Driver position monitoring system

Abstract

A computer-implemented method that, when executed by data processing hardware, causes the data processing hardware to perform operations. The operations include identifying, via a driver positioning algorithm, a driver profile related to a driver of a vehicle; receiving, at the driver positioning algorithm, sensor data from a plurality of sensors within the vehicle; and determining, based on the sensor data, a conditional performance of the driver, the conditional performance corresponding to a location of the driver. The operations further include generating, via the driver positioning algorithm, a suggestion based on the determined conditional performance of the driver; and monitoring, via the driver positioning algorithm, the conditional performance of the driver.

Description

[0001] introduce

[0002] The information provided in this section is intended to generally present the background of this disclosure. The work of the inventors listed herein (within the scope described in this section) and aspects of the specification that may otherwise not be considered prior art at the time of filing are neither expressly nor implied to be prior art to this disclosure.

[0003] This disclosure generally relates to a driver position monitoring system for vehicles.

[0004] Vehicles are often operated for extended periods, causing operators to adjust their seat positions for comfort. However, these adjustments can create unexpected blind spots or other problems, potentially affecting vehicle operation. For example, the operator might recline the seat or shift their position within the seat. In some cases, the operator's natural posture can also affect driving performance.

[0005] Many vehicles are equipped with driver monitoring systems, such as cameras or other sensors, designed to detect driver distraction or other impairments in driving ability. While these systems are practical, they can be enhanced to monitor other factors that may ultimately affect driving performance. Summary of the Invention

[0006] In some aspects, a computer-implemented method causes data processing hardware to perform various operations when executed by data processing hardware. These operations include: identifying a driver profile associated with a driver of a vehicle via a driver localization algorithm; receiving sensor data from multiple sensors within the vehicle at the driver localization algorithm; and determining the driver's conditional performance based on the sensor data, the conditional performance corresponding to the driver's position. These operations also include: generating recommendations based on the determined driver's conditional performance via the driver localization algorithm; and monitoring the driver's conditional performance via the driver localization algorithm.

[0007] In some examples, generating recommendations may include issuing a prompt that includes a position adjustment. The operation may also include: receiving input at a driver positioning algorithm in response to the issued prompt; and performing a position change implementation via the driver positioning algorithm. Optionally, performing a position change implementation may include automatically performing the position change implementation. In some cases, a position change implementation may include one or more of the following: seat adjustment, steering wheel adjustment, and lumbar support adjustment. The operation may also include collecting crowdsourced data corresponding to the conditional performance of multiple drivers at a backend server. The operation may also include transmitting sensor data, including driver position data, to the backend server via the driver positioning algorithm. Optionally, determining conditional performance includes analyzing the sensor data at the backend server.

[0008] In other aspects, the driver position monitoring system includes data processing hardware and memory hardware that communicates with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform various operations. These operations include: identifying a driver profile associated with the vehicle's driver via a driver localization algorithm; receiving sensor data from multiple sensors within the vehicle at the driver localization algorithm; and determining the driver's conditional performance based on the sensor data, which corresponds to the driver's position. These operations also include: generating recommendations based on the determined driver's conditional performance via the driver localization algorithm; and monitoring the driver's conditional performance via the driver localization algorithm.

[0009] In some examples, generating recommendations may include issuing a prompt that includes a position adjustment. The operation may also include: receiving input at a driver positioning algorithm in response to the issued prompt; and performing a position change implementation via the driver positioning algorithm. Optionally, performing a position change implementation may include providing the driver with instructions corresponding to the position change implementation. In some cases, a position change implementation may include one or more of the following: seat adjustment, steering wheel adjustment, and lumbar support adjustment. The operation may also include collecting crowdsourced data corresponding to the conditional performance of multiple drivers at a backend server. The operation may also include transmitting sensor data, including driver position data, to the backend server via the driver positioning algorithm. Optionally, determining conditional performance may include analyzing the sensor data at the backend server.

