Method for adjusting at least one adjustable feature of a motor vehicle, data processing device, computer program, computer-readable storage medium and motor vehicle

Abstract

The present disclosure relates to a method for adjusting at least one adjustable feature (110) of a motor vehicle (100). The method comprises obtaining first data indicative of a state of a chassis of the motor vehicle (100) before and after at least one occupant (210) boards or disembarks from the motor vehicle (100). Furthermore, the method comprises obtaining second data indicative of a weight of the at least one occupant (210). The second data is obtained based on the obtained first data. Furthermore, the method comprises causing an adjustment of the at least one adjustable feature (110) of the motor vehicle (100) based on the second data. The present disclosure also relates to a data processing device (120), a computer program (150), a computer readable storage medium (140) and a motor vehicle (100).

Description

Technical Field

[0001] This disclosure relates to a method for adjusting at least one adjustable feature of a motor vehicle.

[0002] In addition, this disclosure relates to data processing apparatus, computer programs, and computer-readable storage media.

[0003] Furthermore, this disclosure relates to a motor vehicle. Background Technology

[0004] Modern motor vehicles include adjustable features, such as one or more airbags or one or more seat belts, to prevent or mitigate injury to vehicle occupants in the event of an accident. In this case, it is known whether a seat in the motor vehicle is occupied, and the adjustable features associated with an occupied seat are used only in the event of an accident. It is also known to classify occupants as adults or children. Based on this classification, the adjustable features can be enabled or disabled. Summary of the Invention

[0005] The purpose of this disclosure is to further improve the adjustable features of motor vehicles and the operation of such adjustable features.

[0006] The problem is solved or mitigated at least in part by the subject matter of the independent claims of this disclosure, wherein further examples are incorporated in the dependent claims.

[0007] According to a first aspect, a method for adjusting at least one adjustable feature of a motor vehicle is provided. The method includes acquiring first data indicating at least one of the following: the state of the motor vehicle's chassis before and after at least one occupant boards or disembarks, and a transition between the states of the motor vehicle before and after at least one occupant boards or disembarks. Furthermore, the method includes acquiring second data indicating the weight of at least one occupant. The second data is obtained based on the acquired first data. Additionally, the method includes causing adjustment of at least one adjustable feature of the motor vehicle based on the second data.

[0008] The method disclosed herein can be implemented at least in part by a computer, and can be implemented in software or hardware, or both. Furthermore, the method can be executed by computer program instructions running on a component providing data processing functionality. The data processing component can be a suitable computing component, such as an electronic control module, or it can be a distributed computer system. The data processing component or the computer can each include one or more of a processor, memory, data interface, etc.

[0009] In this document, unless otherwise stated, the term "obtain" can include or be interpreted as sensing, measuring, monitoring, determining, calculating, processing, receiving, etc. For example, any data described herein can be obtained by performing at least one of sensing, measuring, monitoring, determining, calculating, processing, and receiving. More specifically, obtaining first data can be or includes determining first data or receiving first data from another entity.

[0010] Furthermore, in this document, when data indicates a parameter, unless otherwise stated, the data may include information that can be obtained by performing at least one of processing, transitioning, correlation, and calculation to produce or provide insight into the parameter. For example, when a vertical force corresponds to referenced data, the parameter could be the state of the chassis of a motor vehicle. Assuming that the vertical acceleration corresponds to gravitational acceleration, the mass can be determined or calculated using the following formula: , where f, m, and a represent force, mass, and acceleration, respectively. The determined mass can be used to determine whether the chassis is carrying a certain mass (e.g., the weight of one or more occupants occupying seats in a motor vehicle).

[0011] The state of a motor vehicle chassis can include at least one of a loaded state, an unloaded state, a transitional state, a seat-occupied state, and a seat-free state. A loaded state can refer to a situation or condition where the body mass, such as a load / cargo / burden / goods / luggage, applies forces to the chassis. An unloaded state can refer to the opposite of a loaded state, i.e., no body mass applies forces to the chassis. A transitional state can refer to a situation, condition, or process in which at least one occupant boards or disembarks from the motor vehicle. For example, when an occupant enters or leaves the vehicle. A seat-occupied state can refer to a situation or condition in which one or more seats are occupied, for example, occupied by a corresponding vehicle occupant or occupant.

[0012] The states of a vehicle's chassis can be distinguished from each other based on first data. For example, the first data could be forces applied to the chassis, measured at different locations on the chassis. A force distribution profile on the chassis can then be determined, based on which the locality of the applied forces can be determined. The determined locality can be used to distinguish one state of the vehicle's chassis from another. In another example, the first data could include at least one of the chassis's angular velocity and acceleration. Such data can indicate the transition between the states of the vehicle before and after at least one occupant boards or disembarks from the vehicle.

[0013] The initial data obtained before and after at least one occupant boards or disembarks from the motor vehicle can form differential data based on the differences between them. Such differential data can indicate the weight of at least one occupant who has boarded or disembarked from the vehicle. In this respect, the initial data obtained before at least one occupant boards or disembarks from the motor vehicle can be used as a reference.

[0014] The first data can be measured when at least one occupant boards or disembarks from the motor vehicle, i.e., during the transition between the states of the motor vehicle before and after at least one occupant boards or disembarks from the motor vehicle.

[0015] It allows adjustment of at least one adjustable feature to be made before any accident occurs.

[0016] In this document, unless otherwise stated, the term “adjustment” may include, or may be, alteration, modification, change, update, tuning, calibration, adaptation, etc.

[0017] In this document, causing adjustment may include determining adjustment data (e.g., updated data), adjusting at least one parameter related to at least one adjustable feature of a motor vehicle based on the determined adjustment data, and operating at least one adjustable feature of the motor vehicle based on the adjusted parameter.

[0018] Alternatively or additionally, the adjustment may include at least one of the following: determining a control signal for controlling at least one safety feature of a motor vehicle based on the adjusted parameters of at least one safety feature, and controlling at least one safety feature based on the adjusted parameters of at least one safety feature.

[0019] The method described above according to this disclosure can advantageously improve the determination of the weight of at least one occupant, and further improve at least one aspect of the motor vehicle by adjusting at least one feature of the motor vehicle. For example, occupant safety can be improved in the event of a collision.

[0020] According to the example, at least one adjustable feature includes one or more of at least one performance feature, at least one safety feature, at least one comfort feature, and at least one connectivity feature.

[0021] The method described in the above example can advantageously provide at least one of improved performance, improved safety, improved comfort, and improved connectivity for at least one occupant of a motor vehicle.

[0022] According to the example, at least one performance characteristic includes, or is included in, at least one of the engine, suspension, and transmission systems of a motor vehicle.

