Vehicle control method and vehicle

Abstract

This disclosure relates to the field of autonomous driving technology, specifically providing a vehicle control method and a vehicle. The method includes: acquiring global information, including the lateral tilt angle and longitudinal slope angle of the driving road; and calculating the vehicle driving control quantity: Step 1: predicting the vehicle driving state quantity for multiple future time steps based on the dynamic relationship between the vehicle driving state quantities of adjacent sampling time steps; the current vehicle driving state quantity is determined based on the global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, the vehicle dynamic state information including: vehicle roll angle and roll angular velocity; Step 2: determining a risk index and a resource allocation weight for control preference based on the predicted vehicle driving state quantity and a specified threshold, the control preference including stability control preference or precision control preference; Step 3: generating the target vehicle driving control quantity based on the resource allocation weight of the control preference, and controlling the vehicle.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to a vehicle control method and a vehicle. Background Technology

[0002] When unmanned mining trucks operate in mining environments, they frequently encounter emergency conditions such as slippery slopes, sharp bends, and soft road surfaces. Their dynamic characteristics exhibit strong nonlinearity and multi-dimensional, highly coupled effects across longitudinal, lateral, and roll dimensions. Traditional vehicle control methods typically design controllers based on simplified vehicle dynamics models with two degrees of freedom, making it difficult to accurately predict and compensate for the coupling effects of vehicle roll motion, resulting in large model prediction errors. Furthermore, in emergency conditions, prioritizing vehicle stability often comes at the expense of trajectory tracking accuracy, or the pursuit of high-precision trajectory tracking leads to vehicle instability, thus limiting the vehicle's adaptability to emergency conditions and operational safety. Summary of the Invention

[0003] This disclosure provides a vehicle control method and a vehicle to address the problems of low control accuracy and inability to dynamically balance stability and trajectory tracking accuracy in existing vehicle dynamics models, making them unsuitable for emergency mining conditions.

[0004] In view of the above problems, in a first aspect, the present disclosure provides a vehicle control method, including: Obtain global information about the vehicle, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road; The vehicle driving control parameters are calculated, and the following steps 1-3 are performed in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, predict vehicle driving state quantities for multiple future time steps based on the current vehicle driving state quantity corresponding to the current sampling time; wherein, the current vehicle driving state quantity is determined based on the vehicle's global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, wherein the vehicle dynamic state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle and roll rate, and the driving trajectory information includes the following indicators: center of gravity lateral position and heading angle; Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference according to the risk index, wherein the control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weights corresponding to the control preferences, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity.

[0005] In conjunction with the first aspect, in one possible implementation, the risk index includes a stability risk index and a precision risk index; determining the resource allocation weight corresponding to the vehicle's control preference based on the risk index includes: Based on the stability risk index, determine the degree of primary impact of the deviation of each index value corresponding to the vehicle dynamic state information on vehicle stability control. Based on the accuracy risk index, determine the degree of secondary impact of the deviation of each indicator value corresponding to the driving trajectory information on the vehicle accuracy control. Based on the first degree of influence and the second degree of influence, determine the resource allocation weights for stability control preference and precision control preference; Based on the resource allocation weights corresponding to control preferences, the target vehicle driving control quantities are generated as follows: The target vehicle driving control quantity is determined based on the first degree of influence, the second degree of influence, the resource allocation weight of stability control preference, the resource allocation weight of precision control preference, and the deviation of each index value corresponding to the vehicle driving state quantity.

[0006] In conjunction with the first aspect, in one possible implementation, the target vehicle driving control quantity is determined based on the first degree of influence, the second degree of influence, the resource allocation weights for stability control preference, the resource allocation weights for precision control preference, and the deviations of various index values ​​corresponding to the vehicle driving state quantity, including: Based on the deviations of various index values ​​corresponding to vehicle driving state quantities and vehicle driving control quantities, a comprehensive performance index function is constructed. The comprehensive performance index function includes a driving state quantity term and a driving control quantity term; the driving state quantity term includes a dynamic state information term and a driving trajectory information term, the dynamic state information term includes at least one vehicle driving state index term, the driving trajectory information term includes at least one driving trajectory index term, the coefficient of each vehicle driving state index term is the corresponding first degree of influence, the coefficient of each driving trajectory index term is the corresponding second degree of influence, the coefficient of the dynamic state information term is the resource allocation weight of the stability control preference, and the coefficient of the driving trajectory information term is the resource allocation weight of the precision control preference. The target vehicle driving control quantity is solved based on the comprehensive performance index function so that the value of the function meets the preset conditions.

[0007] In conjunction with the first aspect, in one possible implementation, a risk index corresponding to the predicted vehicle driving state quantity is determined based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, including: The risk index corresponding to the first indicator is determined by the square of the ratio of the first indicator to the corresponding specified threshold. The first indicator includes one of the following: yaw rate, center of gravity sideslip angle, vehicle roll index, center of gravity lateral position deviation, and heading angle deviation. The vehicle roll index is determined based on the lateral tilt angle, longitudinal slope angle, vehicle roll angle, and roll rate.

[0008] In conjunction with the first aspect, in one possible implementation, the degree of target influence is determined by the following methods, whereby the degree of target influence includes a first degree of influence or a second degree of influence: Vehicle driving state quantities include sub-vehicle driving state quantities, which are vehicle dynamic state information or driving trajectory information, and each sub-vehicle driving state quantity includes at least one indicator. The total risk index is determined by summing the risk indices corresponding to at least one of the indicators. The degree of impact of at least one target is determined based on the ratio of the risk index corresponding to each of the at least one indicator to the total risk index.

[0009] In conjunction with the first aspect, in one possible implementation, resource allocation weights for stability control preferences and precision control preferences are determined based on the first degree of influence and the second degree of influence, including: The maximum target influence level among the various target influence levels is determined, as well as the sum of the influence levels of all targets. A correction coefficient is determined based on the ratio of the maximum target influence level to the sum of the influence levels of all targets. A target quantification coefficient is determined based on the average value of the influence levels of all targets, the maximum target influence level, and the correction coefficient. The target influence level is either a first influence level or a second influence level, and the target quantification coefficient is either a stability quantification coefficient or a precision quantification coefficient. The resource allocation weight for stability control preference is determined by multiplying the ratio of the stability quantification coefficient to the accuracy quantification coefficient with the basic weight of the stability index. The resource allocation weight for precision control preference is determined by multiplying the ratio of the precision quantification coefficient to the stability quantification coefficient with the basic weight of the precision index.

[0010] In conjunction with the first aspect, in one possible implementation, the dynamic state information item, the driving trajectory information item, and the driving control quantity item each include a corresponding time-domain decay factor coefficient; the time-domain decay factor coefficient decreases as the predicted future time step increases.

[0011] In conjunction with the first aspect, in one possible implementation, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps, including: Based on the current longitudinal vehicle speed, the current tangent of the centroid sideslip angle, the current cosine of the heading angle, and the current sine of the heading angle, determine the increment of the centroid's lateral position within the sampling period; The lateral position of the centroid at the next moment is determined by summing the lateral position increment of the centroid within the sampling period with the lateral position of the centroid at the current moment.

[0012] In conjunction with the first aspect, in one possible implementation, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps, including: Based on the sampling period, total vehicle mass, longitudinal force of the left front axle tire at the current moment, cosine value of front wheel steering angle, longitudinal force of the right front axle tire at the current moment, longitudinal force of the left rear axle tire at the current moment, and longitudinal force of the right rear axle tire at the current moment, determine the longitudinal speed increment within the sampling period. The longitudinal speed at the next moment is determined by summing the longitudinal speed increment within the sampling period with the longitudinal speed at the current moment.

[0013] In conjunction with the first aspect, in one possible implementation, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps, including: Obtain the vehicle's center of gravity sideslip angle self-coupling coefficient, yaw rate self-coupling coefficient, center of gravity sideslip angle yaw rate cross-coupling coefficient, and yaw rate center of gravity sideslip angle cross-coupling coefficient. The determinant of the vehicle lateral dynamics system state matrix is ​​determined based on the sampling period, the self-coupling coefficient of the center of mass sideslip angle, the self-coupling coefficient of the yaw rate, the cross-coupling coefficient of the center of mass sideslip angle and the cross-coupling coefficient of the yaw rate and the center of mass sideslip angle. The sideslip angle of the center of mass at the next moment is determined based on the determinant of the state matrix of the vehicle's lateral dynamics system.

[0014] In conjunction with the first aspect, in one possible implementation, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps, including: Based on the current centroid sideslip angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle, the current vehicle body roll angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle velocity, the lateral tilt angle and longitudinal slope angle of the driving road, determine the first-order lateral dynamic equivalent disturbance term; Based on the yaw rate at the current moment, the front wheel steering angle at the current moment, the longitudinal forces of the left and right tires of the front axle, and the difference between the left and right longitudinal forces of the front and rear axles at the current moment, determine the equivalent disturbance term of the second-order lateral dynamics. The yaw rate at the next moment is determined based on the sampling period, the first-order lateral dynamic equivalent perturbation term, and the second-order lateral dynamic equivalent perturbation term.

[0015] In conjunction with the first aspect, in one possible implementation, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps, including: The excitation gain coefficient of the front wheel steering angle on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle. The gain coefficient of the longitudinal force on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total mass of the vehicle, the moment of inertia of the vehicle about the roll axis, and the front wheel angle. The roll rate at the next moment is determined based on the sampling period, the front wheel steering angle to roll angle excitation gain coefficient, and the front axle longitudinal force to roll angle excitation gain coefficient.

[0016] In a second aspect, a vehicle is provided, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, perform steps of the vehicle control method as described in the first aspect or in any possible embodiment of the first aspect.

[0017] Thirdly, a vehicle control device is provided, comprising: A global information acquisition module is used to acquire global information about the vehicle, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road. The vehicle driving control quantity determination module is used to calculate the vehicle driving control quantity, and performs the following steps 1-3 in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, predict vehicle driving state quantities for multiple future time steps based on the current vehicle driving state quantity corresponding to the current sampling time; wherein, the current vehicle driving state quantity is determined based on the vehicle's global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, wherein the vehicle dynamic state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle and roll rate, and the driving trajectory information includes the following indicators: center of gravity lateral position and heading angle; Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference according to the risk index, wherein the control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weights corresponding to the control preferences, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity.

