Predictive control systems for vehicles

Abstract

An apparatus, system (100), and method for mitigating adverse effects on an occupant based on predicted motion are provided. Prediction of the motion of a vehicle (105) may be accomplished by detecting the operation of one or more speed control mechanisms, and / or using various types of sensors (110, 120, 130, 140). One or more processors (150) may be used to derive the predicted motion and to send an actuation signal to an actuator (160A, 160B, 160C) to alter the operation of the vehicle (105) and / or the operation of a seat or other controlled device (170) within the vehicle that interacts with an occupant.

Description

Technical Field

[0001] The field of application is automotive and other vehicle control. Background Art

[0002] The following description includes information that is helpful in understanding the present application.

[0003] Whether traveling on a private vehicle or public transportation, the occupants of these vehicles (e.g., drivers, pilots, riders, passengers, pets, cargo or goods, etc.) are sometimes subject to adverse transportation conditions caused by unexpected or sudden movements of the vehicle. Vehicles herein may include various vehicle types (e.g., motor vehicles, trucks, cars, motorcycles, bicycles, ships, cruise ships, airplanes, trains, subways) or any other means of transportation. Unexpected movements include sudden acceleration and deceleration, bumps, turns, vibrations, etc., which have adverse effects on these types of vehicles. The adverse effects on the occupants of the vehicle can range from mild annoyance to severe physical or psychological discomfort.

[0004] Existing systems provide solutions by adjusting for unwanted vehicle movement. For example, it is known that automobiles use suspension components (e.g., shock absorbers including various springs or hydraulic pistons) to reduce bumps and vibrations. However, any given suspension component may not be suitable for all adverse conditions. For example, in the event that excessive suspension movement occurs due to extremely rough road conditions, the results of applying shock absorbers to damp spring vibrations may be limited by the mechanical structure of the suspension components. Some vehicles have adjustable suspensions that can be actuated by the occupants, and some vehicles have automatically actuated adjustable suspensions, for example, when the vehicle turns, the adjustable suspension can be automatically actuated. However, manually or automatically actuated adjustable suspension systems have similar problems as described above caused by the structural limitations of the suspension components and are therefore not suitable for absorbing and dissipating all excess energy from the springs under all adverse conditions.

[0005] One problem that remains unsolved in known systems is that the systems are designed to be reactive rather than predictive. More specifically, both adjustable and non-adjustable suspension systems are designed to react after an unexpected motion has taken effect on the vehicle, for example, absorbing excess energy from the springs to dampen spring oscillations after a jolt or severe vibration. This problem is particularly acute when the execution of the required suspension changes for the vehicle requires a reaction time of several seconds or more. Continuing with the example above, a car may be traveling over several meters of rough terrain, and by the time the suspension change is completed, the car has already passed the rough terrain. Similarly, if the driver presses hard on the accelerator pedal, there is typically a delay of several seconds before significant acceleration can occur. If the vehicle waits for acceleration to occur before adjusting the suspension, it may be too late to properly account for the driver in the vehicle seat.

[0006] Therefore, there is still a need for devices, systems and methods for predicting the movement of a vehicle in advance, so as to actively change the carrying environment of the occupants of the vehicle to reduce or compensate for the adverse effects on the occupants caused by the predicted movement. Summary of the invention

[0007] The subject matter of the present application provides apparatus, systems, and methods in which the movement of a vehicle is predicted and used to proactively change the occupant's transportation environment to mitigate adverse effects on the occupant caused by the predicted movement.

[0008] The examples of the present application provide predictions of vehicle motion and active changes to the carrying environment to improve the riding experience of vehicle occupants. The prediction of vehicle motion can be completed in any suitable manner, including detection operations through a speed control mechanism. In various embodiments of the present application, the speed control mechanism may include a mechanical control mechanism and / or an electrical control mechanism. For example, the mechanical control mechanism may be associated with the operation of an accelerator pedal, a brake pedal, and / or a steering wheel. The electrical control mechanism may be associated with a drive-by-wire (i.e., drive-by-wire, steering-by-wire, fly-by-wire, or other control-by-wire technologies) system that utilizes an electrical or electromechanical system to perform vehicle functions that are typically operated by a mechanical linkage. In various embodiments, the operation of the accelerator pedal, brake, steering wheel, and / or wheel drive system (or other speed control mechanism) generates a signal for predicting the future motion of the vehicle. Based on the prediction, the system may use an actuator to actively change the position or direction of the passenger seat to mitigate possible adverse effects on the occupants.

[0009] It is conceivable that the speed control mechanism can be operated from inside the vehicle, outside the vehicle, or a combination of the two. Inputs from one or more sensors such as active sensors or passive sensors (i.e., sensors that are not actively operated by the occupants or other media) can also be used to obtain predictions of the movement of the vehicle. In various embodiments of the present application, example sensors include pressure sensors, motion sensors (e.g., inertial measurement units (IMUs)), light detection and ranging (LIDAR) sensors, light sensors (infrared sensors, ultraviolet sensors, laser sensors, etc.), cameras, position sensors (e.g., global positioning system (GPS) receivers), sound sensors (ultrasonic sensors, etc.), etc. Some of these sensors can be configured to predict the movement of the vehicle based on changes in upcoming road conditions, changes in upcoming traffic, or changes in surrounding weather. Based on such predictions, the carrying environment of the vehicle occupants can be changed, for example, by engaging four-wheel drive (4WD) or other anti-skid technology, or by slowing down the speed of the vehicle.

[0010] Any one or more of the above sensors may be located inside or on the vehicle (hereinafter referred to as onboard sensors), or may be located away from the vehicle (hereinafter referred to as off-board sensors).

[0011] In various embodiments of the present application, a processor is used to derive the predicted motion from one or more sensors, and an actuation signal is sent to one or more actuators. In some embodiments, the processor may be configured to generate an actuation signal based at least in part on a past actuation signal, which is obtained from historical experience corresponding to a historical motion similar to the currently predicted motion. In other embodiments, an actuation signal may also be generated by considering the characteristics of the vehicle occupant. In such an embodiment, the characteristics of the occupant may include one or more information associated with the occupant's weight, height, spinal curvature, flexibility, body temperature, seat height, shoulder width, hip circumference, waist circumference, chest circumference, age, gender, physical disability, health status, or mental condition. For example, when a rough road is expected, the configuration of the impact of the vehicle can be appropriately changed to one degree for a light occupant, and the configuration of the impact can be changed to another degree for a heavier occupant.

[0012] In various embodiments of the present application, exemplary changes to the transport environment include seat, steering wheel or other physical adjustments, music or other auditory adjustments, and / or display or other visual adjustments. For example, seat adjustment in the present application may include adjusting at least one of the distance, direction, layout, inclination, height, temperature, or stiffness of any one or more components of the seat. Other exemplary changes to the transport environment include changing at least one of the spatial position, seat restraints or tension of seat belts, or the suspension system of the vehicle.

[0013] All modes of vehicles can be considered, whether the vehicle is used for land, sea, air or a combination of these modes. In various embodiments of the present application, exemplary vehicles include passenger-driven or autonomous vehicles, drones or other non-passenger-driven vehicles. Exemplary vehicles also include vehicles for specific purposes, including vehicles for private, public, government or military use.

[0014] Various embodiments of the present application solve the significant technical problem of how to proactively adapt to vehicle motion that may have undesirable effects on occupants. A processor connected to various sensors and / or control units is provided to predict potential adverse motions. A processor is also provided to predictively adjust the occupant's seat, other aspects of the vehicle, or the carrying environment to mitigate adverse effects that may occur based on the predicted motion.

[0015] Various objects, features, aspects, and advantages of the subject matter of the present application will become more apparent from the following detailed description of preferred embodiments and the accompanying drawings, in which like reference numerals represent like components. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an exemplary schematic diagram of a predictive control system for a vehicle that is configured to proactively mitigate or compensate for adverse effects on occupants based on predicted movement of the vehicle.

[0017] Figure 2 is a diagram identifying various exemplary onboard speed control sensors and offboard speed control sensors.