[0023] Adjusting an engine can include adjusting one or more of the following: air-fuel ratio, fuel injection timing, throttle response sensitivity, intake manifold runner length, boost pressure, ignition timing, rev limiter, start-up control RPM, variable valve timing, exhaust valve timing, idle speed control, exhaust gas recirculation, catalytic converter bypass, pedal response configuration, exhaust valve actuation, regenerative braking, electric power of an electric or hybrid engine, timing of electric or hybrid engine current injection (e.g., to rotor position), wheel torque, torque distribution or partitioning between wheels, and torque mapping.

[0024] The method described in the above example can advantageously provide selective and specific improvements to engine performance, thereby improving at least one of the following: fuel efficiency, charging efficiency, driving performance, comfort, and safety of a motor vehicle.

[0025] Adjusting the suspension can include adjusting one or more of the following: damping rate, spring stiffness, ride height, suspension mode, load leveling suspension sensitivity, anti-dive settings, anti-squat settings, camber angle, kingpin inclination angle, toe angle, active anti-roll bar stiffness, electronic stability control sensitivity, traction control intervention level, ride height, downforce control, and drag reduction settings.

[0026] The method described in the above example can advantageously provide selectivity and specific improvements to suspension performance, thereby improving at least one of the following: fuel efficiency, charging efficiency, driving performance, comfort, and safety of a motor vehicle.

[0027] Adjustments to the drivetrain may include adjustments to one or more of the following: shift timing, shift speed, start-up control engagement RPM, speed matching sensitivity, gear ratio, kickdown sensitivity, transmission cooler flow rate, front or rear power bias, limited-slip differential lock percentage, torque vectoring sensitivity, differential lock sensitivity, drivetrain mode, tire pressure monitoring sensitivity, active yaw control, clutch engagement point, dual-clutch transmission, and creeping behavior.

[0028] The methods described in the above examples can advantageously provide selectivity and specific improvements to the performance of the drivetrain, thereby improving at least one of the following aspects of a motor vehicle: fuel efficiency, driving performance, comfort, and safety.

[0029] According to the example, at least one safety feature includes, or is at least one of the passive safety features and active safety features included in a motor vehicle.

[0030] Passive safety features, sometimes also called protective or proactive safety features, may include, or may be features provided to reduce injury and damage to at least one occupant during and after an accident (such as a collision).

[0031] Passive safety features may include, or may be, a restraint system.

[0032] At least one safety feature may be a feature designed to protect at least one occupant or passenger compartment in the event of a collision. Adjustment of at least one safety feature may be made prior to a collision or accident.

[0033] At least one safety feature, particularly a passive safety feature, may include, or may be, at least one of an airbag and a seatbelt included in a motor vehicle. At least one of the airbag and seatbelt may be configured to protect either occupant in the event of a collision. Therefore, using the method of this disclosure, the operation of at least one of the airbag and seatbelt can be adjusted according to the weight of the occupant to be protected by at least one of the airbag and seatbelt. This enhances occupant safety in the event of an accident.

[0034] According to the example, making adjustments to at least one safety feature may include making adjustments to one or more of the following: airbag deployment speed, airbag deployment pressure, airbag ventilation, airbag deployment shape, airbag deployment position, seat belt pre-tensioning timing, seat belt pre-tensioning force level, seat belt tensioning force level, and seat belt force level retention curve.

[0035] Airbag deployment can refer to the process of airbag inflation (i.e., at least one of before, during, and after airbag inflation). An airbag can be deployed by triggering an igniter, which in turn releases a gas (e.g., nitrogen) into a gas container. The rate of gas release can vary over time. Airbag deployment speed can refer to the rate of airbag inflation. Airbag deployment speed can be measured as the distance traveled by one end of the airbag per given time interval. Here, a given time interval can be the time between collision detection and another subsequent moment (e.g., when a occupant comes into contact with the airbag due to a collision). In this respect, adjusting the airbag deployment speed can be adjusting any parameter associated with the airbag, which in turn adjusts the deployment speed. For example, adjusting the airbag deployment speed can include, or can be, adjusting the timing at which the igniter is triggered. If the gas release rate (and thus the airbag inflation rate) varies over time, adjusting the ignition timing of the gas release can, in turn, adjust the airbag deployment speed, particularly when an occupant comes into contact with the airbag due to a collision. Alternatively or additionally, adjusting the airbag deployment speed may be, or may include, adjusting the amount of gas entering the airbag (particularly its gas container). For example, the airbag may include a gas vent port arranged to controllably release a portion of the released gas once the igniter is triggered. The gas vent port can be controlled to release at least a portion of the gas to be released outside the airbag, such that only a portion of the gas enters the gas container of the airbag that is inflated to protect at least one occupant. This, in turn, adjusts the airbag deployment speed.

[0036] The deployment shape of the airbag can refer to the fully or partially deployed form of the airbag, preferably before the occupant comes into contact with the airbag. Adjustment of the airbag deployment shape can be performed at least one of the following: before, during, and after airbag inflation. For example, a coupling module, such as a rope or line, can be arranged to couple one or more ends of the airbag's gas container to the airbag's housing. In this case, adjusting the airbag deployment shape may include adjusting the coupling module, for example, by adjusting the tension of the rope or a positioning rope, to adjust the position of one or more ends of the coupled gas container.

[0037] The deployment position of an airbag can refer to its location at any point during the airbag inflation process. For example, an airbag may include a positioning module arranged between openings through which gas is released into the airbag's gas container, and the positioning module is used to position the gas container. Using the positioning module, the deployment position can be adjusted in any direction in 3D space relative to a reference position (i.e., the initial unadjusted position).

[0038] Seat belt pretensioning can refer to the process or action of tightening (e.g., loosening or tightening) the seat belt before the full impact force of a collision in a motor vehicle, thereby pulling the occupant's body closer to their seat. The timing of this pretensioning and / or the level of this pretensioning force can be adjusted, for example, before a collision and / or by a control signal fed to the pretensioner of the seat belt.

[0039] Seatbelt tension can refer to the process or action of tensioning (e.g., loosening or tightening) the seatbelt during a motor vehicle collision. This tension level can be adjusted, for example, before a collision and / or via control signals fed to the seatbelt tensioner.

[0040] Seatbelt maintenance can refer to the process or action of tensioning (e.g., loosening or tightening) the seatbelt after a motor vehicle collision. The force level of this maintenance, more specifically the force level maintenance curve, can be adjusted, for example, before a collision and / or by a control signal fed to the tensioner of the seatbelt.