[0018] The beneficial effects of the embodiments disclosed herein include: This disclosure provides a vehicle control method and a vehicle, comprising: acquiring global information of the vehicle, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road; calculating vehicle driving control quantities, and performing the following steps 1-3 in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current sampling time, predicting vehicle driving state quantities for multiple future time steps; wherein the current vehicle driving state quantity is determined based on the vehicle's global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, vehicle dynamics... The vehicle's driving state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, vehicle roll angle, and roll rate. The driving trajectory information includes the following indicators: center of gravity lateral position and heading angle. Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference based on the risk index. The control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weight corresponding to the control preference, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity. The vehicle control method provided in this embodiment introduces a dynamic relationship with roll degrees of freedom, incorporating the lateral tilt angle and longitudinal slope angle of the driving road into the modeling calculation. This fully restores the force and attitude change laws of the vehicle under emergency conditions such as slippery slopes and sharp bends, predicting outputs that conform to the vehicle's true dynamic characteristics, eliminating prediction bias from the root of modeling, and ensuring that the solved target driving control quantity matches the actual operating state of the vehicle. Based on the quantification of risk index by the deviation between predicted driving state quantities and specified thresholds, the system dynamically allocates resource weights for stability control preferences and precision control preferences, adaptively coordinating vehicle stability and trajectory tracking accuracy. This avoids the problem of traditional methods using fixed weight configurations, which cannot flexibly switch control priorities when operating conditions become riskier, easily leading to situations where trajectory tracking accuracy is sacrificed to maintain stability, or where pursuing trajectory tracking accuracy causes vehicle instability. By automatically identifying the current dominant risk and adjusting the priority of control resources, the system achieves "strong intervention to maintain stability in critical situations, and fine-tuning to pursue accuracy in stable situations." Under the premise of ensuring a safety baseline, it maximizes the potential of vehicle traffic efficiency and fully leverages the operational efficiency of mining vehicles. Attached Figure Description

[0019] Figure 1 A flowchart of a vehicle control method provided in an embodiment of this disclosure; Figure 2 This is a structural diagram of a vehicle control device provided in an embodiment of the present disclosure. Detailed Implementation

[0020] This disclosure provides a vehicle control method and a vehicle. Preferred embodiments of this disclosure are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of this disclosure. Furthermore, the embodiments and features described herein can be combined with each other unless otherwise specified.

[0021] This disclosure provides a vehicle control method, including: S101. Obtain the vehicle's global information, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road. S102. Calculate the vehicle driving control quantities, and perform the following steps 1-3 in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, predict vehicle driving state quantities for multiple future time steps based on the current vehicle driving state quantity corresponding to the current sampling time; wherein, the current vehicle driving state quantity is determined based on the global information of the vehicle; vehicle driving state quantities include: vehicle dynamic state information and driving trajectory information, the vehicle dynamic state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle and roll rate, and the driving trajectory information includes the following indicators: center of gravity lateral position and heading angle; Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference according to the risk index. The control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weights corresponding to the control preferences, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity.

[0022] In this embodiment, unmanned mining trucks frequently encounter emergency conditions in mining environments, such as slippery slopes, sharp bends, and soft road surfaces. Their dynamic characteristics exhibit strong nonlinearity and multi-dimensional, highly coupled effects in longitudinal, lateral, and roll directions. Traditional control methods typically design controllers based on simplified (e.g., two-degree-of-freedom) vehicle dynamics models, making it difficult to accurately predict and compensate for the coupling effects of vehicle roll motion, resulting in large model prediction errors. Furthermore, multiple objectives, such as vehicle stability and trajectory tracking accuracy, often conflict under emergency conditions. Traditional methods typically use fixed weights, failing to dynamically adjust the priority of each objective, leading to compromises in emergency situations. Either trajectory tracking accuracy is sacrificed to ensure vehicle stability, or vehicle instability is caused by pursuing higher trajectory tracking accuracy. This restricts the vehicle's adaptability to emergency conditions and operational safety.

[0023] In this embodiment, a nonlinear vehicle dynamics relationship incorporating degrees of freedom such as lateral, yaw, and tilt is employed, fully considering the influence of the lateral tilt angle and longitudinal slope angle of the driving road to accurately characterize the strong coupling effects of the vehicle in the three-dimensional complex terrain of the mine, encompassing longitudinal, lateral, and tilt dimensions. Secondly, an adaptive weight allocation strategy based on a real-time risk index is designed to dynamically adjust the priority of each control objective, achieving intelligent decision-making of "maintaining stability in critical situations and pursuing accuracy in stable situations." Finally, the target vehicle driving control quantity is generated to control the vehicle. This improves the dynamic control accuracy, multi-objective collaborative capability, and vehicle driving safety boundary of the unmanned mining truck in emergency conditions. In the mining operation environment, vehicles are typically unmanned mining trucks. Unmanned mining trucks are usually equipped with various sensors. By analyzing the data collected by various sensors and combining them with high-precision maps, vehicle intelligent decision-making modules, and other modules, global information about the vehicle can be obtained. For example, sensors may include: lidar, cameras, etc. Global information consists of parameters characterizing the vehicle's state. Global information may include at least one of the following: road information, vehicle body information, current vehicle driving state quantity, and current vehicle driving control quantity. Road information can include: the lateral tilt angle and longitudinal slope angle of the road. The lateral tilt angle is the angle formed by the difference in elevation between the left and right sides of the road surface, perpendicular to the vehicle's direction of travel. The longitudinal slope angle is the angle formed by the road surface and the horizontal plane along the vehicle's direction of travel. Vehicle body information is a set of parameters reflecting the vehicle's physical, kinematic, and load properties, such as the vehicle's total mass and moment of inertia about its roll axis. Vehicle driving state quantities are used to characterize the vehicle's driving state. Vehicle driving state quantities include: vehicle dynamics state information and driving trajectory information. Vehicle dynamics state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle, and roll rate. Driving trajectory information includes the following indicators: center of gravity lateral position and heading angle. For example, by combining high-precision maps with intelligent decision-making modules, the system acquires the lateral tilt angle and longitudinal slope angle of the road ahead for the autonomous vehicle, the lateral position of its center of gravity (the coordinates of the vehicle's center of gravity in the lateral direction relative to a reference coordinate system, which can be determined based on the centerline of the road or the vehicle's pre-aimed trajectory), the vehicle's pre-aimed trajectory, the position of the trajectory point corresponding to the pre-aimed trajectory, and the reference heading angle of the trajectory point corresponding to the pre-aimed trajectory. Various onboard sensors acquire longitudinal vehicle speed and yaw rate. The state observer software estimates the vehicle's current center of gravity sideslip angle, body roll angle, and roll rate in real time.

[0024] A dynamic equation is constructed to represent the dynamic relationship between vehicle driving state variables at adjacent sampling time steps. This dynamic equation can include degrees of freedom such as lateral, yaw, and roll. The backward Euler method is used to discretize the dynamic equation, transforming it into a discrete form for predicting the next time step. Using the current vehicle driving state variable obtained from actual measurement or calculation at the current sampling time as the initial value for recursion, iterative calculations are performed along the time axis to successively calculate the vehicle driving state variables corresponding to multiple future time steps in the prediction time domain, completing the multi-step state pre-deduction. For example, if the controller sampling time step is set to 0.05s and the prediction time domain contains 10 time steps, then it is necessary to predict 10 vehicle driving state variables in the future at 0.05s, 0.10s, 0.15s...0.50s.

[0025] Vehicle driving state quantities correspond to specified thresholds. These specified thresholds can refer to safety critical values ​​used to ensure that the vehicle does not become unstable and that trajectory tracking does not exceed tolerances. Instability critical thresholds are set for vehicle dynamics state information, and trajectory allowable deviation thresholds are set for driving trajectory information. Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, a risk index corresponding to the predicted vehicle driving state quantity is determined. The risk index reflects the degree to which the vehicle driving state tends towards the safety critical value. The smaller the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, the higher the risk index. Control preference can refer to the focus of the vehicle control strategy, including stability control preference or accuracy control preference. Stability control preference prioritizes vehicle driving stability and is related to vehicle dynamics state information. Accuracy control preference prioritizes trajectory tracking accuracy and is related to driving trajectory information. Risk indices include stability risk index and accuracy risk index. A correspondence is constructed between the risk index and the corresponding resource allocation weights of the control preference. When the stability risk index is larger than the accuracy risk index, the resource allocation weight of the stability control preference is increased to make the control preference a stability control preference. When the accuracy risk index is greater than the stability risk index, the resource allocation weight of the accuracy control preference is increased to align the control preference with the accuracy control preference. For example, the closer the vehicle's yaw rate is to its limit (a specified threshold), the higher the corresponding yaw stability risk index. The yaw stability risk index is then converted into a resource allocation weight for the stability control preference, thereby determining whether there is a greater preference for "ensuring vehicle driving stability" or "prioritizing trajectory tracking accuracy," and dynamically adjusting the resource allocation weights for the stability control preference and the accuracy control preference.

[0026] Furthermore, using dynamically adjusted resource allocation weights as the optimization criterion, an optimization problem with multiple future time steps is constructed. The goal of this problem is to find an optimal control sequence, which represents the generated target vehicle driving control variables, taking into account the physical limits of front wheel steering angle and braking force. This sequence aims to ensure that the predicted driving trajectory information closely approximates the vehicle's intended trajectory and mitigates instability risks as quickly as possible. The target vehicle driving control variables should also be as smooth as possible. Finally, the target vehicle driving control variables are distributed to the execution structure to control the vehicle.