[0018] Figure 3 is a diagram identifying various exemplary onboard occupant sensors and offboard occupant sensors.

[0019] Figure 4 is a diagram identifying various exemplary onboard environmental sensors and offboard environmental sensors.

[0020] Figure 5 is a diagram identifying various exemplary vehicle actuators, seat actuators, and environmental actuators.

[0021] Figure 6 A flowchart exemplarily illustrates steps of a method for implementing an embodiment of the present disclosure.

[0022] Figure 7 is an exemplary schematic diagram of a predictive control system for a vehicle, the system including various modules configured to change the vehicle environment based on predicting the movement of the vehicle. DETAILED DESCRIPTION

[0023] In the following discussion, there will be extensive reference to processors, servers, services, interfaces, portals, platforms, or other systems formed by computing devices. It should be understood that the use of these terms is considered to represent one or more computing devices having at least one processor configured to execute software instructions stored on a non-transitory computer-readable tangible medium. For example, as used herein, a "processor" may include any cloud computing service, one or more separate computers, and / or one or more processing chips or processing cores that implement the roles, responsibilities, or functions described herein in any operational manner.

[0024] In certain embodiments, the numerals for describing and claiming the expression component quantity, property (for example, concentration), reaction conditions, etc. of some embodiments of the present application should be understood to be modified by the term "about" in some cases.Therefore, in certain embodiments, the numerical parameter set forth in the written description and the appended claims is an approximate value, and this numerical parameter can change according to the desired characteristic that a particular embodiment attempts to obtain.In certain embodiments, the numerical parameter should be explained according to the number of significant figures reported and by applying common rounding techniques.Although the wide numerical range and parameter of some embodiments of the present application are listed to be approximate values, the numerical value listed in the specific examples is as accurate as possible.The numerical value presented in some embodiments of the present application may include certain errors, and these errors are necessarily caused by the standard deviation found in their respective test measurements.

[0025] As used in the specification and throughout the claims herein, the meaning of "a", "an", and "the" includes plural forms unless the context clearly indicates otherwise. Similarly, as used in the specification herein, the meaning of "in" includes "in" and "on", unless the context clearly indicates otherwise.

[0026] The enumeration of numerical ranges herein is only intended to refer to the shorthand method of each individual numerical value in the range respectively. Each individual value is incorporated into the specification, as if separately enumerated herein, unless otherwise noted herein. All methods described herein can be performed in any suitable order, unless otherwise noted herein or clearly contradictory to the context. The use of any and all examples or exemplary language (e.g., "such as") provided relative to some embodiments of this invention is only intended to better illustrate the application, without limiting the scope of the application otherwise claimed. Any language in the specification should not be interpreted as representing any unclaimed element indispensable for implementing the application.

[0027] The grouping of the alternative elements or embodiments of the present application disclosed herein should not be construed as limiting. The member of each group can be quoted separately, or can be used in combination with other members of the group or other elements herein. For convenience and / or patentability reasons, one or more members in a group can be included in the group or deleted from the group. When any such inclusion or deletion occurs, the specification is considered to include the modified group in this article, so as to meet the written description of all Markush groups used in the appended claims.

[0028] The following discussion provides many example embodiments of the subject matter of the present application. Although each embodiment represents a single combination of inventive elements, the subject matter of the present application is considered to include all possible combinations of the disclosed elements. Therefore, if one embodiment includes elements A, B, and C, and a second embodiment includes elements B and D, then even if not explicitly stated, the subject matter of the present application is also considered to include other remaining combinations of A, B, C, or D.

[0029] As used herein, the term "connected to" is intended to include direct connections (where two elements connected to each other are in contact with each other) and indirect connections (where at least one additional element is located between the two elements connected to each other), unless the context indicates otherwise. Therefore, the terms "connected to" and "connected to" are used synonymously.

[0030] Figure 1 is an exemplary schematic diagram of a predictive control system 100 for a vehicle 105 that is configured to proactively mitigate or compensate for adverse effects on occupants based on predicted movement of the vehicle 105.

[0031] exist Figure 1 In the present invention, the predictive control system 100 generally includes various onboard sensors 110, 120 and / or 130 and various offboard sensors 140 (e.g., offboard speed control sensors, offboard occupant sensors, and / or offboard environmental sensors) that provide information to a processor 150. The processor 150 then uses the provided information to operate various actuators 160A, 160B, 160C to control controlled devices 170 that affect the occupants. Of particular importance in this application is that the processor 150 is programmed to use the information obtained by the sensors 110, 120, 130 and / or 140 to predict acceleration, jerk, bump or other motion of the vehicle 105, and use the predicted motion to perform control to at least partially mitigate or compensate for the adverse effects of the predicted motion on the occupants.

[0032] The dashed lines schematically indicate that some of these components (e.g., sensors 110, 120, 130) are onboard sensors, i.e., sensors that are physically located at least partially within or on the vehicle 105. Other components (e.g., sensor 140) are offboard sensors and are only informationally connected to the vehicle 105. The offboard sensors 140 may include offboard speed control sensors, offboard occupant sensors, or offboard environmental sensors. In an embodiment, a sensor that detects the operation of an accelerator pedal or a brake pedal performed by an occupant in a fuel-powered car belongs to the category of onboard speed control sensor 110, while a sensor that detects the operation of a vehicle joystick performed by a remote pilot belongs to the category of offboard sensor 140.

[0033] As a schematic diagram, Figure 1 The scope includes any type of vehicle having a seat, bed or other component that is configured to keep the occupant in a sitting position, a lying position or other position that can be actively changed under the control of the processor 150. Examples of vehicle 105 include cars, buses, trucks, trains, cable cars, carts, amusement rides for people, ships, boats and / or airplanes. Vehicle 105 should also be broadly interpreted as including a vehicle driven by an occupant, a vehicle operated by an external control person and / or an autonomous vehicle. For example, one embodiment of vehicle 105 is an unmanned aircraft with a passenger seat or a cot for evacuating soldiers from a war zone. Another example of vehicle 105 is an autonomous truck with a driver's seat for transporting goods from one city to another, and the driver can operate the truck in difficult traffic conditions. Yet another example of vehicle 105 is an autonomous car or an autonomous bus for transporting passengers in seats without requiring any passengers to drive the vehicle.

[0034] As used herein, the term "occupant" preferably refers to a person, driver, pilot, rider or passenger, but may also refer to an indoor pet (e.g., a dog or cat), a farm animal (e.g., a pig, horse or cow), other animals, cargo or merchandise. For example, the occupant may be a driver operating a vehicle or a passenger occupying a seat of a vehicle. In such an example, the proposed active seat control system can predict the movement of the vehicle to actively adjust the carrying environment (e.g., adjust the direction, inclination, tilt angle or stiffness of the seat) to mitigate, eliminate, offset or compensate for the adverse effects that may occur due to the predicted movement. For another example, the occupant may be an animal or pet to be transported to a farm or zoo. In order to transport the animals or pets, these animals or pets may occupy adjustable seats or removable trays / cages of trucks, trains or airplanes. In such an example, the proposed active seat control system can predict the future movement of the truck to actively change the truck environment (e.g., adjust the position, direction or inclination of the adjustable seat or movable tray / cage) to keep the animal or pet in a stable position and prevent the bad mood (e.g., anger, anxiety, fear, etc.) or discomfort of the animal or pet that may occur due to the bad carrying environment caused by the upcoming bad transportation conditions. For another example, the occupant may be fragile goods to be transported from merchants / suppliers to consumers / customers, occupying the seat of the carrier or delivery truck for cargo transportation. In such an example, the proposed active seat control system can predict the future movement of the truck based on the detected upcoming road conditions, delivery routes or map data, etc. The predicted future movement can be used to actively change the carrying environment (e.g., tighten the cargo rope) to prevent the slamming movement or vibration caused by the upcoming bad road conditions from causing damage to the fragile goods. For another example, the occupant may be a product to be transported from one location to another in an indoor environment (e.g., a warehouse or retailer) to be stored separately based on different categories of products. The product can occupy the storage structure of the autonomous driving carrier to send different categories of products to different designated areas within the warehouse. In such an example, the proposed active seat control system can predict the future movement of the autonomous vehicle based on information associated with the indoor layout of the warehouse or the dispatch routes used to sort different products, so as to prevent undesirable situations (e.g., product damage or falling from the autonomous vehicle). References to "one" or "the" occupant in this article should be understood to take into account that the vehicle may or may not have other occupants.

