Vehicle control method, apparatus, device, storage medium, and program product

Abstract

The application provides a vehicle control method, device, equipment, storage medium and program product, and relates to the technical field of vehicle control. The method comprises the following steps: determining a posture risk coefficient according to constraint state information and seat angle information of a passenger when the passenger is sitting on a zero-gravity seat; adjusting a preset brake triggering threshold value according to the posture risk coefficient to obtain a target brake triggering threshold value; when it is detected that the vehicle has a collision risk, judging whether to trigger braking according to the target brake triggering threshold value; and when it is judged to trigger braking, adjusting a preset deceleration curve according to the posture risk coefficient to obtain a target deceleration curve and performing braking control. The method can generate a target deceleration curve that is adapted to the posture of the passenger when triggering braking, by fusing and quantifying the constraint state of the passenger and the seat angle into the posture risk coefficient, dynamically adjusting the brake triggering threshold value and the deceleration curve, and improving the safety of the passenger when the zero-gravity seat brakes in a non-standard position.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle control method, device, equipment, storage medium, and program product. Background Technology

[0002] With the rapid development of intelligent driving technology, zero-gravity seats, offering a near-reclining comfort experience, have become a standard feature in high-end vehicles. However, zero-gravity seats pose safety risks when used while driving. When occupants are in a reclining position at a large angle, the center of gravity shifts, the seatbelt's fit path changes, and the risk of serious injury in a collision is significantly increased compared to a standard sitting position. Therefore, ensuring driving safety while maintaining the comfort of zero-gravity seats has become an important research direction.

[0003] In related technologies, a preset braking strategy is typically formulated based on the seat being in a standard position to control braking in the event of a vehicle accident. However, in this approach, when the occupant adjusts the seat to a position deviating from the standard position, braking control according to the preset braking strategy is insufficient to protect the occupant, resulting in low occupant safety. Summary of the Invention

[0004] This application provides a vehicle control method, device, equipment, storage medium, and program product for improving occupant safety when a zero-gravity seat brakes in a non-standard position.

[0005] In a first aspect, this application provides a vehicle control method applied to a vehicle with a zero-gravity seat, the method comprising:

[0006] The posture risk coefficient is determined based on the constraint state information of the occupant when sitting in the zero-gravity seat and the seat angle information of the zero-gravity seat.

[0007] Based on the attitude risk coefficient, the preset braking trigger threshold of the vehicle is adjusted to obtain the target braking trigger threshold;

[0008] When a collision risk is detected in the vehicle, it is determined whether to trigger braking based on the target braking trigger threshold.

[0009] When braking is triggered, the preset deceleration curve of the vehicle is adjusted according to the attitude risk coefficient to obtain the target deceleration curve, and braking control is performed on the vehicle according to the target deceleration curve.

[0010] In one possible implementation, adjusting the preset braking trigger threshold of the vehicle based on the attitude risk coefficient to obtain the target braking trigger threshold includes:

[0011] Obtain the preset braking trigger threshold from the vehicle's preset braking strategy;

[0012] Obtain a safety gain coefficient, which is used to amplify the adjustment range of the attitude risk coefficient to the preset braking trigger threshold;

[0013] The threshold adjustment amount of the preset braking trigger threshold is calculated based on the safety gain coefficient and the attitude risk coefficient.

[0014] The preset braking trigger threshold is adjusted according to the threshold adjustment amount to obtain the target braking trigger threshold.

[0015] In one possible implementation, when a collision risk is detected in the vehicle, determining whether to trigger braking based on the target braking trigger threshold includes:

[0016] Obtain the current collision time between the vehicle and the obstacle;

[0017] If the current collision time is less than or equal to the target braking trigger threshold, then braking is determined to be triggered.

[0018] In one possible implementation, the preset deceleration curve of the vehicle is adjusted according to the attitude risk coefficient to obtain a target deceleration curve, including:

[0019] Obtain the preset deceleration curve from the preset braking strategy;

[0020] Obtain a time constant, which is used to control the smoothness of the deceleration curve;

[0021] Based on the attitude risk coefficient and the time constant, the preset deceleration curve is adjusted to obtain the target deceleration curve.

[0022] In one possible implementation, performing braking control on the vehicle according to the target deceleration curve includes:

[0023] The target deceleration curve is sent to the vehicle's brake-by-wire system so that the brake-by-wire system performs braking control on the vehicle according to the target deceleration curve.

[0024] In one possible implementation, determining the posture risk coefficient based on the occupant's constraint state information while sitting in the zero-gravity seat and the seat angle information of the zero-gravity seat includes:

[0025] Calculate the constraint risk value based on the constraint status information;

[0026] Calculate the angle risk value based on the seat angle information;

[0027] The attitude risk coefficient is calculated based on the constraint risk value and the angle risk value.

[0028] In one possible implementation, calculating the attitude risk coefficient based on the constraint risk value and the angle risk value includes:

[0029] Obtain the first weighting coefficient corresponding to the angle risk value and the second weighting coefficient corresponding to the constraint risk value;

[0030] The attitude risk coefficient is obtained by weighting and summing the angle risk value and the constraint risk value according to the first weighting coefficient and the second weighting coefficient.

[0031] In one possible implementation, calculating the angle risk value based on the seat angle information includes:

[0032] The seat angle information is mapped according to a preset angle risk function to obtain the angle risk value corresponding to the seat angle information.

[0033] In one possible implementation, the constraint state information is the degree of constraint of the occupant; the method further includes:

[0034] The first constraint determination result is obtained by identifying the fit between the seat belt path and the occupant's body through the cockpit camera;

[0035] The second constraint determination result is obtained by detecting whether the pressure distribution of the occupant on the seat conforms to the preset constraint mode using a seat pressure distribution sensor.

[0036] Based on the first constraint determination result and the second constraint determination result, the constraint degree of the occupant is determined, and the constraint degree is used to characterize the effectiveness of the occupant being restrained by the seat belt.

