Methods for predicting and reducing kinetosis-related disorders

Abstract

Method for predicting and reducing kinetosis-related disturbances of an occupant (2) when driving a vehicle (1), wherein the occupant (2) is detected at least by means of a vehicle camera (4), wherein - depending on the stimuli acting on the occupant (2), an individual susceptibility of the occupant (2) to kinetosis and a type of activity performed during driving, a key figure is determined which indicates the probability of the onset of kinetosis-related disorders and - depending on the determined key figure, at least one individual measure from a catalogue of measures for the prevention of kinetosis-related disorders is recommended to the occupant (2) or automatically initiated, - an intra- and inter-individual susceptibility of the occupant (2) with regard to the occurrence of kinetosis-related disorders is determined, - a determined vulnerability value is then stored in a profile of the corresponding occupant (2), - in an iterative process, the value of the vulnerability is adjusted by adjusting the value of the vulnerability of the occupant (2) when a learning effect or an adaptation of the occupant (2) to certain circumstances is detected by means of a learning system, and - in order to reduce the stimuli acting on the occupant (2), depending on predicted vehicle movements for a route ahead of the vehicle (1), the occupant (2) is recommended to take an alternative route.

Description

The invention relates to a method for predicting and reducing or preventing chinetosis-related disturbances of an occupant during the driving operation of a vehicle.DE 10 2017 206 012 B3 discloses a method for operating a vehicle. The method provides that at least one first sensor unit detects movements of at least one region of the body of at least one vehicle occupant and / or of at least one object with which the vehicle occupant interacts. At least one driving system is activated by means of a control unit as a function of the detected movements. In this case, the detected movement is at least one oscillation profile as a function of time, and the oscillation profile is detected on the basis of the amplitude and / or frequency of the at least one oscillation.Furthermore, DE 10 2015 011 708 A1 discloses a method for reducing chinetosis-related disturbances of an occupant in a vehicle, wherein an individual inclination of the occupant to chinetosis is detected by means of a manual input. Furthermore, vehicle parameters, occupant health parameters and parameters relating to a route are detected for determining the occurrence of chinetosis-related disturbances and a discomfort threshold is determined on the basis of these parameters. Before reaching the discomfort threshold, a warning message is automatically issued to the occupant with regard to the occurrence of an imminent chinetosis-related disturbance. The method is used for a driver of a vehicle, who is moved in the autonomous driving mode, since the driver is given the possibility in this driving mode of dealing with other activities.Further methods for reducing chinetosis-related disturbances of a vehicle occupant are described in DE 10 2017 219 585 A1 and DE 10 2018 111 266 A1.The object of the invention is to specify a method, improved compared to the prior art, for predicting and reducing chinetosis-related disturbances of an occupant during the driving operation of a vehicle.The object is achieved according to the invention by the features specified in claim 1.Advantageous embodiments of the invention are the subject matter of the dependent claims.The invention relates to a method for predicting and reducing or avoiding chinetosis-related disturbances of an occupant during the driving operation of a vehicle, wherein the occupant is detected at least by means of a vehicle camera. In this case, as a function of stimuli acting on the occupant, an individual susceptibility of the occupant with respect to kinetosis and a type of activity exerted during the driving operation, an index is determined which indicates or predicts the probability of a onset of kinetosis-related disturbances. Depending on the determined characteristic number, at least one individual measure of a measure catalog for preventing chinetosis-related disturbances is recommended to the occupant or automatically initiated.An intra- and inter-specific susceptibility to chinetosis-related disturbances of an occupant is determined and a determined value of the susceptibility is then stored in the profile of the corresponding occupant. In an iterative process, the value of the susceptibility is adjusted by adjusting the value of the susceptibility of the occupant when a learning effect or an adjustment of the occupant to specific circumstances is detected by means of a learning system. Furthermore, in order to reduce the stimuli acting on the occupant as a function of predicted vehicle movements for a route ahead of the vehicle, the occupant is recommended to travel an alternative route. In particular, driving along an alternative route is recommended if all other countermeasures available cannot counteract the occurrence of chinetosis-related disorders and the occupant would suffer from nausea and / or headaches, among other things.By using the method, the occupant is given the possibility of being able to efficiently use a travel time for carrying out activities without there being the risk of the occupant being afflicted with chinetosis-related disorders. In the case of particularly susceptible persons, the program can also be used to prevent or alleviate kinetosis without any side activities being carried out.For example, the occupant is provided with the use of the