Autonomous preference system for vehicle

Abstract

A vehicle autonomous preference system includes a plurality of sensors located within a vehicle cabin, and data processing hardware including a memory storing a user profile including user preferences and a trainable preference model including a model trainer. The data processing hardware is configured to execute the model trainer in response to sensor data received from one or more of the plurality of sensors to update user preferences in the user profile. The data processing hardware is further configured to adjust environmental controls of the vehicle based on the trainable preference model and the updated user preferences.

Description

[0001] The information provided in this section is for the purpose of generally presenting the content of the present disclosure. The work of the presently named inventors, to the extent it is described in this section and in aspects of the description that may not qualify as prior art at the time of filing, is neither explicitly nor implicitly admitted to be prior art against the present disclosure. Technical Field

[0002] The present disclosure generally relates to autonomous preference systems. Background Art

[0003] Vehicles often provide users with preset functions. For example, a user can save preset seat functions that can be automatically adjusted in response to user activation. Vehicles also include heating and cooling controls that can be adjusted by the user to set the vehicle's interior environment for comfort. In addition, the user can adjust the audio settings within the vehicle and select various playback options. Therefore, while the user can select and adjust each in-vehicle setting, the vehicle is often a passive component waiting for input selections to be made. Summary of the invention

[0004] In some aspects, an autonomous preference system for a vehicle includes a plurality of sensors located within a cabin of the vehicle and an environmental control of the vehicle, the environmental control being electrically coupled to one or more of the plurality of sensors and configured to detect user input. The autonomous preference system also includes data processing hardware communicatively coupled to the plurality of sensors and the environmental control. The data processing hardware includes a memory storing a user profile including user preferences. The data processing hardware also includes a trainable preference model including a model trainer. The data processing hardware is configured to execute the model trainer in response to detected user input received from the environmental control and sensor data received from one or more of the plurality of sensors. The trainable preference model updates the user preferences in the user profile and is configured to adjust the environmental control based on the trainable preference model and the updated user preferences.

[0005] In some examples, the trainable preference model can be a machine learning model. Optionally, the sensor data can include one or more of audio data, image data, and weight data. The data processing hardware can be configured to execute a model trainer in response to the audio data. In some cases, the user profile can include routines, and the training preference model can be configured to generate routines based on environmental controls and sensor data. The data processing hardware can be configured to execute one or more of the user preferences based on the routines. In other examples, the data processing hardware can include a navigation application that includes a saved route, and the model trainer can be configured to update the training preference model based on the saved route.

[0006] In other aspects, a vehicle autonomous preference system includes a plurality of sensors located within a vehicle cabin, and data processing hardware including a memory storing a user profile including user preferences and a trainable preference model including a model trainer. The data processing hardware is configured to execute the model trainer in response to sensor data received from one or more of the plurality of sensors to update the user preferences in the user profile. The data processing hardware is further configured to adjust environmental controls of the vehicle via the trainable preference model based on the updated user preferences.

[0007] In some configurations, the model trainer may be configured to identify a routine for a user in response to sensor data, and may be configured to update a trainable preference model and execute the routine. The routine may include one or more of the user preferences. The model trainer may be configured to update the routine during operation of the vehicle in response to at least one of the user input and the sensor data. In some examples, the data processing hardware may be configured to identify a user based on the sensor data, and may be configured to execute a user profile in response to the identified user. The data processing hardware may be configured to identify a new user based on the sensor data, and the model trainer may be configured to update the trainable preference model in response to the identified new user. Optionally, the vehicle autonomous preference system may include a user device communicatively coupled to the data processing hardware. The user device may be configured to transmit one or more of route data and calendar data, and the trainable preference model may be configured to generate new user preferences based on at least one of the route data and calendar data.