[0035] Figure 2 Various exemplary onboard speed control sensors 110 and offboard speed control sensors 140 are shown.

[0036] like Figure 2As shown, the onboard speed control sensor 110 should be interpreted herein as representing any device at least partially located in or on the vehicle for detecting operations related to the speed control of the vehicle. In some embodiments, the onboard speed control sensor 110 may include, for example, a sensor 112 that detects operations associated with a gas pedal, an accelerator pedal, other accelerator pedals 112A, or other acceleration mechanisms 112B. In such an embodiment, the processor 150 may receive a control signal generated based on the operation detected by the sensor 112 (e.g., the accelerator pedal) and predict the acceleration movement of the vehicle.

[0037] In an embodiment, the calculation / determination of the prediction may use not only the currently received control signal obtained from the sensor detection, but also the historical control signal obtained by the processor 150 from the memory 155, in which the historically detected sensor data and the historical control signal are stored. Specifically, the historical control signal may be associated with an occupant profile such as a driver profile. In such an example, the driver profile may record historical control signals associated with the driver's driving habits, commuting routes and times, posture or seating habits, etc. By using signal mixing or fusion techniques (e.g., a sensor fusion process), the currently detected control signal and the historical control signal may be used jointly or mixed to provide a more accurate prediction result.

[0038] For example, the historical control signal may include information about the driver's driving habits, such as the average rotation angle that the driver may use when steering. Based on the average rotation angle, the proposed active seat control system can provide more accurate predictions by calculating the corresponding rotation angle of the seat when a steering movement may occur, offsetting / compensating the average rotation angle predicted based on the driver's driving habits.

[0039] For another example, the historical control signal may also include information about the driver's commuting route and time. The historical control signal may include information about the commuting route, including the commuting route from the driver's residence to the workplace. The historical control signal may also include other traffic information stored in the memory detected from the onboard or off-board speed control sensors 110, 140, such as the location of traffic lights, stop signs, or estimated departure / arrival times associated with the commuting route and the driver. In such an example, the proposed active seat control system can use the information of the commuting route to predict where the vehicle may go straight, turn left or turn right, and use other information to determine when the vehicle may accelerate and decelerate to provide a better prediction of the vehicle's movement. For example, predict deceleration when approaching the location of a traffic light or approaching a stop sign on the route. In an embodiment, the historical control signal can also be based on the time of the date (e.g., daytime or night) or the time of the year (e.g., long weekends or holidays), merge additional traffic information. For example, during the day or long weekend, due to heavy traffic, the vehicle may travel slower than at night or on a regular weekend. In such an example, deceleration movement may be predicted due to heavy traffic and multiple stops / pauses.

[0040] For another example, the historical control signal may also include information about the driver's posture or seating habits. The historical control signal may include information such as seat height, tilt angle, or stiffness that records a specific driver or passenger's preferences. In such an example, the proposed active seat control system may be customized to adjust the carrying environment while keeping the driver or passenger in the seat. In an embodiment, the proposed active seat control system may combine the currently detected speed control signal and the historical signal about the driver's or passenger's posture or seating habits to change the height of the seat (e.g., increase the height of the seat when an impending hole is detected, or reduce the height of the seat when an impending bump is detected), direction (e.g., rotate the seat when a curved road or steering motion is detected), stiffness (e.g., reduce the stiffness of the seat when vibration is detected due to uneven roads) or the tilt angle of the seat (e.g., tilt the seat backward to slow down when a red light or stop sign is detected) in order to better predict the movement of the vehicle and make corresponding compensatory adjustments on the seat.

[0041] It should be understood that the prediction of vehicle acceleration can be based on one or more of the currently detected control signals generated by the sensor 112, which detects operations related to the accelerator pedal, the accelerator pedal, other accelerator pedals 112A or other acceleration mechanisms 112B. The prediction of vehicle acceleration can be based on historical control signals associated with similar acceleration movements. Alternatively, the prediction of vehicle acceleration can be based on a combination of the currently detected control signal and the historical control signal. Specifically, the prediction of vehicle acceleration can be calculated based on the currently detected control signal and / or the historical control signal obtained from similar acceleration operations (e.g., accelerator pedals) detected by the sensor 112, using sensor fusion processing to provide a more accurate prediction result. The historical control signal can be stored in the memory 155, and the historical control signal can be retrieved whenever a similar acceleration operation is detected.

[0042] In other embodiments, the onboard speed control sensor 110 may include a sensor 113 for detecting an operation associated with a brake pedal 113A or other braking mechanism 113B. In such an embodiment, the processor 150 can receive a control signal generated based on the operation detected by the sensor 113 (e.g., a brake pedal) and predict the deceleration movement of the vehicle. The prediction of the deceleration of the vehicle can also be calculated based on the currently detected control signal and / or the historical control signal obtained from similar deceleration operations (e.g., a brake pedal) detected by the sensor 113, using sensor fusion processing to provide a more accurate prediction result. The historical control signal can be stored in the memory 155 and can be retrieved each time a similar deceleration operation is detected.

[0043] In other embodiments, the onboard speed control sensor 110 may include a sensor 114 for detecting operations associated with a steering wheel 114A or other steering mechanism 114B. In such an embodiment, the processor 150 may receive a control signal generated based on the operation detected by the sensor 114 (e.g., a steering wheel) and predict the steering movement of the vehicle (e.g., a right turn, a left turn, or a U-turn). The prediction of the steering of the vehicle may also be calculated based on the currently detected control signal and / or the historical control signal obtained from similar steering operations detected by the sensor 114 (e.g., a steering wheel) by using sensor fusion processing to provide a more accurate prediction result. The historical control signal may be stored in the memory 155 and the historical control signal may be retrieved each time a similar steering operation is detected.

[0044] In other embodiments, the onboard speed control sensor 110 may include a sensor 115 that detects an operation associated with an IMU 115A. In such an embodiment, the processor 150 may receive a control signal generated based on the operation detected by the sensor 115 (e.g., an IMU) and predict a three-dimensional (3D) dynamic motion (e.g., a vibration motion, a rotational motion, a tilt motion, etc.) of the vehicle. The prediction of the 3D dynamic motion of the vehicle may also be calculated using sensor fusion processing based on the currently detected control signal and / or historical control signal obtained from similar 3D dynamic operations detected by the sensor 115 (e.g., an IMU). The historical control signal may be stored in the memory 155 and the historical control signal may be retrieved whenever a similar 3D dynamic operation is detected.

[0045] In various embodiments of the present application, the processor 150 may also receive additional control signals from the off-board sensor 140. The processor 150 may use control signals received from on-board sensors, off-board sensors, or a combination of the two to predict the future movement of the vehicle. An embodiment of an example control signal received from the off-board speed control sensor 140 may include a signal related to map data, which is received from an off-board sensor connected to a remote map database, and various map data is stored in the remote map database. An embodiment of an example control signal received from the off-board speed control sensor 140 may also include a GPS signal received from the off-board sensor, which is connected to a remote GPS database, and various latitude and longitude coordinates are stored in the remote GPS database. An embodiment of an example control signal received from the off-board speed control sensor 140 may also include a control signal received from a remote device (e.g., a sensor located on a traffic signal pole) or other control signals sent from sensors embedded in other nearby vehicles.