[0037] In one possible implementation, the seat angle information is the angle between the backrest of the zero-gravity seat and the vertical direction, and the angle is obtained by an angle sensor.

[0038] In one possible implementation, the method further includes:

[0039] When a collision risk is detected in the vehicle and the attitude risk coefficient is zero, braking control is performed on the vehicle according to the braking trigger threshold and the preset deceleration curve.

[0040] Secondly, this application provides a vehicle control device, the device comprising:

[0041] The first processing module is used to determine the posture risk coefficient based on the constraint state information of the occupant when sitting in the zero gravity seat and the seat angle information of the zero gravity seat.

[0042] The second processing module is used to adjust the preset braking trigger threshold of the vehicle according to the attitude risk coefficient to obtain the target braking trigger threshold.

[0043] The judgment module is used to determine whether to trigger braking based on the target braking trigger threshold when a collision risk is detected in the vehicle.

[0044] The third processing module is used to adjust the preset deceleration curve of the vehicle according to the attitude risk coefficient when it is determined that braking is triggered, to obtain a target deceleration curve, and to perform braking control on the vehicle according to the target deceleration curve.

[0045] Thirdly, this application provides a controller, including: a memory and a processor;

[0046] The memory stores computer-executed instructions;

[0047] The processor executes computer execution instructions stored in the memory to implement the method as described in any of the first aspects.

[0048] Fourthly, this application provides a vehicle, including a vehicle body and a controller as described in the third aspect.

[0049] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in any of the first aspects above.

[0050] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0051] This application provides a vehicle control method, device, equipment, storage medium, and program product. It can determine an attitude risk coefficient based on the constraint state information and seat angle information of an occupant sitting in a zero-gravity seat; adjust a preset braking trigger threshold based on the attitude risk coefficient to obtain a target braking trigger threshold; when a collision risk is detected, determine whether to trigger braking based on the target braking trigger threshold; when braking is determined to be triggered, adjust a preset deceleration curve based on the attitude risk coefficient to obtain a target deceleration curve, and execute braking control according to the target deceleration curve. Through this method, dynamic adaptation of the braking trigger threshold and deceleration curve to the occupant's seat posture and constraint state is achieved, thereby improving occupant safety when the zero-gravity seat brakes in non-standard positions. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0053] Figure 1 A schematic diagram illustrating the application scenarios provided in the embodiments of this application;

[0054] Figure 2 A flowchart illustrating an embodiment of the vehicle control method provided in this application;

[0055] Figure 3 A flowchart illustrating Embodiment 2 of the vehicle control method provided in this application;

[0056] Figure 4 A flowchart illustrating Embodiment 3 of the vehicle control method provided in this application;

[0057] Figure 5 This is a schematic diagram of the structure of the vehicle control device provided in the embodiments of this application;

[0058] Figure 6 This is a schematic diagram of the controller provided in an embodiment of this application.

[0059] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0061] With the rapid development of intelligent driving technology and the continuous improvement of vehicle intelligence, passengers are placing higher demands on driving and riding comfort during their journeys. Zero-gravity seats, designed based on the concept of astronaut zero-gravity posture, adjust the seat back to a large tilt angle (such as 40° to 90°) to distribute body pressure more evenly, effectively relieving muscle fatigue and spinal pressure during long journeys and significantly improving riding comfort. Therefore, they have gradually been introduced from the aviation and aerospace fields into mass-produced passenger vehicles and have become an important feature in mid-to-high-end models.

[0062] However, the safety issues of zero-gravity seats while in motion are becoming increasingly prominent. When occupants are in a reclining position at a large angle, their center of gravity shifts significantly backward compared to a standard sitting position. This causes a shift in the fit between the seatbelt webbing and the occupant's body, making them more prone to abnormal displacements such as "lurching" or "forward lurching" during emergency braking or collisions. The risk of injury to the neck, chest, and abdomen is significantly increased compared to a standard sitting position. Therefore, how to ensure driving safety while retaining the comfort advantages of zero-gravity seats has become an important research direction in the field of automotive safety technology.

[0063] In related technologies, a preset braking strategy is typically formulated based on the seat being in a standard position to control braking in the event of a vehicle accident. This preset braking strategy includes a fixed braking trigger threshold and a standard deceleration curve. However, when the occupant adjusts the seat to a position deviating from the standard position, the occupant's center of gravity and seatbelt fit change, but the braking trigger threshold and deceleration curve in the preset braking strategy still execute according to fixed parameters, without adjusting according to the occupant's actual posture. This results in insufficient protection for the occupant from the preset braking strategy, leading to low occupant safety.

[0064] To address the aforementioned problems, the inventors considered dynamically adjusting the braking strategy based on the constraint state information and seat angle information of the occupant while sitting in the zero-gravity seat. Based on this, after numerous experiments, the inventors discovered that the constraint state information of the occupant while sitting in the zero-gravity seat and the seat angle information of the zero-gravity seat can be acquired during vehicle operation. An attitude risk coefficient is determined based on this information, and a preset braking trigger threshold is adjusted accordingly. When a collision risk is detected, the adjusted threshold is used to determine whether to trigger braking. If braking is triggered, a preset deceleration curve is adjusted based on the attitude risk coefficient, and braking control is executed. In this way, the braking trigger threshold and deceleration curve can be dynamically adjusted based on seat angle and constraint state information, effectively improving occupant safety when braking in non-standard positions with the zero-gravity seat. Based on this, this application proposes a vehicle control method to improve occupant safety when braking in non-standard positions with a zero-gravity seat.

[0065] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. Please refer to [link / reference]. Figure 1 The vehicle is equipped with zero-gravity seats and a controller. The controller can acquire information about the occupant's constraint status and the seat angle when sitting in the zero-gravity seat, and based on this information, it can apply braking control to the vehicle when a collision risk is detected.