[0010] In other aspects, the vehicle's driver position monitoring system includes data processing hardware and memory hardware that communicates with the data processing hardware. The memory hardware stores instructions that, when executed on the data processing hardware, cause the data processing hardware to perform various operations. These operations include: identifying a driver profile associated with the vehicle's driver via a driver localization algorithm; receiving sensor data from multiple sensors within the vehicle at the driver localization algorithm; transmitting the sensor data, including driver position data, to a back-end server via the driver localization algorithm; and analyzing the sensor data at the back-end server. These operations also include: determining the driver's conditional performance based on the sensor data, the conditional performance corresponding to the driver's position; generating recommendations based on the determined driver's conditional performance via the driver localization algorithm; and monitoring the driver's conditional performance via the driver localization algorithm.

[0011] In some examples, generating suggestions includes issuing a prompt that includes a position adjustment. The operation may also include: receiving input at a driver positioning algorithm in response to the issued prompt; and performing a position change implementation via the driver positioning algorithm. Optionally, performing a position change implementation may include providing the driver with instructions corresponding to the position change implementation. The position change implementation may include one or more of the following: seat adjustment, steering wheel adjustment, and lumbar support adjustment.

[0012] This disclosure provides the following examples:

[0013] Example 1. A computer-implemented method, when executed by data processing hardware, causes the data processing hardware to perform operations including the following:

[0014] Driver profiles related to the vehicle's driver are identified through a driver location algorithm;

[0015] Sensor data from multiple sensors within the vehicle is received at the driver positioning algorithm.

[0016] The driver's conditional performance is determined based on the sensor data, and the conditional performance corresponds to the driver's position;

[0017] Based on the determined driver performance, suggestions are generated using the driver localization algorithm; and

[0018] The driver's conditional performance is monitored via the driver positioning algorithm.

[0019] Example 2. The method described in Example 1, wherein generating a suggestion includes issuing a prompt that includes a position adjustment.

[0020] Example 3. The method according to Example 2 further includes: receiving input at the driver positioning algorithm in response to a given prompt; and performing a location change implementation via the driver positioning algorithm.

[0021] Example 4. The method according to Example 3, wherein performing the location change implementation includes automatically performing the location change implementation.

[0022] Example 5. The method according to Example 4, wherein the position change implementation includes one or more of the following: seat adjustment, steering wheel adjustment, and lumbar adjustment.

[0023] Example 6. The method according to Example 1 further includes: collecting crowdsourced data corresponding to the conditional performance of multiple drivers at a backend server.

[0024] Example 7. The method according to Example 1 further includes: transmitting the sensor data to a backend server via the driver positioning algorithm, wherein the sensor data includes driver position data.

[0025] Example 8. The method according to Example 7, wherein determining the conditional performance includes analyzing the sensor data at the backend server.

[0026] Example 9. A driver position monitoring system, comprising:

[0027] Data processing hardware; and

[0028] Memory hardware communicating with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including the following:

[0029] Driver profiles related to the vehicle's driver are identified through a driver location algorithm;

[0030] Sensor data from multiple sensors within the vehicle is received at the driver positioning algorithm.

[0031] The driver's conditional performance is determined based on the sensor data, and the conditional performance corresponds to the driver's position;

[0032] Based on the determined driver performance, suggestions are generated using the driver localization algorithm; and

[0033] The driver's conditional performance is monitored via the driver positioning algorithm.

[0034] Example 10. The driver position monitoring system according to Example 9, wherein generating a suggestion includes issuing a prompt including a position adjustment.

[0035] Example 11. The driver position monitoring system according to Example 10 further includes: receiving input at the driver positioning algorithm in response to a given prompt; and performing a position change implementation via the driver positioning algorithm.

[0036] Example 12. The driver position monitoring system according to Example 11, wherein performing the position change implementation includes providing the driver with an instruction corresponding to the position change implementation.