[0046] For example, the processor 150 can receive a signal related to the map data from an off-board sensor connected to a remote map database. In such an example, the received signal can be used to predict the movement of the vehicle based on the relationship between the vehicle and the surrounding area associated with the map. In the case where the vehicle position shows that the vehicle is approaching intersections and junctions according to the map data, the vehicle may slow down due to turning and accelerate at the intersection or junction, so future deceleration, turning or acceleration movement can be predicted. For another example, the processor 150 can receive a signal from an off-board sensor connected to a remote GPS database, and the remote database contains various GPS coordinates. In such an example, the received signal can be used to predict the movement of the vehicle based on the received GPS coordinates associated with the geographic location of the vehicle. In the case where the GPS coordinates around the vehicle GPS coordinates show that the vehicle is approaching an area where the height / altitude increases or decreases, the vehicle may slow down on the uphill section and accelerate on the downhill section, so future deceleration or acceleration can be predicted.

[0047] For another example, the processor 150 may receive a control signal from a remote device embedded in a fixed object (e.g., a traffic light pole or a road sign). In such an example, the received signal may be used to predict the movement of the vehicle based on traffic conditions, the distance between the vehicle and the traffic light pole or road sign, or the distance between the vehicle and other vehicles. In the case where the processor 150 receives a control signal from a distance sensor (e.g., LIDAR or any type of light sensor, camera or any type of image sensor, any type of sound sensor, etc.) located on a traffic light pole or road sign (e.g., a stop sign), the vehicle may slow down and stop at the location of the traffic light pole or road sign (e.g., a stop sign), so when the distance sensor detects that the distance between the vehicle and the traffic light pole or road sign is reduced, future deceleration and stopping movement can be predicted.

[0048] As another example, the processor 150 may receive control signals emitted by sensors embedded in other nearby vehicles. In an embodiment, the processor 150 may receive control signals emitted by sensors embedded in a previous vehicle that is located in front of the current vehicle being operated by the occupant. In such an embodiment, the sensors embedded in the previous vehicle may include various types of distance sensors, including LIDAR sensors or other light sensors (e.g., visible light sensors, infrared sensors, ultraviolet sensors, laser sensors, etc.), ultrasonic sensors or other sound sensors, GPS receivers or other location determination sensors, visual sensors or other image detection sensors (e.g., cameras), and / or any type of distance or proximity sensors, etc. These sensors may, for example, send a control signal to the current vehicle when the distance between the previous vehicle and the current vehicle is less than a threshold. In such an example, in order to avoid colliding with the previous vehicle, the current vehicle may slow down or stop, so the processor 150 can predict the deceleration or stop movement.

[0049] The corresponding off-board speed control sensor 140 can be operated additionally or alternatively by a person, a computer, a computing entity, or other operating entity external to the vehicle. In an embodiment, the processor 150 can receive a control signal from the off-board sensor 140, which is generated based on the operation detected by the off-board sensor 140 by a person, a computer and / or non-human operation. The processor 150 can predict the movement of the vehicle based on the control signal received by the off-board sensor 140. The prediction of the movement of the vehicle can be generated based on the detected control signal and / or a historical control signal, which is obtained by similar operations historically detected by the off-board sensor 140. The historical control signal can be stored in the memory 155, and the historical control signal can be retrieved each time a similar operation detected by the off-board sensor is sent to the processor 150.

[0050] In some embodiments, the above-mentioned onboard speed control sensor 110 or offboard speed control sensor 140 may also include an electrical control mechanism associated with a drive-by-wire (i.e., drive-by-wire, steer-by-wire, fly-by-wire, or other drive-by-wire technologies) system that utilizes an electrical or electromechanical system to perform vehicle functions or movements that are typically operated by mechanical linkages. In an embodiment, the electrical control mechanism may use onboard or offboard electromechanical actuators and a human-machine interface, such as a pedal and steering feel simulator. In such an embodiment, the onboard speed control sensor 110 or offboard speed control sensor 140 may be used to predict future movement of the vehicle by detecting electronic or electromagnetic signals associated with the drive-by-wire system.

[0051] Figure 3is a diagram with various exemplary onboard occupant sensors 120 and offboard occupant sensors 140 identified.

[0052] like Figure 3 As shown, optionally, an onboard occupant sensor 120 may be included in the predictive control system 100 to provide additional control signals to the processor 150. In an embodiment, the onboard occupant sensor 120 may generate additional control signals in response to the detection of occupant characteristics. In an embodiment, example characteristics of the occupant that may be sensed include weight, height, spinal curvature, flexibility, body temperature, seat height, shoulder width, hip circumference, waist circumference, chest circumference, age, gender, physical disability, health condition, mental condition and / or other biological characteristics of the occupant. In an embodiment, the onboard occupant sensor 120 may include: a scale 124, a camera 125, an acoustic sensor 126, a chemical sensor 127 and / or a miscellaneous sensor 128. The scale 124 is operably connected to the seat 122 to measure the weight of the occupant. The camera 125 may measure the size of the occupant. The acoustic sensor 126 may detect the volume of the occupant's voice or audio commands, or identify the occupant's identity through voice recognition technology. The chemical sensor 127 may detect pheromones or other chemicals emitted by the occupant. The miscellaneous sensors 128 may be sensors for detecting stretching or other self-controlled movements of the occupant. Alternatively, the characteristics of the occupant may also be sensed by an off-board occupant sensor located outside the vehicle 105. The off-board occupant sensor may perform similar functions to detect the characteristics of the occupant and provide additional control signals connected to the processor 150.

[0053] In an embodiment, the processor 150 may receive additional control signals generated based on the operations detected by the onboard occupant sensor 120 and / or the offboard occupant sensor 140. The processor 150 may also utilize various actuators (hereinafter referred to as Figure 5 ) provides adjustments to the carrying environment, for example, by using the seat actuator 160B to change the physical aspects of the seat. For example, in the case where the sound sensor 126 recognizes the voice of driver A via voice recognition technology, the processor 150 can receive additional control signals from the onboard occupant sensor 120 and / or the offboard occupant sensor 140 to adjust the height, inclination, or stiffness of the seat of driver A. In an embodiment, in the case where driver A has historically operated the carrying vehicle and adjusted the seat appropriately, information on the height, inclination, or stiffness of the seat suitable for driver A can be retrieved from the memory 155 based on historical data related to the profile of driver A. For another example, in the case where the onboard occupant sensor 120 and / or the offboard occupant sensor 140 detects a stretching movement of the occupant, the processor 150 can cause the seat actuator 160B to change the stiffness of the seat (e.g., change the stiffness of the seat from a hard degree to a soft degree) in response to the detected stretching movement of the occupant.

[0054] It should be understood that any one or any combination of the exemplary onboard and offboard speed control sensors 110, 140 and the exemplary onboard and offboard occupant sensors 120, 140 can be used to predict the movement of the vehicle and change the carrying environment based on the predicted movement. For example, when the driver (or a computing entity embedded in an autonomous vehicle) is operating the accelerator pedal, brake pedal, or throttle, the onboard or offboard speed control sensors 110, 140 can be used to predict the acceleration or deceleration movement of the (driver-driven or autonomous driving) vehicle. At the same time, in addition, the onboard or offboard occupant sensors 120, 140 can be used to detect the characteristics of a specific occupant and predict the adjustment of the carrying environment to suit the specific occupant. In such an example, a first control signal can be generated based on the detection results of the onboard or offboard speed control sensors 110, 140. A second control signal can be generated based on the detection results of the onboard or offboard occupant sensors 120, 140. Figure 1 The illustrated processor 150 may utilize the first control signal and the second control signal together to predict future movement or changes in the carrying environment of the carrying vehicle.

[0055] More specifically, in examples where the onboard or offboard speed control sensor 110, 140 includes a sensor for a steering wheel or other steering mechanism, the sensor can be used when the driver operates the steering wheel to steer. At the same time, the onboard or offboard occupant sensor 120, 140 can also include a scale connected to a seat, camera, sound sensor, chemical sensor or other miscellaneous sensor for detecting the characteristics of the occupant when the driver uses the steering wheel to steer. For example, a scale connected to a seat can detect the weight of the occupant and adjust the direction of the occupant when the vehicle is turning (for example, when the vehicle is turning, in order to better hold the occupant, the direction is changed to a greater extent for lighter occupants). In such an example, the first control signal detected by the sensor of the steering wheel and the second control signal detected by the scale are used together to predict future movements or adjust the carrying environment of the vehicle.