[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0067] Figure 2 This is a schematic flowchart illustrating an embodiment of the vehicle control method provided in this application. Please refer to [link / reference]. Figure 2 This method is applied to vehicles with zero-gravity seats, including:

[0068] S201. Determine the attitude risk coefficient based on the constraint state information of the occupant when sitting in the zero-gravity seat and the seat angle information of the zero-gravity seat.

[0069] The execution entity of this application embodiment can be a controller located in a vehicle, or a vehicle control device located in the controller. Vehicle control can be implemented through software or through a combination of software and hardware. The vehicle control device can be a processor in the controller. For ease of understanding, the technical solution of this application will be described below using a controller as an example.

[0070] In this step, during vehicle operation, the controller uses a sensor system deployed in the cabin to collect real-time information on the occupant's constraint status and seat angle while seated in the zero-gravity seat, and determines an attitude risk coefficient based on this information. This attitude risk coefficient characterizes the overall risk level of the occupant in the current seat posture, and its value ranges from 0 to 1. The higher the value of the attitude risk coefficient, the higher the current attitude risk, and the more early and gentler the braking strategy required by the vehicle.

[0071] In practice, the controller calculates the constraint risk value based on the constraint state information, the angle risk value based on the seat angle information, and then calculates the posture risk coefficient based on both the constraint risk value and the angle risk value. The constraint risk value is determined by the degree of constraint of the occupant; the higher the degree of constraint, the lower the constraint risk value. The angle risk value can be obtained by mapping the seat angle information using a preset angle risk function, which is a piecewise function: the angle risk value is lowest when the angle between the seat back and the vertical direction is within the standard sitting posture range; the angle risk value increases linearly with the angle increase when the angle is within the tilted sitting posture range; and the angle risk value is highest when the angle is within the fully reclined posture range.

[0072] Furthermore, the angle risk value and constraint risk value can be weighted and summed to obtain the attitude risk coefficient. The angle risk value corresponds to the first weighting coefficient, and the constraint risk value corresponds to the second weighting coefficient; the sum of the first and second weighting coefficients is 1. The attitude risk coefficient is equal to the product of the angle risk value and the first weighting coefficient plus the product of the constraint risk value and the second weighting coefficient. The first weighting coefficient is used to adjust the weight of the seat angle's influence on the overall risk, and the second weighting coefficient is used to adjust the weight of the constraint state's influence on the overall risk.

[0073] S202. Based on the attitude risk coefficient, adjust the vehicle's preset braking trigger threshold to obtain the target braking trigger threshold.

[0074] In this step, the controller adjusts the vehicle's preset braking trigger threshold based on the attitude risk coefficient to obtain the target braking trigger threshold.

[0075] The vehicle's preset braking trigger threshold is the braking control parameter calibrated at the factory based on the standard sitting posture condition. It is used to trigger the vehicle's automatic emergency braking function, with the time to collision (TTC) as the judgment criterion. When the predicted collision time is lower than the threshold, the vehicle automatically triggers braking.

[0076] Optionally, the controller determines a preset braking trigger threshold in the vehicle's preset braking strategy and obtains a safety gain coefficient. The safety gain coefficient is used to amplify the adjustment range of the attitude risk coefficient on the preset braking trigger threshold. Based on the safety gain coefficient and the attitude risk coefficient, the controller calculates the threshold adjustment amount of the preset braking trigger threshold, and adjusts the preset braking trigger threshold according to the threshold adjustment amount to obtain the target braking trigger threshold.

[0077] S203. When a collision risk is detected, determine whether to trigger braking based on the target braking trigger threshold.

[0078] In this step, the controller monitors for collision risks in real time while the vehicle is in motion. When a collision risk is detected, the controller determines whether to trigger braking based on the target braking trigger threshold.

[0079] In one specific implementation, the controller can obtain the current collision time between the vehicle and the obstacle; if the current collision time is less than or equal to the target braking trigger threshold, it determines to trigger braking; if the current collision time is greater than the target braking trigger threshold, it determines not to trigger braking, and the controller continues to monitor.

[0080] S204. When it is determined that braking is triggered, the preset deceleration curve of the vehicle is adjusted according to the attitude risk coefficient to obtain the target deceleration curve, and braking control of the vehicle is performed according to the target deceleration curve.

[0081] In this step, when the controller determines to trigger braking, it no longer uses the preset deceleration curve calibrated based on the standard sitting posture condition when the vehicle leaves the factory. Instead, it dynamically adjusts the preset deceleration curve according to the posture risk coefficient to obtain the target deceleration curve, and then performs braking control on the vehicle according to the target deceleration curve.

[0082] The vehicle's preset deceleration curve is the braking control parameter calibrated at the factory based on the standard sitting posture condition. It is used to control the deceleration change pattern after braking is triggered, including parameters such as maximum deceleration and deceleration change rate.

[0083] Optionally, the controller can determine a preset deceleration curve in a preset braking strategy and obtain a time constant, which is used to adjust the smoothness of the deceleration curve. Based on the attitude risk coefficient and the time constant, the controller adjusts the preset deceleration curve to obtain the target deceleration curve.

[0084] Furthermore, the controller can send a target deceleration curve to the vehicle's brake-by-wire system so that the brake-by-wire system can perform braking control on the vehicle according to the target deceleration curve.

[0085] In this embodiment, the controller can determine an attitude risk coefficient based on the occupant's constraint state information and the seat angle information of the zero-gravity seat. Based on the attitude risk coefficient, a preset braking trigger threshold is adjusted to obtain a target braking trigger threshold. When a collision risk is detected, the controller determines whether to trigger braking based on the target braking trigger threshold. Furthermore, when braking is triggered, a preset deceleration curve is adjusted based on the attitude risk coefficient, and braking control is executed. By fusing the constraint state and seat angle into an attitude risk coefficient, and dynamically adjusting the braking trigger threshold and deceleration curve based on this coefficient, the braking strategy can adaptively adjust according to the occupant's actual posture and constraint state. This ensures braking safety while also considering occupant comfort, effectively improving occupant safety when braking in non-standard positions with the zero-gravity seat.