[0008] In other aspects, an autonomous preference system includes a plurality of sensors and data processing hardware within a cabin of a vehicle, the data processing hardware including a memory storing a plurality of user profiles, each of the user profiles including user preferences. The data processing hardware includes a trainable preference model, the trainable preference model includes a model trainer, and the data processing hardware is configured to execute the model trainer in response to sensor data received from one or more of the plurality of sensors to update the user preferences in one or more of the plurality of user profiles. The trainable preference model is configured to select one of the user profiles to execute the corresponding updated user preferences based on the sensor data.

[0009] In some examples, the trainable preference model can be configured to autonomously learn new user preferences based on user input to the vehicle and sensor data from multiple sensors. Optionally, the autonomous preference system can include a navigation application in communication with the data processing hardware, and the model trainer can be configured to update the trainable preference model with one or more repeating routes from the navigation application. The trainable preference model can be configured to select one or more of the user preferences based on the identified repeating routes from the navigation application. In some cases, the multiple sensors can include a weight sensor configured to detect a first weight and at least one second weight, and the trainable preference model can be configured to select a first user profile from multiple user profiles in response to receiving the first weight from the weight sensor. The data processing hardware can be configured to determine the time of day, and the trainable preference model can be configured to select corresponding user preferences from one or more multiple user profiles in response to the determined time of day. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are for illustrative purposes only of selected configurations and are not intended to limit the scope of the present disclosure.

[0011] Figure 1 is a perspective view of a vehicle with occupants according to the present disclosure;

[0012] Figure 2 is a functional block diagram of an autonomous preference system according to the present disclosure;

[0013] Figure 3 is a partial perspective view of an interior cabin of a vehicle according to the present disclosure;

[0014] Figure 4 is a flow chart of an autonomous preference system according to the present disclosure;

[0015] Figure 5 According to the present disclosure Figure 4 A flowchart of the autonomous preference system; and

[0016] Figure 6 According to the present disclosure Figure 5 Flowchart of the autonomous preference system.

[0017] Corresponding reference characters indicate corresponding parts throughout the several views of the drawings. DETAILED DESCRIPTION

[0018] Example configurations will now be described more fully with reference to the accompanying drawings. Example configurations are provided so that the present disclosure will be thorough and will fully convey the scope of the present disclosure to those of ordinary skill in the art. Specific details, such as examples of specific components, devices, and methods, are set forth to provide a thorough understanding of the configurations of the present disclosure. It will be apparent to those of ordinary skill in the art that specific details need not be employed, that example configurations may be embodied in many different forms, and that the specific details and example configurations should not be construed as limiting the scope of the present disclosure.

[0019] The terms used herein are only used for the purpose of describing specific exemplary configurations and are not intended to be limiting. As used herein, the singular articles "a", "an" and "the" may also be intended to include plural forms unless the context clearly states otherwise. The terms "comprises", "comprising", "including" and "having" are inclusive and therefore specify the presence of features, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, steps, operations, elements, parts and / or groups thereof. The method steps, processes and operations described herein should not be interpreted as necessarily requiring them to be performed in the specific order discussed or shown, unless specifically identified as an execution order. Additional or alternative steps may be adopted.

[0020] When an element or layer is referred to as being "on another element or layer," "engaged to," "connected to," "attached to," or "coupled to" another element or layer, it may be directly on, directly engaged with, connected to, attached to, or coupled to another element or layer, or there may be intermediate elements or layers. Conversely, when an element is referred to as being "directly on another element or layer," "directly engaged to," "directly connected to," "directly attached to," or "directly coupled to" another element or layer, there may be no intermediate elements or layers. Other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.). As used herein, the term "and / or" includes any and all combinations of one or more associated listed items.

[0021] The terms first, second, third, etc. can be used in this article to describe various elements, components, regions, layers and / or parts. These elements, components, regions, layers and / or parts should not be limited by these terms. These terms can only be used to distinguish an element, component, region, layer or part from another region, layer or part. Unless the context clearly indicates, terms such as "first", "second" and other numerical terms do not imply order or sequence. Therefore, without departing from the teaching of the example configuration, the first element, component, region, layer or part discussed below can be referred to as the second element, component, region, layer or part.

