Patent Publication Number: US-2021176298-A1

Title: Private cloud processing

Description:
INTRODUCTION 
     The present disclosure relates to a system and a method for private cloud processing. 
     Advanced vehicles are incorporating face monitoring, voice monitoring, posture assessment and detection of occupants inside the vehicles. The monitoring and detection features are used to facilitate autonomous driving applications, and advanced human machine applications. For example, the face recognition allows automatic validation of occupants in an autonomous fleet of vehicles. Occupant detection can determine if someone has been left alone in a back seat of a given vehicle. 
     Due to increased data rates and computational complexities, the applications increasingly rely on cloud computing. However, sending data related to the occupants into the cloud exposes the occupants to potential privacy violations. Even where the data is encrypted before being sent to the cloud, the data is no longer private after being decrypted in the cloud to permit neural-network operations. What is desired is a technique for cloud processing of occupant data with built in privacy protection. 
     SUMMARY 
     A privacy system is provided herein. The privacy system comprises at least one sensor and a device. The at least one sensor is operational to generate sensor data in response to a user. The device is in communication with the at least one sensor, in communication with a plurality of distributed cloud processing nodes, and operational to decompose the sensor data into a plurality of data items, transmit the plurality of data items to the plurality of distributed cloud processing nodes, receive a plurality of processed items from the plurality of distributed cloud processing nodes, and generate output data based on the plurality of processed items. Individual ones of the plurality of distributed cloud processing nodes are operational to generate a corresponding one of the plurality of processed items in response to the corresponding one of the plurality of data items, a privacy aspect of the user is indeterminable from individual ones of the plurality of data items, and the privacy aspect of the user is determinable from the output data. 
     In one or more embodiments of the privacy system, the privacy aspect of the user is indeterminable from individual ones of the plurality of processed items. 
     In one or more embodiments of the privacy system, the decomposition of the sensor data comprises at least one of spatial decomposition and spectral decomposition of the sensor data. 
     In one or more embodiments of the privacy system, the decomposition of the sensor data comprises temporal decomposition of the sensor data. 
     In one or more embodiments of the privacy system, the device is operational to generate intermediate data by fusing the plurality of processed items. 
     In one or more embodiments of the privacy system, the device is operational to generate the output data by classifying the intermediate data. 
     In one or more embodiments of the privacy system, the fusing of the plurality of processed items comprises spatial fusing of the plurality of processed items. 
     In one or more embodiments of the privacy system, the fusing of the plurality of processed items comprises temporal fusing of the plurality of processed items. 
     In one or more embodiments of the privacy system, the sensor data comprises one or more of a video of the user, an image of the user, and audio generated by the user. 
     In one or more embodiments of the privacy system, the at least one sensor and the device are mountable in a vehicle, and the device communicates wirelessly with the plurality of distributed cloud processing nodes. 
     A method for cloud processing with privacy protection is provided herein. The method comprises: generating sensor data in response to a user; decomposing the sensor data into a plurality of data items using a device; transmitting the plurality of data items from the device to a plurality of distributed cloud processing nodes, wherein individual ones of the plurality of distributed cloud processing nodes are operational to generate a corresponding one of a plurality of processed items in response to the corresponding one of the plurality of data items, and a privacy aspect of the user is indeterminable from individual ones of the plurality of data items; receiving the plurality of processed items from the plurality of distributed cloud processing nodes at the device; and generating output data based on the plurality of processed items, wherein the privacy aspect of the user is determinable from the output data. 
     In one or more embodiments, the method further comprises generating intermediate data by fusing the plurality of processed items. 
     In one or more embodiments of the method, the output data is generated by classifying the intermediate data. 
     In one or more embodiments of the method, the sensor data comprises one or more of a video of the user, an image of the user, and audio generated by the user. 
     In one or more embodiments of the method, the device is mountable in a vehicle, and the device communicates wirelessly with the plurality of distributed cloud processing nodes. 