[0086] exist Figure 2 Based on the illustrated embodiment, the following is combined with Figure 3 The above vehicle control method will be further explained in detail.

[0087] Figure 3 This is a flowchart illustrating a second embodiment of the vehicle control method provided in this application. Please refer to [link / reference]. Figure 3 The method includes:

[0088] S301. Calculate the constraint risk value based on the constraint status information.

[0089] Optionally, the constraint status information can be the occupant's degree of restraint, which characterizes the effectiveness of the occupant being restrained by the seat belt, and has a value ranging from 0 to 1. The controller determines the constraint risk value based on the degree of restraint; the higher the degree of restraint, the lower the constraint risk value.

[0090] In one specific implementation, the constraint risk value is equal to 1 minus the constraint degree.

[0091] For example, when the restraint degree is 1, it means that the occupant is properly fastened with the seat belt and in an upright posture, and the restraint risk value is 0; when the restraint degree is 0.5, it means that the seat belt is "falsely fastened" or loose, and the restraint risk value is 0.5; when the restraint degree is 0, it means that the occupant is not wearing a seat belt, and the restraint risk value is 1.

[0092] S302. Calculate the angle risk value based on the seat angle information.

[0093] Optionally, the seat angle information can be the angle between the seat back of the zero-gravity seat and the vertical direction, which can be obtained in real time by an angle sensor (such as a rotary encoder or tilt sensor) located at the rotation axis of the seat back. The controller maps the seat angle information according to a preset angle risk function to obtain the angle risk value corresponding to the seat angle information.

[0094] The angle risk function is a piecewise function: when the angle between the seat back and the vertical direction is in the standard sitting posture range (e.g., θ≤30°), the angle risk value is the lowest; when the angle is in the tilted sitting posture range (e.g., 30°<θ<90°), the angle risk value increases linearly with the angle (specifically equal to (θ-30°) / 60°); when the angle is in the lying posture range (e.g., θ≥90°), the angle risk value is the highest.

[0095] S303. Calculate the attitude risk coefficient based on the constraint risk value and the angle risk value.

[0096] In this step, the controller performs a weighted sum of the angle risk value and the constraint risk value to obtain the attitude risk coefficient.

[0097] In practice, the controller can obtain the first weighting coefficient corresponding to the angle risk value and the second weighting coefficient corresponding to the constraint risk value. The angle risk value is multiplied by the first weighting coefficient to obtain the weighted angle risk component, and the constraint risk value is multiplied by the second weighting coefficient to obtain the weighted constraint risk component. The weighted angle risk component and the weighted constraint risk component are then added together to obtain the attitude risk coefficient.

[0098] Optionally, the specific values ​​of the first and second weighting coefficients can be adjusted according to the actual application scenario or vehicle type. For example, in scenarios focusing on seat angle risk, the first weighting coefficient can be set to 0.7 and the second weighting coefficient to 0.3; in scenarios focusing on constraint state risk, the first weighting coefficient can be set to 0.5 and the second weighting coefficient to 0.5; in high-speed driving scenarios, the first weighting coefficient can be set to 0.65 and the second weighting coefficient to 0.35 to more sensitively respond to the impact of seat angle changes on braking risk; in low-speed urban driving scenarios, the first weighting coefficient can be set to 0.55 and the second weighting coefficient to 0.45 to more evenly assess the impact of angle and constraint state on overall risk. It should be noted that the above values ​​are merely illustrative and do not constitute a limitation on this application.

[0099] For example, when the occupant is in a fully reclined position, the angle between the seat back and the vertical direction is 90°, and the angle risk value is 1. Simultaneously, the occupant's seatbelt is either partially fastened or loose, with a restraint degree of 0.5, resulting in a restraint risk value of 0.5. With a first weighting coefficient of 0.6 and a second weighting coefficient of 0.4, the posture risk coefficient is 0.8. This posture risk coefficient is relatively high, indicating a high safety risk for the occupant in the current seat posture.

[0100] S304. Based on the attitude risk coefficient, adjust the vehicle's preset braking trigger threshold to obtain the target braking trigger threshold.

[0101] The controller determines a preset braking trigger threshold within the vehicle's preset braking strategy and obtains a safety gain coefficient. Based on the safety gain coefficient and the attitude risk coefficient, it increases and adjusts the preset braking trigger threshold to obtain the target braking trigger threshold. The safety gain coefficient amplifies the adjustment range of the attitude risk coefficient on the preset braking trigger threshold; the higher the attitude risk coefficient, the larger the target braking trigger threshold, and the earlier the braking is triggered.

[0102] Optionally, the safety gain coefficient is a preset constant greater than 0, and its specific value can be calibrated according to the vehicle type, braking system response characteristics, or occupant safety protection requirements. The larger the safety gain coefficient, the stronger the amplification effect of the attitude risk coefficient on the braking trigger threshold, and the greater the advance of braking. For example, when the safety gain coefficient is 0.3, the braking trigger threshold under the maximum risk attitude is only advanced by 30%; when the safety gain coefficient is 0.5, the braking trigger threshold under the maximum risk attitude can be advanced by 50%. Optionally, the safety gain coefficient can be set to 0.5.

[0103] For example, if the preset braking trigger threshold is 2s and the safety gain coefficient is 0.5, when the attitude risk coefficient is 0.8, the target braking trigger threshold is 2.8s, which means that the vehicle triggers braking 0.8 seconds earlier than the standard condition.

[0104] S305. Obtain the current collision time between the vehicle and the obstacle.

