Methods and systems for controlling motor vehicle functions for controlling motion sickness
Systems and methods for avoiding or mitigating motion sickness in a vehicle are disclosed herein. The systems and methods may include (a) determining a profile of the individual inclination towards motion sickness; (b) predicting an individual effectiveness of countermeasures based on typing using the data from step (a); (c) evaluating the actual effectiveness of the predicted countermeasures after their implementation when travelling in the vehicle; wherein step (a) is repeated regularly and all data and results from step (c) are fed into a self-learning system in order to obtain an improved statement on the individual actual effectiveness of countermeasures taken and to make the selection of effective countermeasures based on them.
1. A method for avoiding or mitigating motion sickness in a vehicle, comprising:
(a) determining a profile of individual inclination towards motion sickness for a passenger of the vehicle, wherein the profile of individual inclination towards motion sickness comprises one or more conditions having a tendency to cause the passenger of the vehicle to experience motion sickness;
(b) predicting individual effectiveness of countermeasures against the one or more conditions based on typing using the profile of step (a), wherein the countermeasures comprise automatically setting a damper hardness of the vehicle;
(c) evaluating actual effectiveness of the predicted countermeasures after their implementation when travelling in the vehicle;
where step (a) is repeated and all data and results from step (c) are fed into a self-learning system in order to obtain an improved statement on the actual effectiveness of countermeasures taken and to carry out a selection of effective countermeasures based on them, wherein each countermeasure is given a weight based on the actual effectiveness of the countermeasure; and
where steps (a)-(c) are repeated for other passengers of the vehicle, wherein the weight assigned to the countermeasure is further increased if the countermeasure is also taken against the one or more conditions shared by the other passengers of the vehicle.
2. The method according to claim 1 , wherein data and results of other passengers are also fed into the self-learning system.
3. The method according to claim 1 , wherein habituation effects are taken into account.
4. The method according to claim 1 , wherein further vehicle settings are also fed into the self-learning system for consideration during the analysis.
5. A system for avoiding or mitigating motion sickness in a vehicle, comprising:
a processor and memory, wherein the processor is configured to:
(a) determine a profile of individual inclination towards motion sickness for a passenger of the vehicle, wherein the profile of individual inclination towards motion sickness comprises one or more conditions having a tendency to cause the passenger of the vehicle to experience motion sickness;
(b) predicting individual effectiveness of countermeasures against the one or more conditions based on typing using the profile of step (a), wherein the countermeasures comprise automatically setting a damper hardness of the vehicle;
(c) evaluate actual effectiveness of the predicted countermeasures after their implementation when travelling in the vehicle;
where step (a) is repeated and all data and results from step (c) are fed into a self-learning system in order to obtain an improved statement on the actual effectiveness of countermeasures taken and to carry out a selection of effective countermeasures based on them, wherein each countermeasure is given a weight based on the actual effectiveness of the countermeasure; and
where steps (a)-(c) are repeated for other passengers of the vehicle, wherein the weight assigned to the countermeasure is further increased if the countermeasure is also one taken against the one or more conditions shared by the other passengers of the vehicle.
6. The system according to claim 5 , wherein data and results of other passengers are also fed into the self-learning system.
7. The system according to claim 5 , wherein habituation effects are taken into account.
8. The system according to claim 5 , wherein further vehicle settings are also fed into the self-learning system for consideration during the analysis.