[0037] Example 13. The driver position monitoring system according to Example 12, wherein the position change implementation includes one or more of the following: seat adjustment, steering wheel adjustment, and lumbar adjustment.

[0038] Example 14. The driver location monitoring system according to Example 9 further includes collecting crowdsourced data corresponding to the conditional performance of multiple drivers at a back-end server.

[0039] Example 15. The driver position monitoring system according to Example 9 further includes: transmitting the sensor data to a backend server via the driver positioning algorithm, wherein the sensor data includes driver position data.

[0040] Example 16. The driver position monitoring system according to Example 15, wherein determining the conditional performance includes analyzing the sensor data at the back-end server.

[0041] Example 17. A driver position monitoring system for a vehicle, the driver position monitoring system comprising:

[0042] Data processing hardware; and

[0043] Memory hardware communicating with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including the following:

[0044] Driver profiles related to the vehicle's driver are identified through a driver location algorithm;

[0045] Sensor data from multiple sensors within the vehicle is received at the driver positioning algorithm.

[0046] The sensor data, including driver location data, is transmitted to the backend server via the driver positioning algorithm.

[0047] The sensor data is analyzed at the backend server.

[0048] The driver's conditional performance is determined based on the sensor data, and the conditional performance corresponds to the driver's position;

[0049] Based on the determined driver performance, suggestions are generated using the driver localization algorithm; and

[0050] The driver's conditional performance is monitored via the driver positioning algorithm.

[0051] Example 18. The driver position monitoring system according to Example 17, wherein generating a suggestion includes issuing a prompt including a position adjustment.

[0052] Example 19. The driver position monitoring system according to Example 18 further includes: receiving input at the driver positioning algorithm in response to a given prompt; and performing a position change implementation via the driver positioning algorithm.

[0053] Example 20. A driver position monitoring system according to Example 19, wherein performing the position change implementation includes providing the driver with an instruction corresponding to the position change implementation, the position change implementation including one or more of the following: seat adjustment, steering wheel adjustment, and lumbar adjustment. Attached Figure Description

[0054] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure.

[0055] Figure 1 This is a schematic diagram of a vehicle equipped with a driver position monitoring system according to this disclosure;

[0056] Figure 2 This is an example block diagram of a driver location monitoring system based on this disclosure;

[0057] Figure 3 This is another example block diagram of a driver location monitoring system according to this disclosure;

[0058] Figure 4 This is an exemplary flowchart of the operation of the driver location monitoring system according to this disclosure; and

[0059] Figure 5 This is an exemplary flowchart of the operation method of the driver location monitoring system according to the present disclosure.

[0060] Throughout the accompanying figures, the corresponding figure labels indicate the relevant parts. Detailed Implementation

[0061] The example configuration will now be described more fully with reference to the accompanying drawings. The example configuration is provided so that this disclosure will be comprehensive and will fully convey the scope of this disclosure to those skilled in the art. Specific details, such as examples of specific components, devices, and methods, are set forth to provide a comprehensive understanding of the configuration of this disclosure. It will be apparent to those skilled in the art that the specific details are not required, the example configuration may be embodied in many different forms, and the specific details and example configuration should not be construed as limiting the scope of this disclosure.

[0062] The terminology used herein is for describing specific exemplary configurations only and is 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 explicitly indicates otherwise. The terms “comprising,” “including,” and “having” are inclusive and therefore specify the presence of features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein should not be construed as necessarily requiring them to be performed in the specific order discussed or illustrated, unless explicitly identified as such. Additional or alternative steps may be employed.

[0063] When an element or layer is referred to as “on another element or layer,” “joined to,” “connected to,” “attached to,” or “coupled to” another element or layer, it may be located directly on, joined to, connected to, attached to, or coupled to that other element or layer, or there may be intermediate elements or layers present. Conversely, when an element is referred to as “directly on another element or layer,” “directly joined to,” “directly connected to,” “directly attached to,” or “directly coupled to” another element or layer, there may be no intermediate elements or layers present. Other terms used to describe relationships between elements should be interpreted in a similar manner (e.g., “between” vs. “directly between,” “adjacent” vs. “directly adjacent,” etc.). As used herein, the term “and / or” includes any and all combinations of one or more of the related listed items.