[0022] In this application, including the definitions below, the term module may be replaced by the term circuit. The term module may refer to, be part of, or include: an application specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field programmable gate array (FPGA); a processor (shared, dedicated, or group) that executes code; a processor (shared, dedicated, or group) that stores code executed by a memory; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system on a chip.

[0023] The term code as used above may include software, firmware and / or microcode, and may refer to a program, a routine, a function, a class and / or an object. The term shared processor includes a single processor that executes some or all codes from multiple modules. The processor term group includes a processor that executes some or all codes from one or more modules in combination with an additional processor. The term shared memory includes a single memory that stores some or all codes from multiple modules. The memory term group includes a memory that stores some or all codes from one or more modules in combination with an additional memory. The term memory may be a subset of the term computer-readable medium. The term computer-readable medium does not include transient electrical signals and electromagnetic signals propagated through the medium, and therefore can be considered to be tangible and non-transient memory. Non-limiting examples of non-temporary memory include tangible computer-readable media, which include non-volatile memory, magnetic storage, and optical storage.

[0024] The apparatus and methods described in this application may be implemented in part or in whole by one or more computer programs executed by one or more processors. The computer program includes processor executable instructions stored on at least one non-transitory tangible computer readable medium. The computer program may also include and / or rely on stored data.

[0025] Software applications (i.e., software resources) may refer to computer software that enables a computing device to perform tasks. Example applications include, but are not limited to, system diagnostic applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.

[0026] Non-transitory memory can be a physical device used to temporarily or permanently store programs (e.g., sequences of instructions) or data (e.g., program state information) for use by a computing device. Non-transitory memory can be volatile and / or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM) / programmable read-only memory (PROM) / erasable programmable read-only memory (EPROM) / electrically erasable programmable read-only memory (EEPROM) (e.g., commonly used for firmware, such as bootloaders). Examples of volatile memory include, but are not limited to, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), phase change memory (PCM), and disk or tape.

[0027] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and may be implemented in high-level procedural and / or object-oriented programming languages ​​and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, non-transitory computer-readable medium, memory, and / or device (e.g., disks, optical disks, processors, programmable logic devices (PLDs)) for providing machine instructions and / or data to a programmable device, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0028] Various implementations of the systems and techniques described herein can be realized in digital electronic and / or optical circuits, integrated circuits, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs executable and / or interpretable on a programmable system that includes at least one programmable processor, which may be special purpose or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to send data and instructions to the storage system, at least one input device, and at least one output device.

[0029] The process and logic flow described in this specification can be performed by one or more programmable processors (also referred to as data processing hardware), which execute one or more computer programs to perform functions by operating on input data and generating outputs. The process and logic flow can also be performed by a dedicated logic circuit (e.g., FPGA (field programmable gate array) or ASIC (application-specific integrated circuit)). As an example, a processor suitable for executing a computer program includes both general-purpose and special-purpose microprocessors, and any one or more processors of any type of digital computer. Typically, the processor will receive instructions and data from a read-only memory or a random access memory or both. The basic elements of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more large-capacity storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, or be operably connected to receive data from it or transmit data to it or both. However, a computer does not need to have such a device. Computer-readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media, and memory devices, including, for example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0030] To provide interaction with a user, one or more aspects of the present disclosure may be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touch screen) for displaying information to the user and optionally a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including sound, voice, or tactile input. In addition, the computer may interact with the user by sending documents to and receiving documents from a device used by the user; for example, by sending a web page to a web browser on a user's client device in response to a request received from the web browser.