[0027] This application's embodiments incorporate the lateral tilt angle and longitudinal slope angle of the driving road into the modeling calculation by utilizing the dynamic relationship including the roll degree of freedom. This fully reproduces the force and attitude change patterns of the vehicle under emergency conditions such as slippery slopes and sharp bends. The prediction results closely match the actual dynamic response of the vehicle, eliminating prediction errors from the modeling source and ensuring that the output target vehicle driving control quantity matches the actual driving state. Resource allocation weights are dynamically allocated based on a risk index, adaptively balancing driving stability and trajectory tracking accuracy. Traditional multi-objective control uses fixed weight ratios, which cannot adjust control priorities when the risk of the operating condition increases, easily leading to the defects of sacrificing trajectory tracking accuracy for stability or instability induced by pursuing trajectory tracking accuracy. This application quantifies the risk index based on the deviation between the predicted vehicle driving state quantity and a specified threshold, determining the resource allocation weights for stability control preference and accuracy control preference; low-stability-risk operating conditions prioritize trajectory tracking accuracy, while high-stability-risk operating conditions prioritize suppressing roll and yaw instability. It automatically identifies the current dominant risks and adjusts the priority of control resources to achieve "strong intervention to maintain stability in critical situations and fine-tuning to pursue precision in stable situations," maximizing the potential of vehicle traffic efficiency while ensuring the bottom line of safety.

[0028] In another embodiment of this disclosure, the risk index includes a stability risk index and a precision risk index; In step 2 above, the resource allocation weights corresponding to the vehicle's control preferences are determined based on the risk index, including: Step 1: Based on the stability risk index, determine the degree of primary impact of the deviation of each index value corresponding to the vehicle dynamic state information on vehicle stability control. Step 2: Based on the accuracy risk index, determine the degree of secondary impact of the deviation of each indicator value corresponding to the driving trajectory information on the vehicle accuracy control. Step 3: Determine the resource allocation weights for stability control preference and precision control preference based on the first and second degree of influence. In step 3 above, the target vehicle driving control quantity is generated based on the resource allocation weights corresponding to the control preferences, including: The target vehicle driving control quantity is determined based on the first degree of influence, the second degree of influence, the resource allocation weight of stability control preference, the resource allocation weight of precision control preference, and the deviation of each index value corresponding to the vehicle driving state quantity.

[0029] In this embodiment, a first degree of influence on stability control and a second degree of influence on accuracy control are determined based on the stability risk index and the accuracy risk index, respectively. Based on the first and second degrees of influence, resource allocation weights for stability control preference and accuracy control preference are determined. The target vehicle driving control quantity is calculated and generated by combining the first degree of influence, the second degree of influence, the resource allocation weights, and the deviations of each index value. The risk indices include the stability risk index and the accuracy risk index. The stability risk index is a quantitative indicator characterizing the probability of vehicle dynamics instability. It is calculated based on the deviation between the predicted vehicle dynamics information and the corresponding specified stability safety threshold, reflecting the risk level of vehicle instability phenomena such as sideslip and rollover. The accuracy risk index is a quantitative indicator characterizing the probability of the vehicle deviating from the predicted driving trajectory. It is calculated based on the deviation between the predicted driving trajectory information and the corresponding specified trajectory tracking error threshold, reflecting the risk level of the vehicle deviating from the lane or planned path.

[0030] Regarding step one above, based on the stability risk index, the degree of influence of deviations in various indicators corresponding to vehicle dynamics information on vehicle stability control is determined. For example, based on the magnitude of the stability risk index, the coefficients of vehicle driving state indicators corresponding to vehicle dynamics information such as sideslip angle, yaw rate, body roll angle, and roll rate are dynamically adjusted. When the stability risk index is high, the coefficients of vehicle driving state indicators approaching the limit state are increased, thereby quantifying the degree of influence of deviations in each indicator on maintaining vehicle stability.

[0031] Regarding step two above, based on the accuracy risk index, the degree of influence of the deviation of each indicator value corresponding to the driving trajectory information on vehicle accuracy control is determined. For example, based on the magnitude of the accuracy risk index, the coefficients of the driving trajectory information items corresponding to the center of gravity lateral position and heading angle are dynamically adjusted. When the accuracy risk index is high, the coefficients of the driving trajectory information items are increased, thereby quantifying the degree of influence of the deviation of each indicator on improving trajectory tracking accuracy.

[0032] Regarding step three above, based on the first and second degrees of influence, the resource allocation weights for stability control preference and accuracy control preference are determined. For example, the first and second degrees of influence are compared or weighted to assess the priority of stability control and accuracy control under the current operating condition. If the first degree of influence is significantly higher than the second degree of influence, a higher resource allocation weight is assigned to the stability control preference; conversely, a higher resource allocation weight is assigned to the accuracy control preference, thereby achieving a dynamic allocation of control resources between stability and accuracy.

[0033] Furthermore, based on the first degree of influence, the second degree of influence, the resource allocation weights for stability control preference and accuracy control preference, and the deviations of various index values ​​corresponding to the vehicle's driving state, the target vehicle driving control quantity is determined. For example, the deviations of each index value are multiplied by the corresponding first degree of influence or the second degree of influence to obtain the basic control components; then, the basic control components are weighted and summed or optimized using the resource allocation weights for stability control preference and accuracy control preference, ultimately generating the target vehicle driving control quantity that can simultaneously consider vehicle stability and trajectory tracking accuracy. By distinguishing between the stability risk index and the accuracy risk index, and calculating the first degree of influence and the second degree of influence respectively, the main contradiction of the current vehicle state can be accurately identified as whether it tends towards instability or deviation from the trajectory, thus avoiding a one-size-fits-all control strategy. Determining the resource allocation weights based on the degree of influence allows for a dynamic trade-off between vehicle dynamics stability and trajectory tracking accuracy. First, priorities are dynamically allocated within vehicle stability control and vehicle accuracy control, and then intelligent resource allocation is performed between the global objectives of "ensuring stability" and "accurate tracking," thereby achieving an adaptive control capability of "prioritizing safety in dangerous situations and striving for accuracy in stable situations."

[0034] In another embodiment of this disclosure, the target vehicle driving control quantity is determined based on the first degree of influence, the second degree of influence, the resource allocation weight of stability control preference, the resource allocation weight of precision control preference, and the deviation of each index value corresponding to the vehicle driving state quantity, including: Step (1): Based on the deviation of each index value corresponding to the vehicle driving state quantity and the vehicle driving control quantity, construct a comprehensive performance index function; The comprehensive performance index function includes driving state quantity terms and driving control quantity terms; the driving state quantity terms include dynamic state information terms and driving trajectory information terms. The dynamic state information terms include at least one vehicle driving state index term, and the driving trajectory information terms include at least one driving trajectory index term. The coefficient of each vehicle driving state index term represents the corresponding first degree of influence, and the coefficient of each driving trajectory index term represents the corresponding second degree of influence. The coefficient of the dynamic state information terms represents the resource allocation weight of the stability control preference, and the coefficient of the driving trajectory information terms represents the resource allocation weight of the precision control preference. Step (2): Solve for the target vehicle driving control quantity based on the comprehensive performance index function so that the value of the function meets the preset conditions.

[0035] In this embodiment, a comprehensive performance index function is constructed based on the deviations of various index values ​​of vehicle driving state quantities and vehicle driving control quantities. This function includes driving state quantity terms and driving control quantity terms. The coefficients of each term are configured using a first degree of influence, a second degree of influence, and resource allocation weights. The comprehensive performance index function is then solved to obtain the target vehicle driving control quantity that satisfies preset conditions.

[0036] Regarding step (1) above, the expression for the comprehensive performance index function is: ; in, The function value represents the comprehensive performance index function. This represents the driving state parameters. Driving state parameters include dynamic state information and driving trajectory information. The resource allocation weight, representing the stability control preference, is a coefficient of the dynamic state information term and is used to adjust the proportion of the dynamic state information term in the comprehensive performance index function. This value increases when the vehicle is in a high-stability-risk state. This represents a dynamic state information item. The dynamic state information item includes at least one vehicle driving state indicator item. , , These are indicators representing vehicle driving conditions, including: yaw stability, sideslip stability, and roll stability. This indicates the yaw stability index. This indicates the sideslip stability index. This indicates the roll stability index. Indicates yaw rate. This represents the desired yaw rate. Indicates the centroid sideslip angle. Indicates the expected centroid sideslip angle. The vehicle roll index is determined based on the lateral tilt angle, longitudinal slope angle, body roll angle, and roll rate. , , This indicates the degree of primary impact of deviations in various index values ​​corresponding to vehicle dynamics information on vehicle stability control. The degree of primary impact includes: the degree of impact on yaw stability, the degree of impact on sideslip stability, and the degree of impact on roll stability. This indicates the degree of influence on yaw stability, i.e., the coefficient of the yaw stability index item. This indicates the degree of influence on sideslip stability, i.e., the coefficient of the sideslip stability index term. This indicates the degree of influence on roll stability, i.e., the coefficient of the roll stability index item.

[0037] The resource allocation weight, representing the preference for precision control, is a coefficient for the driving trajectory information item and is used to adjust the proportion of the driving trajectory information item in the comprehensive performance index function. This value increases when the vehicle is at high risk of trajectory tracking deviation. This represents the driving trajectory information item. The driving trajectory information item includes at least one driving trajectory indicator item. , This indicates the driving trajectory indicators. The driving trajectory indicators include: position accuracy indicators and heading accuracy indicators. This indicates the position accuracy index. This indicates the heading accuracy indicator. Indicates the lateral position of the centroid. Indicates the desired lateral position of the centroid. Indicates the heading angle. This represents the desired heading angle. The desired yaw rate, desired sideslip angle, desired lateral position of the center of mass, and desired heading angle are determined according to the vehicle driving planning strategy, that is, the desired yaw rate, desired sideslip angle, desired lateral position of the center of mass, and desired heading angle are obtained according to the path planning instructions from the upper layer. , This indicates the degree of influence of deviations in various indicator values ​​corresponding to the driving trajectory information on vehicle precision control. The degree of influence includes: the degree of influence on position accuracy and the degree of influence on heading accuracy. This indicates the degree of impact of positional accuracy, i.e., the coefficient of the positional accuracy index item. This indicates the degree of influence on heading accuracy, i.e., the coefficient of the heading accuracy index item. Indicates driving control parameters. This indicates the vehicle's driving control parameters. The coefficient representing the driving control quantity can be a preset value.