method in the vehicle as an activatable anti-chinetosis program, in particular during autonomous driving operation of the vehicle.In this case, the method includes, on the one hand, the prediction of potentially kinetosis-related disturbances of the occupant and, on the other hand, individual measures, i.e. countermeasures, for preventing the occurrence of the kinetosis-related disturbances.In one embodiment of the method, a type of activity is assigned, i.e. clustered, to an activity category depending on a degree of distraction and attention and / or a stress on attention of the occupant, a required visual dynamics of the occupant, a required permanent attention and / or a slight lack of gaze of the occupant and / or a seating position of the occupant in the vehicle. Thus, the actions are summarized with respect to their characteristics, so that the determination of a countermeasure reducing chinetosis-related disorders can be simplified.In order to ascertain some of the stimuli acting on the occupant, in a further embodiment of the method, signals for ascertaining a body movement, a lateral dynamics, a roll movement and / or a pitch movement of the vehicle are detected by means of a vehicle-side acceleration sensor system. This makes it possible to counteract this by means of at least one suitable countermeasure, such as a corresponding chassis setting.A further development of the method provides that, in order to determine the stimuli acting on the occupant, vehicle movements for a route lying ahead of the vehicle are determined on the basis of information from a digital map and / or on the basis of data from a navigation system of the vehicle. Thus, movements of the vehicle can be predicted which lead to chinetosis-related disturbances, so that corresponding countermeasures can be taken in advance in order to be able to significantly reduce the risk of an occurrence of chinetosis-related disturbances.In a further possible embodiment of the method, the individual susceptibility of the occupant is determined on the basis of a question sheet to be filled. To this end, the occupant answers in particular a standardized question sheet, the question sheet being filled only once.In order to reduce the chinetosis-related disturbances, in a further embodiment of the method it is provided that driving dynamics of the vehicle are adapted, trajectory planning is carried out as a function of a driving profile and / or a traffic volume, the occupant is stabilized on the basis of seat settings, illumination in the vehicle is controlled, settings of a display unit are adapted and / or comfort settings such as scent, temperature, massage, etc. are carried out.Furthermore, the method provides in one embodiment that feedback of setting parameters of initiated individual measures into a central computer unit connected to the vehicle takes place in order to validate an effect of initiated individual measures, whereby the catalog of measures can be improved with respect to the reduction of disorders caused by chinetosis. For example, a seat setting compared to another proposed seat setting may result in a vertigo completely failing, whereas the occupant became vulnerable despite proposed seat settings. For this reason, the feedback is effected, so that this seat adjustment can be included additionally or alternatively in the measures catalog and thus made available to occupants of other vehicles.Exemplary embodiments of the invention are explained in more detail below with reference to a drawing.The following shows: FIG. 1 schematically shows a perspective view of a detail of a vehicle with a passenger.FIG. 1 shows a section of a vehicle 1 with an occupant 2 on a vehicle seat 3.The occupant 2 is a vehicle user, i.e. a driver of the vehicle 1, who carries out a driving task during manual driving operation of the vehicle 1.The vehicle 1 has an assistance system for autonomous driving operation, so that the occupant 2 can follow other activities during autonomous driving operation. According to the present embodiment in FIG. 1, the occupant 2 reads while the vehicle 1 autonomously travels to his or her predetermined destination.Since the occupant 2, which may also be a passenger or another occupant 2, is distracted from the driving situation and focuses on the other activity, in particular reading, there is the risk of a kinetosis-related disturbance occurring, kinetosis being referred to as travel or motion maladies.The chinetosis can also occur in the case of a passenger or another occupant 2 in the rear region of the vehicle 1 if the vehicle 1 does not have the assistance system for autonomous driving operation.Kinetosis is understood to mean physical reactions such as bloating, headaches, nausea, vomiting and dizziness, which are triggered by an uneasy movement, in particular in a vehicle 1, in the case of an occupant 2. The physical reactions are referred to as chinetosis-related disorders or symptoms.In order to make the driving operation of the vehicle 1 for the occupant 2 as comfortable as possible, a method described below for predicting and reducing chinetosis-related disturbances is provided.A vehicle camera 4 is arranged in the vehicle 1, i.e. in the interior thereof, which continuously captures image data during driving operation, wherein the vehicle camera 4 is designed and oriented such that occupants 2 located in the vehicle 1 are located in the capture range of the vehicle camera 4. Alternatively, the number of vehicle cameras 4 arranged in the vehicle 1 can vary, so that at least two vehicle cameras 4 are present.The method comprises an algorithm for predicting chinetosis-related disturbances, wherein different input variables are required for determining a