[0031] refer to Figure 1-6, an autonomous preference system 100 for a vehicle 10 includes a vehicle processor 200 configured with a trainable preference model 202 and including one or more user profiles 204 stored in a memory 206 of the vehicle processor 200. The vehicle processor 200 is configured to monitor an interior cabin 12 of the vehicle 10. The vehicle 10 includes a body 14 having an interior cabin 12 therein. The interior cabin 12 defines a passenger compartment, which may include a first row of seats 16 and a second row of seats 18. In other examples, fewer or more rows of seats may be included in the vehicle 10. The first row of seats 16 includes a driver's seat 20 and a passenger seat 22. The second row of seats 18 may include a rear passenger seat 24. As described below, the vehicle processor 200 is configured to receive a signal indicating occupancy of one or more of the row of seats 16, 18.

[0032] The interior cabin 12 also includes environmental controls 30 that control a plurality of outputs 32 within the interior cabin 12. For example, the environmental controls 30 may be directed to lighting features 34, temperature settings 36, accessory features 38, audio settings 40, and other environmental settings of the vehicle 10. The vehicle processor 200 may be communicatively coupled with the environmental controls 30 to collect environmental data 42. The environmental data 42 may collectively include data from each of the lighting features 34, temperature settings 36, accessory features 38, and audio settings 40. As described below, the vehicle processor 200 may utilize the environmental data 42 in conjunction with the trainable preference model 202. For example, the vehicle processor 200 may utilize the environmental controls 30 to update one or both of the trainable preference model 202 and the user profile 204.

[0033] Reference now Figure 1-3, the vehicle processor 200 includes data processing hardware 208 configured to execute the trainable preference model 202 and execute user preferences 210 associated with each corresponding user profile 204. The trainable preference model 202 includes a model trainer 212 configured to obtain training data 214 for training the trainable preference model 202. The trainable preference model 202 is configured as a machine learning model 202, such that the model trainer 212 is configured to train the machine learning model 202 based on the training data 214. The model trainer 212 can retrieve the training data 214 from, for example, the user profile 204. The training data 214 includes, but is not limited to, the user preferences 210. The training data 214 can also include any type of data (e.g., text, images, audio, etc.) that the trainable preference model 202 is trained to receive. For example, the training data 214 can include sensor data 300 from a plurality of sensors 302 disposed within the interior cabin 12 of the vehicle 10 and environmental data 42 from the environmental controls 30. Training the trainable preference model 202 to predict future user preferences 210 based on the training data 214. For example, the model trainer 212 can utilize the sensor data 300 to update the user preferences 210.

[0034] A plurality of sensors 302 are disposed at various locations within the interior cabin 12. The sensors 302 may include, but are not limited to, weight sensors 304, capacitive sensors 306, image sensors 308, and audio sensors 310. For example, a camera or other imager 308 may be positioned within the interior cabin 12 near the driver's seat 20, and an additional imager 308 may be positioned near the rear passenger seat 24 to monitor the occupant 50 positioned in one or more rear passenger seats 24. The sensor data 300 are collectively referred to as weight data 304a, capacitive data 306a, image data 308a, and audio data 310a. For example, the image data may include image recognition data, image size data, and / or image mapping data. In some examples, the weight sensor 304 may be positioned within each of the driver's seat 20, the passenger seat 22, and the rear passenger seat 24. The weight sensor 304 is configured to detect the weight positioned on each respective seat 20-24, and communicate the weight data 304a to the vehicle processor 200 for analysis. Accordingly, the weight sensor 304 is configured to provide differentiation data to assist in identifying one occupant 50 as compared to another occupant.

[0035] Audio data 310a may be collected from an audio sensor 310 that detects an occupant 50 within the vehicle 10. The audio sensor 310 may include one or more microphones positioned within the interior cabin 12 to collect the audio data 310a. For example, the audio data 310a may include talking, crying, barking, whistling, and other occupant noises. As described below, the vehicle processor 200 receives the audio data 310a and may execute the trainable preference model 202 in response to the audio data 310a. The capacitance data 306a may include input from the occupant 50 on the display console 44 of the vehicle 10. The model trainer 212 may train the trainable preference model 210 with the capacitance data 306a to learn the input selection of the corresponding occupant 50. The trainable preference model 210 may first identify the occupant 50 based on one or more of the weight data 304a, the image data 308a, and the audio data 310a, and may then execute the function of the vehicle 10 corresponding to the capacitance data 306a.