[0064] The terms “first,” “second,” “third,” etc., are used herein to describe various elements, components, regions, layers, and / or sections. These elements, components, regions, layers, and / or sections should not be limited by these terms. These terms are used only to distinguish one element, component, region, layer, or section from another. Unless the context explicitly indicates otherwise, terms such as “first,” “second,” and other numerical terms do not imply order or sequence. Therefore, the first element, component, region, layer, or section discussed below may be referred to as the second element, component, region, layer, or section without departing from the teachings of the example configuration.

[0065] In this application, the term "module" is replaced by the term "circuit" as defined below. The term "module" may refer to, be part of, or include the following: application-specific integrated circuit (ASIC); digital, analog, or mixed-signal analog / digital discrete circuit; digital, analog, or mixed-signal analog / digital integrated circuit; combinational logic circuit; field-programmable gate array (FPGA); processor (shared, dedicated, or grouped) that executes code; memory (shared, dedicated, or grouped) that stores code executed by the processor; other suitable hardware components that provide the aforementioned functionality; or combinations of some or all of the foregoing, such as in a system-on-a-chip.

[0066] The term "code" as used above can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, and / or objects. The term "shared processor" covers a single processor that executes some or all of the code from multiple modules. The term "group processor" covers a processor that, in combination with additional processors, executes some or all of the code from one or more modules. The term "shared memory" covers a single memory that stores some or all of the code from multiple modules. The term "group memory" covers memory that, in combination with additional memory, stores some or all of the code from one or more modules. The term "memory" can be a subset of the term "computer-readable medium." The term "computer-readable medium" does not include transient electrical and electromagnetic signals propagating through a medium, and therefore can be considered tangible and non-transient memory. Non-limiting examples of non-transient memory include tangible computer-readable media, including non-volatile memory, magnetic storage devices, and optical storage devices.

[0067] 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 programs include processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer programs may also include and / or depend on stored data.

[0068] A software application (i.e., a software resource) can refer to computer software that instructs a computing device to perform a task. In some examples, a software application may be referred to as an "application," "app," or "program." 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 game applications.

[0069] Non-transient memory can be a physical device used for temporary or permanent storage of programs (e.g., instruction sequences) or data (e.g., program state information) for use by a computing device. Non-transient 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) / electronically erasable programmable read-only memory (EEPROM) (e.g., commonly used in 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 magnetic disks or magnetic tapes.

[0070] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using 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, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, 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.

[0071] Various implementations of the systems and techniques described herein can be implemented 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 that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be dedicated 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 transmit data and instructions to the storage system, at least one input device, and at least one output device.

[0072] The processes and logic described in this specification can be executed by one or more programmable processors (also known as data processing hardware) that execute one or more computer programs to perform functions by manipulating input data and generating output. The processes and logic can also be executed by special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits). Processors suitable for executing computer programs include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any kind of digital computer. Typically, the processor receives instructions and data from read-only memory or 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 mass storage devices for storing data, or operably coupled to receive data from or transfer data to, or both, such as magnetic disks, magneto-optical disks, or optical disks. However, a computer does not necessarily need to have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile 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-ROMs and DVD-ROMs. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0073] To provide interaction with a user, one or more aspects of this disclosure can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube), LCD (liquid crystal display) monitor, or touchscreen) for displaying information to the user and optionally having a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual, auditory, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. Furthermore, the computer can interact with the user by sending documents to and receiving documents from the device used by the user; for example, by sending web pages to a web browser on the user's client device in response to a request received from a web browser.