     A private cloud processing system is provided herein. The private cloud processing system comprising a network, at least one sensor, a device and a plurality of distributed cloud processing nodes. The at least one sensor is operational to generate sensor data in response to a user. The device is in communication with the at least one sensor and the network, and operational to decompose the sensor data into a plurality of data items, transmit the plurality of data items to the network, receive a plurality of processed items from the network, and generate output data based on the plurality of processed items. The plurality of distributed cloud processing nodes are in communication with the network. Individual ones of the plurality of distributed cloud processing nodes are operational to receive a corresponding one of the plurality of data items from the device through the network, generate a corresponding one of the plurality of processed items in response to the corresponding one of the plurality of data items, and transmit the corresponding one of the plurality of processed items to the device through the network. A privacy aspect of the user is indeterminable from individual ones of the plurality of data items, and the privacy aspect of the user is determinable from the output data. 
     In one or more embodiments, the private cloud processing system further comprises a network node operational to transfer the plurality of data items from the device to the plurality of distributed cloud processing nodes, and transfer the plurality of processed items from the plurality of distributed cloud processing nodes to the device. 
     In one or more embodiments of the private cloud processing system, the device comprises a transceiver operational to communicate wirelessly with the network node. 
     In one or more embodiments of the private cloud processing system, the individual ones of the plurality of distributed cloud processing nodes are operational to generate first internal data by spatially convoluting the corresponding one of the plurality of data items, generate second internal data by temporally convoluting the first internal data, and generate the corresponding one of the plurality of processed items by temporally fusing the second internal data. 
     In one or more embodiments of the private cloud processing system, the individual ones of the plurality of distributed cloud processing nodes are operational to generate third internal data by spectral binning the corresponding one of the plurality of data items, generate fourth internal data by temporally convoluting the third internal data, and generate the corresponding one of the plurality of processed items by temporally fusing the fourth internal data. 
     The above features and advantages and other features and advantages of the present disclosure are readily apparent from the following detailed description of the best modes for carrying out the disclosure when taken in connection with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a private cloud processing system in accordance with an exemplary embodiment. 
         FIG. 2  is a schematic diagram of a device of the private cloud processing system in accordance with an exemplary embodiment. 
         FIG. 3  is a schematic diagram of a generic processing operation of the private cloud processing system in accordance with an exemplary embodiment. 
         FIG. 4  is a schematic diagram of a distributed machine learning operation in accordance with an exemplary embodiment. 
         FIG. 5  is a flow diagram of a method for private cloud processing in accordance with an exemplary embodiment. 
         FIG. 6  is a schematic diagram of a private video processing operation in accordance with an exemplary embodiment. 
         FIG. 7  is a schematic diagram of a private audio processing operation in accordance with an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Various embodiments of the disclosure provide a technique for protecting occupant privacy in applications where cabin content processing is aided by cloud computing. The technique generally involves distributed cloud-based machine learning where individual distributed cloud processing nodes receive a corresponding data portion (or data item) of the cabin content for processing. The data items may be parsed from the cabin content within the vehicle such that a privacy aspect(s) (e.g., identities, recognition, personal features and/or the like) of the occupant(s) cannot be detected at the individual distributed cloud processing nodes. Cloud processed data portions (or processed items) are subsequently returned to the vehicle. Meaningful processing that may facilitate identification of the occupant(s), recognition of the occupant(s) and/or determining personal features of the occupant(s) is possible after the processed items are merge locally back within the vehicle. 
     The individual distributed cloud processing nodes may perform processing and model adjustments on the data items. The data items may be configured inside the vehicle such that the privacy aspects of the occupants cannot be detected at the distributed cloud processing nodes. The data items may also be configured such that meaningful processing may be performed in the distributed cloud processing nodes. Merger of the processed items is limited to within the vehicle such that privacy information may be understood only inside the vehicle. 
     Referring to  FIG. 1 , a schematic diagram of an example implementation of a private cloud processing system  100  is shown in accordance with an exemplary embodiment. The private cloud processing system  100  generally comprises a vehicle  102 , multiple network nodes  104  (one shown for clarity), a distributed processing cloud  106  and a network  108 . The vehicle  102  may include a device  110 . The distributed processing cloud  106  generally comprises multiple distributed cloud processing nodes  112   a - 112   n.    
     A bidirectional radio-frequency signal (e.g., RF) may be exchanged between the device  110  and the network node  104 . The radio-frequency signal RF generally conveys the data items from the device  110  to the network node  104  and the processed items from the network node  104  to the device  110 . The data items and the processed items are generally configured such that the privacy aspects of the occupants (or users) of the vehicle  102  cannot be determined. 
