Control unit and control method for comfort braking of vehicle

Abstract

A control unit (20) for comfort braking of a vehicle, the control unit comprising: a detection module (22), which is configured to detect a driving behavior of a vehicle so as to obtain a first detection result, detect the road condition of a road on which the vehicle is travelling so as to obtain a second detection result, and detect a travelling state of the vehicle so as to obtain a third detection result; a processing module (23), which is configured to determine, on the basis of the first detection result, the second detection result and the third detection result, whether a trigger condition for a virtual sensor used for calculating the load of the vehicle is satisfied, and if it is determined that the trigger condition is satisfied, use the virtual sensor to obtain a load state of the vehicle on the basis of a plurality of groups of parameters representative of the current state of the vehicle; and a comfort braking module (26), which is configured to determine, on the basis of the load state of the vehicle, an adjustment to comfort braking parameters of the vehicle. Further provided are a vehicle, a control method for comfort braking of a vehicle, and a computer program product.

Description

Vehicle comfort braking control unit and control method Technical Field

[0001] This invention generally relates to the technical field of vehicle control. Specifically, this invention relates to a control unit and control method for vehicle comfort braking. Background Technology

[0002] Comfort braking offers numerous advantages. For example, it effectively eliminates the jerkiness during braking, making driving smoother, and in congested traffic, it softens frequent braking actions, significantly reducing passenger discomfort from frequent starts and stops. Currently, to ensure reliability in various scenarios, some performance compromises have been made, resulting in the comfort braking function's full potential not being realized. Therefore, solutions to improve the performance of comfort braking are urgently needed. Summary of the Invention

[0003] Against this backdrop, the present invention aims to provide a solution that uses a virtual sensor based on an artificial intelligence model to obtain the vehicle load status and determines the adjustment of the vehicle's comfort braking parameters based on the vehicle load status.

[0004] According to one aspect of the present invention, a control unit for vehicle comfort braking is provided, comprising: a detection module configured to detect driving behavior of the vehicle to obtain a first detection result, detect road conditions of the road on which the vehicle travels to obtain a second detection result, and detect the driving state of the vehicle to obtain a third detection result; a processing module configured to determine, based on the first, second, and third detection results, whether a triggering condition for a virtual sensor used to calculate the vehicle load is met; if it is determined that the triggering condition is met, then using the virtual sensor and based on multiple sets of parameters characterizing the current state of the vehicle to obtain the vehicle load state; and a comfort braking module configured to determine the adjustment of comfort braking parameters of the vehicle based on the vehicle load state.

[0005] According to another aspect of the present invention, a vehicle is provided that includes the control unit described above.

[0006] According to another aspect of the present invention, a control method for vehicle comfort braking is provided, comprising: detecting the driving behavior of the vehicle to obtain a first detection result; detecting the road conditions of the road on which the vehicle is traveling to obtain a second detection result; detecting the driving state of the vehicle to obtain a third detection result; determining, based on the first, second, and third detection results, whether a triggering condition for a virtual sensor used to calculate the vehicle load state is met; if it is determined that the triggering condition is met, obtaining the vehicle load state using the virtual sensor and based on multiple sets of parameters characterizing the current state of the vehicle; and determining, based on the vehicle load state, an adjustment of comfort braking parameters for vehicle comfort braking.

[0007] According to another aspect of the present invention, a computer program product is provided, including computer executable instructions that, when executed, cause one or more processors to perform the method described above.

[0008] The foregoing provides a summary of the main aspects of the invention to enable a basic understanding of these aspects. This summary is not intended to describe all key or essential elements of the invention, nor is it intended to limit the scope of any or all aspects of the invention. The purpose of this summary is to present some implementations of these aspects in a simplified form as a prelude to the detailed description that follows. Attached Figure Description

[0009] The technical solution of the present invention will become clearer from the following detailed description taken in conjunction with the accompanying drawings. It is to be understood that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0010] Figure 1 is a schematic block diagram of an on-board system for vehicle comfort braking according to an embodiment of the present invention.

[0011] Figure 2 is a schematic block diagram of a control unit for vehicle comfort braking according to an embodiment of the present invention.

[0012] Figure 3 schematically illustrates the comfort braking parameters according to an embodiment of the present invention.

[0013] Figure 4 is a flowchart of a vehicle comfort braking control method according to an embodiment of the present invention.

[0014] Figures 5-8 illustrate implementations of some of the main steps of the method shown in Figure 4. Detailed Implementation

[0015] The inventors discovered that the performance level of a vehicle's comfort braking function is closely related to the vehicle's load capacity, as load capacity is a crucial factor in calibrating many key parameters of the comfort braking function. However, in actual vehicle use, the load capacity varies, but the impact of these load changes was not fully considered when calibrating these comfort braking parameters. Therefore, when the vehicle load capacity changes, the performance level of the comfort braking function will be affected.

[0016] Based on this, the present invention proposes a virtual sensor based on an artificial intelligence model to calculate the vehicle load condition and adjust the comfort braking parameters according to the vehicle load condition so that the comfort braking function can meet the comfort braking performance under different vehicle loads.