[0038] Regarding step (2) above, the preset condition can refer to the target condition when optimizing the comprehensive performance index function. This is usually the minimization of the comprehensive performance index function, meaning minimizing its value while satisfying the vehicle's physical constraints. Alternatively, it can mean making the value of the comprehensive performance index function less than a first preset value. For example, when the front wheel angle is less than the maximum front wheel angle the vehicle can provide and the braking force is less than the maximum braking force the vehicle can provide, the constructed comprehensive performance index function is optimized using a rolling optimization method. By finding the optimal solution, the value of the comprehensive performance index function satisfies the preset condition (usually the minimum value). The current control quantity in the optimal control sequence obtained from the solution is used as the target vehicle driving control quantity to drive the actuator to adjust the vehicle's driving state.

[0039] By constructing a comprehensive performance index function that incorporates vehicle dynamics state information and driving trajectory information, vehicle stability and trajectory tracking accuracy can be considered simultaneously within a unified mathematical framework, avoiding performance shortcomings that may result from single-objective control. The first degree of influence, the second degree of influence, and resource allocation weights are directly used as coefficients of the function, allowing the objective function to dynamically adjust its optimization direction based on real-time risk assessment results. When the stability risk is high, the comprehensive performance index function automatically increases the penalty weight of the dynamics state information term, forcing the solver to prioritize outputting driving control quantities conducive to stability; conversely, it prioritizes ensuring trajectory tracking accuracy.

[0040] In another embodiment of this disclosure, step 2 above, determining the risk index corresponding to the predicted vehicle driving state quantity based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, includes: The risk index corresponding to the first indicator is determined by the square of the ratio of the first indicator to the corresponding specified threshold. The first indicator includes one of the following: yaw rate, center of gravity sideslip angle, vehicle roll index, center of gravity lateral position deviation, and heading angle deviation. The vehicle roll index is determined based on the lateral tilt angle, longitudinal slope angle, vehicle roll angle, and roll rate.

[0041] In this embodiment of the disclosure, the ratio of the first indicator to the corresponding specified threshold is calculated, and the ratio is squared to determine the risk index corresponding to the first indicator.

[0042] The risk indices include: stability risk index and accuracy risk index. The stability risk indices include: yaw stability risk index, sideslip stability risk index, and roll stability risk index. The accuracy risk indices include: position accuracy risk index and heading accuracy risk index. The risk index corresponding to the first indicator is determined based on the square of the ratio of the first indicator to the corresponding specified threshold, including the following 1-5: 1. The yaw stability risk index is determined by the square of the ratio of the yaw angular velocity to the yaw angular velocity threshold. The formula is as follows: ; in, This indicates the yaw stability risk index. Indicates yaw rate. This represents the yaw rate threshold, which can be a preset value.

[0043] 2. The sideslip stability risk index is determined based on the square of the ratio of the sideslip angle to the sideslip angle threshold. The formula is as follows: ; in, Indicates the sideslip stability risk index. Indicates the centroid sideslip angle. This represents the centroid sideslip angle threshold, which can be a preset value.

[0044] 3. The roll stability risk index is determined by the square of the ratio of the vehicle roll index to the vehicle roll index threshold. The formula is as follows: ; in, This indicates the roll stability risk index. This indicates the vehicle roll index. This represents the vehicle roll index threshold, which can be a preset value.

[0045] 4. Determine the lateral position deviation of the centroid based on the difference between the lateral position of the centroid and the corresponding trajectory point position of the vehicle's pre-aimed trajectory; determine the position accuracy risk index based on the square of the ratio of the lateral position deviation of the centroid to the lateral position deviation threshold, expressed by the formula: ; ; in, Indicates the lateral positional deviation of the centroid. Indicates the lateral position of the centroid. Indicates the desired lateral position of the centroid. This indicates the risk index for location accuracy. This represents the threshold value for the lateral position deviation of the centroid, which can be a preset value.

[0046] 5. Determine the heading angle deviation based on the difference between the heading angle and the reference heading angle of the corresponding trajectory point of the vehicle's pre-aimed trajectory; determine the heading accuracy risk index based on the square of the ratio of the heading angle deviation to the heading angle deviation threshold, expressed by the formula: ; ; in, Indicates the deviation of the heading angle. Indicates the heading angle. Indicates the desired heading angle. This indicates the risk index for heading accuracy. This represents the heading angle deviation threshold, which can be a preset value.

[0047] The vehicle roll index is determined based on the lateral tilt angle, longitudinal slope angle, vehicle roll angle, and roll angular velocity, expressed by the following formula: ; ; ; ; ; in, This indicates the vehicle roll index. This indicates the vehicle's equivalent roll stiffness. Indicates the body roll angle. This indicates the vehicle's equivalent roll damping. Indicates the roll rate. This represents the baseline value for the vehicle roll index. This represents the vehicle's roll equivalent mass height moment. Indicates the total mass of the vehicle. Represents gravitational acceleration. Indicates the wheel track. Indicates the lateral inclination angle of the road. Indicates the longitudinal slope angle. Indicates the inherent roll stiffness of the suspension. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the inherent roll damping of the suspension. Indicates unsprung mass. This indicates the distance from the roll center to the center of mass of the unsprung mass.

[0048] The risk index is determined by using the square of the ratio, so that when the first indicator approaches a specified threshold, the risk index rises sharply and non-linearly, thus enabling more sensitive detection of emergency situations where the vehicle approaches the instability boundary or deviates significantly from the tracking trajectory. When determining the vehicle roll index, lateral tilt angle, longitudinal slope angle, body roll angle, and roll rate are comprehensively considered, which can more fully and accurately reflect the vehicle's rollover tendency on complex road surfaces (such as slopes) or under severe maneuvering, improving the accuracy of risk identification.

[0049] In another embodiment of this disclosure, the target influence level is determined by the following method, wherein the target influence level includes a first influence level or a second influence level, including: Vehicle driving state quantities include sub-vehicle driving state quantities, which are vehicle dynamic state information or driving trajectory information, and each sub-vehicle driving state quantity includes at least one indicator. The total risk index is determined by summing the risk indices corresponding to at least one indicator. The degree of impact of at least one target is determined by the ratio of the risk index corresponding to each of the at least one indicators to the total risk index.

[0050] In this embodiment, vehicle driving state quantities are divided into vehicle dynamics state information or driving trajectory information as sub-vehicle driving state quantities, and at least one index is determined for each sub-vehicle driving state quantity. The vehicle dynamics state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle, and roll rate. The driving trajectory information includes the following indicators: center of gravity lateral position and heading angle. Risk indices corresponding to at least one indicator are obtained, and these risk indices are summed to determine the total risk index. The ratio of the risk index corresponding to each indicator to the total risk index is calculated, and this ratio is determined as the target influence degree of the corresponding indicator. The target influence degree includes a first influence degree or a second influence degree. The coefficient of each vehicle driving state indicator item is the corresponding first influence degree, and the coefficient of each driving trajectory indicator item is the corresponding second influence degree. The first influence degree includes: yaw stability influence degree, sideslip stability influence degree, and roll stability influence degree. The second influence degree includes: position accuracy influence degree and heading accuracy influence degree. The total risk index may include: a stability risk index and a precision risk index.

[0051] The total stability risk index is determined by summing the yaw stability risk index, sideslip stability risk index, and roll stability risk index. The degree of yaw stability impact is determined by the ratio of the yaw stability risk index to the total stability risk index; the degree of sideslip stability impact is determined by the ratio of the sideslip stability risk index to the total stability risk index; and the degree of roll stability impact is determined by the ratio of the roll stability risk index to the total stability index. The formulas are as follows: ; ; ; in, This indicates the degree of influence on yaw stability, i.e., the coefficient of the yaw stability index item. This indicates the degree of influence on sideslip stability, i.e., the coefficient of the sideslip stability index term. This indicates the degree of influence on roll stability, i.e., the coefficient of the roll stability index item. This indicates the yaw stability risk index. Indicates the sideslip stability risk index. This indicates the roll stability risk index. This represents the overall stability risk index.

[0052] The total accuracy risk index is calculated by summing the position accuracy risk index and the heading accuracy risk index. The degree of impact on position accuracy is determined by the ratio of the position accuracy risk index to the total accuracy risk index. Similarly, the degree of impact on heading accuracy is determined by the ratio of the heading accuracy risk index to the total accuracy risk index. The formula is as follows: ; ; in, This indicates the degree of impact of positional accuracy, i.e., the coefficient of the positional accuracy index item. This indicates the degree of influence on heading accuracy, i.e., the coefficient of the heading accuracy index item. This indicates the risk index for location accuracy. This indicates the risk index for heading accuracy. This represents the overall accuracy risk index.

[0053] In this embodiment, the fixed weight allocation method is abandoned, and the target influence degree (first influence degree or second influence degree) is dynamically adjusted according to the real-time risk index of the vehicle. When the risk index corresponding to a certain indicator (such as the centroid sideslip angle) increases, the corresponding target influence degree automatically increases, so that the generated target vehicle driving control quantity pays more attention to the convergence of the vehicle driving state quantity.

[0054] In another embodiment of this disclosure, step three above, determining the resource allocation weights for stability control preference and precision control preference based on the first degree of influence and the second degree of influence, includes: Step (1): Determine the maximum impact level of each target and the sum of the impact levels of all targets. Determine the correction coefficient based on the ratio of the maximum impact level to the sum of the impact levels of all targets. Determine the target quantification coefficient based on the average of the impact levels of each target, the maximum impact level, and the correction coefficient. The impact level of the target is either the first impact level or the second impact level, and the target quantification coefficient is either the stability quantification coefficient or the accuracy quantification coefficient. Step (II): Determine the resource allocation weight of stability control preference based on the ratio of stability quantification coefficient to accuracy quantification coefficient and the product of the basic weight of stability index. Step (3): Determine the resource allocation weight for precision control preference based on the ratio of the precision quantification coefficient to the stability quantification coefficient and the product of the basic weight of the precision index.

[0055] In this embodiment of the disclosure, stability quantification coefficients and accuracy quantification coefficients are determined based on statistical data (maximum value, sum, and average value) of the first and second levels of influence. The resource allocation weight for stability control preference is calculated using the ratio of the stability quantification coefficient to the accuracy quantification coefficient, combined with the basic weight of the stability index. Similarly, the resource allocation weight for accuracy control preference is calculated using the ratio of the accuracy quantification coefficient to the stability quantification coefficient, combined with the basic weight of the accuracy index.