     The vehicle  102  may be implemented as an automobile (or car). In various embodiments, the vehicle  102  may include, but is not limited to, a passenger vehicle, a truck, an autonomous vehicle, a gas-powered vehicle, an electric-powered vehicle, a hybrid vehicle, a motorcycle, a boat, a train and/or an aircraft. In some embodiments, the vehicle  102  may include stationary objects such as rooms, booths and/or structures suitable for one or more users to occupy. Other types of vehicles  102  may be implemented to meet the design criteria of a particular application. 
     The network nodes  104  may implement wireless transceiver nodes (or towers). The network nodes  104  are generally operational to communicate with the device  110  via the radio-frequency signal RF. The network nodes  104  may also be operational to communicate with the processing cloud  106  via the network  108 . The data items received by the network nodes  104  from the device  110  in the radio-frequency signal RF may be presented to the processing cloud  106 . The processed items received by the network nodes  104  from the processing cloud  106  may be relayed to the device  110 . In various embodiments, the network nodes  104  may be implemented as cellular network nodes. In other embodiments, the network nodes  104  may be implemented as Wi-Fi network nodes and/or WiGig (60 GHz Wi-Fi) nodes. Other types of wireless nodes (or access points) may be implemented to meet a design criteria of a particular application. 
     The processing cloud  106  may implement a distributed collection of computers. The processing cloud  106  is generally operational to process the data items generated by the device  110  to create the processed items. 
     The network  108  may implement a backbone network. The network  108  may include one or more wired networks and/or one or more wireless networks. In various embodiments, the network  108  may include the Internet. The network  108  is generally operational to transfer data between the network nodes  104  and the processing cloud  106 . 
     The device  110  may be implemented as an electronic circuit in the vehicle  102 . The device  110  is generally operational to generate sensor data by sensing one or more characteristics (e.g., position, posture, voice, images, video, weight, etc.) of one or more users within the vehicle  102 . The device  110  may decompose the sensor data into multiple data items. The data items may be decomposed (or parsed) such that the privacy aspects of the occupants cannot be determined from an individual data item. Thereafter, the device  110  may transmit the data items to the distributed cloud processing nodes  112   a - 112   n  through the network node  104  and the network  108 . After the distributed cloud processing nodes  112   a - 112   n  have performed various transformations of the data items, the resulting processed items may be returned to the device  110  via the network  108  and the network node  104 . Upon reception of the processed items, the device  110  may generate output data based on the processed items. The output data may be configured such that the privacy aspects (or privacy information) of the users may be determinable. 
     The distributed cloud processing nodes  112   a - 112   n  may implement a distributed set of computers operating independent of each other. Individual ones of the distributed cloud processing nodes  112   a - 112   n  are generally operational to generate a corresponding one of the processed items by performing one or more operations on a corresponding one of the data items. The operations may include, but are not limited to, video processing operations, still image (or picture) processing operations and/or audio processing operations. 
     Referring to  FIG. 2 , a schematic diagram of an example implementation of the device  110  is shown in accordance with an exemplary embodiment. The device  110  generally comprises one or more sensors  120   a - 120   m , an electronic control unit  122  and a transceiver  124 . The electronic control unit  122  may include a decomposition circuit (or block)  126  and a processing circuit (or block)  128 . The decomposition circuit  126  and the processing circuit  128  may be implemented in hardware and/or software executing on the hardware. 
     One or more input signals (e.g., INa-INm) may be received by the sensors  120   a - 120   m . The input signals INa-INm may be one or more video signals, one or more still image signals and/or one or more acoustic signals that carry input information. The sensors  120   a - 120   m  may generate sensor data signals (e.g., Sa-Sm) that are presented to the decomposition circuit  126 . The sensor data signals Sa-Sm may convey digitized versions of the input information received in the input signals INa-INm. The processing circuit  128  may generate and present an output signal (e.g., OUT). The output signal OUT may carry the output data (e.g., the privacy aspect information of the users) to additional circuitry within the vehicle  102 . The privacy aspect information (e.g., identification information, recognition information and/or personal feature information) may be used to facilitate validation applications, autonomous driving applications, advanced human machine applications and/or similar applications within the vehicle  102  that rely on knowing who is driving the vehicle and/or who is situated within the vehicle. 