characteristic number. For example, the algorithm is based on a clustering method in conjunction with a learning system and / or regression models.The algorithm consists of two independent or collaborative expansion stages.Expansion stage 1 of the algorithm is defined via a statistical / stochastic model, which was created and validated via a linear mixed model in the real driving environment. Potential variables are gradually integrated ("forward selection") into a regression model. Statistical quality criteria show the potential improvement of the model fitting and thus also the relevance of the variable to the expression of kinetosis. The corresponding characteristics of the identified variables are now classified with regard to their kinetosis provocation, i.e. the extent to which the occurrence of kinetosis is caused or enhanced by them, and integrated into the statistical model. Corresponding sensors record or predict the status of the variable permanently or at regular intervals. If a change in the form of the variable takes place, by manual adaptation or by automated introduction, the status of the prediction model also adapts itself. The prediction model can be extended or reduced permanently by further variables, their expression levels, interaction effects, etc.Extension stage 2 is a learning statistical model (machine learning) which improves or intersects with the occupant in the course of the application on the basis of collected data including individual preferences. A training data set and on the basis of the model of the positive and negative feedback are used to perform the corresponding adaptation.An input variable of the algorithm forms stimuli which act on the occupant 2 during the driving operation, wherein an individual susceptibility of the occupant 2 and a type of the activity exerted by the occupant 2 during the driving operation, a so-called secondary activity, form further input variables.Stimuli which act on the occupant 2 during the driving operation, in particular during the autonomous driving operation, are detected or determined as driving dynamic characteristic variables, such as a seat setting, a driving profile and / or a driving duration.The individual susceptibility of the occupant 2 is determined, for example, by means of at least one question sheet, which can be answered, for example, by means of a mobile data processing unit connected to a central computer unit, in particular a smartphone, and / or by means of an infotainment system of the vehicle 1.The type of activity carried out by the occupant 2, for example reading, playing, looking from the window and / or whether the occupant 2 is being accommodated, can be determined on the basis of recorded image data of the vehicle camera 4.On the basis of the input variables, a characteristic number is determined which specifies or predicts how likely chinetosis-related disturbances occur in the occupant 2.In order to counteract the chinetosis-related disturbances, in particular to prevent them from occurring at all, a catalog of measures for prevention based on this characteristic number is stored in the vehicle 1 with a plurality of individual measures, i.e. countermeasures.Examples of individual measures are driving dynamics measures, a change in route planning, changes in the seat setting and comfort settings, wherein feedback of the countermeasures with respect to the algorithm is provided.With respect to the stimulus or stimuli acting on the occupant 2, a type and duration of the stimulus or stimuli are determined.For this purpose, it is provided that an activity of the occupant 2, i.e., the activity being carried out, and the duration thereof, are ascertained on the basis of the captured image data of the vehicle camera 4. If present, the type of activity is summarized, i.e. clustered, for example according to the degree of distraction and attention / or attention stress, according to the presence of visual dynamics, in relation to the requirement of permanent attention, e.g. based on a slight lack of gaze, and / or depending on a seating position of the occupant 2 in the vehicle 1. In particular, the activities are assigned to an activity category, wherein activities of an activity category are identical with one another in respect of a number of properties mentioned above.With respect to the equilibrium organ and the proprioception, detected signals of an acceleration sensor system on the vehicle side are evaluated, for example with respect to a body movement, a lateral dynamics, a roll movement and / or a pitch movement.In particular, the detected signals of the acceleration sensor system are analyzed with respect to physical movement stimuli to which the occupant 2 is exposed. In addition to analyses in the time domain, for example a mean value calculation and / or by applying moving effective values with evaluation functions, additional analyses in the frequency domain are possible. In this case, an energy and / or power density can be analyzed, for example. In particular, a frequency in the range of 0.2 Hz proves to be critical with respect to the occurrence of kinetosis-related disturbances.In addition, the image data of the vehicle camera 4 can be evaluated with respect to head movements of the occupant 2. If it is determined that a relative movement between the head of the occupant 2 and a headrest 5 is comparatively large, there is the risk of chinetosis.In order to obtain information on the travel profile, a set travel duration and a traffic jam, for example on a travel route lying ahead of the vehicle 1, map data and data of a navigation system of the vehicle 1 are used. By means of this information, it is possible not only to view a past, but also