[0041] The method described in the above example can advantageously improve occupant safety in the event of a collision, particularly by adjusting or calibrating parameters related to at least one safety feature based on data indicating occupant weight.

[0042] Active safety features may include, or may be features provided to help prevent accidents from occurring.

[0043] Active safety features may include, or may be at least one of sensors, cameras, radar, data processing units, and control systems.

[0044] Active safety features of a motor vehicle may include, or may be at least one of, the following: automatic emergency braking, forward collision warning, rear collision warning, automatic braking, cross-traffic alert, adaptive cruise control, lane departure warning, lane keeping assist, driver attention monitoring, traffic sign recognition, parking sensors, proximity warning, automatic parking assist, rearview camera, electronic stability control, traction control, anti-lock braking system, hill start assist, hill descent control, blind spot monitoring, side collision avoidance, door presence warning, headlights, high beams, and fog lights.

[0045] The method described in the above example can advantageously improve occupant safety by helping to prevent collisions, particularly by adjusting or calibrating parameters related to at least one safety feature based on data indicating occupant weight.

[0046] According to the example, at least one comfort feature includes, or is at least one of, the following: an infotainment system, steering wheel controls, rearview mirror controls, interior mirror controls, climate controls, and seats (which may include at least one of seat belt positioning and tension controls) in a motor vehicle.

[0047] Adjusting at least one comfort feature may include, or may be, adjusting one or more of the following: seat height, seat forward or backward position, seat tilt angle, seat cushion tilt, lumbar support, side pads, extendable seat cushion length, heating intensity, seat ventilation intensity, heating timer off, cooling timer off, massage mode, massage intensity, massage duration timer, massage area, headrest height, headrest tilt, fan speed, airflow direction, ventilation mode, air filtration mode, display settings, audio system settings, voice control settings, auxiliary settings, head-up display settings, digital instrument display settings, and parking assistance settings.

[0048] The method described in the above example can advantageously provide at least one occupant with options and specific improvements in comfort.

[0049] According to the example, at least one connectivity feature includes, or is at least one of a navigation system and vehicle to everything (V2X).

[0050] V2X communication may include, or may be at least one of, the following: vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication, vehicle-to-pedestrian (V2P) communication, vehicle-to-network (V2N) communication, and vehicle-to-grid (V2G) communication.

[0051] Adjusting at least one connectivity feature (particularly V2V communication) may include, or may include, adjusting one or more of the following: transmission interval, data priority, encryption mode, message range setting, emergency braking warning, lane change warning, cooperative adaptive cruise control, intersection collision warning, vehicle-to-vehicle distance setting, speed synchronization, and communication priority rules.

[0052] Adjusting at least one connectivity feature (particularly V2I communication) may include, or may be at least one of, the following: dynamic route optimization, road hazard warning, parking availability settings, charging point availability settings, and parking reservation priority settings.

[0053] Adjusting at least one connectivity feature (particularly V2P communication) may include, or may be at least one of the following: pedestrian detection sensitivity, pedestrian warning type, vehicle response mode, pedestrian approach warning range, audible alarm intensity, hazard light activation duration, and automatic braking activation delay for pedestrians.

[0054] Adjusting at least one connectivity feature (particularly V2N communication) may include, or may be, at least one of the following: traffic data refresh rate, weather alert settings, dynamic route lookup settings, software update times throughout the day, remote system diagnostics, and connectivity settings.

[0055] Adjusting at least one connectivity feature (particularly V2G communication) may include, or may be at least one of, the following: charging priority settings, grid demand update settings, charging source preferences, and excess energy sales settings.

[0056] The method described in the above example can advantageously provide selectivity and specific improvements in the connectivity of a vehicle to another entity or unit.

[0057] According to the example, the first data may indicate at least one of the following: the position, angle, displacement, pressure, angular velocity, acceleration, or vertical force of the chassis of the motor vehicle affected by at least one occupant boarding or disembarking from the motor vehicle. That is, the first data may be associated with one or both of the following: the absolute values ​​before and after at least one occupant boards or disembarks from the motor vehicle, and the transition caused by the occupant boarding or disembarking from the motor vehicle. Specifically, the first data may be, for example, an absolute value before and after (e.g., angle or position), a value related to the transition itself (e.g., acceleration, angular velocity), or an incremental value (the difference between the before and after positions or angles).

[0058] In this document, the term "affected" can include, or may be, altered, modified, changed, modified, etc. Furthermore, in this document, when an action performed results in such a change or alteration of a parameter, the parameter is considered to be affected by at least one occupant performing the action. For example, when at least one occupant boards the motor vehicle, the chassis of the motor vehicle may undergo vertical displacement. First data (e.g., vertical acceleration) can indicate this displacement. This indication can be determined by integrating the measured vertical acceleration data twice with respect to time.

[0059] According to the example, the method may further include: obtaining third data indicating the confidence level of the second data; comparing the third data with a predefined confidence threshold; and if the third data is equal to or exceeds the confidence threshold, causing adjustment of at least one adjustable feature based on the second data, or if the third data is below the confidence threshold, causing adjustment of at least one adjustable feature to a predefined setting. Note that when referring to a predefined threshold / setting herein, the term considered refers to a threshold / setting that may be, but is not limited to, a pre-set threshold / setting. The threshold / setting may, for example, be pre-set / determined / stored, retrieved from a repository, or calculated / determined using a previously determined (predefined) algorithm or function.

[0060] In this document, the term "confidence level" can be a statistical indicator. More specifically, when a data point is considered to indicate the confidence level of another data point, the first data point can include statistical information about the second data point. The statistical information can include, or may be, at least one of a confidence interval and a confidence level. In this case, probabilistic properties, such as the probability that the second data point resides within the confidence interval (an interval of parameters in a distribution) of the second data point, can be indicated by a single data point.

[0061] It should be understood that comparing third data with a predefined confidence threshold is one example of obtaining (e.g., determining or evaluating) the confidence level of second data. It should also be understood that the confidence level of second data can typically be obtained by performing (pre)processing and evaluating at least one of the third data against any predefined at least one criterion. Furthermore, this allows for adjustments to at least one adjustable feature to be made based on the second data and the results of such (pre)processing and evaluation.

[0062] The method according to the above example can advantageously provide particular improvements in at least one aspect of a motor vehicle (e.g., occupant safety in the event of a collision), particularly by improving at least one of the accuracy and precision of data indicating occupant weight, which in turn can be used to adjust at least one safety feature of the motor vehicle.