[0105] In this step, the controller can detect obstacle information in front of the vehicle in real time through radar and / or cameras pre-deployed on the vehicle, obtain the relative distance and relative speed between the vehicle and the obstacle, and calculate the current collision time based on the ratio of relative distance to relative speed.

[0106] Optionally, when the controller detects an obstacle in front of the vehicle, it continuously tracks the obstacle's motion and updates the current collision time in real time based on the relative distance and speed between the vehicle and the obstacle. The controller can also filter the acquired relative distance and speed to eliminate the impact of sensor noise and environmental interference on the accuracy of the collision time calculation.

[0107] S306. If the current collision time is less than or equal to the target braking trigger threshold, then determine to trigger braking.

[0108] In this step, the controller compares the current collision time with the target braking trigger threshold. If the current collision time is less than or equal to the target braking trigger threshold, braking is triggered; if the current collision time is greater than the target braking trigger threshold, braking is not triggered, and the controller continues to monitor and update the current collision time.

[0109] For example, if the target braking trigger threshold is 2.8 seconds, and the controller calculates the current collision time to be 3.5 seconds, then the current collision time is greater than the target braking trigger threshold. In this case, the controller determines not to trigger braking and continues to monitor the obstacle information ahead and update the collision time. If, after a period of time, the vehicle in front decelerates, causing the relative distance between the vehicle and the obstacle to shorten, and the current collision time becomes 2.5 seconds, then the current collision time is less than the target braking trigger threshold, and the controller determines to trigger braking.

[0110] S307. When it is determined that braking is triggered, the preset deceleration curve of the vehicle is adjusted according to the attitude risk coefficient to obtain the target deceleration curve.

[0111] In this step, when the controller determines to trigger braking, it can determine a preset deceleration curve in the preset braking strategy and obtain the time constant. Then, the controller can adjust the deceleration peak value and curve shape of the preset deceleration curve according to the attitude risk coefficient and the time constant to obtain the target deceleration curve.

[0112] The time constant is used to adjust the smoothness of the deceleration curve. It can be a preset fixed value or dynamically determined based on the attitude risk coefficient. When the time constant is a fixed value, it is calibrated before the vehicle leaves the factory and stored in the preset braking strategy. When the time constant is dynamically determined based on the attitude risk coefficient, the larger the attitude risk coefficient, the larger the value of the time constant, in order to further slow down the rate of increase of deceleration in the initial stage and make the braking intervention smoother.

[0113] In one optional implementation, the target deceleration curve at each moment is the peak value of a preset deceleration curve multiplied by a product of an attitude risk coefficient and a time-decreasing exponential term. This exponential term gradually approaches 0 from 1 over time. In the initial braking phase, the exponential term is larger, indicating a higher degree of deceleration suppression and a slow curve rise; as time progresses, the exponential term gradually approaches zero, and the deceleration gradually approaches the preset peak value. The time constant controls the rate at which the exponential term decays from 1 to 0; a larger time constant results in a slower decay of the exponential term and a smoother rise in deceleration.

[0114] For example, when the attitude risk coefficient is 0.8 and the time constant is 0.5, the controller significantly reduces the peak value of the preset deceleration curve, while the deceleration slowly increases from a lower value, resulting in a target deceleration curve that is significantly smoother than the standard curve. That is, the deceleration increases slowly in the initial stage of braking and gradually approaches the maximum deceleration capability in the later stage.

[0115] In this way, the target deceleration curve generated by the controller has a lower peak value and a gentler initial slope compared to the preset deceleration curve, exhibiting a gradual characteristic of "slow at first, strong later". The deceleration increases slowly in the initial stage of braking and gradually approaches the maximum deceleration capacity in the later stage, thereby effectively reducing the risk of secondary injury to occupants caused by sudden braking.

[0116] In another alternative implementation, when a collision risk is detected and the attitude risk coefficient is zero, the controller may not make any adjustments to the preset braking trigger threshold and preset deceleration curve, but may directly perform braking control on the vehicle according to the preset braking trigger threshold and preset deceleration curve.

[0117] S308. Send the target deceleration curve to the vehicle's brake-by-wire system so that the brake-by-wire system performs braking control on the vehicle according to the target deceleration curve.

[0118] In this step, the controller can send the target deceleration curve to the vehicle's brake-by-wire system. After receiving the target deceleration curve, the brake-by-wire system can use it as the target command for braking control, controlling the output of braking force according to the curve, so that the actual deceleration of the vehicle follows the changes in the target deceleration curve.

[0119] Furthermore, the brake-by-wire system can monitor the deviation between the actual deceleration and the target deceleration in real time through closed-loop feedback control, and dynamically adjust the braking pressure to ensure that the actual deceleration curve is consistent with the target deceleration curve. For example, when the actual deceleration is lower than the target value, the brake-by-wire system increases the braking pressure; when the actual deceleration is higher than the target value, the brake-by-wire system decreases the braking pressure, thereby achieving precise tracking of the deceleration curve.

[0120] Optionally, the brake-by-wire system can be an Electronic Stability Program (ESP) and / or an electronic brake booster (ibooster). ESP is used for stability control and brake force distribution during vehicle braking, while ibooster is used to drive the master cylinder based on electronic signals to achieve precise brake pressure control.

[0121] Optionally, after receiving the target deceleration curve, the brake-by-wire system can also adaptively modify the target deceleration curve based on the current vehicle state (such as vehicle speed, wheel speed, and road surface adhesion coefficient) to adapt to the braking control requirements under different road conditions. For example, on roads with low adhesion coefficients, the brake-by-wire system can appropriately reduce the peak value of the target deceleration curve to avoid wheel lock-up; on roads with high adhesion coefficients, the brake-by-wire system can precisely execute braking control according to the target deceleration curve.