[0056] Regarding step (i) above, the correction coefficient includes a stability correction coefficient or an accuracy correction coefficient. The correction coefficient is determined based on the ratio of the maximum target's influence to the sum of the influences of all targets, expressed by the following formula: , ; , ; in, This represents the stability correction coefficient. This indicates the maximum impact level of the target within the first level of impact. This represents the sum of the impact levels of all targets corresponding to the first level of impact. Indicates yaw rate. Indicates the centroid sideslip angle. This indicates the vehicle roll index. Indicates the degree of influence on yaw stability. Indicates the degree of impact on sideslip stability. This indicates the degree of impact on roll stability. Indicates the precision correction factor. This indicates the maximum degree of target impact within the second level of impact. This represents the sum of the impact levels of all objectives corresponding to the second level of impact. Indicates the lateral positional deviation of the centroid. Indicates the deviation of the heading angle. Indicates the degree of impact of positional accuracy. This indicates the degree to which heading accuracy is affected.

[0057] The target quantification coefficient is determined based on the average impact level of each target, the maximum impact level of the target, and the correction coefficient. The formula is as follows: ; ; in, Represents the stability quantization coefficient. Indicates the precision quantization coefficient. This represents the average impact level of each target corresponding to the first level of impact. This represents the average impact level of each target corresponding to the second level of impact.

[0058] Regarding step (ii) above, the resource allocation weight for stability control preference is determined by multiplying the ratio of the stability quantification coefficient to the accuracy quantification coefficient with the basic weight of the stability index. The formula is as follows: ; in, Resource allocation weights representing stability control preferences. This represents the basic weight of the stability index, which can be a preset value.

[0059] Regarding step (iii) above, the resource allocation weight for accuracy control preference is determined by multiplying the ratio of the accuracy quantification coefficient to the stability quantification coefficient with the basic weight of the accuracy index. The formula is as follows: ; in, Resource allocation weights representing precision control preferences. This represents the basic weight of the accuracy index, which can be a preset value.

[0060] By introducing basic weights for stability and accuracy indicators, developers can pre-set these weights based on vehicle characteristics. Then, the resource allocation weights for control preferences are dynamically adjusted using real-time calculated ratios, balancing versatility and personalization. By introducing a ratio between the stability quantification coefficient and the accuracy quantification coefficient, the resource allocation weights for stability control preferences and accuracy control preferences exhibit an inverse linkage. For example, when vehicle dynamics risk (stability quantification coefficient) increases, the resource allocation weight for stability control preferences automatically increases, while the resource allocation weight for accuracy control preferences automatically decreases, thereby prioritizing vehicle stability and preventing instability.

[0061] In another embodiment of this disclosure, the dynamic state information item, the driving trajectory information item, and the driving control quantity item each include a corresponding time-domain decay factor coefficient; the time-domain decay factor coefficient decreases as the predicted future time step increases.

[0062] In this embodiment of the disclosure, the comprehensive performance index function is configured with corresponding time-domain decay factor coefficients for the dynamic state information item, the driving trajectory information item, and the driving control quantity item. The time-domain decay factor coefficient decreases with the increase of the predicted future time step; that is, as the prediction time domain extends into the future, the corresponding time-domain decay factor coefficient gradually decreases. Therefore, the optimized comprehensive performance index function expression is: ; This represents the time domain for predicting vehicle dynamics state information. This represents the time domain for predicting driving trajectory information. Indicates control of the time domain, Represents the time-domain decay factor. . This represents the index for predicting future time steps.

[0063] By introducing a time-domain decay factor, greater attention is paid to the deviations in vehicle dynamics and trajectory information indicators in the near term (current and short future), while less attention is paid to long-term predictions. This effectively avoids the "farsightedness" bias caused by the accumulation of model prediction errors over time, ensuring that the vehicle can quickly respond to dynamic changes in recent vehicle driving state parameters and improving the real-time response capability of control.

[0064] In another embodiment of this disclosure, step 1 above, predicting vehicle driving state quantities for multiple future time steps based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps and the current vehicle driving state quantity corresponding to the current sampling time, includes: Based on the current longitudinal vehicle speed, the current tangent of the centroid sideslip angle, the current cosine of the heading angle, and the current sine of the heading angle, determine the increment of the centroid's lateral position within the sampling period; The lateral position of the centroid at the next moment is determined by summing the lateral position increment of the centroid within the sampling period with the lateral position of the centroid at the current moment.

[0065] In this embodiment of the disclosure, based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity corresponding to the current discrete sampling moment, the vehicle driving state quantities for multiple future time steps are predicted, including: Perform the following single-step prediction steps: Based on the dynamic relationship between the current vehicle driving state quantity at the current sampling time and the vehicle driving state quantity at adjacent sampling time steps, predict the vehicle driving state quantity at the next sampling time. Take the next sampling time as the new current sampling time, return to execute the single-step prediction step, until the vehicle driving state quantity for multiple future time steps is predicted; Define vehicle driving state variables and vehicle driving control variables as follows: , ; in, This indicates the vehicle's current driving status. Indicates the horizontal position of the center of mass at the current moment. Indicates the heading angle at the current moment. Indicates the longitudinal speed of the vehicle at the current moment. This indicates the sideslip angle of the center of mass at the current moment. This represents the yaw rate at the current moment. This indicates the current vehicle body roll angle. This represents the current roll rate. This indicates the vehicle's driving control parameters. This indicates the current steering angle of the front wheels. This indicates the longitudinal force on the left tire of the front axle at the current moment. This indicates the longitudinal force on the right tire of the front axle at the current moment. This indicates the longitudinal force on the right rear axle tire at the current moment. This indicates the longitudinal force on the left rear axle tire at the current moment.

[0066] Based on the dynamic relationship between the current vehicle driving state quantity at the current sampling time and the vehicle driving state quantity at adjacent sampling time steps, the vehicle driving state quantity at the next sampling time is predicted, including: Based on the current longitudinal vehicle speed, the current tangent of the centroid sideslip angle, the current cosine of the heading angle, and the current sine of the heading angle, determine the centroid lateral position increment within the sampling period; based on the sum of the centroid lateral position increment within the sampling period and the current centroid lateral position, determine the centroid lateral position at the next moment. Based on the yaw rate at the current moment, determine the heading angle increment within the sampling period; based on the sum of the heading angle increment within the sampling period and the heading angle at the current moment, determine the heading angle at the next moment. Based on the sampling period, total vehicle mass, longitudinal force of the left front axle tire at the current moment, cosine value of front wheel steering angle, longitudinal force of the right front axle tire at the current moment, longitudinal force of the left rear axle tire at the current moment, and longitudinal force of the right rear axle tire at the current moment, determine the longitudinal speed increment within the sampling period; based on the sum of the longitudinal speed increment within the sampling period and the longitudinal speed at the current moment, determine the longitudinal speed at the next moment. Obtain the vehicle's center of gravity sideslip angle self-coupling coefficient, yaw rate self-coupling coefficient, and cross-coupling coefficients of center of gravity sideslip angle, yaw rate, and center of gravity sideslip angle; determine the determinant of the vehicle's lateral dynamics system state matrix based on the sampling period, center of gravity sideslip angle self-coupling coefficient, yaw rate self-coupling coefficient, center of gravity sideslip angle, yaw rate, and center of gravity sideslip angle; determine the center of gravity sideslip angle at the next moment based on the determinant of the vehicle's lateral dynamics system state matrix. Based on the current center of gravity sideslip angle, the cross-coupling coefficient of the center of gravity sideslip angle and roll angle, the current vehicle body roll angle, the cross-coupling coefficient of the center of gravity sideslip angle and roll angular velocity, the lateral tilt angle and longitudinal slope angle of the driving road, determine the first-order lateral dynamics equivalent disturbance term; based on the current yaw rate, the current front wheel steering angle, the longitudinal forces of the left and right tires of the front axle, and the current difference between the left and right longitudinal forces of the front and rear axles, determine the second-order lateral dynamics equivalent disturbance term; based on the sampling period, the first-order lateral dynamics equivalent disturbance term, and the second-order lateral dynamics equivalent disturbance term, determine the yaw rate at the next moment; Based on the current roll rate, determine the roll angle increment within the sampling period; based on the sum of the roll angle increment within the sampling period and the roll angle at the current moment, determine the roll angle at the next moment. Based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle, determine the front wheel steering angle excitation gain coefficient on the roll angle. Based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, and the front wheel steering angle, determine the front axle longitudinal force excitation gain coefficient on the roll angle. Based on the sampling period, the front wheel steering angle excitation gain coefficient on the roll angle, and the front axle longitudinal force excitation gain coefficient on the roll angle, determine the roll angular velocity at the next moment. To address the issue of numerical instability in predicted vehicle driving state variables, a backward Euler discretization method is employed to suppress divergence and oscillations in the vehicle driving state variable values ​​during calculation. For example, based on the backward Euler discretization method, the following formula is used: The lateral position increment of the centroid within the sampling period is determined based on the current longitudinal vehicle speed, the current centroid sideslip tangent, the current heading angle cosine, and the current heading angle sine; the lateral position of the centroid at the next moment is determined by the sum of the lateral position increment within the sampling period and the current lateral position of the centroid. This is expressed by the following formula: ; in, Indicates the lateral position of the center of mass at the next moment. Indicates the horizontal position of the center of mass at the current moment. This represents the increment of the centroid's lateral position within the sampling period. Indicates the sampling period. Indicates the longitudinal speed of the vehicle at the current moment. This represents the tangent value of the centroid sideslip angle at the current moment. This represents the cosine value of the heading angle at the current moment. This represents the sine value of the heading angle at the current moment.

[0067] For example, based on the backward Euler discretization method, the following is obtained: The heading angle increment within the sampling period is determined based on the yaw rate at the current moment; the heading angle at the next moment is determined based on the sum of the heading angle increment within the sampling period and the heading angle at the current moment. The formula is expressed as: ; in, Indicates the heading angle at the next moment. Indicates the heading angle at the current moment. This represents the increment of the heading angle within the sampling period. Indicates the sampling period. This represents the yaw rate at the current moment.