[0017] In embodiments of the present invention, "vehicle mass" should be understood as the total load capacity or total weight of the vehicle. Vehicle mass includes the weight of the vehicle itself, the weight of the passengers and cargo carried by the vehicle, and, in the case of a trailer, the additional weight generated by the trailer. It may also include weight changes caused by dynamic forces generated or experienced during travel. Vehicle mass can be understood as the force exerted on a structure (such as a highway, bridge, tunnel, etc.) by the vehicle's wheels when stationary or in motion.

[0018] It is worth noting that various thresholds, predetermined values, and predetermined ranges are employed in the embodiments of the present invention, all of which are predetermined, for example, based on actual vehicle test results and / or calculations based on the test results. The present invention does not limit their specific values. Moreover, these thresholds, predetermined values, and predetermined ranges can also be adjusted based on the needs or preferences of vehicle users (e.g., OEMs or vehicle drivers).

[0019] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0020] Figure 1 illustrates an on-board system 100 for vehicle comfort braking according to an embodiment of the present invention, which includes a sensor unit 10, a control unit 20, and an execution unit 30.

[0021] Sensor unit 10 is used to capture vehicle state parameters. Vehicle state parameters include multiple parameters that characterize the current state of the vehicle (e.g., the vehicle's operating state or driving state). For example, vehicle state parameters include, but are not limited to: the drive system's transmission ratio (e.g., the amplification ratio of the torque transmitted from the front and rear axle motors to the wheel ends), wheel moment of inertia (e.g., the resistance that needs to be overcome to make the wheel rotate), tire pressure (e.g., the tire pressure of each wheel), suspension height (e.g., the front suspension height and rear suspension height), suspension spring stiffness (e.g., the force required to compress the suspension for a certain distance), door status (e.g., whether all doors are closed or some doors are open), trunk opening / closing status (e.g., whether the trunk is open), motor drive torque (e.g., the motor torque of the front and rear axle motors, or the motor torque of the motors coupled to each wheel), and energy recovery. Torque (e.g., motor torque of the front and rear axle motors for regenerative braking), hydraulic braking force (e.g., braking pressure applied by a hydraulic braking system), longitudinal and lateral vehicle speeds, longitudinal and lateral accelerations, wheel speeds of each wheel, vehicle yaw angle, vehicle pitch angle, accelerator pedal position, accelerator pedal force application speed (e.g., when the driver rapidly or gradually depresses the accelerator pedal), brake pedal position, brake pedal force application speed (e.g., when the driver rapidly or gradually depresses the brake pedal), steering wheel angle, current gear, road slope and road type (e.g., current road slope and road type obtained from a vehicle driver assistance or autonomous driving system), etc.

[0022] In one embodiment, sensor unit 10 includes sensors for capturing vehicle state parameters, such as door sensors, tire pressure sensors, suspension height sensors, lateral acceleration sensors, longitudinal acceleration sensors, wheel speed sensors, accelerator pedal displacement sensors, yaw rate sensors, and so on. Sensor unit 10 may also include a receiving unit for receiving one or more vehicle state parameters from cloud services, roadside units, or other vehicles via V2X (Vehicle-to-everything).

[0023] According to embodiments of the present invention, vehicle state parameters can be obtained directly based on sensor measurements, or they can be calculated based on sensor measurements.

[0024] The control unit 20 is communicatively connected to the sensor unit 10 and receives vehicle status parameters from the sensor unit 10. Based on the received parameters and using virtual sensors, the control unit 20 obtains the vehicle load status and adjusts the vehicle's comfort braking parameters according to the vehicle load status, so that the performance of the comfort braking function can be satisfied under different vehicle load conditions.

[0025] The virtual sensor is a vehicle load model (hereinafter referred to as the model) used to calculate the vehicle's load. This model can be an AI (artificial intelligence) model. In one embodiment, the model is a trained machine learning model stored in the vehicle, for example, in control unit 20. In another embodiment, the AI ​​model can be updated through relearning during vehicle use, for example, by retraining the model using parameters collected during vehicle use (i.e., performing prior training on an offline-trained model), thereby obtaining a more robust AI model. It is invoked when a triggering condition (described in detail below) is met to calculate the vehicle load. This invention does not limit the specific implementation of this model.

[0026] In one embodiment, referring to FIG2, the control unit 20 includes an acquisition module 21, a detection module 22, a processing module 23, a first recording module 24, a second recording module 25, and a comfort braking module 26. The lines and arrows between these modules shown in FIG2 are illustrative of the information interaction between these modules, which will be described in more detail in the Methods section below.

[0027] Understandably, the naming of these modules is functional, not intended to limit their implementation or physical location. For example, these modules may be implemented on the same chip or circuit, or on different chips or circuits. Each module can be further divided into multiple sub-modules. Two or more of these modules can be combined into a single module.

[0028] The control unit 20 and its various modules can be implemented in hardware, software, or a combination of both. For hardware implementations, they can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), data signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic units designed to perform their functions, or combinations thereof. For software implementations, they can be implemented using microcode, program code, or code segments, and can also be stored in machine-readable storage media such as storage components.

[0029] In one embodiment, the control unit 20 may be located in the vehicle's electronic control unit (ECU), the vehicle's body control unit (VCU), or the vehicle's domain controller. Alternatively, the multiple modules of the control unit 20 may be configured such that some modules are located in one ECU, while others are located in another ECU or VCU.

[0030] In one embodiment, the control unit 20 is implemented including a memory and a processor. The memory contains instructions that, when executed by the processor, cause the processor to perform the comfort braking control method according to an embodiment of the invention.