[0122] In this embodiment, the controller can calculate the constraint risk value based on the constraint state information and the angle risk value based on the seat angle information. The constraint risk value and the angle risk value are then weighted and summed to obtain the attitude risk coefficient. Based on this, the controller can adjust the preset braking trigger threshold according to the attitude risk coefficient, enabling the vehicle to intervene earlier when a collision risk occurs. Simultaneously, the controller dynamically adjusts the peak value and curve shape of the preset deceleration curve based on the attitude risk coefficient and the time constant, generating a target deceleration curve that is "gradually strong." Through this dual optimization strategy of "early intervention and gradual braking," the braking trigger timing and deceleration curve can be synergistically optimized. This allows the braking strategy to adaptively adjust according to the actual constraint state and seat posture of the occupant, thereby significantly reducing the risk of additional damage caused by inertial forward thrust or sliding in non-standard seating positions while ensuring emergency braking effectiveness. This enhances the vehicle's occupant protection capability in complex seating postures.

[0123] Figure 4 This is a flowchart illustrating a third embodiment of the vehicle control method provided in this application. Please refer to [link / reference]. Figure 4 Based on any of the above embodiments, where the constraint state information is the degree of constraint of the occupants, the method further includes:

[0124] S401. The seat belt path and the fit between the seat belt and the occupant's body are identified by the cockpit camera to obtain the first constraint determination result.

[0125] In this step, the controller can collect image information of the occupants through cameras installed in the cockpit, and use computer vision algorithms to identify the extension path of the seat belt webbing on the occupant's body surface, and analyze the degree of fit between the seat belt and the occupant's shoulders and waist.

[0126] In practice, the controller can use deep learning object detection algorithms to detect key human features and seat belt paths in the image. For example, the controller can use human pose recognition algorithms (such as the OpenPose algorithm) to detect key human features of the occupant (such as shoulders, chest, waist, etc.) in the image, and at the same time use single-stage object detection algorithms (such as the YOLO algorithm) to perform object detection and path recognition on the seat belt webbing. By comparing the seat belt path with the key human features, it can determine whether the seat belt crosses diagonally from the shoulder to the chest and waist and fits snugly against the occupant's body surface.

[0127] Optionally, the controller can also identify the buckle status of the seat belt to help determine whether the seat belt is properly fastened. By detecting the buckle switch signal or visual features of the buckle area in the image, it can be confirmed whether the seat belt is inserted into the buckle. If the buckle signal indicates that it is not inserted, it is directly determined that the seat belt is not fastened, and there is no need to perform path fit analysis.

[0128] In one specific implementation, the controller divides the first constraint determination result into three levels based on the seat belt path fit and buckle status: when the seat belt path is consistent with the preset path, the fit is good, and the buckle is locked, it is determined as "the seat belt is fastened and fits well"; when the seat belt path deviates or the fit is poor but the buckle is locked, it is determined as "the seat belt fit is abnormal"; when the buckle is not locked, it is determined as "the seat belt is not fastened".

[0129] In some embodiments, after acquiring image information, the controller can also preprocess the image, including image enhancement, noise reduction, and normalization, to improve image quality and thus improve the accuracy of subsequent seat belt path recognition.

[0130] S402. The seat pressure distribution sensor detects whether the pressure distribution of the occupant on the seat conforms to the preset constraint mode, and obtains the second constraint judgment result.

[0131] In this step, the controller can collect contact pressure distribution data between the occupant and the seat through an array of pressure distribution sensors installed in the seat cushion and backrest, and detect whether the pressure distribution conforms to the standard restraint pattern when the seat belt is properly fastened.

[0132] In practice, the pressure distribution sensor array can include multiple pressure sensing units, evenly distributed in the seat cushion and backrest areas, to collect static pressure distribution data under occupant seating posture. The controller compares the collected pressure distribution data with a pre-stored standard pressure distribution template. The standard pressure distribution template is the pressure distribution data collected when the occupant is in a standard sitting posture and the seatbelt is properly fastened.

[0133] Optionally, when the consistency between the pressure distribution and the standard template exceeds a preset threshold, the controller determines that the occupant pressure distribution is normal, and the second constraint determination result is "normal pressure distribution"; when the deviation between the pressure distribution and the standard template exceeds a preset threshold, the controller determines that the occupant pressure distribution is abnormal, and the second constraint determination result is "abnormal pressure distribution". For example, when the occupant's body pressure distribution exhibits an asymmetrical pattern, the pressure center deviates from the normal position, or there is a lack of local pressure, it may indicate that the seat belt is not properly fastened or the occupant's posture is abnormal.

[0134] In addition, the controller can determine whether there is an occupant on the seat based on pressure distribution data. When the pressure distribution data indicates that there is no effective pressure distribution on the seat, the controller can directly determine the constraint value to be 0 and skip the subsequent constraint determination steps to save computational resources.

[0135] In some embodiments, the controller can also identify the occupant's body shape characteristics based on pressure distribution data, including the occupant's weight distribution and sitting posture. The controller can adaptively adjust the standard pressure distribution template based on body shape characteristics to suit occupants of different body shapes, thereby improving the accuracy of pressure distribution detection.

[0136] S403. Determine the degree of constraint of the occupants based on the results of the first constraint determination and the second constraint determination.

[0137] In this step, the controller can comprehensively evaluate the obtained first constraint determination result and the obtained second constraint determination result to determine the degree of restraint of the occupant. The degree of restraint is used to characterize the effectiveness of the occupant being restrained by the seat belt.

[0138] Specifically, when the first constraint determination result is "the seat belt is fastened and fits well" and the second constraint determination result is "the pressure distribution is normal", the controller determines the constraint degree C=1, indicating that the occupant is correctly restrained by the seat belt and has a proper posture.

[0139] When the first constraint determination result is "abnormal seat belt fit" or the second constraint determination result is "abnormal pressure distribution", the controller determines the constraint degree C=0.5, indicating that the occupant's seat belt is "falsely fastened" or loose, and the constraint effect is poor.