[0036] Similarly, the vehicle processor 200 receives image data 308a, which can be utilized in conjunction with the trainable preference model 202. The image data 308a can include, but is not limited to, images of the occupants 50 within the vehicle 10. While the image data 308a can confirm the location of the occupants within the vehicle 10, the image data 308a can also be used to monitor behavior and actions within the vehicle 10. The trainable preference model 202 can be configured to implement the user preferences 210 in response to the image data 308a, described in more detail below.

[0037] Still reference Figure 1-3 , the vehicle processor 200 can be communicatively coupled to the user device 400 via a wired or wireless connection. For example, the user device 400 can be coupled to the display console 44 of the vehicle 10. In some examples, the user device 400 can be communicatively coupled to the vehicle processor 200 via a network 404, which facilitates the transmission of signals between the user device 400 and the vehicle processor 200. The user device 400 includes a navigation application 406, which is configured to provide routes 408 to the user. The user can save or otherwise store routes 408 within the navigation application 406, and the navigation application 406 can be configured to detect or otherwise identify repeating routes 408 based on regularly visited locations. Repeating routes 408 can also be stored within the navigation application 406, and all routes 408 can be communicated with the vehicle processor 200 to be stored in the corresponding user profile 204.

[0038] The user device 400 may also be configured with a calendar 410 that may include appointments, meetings, and other scheduled events. As described herein, the vehicle processor 200 may collect user data 412 from the user device 400, and the model trainer 212 may utilize the user data 412 as training data 214 for the trainable preference model 202. The user data 412 includes, but is not limited to, route data 414, calendar data 416, and audio preferences 418. For example, the route data 414 includes the route 408 described above, and the audio preferences 418 may include, but are not limited to, music selections, audiobook selections, podcast selections, and other applications or features of the user device 400 that may be selected by the user to output audio within the interior cabin 12 of the vehicle 10.

[0039] It is also contemplated that in some examples, the user may input the user preferences 210 via the user device 400. The input user preferences 210 may then be communicated to the vehicle processor 200 and stored in the user profile 204. As mentioned above, the user preferences 210 may include user data 412 and may be communicated with the trainable preference model 202. The trainable preference model 202 may continuously update the user preferences 210, regardless of whether the user preferences 210 are input by the user via the user device 400, the display console 44, and / or added to by the vehicle processor 200 over a period of time.

[0040] Further references Figure 1-3 , describes an example related to the autonomous preference system 100. In some examples, the occupants 50 of the vehicle 10 may include a first occupant 50a, a second occupant 50b, and an animal occupant 50c. In other examples, the occupants 50 of the vehicle 10 may include a first occupant 50a, a second occupant 50b, and a child occupant 50d. The autonomous preference system 100 is configured to detect and identify the occupants 50 using a trainable preference model 202 and based on a user profile 204 and saved user preferences 210. It is contemplated that, upon first use, the occupant 50 may enter information into the display console 44 or via the user device 400 to establish each respective user profile 204. Additionally or alternatively, the model trainer 212 may train the trainable preference model 202 over time and generate each respective user profile 204 based on the training data 214.

[0041] For example, the trainable preference model 202 can generate user preferences 210 based on the sensor data 300 and the environmental data 42 received by the data processing hardware 208 and the model trainer 212. The trainable preference model 202 can identify various user preferences 210 and automatically execute the user preferences 210 in response to a detected user. Thus, the trainable preference model 202 is configured to autonomously execute the user preferences 210 based on the detected or otherwise identified user 50.