[0031] The following section introduces the vehicle's comfort braking parameters.

[0032] The vehicle's comfort braking has multiple comfort braking levels. These levels are predetermined and correspond to different levels of comfort braking during the vehicle's braking process. For example, there are high comfort braking level, medium comfort braking level, and low comfort braking level. The high comfort braking level indicates the highest level of comfort during the comfort braking process; the medium comfort braking level indicates the moderate level of comfort during the comfort braking process; and the low comfort braking level indicates the average level of comfort during the comfort braking process.

[0033] Each comfort braking level includes multiple comfort braking parameters corresponding to that level. For example, a high comfort braking level includes multiple comfort braking parameters corresponding to that level; a medium comfort braking level includes multiple comfort braking parameters corresponding to that level; and a low comfort braking level includes multiple comfort braking parameters corresponding to that level.

[0034] Comfort braking parameters for each level include, for example, the target braking force and its rate of change, the target driving force and its rate of change, the target deceleration, and so on. Comfort braking parameters can be constant values ​​or functions that vary arbitrarily over time. For instance, the target braking force can be expressed as a curve that changes over time during vehicle braking.

[0035] For clarity, Figure 3 schematically illustrates the comfort braking levels and comfort braking parameters for each level according to an embodiment of the present invention. Referring to Figure 3, the vehicle V has multiple selectable comfort braking levels CST_L1, CST_L2, CST_L3...CST_Ln. Each comfort braking level includes multiple comfort braking parameters; that is, comfort braking level CST_L1 includes multiple comfort braking parameters m11, m12, m13...m1m; comfort braking level CST_L2 includes multiple comfort braking parameters m21, m22, m23...m2m... and comfort braking level CST_Ln includes multiple comfort braking parameters mn1, mn2, mn3...mnm.

[0036] The execution unit 30 is communicatively connected to the control unit 20 and is used to execute comfort braking parameters decided by the control unit 20, such as adjustment amounts or adjustment schemes for comfort braking parameters. The execution unit 30 may include, for example, the vehicle's braking system, for executing comfort braking parameters related to vehicle braking decided by the control unit 20. The execution unit 30 may also include, for example, the vehicle's drive system, for executing comfort braking parameters related to vehicle drive determined by the control unit 20. The execution unit 30 may also include other control units communicatively connected to the control unit 20, such as a vehicle control unit (VCU).

[0037] Figure 4 illustrates a control method 400 for vehicle comfort braking according to an embodiment of the present invention. This method 400 can be executed by the aforementioned control unit 20.

[0038] In block 410, vehicle state parameters are acquired from sensor unit 10, including vehicle operating state-related parameters and vehicle driving state-related parameters. The above descriptions of vehicle operating state parameters and vehicle driving state-related parameters also apply here and will not be repeated. Block 410 can be executed by acquisition module 21.

[0039] In one embodiment, the acquisition module 21 acquires multiple vehicle state parameters from the sensor unit 10 and then preprocesses these parameters. Preprocessing includes time synchronization and noise cancellation (e.g., removing signal defects) of these parameters to ensure that the vehicle state parameters used in subsequent calculations and processing are preprocessed and have higher accuracy and reliability.

[0040] In block 420, multiple detections related to the triggering conditions of the virtual sensor are performed. Block 420 can be performed by detection module 22. These multiple detections include three aspects (three detections): a first detection (block 421) detecting whether the driving behavior results in the vehicle being in a steady-state linear acceleration state; a second detection (block 422) detecting whether the current road surface condition is a paved road surface where the gradient change is less than a gradient change threshold; and a third detection (block 423) detecting whether the vehicle's driving state meets a predetermined driving state. In the first detection, the driving behavior can include the driver's driving behavior in driver-driven mode, the driving behavior of the driver assistance system in assisted driving mode, and the driving behavior of the autonomous driving system in autonomous driving mode.

[0041] The specific implementation methods for each test are described below.

