, through interaction with the processor 310, constitutes an upcoming driving situation determination module 322 for determining an upcoming driving situation of the vehicle based at least in part on sensor data obtained by the vehicle system. The memory device 320 also stores program code that constitutes an expected behavior determination module 324 for determining an expected behavior of the vehicle driver relative to the upcoming driving situation using a trained algorithm at least in part on the upcoming driving situation. The trained algorithm has been trained based at least in part on the driver's previous behavior and previous driving situations. The memory device 320 also stores program code that constitutes a target setting determination module 326 for determining a target setting of at least one active aerodynamic device of the vehicle based at least in part on the upcoming driving situation and the driver's expected behavior. The memory device 320 also stores program code that constitutes an active aerodynamic device control module 328 for controlling at least one active aerodynamic device according to the determined target setting of the at least one active aerodynamic device.

[0059] Figure 4 4 is a flow chart showing a method 400 for operating a vehicle system of a vehicle according to another example. For example, the method 400 may be implemented by means of the vehicle system 110 according to some examples, as described above in conjunction with Figure 1 described.

[0060] Unless otherwise stated, steps 410 to 440 of method 400 substantially correspond to steps 210 to 240 of method 200. Similar to method 200, method 400 includes: determining an upcoming driving situation of the vehicle (step 410), determining the expected behavior of the driver of the vehicle with respect to the upcoming driving situation using a trained algorithm (step 420), determining a target setting for at least one active aerodynamic device of the vehicle (step 430), and controlling the at least one active aerodynamic device accordingly (step 440). In addition, as indicated by the arrow on the left side of box 440, the above steps of method 400 are repeatedly implemented, for example, in a closed loop as described above.

[0061] Unlike method 200, method 400 also includes determining the current vehicle state of the vehicle by means of a vehicle system (step 405). Figure 1 As described with respect to some examples, the determined current vehicle state is additionally used in at least one of the determination operations performed in any one of steps 410 to 430 .

[0062] The method 400 also includes continuously training the trained algorithm (step 450). To this end, the method 400 includes: detecting the (actual) behavior of the driver relative to the upcoming driving situation (step 452), and updating the trained algorithm based on the detected driver's behavior by means of the vehicle system (step 454).

[0063] The method 400 also includes externally updating the trained algorithm (step 460). To this end, the method 400 includes: receiving updated algorithm data for the trained algorithm by the communication module of the vehicle system (step 462), and updating the trained algorithm according to the received updated algorithm data (step 464).

[0064] In any case before, during, or after any of the continuous training operations (step 450) or external updates (step 460), method 400 may be repeated in some examples, such as Figure 4 As indicated by arrows originating from the left side of each of boxes 440, 450, 460.

Claims

1. A method (200); of operating a vehicle system (110) of a vehicle (100); 400), the method comprising: - determining (210; 410) an upcoming driving situation of the vehicle (100) by means of the vehicle system (110) at least partly based on sensor data obtained by the vehicle system (110); - determining (220; 420) by means of the vehicle system (110) an expected behavior of a driver (D) of the vehicle (100) with respect to the upcoming driving situation based at least in part on the upcoming driving situation using a trained algorithm, the trained algorithm being trained at least in part on previous behavior of the driver (D) and previous driving situations; - determining (230; 430) by means of the vehicle system (110) a target setting of at least one active aerodynamic device (140) of the vehicle (100) based at least in part on the upcoming driving situation and the expected behavior of the driver (D), and - controlling (240; 440) the at least one active aerodynamic device (140) by means of the vehicle system (110) in accordance with the determined target setting of the at least one active aerodynamic device (140).

2. The method according to claim 1, further comprising: - determining (405) a current vehicle state of the vehicle (100) by means of the vehicle system (110), Wherein, determining (410) the upcoming driving situation, determining (420) the expected behavior of the driver (D) and / or determining (430) at least one of the target settings of the at least one active aerodynamic device (140) is also implemented at least in part based on the current vehicle state of the vehicle (100).

3. The method according to claim 2, wherein: The current vehicle state includes a selectable driving mode of the vehicle (100), the driving mode being selected from a group of driving modes including at least one of economy, comfort, sport and / or racing.

4. The method according to any one of the preceding claims, further comprising: - detecting (450, 452) the driver's (D) behavior relative to the impending driving situation by means of the vehicle system (110), and - Updating (450, 454) the trained algorithm based on detected behavior of the driver (D) by means of the vehicle system (110).

5. A method according to any one of the preceding claims, wherein: The trained algorithm is stored with the aid of an onboard device (120, 122, 124) of the vehicle system (110), in particular with the aid of a storage device (124) of an onboard control unit (120), and wherein, with the aid of the vehicle system (110), determining (220; 420) the expected behavior of the driver (D) with respect to the upcoming driving situation using the trained algorithm is implemented with the aid of the onboard device (120, 122, 124), in particular with the aid of a processor (122) of the onboard control unit (120).

6. The method according to claim 5, further comprising: - receiving (460, 462) updated algorithm data for the trained algorithm via a communication module (150) of the vehicle system (110), and - updating (460, 464) the trained algorithm according to the received updated algorithm data by means of the on-board device (120, 122, 124), The updated algorithm data may include at least one of the following: - one or more updated types of driving situations, or - Updated classification data for analyzing acquired sensor data with respect to one or more driving situations.

7. A method according to any one of the preceding claims, wherein: The at least one active aerodynamic device (140) includes at least one of an active rear spoiler, an active grille shutter, an active front chassis device, an active ride height, and / or an active rear diffuser of the vehicle (100).

8. A method according to any one of the preceding claims, wherein: The method is repeatedly implemented relative to the determining steps (210-230; 405-430) and the controlling steps (240; 440), and optionally, wherein the imminent occurrence of the impending driving situation corresponds to a time period allowing at least two implementations of the determining steps (210-230; 405-430) and the controlling steps (240; 440).

9. A method according to any one of the preceding claims, wherein: The sensor data is obtained from at least one sensor (130, 132) of the vehicle system (110), the at least one sensor (130, 132) comprising at least one of a position sensor (132), a camera (130), a lidar sensor, a radar sensor and / or an ultrasonic sensor.

10. A method according to any one of the preceding claims, wherein: Any of the upcoming driving situation and the previous driving situation is associated with one or more of: road trajectory, road conditions, crosswind conditions, time of day, weather conditions, traffic conditions, environment (E), in particular terrain environment, vehicle status, in particular any of various possible settings of the at least one active aerodynamic device (140) and / or driver status.

11. A method according to any one of the preceding claims, wherein: any one of the expected behavior and the previous behavior of the driver (D) includes at least one of the activities and / or preferences of the driver (D), and / or Wherein, determining (220; 420) the expected behavior of the driver (D) at least in part based on the impending driving situation includes: determining the expected behavior of the driver at least in part based on any one of various possible settings of the at least one active aerodynamic device (140).

12. A method according to any one of the preceding claims, wherein: Determining (230; 430) the target setting of the at least one active aerodynamic device (140) is also performed based on a stored adjustment time of the at least one active aerodynamic device (140) for adopting a position according to the received control signal.

13. A computer program product comprising portions of program code which, when executed by a processor (122, 310) of a vehicle system (110), configure the vehicle system (110) to implement the method according to any one of the preceding claims.

14. A vehicle system (110) comprising a processor (122, 310) and a storage device (124, 320) operably coupled to the processor (122, 310), wherein: The storage device (124, 320) includes portions of program code which, when executed by the processor (122, 310), cause the vehicle system (110) to implement a method according to any one of claims 1 to 12.

15. A vehicle (100) comprising the vehicle system according to claim 14.

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