[0042] The data processing hardware 208 is configured to identify a user based on the sensor data 300, and is configured to execute the user profile 204 in response to the identified user. In other examples, the data processing hardware 208 can identify a new user based on the sensor data 300. The model trainer 212 is configured to update the trainable preference model 202 in response to the identified new user. For example, when a new user is identified, the data processing hardware 208 generates a new user profile 204. The model trainer 212 can then train the trainable preference model 202 with the new user profile 204. If the new user profile 204 does not contain the user preferences 210, one or both of the data processing hardware 208 and the trainable preference model 202 can update the new user profile 204 when the vehicle processor 200 collects data.

[0043] For example, the vehicle processor 200 may collect audio data 310a from within the interior cabin 12. The data processing hardware 208 may execute the model trainer 212 in response to the audio data 310a to execute one or more of the user preferences 210. For example, the audio data 310a may be a child occupant 50d crying in the rear passenger seat 24. The audio data 310a may include the crying sound, and the trainable preference model 202 may identify one of the user preferences 210 corresponding to the calming routine 220. For example, the calming routine 220 may include the trainable preference model 202 executing the audio preference 418 associated with the calming routine 220. In the example of the child occupant 50d crying, the trainable preference model 202 may select the audio preference 418 of the calming routine 220 to relax or otherwise calm the child occupant 50d.

[0044] In some cases, the trainable preference model 202 may utilize the image data 308a to execute one or more of the user preferences 210. For example, the image data 308a may include content of an animal occupant 50c chewing an object within the vehicle 10, otherwise the first occupant 50a may choose to remain untouched. The trainable preference model 202 may execute an alarm or other notification to notify the first occupant 50a of the situation. It is also contemplated that the trainable preference model 202 may output an audio preference 418 of the user preferences 210 that may distract the animal occupant 50d from the action. In another example, the image data 308a may be used to detect that an occupant 50 (including both the animal occupant 50c and the child occupant 50d) that may be outside the field of view of the driver occupant 50 is experiencing discomfort or otherwise may benefit from the calming routine 220. The trainable preference model 202 is configured to execute the calming routine 220 and / or notify the driver occupant 50 of the status of the occupant 50 outside the field of view. Trainable preference model 202 is configured to implement other user preferences 210 , such that calming routine 220 is one example of many user preferences 210 .

[0045] For example, the user profile 204 may also include routines 222. The routines 222 may be generated by the trainable preference model 202 based on one or more of the sensor data 300, the environmental data 42, and the user data 412. The routines 222 may be configured to execute the user preferences 210, so that the data processing hardware 208 may execute one or more of the user preferences 210 in response to the selection of the routines 222. For example, the routines 222 may include, but are not limited to, a morning routine, an afternoon routine, and an evening routine. The model trainer 212 may identify one of the routines 222 of the user in response to the sensor data 300. The model trainer 212 may update the trainable preference model 202 based on the sensor data 300 to include the routines 222 and execute the routines 222. During operation of the vehicle 10, the model trainer 212 may continuously update the trainable preference model 202, which may update the routines 222 in response to at least one of the user input and the sensor data 300.

[0046] Model trainer 212 is configured to continuously and autonomously update routine 222. For example, model trainer 212 continuously receives various data from sensors 302, environmental controls 30, and user devices 400 during operation of vehicle 10. Model trainer 212 uses the data to train trainable preference model 202 to ultimately execute user preferences 210 and routine 222 independently of activation by user input. In contrast, trainable preference model 202 is a machine learning model that autonomously adapts and executes user preferences 210 and / or routine 222 based on received data.

[0047] In some examples, the received data may include route data 414. As described above, route data 414 may be input by a user or saved within navigation application 406 as route 408. In either example, model trainer 212 may train trainable preference model 202 to recognize route 408. For example, occupant 50 may operate vehicle 10 in a direction associated with route 408, and trainable preference model 202 may autonomously execute user preferences 210 associated with route 408. In some examples, route 408 may direct vehicle 10 toward a sporting event, which trainable preference model 202 may recognize as being associated with upbeat music from audio preferences 418.