     The sensors  120   a - 120   m  may implement a variety of image, video, acoustic, pressure and/or ultrasound sensors. The sensors  120   a - 120   m  are generally operational to sense characteristics of the users inside a cabin of the vehicle  102  and/or in near proximity outside the vehicle  102  (e.g., visible through a window). One or more video sensors (e.g., the sensor  120   a ) and/or one or more image sensors (e.g., the sensor  120   b ) may be operational in the visible spectrum and/or in the infrared spectrum. Other types of sensors may be implemented to meet a design criteria of a particular application. 
     The electronic control unit  122  may implement the electronic circuitry used to partially process the sensor information received in the sensor data signals Sa-Sm and finish processing the processed items to generate the output signal OUT. The partial processing of the sensor data signals Sa-Sm may include decomposition of the sensor information to generate multiple data items. The data items may be presented to the transceiver  124  for transmission in the radio-frequency signal RF outside the vehicle  102 . The processed items may be received in the radio-frequency signal RF, through the transceiver  124 , and transferred into the electronic control unit  122 . The processed items may be fused together and classified to generate the output data. The output data may be presented in the output signal OUT. 
     The transceiver  124  may implement a bidirectional wireless transceiver. The transceiver  124  is generally operational to transmit the data items received from the electronic control unit  122  in the radio-frequency signal RF. The transceiver  124  is also operational to receive the processed items from the network nodes  104 . The processed items may be provided to the electronic control unit  122  for the final processing. 
     The decomposition circuit  126  may implement electronic circuitry operational to receive the sensor signals Sa-Sm and decompose (or parse) the sensor information within into the data items. The type of decomposition performed generally depends on the type of sensor information. For example, video information may be parsed into different fields or frames, different slices within the fields/frames and/or different components of the slices. Image information may be parsed into different regions of the images and/or different components of the images. Audio information may be parsed into different time slices and/or different frequency components. In various embodiments, the decomposition circuit  126  may also be operational to perform spectral decomposition and/or other types of data decomposition. 
     The processing circuit  128  may implement electronic circuitry configured to generate the output signal OUT in response to the processed items received from the distributed cloud processing nodes  112   a - 112   n  via the transceiver  124 . The processing circuit  128  is generally operational to fuse the processed items together and subsequently classify the fused processed items. The classification information may form the output data presented in the output signal OUT. 
     Referring to  FIG. 3 , a schematic diagram of an example generic processing operation  130  of the private cloud processing system  100  is shown in accordance with an exemplary embodiment. A video camera in a steering wheel of the vehicle  102  may capture a face of a driver. Multiple aspects of the resulting video (e.g., various luminance features and various chrominance features in spatially different locations and/or temporally different positions) may be captured by the device  110 . The individual aspects may be divided into different data items (e.g., DATAa-DATAn) by the device  110  and transmitted to the distributed cloud processing nodes  112   a - 112   n.    
     Individual ones of the distributed cloud processing nodes  112   a - 112   n  may receive corresponding ones of the data items for intermediate processing. The intermediate processing may include signal processing and/or model adjustment processing. The distributed cloud processing nodes  112   a - 112   n  generally get a partial representation of the cabin data such that the privacy aspects of the users cannot be detected at a single node, but meaningful processing is possible. In some embodiments, one or more distributed cloud processing nodes  112   a - 112   n  may receive and/or process multiple data items concurrently as long as the privacy of the users is maintained. The distributed cloud processing nodes  112   a - 112   n  may generate the processed items that are returned to the vehicle  102 . Merging of the processed items may be performed locally in the vehicle  102 . Therefore, the private information may be understood only by the electronic circuitry in the vehicle  102 . 
     Referring to  FIG. 4 , a schematic diagram of an example implementation of a distributed machine learning operation  140  is shown in accordance with an exemplary embodiment. The sensor data signals Sa-Sm may be received by the decomposition circuit  126 . The decomposition circuit  126  may generate multiple data item signals (e.g., DI) carrying the data items in response to the sensor information in the sensor data signals Sa-Sm. In various embodiments, a number of the sensor data signals Sa-Sm may be different than a number of data items. In some embodiments, the number of sensor data signals Sa-Sm may match the number of data items. 
     The data items may be transferred to the distributed cloud processing nodes  112   a - 112   n  in the processing cloud  106 . The distributed cloud processing nodes  112   a - 112   n  may generate the processed items in response to the data items. The processed items may be transferred back to the device  120  in multiple processed item signals (e.g., PI). 