[0056] Figure 4 is a diagram with various exemplary onboard environmental sensors 130 and offboard environmental sensors 140 identified.

[0057] exist Figure 4Optionally, an onboard environmental sensor 130 may be included in the predictive control system 100 to provide other additional signals to the processor 150 to at least partially mitigate the impact on the occupant based on the predicted motion. In an embodiment, the onboard environmental sensor 130 may include a LIDAR sensor or other optical sensor 132 (e.g., a visible light sensor, an infrared sensor, an ultraviolet sensor, a laser sensor, etc.), an ultrasonic sensor or other sound sensor 134, a GPS receiver or other position determination sensor 136, an inertial measurement sensor 138, a chemical measurement sensor 139 (e.g., a humidity sensor), an ambient meteorological sensor (e.g., a wind speed sensor, a wind direction sensor, an ambient temperature sensor, a thermometer, etc.), a distance or proximity sensor, a visual sensor, and / or an image detection sensor (e.g., a camera).

[0058] It should be understood that any one or any combination of the exemplary onboard and offboard speed control sensors 110, 140 and the exemplary onboard and offboard environmental sensors 130, 140 can be used to predict the movement of the vehicle and change the carrying environment based on the predicted movement. For example, when the driver (or a computing entity embedded in an autonomous vehicle) is operating the accelerator pedal, brake pedal, or throttle, the onboard or offboard speed control sensors 110, 140 can be used to predict the acceleration or deceleration movement of the (driver-driven or autonomous driving) vehicle. At the same time, in addition, the onboard or off-board environmental sensors 130, 140 can be used to detect upcoming road conditions, weather conditions, and / or other surrounding environmental conditions to predict potential changes in the carrying environment. For example, in the case of predicting that there are holes, bumps, or unevenness in the upcoming road conditions based on signals received from image sensors, light sensors, sound sensors, etc., these signals can be used separately and / or together with the vehicle control signal to determine the future movement of the vehicle. Specifically, when holes or bumps are detected, vibratory motion may occur. For another example, in the case of rainy, foggy, or snowy weather detected based on signals received from onboard or offboard environmental sensors (e.g., humidity sensors, thermometers, wind speed sensors, etc.), these signals can be used separately and / or together with the vehicle control signal to determine the future movement of the vehicle. Specifically, when rainy, foggy, or snowy weather is detected, the driver may drive slower than normal due to slippery road conditions and poor visibility, so deceleration movement can be predicted. In various embodiments, a first control signal can be generated based on the detection results of the onboard or offboard speed control sensors 110, 140. A second control signal can be generated based on the detection results of the onboard or offboard environmental sensors 130, 140. Figure 1 The illustrated processor 150 may utilize the first control signal and the second control signal, either individually or collectively, to predict future movement of the vehicle.

[0059] More specifically, in an example where the onboard or offboard speed control sensor 110, 140 includes a sensor for a steering wheel or other steering mechanism, the sensor can be used when the driver operates the steering wheel to steer. At the same time, the onboard or offboard environmental sensor 130, 140 including a light sensor, a sound sensor, or any other distance / proximity sensor can also be used to detect the distance or proximity between the vehicle and an upcoming obstacle (e.g., another vehicle in front of the vehicle that the driver is occupying) when the driver uses the steering wheel to steer. In such an example, the first control signal detected by the sensor of the steering wheel and the second control signal detected by the distance or proximity sensor are used individually or collectively to predict the future movement of the vehicle.

[0060] In various embodiments, a LIDAR implementation of the optical sensor 132 can be used to predict an impending collision. An ordinary visual camera can detect impending rough road conditions, impending wet roads, etc. The sound sensor 134 can detect instructions such as "slow down", "quick, go to the hospital" or screaming from an occupant or other person. GPS or other location determination sensors 136 can be used to predict upcoming curves, speed bumps, gravel or other adverse road conditions in a road or track. The inertial measurement sensor 138 can, for example, detect skidding on a wet or snowy road. The chemical measurement sensor 139 can be used to detect fog or dust in the air around the vehicle, or detect a breath indicating fear in the occupant. Any of these sensors can be used to trigger a sudden deceleration of the vehicle or a rapid turn of the vehicle, and trigger corresponding adjustments to the occupant's seat or other carrying environment to at least partially mitigate the impact of the predicted future movement on the occupant.

[0061] As used herein, the term "prediction" refers to an algorithmically generated identification of an event that has at least a threshold probability of occurring within the next few seconds or minutes. In various embodiments, the prediction is for an event that may occur within no more than 5 seconds, 10 seconds, 30 seconds, 1 minute, or 5 minutes of event occurrence time. The event occurrence time may also vary depending on the severity of the predicted event. In the case where the prediction is associated with a calculated probability, the threshold for performing the prediction may have a probability of at least 5% to 100%, including, for example, at least 10%, 25%, 50%, 75%, and 90%, and the threshold may vary depending on the severity of the predicted event.

[0062] Information used to predict speed changes may also be obtained from onboard or offboard data stores. For example, the step of predicting vehicle movement may include predicting upcoming changes in traffic based on data such as previous traffic patterns, time of day (e.g., rush hour), day of the week (e.g., weekday or weekend), or time of year (e.g., holiday), traffic reports, traffic broadcasts, satellite data, etc. Predictions of vehicle movement may also include predicting changes in surrounding weather using national or regional weather data. Occurrences of rain, snow, hail, and fog are weather conditions that are considered particularly relevant.

[0063] Predicted motion may include any movement of the vehicle 105, whether voluntary or involuntary, including acceleration and deceleration, jolts, turns, vibrations, etc. The effects of the movement of the vehicle 105 on the occupants may range from mild irritation to motion sickness or other severe physical or psychological discomfort.

[0064] The information obtained from the sensor can be transmitted to the processor using signals in any suitable manner. The transmission of signals can include, for example, communication using electronic, optical, mechanical means, and through hard-wired, wireless or optical connections.

[0065] The processor 150 is any hardware with the computing power to execute a program or algorithm. The example processor 150 includes a digital / deep neural network (DNN) or other machine learning system. In an embodiment, the processor 150 is used to predict the future action movement of the carrier 105 using one or more signals. The one or more signals are obtained from any one of the sensors 110, 120, 130, 140 and the onboard memory / database 155 or the off-board memory / database. Preferably, the processor 150 is configured so that when an inconsistent second signal is received at the same time, or when an inconsistent second signal is received within a few seconds after receiving the first signal, the first signal and the second signal are combined by ignoring the first or second signal, averaging, or otherwise, using a signal mixing or fusion method (e.g., sensor fusion processing) to complete the prediction of future movement. Such a result can be obtained at least in part based on a machine learning model, for example, using sample data of the control signal to train the DNN and evaluate the corresponding movement of the carrier 105.

[0066] like Figure 1As shown, the processor 150 is used to provide an actuation signal to each actuator (e.g., a vehicle actuator 160A, a seat actuator 160B, or an environmental actuator 160C) to mitigate the impact on the occupants caused by the predicted movement of the vehicle. The actuator can be any device, machine, or equipment that can change the carrying environment to improve the riding experience of the occupants. In an embodiment, the actuation signal may include a signal for changing or adjusting a seat component. For example, the actuation signal can be used to adjust the direction, inclination, spatial position, distance, size, tension, or stiffness of the seat by changing the components of the seat (including support, cushion, back support, head support, lumbar support, seat restraints such as seat belts, or armrests, etc.).

[0067] For example, when the processor 150 predicts the acceleration of the vehicle due to the operation of the accelerator pedal or the sensor detects an upcoming downhill road condition, in order to compensate for the predicted acceleration of the vehicle, the processor 150 can generate an actuation signal to adjust the inclination of the seat so that the seat can tilt forward. Similarly, when the processor 150 predicts the deceleration of the vehicle due to the operation of the brake pedal or the sensor detects an upcoming uphill road condition, in order to compensate for the predicted deceleration of the vehicle, the processor 150 can generate an actuation signal to adjust the inclination of the seat so that the seat can tilt backward.

