An Active Vehicle Posture Control Method Based on Vehicle Damage Assessment Model After a Rear-End Collision

Abstract

This invention relates to the field of active vehicle safety control technology, and more particularly to a method for actively adjusting the posture of a vehicle after a rear-end collision based on a vehicle damage assessment model. First, the mechanical parameters of the rear section of the vehicle are obtained through finite element simulation, and a vulnerability coefficient database is constructed. Then, the vehicle state is sensed within the collision window, and the relative velocity between the collision point and the normal direction is predicted. The real-time damage index is calculated based on the database. Subsequently, with the goal of minimizing damage, a dynamic model containing yaw and pitch degrees of freedom is constructed, and the optimal control sequence is obtained through model predictive control and roll optimization. Finally, the vehicle posture is coordinated and adjusted through steer-by-wire and four-wheel brake-by-wire to allow the robust rear section to absorb the impact. This invention addresses the problems in existing vehicle safety technologies, such as the lack of a real-time quantifiable vehicle damage assessment model and the inability to actively optimize collision conditions to reduce vehicle damage in situations where a rear-end collision is unavoidable.

Description

Technical Field

[0001] This invention relates to the field of vehicle active safety control technology, and in particular to a method for actively adjusting the posture of a vehicle after a rear-end collision based on a vehicle damage assessment model. Background Technology

[0002] Rear-end collisions are a common type of road traffic accident. The structural damage to the rear-ended vehicle not only incurs high repair costs but also reduces its residual value and can even trigger secondary collisions, threatening the safety of occupants. Current vehicle collision avoidance safety technologies are mainly divided into passive and active safety technologies. However, both existing technologies have shortcomings in adapting to rear-end collision scenarios and in minimizing damage before a collision, as detailed below: 1. Passive safety technologies have inherent limitations: Current rear-end collision protection designs (such as energy-absorbing boxes and reinforced longitudinal beams) are all passive safety technologies. Their protection mechanism relies on the dissipation of energy through the plastic deformation of structural components after a collision. The core flaw is that they "only absorb, but do not intervene." When the vehicle being rear-ended senses the risk of a rear collision, this type of structure cannot make any active response to change the collision angle, overlap, or other collision conditions. The final extent of vehicle damage depends entirely on the instantaneous state of the collision, and its role in reducing vehicle property damage is extremely limited.

[0003] 2. Traditional active safety technologies are not applicable to rear-end collision situations: Traditional active safety technologies, represented by Automatic Emergency Braking (AEB), are designed for frontal collisions where the vehicle rear-ends the vehicle in front. They cannot directly and effectively intervene in the braking of vehicles with rear-end collision threats. Furthermore, their core decision-making logic is a binary mode: if the algorithm determines that the collision can be avoided, it will brake at full force; if it determines that the collision is unavoidable, the system usually only issues a warning or stops active intervention. It lacks a continuous active control strategy aimed at reducing the damage to the rear-ended vehicle itself during the "last window" when a collision is unavoidable.

[0004] 3. Limitations in the Objectives and Dimensions of Cutting-Edge Pre-Collision Intervention Research: In recent years, most academic research on active intervention technologies for unavoidable collision scenarios has focused on reducing occupant injury by pre-tensioning seat belts and adjusting seat angles to optimize human dynamics response. Research specifically targeting "minimizing damage to the structural integrity of the rear-ended vehicle" is almost nonexistent, seriously neglecting the structural integrity and repair economy requirements of vehicles as high-value assets. The few studies involving vehicle motion intervention focus only on emergency braking or steering to avoid collisions with the vehicle in front. For the rear-ended vehicle, there is still no systematic solution on how to actively adjust the yaw and pitch angles and other multi-degree-of-freedom attitudes in a very short time using steering and braking actuators to allow the strongest rear structural area of ​​the vehicle to absorb the impact and optimize the collision energy transfer path.

[0005] The fundamental reason for the aforementioned defects in existing technology is that: First, there is a lack of quantitative vehicle damage assessment models. Existing systems cannot predict the extent of damage to the rear structure of a rear-ended vehicle in different collision scenarios in real time before a collision occurs, and there is a lack of objective quantitative basis for judging "which posture is better". Second, there is a lack of targeted decision-making logic. The control logic of existing vehicle electronic stability programs or driver assistance systems does not include a decision branch for "actively adjusting attitude to minimize damage to its own structure," making it impossible to perform targeted control for situations where rear-end collisions are unavoidable. Third, there is a lack of suitable cooperative control algorithms. The coordinated adjustment of yaw and pitch of the rear-ended vehicle in the forward state is a strongly coupled, multi-constrained real-time optimal control problem that traditional control methods cannot handle. Existing model predictive control algorithms lack objective function design that is directly linked to vehicle structural damage, and cannot achieve attitude control with damage minimization as the core. Summary of the Invention

[0006] The technical problem solved by this invention is to provide a method for actively controlling the posture of a vehicle after a rear-end collision based on a vehicle damage assessment model, so as to solve the problem in existing vehicle safety technologies that lack a real-time quantifiable vehicle damage assessment model for rear-end collisions where collisions are unavoidable, and cannot actively optimize collision conditions to reduce vehicle damage.

[0007] The basic solution provided by this invention is: a method for actively controlling the posture of a vehicle after a rear-end collision based on a vehicle damage assessment model, comprising: S1: Based on the whole vehicle finite element model of the target vehicle, extract the mesh partition map of the rear of the vehicle, and obtain the mechanical response parameters of each partition of the rear of the vehicle under different collision conditions through simulation software based on the preset collision condition matrix. S2: After preprocessing the mechanical response parameters, generate the comprehensive vulnerability coefficient of each zone, establish a mapping relationship between the ID of each zone at the rear of the vehicle and the vulnerability coefficient of each zone, and generate a vulnerability coefficient lookup table database. S3: Within a preset window period before a collision occurs, the vehicle acquires the status information of the following vehicle and the motion status information of the vehicle in real time based on the on-board environmental perception sensor, and calls the kinematic model to predict the predicted collision point between the vehicle and the following vehicle and the normal relative velocity of the two vehicles along the normal direction of the contact surface. S4: Based on the predicted collision point mapping to the vehicle rear mesh partition map, determine the partition ID to which the collision point belongs, obtain the vulnerability coefficient corresponding to the partition ID by querying the vulnerability coefficient lookup table database, input the normal relative velocity and vulnerability coefficient into the preset vehicle damage assessment model, and calculate the real-time damage index. S5: Based on minimizing the total damage index of the vehicle as the optimization objective, a vehicle dynamics prediction model including the vehicle's yaw and pitch degrees of freedom is constructed to perform rolling optimization for the optimization objective, and the optimal control sequence at the current moment is obtained respectively. S6: Based on the optimal control sequence at the current moment, it is sent to the vehicle's steer-by-wire system and brake-by-wire system respectively. The steer-by-wire system executes the front wheel steering angle command to adjust the vehicle's yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to adjust the vehicle's pitch degree of freedom.