     The device  120  generally comprises a fusion circuit (or block)  142  and a classifier circuit (or block)  144 . The fusion circuit  142  and the classifier circuit  144  may be implemented in hardware and/or software executing on the hardware. 
     The processed items may be received by the fusion circuit  142 . The fusion circuit  142  may generate an intermediate signal (e.g., IM) that is conveyed to the classifier circuit  144 . The intermediate signal IM may convey intermediate data within the processing circuit  128 . The output signal OUT may be generated and presented by the classifier circuit  144 . 
     The fusion circuit  142  may implement a spatial fusion circuit and/or spectral fusion circuit. The fusion circuit  142  is generally operational to combine the processed items received from the processing cloud  106  to create the intermediate data. The intermediate data may contain sufficient information that the users are recognizable (or distinguishable). The intermediate data may be presented in the intermediate signal IM to the classifier circuit  144 . 
     The classifier circuit  144  is generally operational to perform one or more classification operations. The classification operation may be configured to determine the privacy aspects of the users. The classification operations may generate the output data in response to the intermediate data. The output data may be presented in the output signal OUT to other circuits within the vehicle  102 . 
     Referring to  FIG. 5 , a flow diagram of an example method  150  for private cloud processing is shown in accordance with an exemplary embodiment. The method (or process)  150  generally comprises a step  152 , a step  154 , multiple steps  156   a - 156   n , a step  158  and a step  159 . The sequence of steps  152  to  159  is shown as a representative example. Other step orders may be implemented to meet the criteria of a particular application. 
     In the step  152 , the sensors  120   a - 120   m  may convert the input information (e.g., cabin content) received in the input signals INa-INm into electrical signals (e.g., the sensor information in the sensor data signals Sa-Sm) that are conveyed to the decomposition circuit  126 . The decomposition circuit  126  may decompose the sensor information into privacy protected data items (or sub-components) inside the vehicle  102 /the device  110  in the step  154 . The data items may be transferred at the end of the step  154  to the distributed cloud processing nodes  112   a - 112   n.    
     In the steps  156   a - 156   n , the distributed cloud processing nodes  112   a - 112   n  may process the process data items concurrently (at N places in the cloud) to create the processed items. At the end of the steps  156   a - 156   n , the processed items may be transferred back to the device  110  within the vehicle  102 . In the step  158 , the fusion circuit  142  may fuse the processed items together to create the intermediate data. The intermediate data may be processed further by the classifier circuit  144  in the step  159  to generate the output data in the output signal OUT. 
     Referring to  FIG. 6 , a schematic diagram of an example implementation of private video processing operation  160  is shown in accordance with an exemplary embodiment. The private video processing operation  160  may be a variation of the distributed machine learning operation  140 . 
     A video sensor (e.g., the sensor  120   a ) may record a video sequence as the input information in the input signal INa. The sensor data signal Sa may be received by the decomposition circuit  126  where the video sequence is divided into the data items. The data items may be transmitted to the distributed cloud processing nodes  112   a - 112   n . A particular distributed cloud processing node (e.g.,  112   x ) may receive several data items from a similar spatial portion of the video sequence with the portions taken at different times in the sequence. Other spatial portions of the video sequence may be transferred to other ones of the distributed cloud processing nodes  112   a - 112   n.    
     The particular distributed cloud processing node  112   x  may be configured as one or more spatial convolution nodes  162 , one or more temporal convolution nodes  164  and one or more temporal fusion nodes  166 . The spatial convolution nodes  162  may generate a first internal signal (e.g., A) transferred to the temporal convolution nodes  164 . The first internal signal A may convey first internal data of the spatially convoluted video. A second internal signal (e.g., B) may be generated by the temporal convolution nodes  164  and transferred to the temporal fusion nodes  166 . The second internal signal B may convey second internal data of the temporally convoluted first data. The other distributed cloud processing nodes  112   a - 112   n  may have a similar configuration. 
     The spatial convolution nodes  162  are generally operational to perform multidimensional (e.g., 3-dimensional) spatial convolutions on the data items received for the corresponding spatial portion. The spatial convolutions may generate the first internal data in response to the corresponding data items. 