[0063] According to the example, the method may further include: obtaining fourth data indicating possible boarding or alighting of at least one occupant within a predetermined time window before at least one occupant boards or alights from the motor vehicle; and, based on the obtained fourth data, causing at least one data monitoring component configured to monitor the first data to begin monitoring the first data before at least one occupant boards or alights from the motor vehicle.

[0064] In this text, the phrase "at least one occupant may board the vehicle within a predefined time window" can refer to a situation where at least one occupant did not board the vehicle when the fourth data was obtained, but may or may not board the vehicle within the predetermined time window or period. In this case, "at least one occupant" can refer to at least one occupant who ultimately occupies at least one seat in the motor vehicle. Alternatively or additionally, "at least one occupant" can refer to an identified person who will not ultimately occupy at least one seat in the motor vehicle. Similarly, the phrase "at least one occupant may disembark within a predefined time window" can refer to a situation where at least one occupant has boarded the motor vehicle when the fourth data was obtained, but may or may not disembark within the predefined time window or period.

[0065] For example, the fourth data may include or may be status information about the vehicle's surroundings. Such information may be at least one of proximity data, images, and videos of the vehicle's surroundings. In another example, the fourth data may include or may be communication data received by the vehicle. Such data may include or may be: wireless signals transmitted from the vehicle's key or a mobile device communicatively connected to the vehicle, or location information of at least one occupant (more specifically, the mobile device of at least one occupant) transmitted by a base station communicatively coupled to the vehicle and the mobile device. In yet another example, the fourth data may include or may be a key insertion signal indicating that a physical key has been inserted into the vehicle's door key slot.

[0066] Such fourth data can be used to obtain (e.g., determine) at least one of a person approaching a motor vehicle and about to board a motor vehicle. This could be an indication of the possibility of at least one occupant boarding the vehicle within a predefined time window.

[0067] Similarly, the fourth data may include, or may be, information about the interior of the vehicle. This information may include, or may be, at least one of the state and posture of at least one occupant already occupying a seat in the vehicle, or a grasp on a door handle. Such information may be obtained from at least one of the following: images of the vehicle's interior, video of the vehicle's interior, measured proximity data, and measured thermal data.

[0068] Such fourth data can be used to obtain (e.g., determine) that at least one occupant is about to disembark from the vehicle. This could be an indication of the possible disembarkation of at least one occupant within a predetermined time window.

[0069] The method according to the above example can advantageously provide particular improvements in at least one aspect of a motor vehicle (e.g., occupant safety in the event of a collision), particularly by improving at least one of the accuracy and precision of data indicating occupant weight, which in turn can be used to adjust at least one safety feature of the motor vehicle.

[0070] According to the example, at least one occupant may be multiple occupants, and the method further includes: obtaining fifth data indicating the weight of a subgroup of multiple occupants; obtaining second data indicating the weight of another subgroup of multiple occupants based on the obtained first data and the obtained fifth data; and causing an adjustment of at least one adjustable feature of the motor vehicle based on the second data.

[0071] Here, the term "subgroup" can refer to one or more of a group of occupants. A subgroup of multiple occupants can refer to all of the multiple occupants. The term "another subgroup" can refer to the remaining occupants who are not part of that subgroup. A group can refer to the entirety of multiple occupants.

[0072] The fifth piece of data may include, or may be, the weight of an individual occupant included in a subgroup. This subgroup may be identified or determined as having previously occupied the vehicle. Previously measured weights of the individual occupants may be stored, for example, in a database. The stored data may be retrieved for further processing.

[0073] The fifth data, together with the obtained first data, can be used to obtain (e.g., determine or calculate) second data indicating the weight of another subgroup of multiple occupants. The obtained second data, in turn, can be used to obtain revised fifth data. The revised data can be used to update the stored fifth data.

[0074] The method according to the above example can advantageously provide particular improvements in at least one aspect of a motor vehicle (e.g., occupant safety in the event of a collision), particularly by improving at least one of the accuracy and precision of data indicating occupant weight, which in turn can be used to adjust at least one safety feature of the motor vehicle.

[0075] According to the example, the method may further include: providing the second data to at least one application including a trainable data model.

[0076] In this document, the term “provide” may include, or may be, sending, transmitting, sharing, communicating, etc.

[0077] At least one application may be stored or implemented within a motor vehicle, or both. Alternatively or additionally, at least one application may be stored or implemented by another entity (e.g., at least one of a database, a central processing unit, and another vehicle), or both.

[0078] According to the example, the second data is used as training data for training a trainable data model.

[0079] Trainable data models can include or be machine learning models.

[0080] The trained data model can be provided to and subsequently used by another vehicle to adjust at least one adjustable feature included therein. Any or more of the first to fifth data described herein can be obtained by another vehicle and subsequently provided to the vehicle that has implemented the data model. However, at least one of the accuracy and precision of such data obtained by the other vehicle may be worse or less accurate. Alternatively or additionally, some of the first to fifth data may not be obtained due to the lack of monitoring equipment responsible for obtaining the corresponding data.

[0081] The method according to the above example can advantageously provide particular improvements in at least one aspect of a motor vehicle (e.g., occupant safety in the event of a collision), particularly by improving at least one of the accuracy and precision of the data indicating occupant weight, which in turn can be used to adjust at least one safety feature of the motor vehicle. Furthermore, using a trained data model in another vehicle can advantageously improve at least one of the accuracy and precision of the data indicating occupant weight.

[0082] According to an example, the method may further include: obtaining a second set of first data indicating at least one of the following: the state of the vehicle chassis before and after at least one specific occupant boards or disembarks from the vehicle, and the transition between the states of the vehicle before and after at least one specific occupant boards or disembarks from the vehicle; obtaining a second set of second data indicating the weight of the at least one occupant based on the second set of the obtained first data; and causing an adjustment of at least one adjustable feature of the vehicle based on the second data and the second set of the second data.

[0083] The second set of data obtained can be used to improve, fine-tune, calibrate, etc., the first set of data obtained.

[0084] The method according to the above example can advantageously provide particular improvements in at least one aspect of a motor vehicle (e.g., occupant safety in the event of a collision), particularly by improving at least one of the accuracy and precision of data indicating occupant weight, which in turn can be used to adjust at least one safety feature of the motor vehicle.

[0085] According to the example, obtaining second data indicating the weight of at least one occupant based on the obtained first data may further include comparing it with at least one of historical second data and predefined second data.

[0086] Historical second data may include, or may be, the stored weight of one or more occupants who have previously occupied the motor vehicle. Predefined second data may include, or may be, the initially defined weight of the occupants, such as average weight. At least one of the historical second data and the predefined second data may be used as an evaluation criterion or reference for determining deviations. When the difference produced by the comparison does not meet at least one predefined criterion, at least one of the historical second data and the predefined second data may be used to adjust at least one safety feature of the motor vehicle.