[0008] Furthermore, S1 includes: S1-1: Based on the whole vehicle finite element model of the target vehicle, the continuous structure of the rear of the vehicle is discretized into several independent damage assessment units, and typical partitions are performed to generate a mesh partition map of the rear of the vehicle; typical partitions include but are not limited to: the central area of ​​the rear bumper beam, the left energy absorption box area, the right energy absorption box area, the end of the left longitudinal beam, the rear panel area, the left taillight corner area, the right taillight corner area, and the trunk lid area. S1-2: Construct a high-fidelity explicit dynamic finite element model of the rear of the target vehicle, design a standard simulation condition matrix, and perform simulation calculations using explicit dynamic finite element software to obtain the mechanical response parameters of each section of the rear of the vehicle under different collision conditions. The mechanical response parameters include the maximum dynamic intrusion amount. Total energy absorption Average contact force on the contact surface Structural failure state .

[0009] Furthermore, S2 includes: S2-1: Select the central area of ​​the rear bumper beam at the rear of the target vehicle as the reference area, and define its vulnerability coefficient as the reference coefficient; S2-2: For each partition i in the vehicle rear mesh partitioning diagram and for each simulation condition j, calculate the ratio of each mechanical response parameter of that partition under condition j to the parameters of the reference region under the same condition. The expression is:

[0010]

[0011]

[0012]

[0013] in, This represents the ratio of the maximum dynamic intrusion amount of partition i under operating condition j. Let i be the maximum dynamic intrusion amount of partition i under operating condition j. The maximum dynamic intrusion of the reference area under operating condition j; Let i be the ratio of total energy absorbed by partition i under operating condition j. The total energy absorbed in the reference region under operating condition j is given. Let i be the total energy absorbed by partition i under operating condition j; Let i be the ratio of the average contact force of the contact surface in zone i under working condition j. The average contact force of the contact surface in the reference region under operating condition j. The average contact force of the contact surface of zone i under working condition j; The ratio of structural failure states in partition i under operating condition j. The structural failure state of the reference region under operating condition j is given. The structural failure state of partition i under operating condition j; It is the minimum value; S2-3: Calculate the comprehensive working condition ratio of partition i under single working condition j, the expression is:

[0014] in, This is the ratio under comprehensive operating conditions. , , , This is a weighting coefficient for the ratio of maximum dynamic intrusion, the ratio of total energy absorption, the ratio of average contact force on the contact surface, and the ratio of structural failure states. ; S2-4: Calculate the vulnerability coefficient of partition i based on the comprehensive operating condition ratio, with the following expression:

[0015] in, Let i be the vulnerability coefficient of partition i. , where is the weighting coefficient for working condition j; S2-5: Establish partition IDs and corresponding vulnerability coefficients The mapping relationship of values ​​forms a structured structure. Look up a table in the database.

[0016] Furthermore, S4 includes: S4-1: Map the predicted collision point to the vehicle's rear structure partition map, determine the partition ID to which the collision point belongs, and then query... Look up the database table to obtain the vulnerability coefficient corresponding to the partition; S4-2: Input the normal relative velocity and the vulnerability coefficient corresponding to the zone into the preset vehicle damage assessment model to calculate the real-time damage index, the expression of which is:

[0017] in, Real-time damage index; It represents the trend of kinetic energy input during a collision, and is positively correlated with the energy absorbed by structural deformation during the collision.

[0018] Furthermore, S5 includes: S5-1: Based on minimizing the total damage as the optimization objective and the real-time damage index as the optimization objective, construct an objective function that includes damage cost, control cost, and control smoothing cost. S5-2: Define a 7-dimensional state vector x and a 5-dimensional control vector u. The 7-dimensional state vector x includes the vehicle's geodetic coordinates, yaw motion, longitudinal motion, and pitch motion states, defined as follows:

[0019] in, , Let x and y be the vehicle's center of mass in the geodetic coordinate system; This refers to the vehicle's yaw angle; This refers to the yaw rate; The longitudinal speed at the vehicle's center of gravity; The vehicle's pitch angle; The vehicle's pitch angular velocity; ; The 5-dimensional control vector u is defined as:

[0020] in, The steering angle of the vehicle's front wheels; This refers to the braking torque of the left front wheel; This refers to the braking torque of the right front wheel; This refers to the braking torque of the left rear wheel; This refers to the braking torque of the right rear wheel; S5-3: Construct the continuous-time vehicle dynamics differential equations and discretize them using the forward Euler method to obtain the discrete-time state transition equations; the expressions for the continuous-time vehicle dynamics differential equations include: Differential equation of yaw motion dynamics:

[0021]

[0022]

[0023] in, Let be the yaw moment of inertia of the vehicle about the Z-axis. The yaw rate is angular velocity. The yaw acceleration is... The horizontal distance from the vehicle's center of gravity to the front axle; This is the horizontal distance from the center of mass to the rear axle; The resultant force is the lateral force on the front axle. This is the resultant force on the rear axle lateral side; The additional yaw moment generated by independent braking of the four wheels; Differential equations of pitch motion dynamics:

[0024]

[0025]

[0026] in, Let be the pitch inertia of the vehicle about the Y-axis. The vehicle's pitch angular velocity, It is the pitch acceleration; For the front axle dynamic vertical load. For the dynamic vertical load on the rear axle; For the front axle static vertical load; For the static vertical load on the rear axle; Longitudinal motion dynamics differential equation:

[0027]

[0028] in, For vehicle curb weight, For longitudinal deceleration, For the longitudinal resultant force of the front axle, This is the longitudinal resultant force of the rear axle; For the braking torque of each wheel, The effective rolling radius of the wheel; The state transition equation is expressed as follows:

[0029] in, For discrete time steps, This encapsulates the discrete state transition function of the continuous-time vehicle dynamics differential equation. Let k be the system state vector at step k. This is the control vector for the k-th step; S5-4: Based on each prediction step k, according to the predicted target vehicle state and the predicted status of the following vehicle Calculate the instantaneous damage index corresponding to the prediction step size, the expression is:

[0030]

[0031] in, The instantaneous damage index, To predict collision points Vulnerability coefficient of the corresponding region The normal relative velocity, Let the velocity vector of the following vehicle be the velocity vector at the k-th prediction step. Let be the longitudinal velocity vector of the target vehicle at the k-th prediction step; The unit normal vector of the contact surface; S5-5: Taking the vehicle state x(t) at the current sampling time t as the initial state, a finite time domain open-loop optimization problem is preset, including discrete dynamic constraints, actuator amplitude constraints, actuator rate constraints, pitch attitude constraints, tire adhesion circle constraints, forward safety distance constraints, and control time domain external constraints. The nonlinear programming solver is called to solve the problem online to obtain the optimal control sequence. S5-6: In the next sampling period, repeat the above steps to perform adaptive rolling optimization control.