[0048] Data processing hardware 208 can also be configured to identify repetitive routes 408 from navigation application 406 and user device 400. Model trainer 212 updates trainable preference model 202 in response to repetitive routes 408 from navigation application 406. In response to identified repetitive routes 408, trainable preference model 202 can select one or more of user preferences 210.

[0049] The trainable preference model 202 may also confirm the selection of the route 408 and the user preferences 210 with the calendar data 416. For example, the calendar data 416 may include scheduled events indicating a sporting event. Thus, the trainable preference model 202 may confirm the content of the route 408 before executing the user preferences 210. It is also contemplated that the model trainer 212 may identify new routes 408 based on the route data 414. For example, a user may start a new course or scheduled meeting that was not previously identified as a saved route 408. Thus, the model trainer 212 may generate new user preferences 210 in response to the route data 414 and the calendar data 416. It is contemplated that the occupant 50 may adjust various features of the vehicle 10, such as audio output, that are otherwise selected by the trainable preference model 202. The model trainer 212 is configured to train and update the trainable preference model 202 in response to the adjustments made by the occupant 50 to subsequently learn new user preferences 210.

[0050] In other examples, new user preferences 210 can be learned by model trainer 212 in response to user input into vehicle 10 and sensor data 300 from sensor 302. Additionally, as described above, data processing hardware 208 can be configured to determine time of day 224. Trainable preference model 202 can select one or more user preferences 210 from user profile 204. Trainable preference model 202 can further select routine 222 using the determination of time of day 224, as described above.

[0051] As described above, the data processing hardware 208 is configured to receive weight data 304a from the weight sensor 304, which can be stored in the corresponding user profile 204. In some cases, the weight sensor 304 can detect a first weight 312 and at least one second weight 314. The first weight 312 can be associated with the first occupant 50a, and the at least one second weight 314 can be associated with the second occupant 50b. Additionally or alternatively, the second weight 314 can be associated with the animal occupant 50c and / or the child occupant 50d, so that the second weight 314 can be distinguished from the first weight 312.

[0052] Although the first occupant 50a is shown as a driver, the autonomous preference system 100 is configured to recognize the repositioning of the occupant 50 within the vehicle 10. For example, the trainable preference model 202 can recognize the difference between the first weight 312 and the second weight 314 and compare the detected weights 312, 314 to the stored user profile 204. The trainable preference model 202 can also utilize content from the sensor data 300 and the environmental data 42.

[0053] The user preferences 210 associated with the first occupant 50a can be adjusted based on the position or location of the first occupant 50a within the cabin 12. For example, the trainable preference model 202 can identify that the first occupant 50a moves to the passenger seat 22 when the first occupant 50a was previously located in the driver's seat 20. In some examples, the trainable preference model 202 identifies the change in position of the first occupant 50a based on the weight data 304a collected from the weight sensor 304. The weight data 304a can indicate that a first weight 312 previously detected in the driver's seat 20 is detected in the passenger seat 22. In response, the trainable preference model 202 can identify the user preferences of the first occupant 50a and execute the user preferences 210 relative to the passenger seat 22. The trainable preference model 202 can confirm the change in position of the first occupant 50a by comparing the first weight 312 detected in the passenger seat 22 with the second weight 314 detected in the driver's seat 20.

[0054] Once the first occupant 50a is identified, the trainable preference model 202 can implement the user preferences 210 associated with the first occupant 50a. For example, the trainable preference model 202 can adjust the environmental controls 30 based on the environmental data 42 associated with the user profile 204 of the first occupant 50a. For example, the trainable preference model 202 can adjust the temperature settings 36 based on the temperature settings 36 stored in the corresponding user profile 204 of the first occupant 50a. The adjustment of the user preferences 210 can occur regardless of the position of the occupant 50 within the vehicle 10.