[0074] refer to Figure 1-3The driver position monitoring system 10 is configured as part of vehicle 100 and a backend server 200. For example, the driver position monitoring system 10 includes a controller 12 configured with a driver positioning algorithm 14, which is communicatively coupled to the backend server 200. The controller 12 and the backend server 200 may be communicatively coupled via a network 300. The network 300 may include any wireless communication and / or direct server communication between the controller 12 and the backend server 200. The backend server 200 is configured to collect crowdsourced data 202 from multiple sensor data 102 from one or more vehicles 100 equipped with the driver position monitoring system 10. During operation of the driver position monitoring system 10, the backend server 200 utilizes the crowdsourced data 202 to assess trends and / or specific driving patterns associated with vehicle 100 based on the sensor data 102.

[0075] Sensor data 102 is captured by a plurality of sensors 104 disposed inside and around the vehicle 100. Sensors 104 may include, but are not limited to, driver monitoring sensors such as cameras, pressure sensors, lane detection sensors, blind spot monitoring sensors, LiDAR sensors, radar sensors, and other sensors utilized by various driver monitoring systems of the vehicle 100, including the driver position monitoring system 10. At least some of the sensors 104 are configured to monitor the operator or driver 110 of the vehicle 100. The sensor data 102 captured by the sensors 104 may be directly correlated with the driver 110 (i.e., images captured by the camera sensors 104) and / or may be indirectly correlated with the driver 110 (i.e., blind spot monitoring data and / or lane detection data). The driver position monitoring system 10 utilizes the sensor data 102 to determine the conditional performance 112 of the driver 110 when executing the driver positioning algorithm 14, as described in more detail below.

[0076] Further reference Figure 1-3 The driver positioning algorithm 14 is executed by the data processing hardware 16 of the controller 12. The controller 12 also includes memory hardware 18 that communicates with the data processing hardware 16. The memory hardware 18 stores instructions that, when executed on the data processing hardware 16, cause the data processing hardware 16 to perform the operations described herein. The memory hardware 18 may also store a driver profile 20 associated with the driver 110. The driver profile 20 may be initially configured or established by the driver 110 and may include various driver attributes 22. Driver attributes 22 may include, but are not limited to, height 22a, weight 22b, gender 22c, age 22d, seat position 22e, and other physical characteristics that can inform the driver 110 of the driving position 24.

[0077] Driver localization algorithm 14 can utilize driver attribute 22 to establish a baseline for how driver 110 might be located within vehicle 100. For example, a driver 110 with a shorter height 22a might be located differently within vehicle 100 compared to a driver 110 with a larger height 22a. Differences in driver attribute 22 can inform driver localization algorithm 14 when determining or otherwise identifying conditional performance 112 associated with driver 110. Driver profile 20 can also be updated based on learned driver localization 26. Driver localization algorithm 14 can periodically evaluate the learned driver localization 26 based on received sensor data 102. Driver localization algorithm 14 can periodically update driver profile 20 with the learned driver localization 26. Driver localization algorithm 14 can also transmit the learned driver localization 26 to backend server 200 as part of crowdsourced data 202 collected by backend server 200.

[0078] Driver localization algorithm 14 is configured to identify a driver profile 20 associated with driver 110 of vehicle 100 and receive sensor data 102 from sensor 104. In some cases, driver localization algorithm 14 may identify driver profile 20 based on the last used driver profile 20, thereby identifying driver profile 20 before receiving sensor data 102. In other cases, driver localization algorithm 14 may identify driver profile 20 after receiving and analyzing sensor data 102. In either configuration, driver profile 20 is identified first before driver localization algorithm 14 evaluates or otherwise determines the conditional performance 112 of driver 110.

[0079] Still referencing Figure 1-3 The driver positioning algorithm 14 uses received sensor data 102 to measure the conditional performance 112 of the driver 110. As described above, the sensor data 102 may correspond to blind spot sensors, automatic braking, cross-traffic sensors, and other driver monitoring sensors 104. The driver positioning algorithm 14 can determine when one or more sensors 102 are triggered, and also analyzes sensor data 102 from the in-cabin camera 104. The driver positioning algorithm 14 can determine that the conditional performance 110 is related to the position, posture, or other environmental factors resulting from the driver 110's selected seating angle, which may affect the operability of the vehicle 100.