[0032] Furthermore, S6 includes: S6-1: Send the first control quantity of the optimal control sequence to the vehicle's steer-by-wire system and brake-by-wire system; S6-2: The steer-by-wire system executes the front wheel steering angle command to control the vehicle yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to control the vehicle pitch angle and longitudinal speed, achieving coordinated control of the vehicle's yaw and pitch multi-degree-of-freedom attitude.

[0033] The principle of this invention lies in minimizing vehicle structural damage under rear-end collision conditions where the collision is unavoidable. It integrates vehicle structural crashworthiness analysis, vehicle damage quantification assessment, multi-degree-of-freedom vehicle dynamics, and model predictive control technology. Through a two-layer architecture of offline calibration and online real-time control, it achieves active coordinated control of the yaw and pitch multi-degree-of-freedom attitudes of the rear-ended vehicle. The core principle consists of two main stages: offline basic calibration and online real-time control, with each stage progressing progressively and linked in a closed loop. The first step is offline calibration: Based on the finite element model of the target vehicle, the rear structure of the vehicle is divided into sections. The mechanical response parameters of each section are obtained through multi-condition collision simulation. Taking the center area of ​​the rear anti-collision beam as the benchmark, the ratio of individual parameters and the comprehensive vulnerability coefficient of each section are calculated. A mapping database between section ID and vulnerability coefficient is established to provide a quantitative benchmark for online vehicle damage assessment and realize the standardization and callability of the damage characteristics of the rear structure of the vehicle. The next step is online real-time control: within a preset window before the collision, the vehicle's motion state and that of the following vehicle are acquired through onboard sensors, the relative velocity at the collision point and in the normal direction are predicted, and the vulnerability coefficient of the corresponding partition at the collision point is queried from an offline database. The real-time damage index is quantified through a vehicle damage assessment model. Then, with minimizing the cumulative damage index in the prediction time domain as the core optimization objective, a vehicle dynamics prediction model including yaw, pitch, and longitudinal degrees of freedom is constructed. A model predictive control (MPC) algorithm under multiple constraints is designed for rolling optimization, and the optimal control sequence of front wheel steering angle and four-wheel independent braking torque is obtained. Finally, the vehicle's yaw and pitch angles are dynamically adjusted by the coordinated execution of control commands through the steer-by-wire and brake-by-wire systems, so that the strongest area of ​​the vehicle's rear structure can withstand the impact, optimize the collision energy transfer path, and achieve optimal control of the vehicle's anti-collision attitude before the collision.

[0034] The technical effect is as follows: This invention addresses the core technical problem in existing vehicle safety technologies where, in rear-end collisions where the collision is unavoidable, there is a lack of real-time quantifiable vehicle damage assessment models and a lack of suitable multi-degree-of-freedom attitude collaborative control methods, making it impossible to proactively optimize collision conditions to reduce vehicle structural damage. The invention achieves targeted technical effects, as detailed below: 1. This invention establishes a vulnerability coefficient database for each section of the vehicle's rear end through offline calibration, and constructs a vehicle damage assessment model by combining normal relative velocity. It transforms the collision resistance characteristics of the vehicle's rear structure into quantifiable vulnerability coefficients and the collision impact intensity into a real-time damage index. For the first time, it provides an objective, accurate, and real-time quantitative assessment basis for vehicle damage in rear-end collision scenarios, solving the problem in existing technologies that cannot determine "which vehicle posture is better," and providing a clear optimization direction for subsequent posture control.

[0035] 2. This invention breaks through the binary decision-making logic of traditional active safety technology, which is "only avoiding collisions and not controlling damage". For the "last window period" when a rear-end collision is inevitable, a special decision-making branch with minimizing vehicle structural damage as the core is designed. The goal of active control is extended from "avoiding collisions" to "collision damage control". This fills the technical gap in active protection of existing vehicle electronic stability programs and driver assistance systems in the case of rear-end collisions, and improves the full-scenario coverage of vehicle active safety protection.

[0036] 3. This invention constructs a vehicle dynamics differential equation that includes yaw, pitch, and longitudinal degrees of freedom, designs a multi-objective optimization function that integrates damage cost, control cost, and control smoothing cost, and achieves rolling optimization of model predictive control by combining multiple physical constraints. This solves the problem of strong coupling and multi-constraint control of yaw and pitch adjustment in the forward state of a rear-ended vehicle. At the same time, through the coordinated execution of steer-by-wire and four-wheel independent steer-by-wire braking, it achieves precise matching of front wheel steering angle and braking torque, ensuring that the vehicle can quickly adjust to the optimal anti-collision posture within a very short window period. This solves the problem in the prior art that it is impossible to systematically adjust the multi-degree-of-freedom posture of the vehicle to optimize collision conditions.

[0037] 4. This invention actively adjusts the vehicle's yaw and pitch angles to ensure that the strongest structural area at the rear of the vehicle absorbs the impact. At the same time, it optimizes the collision energy transfer path, transforming a frontal collision and a collision in a vulnerable area into an oblique collision and a collision in a strong area. This significantly reduces the maximum dynamic intrusion of the vehicle's rear structure and the probability of structural failure, maximizes the use of the vehicle's original crashworthiness design, effectively reduces vehicle repair costs and depreciates the vehicle's residual value, and solves the problem that existing passive protection technologies "only absorb, do not intervene," causing the degree of vehicle damage to depend entirely on the instantaneous state of the collision. Attached Figure Description

[0038] Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2 This is a schematic diagram of the perception and decision processing in an embodiment of the present invention; Figure 3 This is a schematic diagram of the offline calibration process according to an embodiment of the present invention. Detailed Implementation

[0039] The following detailed description illustrates the specific implementation method: This application presents a method for active control of a vehicle's rear-end collision posture based on a vehicle damage assessment model. Before implementing the active control method, a decision-making assessment is first performed through a perception layer and a decision-making layer, such as... Figure 2 As shown, in the perception layer, data from millimeter-wave radar (speed and distance of following vehicles), cameras (lane line recognition, outlines of vehicles in front / to the side), and the vehicle CAN bus (speed and yaw angle of the vehicle itself) are integrated to uniformly transform the position and speed of all external targets into the vehicle coordinate system with the center of gravity of the vehicle as the origin, so as to perform geometric and kinematic calculations.