[0068] For example, based on the backward Euler discretization method, the following is obtained: The roll angle increment within the sampling period is determined based on the roll angle velocity at the current moment; the roll angle at the next moment is determined based on the sum of the roll angle increment within the sampling period and the roll angle at the current moment. The formula is expressed as: ; in, Indicates the vehicle body roll angle at the next moment. This indicates the current vehicle body roll angle. This indicates the increase in vehicle body roll angle within the sampling period. Indicates the sampling period. This represents the roll rate at the current moment.

[0069] In another embodiment of this disclosure, step 1 above, predicting vehicle driving state quantities for multiple future time steps based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps and the current vehicle driving state quantity corresponding to the current sampling time, includes: Based on the sampling period, total vehicle mass, longitudinal force of the left front axle tire at the current moment, cosine value of front wheel steering angle, longitudinal force of the right front axle tire at the current moment, longitudinal force of the left rear axle tire at the current moment, and longitudinal force of the right rear axle tire at the current moment, determine the longitudinal speed increment within the sampling period. The longitudinal speed at the next moment is determined by summing the longitudinal speed increment within the sampling period with the longitudinal speed at the current moment.

[0070] In this embodiment of the disclosure, the longitudinal speed increment within the sampling period is determined based on the sampling period, the total vehicle mass, the longitudinal force of the left front axle tire at the current moment, the cosine value of the front wheel steering angle, the longitudinal force of the right front axle tire at the current moment, the longitudinal force of the left rear axle tire at the current moment, and the longitudinal force of the right rear axle tire at the current moment. The longitudinal speed at the next moment is determined based on the sum of the longitudinal speed increment within the sampling period and the longitudinal speed at the current moment, expressed by the formula as follows: ; in, Indicates the longitudinal speed of the vehicle at the next moment. Indicates the longitudinal speed of the vehicle at the current moment. This represents the longitudinal vehicle speed increment within the sampling period. Indicates the sampling period. Indicates the total mass of the vehicle. This represents the longitudinal force on the left tire of the front axle at the current moment. This represents the cosine value of the front wheel steering angle. This represents the longitudinal force on the right tire of the front axle at the current moment. This represents the longitudinal force on the left rear axle tire at the current moment. This represents the longitudinal force on the right tire of the rear axle at the current moment.

[0071] In another embodiment of this disclosure, step 1 above, predicting vehicle driving state quantities for multiple future time steps based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps and the current vehicle driving state quantity corresponding to the current sampling time, includes: Obtain the vehicle's center of gravity sideslip angle self-coupling coefficient, yaw rate self-coupling coefficient, center of gravity sideslip angle yaw rate cross-coupling coefficient, and yaw rate center of gravity sideslip angle cross-coupling coefficient. The determinant of the vehicle lateral dynamics system state matrix is ​​determined based on the sampling period, the self-coupling coefficient of the center of mass sideslip angle, the self-coupling coefficient of the yaw rate, the cross-coupling coefficient of the center of mass sideslip angle and the cross-coupling coefficient of the yaw rate and the center of mass sideslip angle. The sideslip angle of the center of mass at the next moment is determined based on the determinant of the state matrix of the vehicle's lateral dynamics system.

[0072] In this embodiment of the disclosure, the determinant of the vehicle lateral dynamics system state matrix is ​​determined based on the sampling period, the self-coupling coefficient of the center of mass sideslip angle, the self-coupling coefficient of the yaw rate, the cross-coupling coefficient of the center of mass sideslip angle and the cross-coupling coefficient of the yaw rate and the center of mass sideslip angle; the determinant of the vehicle lateral dynamics system state matrix is ​​then determined based on the determinant of the vehicle lateral dynamics system state matrix, as expressed by the formula: ; ; in, Indicates the sideslip angle of the center of mass at the next moment. Indicates the sampling period. This represents the self-coupling coefficient of the yaw rate. This represents the first-order lateral dynamic equivalent perturbation term. The cross-coupling coefficient represents the sideslip angle and yaw rate of the center of mass. This represents the equivalent perturbation term of the second-order lateral dynamics. This represents the determinant of the state matrix of the vehicle's lateral dynamics system. This represents the self-coupling coefficient of the centroid sideslip angle. This represents the cross-coupling coefficient of the yaw rate, center of mass deflection angle, and cross-coupling factor.

[0073] Based on the vehicle's moment of inertia about the yaw axis, longitudinal speed, longitudinal distance from the center of gravity to the front axle, equivalent tire lateral stiffness of the front axle, front wheel steering angle, longitudinal distance from the center of gravity to the rear axle, and equivalent tire lateral stiffness of the rear axle, the yaw rate self-coupling coefficient is determined, expressed by the following formula: ; in, This represents the self-coupling coefficient of the yaw rate. This represents the moment of inertia of the vehicle about its yaw axis. Indicates longitudinal vehicle speed. Indicates the longitudinal distance from the center of gravity to the front axle. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the front wheel steering angle. This indicates the longitudinal distance from the center of mass to the rear axle. This indicates the equivalent tire lateral stiffness of the rear axle.

[0074] Based on the vehicle's total mass, longitudinal speed, sprung mass, distance from the roll center to the sprung mass's center of mass, vehicle moment of inertia about the roll axis, longitudinal distance from the center of mass to the front axle, equivalent tire lateral stiffness of the front axle, front wheel steering angle, longitudinal distance from the center of mass to the rear axle, and equivalent tire lateral stiffness of the rear axle, the cross-coupling coefficient of the center of mass sideslip angle and yaw rate is determined, expressed by the formula: ; in, The cross-coupling coefficient represents the sideslip angle and yaw rate of the center of mass. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. This represents the moment of inertia of the vehicle about its roll axis. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the longitudinal distance from the center of gravity to the front axle. Indicates the front wheel steering angle. This indicates the equivalent tire lateral stiffness of the rear axle. This indicates the longitudinal distance from the center of mass to the rear axle.

[0075] Based on the vehicle's total mass, longitudinal speed, sprung mass, distance from the roll center to the sprung mass's center of mass, vehicle moment of inertia about the roll axis, equivalent tire lateral stiffness of the front axle, front wheel steering angle, and equivalent tire lateral stiffness of the rear axle, the self-coupling coefficient of the center of mass lateral slip angle is determined, expressed by the formula: ; in, This represents the self-coupling coefficient of the centroid sideslip angle. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. This represents the moment of inertia of the vehicle about its roll axis. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the front wheel steering angle. This indicates the equivalent tire lateral stiffness of the rear axle.

[0076] Based on the vehicle's moment of inertia about the yaw axis, the longitudinal distance from the center of mass to the front axle, the equivalent tire lateral stiffness of the front axle, the front wheel steering angle, the longitudinal distance from the center of mass to the rear axle, and the equivalent tire lateral stiffness of the rear axle, the yaw rate-center-of-mass lateral angle cross-coupling coefficient is determined, expressed by the formula: ; in, This represents the cross-coupling coefficient of the yaw rate, center of mass sideslip angle. This represents the moment of inertia of the vehicle about its yaw axis. Indicates the longitudinal distance from the center of gravity to the front axle. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the front wheel steering angle. This indicates the longitudinal distance from the center of mass to the rear axle. This indicates the equivalent tire lateral stiffness of the rear axle.

[0077] Based on the current centroid sideslip angle, the cross-coupling coefficient of the centroid sideslip angle and roll angle, the current vehicle body roll angle, the cross-coupling coefficient of the centroid sideslip angle and roll angular velocity, and the lateral tilt angle and longitudinal slope angle of the driving road, the first-order lateral dynamic equivalent disturbance term is determined, expressed by the formula: ; in, This represents the first-order lateral dynamic equivalent perturbation term. This indicates the sideslip angle of the center of mass at the current moment. Indicates the sampling period. Represents the cross-coupling coefficient of the centroid sideslip angle and sideslip angle. This indicates the current vehicle body roll angle. The cross-coupling coefficient represents the sideslip angle and roll velocity of the center of mass. This represents the current roll rate. This represents the excitation gain coefficient of the front wheel steering angle on the sideslip angle of the center of gravity. This indicates the current steering angle of the front wheels. This represents the excitation gain coefficient of the longitudinal force on the front axle relative to the sideslip angle of the center of gravity. This represents the longitudinal force on the front axle at the current moment. Represents gravitational acceleration. Indicates longitudinal vehicle speed. Indicates the lateral inclination angle of the road. Indicates the longitudinal slope angle.

[0078] Based on the sprung mass, the distance from the roll center to the sprung mass center of gravity, the total vehicle mass, longitudinal speed, gravitational acceleration, the lateral tilt angle of the road, the longitudinal slope angle, the vehicle's equivalent roll stiffness, and the vehicle's moment of inertia about the roll axis, the cross-coupling coefficient of the center of gravity sideslip angle and roll angle is determined, expressed by the following formula: ; in, Represents the cross-coupling coefficient of the centroid sideslip angle and sideslip angle. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. Represents gravitational acceleration. Indicates the lateral inclination angle of the road. Indicates the longitudinal slope angle. This indicates the vehicle's equivalent roll stiffness. This represents the moment of inertia of a vehicle about its roll axis.

[0079] Based on the sprung mass, the distance from the roll center to the sprung mass center of gravity, the total vehicle mass, the longitudinal speed, the vehicle's equivalent roll damping, and the vehicle's moment of inertia about the roll axis, the cross-coupling coefficient of the center of gravity sideslip angle and roll angular velocity is determined, expressed by the following formula: ; in, The cross-coupling coefficient represents the sideslip angle and roll velocity of the center of mass. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. This indicates the vehicle's equivalent roll damping. This represents the moment of inertia of a vehicle about its roll axis.

[0080] Based on the vehicle's total mass, longitudinal speed, sprung mass, distance from the roll center to the sprung mass's center of mass, vehicle moment of inertia about the roll axis, equivalent tire lateral stiffness of the front axle, and front wheel steering angle, determine the excitation gain coefficient of the front wheel steering angle on the center of mass's lateral slip angle. The formula is as follows: ; in, This represents the excitation gain coefficient of the front wheel steering angle on the sideslip angle of the center of gravity. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. This represents the moment of inertia of the vehicle about its roll axis. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the steering angle of the front wheels.