[0042] Figure 5 illustrates one implementation of the first detection. Referring to Figure 5, in block 4210, it is determined whether the driving behavior of the driver, driving assistance system, or autonomous driving system causes the vehicle to be in a steady-state acceleration state. For example, the detection module 22 determines whether the vehicle is in a steady-state acceleration state based on the drive torque from sensor unit 10, the vehicle's longitudinal acceleration, accelerator pedal opening, and brake pedal travel. In one implementation, the detection module 22 determines that the vehicle is in a steady-state acceleration state when all of the following conditions are met: 1) the vehicle's power output is stable, for example, the fluctuation amplitude of the drive torque is less than a drive torque fluctuation amplitude threshold; 2) the vehicle's longitudinal acceleration is stable, for example, the rate of change of the vehicle's longitudinal acceleration is less than a longitudinal acceleration change rate threshold; 3) the driving behavior indicates a stable acceleration demand for the vehicle, for example, the fluctuation amplitude of the accelerator pedal opening is less than a accelerator pedal opening fluctuation amplitude threshold; 4) the driving behavior indicates no braking operation, for example, the brake pedal travel is close to zero. If the determination result of block 4210 is negative, that is, the vehicle is not in a steady-state acceleration state, then a first detection result that does not meet the triggering conditions is obtained (block 4212). If the judgment result of box 4210 is positive, that is, the vehicle is in a stable acceleration state, then proceed to box 4214. In box 4214, it is further determined whether the driving behavior of the driver, the driving assistance system, or the autonomous driving system causes the vehicle to be in a longitudinal straight-line driving state. For example, the detection module 22 determines whether the vehicle is in a longitudinal straight-line driving state based on the vehicle's steering angle and lateral acceleration (i.e., lateral acceleration) from the sensor unit 10. In one embodiment, the detection module 22 determines that the vehicle is in a longitudinal straight-line driving state when all of the following conditions are met: 1) the driving behavior indicates that the vehicle is in a non-steering state, for example, the absolute value of the steering angle is less than a steering angle threshold (which can be predetermined based on the vehicle's steering system accuracy and current road conditions); 2) the driving behavior indicates that the vehicle is not subjected to steering force or lateral force, for example, the absolute value of the vehicle's lateral acceleration is less than a lateral acceleration threshold (which can be predetermined based on the vehicle's suspension system performance and current road conditions). If the judgment result of box 4214 is negative, that is, the vehicle is not in a longitudinal straight-line driving state, then the first detection result that does not meet the trigger condition is obtained (box 4212). If the judgment result of box 4214 is positive, that is, the vehicle is in a longitudinal straight-line driving state and in a steady-state acceleration state, then the first detection result that meets the trigger condition is obtained (box 4216).

[0043] In another embodiment of the first detection, in block 4210, a similar method as described above is used to determine whether the driving behavior of the driver, driving assistance system, or autonomous driving system causes the vehicle to be in a steady-state deceleration state. The rest of this embodiment is the same as the embodiment described above. Thus, this embodiment is suitable for detecting whether the vehicle is traveling in a straight line and in a steady-state deceleration state.

[0044] Figure 6 illustrates one implementation of the second detection. Referring to Figure 6, in block 4220, it is determined whether the driving behavior of the driver, driving assistance system, or autonomous driving system causes the vehicle to be in a steady-state acceleration or steady-state deceleration state. Block 4220 is the same as block 4210 described above, therefore the above description of block 4210 also applies here. In other words, both the first and second detections include a determination of a steady-state acceleration or steady-state deceleration state. In one embodiment, after performing the determination process of a steady-state acceleration or steady-state deceleration state, the determination result can be used for both the first and second detections. In this embodiment, block 4220 is implemented using block 4210. If the determination result of block 4220 is negative, that is, a second detection result that does not meet the triggering condition is obtained (block 4222). If the determination result of block 4220 is positive, that is, the vehicle is in a steady-state acceleration or steady-state deceleration state, then proceed to block 4224. In block 4224, the change in the gradient value of the road currently being driven by the vehicle is calculated, wherein the change in the gradient value is the change after removing the pitch interference caused by the vehicle's acceleration or deceleration. In one embodiment, the detection module 22 calculates the change in road slope value, excluding vehicle pitch interference, based on the vehicle body acceleration measured by the longitudinal acceleration sensor from the sensor unit 10 and the vehicle acceleration calculated from the wheel speed sensor. In another embodiment, the detection module 22 obtains the change in road slope value based on the measurement results from the camera or radar sensors of the driver assistance system or autonomous driving system. In yet another embodiment, the detection module 22 calculates the superposition angle value of the road slope value and the pitch angle caused by vehicle acceleration and deceleration based on the height value measured by the front and rear axle suspension height sensors, and also calculates the change in road slope value, excluding vehicle pitch interference, based on the vehicle acceleration calculated from the wheel speed sensor and the pitch angle caused by vehicle acceleration and deceleration estimated from the suspension spring stiffness; and determines whether the change in slope value is less than the slope change range threshold. In block 4226, the detection module 22 receives the detection result regarding whether the current road surface is a paved road surface. In box 4228, if the change in slope value is less than the slope change threshold and the detection result for the paved road surface is positive (i.e., the current road surface is not a road surface such as an off-road road, and there is no significant uphill or downhill change), a second detection result that meets the triggering condition is obtained. Conversely, if the change in slope value is greater than or equal to the slope change threshold or the detection result for the paved road surface is negative (i.e., the current road surface is a road surface such as an off-road road), a second detection result that does not meet the triggering condition is obtained (box 4222).

[0045] The detection of paved road surfaces can be performed by the detection module 22, or by other ECUs of the vehicle, which can then transmit the detection results to the detection module 22. This invention does not limit the specific implementation of unpaved road surface detection. One implementation method for unpaved road surface detection is described below by way of example.

[0046] In one implementation, the wheel speed of each wheel is monitored to obtain a curve showing the deviation between the wheel speed and the vehicle speed over time. Based on this curve, it is determined whether the wheel speed fluctuation amplitude of each wheel is less than a wheel speed fluctuation amplitude threshold. If it is determined that the wheel speed fluctuation amplitude of each wheel is less than the wheel speed fluctuation amplitude threshold, the detection result of the paved road surface is positive; conversely, if it is determined that the wheel speed fluctuation amplitude of at least one wheel is greater than or equal to the wheel speed fluctuation amplitude threshold, the detection result of the paved road surface is negative.

[0047] In another implementation, the vehicle's camera module can infer the road surface's adhesion coefficient by observing the road surface's wetness, water accumulation, and material, and obtain the detection result of whether the vehicle is on a paved road by scanning the road surface's smoothness with the vehicle's radar.

