Vehicle sensory feedback management
View Patent ↗Systems, methods, and computer-readable media for managing vehicle sensory feedback are provided. For example, an electric vehicle can be configured to replicate the feeling and response of a combustion engine powered supercar, adjusting for its real feedback and adjusting the suspension, and adding or countering vibrations in different areas and components of the vehicle in real time so the driver and/or passenger can have the feeling of any car they wish, with each passenger choosing their own feel.
1 . A method for managing sensory feedback using a sensory feedback model custodian system, the method comprising:
initially configuring, at the sensory feedback model custodian system, a learning engine for an experiencing entity;
receiving, at the sensory feedback model custodian system, scenario data for at least one scenario data category sensed from the experiencing entity during an experiencing entity scenario and a sensory feedback output state sensed from the experiencing entity during the experiencing entity scenario;
training, at the sensory feedback model custodian system, the learning engine using the received scenario data and the received sensory feedback output state;
accessing, at the sensory feedback model custodian system, scenario data for the at least one scenario data category sensed from another experiencing entity during another experiencing entity scenario;
determining a sensory feedback output state for the other experiencing entity scenario, using the trained learning engine for the experiencing entity at the sensory feedback model custodian system, with the accessed scenario data for the other experiencing entity scenario;
generating sensory feedback control data based on the determined sensory feedback output state for the other experiencing entity scenario; and
using the sensory feedback control data to affect a user experience of a user of the other experiencing entity during the other experiencing entity scenario.
2 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to provide a recommendation to adjust a control variable of the other experiencing entity.
3 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to automatically adjust a control variable of the other experiencing entity.
4 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to automatically adjust a haptic sensory feedback actuator of the other experiencing entity.
5 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to automatically adjust an olfactory sensory feedback actuator of the other experiencing entity.
6 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to automatically adjust an auditory sensory feedback actuator of the other experiencing entity.
7 . The method of claim 1 , wherein the using comprises using the sensory feedback control data to automatically adjust a visual sensory feedback actuator of the other experiencing entity.
8 . The method of claim 1 , wherein the at least one scenario data category comprises one of the following:
type of tires of the experiencing entity;
type of fuel used by the experiencing entity;
status of each seat of the experiencing entity;
status of occupancy of each seat of the experiencing entity;
status of each window and/or sunroof and/or convertible roof of the experiencing entity;
status of a heating, ventilation, and air conditioning (“HVAC”) system of the experiencing entity;
status of a media system of the experiencing entity;
status of weather of the environment of the experiencing entity;
status of smell of the environment of the experiencing entity;
status of sound of the environment of the experiencing entity;
status of light of the environment of the experiencing entity;
status of the driving surface of the experiencing entity; or
status of the driving operation of the experiencing entity.
9 . The method of claim 1 , further comprising:
initially configuring, at the sensory feedback model custodian system, a learning engine for the other experiencing entity;
obtaining, at the sensory feedback model custodian system, scenario data for the at least one scenario data category sensed from the other experiencing entity during a third experiencing entity scenario and a sensory feedback output state sensed from the other experiencing entity during the third experiencing entity scenario;
training, at the sensory feedback model custodian system, the learning engine for the other experiencing entity using the obtained scenario data and the obtained sensory feedback output state; and
determining another sensory feedback output state for the other experiencing entity scenario, using the trained learning engine for the other experiencing entity at the sensory feedback model custodian system, with the accessed scenario data for the other experiencing entity scenario, wherein the generating comprises generating the sensory feedback control data based on:
the determined sensory feedback output state for the other experiencing entity scenario; and
the determined other sensory feedback output state for the other experiencing entity scenario.
10 . The method of claim 9 , wherein the generating comprises generating the sensory feedback control data based on a difference between the determined sensory feedback output state for the other experiencing entity scenario and the determined other sensory feedback output state for the other experiencing entity scenario.
11 . The method of claim 9 , further comprising:
after the using, further accessing, at the sensory feedback model custodian system, further scenario data for the at least one scenario data category sensed from the other experiencing entity during the other experiencing entity scenario and a sensory feedback output state sensed from the other experiencing entity during the other experiencing entity scenario; and
further training, at the sensory feedback model custodian system, the learning engine for the other experiencing entity using the further accessed further scenario data and the further accessed sensory feedback output state.