[0040] Subsequently, based on the data obtained above, the decision-making body calculated the following core decision indicators in real time, including: Relative motion: Accurately calculates the relative speed and collision time between this vehicle and the vehicle behind it; Lateral conflict time: Calculates the time required for vehicles in the lateral lane to reach the planned lane change area of ​​this vehicle; Lane line semantic information: Through image recognition, output the lane line type (solid line / dashed line) of the target lane and a Boolean judgment on whether there is a no-lane-change sign.

[0041] The calculation methods for the above decision indicators are all existing mature technologies, and this application will not elaborate on them further. After the decision indicators are calculated, the first level of decision-making is carried out, namely, the feasibility judgment of lane changing without collision. This step requires dual judgment, namely, physical feasibility judgment and regulatory feasibility judgment. Both must be met before lane changing is allowed. Among them, the physical feasibility judgment is as follows: Calculate the minimum safe lane change distance ( Based on the classic "double lane change" trajectory model, and combined with the vehicle's current speed and maximum permissible lateral acceleration, the minimum longitudinal travel distance required to complete a safe lane change is calculated. Determine distance to the vehicle in front: Obtain the real-time distance between your vehicle and the vehicle in front. ),like If so, then the vertical geometric feasibility is satisfied; Determine the safe lateral distance: Calculate the time required for the vehicle to complete the lane change ( If a vehicle is approaching from the side +Safety margin, then lateral safety is satisfied.

[0042] Feasibility assessment of regulations: Directly read the lane line type and traffic sign information obtained in the aforementioned steps; Establish judgment rules: If the target lane line is a solid line or there is a no-lane-change sign, then changing lanes is absolutely prohibited by law. This is a hard constraint based on the national standard "GB / T 41798-2022". Logical composition and output: If physically feasible and legally feasible, the decision framework outputs the "execute collision-free lane change" instruction and calls the standard trajectory planning and tracking module; if either condition is not met, the second-level decision is triggered immediately.

[0043] Second-level decision-making: After the first-level decision determines that changing lanes is not feasible, the second-level decision assesses the severity of the impending rear-end collision and determines the final defense mode; the first step is to calculate the estimated collision energy. :

[0044] in, For the sake of the vehicle's quality, For the quality of the following vehicle, It is the relative velocity; The next step is to determine whether the energy threshold has been exceeded. This threshold is determined through preliminary finite element simulation, which analyzes the maximum effective energy absorption capacity of the vehicle's rear structure to establish an energy threshold. And set a safety factor. The final threshold is ; Logical judgment: If This means that the collision is likely to cause irreversible and catastrophic crushing of the vehicle's rear structure, posing an extremely high risk to the safety of the passenger compartment, and is therefore classified as a catastrophic collision. In the event of a devastating collision, the system will output the "execute forced lane change" command. At this time, the system will ignore the lateral safety margin, but will still comply with the regulations and replace a high-energy rear-end collision with a lateral scrape. This is an extreme risk avoidance strategy. If the destructive threshold is not reached, the "Enter Active Attitude Adjustment" command is output to execute the vehicle rear-end collision attitude active adjustment based on the vehicle damage assessment model of this application.

[0045] Therefore, the embodiments are basically as shown in the appendix. Figure 1 As shown: A method for active adjustment of vehicle posture after a rear-end collision based on a vehicle damage assessment model, comprising: S1: Based on the finite element model of the target vehicle, extract the mesh partition map of the rear of the vehicle, and obtain the mechanical response parameters of each partition of the rear of the vehicle under different collision conditions using simulation software based on a preset collision condition matrix; wherein, S1 includes: S1-1: Based on the whole vehicle finite element model of the target vehicle, the continuous structure of the rear of the vehicle is discretized into several independent damage assessment units, and typical partitions are performed to generate a mesh partition map of the rear of the vehicle; typical partitions include but are not limited to: the central area of ​​the rear bumper beam, the left energy absorption box area, the right energy absorption box area, the end of the left longitudinal beam, the rear panel area, the left taillight corner area, the right taillight corner area, and the trunk lid area. In this embodiment, the basic model is obtained based on the acquired 3D CAD design drawings of the target vehicle. Using professional mesh generation software such as HyperMesh and ANSYS ICEM, the CAD drawings of the vehicle are converted into a finite element model of the vehicle that meets the requirements of explicit dynamic simulation. The model mesh element type adopts a hybrid form of tetrahedral and hexahedral elements. Among them, the key areas of the vehicle's rear structure (anti-collision beam, energy absorption box, longitudinal beam end, rear panel, etc.) adopt hexahedral structured mesh with a mesh size controlled between 2mm and 5mm, while the non-critical areas (outer panel of the trunk lid, taillight mounting base, etc.) adopt tetrahedral unstructured mesh with a mesh size controlled between 8mm and 12mm, ensuring that the model meets both the simulation accuracy requirements and the computational efficiency. The discretization of the damage assessment units is based on the principles of consistent mechanical properties of the vehicle's rear structure, independence of structural functions, and relevance to maintenance economy. The continuous finite element model structure of the vehicle's rear is discretized into several independent damage assessment units. During the discretization process, the physical boundaries of each core structure of the vehicle's rear are used as the basis for unit division to ensure that each damage assessment unit is an independent structural module, and that the material properties, structural stiffness, and crash resistance characteristics within a single unit remain uniform, and that the structural characteristics between different units are distinguishable. Based on the discretized damage assessment unit, and combined with the actual collision contact characteristics and repair cost of the vehicle's rear, typical zones and unique ID numbers are created. These zones cover all areas of the vehicle's rear that may collide with following vehicles. The typical zone and numbering rules are as follows: Zone 1: Rear bumper beam center area (2 / 3 of the length of the middle section of the bumper beam); Zone 2: Left energy-absorbing box area (the entire area of ​​the energy-absorbing box where the left anti-collision beam and longitudinal beam are connected); Zone 3: Right energy-absorbing box area (the entire area of ​​the energy-absorbing box where the right side anti-collision beam and longitudinal beam are connected); Section 4: End of the left longitudinal beam (the structural area where the rear of the left longitudinal beam extends to the rear panel). Section 5: End of the right longitudinal beam (the structural area where the rear of the right-side longitudinal beam extends to the rear panel). Section 6: Rear panel area (main structure area of ​​the rear panel inside the trunk). Section 7: Left taillight corner area (left taillight mounting location and surrounding sheet metal structure area); Section 8: Right taillight corner area (right taillight mounting location and surrounding sheet metal structure area); Section 9: Trunk lid area (the overall structural area of ​​the trunk lid outer panel + inner panel).