[0080] For example, conditional representation 112 could be the inherent positioning of driver 110 within vehicle 100 and / or an optional seating preference or position selection made by driver 110 during operation of vehicle 100. Driver positioning algorithm 14 can be configured to distinguish driver position 24. Driver position 24 can include attribute positioning 30 and optional positioning 32 based on driver profile 20 and sensor data 102. Attribute positioning 30 is typically associated with driver attribute 22, which is related to the physical aspects of driver 110, such that driver 110 cannot opt ​​out of positioning due to driver attribute 22. Optional positioning 32 is typically associated with a selected or chosen position actively positioned by driver 110.

[0081] In some cases, conditional performance 112 may be related to the driver 110 maintaining a certain position for an extended period. For example, the driver 110 may experience fatigue due to the driver 110 maintaining a fixed position. Driver fatigue due to positional constancy is different from driver fatigue due to drowsiness or other physiological factors. Fatigue associated with conditional performance 112 is caused by maintaining a specific position for an extended period, rather than by physiological factors such as drowsiness. For example, the driver positioning algorithm 14 may be configured to detect the maintained position 34 within a predetermined time frame 36 based on sensor data 102.

[0082] Driver positioning algorithm 14 is configured to determine conditional performance 112 and provide suggestion 40 to driver 110. Suggestion 40 is based on conditional performance 112 determined by driver positioning algorithm 14 and is configured to provide assistance to driver 110 for repositioning, thereby improving the operational performance of driver 110 in vehicle 100. Driver positioning algorithm 14 continuously monitors sensor data 102 provided by sensor 104 to determine conditional performance 112. In some cases, driver 110 may operate vehicle 100 without conditional performance 112 and change position at a later point in time, thereby detecting and determining conditional performance 112.

[0083] Further reference Figure 1-3 The driver positioning algorithm 14 can be executed on the controller 12 of the vehicle 100, and the processing of the driver positioning algorithm 14 can be performed at the backend server 200. Sensor data 102 is continuously uploaded to the backend server 200 for analysis. The backend server 200 can also receive sensor data 102 from other vehicles 100 equipped with the driver position monitoring system 10, so that the backend server 200 can use crowdsourcing technology to identify various conditional behaviors 112 of the driver 110 during operation.

[0084] The driver localization algorithm 14 is configured to measure conditional performance 112 using various driving traits 114 detected by sensor 104 in combination with collected sensor data 102. Driving traits 114 may include, but are not limited to, braking techniques, cornering techniques, aggressive lane changing, ignoring blind spots, and other traits that may affect driver performance. The driver localization algorithm 14 analyzes the sensor data 102 at a background server 200 to generate recommendations 40 based on conditional performance 112.

[0085] Condition 112 can be a result of the driver 110's position within the vehicle 100. For example, the driver 110 may be resting or repositioning themselves while operating the vehicle 100, leaning against the center console. Sensor 104 can detect movement of the driver 110's position, which is transmitted as sensor data 102 to the driver positioning algorithm 14. In other examples, the driver 110 may be seated at a distance from the steering wheel of the vehicle 100, potentially obstructing the driver's view. In response, the backend server 200 and / or controller 12 analyze the sensor data 102, and the driver positioning algorithm 14 generates a suggestion 40.

[0086] Suggestion 40 may include various prompts 44 to assist driver 110 in repositioning, thereby minimizing conditional performance 112. For example, driver positioning algorithm 14 may issue a prompt 44 including position adjustment 44a. Position adjustment 44a is configured to reduce conditional performance 112 by providing driver 110 with a modified position. Driver positioning algorithm 14 waits for input 116 from driver 110 before initiating position adjustment 44a. In some cases, driver 110 may ignore prompt 44. In other cases, driver positioning algorithm 14 may wait until vehicle 100 is stationary or otherwise in a state where it is safe for driver 110 to move position before issuing prompt 44.