[0068] For another example, when the processor 150 predicts a right turn of the vehicle due to the operation of the steering wheel or when the sensor detects an upcoming right turn, in order to compensate for the predicted right turn of the vehicle, the processor 150 may generate an actuation signal to adjust the direction of the seat so that the seat can turn left. Similarly, when the processor 150 predicts a left turn of the vehicle due to the operation of the steering wheel or when the sensor detects an upcoming left turn, in order to compensate for the predicted left turn of the vehicle, the processor 150 may generate an actuation signal to adjust the direction of the seat so that the seat can turn right.

[0069] Figure 5 is a diagram identifying various exemplary vehicle actuators, seat actuators, and environmental actuators.

[0070] like Figure 5 As shown, exemplary actuators include: vehicle actuator 160A, seat actuator 160B and environmental actuator 160C, any of which may be mechanical or non-mechanical. Exemplary mechanical actuators include stepper motor 161, hydraulic piston 162 and pneumatic piston 163. Other example actuators, which may be mechanical or non-mechanical, include speed control actuator 164, steering actuator 165, visual display controller 166, speaker controller 167 and other miscellaneous environmental actuators 168.

[0071] Any of these actuators may be used to control one or more corresponding controlled devices 170, including physical aspects of the seat 171 and / or seat components 171A of the seat, seat belts 172, leg supports 173, seat belts or other seat restraints 174, pedals, throttle or other speed control mechanisms 175, steering wheels or other steering mechanisms 176, visual display systems 177A, and speakers 177B. Miscellaneous environmental devices 178 that are believed to be actuated include air conditioning or seat temperature controls, and actuators for operating windows, curtains, and mirrors. The vehicle's suspension system 179 may also be actuated, for example, by changing the vehicle's height, changing the engagement or disengagement movement with a four-wheel drive or other anti-skid mechanism.

[0072] The actuation signals from the processor 150 to the various actuators 160A, 160B, 160C may be transmitted by any suitable means, including, for example, electronic, optical, or mechanically transmitted signals. Electronic signals may be transmitted by any combination of hardwired, wireless, or optical connections.

[0073] Figure 6 The flowchart exemplarily shows the steps of implementing the method 600 of the embodiment of the present application.

[0074] According to an embodiment of the present application, method 600 includes at least four main steps:

[0075] Step 610 —optionally receiving a signal 612 from a radio frequency receiving circuit 613 ;

[0076] Step 620 - predicting the movement of the vehicle 105 based at least in part on the received signal 612;

[0077] Step 630 - based on the predicted motion, calculating adjustments to the occupant's transportation environment 632 to at least partially counteract or compensate for the effects on the occupant; and

[0078] Step 640 —Perform adjustments to the shipping environment 632 .

[0079] At step 610, signal 612 may include control signals received from onboard speed control sensor 110, onboard occupant sensor 120, onboard environmental sensor 130, or corresponding offboard sensor 140. Signal 612 may be received from RF receiving circuit 613 embedded on these sensors.

[0080] In step 620, the movement of the carrier is predicted based on the received signal 612. In an embodiment, the signal 612 may include a control signal received from one or more onboard sensors or off-board sensors. The signal 612 may be generated by a single control signal or multiple control signals. In the case of receiving multiple control signals, the processor 150 may generate the signal 612 by ignoring a portion of the multiple control signals, calculating an average of the multiple control signals, or applying different weighting parameters to each control signal to combine the multiple control signals. In an embodiment, different weighting parameters may be predefined based on historical data to provide a better carrying environment for the occupants.

[0081] At step 630, after predicting the future movement of the vehicle, corresponding adjustments to the vehicle environment 632 for the occupants may be calculated or determined. The corresponding adjustments to the vehicle environment are calculated or determined in order to preemptively counteract or compensate for the predicted movement. Due to the proactive or predictive nature of the disclosure of the present application, the reaction time and response time to impending changes in the vehicle environment may be significantly reduced to counteract the detected potential adverse effects and provide the occupants with a better vehicle ride experience.

[0082] At step 640, the adjustments calculated or determined in response to the predicted future motion may be performed on the vehicle to change the transport environment. In an embodiment, the adjustments are made by sending actuation signals to trigger various actuators (e.g., vehicle actuator 160A, seat actuator 160B, or environmental actuator 160C) to change the transport environment. The changes in the transport environment may be made by adjusting controlled devices, including one or more components such as seats, speakers, and / or other miscellaneous environmental devices.

[0083] Optionally, method 600 may include sub-steps 615 , 625 , 635 , and 645 .

[0084] Optional step 615 includes: obtaining the signal 612 shown in step 610 by any one or more of the following sensors: light sensor 132, sound sensor 134, position determination sensor 136, accelerator pedal 112A or other acceleration mechanism 112B, brake pedal 113A or other braking mechanism 113B, steering wheel 114A or other steering mechanism 114B, inertial measurement unit 115A, or off-board sensor 140.

[0085] Optional step 625 includes predicting motion, including predicting changes in any one or more of: upcoming road conditions, upcoming changes in traffic, changes in ambient weather, or information received from additional signals.

[0086] Optional step 635 includes calculating an adjustment based at least in part on a characteristic of the occupant, wherein the characteristic is selected from any one or more of: weight, height, age, body temperature, flexibility, spinal curvature, sitting height, shoulder width, hip circumference, waist circumference, chest circumference, gender, physical disability, health condition, or mental condition.

[0087] Optional step 645 includes making adjustments including changing one or more of: the position, orientation, or stiffness of at least one component of the seat 122 in which the occupant is seated, changing the functionality of the suspension system 179 of the vehicle 105, changing the visual display 177A within the vehicle 105, and changing at least one of the sound or volume emitted from a speaker 177B within the vehicle 105.

[0088] Figure 7 is an exemplary schematic diagram of a predictive control system for a vehicle, the system including various modules for changing the vehicle environment based on predicted vehicle movement.

[0089] like Figure 7 As shown, the predictive control system may include a sensing module 710, a decision module 720, or an execution module 730. In an embodiment of the present application, the sensing module 710 may be associated with various onboard sensors (e.g., onboard speed control sensor 110, occupant sensor 120, environmental sensor 130) and offboard sensors (e.g., offboard sensor 140) to provide a control signal or a combined / mixed control signal by mixing information detected from different sensor sources using sensor fusion technology. The sensing module may be connected to various sensors (as described above) that perform different types of sensing mechanisms, including direction sensing, vibration sensing, IMU sensing, occupant feature sensing, or environmental sensing, etc., to provide more information to the decision module 720 for generating more accurate prediction results of future movements of the vehicle.

[0090] In an embodiment of the present application, the decision module 720 may be associated with the processor 150 and the memory 155, and is used to determine and generate an actuation signal based on a prediction result determined based on a received control signal. The generated actuation signal is determined, and the actuation signal is sent to the execution module 730 to trigger a change in the carrying environment, thereby offsetting / compensating for the predicted future movement of the carrying vehicle. For example, when an accelerated movement is predicted based on the received control signal, the decision module 720 may determine to change the inclination of the seat to tilt forward. Similarly, when a decelerated movement is predicted based on the received control signal, the decision module 720 may determine to change the inclination of the seat to tilt backward. For another example, when a right turn / left turn movement is predicted based on the received control signal, the decision module may determine to turn the seat to the left / right to offset / compensate for the predicted right turn / left turn movement. For another example, when a vibration motion is predicted based on the received control signal, the decision module 720 may determine to change the seat on any axis of the 3D dimension (e.g., on the pitch axis, roll axis, or yaw axis of the 3D dimension) to offset / compensate for the predicted vibration motion. In an embodiment, the decision module may also make a determination based on historical control signals stored in the memory 155. The currently detected control signal received from the sensing module and the previously detected historical control signal stored in the memory may be used independently or together to generate an actuation signal to determine a change / adjustment of the carrying environment.