[0046] After partitioning and numbering, different partitions are assigned differentiated color identifiers and ID labels in the finite element simulation software. This generates a vehicle rear mesh partition map containing partition boundaries, unique ID numbers, structural locations, and mesh information. The partition map is stored in a common CAE file format, and high-definition two-dimensional engineering drawings are exported to ensure that subsequent steps can directly call the partition map for collision point mapping and vulnerability coefficient matching.

[0047] S1-2: Construct a high-fidelity explicit dynamic finite element model of the target vehicle's rear end, design a standard simulation condition matrix, and perform simulation calculations using explicit dynamic finite element software to obtain the mechanical response parameters of each section of the vehicle's rear end under different collision conditions; including the maximum dynamic intrusion amount. Total energy absorption Average contact force on the contact surface Structural failure state ; In this embodiment, the construction of a high-fidelity explicit dynamic finite element model first requires extracting the rear model. The finite element model of the vehicle's rear structure is extracted from the whole vehicle finite element model, encompassing all structures behind the B-pillar, including the anti-collision beam, energy-absorbing box, longitudinal beam ends, rear bulkhead, trunk lid, taillight mounting base, and all other collision-related structures. Subsequently, based on the actual component materials of the target vehicle, corresponding material constitutive models are assigned to each structure in the rear finite element model. The material parameters and constitutive model requirements for the core structures are as follows: Anti-collision beams and longitudinal beam ends: High-strength steel materials are selected, and the Johnson-Cook elastoplastic constitutive model is adopted. Key parameters such as the material's elastic modulus, Poisson's ratio, yield strength, tensile strength, strain hardening coefficient, and strain rate sensitivity coefficient are input. Energy Absorbing Box: Low-carbon steel or aluminum alloy material is selected, a plastic kinematic hardening constitutive model is adopted, and material failure criteria (such as ductile failure) are added. The failure parameters such as fracture strain and stress triaxiality of the material are input. Rear panel and trunk lid: ordinary cold-rolled steel plate is selected, and an elastic-plastic constitutive model is adopted, with basic parameters such as elastic modulus, Poisson's ratio, and yield strength input; Connection structure (welding, bolting): The actual connection method is simulated by spot welding connection model and bolting connection model. Parameters such as welding strength, bolt preload, and connection stiffness are input to ensure that the model fits the mechanical transmission characteristics of the actual structure.

[0048] Subsequently, a standard simulation condition matrix was designed. Based on the typical characteristics of rear-end collisions in my country's road traffic accident statistics and combined with the national standards for vehicle rear-end collision protection, a standard simulation condition matrix covering three core variables—collision position, collision angle, and collision speed—was designed to ensure that the condition matrix can simulate all typical rear-end collision scenarios of vehicles in actual roads. The specific design requirements for the conditions are as follows: 1. Collision position variables: Covering all 9 typical partitions divided in S1-1, each partition is an independent collision position case, and the collision contact point is the geometric center position of each partition. 2. Collision angle variables: Set 5 typical collision angles: 0° (frontal collision, the following vehicle is traveling in the same direction as this vehicle), 15° (left diagonal collision, the following vehicle deviates 15° to the left), 30° (left diagonal collision, the following vehicle deviates 30° to the left), -15° (right diagonal collision, the following vehicle deviates 15° to the right), and -30° (right diagonal collision, the following vehicle deviates 30° to the right). The collision angle is defined based on the longitudinal axis of this vehicle. Collision speed variables: Six typical collision speeds are set: 10km / h, 20km / h, 30km / h, 40km / h, 50km / h, and 60km / h, covering the typical rear-end collision speed ranges for urban roads (10-30km / h), suburban roads (30-40km / h), and highways (40-60km / h). Working condition combination: The three variables of collision position, collision angle and collision speed are fully combined to form a standard simulation working condition matrix. The total number of working conditions is 9 (collision position) × 5 (collision angle) × 6 (collision speed) = 270 groups. Each working condition is assigned a unique working condition number to facilitate subsequent data classification and processing.

[0049] Next, explicit dynamic simulation calculations were performed. The simulation software selected included mainstream explicit dynamic finite element simulation software such as LS-DYNA and ABAQUS / Explicit. The software needed to support dynamic simulation functions such as nonlinear structural deformation, material failure, and contact collision. During the simulation, all 270 sets of collision simulation cases were executed sequentially according to the numbering order of the standard simulation case matrix. The convergence of the calculation was monitored in real time during the simulation. If problems such as non-convergence or model penetration occurred, the mesh size, contact parameters, or material model were adjusted in a timely manner to ensure that the simulation results of each set of cases were valid.

[0050] Finally, simulation result post-processing software such as LS-PrePost and ABAQUS / Viewer were used to extract the mechanical response parameters of each of the nine typical partitions under the corresponding collision conditions. The extraction method for each mechanical response parameter is as follows: Maximum dynamic intrusion volume Extract the curves of the normal displacement of all structural elements in the partition as a function of time during the collision process, and take the maximum value of the curve as the maximum dynamic intrusion of the partition under this working condition, in mm. Total energy absorption The total energy absorbed by the partition is calculated by post-processing software, which is the sum of the plastic deformation energy and viscous dissipation energy of all structural units in the partition during the entire collision process. The unit is kJ. Average contact force on the contact surface Extract the curve of the contact force between the partition and the rear vehicle during the collision contact process, and calculate the average value of the curve during the contact time period as the average contact force of the contact surface, in kN. Structural failure state Establish structural failure criteria: If more than 5% of the units in a given zone experience material failure (reaching material fracture strain) or structural connection failure (welding / bolt detachment), then that zone is considered to be in a failed state. The value is assigned to 1; if the above criteria are not met, the state is determined to be not invalid. The value is assigned to 0.

[0051] S2: After preprocessing the mechanical response parameters, a comprehensive vulnerability coefficient for each zone is generated. A mapping relationship is established between the zone IDs of the vehicle rear and the vulnerability coefficients of each zone, generating a vulnerability coefficient lookup table database; where, for example... Figure 3 As shown, S2 includes: S2-1: Select the central area of ​​the rear bumper beam at the rear of the target vehicle as the reference area, and define its vulnerability coefficient as the reference coefficient; S2-2: For each partition i in the vehicle rear mesh partitioning diagram and for each simulation condition j, calculate the ratio of each mechanical response parameter of that partition under condition j to the parameters of the reference region under the same condition. The expression is:

[0052]

[0053]

[0054]

[0055] in, This represents the ratio of the maximum dynamic intrusion amount of partition i under operating condition j. Let i be the maximum dynamic intrusion amount of partition i under operating condition j. The maximum dynamic intrusion of the reference area under operating condition j; Let i be the ratio of total energy absorbed by partition i under operating condition j. The total energy absorbed in the reference region under operating condition j is given. Let i be the total energy absorbed by partition i under operating condition j; Let i be the ratio of the average contact force of the contact surface in zone i under working condition j. The average contact force of the contact surface in the reference region under operating condition j. The average contact force of the contact surface of zone i under working condition j; The ratio of structural failure states in partition i under operating condition j. The structural failure state of the reference region under operating condition j is given. The structural failure state of partition i under operating condition j; For example, the minimum value. ; S2-3: Calculate the comprehensive working condition ratio of partition i under single working condition j, the expression is:

[0056] in, This is the ratio under comprehensive operating conditions. , , , This is a weighting coefficient for the ratio of maximum dynamic intrusion, the ratio of total energy absorption, the ratio of average contact force on the contact surface, and the ratio of structural failure states. ; S2-4: Calculate the vulnerability coefficient of partition i based on the comprehensive operating condition ratio, with the following expression:

[0057] in, Let i be the vulnerability coefficient of partition i. , where is the weighting coefficient for working condition j; S2-5: Establish partition IDs and corresponding vulnerability coefficients The mapping relationship of values ​​forms a structured structure. Look up a table in the database.

[0058] S3: Within a preset window period before a collision occurs, the vehicle's environmental perception sensor acquires real-time status information of the following vehicle and motion status information of the vehicle itself, and calls a kinematic model to predict the predicted collision point between the vehicle and the following vehicle and the normal relative velocity of the two vehicles along the normal direction of the contact surface. The preset window period is defined as the time interval from when the vehicle perceives that a rear collision is unavoidable until the actual occurrence of the rear-end collision. Combining the dynamic characteristics of rear-end collisions on roads and the response time of the vehicle's actuators, the preset window period in this embodiment is set to 0.5s-2.5s. The starting determination node of this window period is: when the vehicle's environmental perception sensor identifies a collision time (TTC) of ≤2.5s between the following vehicle and the vehicle itself for 3 consecutive frames (sampling frequency 10Hz), and the decision layer determines that lane changing without collision is not feasible and the catastrophic collision threshold has not been reached, triggering the active attitude adjustment command.

[0059] The vehicle environment perception sensor adopts a vehicle multi-sensor fusion architecture, which integrates the perception data of millimeter-wave radar, lidar, vision camera and vehicle CAN bus. After the integrated perception data is fused and processed, it outputs the motion status information set of the vehicle and the status information set of the following vehicle relative to the vehicle in real time. Subsequently, based on the relative kinematics analysis of the two vehicles, a linear kinematics prediction model suitable for rear-end collisions is constructed. The basic assumptions of this model are: 1. Within the preset window period, both the vehicle and the following vehicle are undergoing uniformly accelerated linear motion. The longitudinal acceleration of the following vehicle is... lateral acceleration The longitudinal acceleration of this vehicle Obtained by real-time fitting of data collected by sensors; 2. During the preset window period, the heading angles of the following vehicle and this vehicle remain unchanged, that is, the yaw rate is 0, and only translational motion is considered; 3. The collision contact is a rigid surface contact. The collision point is the first contact point between the front of the rear vehicle and the rear of the vehicle. The direction of the normal to the contact surface is determined by the geometric characteristics of the contact position of the two vehicles.

[0060] Subsequently, based on the constructed linear kinematics prediction model and combined with the real-time acquired state information sets of the vehicle and the following vehicle, the predicted collision point at the rear of the vehicle is calculated. : 1. Based on the 3D contour coordinates of the rear vehicle acquired by LiDAR, extract the geometric feature points of the foremost front part of the rear vehicle. As the point of contact between the following vehicle and this vehicle, this point is the first point of contact when the following vehicle collides with this vehicle; 2. Within the preset window period, the longitudinal and lateral displacements of the rear vehicle's collision contact point relative to the vehicle's body coordinate system undergo uniformly accelerated motion over time. Calculate the moment of collision. longitudinal relative displacement and lateral relative displacement ; Combining the geometric contours of the rear of the vehicle, Point of contact between the following vehicle and the point of impact The relative displacement is mapped onto the rear structure of the vehicle to obtain the predicted collision point at the rear of the vehicle. Its coordinates satisfy:

[0061]

[0062] in, , All values ​​are coordinates in the vehicle's body coordinate system, which are directly matched with the coordinate system of the vehicle's rear mesh partition map generated in step S1.

[0063] Normal relative velocity The calculation expression is:

[0064] in, The velocity vector of the following vehicle. This is the velocity vector of the vehicle. To predict the unit normal vector of the contact surface between the two vehicles at the point of collision, the direction is from the rear vehicle to the front vehicle.

[0065] S4: Based on the predicted collision point mapping to the vehicle rear mesh partitioning map, determine the partition ID to which the collision point belongs. Obtain the vulnerability coefficient corresponding to this partition ID by querying a vulnerability coefficient lookup table database. Input the normal relative velocity and vulnerability coefficient into a preset vehicle damage assessment model to calculate the real-time damage index; S4 includes: S4-1: Map the predicted collision point to the vehicle's rear structure partition map, determine the partition ID to which the collision point belongs, and then query... Look up the database table to obtain the vulnerability coefficient corresponding to the partition; S4-2: Input the normal relative velocity and the vulnerability coefficient corresponding to the zone into the preset vehicle damage assessment model to calculate the real-time damage index, the expression of which is:

[0066] in, Real-time damage index; It represents the trend of kinetic energy input during a collision, and is positively correlated with the energy absorbed by structural deformation during the collision.

[0067] S5: Based on minimizing the total damage index of the vehicle as the optimization objective, a vehicle dynamics prediction model including the vehicle's yaw and pitch degrees of freedom is constructed for rolling optimization to obtain the optimal control sequence at the current time. S5 includes: S5-1: Based on minimizing the total damage as the optimization objective and the real-time damage index as the optimization objective, construct an objective function that includes damage cost, control cost, and control smoothing cost. S5-2: Define a 7-dimensional state vector x and a 5-dimensional control vector u. The 7-dimensional state vector x includes the vehicle's geodetic coordinates, yaw motion, longitudinal motion, and pitch motion states, defined as follows:

[0068] in, , Let x and y be the vehicle's center of mass in the geodetic coordinate system; This refers to the vehicle's yaw angle; This refers to the yaw rate; The longitudinal speed at the vehicle's center of gravity; The vehicle's pitch angle; The vehicle's pitch angular velocity; ; The 5-dimensional control vector u is defined as:

[0069] in, The steering angle of the vehicle's front wheels; This refers to the braking torque of the left front wheel; This refers to the braking torque of the right front wheel; This refers to the braking torque of the left rear wheel; This refers to the braking torque of the right rear wheel; S5-3: Construct the continuous-time vehicle dynamics differential equations and discretize them using the forward Euler method to obtain the discrete-time state transition equations; the expressions for the continuous-time vehicle dynamics differential equations include: Differential equation of yaw motion dynamics:

[0070]

[0071]

[0072] in, Let be the yaw moment of inertia of the vehicle about the Z-axis. The yaw rate is angular velocity. This is the yaw acceleration. This is the horizontal distance from the vehicle's center of gravity to the front axle. This is the horizontal distance from the center of mass to the rear axle; The resultant force is the lateral force on the front axle. This is the resultant force on the rear axle lateral side; The additional yaw moment generated by independent braking of the four wheels; Differential equations of pitch motion dynamics:

[0073]

[0074]

[0075] in, Let be the pitch inertia of the vehicle about the Y-axis. The vehicle's pitch angular velocity, It is the pitch acceleration; For the front axle dynamic vertical load. For the dynamic vertical load on the rear axle; For the front axle static vertical load; For the static vertical load on the rear axle; Longitudinal motion dynamics differential equation:

[0076]

[0077] in, For vehicle curb weight, For longitudinal deceleration, For the longitudinal resultant force of the front axle, This is the longitudinal resultant force of the rear axle; For the braking torque of each wheel, The effective rolling radius of the wheel; The state transition equation is expressed as follows:

[0078] in, For discrete time steps, This encapsulates the discrete state transition function of the continuous-time vehicle dynamics differential equation. Let k be the system state vector at step k. This is the control vector for the k-th step; S5-4: Based on each prediction step k, according to the predicted target vehicle state and the predicted status of the following vehicle Calculate the instantaneous damage index corresponding to the prediction step size, the expression is:

[0079]

[0080] in, The instantaneous damage index, To predict collision points Vulnerability coefficient of the corresponding region The normal relative velocity, Let the velocity vector of the following vehicle be the velocity vector at the k-th prediction step. Let be the longitudinal velocity vector of the target vehicle at the k-th prediction step; The unit normal vector of the contact surface; S5-5: Taking the vehicle state x(t) at the current sampling time t as the initial state, a finite-time open-loop optimization problem is pre-defined, including discrete dynamic constraints, actuator amplitude constraints, actuator rate constraints, pitch attitude constraints, tire adhesion circle constraints, forward safety distance constraints, and control time-domain constraints. A nonlinear programming solver is called to solve the problem online, obtaining the optimal control sequence. At each sampling time t, the expression for the finite-time open-loop optimization problem is:

[0081] in, For damage cost item, This is the damage cost weighting coefficient. To predict the time-domain step size; To control quantity cost items and control smoothing cost items, To control the time-domain step size; ; Based on the above finite-time open-loop optimization problem, the pre-defined constraints are as follows: Discrete dynamic constraints:

[0082] Actuator amplitude constraints:

[0083] in, This represents the vector of minimum permissible output values ​​for the vehicle's drive-by-wire actuator. This represents the vector of maximum permissible output values ​​for the vehicle's drive-by-wire actuator. Actuator rate constraints:

[0084] in, This represents the minimum permissible rate of change vector of the control quantity of the vehicle's drive-by-wire actuator. This vector represents the maximum permissible rate of change of the control quantity of the vehicle's drive-by actuator. Pitch attitude constraints:

[0085] in, This indicates the minimum allowable pitch angle for the vehicle. Indicates the maximum allowable pitch angle of the vehicle. This indicates the minimum permissible pitch rate of the vehicle. Indicates the maximum permissible pitch rate of the vehicle; Attached circle constraint:

[0086] in, For the lateral acceleration of the vehicle, For longitudinal acceleration, The coefficient of friction of the road surface; It is the acceleration due to gravity; Forward safety distance constraints:

[0087] in, This represents the real-time distance between this vehicle and the vehicle in front. For the minimum static safety distance, For safe time interval; Lane boundary constraints:

[0088] in, , The left and right boundaries of the current lane are represented by their ordinates. Controlling constraints outside the time domain:

[0089] Of all the constraints mentioned above, .

[0090] Therefore, the above optimization problem is constructed with x(t) as the initial state.

[0091] The optimal control sequence can be obtained by calling a nonlinear programming solver (such as dedicated tools based on sequential quadratic programming (SQP) or interior point methods, like ACADO, CasADi, and FORCE'S Pro). .

[0092] Applying the optimal control input: Obtaining the optimal sequence Then, only the first control variable is used. (i.e., the current front wheel steering angle and four-wheel braking torque command) are sent to the steer-by-wire and brake-by-wire systems for execution.

[0093] S5-6: In the next sampling period, repeat the above steps to perform adaptive rolling optimization control.

[0094] S6: Based on the optimal control sequence at the current moment, it is sent to the vehicle's steer-by-wire system and brake-by-wire system respectively. The steer-by-wire system executes the front wheel steering angle command to adjust the vehicle's yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to adjust the vehicle's pitch degree of freedom; S6 includes: S6-1: Send the first control quantity of the optimal control sequence to the vehicle's steer-by-wire system and brake-by-wire system; S6-2: The steer-by-wire system executes the front wheel steering angle command to control the vehicle yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to control the vehicle pitch angle and longitudinal speed, achieving coordinated control of the vehicle's yaw and pitch multi-degree-of-freedom attitude.

[0095] Therefore, through the aforementioned continuous rolling optimization and execution, the vehicle dynamically adjusts its yaw and pitch angles within the pre-collision window, causing the predicted collision point to move to... Lower tail structure region, while reducing normal relative velocity. This transforms a head-on collision into an oblique collision, optimizes the collision energy transfer path, and gradually adjusts the vehicle to the optimal anti-collision posture.

[0096] When a rear-end collision finally occurs, the vehicle is already in an optimal impact-resistant posture, absorbing the impact through the strongest area of ​​the rear structure. This maximizes the use of the vehicle's original crashworthiness design, significantly reducing irreversible deformation of the rear structure and damage to vulnerable parts, thus minimizing structural damage to the vehicle being rear-ended.