[0087] The driver positioning algorithm 14 may also be configured with a scoring model 50 for associating different conditional performances 112. The scoring model 50 is executed by the backend server 200 and is used to compare the conditional performances 112 among the vehicles 100 equipped with the driver position monitoring system 10. The backend server 200 may utilize the scoring model 50 to identify unique differences in restrictions that may arise between various vehicles 100 (i.e., brands and models) due to different seating arrangements. The scoring model 50 may be configured as a weighted algorithm that assigns a rating 52 to the driver position 24. The rating 52 may also be configured with weights to differentiate the driver position 24 within the driver positioning algorithm 14.

[0088] For example, driver location algorithm 14 can analyze, via backend server 200, how close the driver 110 is to the center, how far from the steering wheel, and how far the driver 110's feet are from the pedals. Backend server 200 uses rating model 50 to evaluate the different driver profiles 20 presented and assesses the number and severity (i.e., rating 52) of various conditional behaviors 112. Backend server 200 transmits the rating 52 of conditional behaviors 112 via driver location algorithm 14, and driver location algorithm 14 executes recommendation 40 based on the rating 52.

[0089] If rating 52 indicates that driver position 24 is unsafe or otherwise results in potentially unsafe conditions 112, driver positioning algorithm 14 may execute suggestion 40 after generating suggestion 40 for driver 110. If rating 52 is low or otherwise indicates that driver position 24 can be improved but is safe, driver positioning algorithm 14 may generate prompt 42 and wait to receive input 116 from driver 110. In response to input 116, driver positioning algorithm 14 executes position change implementation 60. Position change implementation 60 includes performing one or more position adjustments 44a. For example, position change implementation 60 may include one or more of seat adjustment 60a, steering wheel adjustment 60b, and lumbar adjustment 60c.

[0090] Driver positioning algorithm 14 can provide driver 110 with instructions corresponding to location change implementation 60. For example, driver positioning algorithm 14 can provide instructions on user interface 106 of vehicle 100 and / or via audio through speaker system 108 of vehicle 100. In other cases, driver positioning algorithm 14 can automatically deploy location change implementation 60 in response to input 116. If driver 110 rejects suggestion 40, driver positioning algorithm 14 can store suggestion 40 in memory hardware 18 for future use. Even after driver positioning algorithm 14 has executed location change implementation 60, in response to input 116, driver positioning algorithm 14 continues to monitor sensor data 102, and background server 200 continues to execute scoring model 50 to determine whether location change implementation 60 has resulted in a positive change in the operation of vehicle 100. Background server 200 uses updated sensor data 102 to improve scoring model 50, so that scoring model 50 can effectively learn from sensor data 102 and adjustments made by driver positioning algorithm 14.

[0091] refer to Figure 4The diagram illustrates an exemplary operational flowchart of the driver position monitoring system 10. At 400, the driver positioning algorithm 14 identifies that a driver 110 with a known driver profile 20 is operating the vehicle 100. At 402, the driver positioning algorithm 14 determines conditional performance 112. At 404, the backend server 200 combines sensor data 102 with current condition 204. Current condition 204 may include, but is not limited to, time of day, vehicle type, location type, and total driving time. At 406, the driver positioning algorithm 14 continuously uploads sensor data 102 to the backend server 200. At 408, the backend server 200 executes a scoring model 50 to correlate different conditional performances 112. The driver position monitoring system 10 can utilize the correlation of different conditional performances 112 to identify blind spots and other physical limitations associated with the driver's position 24.