[0091] In an embodiment, the decision module 720 may select a suitable compensation strategy from a plurality of compensation strategies based on the prediction obtained from the sensing module 710. The plurality of compensation strategies may include a vibration-based strategy, an inertia-based strategy, and the like. The vibration-based strategy requires a set of predefined vibration-based rules for vibration motions (e.g., vibration motions, oscillation motions, and slamming motions) detected by the IMU. For example, when the vehicle may be lifted to a higher position due to detection of an impending bump, the height of the seat may be lowered, or when the vehicle may be lowered to a lower position due to detection of an impending hole, the height of the seat may be raised, or the direction / attitude of the seat may be changed in 3D dynamics around the center of gravity of the seat (changing the seat in any one direction or combination of the pitch axis, roll axis, and yaw axis), the direction / attitude being opposite to the predicted direction / attitude, and the like. The inertia-based strategy requires a set of predefined inertia-based rules for detected inertial motions (e.g., acceleration motion, deceleration motion, and steering motion). For example, when an accelerated movement is predicted, the seat's tilt angle is adjusted to tilt backward, or when a decelerated movement is predicted, the seat's tilt angle is adjusted to tilt forward, when a left turn is predicted, the seat is rotated to the right at a certain rotation angle and / or tilted to the right at a certain tilt angle, when a right turn is predicted, the seat is rotated to the left at a certain rotation angle and / or tilted to the left at a certain tilt angle, etc.

[0092] Based on the characteristics of the prediction results (e.g., based on vibration or based on inertia), a suitable compensation strategy can be selected from one or more corresponding vibration-based strategies, inertia-based strategies, etc. In the case where the prediction results have multiple characteristics, a suitable compensation strategy can also be selected based on a combination of multiple compensation strategies. For example, when an upcoming bump on an uphill section is detected, the sensing module 710 can simultaneously detect the vibration-based motion caused by the bump and the inertia-based motion caused by the uphill section through different sensors. In such an example, the decision module 720 can determine a suitable compensation strategy, including a combination or aggregation of a vibration-based strategy and an inertia-based strategy, such as a subset of the rules contained in the vibration-based strategy and a subset of the rules contained in the inertia-based strategy. The selection of a suitable compensation strategy can be based on other features, including features of the prediction results (e.g., the type of predicted motion, the amplitude of the predicted motion, etc.), features of the vehicle, features of the occupants (occupant preferences, occupant weight, seat height, arm length, etc.), and / or similar features. Specifically, in the event that the IMU senses a predicted result of the predicted vibration-based motion, the decision module 720 may also consider the preferred seat height of the occupant to determine an appropriate compensation strategy. For example, in the event that the vehicle is predicted to descend to a lower position due to detection of an impending hole, the decision module 720 may also determine a specific height adjustment based on the preferred seat height of a specific occupant (e.g., when one occupant's seat height preference is greater than a default threshold, increase the seat height to a greater extent; when another occupant's seat height preference is less than the default threshold, increase the seat height to a lesser extent, etc.).

[0093] like Figure 7 As shown, the compensation strategy may have a corresponding execution mechanism, which is executed by the execution module 730 via the actuation signal received from the decision module 720, wherein the actuation signal may be generated based on the appropriate compensation strategy selected by the decision module 720. Based on the prediction obtained from the sensing module 710, once the appropriate compensation strategy is selected from the plurality of compensation strategies, the execution module 730 may execute the corresponding execution mechanism in accordance with the rules associated with the appropriate compensation strategy.

[0094] Specifically, in the case where a vibration-based compensation strategy is determined based on the vibration motion (e.g., vibration motion, oscillation motion, and slamming motion) detected by the IMU, the execution module 730 executes a corresponding vibration-based execution mechanism to offset / compensate for the detected vibration motion. For example, if a vibration-based rule for lowering the height of the seat is selected when the carrier may be lifted to a higher position due to detection of an impending bump, the execution module 730 may execute a corresponding vibration-based execution mechanism to lower the height of the seat. For another example, if a vibration-based rule for raising the height of the seat is selected when the carrier may be lowered to a lower position due to detection of an impending hole, the execution module 730 may execute a corresponding vibration-based execution mechanism to increase the height of the seat. For another example, if a vibration-based rule is detected to dynamically change the direction / attitude of the seat in 3D, the execution module 730 can execute the corresponding vibration-based execution mechanism to dynamically reversely change the direction / attitude of the seat in 3D (change any one of the pitch axis, roll axis and yaw axis or a combination of them to the direction opposite to the predicted future direction / attitude change) to offset / compensate for the detected vibration-based direction / attitude movement.

[0095] Specifically, in the case where an inertia-based compensation strategy is determined based on the detected inertial motion (e.g., acceleration motion, deceleration motion, steering motion), the execution module 730 executes a corresponding inertia-based execution mechanism to offset / compensate for the detected inertial motion. For example, if an inertia-based rule is selected to adjust the recline angle of the seat to tilt backward when an acceleration motion is predicted, the execution module 730 can execute a corresponding inertia-based execution mechanism to change the components of the seat (e.g., seat back, seat bottom, etc.) so that the seat can tilt backward. For another example, if an inertia-based rule is selected to adjust the recline angle of the seat to tilt forward when a deceleration motion is predicted, the execution module 730 executes a corresponding inertia-based execution mechanism to change the components of the seat (e.g., seat back, seat bottom, etc.) so that the seat can tilt forward. For another example, if an inertia-based rule is selected to rotate the seat to the right / left at a certain rotation angle and / or tilt the seat to the right / left at a certain tilt angle when a left / right turn movement is predicted, the execution module 730 executes the corresponding inertia-based execution mechanism to change the seat so that the seat can be rotated to the right / left at a certain rotation angle and / or tilted to the right / left at a certain tilt angle.

[0096] In an embodiment, when a suitable compensation strategy is selected from one or more vibration-based compensation strategies, inertia-based strategies and / or other compensation strategies, a corresponding execution mechanism can be jointly executed based on the selection. The corresponding execution mechanism is jointly executed so that the combination or aggregate adjustment of the carrying environment based on different detection characteristics of the predicted undesirable motion (for example, vibration-based or inertia-based) can take effect at the same time to provide better compensation motion and eliminate the predicted undesirable motion. In an embodiment, the selection of a suitable compensation strategy can also be based on other features, including features of the prediction results (for example, the type of predicted motion, the amplitude of the predicted motion, etc.), features of the carrying vehicle, features of the occupants (occupant preferences, occupant weight, seat height, arm length, etc.) and / or similar features. In such an embodiment, corresponding execution mechanisms associated with these additional features (including features of the prediction results, the carrying vehicle, the occupants, etc.) can also be considered to better eliminate the predicted undesirable motion. Specifically, for a specific prediction result (e.g., considering the steering angle of the steering movement, the steepness of the road conditions of the upcoming uphill / downhill section, the vibration amplitude of a specific carrier, etc.), for a specific carrier (e.g., considering that the chassis of a sports car is lower than that of a conventional car; the chassis of a sports utility vehicle (SUV) is higher than that of a conventional car, etc.), and / or for a specific occupant (e.g., considering the seat height, weight, arm length, shoulder width, etc. of a specific occupant), the execution module 730 can execute additional execution mechanisms. Since various characteristics are taken into account, such as the characteristics of the predicted future movement, the characteristics of the predicted results, the characteristics of the carrier, and / or the characteristics of the occupant, the system proposed in the present application can actively adjust the carrying environment based on multiple factors, including the predicted future movement of the carrier, the unique characteristics of the carrier, and / or the unique characteristics of the occupant, and can provide the occupant with precise adjustment and a better riding experience.

[0097] In an embodiment of the present application, the execution module 730 can be associated with various actuators (e.g., a vehicle actuator 160A, a seat actuator 160B, or an environmental actuator 160C). The execution module 730 receives an actuation signal generated by the decision module 720 to change or adjust the seat environment. The vehicle actuator 160A may include a speed actuator (e.g., a throttle or brake) and / or a steering actuator. For example, when a deceleration or stop movement is predicted to avoid a collision with an obstacle or a previous vehicle, the vehicle actuator 160A can be triggered by the execution module to lock the accelerator pedal or trigger the brake pedal, thereby stopping the vehicle or bypassing the obstacle. For another example, when it is determined that the direction, inclination, or stiffness of the seat has changed, the execution module can trigger the seat actuator 160B to adjust one or more components of the seat. For another example, when deceleration or stopped motion of a vehicle is detected, the execution module 730 can turn on the volume of the speaker and provide an audio warning to alert the driver or passengers of the predicted adverse road conditions or possible collision.