[0087] According to the example, the method further includes identifying at least one occupant based on at least one of first data, second data, third data, fourth data, and fifth data; and, upon identifying at least one occupant, making adjustment of at least one adjustable feature of the motor vehicle based on at least one previously set setting of the stored adjustable feature. The method may also include verifying the identity of at least one occupant based on additional data. The additional data may be any one or more of the first data, second data, third data, fourth data, and fifth data. Specifically, verifying the identity of at least one occupant based on additional data may include, or may be based on, further identifying at least one occupant; and comparing the identity of at least one occupant identified by the first identification (i.e., identifying at least one occupant based on at least one of the first data, second data, third data, fourth data, and fifth data) with the identity of at least one occupant identified by the second identification (i.e., identifying at least one occupant based on additional data). At least one occupant may specifically be a driver. For example, the first identification may include, or may be based on data obtained through wireless communication with a personal key card or data obtained from a voice recognition process to identify at least one occupant. The second identification may include, or may be based on, data obtained using a camera to identify at least one occupant, for example, for occupant identification. The identity of at least one occupant may be compared with the identity of a second at least one occupant to determine whether at least one occupant has been correctly identified; that is, verification of the identity of at least one occupant. This verification can improve the reliability of the identification process.

[0088] Alternatively or additionally, the method may include: determining, based on at least one of first data, second data, third data, fourth data, and fifth data, whether at least one occupant has previously occupied the motor vehicle; and, when it is determined that at least one occupant has previously occupied the motor vehicle, causing an adjustment to at least one adjustable feature of the motor vehicle to be made further based on at least one previously set setting of the stored adjustable feature.

[0089] Determining whether at least one occupant has previously occupied the motor vehicle may include, or may be, comparing at least one of first data, second data, third data, fourth data, and fifth data with stored data indicating at least one previous occupant of the motor vehicle.

[0090] Determining whether at least one occupant has previously occupied the vehicle can be further based on identifying at least one occupant.

[0091] The stored at least one prior setting of the adjustable feature may include, or may be, a corresponding stored value of a corresponding parameter of the adjustable feature. In a non-limiting example, the stored at least one prior setting of the adjustable feature may include, or may be, a stored value of any of the following: airbag deployment speed, airbag deployment pressure, airbag ventilation, airbag deployment shape, airbag deployment position, seat belt pretension timing, seat belt pretension force level, seat belt tension force level, and seat belt force level maintenance curve.

[0092] According to a second aspect, a data processing apparatus is provided, comprising components for performing a method according to any example of the first aspect described herein. Using such a data processing apparatus advantageously improves the determination of the weight of at least one occupant and further improves at least one aspect of the motor vehicle by adjusting at least one characteristic of the motor vehicle, such as occupant safety in the event of a collision. According to an embodiment, the data processing apparatus may be the vehicle's core computer.

[0093] According to a third aspect, a computer program including instructions is provided, which, when executed by a computer, cause the computer to perform a method according to any example of the first aspect described herein. Using such a computer program can advantageously improve the determination of the weight of at least one occupant, and further improve at least one aspect of the motor vehicle by adjusting at least one feature of the motor vehicle, such as occupant safety in the event of a collision.

[0094] The computer can be a data processing device according to the second aspect described herein.

[0095] According to a fourth aspect, a computer-readable storage medium storing computer program units of the third aspect is provided. Using such a computer-readable storage medium can advantageously improve the determination of the weight of at least one occupant, and further improve at least one aspect of the motor vehicle by adjusting at least one feature of the motor vehicle, such as occupant safety in the event of a collision.

[0096] According to the fifth aspect, a motor vehicle is provided, which includes at least one adjustable feature and a data processing device according to the second aspect.

[0097] At least one adjustable feature and data processing device are communicatively coupled.

[0098] Such a motor vehicle can advantageously improve the determination of the weight of at least one occupant, and further improve at least one aspect of the motor vehicle by adjusting at least one feature of the motor vehicle. For example, occupant safety in the event of a collision.

[0099] It should be noted that the above examples can be combined with each other, regardless of the aspects involved. Therefore, the method can be combined with structural features, and similarly, the apparatus and system can be combined with the features described above regarding the method.

[0100] These and other aspects of this disclosure will become apparent from the examples described below and will be illustrated with reference to the examples described below. Attached Figure Description

[0101] Examples of this disclosure will now be described with reference to the following figures.

[0102] Figure 1 An example of a motor vehicle according to this disclosure is shown.

[0103] Figure 2 A first scenario including a motor vehicle is shown as an example according to this disclosure.

[0104] Figure 3 A second scenario including a motor vehicle is shown as an example according to this disclosure.

[0105] Figure 4 An example method according to this disclosure is shown. Detailed Implementation

[0106] The accompanying drawings are merely illustrative and are intended to illustrate examples of this disclosure only. Identical or equivalent elements are, in principle, designated by the same reference numerals.

[0107] Figure 1 Motor vehicle 100 is shown.

[0108] The motor vehicle 100 includes at least one adjustable feature 110 and a data processing device 120.

[0109] At least one adjustable feature 110 includes an airbag 111 included in the motor vehicle 100.

[0110] Additionally or alternatively, at least one adjustable feature 110 includes a seat belt 112 included in the motor vehicle 100.

[0111] At least one adjustable feature 110 (e.g., airbag 111 and / or seat belt 112) is communicatively coupled to the data processing device 120.

[0112] The data processing device 120 includes a data storage unit 130 and a data processing unit 160.

[0113] The data storage unit 130 includes a computer-readable storage medium 140.

[0114] A computer program 150 is provided on a computer-readable storage medium 140.

[0115] The computer program 150 and therefore the computer-readable storage medium 140 include instructions that, when executed by the data processing unit 160 (or more generally, the computer), cause the computer or data processing unit 160 to perform a method for adjusting at least one adjustable feature 110.

[0116] The data storage unit 130 stores at least one measured object (measurand) that has been sensed.

[0117] The motor vehicle 100 also includes a communication component 170 for communicating with at least one of external media such as another vehicle, a data cloud, a computer, a mobile user terminal, and a base station, i.e., at least one of sending and receiving.

[0118] The motor vehicle 100 also includes a monitoring component 180.

[0119] Unless otherwise stated, the monitoring component is located anywhere in or on at least one of the motor vehicle.

[0120] The monitoring component may include at least one first sensor.

[0121] For example, at least one first sensor includes, or is a force sensor, configured to measure forces, particularly vertical forces, applied to the motor vehicle 200 when at least one occupant boards or alights from the motor vehicle 100.