[0081] Based on the vehicle's total mass, longitudinal speed, sprung mass, distance from the roll center to the sprung mass's center of mass, vehicle moment of inertia about the roll axis, and front wheel steering angle, determine the excitation gain coefficient of the front axle longitudinal force on the center of mass slip angle. The formula is as follows: ; in, This represents the excitation gain coefficient of the longitudinal force on the front axle relative to the sideslip angle of the center of gravity. Indicates the total mass of the vehicle. Indicates longitudinal vehicle speed. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. This represents the moment of inertia of the vehicle about its roll axis. Indicates the steering angle of the front wheels.

[0082] Based on the current yaw rate, the current front wheel steering angle, the longitudinal forces on the left and right tires of the front axle, and the current difference in longitudinal forces between the left and right sides of the front and rear axles, the equivalent disturbance term of the second-order lateral dynamics is determined, expressed by the following formula: ; in, This represents the equivalent perturbation term of the second-order lateral dynamics. This represents the yaw rate at the current moment. Indicates the sampling period. This represents the gain coefficient of the front wheel steering angle on the yaw rate excitation. This indicates the current steering angle of the front wheels. This represents the gain coefficient of the total longitudinal force on the front axle to the yaw rate excitation. This indicates the longitudinal force on the left tire of the front axle. This indicates the longitudinal force on the right tire of the front axle. This represents the gain coefficient of the longitudinal force on the front axle to the yaw rate excitation. This indicates the difference in longitudinal force between the left and right sides of the front axle at the current moment. This represents the gain coefficient of the longitudinal force on the rear axle to the yaw rate excitation. This indicates the difference in longitudinal force between the left and right sides of the rear axle at the current moment.

[0083] ; ; ; in, This indicates the longitudinal force on the left tire of the front axle at the current moment. This indicates the longitudinal force on the right tire of the front axle at the current moment. This indicates the longitudinal force on the right rear axle tire at the current moment. This indicates the longitudinal force on the left rear axle tire at the current moment.

[0084] Based on the vehicle's moment of inertia about the yaw axis, the longitudinal distance from the center of mass to the front axle, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle, the gain coefficient of the front wheel steering angle on the yaw rate excitation is determined, expressed by the following formula: ; in, This represents the gain coefficient of the front wheel steering angle on the yaw rate excitation. This represents the moment of inertia of the vehicle about its yaw axis. Indicates the longitudinal distance from the center of gravity to the front axle. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the steering angle of the front wheels.

[0085] Based on the vehicle's moment of inertia about the yaw axis, the longitudinal distance from the center of mass to the front axle, and the front wheel rotation angle, the gain coefficient of the total longitudinal force on the yaw rate excitation is determined, expressed by the following formula: ; in, This represents the gain coefficient of the total longitudinal force on the front axle to the yaw rate excitation. This represents the moment of inertia of the vehicle about its yaw axis. Indicates the longitudinal distance from the center of gravity to the front axle. Indicates the steering angle of the front wheels.

[0086] Based on the vehicle's moment of inertia about the yaw axis, the front axle wheel track, and the front wheel steering angle, the gain coefficient of the front axle longitudinal force on the yaw rate excitation is determined, expressed by the following formula: ; in, This represents the gain coefficient of the longitudinal force on the front axle to the yaw rate excitation. This represents the moment of inertia of the vehicle about its yaw axis. Indicates the track width of the front axle wheels. Indicates the steering angle of the front wheels.

[0087] Based on the vehicle's moment of inertia about the yaw axis and the wheelbase of the rear axle, the excitation gain coefficient of the longitudinal force on the yaw rate of the rear axle is determined by the following formula: ; in, This represents the gain coefficient of the longitudinal force on the rear axle to the yaw rate excitation. This represents the moment of inertia of the vehicle about its yaw axis. This indicates the wheel track width of the rear axle.

[0088] In another embodiment of this disclosure, step 1 above, predicting vehicle driving state quantities for multiple future time steps based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps and the current vehicle driving state quantity corresponding to the current sampling time, includes: Based on the current centroid sideslip angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle, the current vehicle body roll angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle velocity, the lateral tilt angle and longitudinal slope angle of the driving road, determine the first-order lateral dynamic equivalent disturbance term; Based on the yaw rate at the current moment, the front wheel steering angle at the current moment, the longitudinal forces of the left and right tires of the front axle, and the difference between the left and right longitudinal forces of the front and rear axles at the current moment, determine the equivalent disturbance term of the second-order lateral dynamics. The yaw rate at the next moment is determined based on the sampling period, the first-order lateral dynamic equivalent perturbation term, and the second-order lateral dynamic equivalent perturbation term.

[0089] In this embodiment of the disclosure, the yaw rate at the next moment is determined based on the sampling period, the first-order lateral dynamic equivalent perturbation term, and the second-order lateral dynamic equivalent perturbation term, as expressed by the formula: ; in, Indicates the yaw rate at the next moment. Indicates the sampling period. This represents the cross-coupling coefficient of the yaw rate, center of mass sideslip angle. This represents the first-order lateral dynamic equivalent perturbation term. This represents the self-coupling coefficient of the centroid sideslip angle. This represents the equivalent perturbation term of the second-order lateral dynamics. This represents the determinant of the state matrix of the vehicle's lateral dynamics system.

[0090] In another embodiment of this disclosure, step 1 above, predicting vehicle driving state quantities for multiple future time steps based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps and the current vehicle driving state quantity corresponding to the current sampling time, includes: The excitation gain coefficient of the front wheel steering angle on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle. The gain coefficient of the longitudinal force on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total mass of the vehicle, the moment of inertia of the vehicle about the roll axis, and the front wheel angle. The roll rate at the next moment is determined based on the sampling period, the front wheel steering angle to roll angle excitation gain coefficient, and the front axle longitudinal force to roll angle excitation gain coefficient.

[0091] In this embodiment of the disclosure, the excitation gain coefficient of the front wheel steering angle on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle. The formula is expressed as follows: ; in, This represents the front wheel steering angle versus roll angle excitation gain coefficient. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. This represents the moment of inertia of the vehicle about its roll axis. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the steering angle of the front wheels.

[0092] Based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, and the front wheel angle, the gain coefficient of the longitudinal force on the roll angle is determined, expressed by the formula: ; in, This represents the gain coefficient of the longitudinal force on the front axle to the roll angle excitation. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. This represents the moment of inertia of the vehicle about its roll axis. Indicates the steering angle of the front wheels.

[0093] Based on the sampling period, the gain coefficient of the front wheel steering angle on the roll angle excitation, and the gain coefficient of the front axle longitudinal force on the roll angle excitation, the roll angular velocity at the next moment is determined by the following formula: = ; in, Indicates the roll rate at the next moment. This represents the current roll rate. Indicates the sampling period. Represents the roll angle, centroid, sideslip angle, and cross-coupling coefficient. This indicates the sideslip angle of the center of mass at the current moment. This represents the cross-coupling coefficient of the roll angle and yaw rate. This represents the yaw rate at the current moment. This represents the self-coupling coefficient of the roll angle. This indicates the current vehicle body roll angle. This represents the front wheel steering angle versus roll angle excitation gain coefficient. This indicates the current steering angle of the front wheels. This represents the gain coefficient of the longitudinal force on the front axle to the roll angle excitation. This represents the longitudinal force on the front axle at the current moment. This represents the roll angle and roll velocity cross-coupling coefficient.

[0094] Based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, the front wheel steering angle, and the equivalent tire lateral stiffness of the rear axle, the roll angle, center of mass, and lateral stiffness cross-coupling coefficient are determined. The formula is as follows: ; in, Represents the roll angle, centroid, sideslip angle, and cross-coupling coefficient. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. This represents the moment of inertia of the vehicle about its roll axis. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the front wheel steering angle. This indicates the equivalent tire lateral stiffness of the rear axle.

[0095] Based on the sprung mass, the distance from the roll center to the sprung mass center of gravity, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the longitudinal vehicle speed, the equivalent tire lateral stiffness of the front axle, the longitudinal distance from the center of gravity to the front axle, the front wheel steering angle, the equivalent tire lateral stiffness of the rear axle, and the longitudinal distance from the center of gravity to the rear axle, the cross-coupling coefficient of roll angle and yaw rate is determined, expressed by the following formula: ; in, The cross-coupling coefficient of roll angle and yaw rate Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Indicates the total mass of the vehicle. This represents the moment of inertia of the vehicle about its roll axis. Indicates longitudinal vehicle speed. This indicates the equivalent tire lateral stiffness of the front axle. Indicates the longitudinal distance from the center of gravity to the front axle. Indicates the front wheel steering angle. This indicates the equivalent tire lateral stiffness of the rear axle. This indicates the longitudinal distance from the center of mass to the rear axle.

[0096] The roll angle self-coupling coefficient is determined based on the sprung mass, gravitational acceleration, distance from the roll center to the sprung mass center of mass, lateral tilt angle of the road, longitudinal slope angle, vehicle equivalent roll stiffness, and vehicle moment of inertia about the roll axis. ; in, This represents the self-coupling coefficient of the roll angle. Indicates the sprung mass. This indicates the distance from the roll center to the center of mass of the sprung mass. Represents gravitational acceleration. Indicates the lateral inclination angle of the road. Indicates the longitudinal slope angle. This indicates the vehicle's equivalent roll stiffness. This represents the moment of inertia of a vehicle about its roll axis.

[0097] Based on the vehicle's equivalent roll damping and the vehicle's moment of inertia about the roll axis, the cross-coupling coefficient of roll angle and roll velocity is determined, expressed by the following formula: ; in, This represents the cross-coupling coefficient of the roll angle and roll velocity. This indicates the vehicle's equivalent roll damping. This represents the moment of inertia of a vehicle about its roll axis.

[0098] Based on the same disclosed concept, this disclosure also provides a vehicle control device and a vehicle. Since the principle by which the device and vehicle solve the problem is similar to that of the aforementioned vehicle control method, the implementation of the device and vehicle can refer to the implementation of the aforementioned method, and the repeated parts will not be described again.