to provide a look-ahead in order to be able to predict future vehicle movements.An intra- and inter-individual susceptibility of the occupant 2 with respect to the occurrence of chinetosis-related disorders is determined, as described above, by responding to at least one standardized questionnaire. A value determined due to the susceptibility is input only once in the vehicle 1 and is stored in the profile of the occupant 2. If a learning effect or an adaptation of the occupant 2 to specific circumstances is detected by using a learning system, this value is adapted to the intra- and inter-individual susceptibility of the occupant 2. This results in the determination of the value of the intra-individual susceptibility of the occupant 2 as an iterative process.Furthermore, for the prediction of the occurrence of chinetosis-related disturbances, a current well-being and comfort criteria are taken into account. The instantaneous well-being or typical chinetosis indicators, such as geezing, perspiration and / or a comparatively nervous sliding back and forth of the occupant 2 are or is determined by means of the recorded image data of the vehicle camera 4 and / or on the basis of detected signals of further suitable sensors in the vehicle 1. In addition to the detection of head pose, acceleration and / or movement, the detection of the stress level is also to be effected via the camera via the detection of the blood circulation of the head (temperature measurement).In particular, a measurement of brain currents, the so-called EEG, is used in the case of disorders caused by chinetosis, since trigger signals for exciting vestibularis nuclei and the formation reticularis can be detected by means of the brain currents. Such data are supplemented with behavior-based measurements and classical physiology data.Together with comfort criteria, such as air conditioning of the vehicle 1, these data are integrated into the algorithm.A type of expression and intensity of the individual input variables of the algorithm is converted into a stochastic prediction model on the basis of the learning system.As the number of users of the described method increases, it will be possible to form clusters that allow a relatively accurate prediction of the potential kinetic expression.In addition to aspects of a learning system, the statistical prediction model contains previous findings, e.g. a dependence of amplitude and frequency with respect to the kinetosis expression.As described above, the individual measures of the measures catalog are used to prevent the occurrence of chinetosis-related disturbances. The catalog of measures includes various setting parameters and influencing variables with corresponding form characteristics.An individual measure, which can be activated manually or automatically, is activated by a proposal that is based on an exceeded threshold value of the characteristic number in the prediction model.The driving dynamics of the vehicle 1 have effects on longitudinal, lateral and vertical dynamics, wherein, in order to reduce the driving dynamics, pitch and roll stabilization, among other things, can be carried out, a rear axle steering can be set, and / or a chassis characteristic curve can be adapted to influence the vertical dynamics.With regard to the trajectory planning, the occupant 2 can be issued the driving of an alternative route which induces no disturbances or at least fewer chinetosis-related disturbances. The driving profile has few curves to avoid the occurrence of chinetosis-related disturbances, avoids city traffic and serpentines. In addition, a selected travel route or the alternative route should have a low traffic volume and therefore no jams, so that a travel duration can be reduced.Furthermore, it is provided that a system for trajectory planning is adapted, wherein limit and threshold values for braking and acceleration processes are specified for longitudinal dynamics and intervention takes place early, so that uniform acceleration and deceleration of the vehicle 1 is possible. With regard to the lateral dynamics, a steering angle is adapted when cornering.As another countermeasure for reducing the occurrence of chinetosis-related disorders, a change in a seat setting on the vehicle seat 3 can be made. For example, the head of the occupant 2 may be supported by advancing the headrest 5.The vehicle seat 3 and / or its seat back 6 can be moved into a position favorable for the occupant 2, for example a reclining position, wherein additionally or alternatively dynamic seat bladders and / or a belt tensioner can be activated for stabilizing the occupant 2 in its position.A light setting in the vehicle 1 can be carried out in order to prevent the occurrence of chinetosis-related disturbances, wherein a dynamic is communicated in the vehicle 1 by means of running lights, in particular a treadmill. When the occupant 2 reads, as illustrated in the embodiment, appropriate illumination of an area where a book 7 is located is ensured.Furthermore, an artificial horizon and / or a reticle can be displayed on a display unit in the vehicle 1, in particular a display unit of an infotainment system, wherein a dynamic of displayed content is adapted in accordance with the driving dynamics.With regard to the comfort settings, it is determined on the basis of map data and data of the navigation system which route section is most suitable for the respective activity. If it is known which activity the occupant 2 intends to carry out during the autonomous driving operation of the vehicle 1, then a suitable route section for carrying out the activity is recommended to the occupant 2 depending on the driving duration and the route.Settings of the air conditioning