[0122] In the example, the force sensor is configured to measure the force applied to the suspension of at least one wheel of the motor vehicle 100.

[0123] In the example, a force sensor is positioned near at least one wheel of the motor vehicle 100.

[0124] For example, a strain gauge can be applied to at least one wheel of a motor vehicle 100 to directly measure the force on the suspension arm.

[0125] In the example, the sum of forces applied to the motor vehicle 100 is obtained based on multiple such force sensors.

[0126] In another example, at least one first sensor includes, or is an gyroscope sensor, which is configured to measure the acceleration of the motor vehicle 100, particularly the vertical acceleration.

[0127] In the example, the displacement of vehicle 100 is determined by integrating the obtained acceleration twice with respect to time.

[0128] When the suspension characteristics of the suspension system of the motor vehicle 200 (e.g., yield strength, Young's modulus, compressive strength, ultimate strength, Poisson's ratio, etc.) are known, the force applied to the motor vehicle 200 can be determined based on these characteristics and the displacement. Therefore, the weight of at least one occupant can be determined based on the determined force.

[0129] In the example, the gyroscope sensor is mounted on or near the front and rear wheel axles of the motor vehicle 100.

[0130] In the example, at least one of the first sensors includes, or is integrated into, at least one of a weight sensor and a pressure sensor.

[0131] Such sensors may not be able to accurately measure the weight of at least one occupant because, for example, the seat belt may add at least one unknown force, and the weight of at least one occupant may also be distributed in areas other than the seat, such as at least one of the floor of vehicle 200 and the armrest of vehicle 100.

[0132] Therefore, the weight measured or determined based on this type of sensor may not be representative of the actual weight.

[0133] Therefore, at least one of the weights measured and determined using such a sensor can be used as the minimum weight. This can be true even if such a sensor takes into account the weight distribution across the entire seat.

[0134] In the example, at least one first sensor is multiple first sensors.

[0135] In the example, a subset of multiple first sensors measures the same object.

[0136] In the example, a subset of the multiple first sensors are located at at least one different location inside and on the carriage.

[0137] In the example, the data processing unit of the motor vehicle 100 is also configured to obtain further data indicating the distribution profile of the measured object determined based on measurements of a subset of multiple sensors.

[0138] In the example, each sensor in a subset of the multiple first sensors measures the force at different locations on the vehicle.

[0139] This force measurement can be used to determine the force distribution profile.

[0140] Additionally, the monitoring component may include at least one additional sensor configured to acquire data indicating the status of at least one occupant.

[0141] In the example, other sensors include proximity sensors, thermal sensors, electromagnetic field sensors, cameras, etc.

[0142] In the example, additional sensors are configured to monitor the interior of the motor vehicle 100.

[0143] For example, at least one additional sensor is a camera configured to monitor the interior of the motor vehicle 100.

[0144] It can process the data obtained from it to determine seating position, identity, and other parameters that indicate the status of at least one occupant.

[0145] Additionally or alternatively, the monitoring component may include at least one fourth sensor configured to acquire fourth data indicating possible boarding or alighting of at least one occupant within a predefined time window before at least one occupant boards or alights from the motor vehicle 100.

[0146] The fourth data can be used to enable any other sensors included in the monitoring component to begin acquiring corresponding data.

[0147] For example, the fourth data could trigger at least one first sensor to begin acquiring the first data before at least one occupant may board or alight.

[0148] In the example, at least one fourth sensor includes, or is at least one of, the following: a proximity sensor, a thermal sensor, an electromagnetic field sensor, a camera, and others.

[0149] For example, at least one fourth sensor is a camera configured to monitor the surrounding environment of the motor vehicle 100.

[0150] In another example, at least one fourth sensor is a proximity sensor configured to monitor the distance between the motor vehicle 100 and objects around the motor vehicle 100.

[0151] In another example, at least one fourth sensor is a sensor configured to monitor data indicating that at least one occupant has entered or exited a door.

[0152] In the example, the motor vehicle 100 also includes a storage medium for storing any data (e.g., at least one of the first, second, third, fourth, and fifth data obtained).

[0153] In the example, the storage medium is also configured to store trainable models for training, such as AI or machine learning models, and the processing component of the data cloud is further configured to train the stored models based on any data obtained (e.g., first data, second data, third data, fourth data, and fifth data). A specific vehicle model with known occupant weights can be used for model training, validation, and testing until the desired test accuracy on irrelevant samples (i.e., relative to the training samples) is achieved.

[0154] Figure 2 The first scene 200 is shown.

[0155] The first scenario includes Figure 1 The vehicle 100, at least one occupant 210, and an external medium 220. The communication components of the vehicle 100 are configured to communicate with the external medium 220, namely, to transmit data D1 and D2 and at least one of models D1 and D2, and to receive data D1 and D2 and at least one of models D1 and D2.

[0156] In the example, external medium 220 includes, or is a data cloud, which includes processing components, storage components, and communication components.

[0157] The communication component of the data cloud is configured to send data to or receive data from the communication component of the motor vehicle 100, or both send data to and receive data from the communication component of the motor vehicle 100.

[0158] The processing component of the data cloud is configured to process any data according to any of the examples described herein to obtain any processed data, such as at least one of second, third, fourth, and fifth data.

[0159] The storage component of the data cloud includes a computer-readable storage medium for storing a computer program that includes instructions that, when executed by a computer or component, cause the computer to perform a method according to any of the examples disclosed herein.

[0160] The storage component is also configured to store at least one of the acquired data and the processed data.

[0161] The communication components of the data cloud are also configured to transmit processed data using the communication components of the vehicle 100.

[0162] For example, at least one of the images or videos of the vehicle's surroundings, along with a measured proximity value of the vehicle's surroundings, can be sent to a data cloud for further processing to obtain any one of the second, third, fourth, and fifth data.

[0163] The obtained data can be sent back to the motor vehicle 100 to adjust at least one safety feature.

[0164] In the example, the storage component of the data cloud is also configured to store trainable models (e.g., AI models) for training, and the processing component of the data cloud is further configured to train the stored models based on any data obtained (e.g., first data, second data, third data, fourth data, and fifth data). A specific vehicle model with known occupant weights can be used for model training, validation, and testing until the desired test accuracy on irrelevant samples (i.e., relative to the training samples) is achieved.

[0165] In the example, the communication component of the data cloud is configured to communicate with a second vehicle, which is different from the first vehicle 100, which is the data source used to train the storage model.

[0166] The data obtained from the second vehicle can be used as input to stored training data, and its output can be communicated back to the second vehicle to adjust at least one safety feature.