[0099] This disclosure provides a vehicle, including a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the vehicle control method as described in any of the above embodiments are performed.

[0100] This disclosure provides a vehicle control device, such as... Figure 2 As shown, it includes: The global information acquisition module 201 is used to acquire global information of the vehicle, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road. The vehicle driving control quantity determination module 202 is used to calculate the vehicle driving control quantity, and performs the following steps 1-3 in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, predict vehicle driving state quantities for multiple future time steps based on the current vehicle driving state quantity corresponding to the current sampling time; wherein, the current vehicle driving state quantity is determined based on the vehicle's global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, wherein the vehicle dynamic state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle and roll rate, and the driving trajectory information includes the following indicators: center of gravity lateral position and heading angle; Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference according to the risk index, wherein the control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weights corresponding to the control preferences, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that the embodiments of this disclosure can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0102] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes in the drawings are not necessarily essential for implementing this disclosure.

[0103] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0104] The sequence numbers of the embodiments disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0105] Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its spirit and scope. Therefore, if such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, this disclosure is also intended to include such modifications and variations.

Claims

1. A vehicle control method, characterized in that, include: Obtain global information about the vehicle, wherein the global information includes at least the lateral tilt angle and longitudinal slope angle of the driving road; The vehicle driving control parameters are calculated, and the following steps 1-3 are performed in each calculation: Step 1: Based on the dynamic relationship between vehicle driving state quantities of adjacent sampling time steps, predict vehicle driving state quantities for multiple future time steps based on the current vehicle driving state quantity corresponding to the current sampling time; wherein, the current vehicle driving state quantity is determined based on the vehicle's global information; the vehicle driving state quantity includes: vehicle dynamic state information and driving trajectory information, wherein the vehicle dynamic state information includes at least the following indicators: center of gravity sideslip angle, yaw rate, body roll angle and roll rate, and the driving trajectory information includes the following indicators: center of gravity lateral position and heading angle; Step 2: Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, determine the risk index corresponding to the predicted vehicle driving state quantity, and determine the resource allocation weight corresponding to the vehicle's control preference according to the risk index, wherein the control preference includes stability control preference or precision control preference. Step 3: Based on the resource allocation weights corresponding to the control preferences, generate the target vehicle driving control quantity, and control the vehicle according to the target vehicle driving control quantity.

2. The method as described in claim 1, characterized in that, The risk index includes a stability risk index and a precision risk index; The resource allocation weights corresponding to the vehicle's control preferences are determined based on the risk index, including: Based on the stability risk index, determine the degree of primary impact of the deviation of each index value corresponding to the vehicle dynamic state information on vehicle stability control. Based on the accuracy risk index, determine the degree of secondary impact of the deviation of each indicator value corresponding to the driving trajectory information on the vehicle accuracy control. Based on the first degree of influence and the second degree of influence, determine the resource allocation weights for stability control preference and precision control preference; Based on the resource allocation weights corresponding to control preferences, the target vehicle driving control quantities are generated as follows: The target vehicle driving control quantity is determined based on the first degree of influence, the second degree of influence, the resource allocation weight of stability control preference, the resource allocation weight of precision control preference, and the deviation of each index value corresponding to the vehicle driving state quantity.

3. The method as described in claim 2, characterized in that, Based on the first degree of influence, the second degree of influence, the resource allocation weights for stability control preference, the resource allocation weights for precision control preference, and the deviations of various index values ​​corresponding to vehicle driving state quantities, the target vehicle driving control quantity is determined, including: Based on the deviations of various index values ​​corresponding to vehicle driving state quantities and vehicle driving control quantities, a comprehensive performance index function is constructed. The comprehensive performance index function includes a driving state quantity term and a driving control quantity term; the driving state quantity term includes a dynamic state information term and a driving trajectory information term, the dynamic state information term includes at least one vehicle driving state index term, the driving trajectory information term includes at least one driving trajectory index term, the coefficient of each vehicle driving state index term is the corresponding first degree of influence, the coefficient of each driving trajectory index term is the corresponding second degree of influence, the coefficient of the dynamic state information term is the resource allocation weight of the stability control preference, and the coefficient of the driving trajectory information term is the resource allocation weight of the precision control preference. The target vehicle driving control quantity is solved based on the comprehensive performance index function so that the value of the function meets the preset conditions.

4. The method as described in claim 1, characterized in that, Based on the deviation between the predicted vehicle driving state quantity and the corresponding specified threshold, a risk index corresponding to the predicted vehicle driving state quantity is determined, including: The risk index corresponding to the first indicator is determined by the square of the ratio of the first indicator to the corresponding specified threshold. The first indicator includes one of the following: yaw rate, center of gravity sideslip angle, vehicle roll index, center of gravity lateral position deviation, and heading angle deviation. The vehicle roll index is determined based on the lateral tilt angle, longitudinal slope angle, vehicle roll angle, and roll rate.

5. The method as described in claim 2 or 3, characterized in that, The degree of impact of the target is determined in the following ways, including either the first degree of impact or the second degree of impact: Vehicle driving state quantities include sub-vehicle driving state quantities, which are vehicle dynamic state information or driving trajectory information, and each sub-vehicle driving state quantity includes at least one indicator. The total risk index is determined by summing the risk indices corresponding to at least one of the indicators. The degree of impact of at least one target is determined based on the ratio of the risk index corresponding to each of the at least one indicator to the total risk index.

6. The method as described in claim 2 or 3, characterized in that, Based on the first degree of influence and the second degree of influence, the resource allocation weights for stability control preference and accuracy control preference are determined, including: The maximum target influence level among the various target influence levels is determined, as well as the sum of the influence levels of all targets. A correction coefficient is determined based on the ratio of the maximum target influence level to the sum of the influence levels of all targets. A target quantification coefficient is determined based on the average value of the influence levels of all targets, the maximum target influence level, and the correction coefficient. The target influence level is either a first influence level or a second influence level, and the target quantification coefficient is either a stability quantification coefficient or a precision quantification coefficient. The resource allocation weight for stability control preference is determined by multiplying the ratio of the stability quantification coefficient to the accuracy quantification coefficient with the basic weight of the stability index. The resource allocation weight for precision control preference is determined by multiplying the ratio of the precision quantification coefficient to the stability quantification coefficient with the basic weight of the precision index.

7. The method as described in claim 3, characterized in that, The dynamic state information item, the driving trajectory information item, and the driving control quantity item each include a corresponding time-domain decay factor coefficient; the time-domain decay factor coefficient decreases as the predicted future time step increases.

8. The method as described in claim 1, characterized in that, Based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity at the current sampling time, predict the vehicle driving state quantities for multiple future time steps, including: Based on the current longitudinal vehicle speed, the current tangent of the centroid sideslip angle, the current cosine of the heading angle, and the current sine of the heading angle, determine the increment of the centroid's lateral position within the sampling period; The lateral position of the centroid at the next moment is determined by summing the lateral position increment of the centroid within the sampling period with the lateral position of the centroid at the current moment.

9. The method as described in claim 1, characterized in that, Based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity at the current sampling time, predict the vehicle driving state quantities for multiple future time steps, including: Based on the sampling period, total vehicle mass, longitudinal force of the left front axle tire at the current moment, cosine value of front wheel steering angle, longitudinal force of the right front axle tire at the current moment, longitudinal force of the left rear axle tire at the current moment, and longitudinal force of the right rear axle tire at the current moment, determine the longitudinal speed increment within the sampling period. The longitudinal speed at the next moment is determined by summing the longitudinal speed increment within the sampling period with the longitudinal speed at the current moment.

10. The method as described in claim 1, characterized in that, Based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity at the current sampling time, predict the vehicle driving state quantities for multiple future time steps, including: Obtain the vehicle's center of gravity sideslip angle self-coupling coefficient, yaw rate self-coupling coefficient, center of gravity sideslip angle yaw rate cross-coupling coefficient, and yaw rate center of gravity sideslip angle cross-coupling coefficient. The determinant of the vehicle lateral dynamics system state matrix is ​​determined based on the sampling period, the self-coupling coefficient of the center of mass sideslip angle, the self-coupling coefficient of the yaw rate, the cross-coupling coefficient of the center of mass sideslip angle and the cross-coupling coefficient of the yaw rate and the center of mass sideslip angle. The sideslip angle of the center of mass at the next moment is determined based on the determinant of the state matrix of the vehicle's lateral dynamics system.

11. The method as described in claim 1, characterized in that, Based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity at the current sampling time, predict the vehicle driving state quantities for multiple future time steps, including: Based on the current centroid sideslip angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle, the current vehicle body roll angle, the cross-coupling coefficient of the centroid sideslip angle and the sideslip angle velocity, the lateral tilt angle and longitudinal slope angle of the driving road, determine the first-order lateral dynamic equivalent disturbance term; Based on the yaw rate at the current moment, the front wheel steering angle at the current moment, the longitudinal forces of the left and right tires of the front axle, and the difference between the left and right longitudinal forces of the front and rear axles at the current moment, determine the equivalent disturbance term of the second-order lateral dynamics. The yaw rate at the next moment is determined based on the sampling period, the first-order lateral dynamic equivalent perturbation term, and the second-order lateral dynamic equivalent perturbation term.

12. The method as described in claim 1, characterized in that, Based on the dynamic relationship between vehicle driving state quantities at adjacent sampling time steps, and based on the current vehicle driving state quantity at the current sampling time, predict the vehicle driving state quantities for multiple future time steps, including: The excitation gain coefficient of the front wheel steering angle on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total vehicle mass, the vehicle's moment of inertia about the roll axis, the equivalent tire lateral stiffness of the front axle, and the front wheel steering angle. The gain coefficient of the longitudinal force on the roll angle is determined based on the sprung mass, the distance from the roll center to the sprung mass center of mass, the total mass of the vehicle, the moment of inertia of the vehicle about the roll axis, and the front wheel angle. The roll rate at the next moment is determined based on the sampling period, the front wheel steering angle to roll angle excitation gain coefficient, and the front axle longitudinal force to roll angle excitation gain coefficient.

13. A vehicle, characterized in that, include: The system includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the vehicle control method as described in any one of claims 1 to 12.

Images