[0048] The third detection can be implemented as follows. Detection module 22 determines whether the vehicle's driving state meets the predetermined driving state based on the following parameters and corresponding preset values, preset ranges, or preset states: 1) Whether the wheel speed of each wheel is within the predetermined wheel speed range; 2) Whether the slippage of each wheel is less than the predetermined slippage amount; 3) Whether the longitudinal vehicle speed is within the predetermined vehicle speed range; 4) Whether the lateral vehicle speed is within the predetermined lateral vehicle speed range; 5) Whether the triggering state of the dynamic controller is a preset state, for example, whether both the traction control system (TCS) and the vehicle dynamics control system (VDC) are in an untriggered state. If the judgment results of all the above judgments are affirmative, a third detection result that meets the triggering conditions is obtained; conversely, if the judgment result of any one or more judgments is negative, a third detection result that does not meet the triggering conditions is obtained.

[0049] According to embodiments of the present invention, the execution order of the first, second, and third detections is not limited. For example, these detections may be performed in a different order than described above, or they may be performed simultaneously.

[0050] Returning to Figure 4, in box 430, the triggering conditions for the virtual sensor are determined based on the first, second, and third detection results. If all three detection results are positive (i.e., all three detection results meet the triggering conditions), it means the current scenario is suitable for using a virtual sensor to calculate vehicle load, and method 400 proceeds to box 440. If any one or more detection results are negative (i.e., any one or more detection results do not meet the triggering conditions), it means the current scenario is not suitable for using a virtual sensor to calculate vehicle load, and method 400 returns to box 420 to continue the above detection. Scenarios unsuitable for using a virtual sensor include, for example, scenarios that might cause the virtual sensor output to contain large errors.

[0051] In block 440, the vehicle load state is obtained using virtual sensors (i.e., a vehicle load model) and multiple sets of parameters characterizing the current state of the vehicle. The vehicle load state includes the calculated value of the vehicle load and its confidence level, as well as the vehicle load level. Block 440 can be executed by processing module 23. A specific implementation of block 440 is described below with reference to Figure 7.

[0052] Referring to Figure 7, in box 441, multiple sets of parameters characterizing the current state of the vehicle are input into the vehicle load model to output multiple first calculated values ​​of the vehicle load. The model outputs one first calculated value for each set of input parameters. This first calculated value is the model-calculated value of the vehicle load. In one embodiment, each set of parameters includes multiple parameters characterizing the vehicle's current driving state, such as the vehicle's longitudinal speed and longitudinal acceleration, lateral speed and lateral acceleration, drive torque and steering angle, and tire pressure. Each parameter can be the average of several measured values; for example, the longitudinal speed can be the average of 10 measured values. This improves the accuracy and reliability of the calculation results.

[0053] In block 442, one or more second calculated values ​​of the vehicle load are obtained based on a plurality of first calculated values, wherein each second calculated value is obtained based on a predetermined number of first calculated values. A specific implementation of block 442 is described below.

[0054] First, the first calculated value output from the model is recorded in the first recording module 24, and the recorded first calculated values ​​are counted to obtain the quantity of the first calculated values. The first recording module 24 records the start time of the first calculated value (i.e., the start time of the count) when the driver, driving assistance system, or autonomous driving system issues a steady-state linear acceleration request or a steady-state linear deceleration request (i.e., the moment the driver, driving assistance system, or autonomous driving system issues an acceleration or deceleration request, and the result of the aforementioned first detection is positive). Furthermore, the count of the first calculated value is read at the end time of steady-state acceleration or steady-state deceleration, thereby obtaining the quantity of the first calculated values. In addition, external environmental factors such as wind resistance, tire rolling resistance, tire pressure, ambient temperature, and ambient pressure, as well as vehicle dynamic changes, are all factors that affect the model calculation; that is, they all affect the model calculation results. In response, the first recording module 24 gradually adjusts the adjustable parameters (e.g., coefficients) of multiple sub-modules within the model based on the magnitude of the influence factors of these factors on the model calculation (i.e., the degree of influence on the model calculation). These sub-modules correspond one-to-one with the aforementioned influencing factors; for example, the model includes sub-modules corresponding to wind resistance, tire rolling resistance, tire pressure, ambient temperature, and ambient pressure. Through iterative adjustments, the change in the first calculated value gradually decreases and eventually converges within a predetermined range. This process can be viewed as filtering the fluctuation components of the first calculated value.

[0055] Next, when the first calculated value recorded in the first recording module 24 reaches a predetermined number (e.g., 100, 200, or 300), a second calculated value of the vehicle load is obtained based on this predetermined number of first calculated values, and this second calculated value is recorded in the second recording module 25. The second calculated value can be the final converged value of the predetermined number of first calculated values, for example, the first calculated value that converges to a predetermined range as described above. The second recording module 25 may be provided with a one-dimensional array for recording the second calculated values, where each number in the array is filled with a second calculated value. For example, the first filling value in the one-dimensional array (i.e., the first filled second calculated value) is the final converged value obtained after iterating from the first first calculated value to the 200th first calculated value. Then, using this final converged value as the initial value of the first calculated value, another 200 iterations are performed to obtain the final converged value of this iteration, which is used as the second second calculated value. And so on. When there is a new second calculated value after the one-dimensional array is filled, the new second calculated value replaces the initially filled second calculated value, and so on.