[0167] In particular, the data obtained from the second motor vehicle is inferior to the data obtained from the first motor vehicle 100.

[0168] For example, compared to the sensors of the first motor vehicle 100, the sensors of the second motor vehicle may be at least one of being less sensitive, less accurate, and less precise, or the data processed by the second motor vehicle may be less accurate than the data processed by the first motor vehicle 200, for example, due to lower computing power.

[0169] In the example, external medium 220 includes, or is a mobile user terminal, such as a mobile phone or a wireless vehicle key.

[0170] The communication component of the motor vehicle 100 is configured to receive data instructing the unlocking of the doors of the motor vehicle 100. Such data can instruct at least one occupant 210 to board the motor vehicle 100 at any time from the date such data is received (e.g., within a predefined time window).

[0171] In the example, external medium 220 includes, or may be, a base station.

[0172] The communication component of the motor vehicle 100 is configured to receive data indicating the location of at least one occupant 210.

[0173] Such data can be processed to determine whether at least one occupant 210 is approaching motor vehicle 100, which can indicate that at least one occupant 210 may soon (e.g., within a predefined time window) board the vehicle.

[0174] In the example, at least one of the communication component, the data processing device 120, and the monitoring component of the motor vehicle 100 can be communicatively coupled to each other.

[0175] Figure 3 A second scenario 300 is shown, including a vehicle 100 and multiple occupants 310.

[0176] Multiple occupants 310 may interact with the vehicle simultaneously or in a manner that partially overlaps in time, for example, by boarding or alighting from the vehicle.

[0177] One or more of the multiple occupants (i.e., a subgroup) can be identified as having previously occupied the motor vehicle, and data indicating the identified subgroup can be retrieved.

[0178] Data can be stored in at least one of the data storage unit 130 and the storage component of the data cloud included in the external medium 220.

[0179] This information also helps to obtain second data, which specifically indicates the weight of the remaining unidentified occupants (i.e., another subgroup) among the multiple occupants.

[0180] Those skilled in the art will understand that any other method described herein can be applied to obtain any data that takes into account multiple occupants.

[0181] For example, second data indicating the weight of an individual among multiple occupants can be obtained.

[0182] Alternatively or additionally, a second set of data indicating the total weight of multiple occupants can be obtained.

[0183] Figure 4 The steps of the method are shown.

[0184] The method begins at position 401.

[0185] At 410, first data indicating at least one of the following is obtained: the state of the chassis of the motor vehicle before and after at least one occupant boards or disembarks from the motor vehicle, and the transition between the states of the motor vehicle before and after at least one occupant boards or disembarks from the motor vehicle.

[0186] The first data is obtained by at least one first sensor included in the monitoring components of the motor vehicle 100.

[0187] At 420, based on the first data obtained, second data indicating the weight of at least one occupant is obtained.

[0188] The second data is obtained by at least one of the vehicle's data processing unit and the data cloud's data processing component.

[0189] At 430, based on the second data, adjustment is made to at least one adjustable feature of the motor vehicle.

[0190] The method ends at position 402.

[0191] In the example, at least one adjustable feature includes, or is included in, at least one of airbags and seat belts in a motor vehicle.

[0192] In the example, adjusting at least one adjustable feature (particularly at least one safety feature) includes adjusting any of the following: airbag deployment speed, airbag deployment pressure, airbag ventilation, airbag deployment shape, airbag deployment position, seat belt pretension timing, seat belt pretension force level, seat belt tension force level, and seat belt force level maintenance curve.

[0193] In the example, the first data indicates at least one of the following: the position, angle, displacement, pressure, angular velocity, acceleration, and vertical force of the chassis of the motor vehicle affected by at least one occupant boarding or alighting from the motor vehicle.

[0194] In the example, the method further includes: obtaining third data indicating the confidence level of the second data, comparing the third data with a predefined confidence threshold, and if the third data is equal to or exceeds the confidence threshold, causing adjustment of at least one adjustable feature based on the second data, or if the third data is below the confidence threshold, causing adjustment of at least one adjustable feature to a predefined setting.

[0195] In the example, the method further includes: obtaining third data indicating the confidence level of the second data; comparing the third data with a predefined confidence level range; and if the third data exceeds the higher end of the predefined confidence level range, causing adjustment of at least one adjustable feature based on the second data; if the third data is below the lower end of the predefined confidence level range, causing adjustment of at least one adjustable feature to a predefined setting; or if the third data is within the predefined confidence level range, obtaining additional data to improve the accuracy of the second data, and updating the second data based on the obtained additional data.

[0196] In the example, the method further includes: obtaining fourth data indicating possible boarding or alighting of at least one occupant within a predefined time window before at least one occupant boards or alights from the vehicle; and, based on the obtained fourth data, causing at least one data monitoring component configured to monitor the first data to begin monitoring the first data before at least one occupant boards or alights from the vehicle.

[0197] The fourth data is obtained by at least one fourth sensor included in the monitoring components of the motor vehicle 100.

[0198] In the example, at least one occupant is multiple occupants, and the method further includes: obtaining fifth data indicating the weight of a subgroup of multiple occupants; obtaining second data indicating the weight of another subgroup of multiple occupants based on the obtained first data and the obtained fifth data; and causing an adjustment of at least one adjustable feature of the motor vehicle based on the second data.

[0199] The fifth data is obtained from at least one of the data storage unit 130 of the motor vehicle and the data storage component of the data cloud.

[0200] In the example, the method further includes providing second data to at least one application that includes a trainable data model.

[0201] In the example, the second data is used as training data for training a trainable data model.

[0202] In the example, the method further includes: obtaining a second set of first data indicating at least one of the following: the state of the chassis of the motor vehicle before and after at least one specific occupant boards or disembarks from the motor vehicle, and the transition between the states of the motor vehicle before and after at least one specific occupant boards or disembarks from the motor vehicle; obtaining a second set of second data indicating the weight of the at least one occupant based on the obtained second set of first data; and causing an adjustment of at least one adjustable feature of the motor vehicle based on the second data and the second set of second data.

[0203] The second set of the first data is obtained by at least one first sensor included in the monitoring components of the motor vehicle 100.

[0204] In the example, obtaining second data indicating the weight of at least one occupant based on the obtained first data also includes comparing it with at least one of historical second data and predefined second data.

[0205] According to the example, this disclosure relates to: an apparatus including at least one processor and at least one memory storing instructions, which, when executed by the at least one processor, cause the apparatus to perform any of the claimed methods.