[0055] Reference again Figure 1-6 ,exist Figure 4-6 . At an initial step 600, the vehicle processor 200 receives the sensor data 300, the environmental data 42, and the user data 412 to classify the occupant 50. At 602, the trainable preference model 202 determines whether a user profile 204 is stored in the memory 206. If no user profile 204 is identified, then at 604, the trainable preference model 202 prompts the occupant 50 to create a user profile 204. At 606, the trainable preference model 202 identifies whether the occupant 50 chooses to create a user profile 204. If the occupant 50 dismisses the prompt, then the trainable preference model 202 stops creating a new user profile 204 at 608 until the next route 408. Figure 5 Occupant 50 is illustrated selecting to create user profile 204 .

[0056] Figure 6A continued flow chart is shown. At 610, trainable preference model 202 may suggest an adjustment to user preference 210 based on user profile 204. If trainable preference model 202 suggests an adjustment based on occupant 50, then trainable preference model 202 automatically selects user preference 210 based on training data 214 at 612. If trainable preference model 202 does not suggest an adjustment, then at 614-618, trainable preference model 202 may evaluate one or more of route data 414, routine 222, and sensor data 300 for occupant 50 identification.

[0057] Reference again Figures 1 to 6 , the autonomous preference system 100 is configured to execute the user preferences 210 independent of user input or other adjustments by the occupant 50. The autonomous preference system 100 utilizes a model trainer 212 to continuously update and train the trainable preference model 202. The trainable preference model 202 utilizes machine learning to continuously modify the execution of the user preferences 210 and to update the user preferences 210 in response to the sensor data 300 and the environmental data 42. Thus, the autonomous preference system 100 advantageously adjusts to the user preferences 210 based on the intelligent trainable preference model 202 to continuously adapt, monitor, and provide an improved user experience.

[0058] Many embodiments have been described. However, it should be understood that various modifications can be made without departing from the spirit and scope of the present disclosure. Therefore, other embodiments are within the scope of the appended claims.

[0059] The foregoing description is provided for the purpose of illustration and description. It is not intended to be exhaustive or to limit the present disclosure. The individual elements or features of a particular configuration are generally not limited to that particular configuration, but are interchangeable where applicable and can be used in a selected configuration, even if not specifically shown or described. It can also be varied in many ways. Such variations should not be considered as departing from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure.

Claims

1. A vehicle autonomous preference system, comprising: a plurality of sensors positioned within the cabin of the vehicle; Data processing hardware comprising a memory storing a user profile and a trainable preference model, wherein the user profile includes user preferences, and the trainable preference model includes a model trainer, the data processing hardware being configured to execute the model trainer in response to sensor data received from one or more of the plurality of sensors to update the user preferences in the user profile, and being configured to adjust environmental controls of the vehicle based on the trainable preference model and the updated user preferences. 2 . The vehicle autonomous preference system of claim 1 , wherein the model trainer is configured to identify a user's routine in response to the sensor data, and is configured to update the trainable preference model and execute the routine. 3 . The vehicle autonomous preference system of claim 2 , wherein the routine includes one or more of the user preferences. 4 . The vehicle autonomous preference system of claim 2 , wherein the model trainer is configured to update the routine during operation of the vehicle in response to at least one of user input and the sensor data. 5 . The vehicle autonomous preference system of claim 1 , wherein the data processing hardware is configured to identify a user based on the sensor data and is configured to execute the user profile in response to the identified user.

6. The vehicle autonomous preference system of claim 5, wherein the data processing hardware is configured to identify new users based on the sensor data, and the model trainer is configured to update the trainable preference model in response to the identified new users.

7. The vehicle autonomous preference system of claim 1 , further comprising a user device, the user device being communicatively coupled to the data processing hardware and configured to transmit one or more of route data and calendar data, the trainable preference model being configured to generate new user preferences based on at least one of the route data and the calendar data.

8. The vehicle autonomous preference system of claim 1, wherein the trainable preference model is a machine learning model. 9 . The vehicle autonomous preference system of claim 1 , wherein the sensor data comprises one or more of audio data, image data, and weight data.

10. The vehicle autonomous preference system of claim 9, wherein the data processing hardware is configured to execute the model trainer in response to the audio data.

Images