     The temporal convolution nodes  164  are generally operational to perform temporal convolutions on the first internal data received from the spatial convolution nodes  162 . The temporal convolution nodes  164  may generate the second internal data in response to the first internal data. 
     The temporal fusion nodes  166  may be operational to combine the second internal data received from the temporal convolution nodes  164  to generate a particular one of the processed items. The particular processed item may be transferred back to the fusion circuit  142  in the device  110 . 
     The fusion circuit  142  may combine the particular processed item created by the particular distributed cloud processing node  112   x  with the other processed items created by the other distributed cloud processing nodes  112   a - 112   n . The combined (e.g., intermediate) information may be transferred to the classifier circuit  144 . The classifier circuit  144  is generally operational to classify the intermediate information to establish the output data in the output signal OUT. 
     Referring to  FIG. 7 , a schematic diagram of an example implementation of private audio processing operation  170  is shown in accordance with an exemplary embodiment. The private audio processing operation  170  may be a variation of the distributed machine learning operation  140 . 
     A microphone sensor (e.g., the sensor  120   m ) may record an audio signal as the input information in the input signal INm. The sensor data signal Sm may be received by the decomposition circuit  126  where a spectrogram (a spectrum of frequencies of the audio signal as the audio signal varies with time) is created from the audio signal and divided into the data items. The data items may be transmitted to the distributed cloud processing nodes  112   a - 112   n . A particular distributed cloud processing node (e.g.,  112   y ) may receive several data items from a similar frequency portion of the spectrogram with the portions taken at different times. Other frequency portions of the spectrogram may be transferred to other ones of the distributed cloud processing nodes  112   a - 112   n.    
     The particular distributed cloud processing node  112   y  may be configured as one or more spectral bin nodes  172 , one or more temporal convolution nodes  174  and one or more temporal fusion nodes  176 . The spectral bin nodes  172  may generate a third internal signal (e.g., C) transferred to the temporal convolution nodes  174 . The third internal signal C may convey third internal data of binned spectrogram information. A fourth internal signal (e.g., D) may be generated by the temporal convolution nodes  174  and transferred to the temporal fusion nodes  176 . The fourth internal signal D may convey fourth internal data of the temporally convoluted third data. The other distributed cloud processing nodes  112   a - 112   n  may have a similar configuration. 
     The spectral bin nodes  172  are generally operational to allocate the data items into spectral bins. The spectral bins may create the third internal data in response to the corresponding data items. 
     The temporal convolution nodes  174  are generally operational to perform temporal convolutions on the first internal data received from the spectral bin nodes  172 . The temporal convolution nodes  174  may generate the fourth internal data in response to the third internal data. 
     The temporal fusion nodes  176  may be operational to combine the fourth internal data received from the temporal convolution nodes  174  to generate a particular one or the processed items. The particular processed item may be transferred back to the fusion circuit  142  in the device  110 . 
     The fusion circuit  142  may combine the particular processed item created by the particular distributed cloud processing node  112   y  with the other processed items created by the other distributed cloud processing nodes  112   a - 112   n . The combined (e.g., intermediate) information may be transferred to the classifier circuit  144 . The classifier circuit  144  is generally operational to classify the intermediate information to establish the output data in the output signal OUT. 
     Various embodiments of the system  100  may provide private cabin content processing in distributed cloud processing nodes  112   a - 112   n . The cabin content may include video content, image content, audio content, ultrasound content and weights. The distributed cloud processing nodes  112   a - 112   n  may be operational to perform multidimensional (e.g., 3-dimensional) spatial convolutions, temporal convolutions, spectral binning, and temporal fusion. The data items transmitted to, and the processed items received from the distributed cloud processing nodes  112   a - 112   n  may be characterized in that the privacy aspects (e.g., identity, recognition and/or personal features) of the occupants of the vehicle  102  cannot be determined outside the vehicle  102  thus protecting the privacy of the occupants. Once the processed data is returned to the vehicle  102 , the device  110  mounted in the vehicle  102  may fuse the processed data together and perform additional processing to establish output data. The output data may be characterized in that the privacy aspects of the occupants may be determinable from the output data thus enabling the vehicle  102  to respond to the privacy aspects of the driver and/or passengers. 
     While the best modes for carrying out the disclosure have been described in detail, those familiar with the art to which this disclosure relates will recognize various alternative designs and embodiments for practicing the disclosure within the scope of the appended claims.