[0206] As used herein, the phrase “at least one” when referring to a list of one or more entities should be understood to mean at least one entity selected from any one or more entities in the list of entities, but not necessarily including at least one of each entity specifically listed in the list of entities, and does not exclude any combination of entities in the list of entities. This definition also allows for the optional presence of entities other than those specifically identified within the list of entities referred to by the phrase “at least one,” whether related to or not related to those specifically identified entities. Thus, as a non-restrictive example, “at least one of A and B” (or equivalently, “at least one of A or B”, or equivalently, “at least one of A and / or B”) could in one example refer to at least one (optionally including more than one) A, without B (and optionally including entities other than B); in another example, it could refer to at least one (optionally including more than one) B, without A (and optionally including entities other than A); and in yet another example, it could refer to at least one (optionally including more than one) A and at least one (optionally including more than one) B (and optionally including other entities). In other words, the phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both connected and separate in operation. For example, each of the expressions “at least one of A, B, and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” and “A, B, and / or C” can mean a single A, a single B, a single C, A and B together, A and C together, B and C together, A, B, and C together, and any of the above, optionally combined with at least one other entity.

[0207] By studying the accompanying drawings, the disclosure, and the appended claims, those skilled in the art can understand and implement other variations of the disclosed examples in practice with respect to the claimed disclosure. In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite articles "a" or "an" do not exclude multiple. A single processor or other unit can perform the functions of several items or steps recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used for benefit. Computer programs can be stored / distributed on suitable media, such as optical storage media or solid-state media provided with or as part of other hardware, but can also be distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. Any reference numerals in the claims should not be construed as limiting the scope of the claims.

[0208] List of reference numerals

[0209] 100 motor vehicles

[0210] 110 At least one adjustable feature

[0211] 111 airbags

[0212] 112 seat belt

[0213] 120 Data Processing Unit

[0214] 130 data storage units

[0215] 140 computer-readable storage media

[0216] 150 computer programs

[0217] 160 Data Processing Units

[0218] 170 communication components

[0219] 180 monitoring components

[0220] 200 Scene 1

[0221] 210 At least one occupant

[0222] 220 external media

[0223] 300 Second Scene

[0224] More than 310 passengers

[0225] The start of the 401 method

[0226] 410 First step

[0227] 420 Step Two

[0228] 430 Step 3

[0229] End of 402 method

Claims

1. A method for adjusting at least one adjustable feature (110) of a motor vehicle (100), the method comprising: Obtain first data indicating at least one of the following: The state of the chassis of the motor vehicle (100) before and after at least one occupant (210) boards or disembarks from the motor vehicle (100), and The transition between the states of the motor vehicle (100) before and after at least one occupant (210) boards or disembarks from the motor vehicle (100); Based on the obtained first data, second data indicating the weight of the at least one occupant (210) is obtained; and This enables the adjustment of at least one adjustable feature (110) of the motor vehicle (100) based on the second data.

2. The method according to claim 1, wherein, The at least one adjustable feature (110) includes, or at least one performance feature, at least one safety feature, at least one comfort feature and / or at least one connectivity feature.

3. The method according to claim 2, wherein: The at least one performance feature includes, or is at least one of, the engine, suspension and transmission system included in the motor vehicle; The at least one safety feature includes, or is at least one of, passive safety features and active safety features included in the motor vehicle (100); The at least one comfort feature includes, or is included in, at least one of the following in the motor vehicle: an infotainment system, steering wheel controls, interior and / or rearview mirror controls, climate controls, and seats. The at least one connectivity feature includes either a navigation system or a vehicle-to-everything (V2X) system.

4. The method according to any one of the preceding claims, wherein, The first data indicates at least one of the vertical force, acceleration, angular velocity, pressure, displacement, angle, and position of the chassis of the motor vehicle (100) affected by the at least one occupant (210) boarding or disembarking from the motor vehicle (100).

5. The method according to any one of the preceding claims further comprises: Obtain third data indicating the confidence level of the second data. The third data is compared with a predefined confidence threshold, and If the third data is equal to or exceeds the confidence threshold, then the at least one adjustable feature (110) is adjusted based on the second data; or if the third data is lower than the confidence threshold, then the at least one adjustable feature (110) is adjusted to a predefined setting.

6. The method according to any one of the preceding claims further comprises: Before the at least one occupant (210) boards or disembarks from the motor vehicle (100), fourth data indicating possible boarding or disembarking of the at least one occupant (210) within a predefined time window is obtained; as well as Based on the obtained fourth data, at least one data monitoring component configured to monitor the first data begins to monitor the first data before the at least one occupant (210) boards the motor vehicle (100) or disembarks from the motor vehicle (100).

7. The method according to any one of the preceding claims, wherein, The at least one occupant (210) is a plurality of occupants (310), and the method further includes: Fifth data indicating the weight of the subgroups of the plurality of occupants (310) is obtained. Based on the first and fifth data obtained, second data indicating the weight of another subgroup of the plurality of occupants (310) is obtained; and This enables the adjustment of at least one adjustable feature (110) of the motor vehicle (100) based on the second data.

8. The method according to any one of the preceding claims further comprises: The second data is provided to at least one application, which includes a trainable data model.

9. The method according to claim 8, wherein, The second data is used as training data for training the trainable data model.

10. The method according to any one of the preceding claims further comprises: Obtain a second set of first data indicating at least one of the following: The state of the chassis of the motor vehicle (100) before and after at least one specific occupant boards or disembarks from the motor vehicle (100), and The transition between the state of the chassis of the motor vehicle (100) before and after at least one specific occupant boards or disembarks from the motor vehicle (100); Based on the second set of the first data obtained, a second set of second data indicating the weight of the at least one occupant (210) is obtained; as well as This enables the adjustment of at least one adjustable feature (110) of the motor vehicle (100) based on the second data and the second set of the second data.

11. The method according to any one of the preceding claims, further comprising: The at least one occupant is identified based on at least one of the first data, the second data, the third data, the fourth data, and the fifth data (210). and Upon identification of the at least one occupant (210), the adjustment of the at least one adjustable feature (110) of the motor vehicle (100) is further based on at least one previously set of the stored adjustable feature (110).

12. A data processing apparatus (120) comprising components for performing the method according to any one of the preceding claims.

13. A computer program (150) comprising instructions which, when executed by a computer, cause the computer to perform the method according to claims 1 to 11.

14. A computer-readable storage medium (140) including instructions that, when executed by a computer, cause the computer to perform the method according to claims 1 to 11.

15. A motor vehicle (100) comprising at least one adjustable feature (110) and a data processing device (120) according to claim 12, wherein, The at least one adjustable feature (110) and the data processing device (120) are communicatively coupled.

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