[0092] At 410, driver localization algorithm 14 identifies driver position 24 and associated conditional behavior 112. At 412, driver localization algorithm 14 determines whether driver 110 has accepted suggestion 40. If driver 110 has not accepted suggestion 40, driver localization algorithm 14 may store suggestion 40 at 414. If driver 110 has accepted suggestion 40, driver localization algorithm 14 determines location adjustment 44a at 416. Driver localization algorithm 14 may prompt driver 110 again at 418 to determine whether driver 110 is ready to deploy location change implementation 60. If not, driver localization algorithm 14 waits for input 116 from driver 110. If input 116 is received, driver localization algorithm 14 executes location change implementation 60 at 420. Driver localization algorithm 14 continues to monitor driver position 24 and any potential resulting conditional behavior 112 at 422.

[0093] Now for reference Figure 5 The diagram illustrates an operation method 500 of the driver position monitoring system 10. At 502, the driver positioning algorithm 14 identifies a driver profile 20 associated with the driver 110 of the vehicle 100, and at 504 receives sensor data 102 from multiple sensors 104 within the vehicle 100. At 506, the driver positioning algorithm 14 transmits the sensor data 102 to a backend server 200. The sensor data 102 includes driver position data 102a. At 508, the backend server 200 analyzes the sensor data 102 and at 510 determines the conditional performance 112 of the driver 110 based on the sensor data 102. The conditional performance 112 corresponds to the driver 110's position. At 512, the driver positioning algorithm 14 generates a suggestion 40 based on the determined conditional performance 112 of the driver 110. At 514, the driver positioning algorithm 14 monitors the conditional performance 112 of the driver 110.

[0094] Several embodiments have been described. However, it should be understood that various modifications can be made without departing from the spirit and scope of this disclosure. Therefore, other embodiments are within the scope of the following claims.

[0095] The foregoing description is provided for illustrative purposes only. It is not intended to be exhaustive or limiting of this disclosure. Individual elements or features of a particular configuration are generally not limited to that particular configuration, but are (where applicable) interchangeable and can be used in selected configurations, even if they are not specifically shown or described. These elements or features can also be varied in many ways. Such variations should not be considered a departure from this disclosure, and all such modifications are intended to be included within the scope of this disclosure.

Claims

1. A computer-implemented method, when executed by data processing hardware, causes the data processing hardware to perform operations including the following: Driver profiles related to the vehicle's driver are identified through a driver location algorithm; Sensor data from multiple sensors within the vehicle is received at the driver positioning algorithm. The driver's conditional performance is determined based on the sensor data, and the conditional performance corresponds to the driver's position; Based on the determined conditional performance of the driver, suggestions are generated using the driver localization algorithm. as well as The driver's conditional performance is monitored via the driver positioning algorithm.

2. The method of claim 1, wherein generating the suggestion includes issuing a prompt including a location adjustment.

3. The method according to claim 2, further comprising: In response to the issued prompt, input is received at the driver positioning algorithm; And the location change is implemented via the driver positioning algorithm.

4. The method of claim 3, wherein performing the location change implementation includes automatically performing the location change implementation.

5. The method according to claim 4, wherein, The position change implementation includes one or more of the following: seat adjustment, steering wheel adjustment, and lumbar adjustment.

6. The method according to claim 1, further comprising: Crowdsourced data corresponding to the conditional performance of multiple drivers is collected on the backend server.

7. The method according to claim 1, further comprising: The sensor data, including driver location data, is transmitted to the backend server via the driver positioning algorithm.

8. The method of claim 7, wherein determining the conditional performance includes analyzing the sensor data at the backend server.

9. A driver position monitoring system, comprising: Data processing hardware; as well as Memory hardware communicating with the data processing hardware, the memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including the following: Driver profiles related to the vehicle's driver are identified through a driver location algorithm; Sensor data from multiple sensors within the vehicle is received at the driver positioning algorithm. The driver's conditional performance is determined based on the sensor data, and the conditional performance corresponds to the driver's position; Based on the determined conditional performance of the driver, suggestions are generated using the driver localization algorithm. as well as The driver's conditional performance is monitored via the driver positioning algorithm.

10. The driver position monitoring system of claim 9, wherein generating a suggestion includes issuing a prompt that includes a position adjustment.

Images