of the vehicle 1 can be changed to cool an air flow and direct it onto the head of the occupant 2, wherein fragrancing can be activated and, for example, the odour of peppermint or ginger in the vehicle 1 is dissipated in order to avoid kinetosis-related disturbances.Furthermore, it can be provided that breathing of the occupant 2 is influenced by acoustic specifications and / or a massage rhythm of a massage system of the vehicle seat 3, in order to at least reduce the risk of a kinetosis-related disturbance occurring.Weighting of the individual measures is effected by means of the learning system and is likewise supplemented by individual preferences. In this case, the measures catalog initially comprises known individual measures, that is to say known countermeasures which have been examined and validated by means of test subject studies carried out.Furthermore, individual measures which have been selected and carried out for an occupant 2 on the basis of the characteristic number are re-entered into the algorithm as further input variables. In this way, it can be judged to what extent the specific measure has effected an improvement. These findings are improved for further improvement of the algorithm. Measures are therefore equally taken into account in the prediction model, with the result that the characteristic value is permanently calculated and updated.The method is iteratively improved by the existing individual profile of the occupant 2 and / or by means of the learning system which recognizes for feedback which countermeasure the respective occupant 2 and / or other persons inside and outside the cluster has selected. For this purpose, the learning system is connected to a central computer unit, wherein the learning system can be a component of the central computer unit.The respective countermeasure is recommended to the occupant 2 or automatically activated. The automatic initiation is performed when there is detailed information, little information or no information about the countermeasure, whereas a countermeasure for manual activation is recommended when there is detailed information about the countermeasure or it is necessary to activate a program.

Claims

Method for predicting and reducing kinetosis-related disturbances of an occupant (2) during the driving operation of a vehicle (1), wherein the occupant (2) is captured at least by means of a vehicle camera (4), wherein - as a function of stimuli acting on the occupant (2), an individual susceptibility of the occupant (2) with respect to kinetosis and a type of activity exerted during the driving operation, an index is determined which indicates the probability of an onset of kinetosis-related disturbances and - as a function of the determined index, at least one individual measure of a measures catalog for preventing kinetosis-related disturbances is recommended to the occupant (2) or automatically initiated, - an intra- and inter-individual susceptibility of the occupant (2) with respect to an occurrence of kinetosis-related disturbances is determined, a determined value of the susceptibility is subsequently stored in a profile of the corresponding occupant (2), an adjustment of the value of the susceptibility takes place in an iterative process in that, when a learning effect or an adjustment of the occupant (2) to specific circumstances is detected by means of a learning system, the value of the susceptibility of the occupant (2) is adjusted, and the driving of an alternative route is recommended to the occupant (2) in order to reduce the stimuli acting on the occupant (2) as a function of predicted vehicle movements for a driving route lying ahead of the vehicle (1).Method according to Claim 1, characterized in that a type of activity is assigned to an activity category as a function of - a degree of distraction and attention and / or a stress on attention of the occupant (2), - a required visual dynamic of the occupant (2), - a required permanent attention and / or a slight lack of gaze of the occupant (2) and / or - a seating position of the occupant (2) in the vehicle (1).Method according to Claim 1 or 2, characterized in that, in order to determine the stimuli acting on the occupant (2), signals for determining a body movement, a transverse dynamics, a roll movement and / or a pitch movement of the vehicle (1) are detected by means of a vehicle-side acceleration sensor system.Method according to one of the preceding claims, characterized in that, in order to determine the stimuli acting on the occupant (2), vehicle movements for a route lying ahead of the vehicle (1) are determined on the basis of information from a digital map and / or on the basis of data from a navigation system of the vehicle (1).Method according to one of the preceding claims, characterized in that the individual susceptibility of the occupant (2) is determined on the basis of a question sheet to be filled.Method according to one of the preceding claims, characterized in that, in order to reduce the stimuli acting on the occupant (2), - driving dynamics of the vehicle (1) are adapted, - trajectory planning is carried out as a function of a driving profile and / or a traffic volume, - the occupant (2) is stabilized on the basis of seat settings, - illumination in the vehicle (1) is controlled, - settings of a display unit are adapted and / or - comfort settings are carried out.Method according to one of the preceding claims, characterized in that, in order to validate an effect of initiated individual measures, feedback of setting parameters of initiated individual measures is effected into a central computer unit connected to the vehicle (1).

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