[0056] In box 443, the calculated value of the vehicle load is determined based on the second calculated value recorded in the second recording module 25. For example, the calculated value of the vehicle load is the average of all the second calculated values ​​in the one-dimensional array, or the average value after removing the value with the largest deviation from the arithmetic mean. If the second recording module 25 records only one second calculated value, that second calculated value is the calculated value of the vehicle load.

[0057] In box 444, the confidence level of the calculated vehicle load is determined based on the number of first and second calculated values, i.e., the reliability of the calculated value. When the number of second calculated values ​​is greater than or equal to 1 (meaning at least one second calculated value is filled in the second recording module, i.e., at least one valid second calculated value is recorded), the confidence level is determined to be acceptable; when the number of second calculated values ​​is zero, the confidence level is determined to be unacceptable. In the case of an acceptable confidence level, the larger the number of second calculated values, the higher the confidence level, i.e., the higher the reliability of the calculated vehicle load. In the case of an unacceptable confidence level, the confidence level is determined based on the number of first calculated values. In this case, in subsequent processing, the calculated vehicle load should not be used or should be selectively used.

[0058] In some other embodiments, the confidence level can be set to a qualified confidence level when the number of the second calculated value is greater than or equal to a quantity threshold, and to an unqualified confidence level when the number of the second calculated value is less than the quantity threshold. Here, the quantity threshold is a positive integer greater than 1.

[0059] According to embodiments of the present invention, the aforementioned "predetermined quantity" can be predetermined based on the needs of vehicle users (e.g., vehicle manufacturers or end users). For example, the predetermined quantity corresponds to the number of first calculated values ​​generated during a complete steady-state linear acceleration behavior of the vehicle (e.g., the driver from pressing the accelerator pedal to fully releasing the accelerator pedal) or a complete steady-state linear deceleration behavior (e.g., the driver from pressing the brake pedal to fully releasing the brake pedal or the vehicle speed decreasing to a predetermined speed), or an integer multiple of that quantity (e.g., 2 times, 3 times, or 5 times the quantity).

[0060] In box 445, the vehicle load status is output, which includes the calculated value of the vehicle load and its confidence level, as well as the vehicle load level. The vehicle load level includes light load, normal load, full load, and overload. The vehicle load level can be obtained by comparing the calculated value of the vehicle load with pre-set reference values ​​for each load level. This invention does not limit the specific method for determining the vehicle load level.

[0061] Referring back to Figure 4, in box 450, when the confidence level of the calculated vehicle load value is acceptable, the adjustment of the vehicle's comfort braking parameters is determined based on the vehicle load level and the calculated vehicle load value. Box 450 can be executed by the comfort braking module 26. A specific implementation of box 450 is described below with reference to Figure 8.

[0062] Referring to Figure 8, in box 451, under the condition that the confidence level of vehicle load is acceptable, different comfort braking modes (i.e., different comfort control strategies) are selected according to different vehicle load levels. For example, when the vehicle is lightly loaded, a more sporty comfort braking mode is selected; when the vehicle is fully loaded, a more comfort-oriented comfort braking mode is selected.

[0063] In addition, the calculated value of the vehicle load is compared with a reference value to obtain the change in vehicle load. This change can be expressed as a specific numerical value (i.e., the change in vehicle load) or as a percentage (i.e., the percentage increase or decrease in vehicle load relative to the reference value). The reference value for vehicle load is the previous valid calculation value of the vehicle load, for example, the calculated value of the vehicle load obtained for the vehicle's previous acceleration, start, or braking / deceleration.

[0064] If the change in vehicle load is less than the vehicle load change threshold (which can be a specific value or a percentage, depending on the form of the change), it means that the change in vehicle load is small and not enough to affect comfort braking performance. In this case, the current comfort braking parameters are kept unchanged, for example, the comfort braking parameters in the current comfort braking mode are kept unchanged (box 452).

[0065] If the change in vehicle load is greater than or equal to the vehicle load change threshold, it means that the change in vehicle load is large and will affect the comfort braking performance. In this case, the comfort braking parameters are adjusted, for example, the comfort braking parameters in the current comfort braking mode are adjusted (box 453).

[0066] According to an embodiment of the present invention, the vehicle load change threshold is predetermined based on the following factors: if the change in load will cause the parking braking force in the comfort braking parameters to no longer match the current vehicle load state, the vehicle load change value at this time corresponds to the threshold.

[0067] Below, some embodiments of box 453 are introduced, namely, examples of adjusting comfort braking parameters according to the determined vehicle load state when the change in vehicle load is greater than the change threshold.

[0068] In one embodiment, when the vehicle is downhill and the comfort braking function is activated, the target braking force or target braking torque in the comfort braking parameters is adjusted based on a calculated value of the determined vehicle load, so that the target braking force is closer to the component of the vehicle's weight along the slope. This adjustment can be achieved through table lookup calculations. For example, a table of vehicle load-gradient-braking compensation coefficients may be pre-created based on real-vehicle testing and / or model calculations, which can be used to determine the target braking force or target braking torque corresponding to the current gradient value and vehicle load value (i.e., the calculated value of the vehicle load).