[0140] When the first constraint determination result is "seat belt not fastened" or the second constraint determination result is "no effective pressure distribution", the controller determines the constraint degree C=0, indicating that the occupant is not fastening the seat belt or there is no occupant in the seat.

[0141] Optionally, when there is a conflict between the first constraint determination result and the second constraint determination result, the controller can make a comprehensive judgment based on a predetermined confidence level priority.

[0142] For example, image recognition may show that the seatbelt is fastened and fits well, but pressure distribution detection results may show abnormal pressure distribution. In this case, the first constraint determination result and the second constraint determination result are inconsistent. In this situation, the controller first detects the image quality of the cockpit camera. If the image quality is good (e.g., sufficient brightness, no obstruction, clear image), the image recognition result is used as the primary factor to determine that the occupant's seatbelt restraint status is fastened. If the image quality deteriorates (e.g., insufficient light at night, camera obstruction, or blurry image), the pressure distribution detection result and the buckle status signal are used as the primary factors for a comprehensive determination.

[0143] In this embodiment, the controller can identify the fit between the seatbelt path and the occupant's body using a cockpit camera to obtain a first constraint determination result; it can also detect whether the pressure distribution of the occupant on the seat conforms to a preset constraint pattern using a seat pressure distribution sensor to obtain a second constraint determination result; and finally, it can comprehensively determine the occupant's constraint degree based on both the first and second constraint determination results. In this process, the controller, through a dual-modal collaborative approach of visual and pressure perception, can achieve comprehensive perception and cross-verification of the occupant's constraint state, effectively improving the accuracy of constraint degree determination and thus providing reliable input parameters for the precise calculation of the attitude risk coefficient.

[0144] Figure 5 This is a schematic diagram of the vehicle control device provided in an embodiment of this application. Please refer to... Figure 5 The vehicle control device 10 includes:

[0145] The first processing module 11 is used to determine the attitude risk coefficient based on the constraint state information of the occupant when sitting in the zero gravity seat and the seat angle information of the zero gravity seat.

[0146] The second processing module 12 is used to adjust the vehicle's preset braking trigger threshold according to the attitude risk coefficient to obtain the target braking trigger threshold.

[0147] The judgment module 13 is used to determine whether to trigger braking based on the target braking trigger threshold when a collision risk is detected in the vehicle.

[0148] The third processing module 14 is used to adjust the vehicle's preset deceleration curve according to the attitude risk coefficient when it is determined that braking is triggered, to obtain the target deceleration curve, and to perform braking control on the vehicle according to the target deceleration curve.

[0149] The vehicle control device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0150] In one possible implementation, the second processing module 12 is specifically used for:

[0151] Determine the preset braking trigger threshold in the vehicle's preset braking strategy;

[0152] Obtain the safety gain coefficient, which is used to amplify the adjustment range of the attitude risk coefficient on the preset braking trigger threshold;

[0153] Based on the safety gain coefficient and attitude risk coefficient, calculate the threshold adjustment amount of the preset braking trigger threshold;

[0154] The preset braking trigger threshold is adjusted according to the threshold adjustment amount to obtain the target braking trigger threshold.

[0155] In one possible implementation, the determination module 13 is specifically used for:

[0156] Obtain the current collision time between the vehicle and the obstacle;

[0157] If the current collision time is less than or equal to the target braking trigger threshold, then braking is determined to be triggered.

[0158] In one possible implementation, the third processing module 14 is specifically used for:

[0159] Determine the preset deceleration curve in the preset braking strategy;

[0160] Obtain the time constant, which is used to adjust the smoothness of the deceleration curve;

[0161] Based on the attitude risk coefficient and time constant, the preset deceleration curve is adjusted to obtain the target deceleration curve.

[0162] In one possible implementation, the third processing module 14 is specifically used for:

[0163] The target deceleration curve is sent to the vehicle's brake-by-wire system so that the brake-by-wire system can perform braking control on the vehicle according to the target deceleration curve.

[0164] In one possible implementation, the first processing module 11 is specifically used for:

[0165] Calculate the constraint risk value based on the constraint status information;

[0166] Calculate the angle risk value based on the seat angle information;

[0167] Calculate the attitude risk coefficient based on the constraint risk value and the angle risk value.

[0168] In one possible implementation, the first processing module 11 is specifically used for:

[0169] Obtain the first weighting coefficient corresponding to the angle risk value and the second weighting coefficient corresponding to the constraint risk value;

[0170] Based on the first and second weighting coefficients, the angle risk value and the constraint risk value are weighted and summed to obtain the attitude risk coefficient.

[0171] In one possible implementation, the first processing module 11 is specifically used for:

[0172] The seat angle information is mapped according to a preset angle risk function to obtain the angle risk value corresponding to the seat angle information.

[0173] In one possible implementation, the constraint state information is the degree of constraint of the occupants, and the first processing module 11 is further used for:

[0174] The first constraint determination result is obtained by identifying the fit between the seat belt path and the occupant's body through the cockpit camera;

[0175] The second constraint determination result is obtained by detecting whether the pressure distribution of the occupant on the seat conforms to the preset constraint mode through the seat pressure distribution sensor.

[0176] Based on the results of the first and second constraint determinations, the degree of constraint of the occupant is determined. The degree of constraint is used to characterize the effectiveness of the occupant being restrained by the seat belt.

[0177] In one possible implementation, the seat angle information is the angle between the backrest of the zero-gravity seat and the vertical direction, which is obtained by an angle sensor.

[0178] In one possible implementation, the third processing module 14 is further configured to:

[0179] When a collision risk is detected and the attitude risk coefficient is zero, braking control is performed on the vehicle according to the braking trigger threshold and the preset deceleration curve.