[0098] It is obvious to those skilled in the art that, without departing from the inventive concept of this article, in addition to the modifications already described, more modifications may be made. Therefore, the subject matter of the present application is not limited except in the spirit of the appended claims. In addition, when interpreting the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms "comprise" and "comprising" should be interpreted as referring to elements, parts or steps in a non-exclusive manner, indicating that the referenced elements, parts or steps may exist, be used or be combined with other elements, parts or steps that are not explicitly referenced. The specification claims protection of at least one of a group containing A, B, C, ..., N, which should be interpreted as requiring only one element in the group, rather than A plus N or B plus N, etc.

Claims

1. An active seat control system for a vehicle having a seat, the system comprising: A speed control sensor for detecting operations related to speed control of a vehicle, including at least one of the following: a sensor for an acceleration mechanism, a sensor for a braking mechanism, a sensor for a steering mechanism, and a sensor for an inertial measurement unit; an actuator connected to the seat; as well as Processor, configured as: The first control signal derived from the speed control sensor is used to predict the future movement of the vehicle, including: predicting at least one of the acceleration movement, deceleration movement, turning movement, and three-dimensional dynamic movement of the vehicle, and providing an actuation signal to the actuator to operate a velocity control mechanism to at least partially mitigate the effects of the predicted future motion; The active seat control system further includes an occupant sensor configured to generate a second control signal based on detecting characteristics of an occupant carried by the seat to predict an adjustment to a carrying environment; The active seat control system further includes a memory, in which historically detected sensor data and historical control signals are stored, and the historical control signals include driver habit information; The processor is specifically configured to: predict the future movement of the vehicle based on the first control signal and the historical control signal; The processor is programmed to generate the actuation signal based on at least historical data stored in the memory and the second control signal.

2. The system according to claim 1, wherein: The actuator includes the speed control mechanism, which is at least partially arranged in or on the vehicle.

3. The system according to claim 1, wherein: The speed control sensor detects operation of an accelerator pedal or a brake pedal.

4. The system according to claim 1, wherein: The speed control sensor detects operation of a steering wheel.

5. The system according to claim 1, wherein: The speed control mechanism is arranged outside the carrying vehicle.

6. The system according to claim 1, wherein: The actuator includes one or more of a stepper motor, a hydraulic piston, or a pneumatic piston.

7. The system according to claim 1, wherein: The actuator controls at least one of a distance, a direction, or a stiffness of at least one component of the seat.

8. The system according to claim 1, wherein: The actuator controls at least one of the spatial position of the vehicle or the tension of a seat restraint.

9. The system according to claim 1, wherein: The occupant's characteristics are associated with one or more of: the occupant's weight, height, flexibility, body temperature, spinal curvature, sitting height, shoulder width, hip circumference, waist circumference, chest circumference, age, gender, physical disability, health condition or mental condition.

10. A method for controlling a carrying environment of a carrying vehicle, comprising: Receiving a first control signal from a speed control sensor, wherein the speed control sensor is used to detect operations related to speed control of the vehicle, and includes at least one of the following: a sensor for an acceleration mechanism, a sensor for a braking mechanism, a sensor for a steering mechanism, and a sensor for an inertial measurement unit; Predicting the movement of the vehicle based at least on the received first control signal, including predicting at least one of the acceleration movement, deceleration movement, turning movement, and three-dimensional dynamic movement of the vehicle; Based on the predicted motion, calculating adjustments to the vehicle environment to at least partially mitigate impacts on the vehicle; and Performing adjustments to the transport environment; Wherein, the step of calculating the adjustment to the transportation environment comprises: calculating the adjustment to the transportation environment based at least on characteristics of the occupant; The method further comprises: Predicting the future movement of the vehicle according to the first control signal and a historical control signal, wherein the historical control signal is stored in a memory and includes driver habit information; The method further includes generating an actuation signal based at least on the historical data stored in the memory and a second control signal to adjust the carrier environment.

11. The method according to claim 10, further comprising: The first control signal is derived from a sensor, the sensor comprising one or more of a light sensor or a sound sensor.

12. The method according to claim 10, further comprising: The first control signal is derived from a position determination sensor.

13. The method according to claim 10, further comprising: The first control signal is derived from a sensor for a brake pedal or other braking mechanism.

14. The method according to claim 10, further comprising: The first control signal is derived from a sensor (115) for an inertial measurement unit.

15. The method according to claim 10, wherein: The first control signal is received from an off-board speed control sensor.

16. The method according to claim 10, wherein: The step of predicting the movement of the vehicle includes predicting upcoming changes in road conditions.

17. The method according to claim 10, wherein: Predicting the movement of the vehicle includes predicting upcoming changes in traffic.

18. The method according to claim 10, wherein: Predicting the movement of the vehicle includes predicting changes in the surrounding weather.

19. The method according to claim 10, further comprising: The movement of the vehicle is further predicted based on the second control signal.

20. The method according to claim 10, wherein: The step of performing the adjustment of the carrying environment includes changing at least one of a position and an orientation of at least one component of a seat in which an occupant is seated.

21. The method according to claim 10, wherein: The step of performing the adjustment of the transport environment includes changing the stiffness of at least one component of a seat in which an occupant is seated.

22. The method according to claim 10, wherein: The step of performing adjustments to the carrying environment includes: changing the functionality of the suspension system of the carrying vehicle.

23. The method according to claim 10, wherein: The step of performing adjustments to the vehicle environment includes changing at least one of: the volume or content of a digital object presented on a visual display within the vehicle or an acoustic signal emitted from a speaker within the vehicle.

24. The method according to claim 10, wherein: The occupant's characteristics are associated with one or more of: the occupant's weight, height, flexibility, body temperature, spinal curvature, sitting height, shoulder width, hip circumference, waist circumference, chest circumference, age, gender, physical disability, health condition or mental condition.

25. A vehicle having a seat and an active seat control system, the vehicle comprising: an actuator connected to the seat; as well as Processor, configured as: Using electronic signals to predict the future movement of the carrier, wherein the electronic signals come from a speed control sensor, the speed control sensor is used to detect operations related to the speed control of the carrier, including at least one of the following: a sensor for an acceleration mechanism, a sensor for a braking mechanism, a sensor for a steering mechanism, and a sensor for an inertial measurement unit, and predicting the future movement of the carrier includes predicting at least one of the acceleration movement, deceleration movement, steering movement, and three-dimensional dynamic movement of the carrier; and providing an actuation signal to the actuator to change one of a distance or an orientation of a component of the seat to at least partially mitigate the effects of the predicted future movement of the vehicle; The processor is further configured to: generate a second control signal based on characteristics of an occupant carried by the seat detected by the occupant sensor to predict adjustments to the carrying environment; The active seat control system further includes a memory, in which historically detected sensor data and historical control signals are stored, and the historical control signals include driver habit information; The processor is specifically configured to: predict the future movement of the vehicle based on the first control signal and the historical control signal; The processor is programmed to generate the actuation signal based on at least historical data stored in the memory and the second control signal.

26. The vehicle according to claim 25 further comprises a sensor for generating the electronic signal, wherein: The sensor includes one or more of a pressure sensor, a motion sensor, a LIDAR sensor, a light sensor, a position sensor, a sound sensor, or a position determination sensor.

27. The carrier according to claim 25 also includes a radio frequency receiving circuit configured to receive the electronic signal.

28. The vehicle according to claim 25, wherein: The vehicle is an autonomous driving vehicle.

29. The vehicle according to claim 25, wherein: The transport vehicle is a public transport vehicle.

30. The vehicle according to claim 25, wherein: The occupant's characteristics are associated with one or more of: the occupant's weight, height, flexibility, body temperature, spinal curvature, sitting height, shoulder width, hip circumference, waist circumference, chest circumference, age, gender, physical disability, health condition or mental condition.

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