[0069] In another embodiment, when the vehicle is uphill and the comfort braking function is activated, in addition to adjusting the target braking force or target braking torque in the comfort braking parameters based on the calculated value of the determined vehicle load, the target driving force in the comfort braking parameters is also adjusted based on the calculated value of the determined vehicle load, so that the target driving force can balance the component of the vehicle's weight along the slope direction. This adjustment can be achieved through table lookup calculation. For example, a table of vehicle load-gradient-driving torque values ​​can be pre-created based on real vehicle testing and / or model calculations. Based on this table, the target driving force or target driving torque corresponding to the current gradient value and vehicle load value (i.e., the calculated value of the vehicle load) can be determined.

[0070] Furthermore, according to an embodiment of the present invention, method 400 further includes a process of recalculating the vehicle load when a situation that may cause a change in vehicle load is detected. For example, when a signal indicating the occurrence of at least one of the following situations is detected, it is considered that a situation causing a change in vehicle load has occurred. At this time, the second calculated value in the second recording module 25 is cleared, and new multiple first calculated values ​​are calculated according to the above method. Based on the new multiple first calculated values, one or more new second calculated values ​​are obtained, and a new vehicle load state is obtained according to the above method, including a new calculated value of vehicle load and its confidence level, and a new vehicle load level. The above multiple situations include one or more of the following: the door is open, the trunk is open, a passenger is getting on or off the vehicle, the vehicle is being attached to a trailer, the vehicle is being unattached from a trailer, and the vehicle load state has changed significantly, for example, the change is greater than a predetermined degree (one situation is: the new first calculated value deviates significantly from the recorded first calculated value of vehicle load, and the reason for this significant deviation may be a situation that requires special attention, such as a sudden occupant jumping out of the vehicle).

[0071] All operations described above are merely exemplary, and this disclosure is not limited to any operation in the method or the order of such operations, but should cover all other equivalent transformations under the same or similar concept.

[0072] Embodiments of the present invention also provide a vehicle that includes the control unit 20 described above, thereby possessing the features and advantages described above.

[0073] Embodiments of the present invention also provide a machine-readable storage medium storing executable instructions that, when executed, cause one or more processors to perform the comfort braking control method as described above.

[0074] Embodiments of the present invention also provide a computer program product including computer-executable instructions that, when executed, cause one or more processors to perform the comfort braking control method as described above.

[0075] It should be noted that a processor can be any combination of one or more of the following: a suitable central processing unit, CPU, multiprocessor, microcontroller, digital signal processor, DSP, application-specific integrated circuit, etc., capable of executing software instructions of a computer program stored in memory. Therefore, memory can be considered part of or constituting part of a computer program product. The processor can be configured to execute the computer program stored therein to cause the controller to perform the required steps.

[0076] It should be noted that software should be broadly considered as representing instructions, instruction sets, code, code segments, program code, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, running threads, procedures, functions, etc. Software may reside on a computer-readable medium. Computer-readable media may include, for example, memory, which may be, for example, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical disks, smart cards, flash memory devices, random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, or removable disks. Although memory is shown as separate from the processor in several aspects set forth in this disclosure, memory may also reside within the processor (e.g., in caches or registers).

[0077] The above description is provided to enable any person skilled in the art to implement the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not intended to be limited to the aspects shown herein. All structural and functional equivalents of the elements of the various aspects described herein, as known or forthcoming to those skilled in the art, are expressly incorporated herein by reference and are intended to be covered by the claims.

Claims

1. A control unit for vehicle comfort braking, comprising: The detection module is configured to detect the driving behavior of the vehicle to obtain a first detection result, detect the road conditions of the road on which the vehicle is traveling to obtain a second detection result, and detect the driving status of the vehicle to obtain a third detection result. The processing module is configured to determine whether the triggering conditions of the virtual sensor used to calculate the vehicle load are met based on the first, second, and third detection results. If the triggering condition is determined to be met, the vehicle load status is obtained by using virtual sensors and based on multiple sets of parameters characterizing the current state of the vehicle. as well as The comfort braking module is configured to determine the adjustment of the vehicle's comfort braking parameters based on the vehicle's load condition.

2. The control unit as claimed in claim 1, wherein, The virtual sensor is an artificial intelligence model (AI model). The model input includes the multiple sets of parameters, the model output includes the calculated value of the vehicle load, and the triggering condition corresponds to the applicable conditions of the AI ​​model.

3. The control unit as claimed in claim 1, wherein, The vehicle load status includes the calculated value of the vehicle load and its confidence level, and the vehicle load level includes light load, normal load, full load, and overload.

4. The control unit as claimed in claim 1, wherein, The processing module is configured to: The multiple sets of vehicle status parameters are input into the virtual sensor to output multiple first calculated values ​​of the vehicle load from the virtual sensor; One or more second calculated values ​​of the vehicle load are obtained based on a plurality of first calculated values, wherein each second calculated value is obtained based on a predetermined number of first calculated values; as well as The calculated value of the vehicle load is determined based on one or more second calculated values.

5. The control unit as claimed in claim 4, wherein, The processing module is configured to determine the confidence level of the calculated value of the vehicle load based on the number of first calculated values ​​and the number of second calculated values. Specifically, when the number of second calculated values ​​is greater than or equal to the number threshold, the processing module determines the confidence level as a qualified confidence level, and the confidence level increases as the number of second calculated values ​​increases; When the number of the second calculated values ​​is less than the quantity threshold, the processing module determines the confidence level as an unqualified confidence level and determines the confidence level based on the number of the first calculated values.