[0180] The vehicle control device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0181] Figure 6 This is a schematic diagram of the controller provided in an embodiment of this application. Please refer to... Figure 6 The controller 20 provided in this embodiment can be a vehicle controller, domain controller, or dedicated controller deployed in a vehicle. The controller 20 includes at least one processor 21 and a memory 22. Optionally, the controller 20 also includes a communication component 23. The processor 21, memory 22, and communication component 23 are connected via a bus 24.

[0182] In the specific implementation process, at least one processor 21 executes computer execution instructions stored in memory 22, causing at least one processor 21 to execute the above-described vehicle control method.

[0183] The specific implementation process of processor 21 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0184] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0185] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0186] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0187] This application also provides a vehicle, including a vehicle body, and... Figure 6 The controller shown is used to implement the vehicle control method in the above embodiments.

[0188] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0189] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0190] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0191] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0192] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0193] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0195] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0196] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0197] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A vehicle control method, characterized in that, Applied to vehicles with zero-gravity seats, the method includes: The posture risk coefficient is determined based on the constraint state information of the occupant when sitting in the zero-gravity seat and the seat angle information of the zero-gravity seat. Based on the attitude risk coefficient, the preset braking trigger threshold of the vehicle is adjusted to obtain the target braking trigger threshold; When a collision risk is detected in the vehicle, it is determined whether to trigger braking based on the target braking trigger threshold. When braking is triggered, the preset deceleration curve of the vehicle is adjusted according to the attitude risk coefficient to obtain the target deceleration curve, and braking control is performed on the vehicle according to the target deceleration curve.

2. The method according to claim 1, characterized in that, The step of adjusting the preset braking trigger threshold of the vehicle based on the attitude risk coefficient to obtain the target braking trigger threshold includes: The preset braking trigger threshold is determined in the preset braking strategy of the vehicle; Obtain a safety gain coefficient, which is used to amplify the adjustment range of the attitude risk coefficient to the preset braking trigger threshold; The threshold adjustment amount of the preset braking trigger threshold is calculated based on the safety gain coefficient and the attitude risk coefficient. The preset braking trigger threshold is adjusted according to the threshold adjustment amount to obtain the target braking trigger threshold.

3. The method according to claim 1, characterized in that, When a collision risk is detected in the vehicle, determining whether to trigger braking based on the target braking trigger threshold includes: Obtain the current collision time between the vehicle and the obstacle; If the current collision time is less than or equal to the target braking trigger threshold, then braking is determined to be triggered.

4. The method according to claim 2, characterized in that, Based on the attitude risk coefficient, the preset deceleration curve of the vehicle is adjusted to obtain the target deceleration curve, including: The preset deceleration curve is determined in the preset braking strategy; Obtain a time constant, which is used to adjust the smoothness of the deceleration curve; Based on the attitude risk coefficient and the time constant, the preset deceleration curve is adjusted to obtain the target deceleration curve.

5. The method according to any one of claims 1-4, characterized in that, The step of performing braking control on the vehicle according to the target deceleration curve includes: The target deceleration curve is sent to the vehicle's brake-by-wire system so that the brake-by-wire system performs braking control on the vehicle according to the target deceleration curve.

6. The method according to any one of claims 1-4, characterized in that, The determination of the posture risk coefficient based on the occupant's constraint state information while sitting in the zero-gravity seat, and the seat angle information of the zero-gravity seat, includes: Calculate the constraint risk value based on the constraint status information; Calculate the angle risk value based on the seat angle information; The attitude risk coefficient is calculated based on the constraint risk value and the angle risk value.

7. The method according to claim 6, characterized in that, The step of calculating the attitude risk coefficient based on the constraint risk value and the angle risk value includes: Obtain the first weighting coefficient corresponding to the angle risk value and the second weighting coefficient corresponding to the constraint risk value; The attitude risk coefficient is obtained by weighting and summing the angle risk value and the constraint risk value according to the first weighting coefficient and the second weighting coefficient.

8. The method according to claim 6, characterized in that, The step of calculating the angle risk value based on the seat angle information includes: The seat angle information is mapped according to a preset angle risk function to obtain the angle risk value corresponding to the seat angle information.

9. The method according to any one of claims 1-4, characterized in that, The constraint state information is the degree of constraint of the occupant; the method further includes: The first constraint determination result is obtained by identifying the fit between the seat belt path and the occupant's body through the cockpit camera; The second constraint determination result is obtained by detecting whether the pressure distribution of the occupant on the seat conforms to the preset constraint mode using a seat pressure distribution sensor. Based on the first constraint determination result and the second constraint determination result, the constraint degree of the occupant is determined, and the constraint degree is used to characterize the effectiveness of the occupant being restrained by the seat belt.

10. The method according to any one of claims 1-4, characterized in that, The seat angle information is the angle between the backrest of the zero-gravity seat and the vertical direction, and the angle is obtained by an angle sensor.

11. The method according to any one of claims 1-4, characterized in that, The method further includes: When a collision risk is detected in the vehicle and the attitude risk coefficient is zero, braking control is performed on the vehicle according to the braking trigger threshold and the preset deceleration curve.

12. A vehicle control device, characterized in that, The device includes: The first processing module is used to determine the posture risk coefficient based on the constraint state information of the occupant when sitting in the zero gravity seat and the seat angle information of the zero gravity seat. The second processing module is used to adjust the preset braking trigger threshold of the vehicle according to the attitude risk coefficient to obtain the target braking trigger threshold. The judgment module is used to determine whether to trigger braking based on the target braking trigger threshold when a collision risk is detected in the vehicle. The third processing module is used to adjust the preset deceleration curve of the vehicle according to the attitude risk coefficient when it is determined that braking is triggered, to obtain a target deceleration curve, and to perform braking control on the vehicle according to the target deceleration curve.

13. A controller, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-11.

14. A vehicle, characterized in that, include: The vehicle body and the controller as described in claim 13.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-11.

16. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-11.

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