12 . A sensory feedback model custodian system comprising:
a communications component; and
a processor operative to:
initially configure a learning engine for an experiencing entity;
receive scenario data for at least one scenario data category sensed from the experiencing entity during an experiencing entity scenario and a sensory feedback output state sensed from the experiencing entity during the experiencing entity scenario;
train the learning engine using the received scenario data and the received sensory feedback output state;
access scenario data for the at least one scenario data category sensed from another experiencing entity during another experiencing entity scenario;
determine a sensory feedback output state for the other experiencing entity scenario, using the trained learning engine for the experiencing entity, with the accessed scenario data for the other experiencing entity scenario;
generate sensory feedback control data based on the determined sensory feedback output state for the other experiencing entity scenario; and
use the sensory feedback control data to affect a user experience of a user of the other experiencing entity during the other experiencing entity scenario.
13 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to provide a recommendation to adjust a control variable of the other experiencing entity.
14 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to automatically adjust a control variable of the other experiencing entity.
15 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to automatically adjust a haptic sensory feedback actuator of the other experiencing entity.
16 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to automatically adjust an olfactory sensory feedback actuator of the other experiencing entity.
17 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to automatically adjust an auditory sensory feedback actuator of the other experiencing entity.
18 . The sensory feedback model custodian system of claim 12 , wherein the processor is operative to use the sensory feedback control data to automatically adjust a visual sensory feedback actuator of the other experiencing entity.
19 . The sensory feedback model custodian system of claim 12 , wherein the at least one scenario data category comprises one of the following:
type of tires of the experiencing entity;
type of fuel used by the experiencing entity;
status of each seat of the experiencing entity;
status of occupancy of each seat of the experiencing entity;
status of each window and/or sunroof and/or convertible roof of the experiencing entity;
status of a heating, ventilation, and air conditioning (“HVAC”) system of the experiencing entity;
status of a media system of the experiencing entity;
status of weather of the environment of the experiencing entity;
status of smell of the environment of the experiencing entity;
status of sound of the environment of the experiencing entity;
status of light of the environment of the experiencing entity;
status of the driving surface of the experiencing entity; or
status of the driving operation of the experiencing entity.
20 . A non-transitory computer-readable storage medium storing at least one program comprising instructions, which, when executed:
initially configure a learning engine for an experiencing entity;
receive scenario data for at least one scenario data category sensed from the experiencing entity during an experiencing entity scenario and a sensory feedback output state sensed from the experiencing entity during the experiencing entity scenario;
train the learning engine using the received scenario data and the received sensory feedback output state;
access scenario data for the at least one scenario data category sensed from another experiencing entity during another experiencing entity scenario;
determine a sensory feedback output state for the other experiencing entity scenario, using the trained learning engine for the experiencing entity, with the accessed scenario data for the other experiencing entity scenario;
generate sensory feedback control data based on the determined sensory feedback output state for the other experiencing entity scenario; and
use the sensory feedback control data to affect a user experience of a user of the other experiencing entity during the other experiencing entity scenario.
21 . A method comprising:
training a target vehicle sensory feedback model using training target vehicle scenario data and training target vehicle Sensory feedback data sensed by any target vehicle of a first target vehicle type during each of at least one target vehicle training scenario;
training a replicating vehicle sensory feedback model using training replicating vehicle scenario data and training replicating vehicle sensory feedback data sensed by any replicating vehicle of a first replicating vehicle type during each of at least one replicating vehicle training scenario, wherein the first replicating vehicle type is different than the first target vehicle type;
obtaining live replicating vehicle scenario data from a live replicating vehicle of the first replicating vehicle type during a replicating vehicle live scenario;
using the trained target vehicle sensory feedback model to predict live target vehicle sensory feedback data for a sensory feedback state of the first target vehicle type during the replicating vehicle live scenario based on the obtained live replicating vehicle scenario data;
using the trained replicating vehicle sensory feedback model to predict live replicating vehicle sensory feedback data for a sensory feedback state of the first replicating vehicle type during the replicating vehicle live scenario based on the obtained live replicating vehicle scenario data;
combining the predicted live target vehicle sensory feedback data and the predicted live replicating vehicle sensory feedback data to generate sensory feedback control data; and
using the sensory feedback control data to adjust a functionality of an output component of the live replicating vehicle during the replicating vehicle live scenario.