6. The control unit as claimed in claim 5, wherein, The processing module is configured to: start counting a first calculated value in response to a steady-state acceleration request or a steady-state deceleration request from a vehicle driver, driving assistance system, or autonomous driving system, and read the count of the first calculated value at the end of steady-state acceleration or steady-state deceleration to obtain the quantity of the first calculated value.

7. The control unit of claim 4, wherein the processing module is configured to: For each predetermined number of first calculated values, the adjustable parameters of multiple sub-modules of the virtual sensor are gradually adjusted so that the change in the first calculated value converges within a predetermined range, and the first calculated value that converges within the predetermined range is determined as the second calculated value. Optionally, the multiple sub-modules correspond to multiple factors that affect the vehicle load calculation, including wind resistance, tire rolling resistance, tire pressure, ambient temperature, and ambient pressure.

8. The control unit as claimed in claim 1, wherein, The comfort braking module is configured to: When the change in vehicle load exceeds a vehicle load change threshold, the comfort braking parameters are adjusted; when the change in vehicle load is less than or equal to the vehicle load change threshold, the comfort braking parameters remain unchanged. The change in vehicle load refers to the difference between the calculated value and the reference value of the vehicle load.

9. The control unit as claimed in claim 1, wherein, The comfort braking module is configured to: Adjust the parameters related to brake control in the comfort braking parameters to match the current vehicle load with the braking force or braking torque required for vehicle comfort braking; and / or The parameters related to drive control in the comfort braking parameters are adjusted to match the current vehicle load with the driving force or driving torque required for vehicle comfort braking.

10. The control unit as claimed in claim 1, wherein, The processing module is configured to determine that the trigger condition is met when the first, second, and third detection results respectively meet the following conditions: - The first detection result indicates that the vehicle's driving behavior causes the vehicle to be in a steady-state linear acceleration state or a steady-state linear deceleration state. The driving behavior includes the driver's driving behavior in the driver driving mode, the driving behavior of the driver assistance system in the driver assistance mode, and the driving behavior of the driver automation system in the autonomous driving mode. - The second test result indicates that the current road condition is a paved road surface where the slope change is less than the slope change threshold; - The third test result indicates that the vehicle's driving status meets the predetermined driving status.

11. The control unit as claimed in claim 1, wherein, The detection module is configured as follows: The vehicle's power output, longitudinal acceleration, acceleration request, and braking request are used to determine whether the vehicle is in a steady-state acceleration or steady-state deceleration state. If the judgment result is positive, then further determine whether the vehicle is in a longitudinal straight-line driving state based on the vehicle's steering condition and lateral force condition. as well as If the judgment result is positive, the first detection result that meets the triggering condition is obtained.

12. The control unit as claimed in claim 1, wherein, The detection module is configured as follows: The vehicle's power output, longitudinal acceleration, acceleration request, and braking request are used to determine whether the vehicle is in a steady-state acceleration or steady-state deceleration state. If the judgment result is positive, determine the change range of the slope value of the road the vehicle is currently traveling on, where the slope value is the slope value after removing the vehicle's pitch interference; Receive the test results regarding whether the current road is paved. If the determined slope value change is less than the slope change threshold and the paved road surface detection result is positive, a second detection result that meets the triggering conditions is obtained.

13. The control unit as claimed in claim 1, wherein, The detection module is configured to obtain a third detection result that meets the triggering conditions when multiple parameters satisfy the corresponding conditions: - The wheel speed of each wheel is within the predetermined wheel speed range; - The slippage of each wheel is within the predetermined slippage range; - The longitudinal vehicle speed is within the predetermined speed range; - The lateral speed is within the predetermined lateral speed range; - The triggering states of multiple dynamic controllers of the vehicle conform to the predetermined states.

14. The control unit as claimed in claim 1, wherein, The processing module is configured to: upon receiving a signal indicating a possible change in vehicle load, re-determine the vehicle load status. The signals indicating a possible change in vehicle load include one or more of the following: - Indicates a signal that the car door is open; - This indicates that the trunk is open; - This indicates that a passenger is boarding or alighting; - This indicates that the vehicle is unloading or loading cargo; - This signal indicates that the vehicle is being attached to a trailer or is being decoupled from an already attached trailer. - This indicates that the vehicle's load condition has changed by a greater than predetermined degree.

15. A vehicle comprising a control unit as claimed in any one of claims 1-14.

16. A control method for vehicle comfort braking, comprising: The vehicle's driving behavior is detected to obtain the initial detection result; The road conditions along the route the vehicle travels are detected to obtain a second detection result; The vehicle's driving status is detected to obtain a third detection result; Based on the first, second, and third detection results, determine whether the triggering conditions of the virtual sensor used to calculate the vehicle's load status are met; If the triggering condition is determined to be met, the vehicle load status is obtained by using virtual sensors and based on multiple sets of parameters characterizing the current state of the vehicle. as well as The adjustment of comfort braking parameters for vehicle comfort braking is determined based on the vehicle's load condition.

17. A computer program product comprising computer-executable instructions that, when executed, cause one or more processors to perform the method of claim 16.

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