[0097] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for actively controlling the posture of a vehicle after a rear-end collision based on a vehicle damage assessment model, characterized in that: include: S1: Based on the whole vehicle finite element model of the target vehicle, extract the mesh partition map of the rear of the vehicle, and obtain the mechanical response parameters of each partition of the rear of the vehicle under different collision conditions through simulation software based on the preset collision condition matrix. S2: After preprocessing the mechanical response parameters, generate the comprehensive vulnerability coefficient of each zone, establish a mapping relationship between the ID of each zone at the rear of the vehicle and the vulnerability coefficient of each zone, and generate a vulnerability coefficient lookup table database. S3: Within a preset window period before a collision occurs, the vehicle acquires the status information of the following vehicle and the motion status information of the vehicle in real time based on the on-board environmental perception sensor, and calls the kinematic model to predict the predicted collision point between the vehicle and the following vehicle and the normal relative velocity of the two vehicles along the normal direction of the contact surface. S4: Based on the predicted collision point mapping to the vehicle rear mesh partition map, determine the partition ID to which the collision point belongs, obtain the vulnerability coefficient corresponding to the partition ID by querying the vulnerability coefficient lookup table database, input the normal relative velocity and vulnerability coefficient into the preset vehicle damage assessment model, and calculate the real-time damage index. S5: Based on minimizing the total damage index of the vehicle as the optimization objective, a vehicle dynamics prediction model including the vehicle's yaw and pitch degrees of freedom is constructed to perform rolling optimization for the optimization objective, and the optimal control sequence at the current moment is obtained respectively. S6: Based on the optimal control sequence at the current moment, it is sent to the vehicle's steer-by-wire system and brake-by-wire system respectively. The steer-by-wire system executes the front wheel steering angle command to adjust the vehicle's yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to adjust the vehicle's pitch degree of freedom.

2. The method for active adjustment of vehicle rear-end collision posture based on a vehicle damage assessment model according to claim 1, characterized in that: S1 includes: S1-1: Based on the whole vehicle finite element model of the target vehicle, the continuous structure of the rear of the vehicle is discretized into several independent damage assessment units, and typical partitions are performed to generate a mesh partition map of the rear of the vehicle; typical partitions include but are not limited to: the central area of ​​the rear bumper beam, the left energy absorption box area, the right energy absorption box area, the end of the left longitudinal beam, the rear panel area, the left taillight corner area, the right taillight corner area, and the trunk lid area. S1-2: Construct a high-fidelity explicit dynamic finite element model of the rear of the target vehicle, design a standard simulation condition matrix, and perform simulation calculations using explicit dynamic finite element software to obtain the mechanical response parameters of each section of the rear of the vehicle under different collision conditions. The mechanical response parameters include the maximum dynamic intrusion amount. Total energy absorption Average contact force on the contact surface Structural failure state .

3. The method for active adjustment of vehicle rear-end collision posture based on a vehicle damage assessment model according to claim 2, characterized in that: S2 includes: S2-1: Select the central area of ​​the rear bumper beam at the rear of the target vehicle as the reference area, and define its vulnerability coefficient as the reference coefficient; S2-2: For each partition i in the vehicle rear mesh partitioning diagram and for each simulation condition j, calculate the ratio of each mechanical response parameter of that partition under condition j to the parameters of the reference region under the same condition. The expression is: in, This represents the ratio of the maximum dynamic intrusion amount of partition i under operating condition j. Let i be the maximum dynamic intrusion amount of partition i under operating condition j. The maximum dynamic intrusion of the reference area under operating condition j; Let i be the ratio of total energy absorbed by partition i under operating condition j. The total energy absorbed in the reference region under operating condition j is given. Let i be the total energy absorbed by partition i under operating condition j; Let i be the ratio of the average contact force of the contact surface in zone i under working condition j. The average contact force of the contact surface in the reference region under operating condition j. The average contact force of the contact surface of zone i under working condition j; The ratio of structural failure states in partition i under operating condition j. The structural failure state of the reference region under operating condition j is given. The structural failure state of partition i under operating condition j; It is the minimum value; S2-3: Calculate the comprehensive working condition ratio of partition i under single working condition j, the expression is: in, This is the ratio under comprehensive operating conditions. , , , This is a weighting coefficient for the ratio of maximum dynamic intrusion, the ratio of total energy absorption, the ratio of average contact force on the contact surface, and the ratio of structural failure states. ; S2-4: Calculate the vulnerability coefficient of partition i based on the comprehensive operating condition ratio, with the following expression: in, Let i be the vulnerability coefficient of partition i. , where is the weighting coefficient for working condition j; S2-5: Establish partition IDs and corresponding vulnerability coefficients The mapping relationship of values ​​forms a structured structure. Look up a table in the database.

4. The method for active adjustment of vehicle rear-end collision posture based on a vehicle damage assessment model according to claim 3, characterized in that: S4 includes: S4-1: Map the predicted collision point to the vehicle's rear structure partition map, determine the partition ID to which the collision point belongs, and then query... Look up the database table to obtain the vulnerability coefficient corresponding to the partition; S4-2: Input the normal relative velocity and the vulnerability coefficient corresponding to the zone into the preset vehicle damage assessment model to calculate the real-time damage index, the expression of which is: in, Real-time damage index; It represents the trend of kinetic energy input during a collision, and is positively correlated with the energy absorbed by structural deformation during the collision.

5. The method for active adjustment of vehicle rear-end collision posture based on a vehicle damage assessment model according to claim 4, characterized in that: S5 includes: S5-1: Based on minimizing the total damage as the optimization objective and the real-time damage index as the optimization objective, construct an objective function that includes damage cost, control cost, and control smoothing cost. S5-2: Define a 7-dimensional state vector x and a 5-dimensional control vector u. The 7-dimensional state vector x includes the vehicle's geodetic coordinates, yaw motion, longitudinal motion, and pitch motion states, defined as follows: in, , Let x and y be the vehicle's center of mass in the geodetic coordinate system; This refers to the vehicle's yaw angle; This refers to the yaw rate; The longitudinal speed at the vehicle's center of gravity; The vehicle's pitch angle; The vehicle's pitch angular velocity; The 5-dimensional control vector u is defined as: in, The steering angle of the vehicle's front wheels; This refers to the braking torque of the left front wheel; This refers to the braking torque of the right front wheel; This refers to the braking torque of the left rear wheel; This refers to the braking torque of the right rear wheel; S5-3: Construct the continuous-time vehicle dynamics differential equations and discretize them using the forward Euler method to obtain the discrete-time state transition equations; the expressions for the continuous-time vehicle dynamics differential equations include: Differential equation of yaw motion dynamics: in, Let be the yaw moment of inertia of the vehicle about the Z-axis. The yaw rate is angular velocity. This is the yaw acceleration. This is the horizontal distance from the vehicle's center of gravity to the front axle. This is the horizontal distance from the center of mass to the rear axle; The resultant force is the lateral force on the front axle. This is the resultant force on the rear axle lateral side; The additional yaw moment generated by independent braking of the four wheels; Differential equations of pitch motion dynamics: in, Let be the pitch inertia of the vehicle about the Y-axis. The vehicle's pitch angular velocity, It is the pitch acceleration; For the front axle dynamic vertical load. For the dynamic vertical load on the rear axle; For the front axle static vertical load; For the static vertical load on the rear axle; Longitudinal motion dynamics differential equation: in, For vehicle curb weight, For longitudinal deceleration, For the longitudinal resultant force of the front axle, This is the longitudinal resultant force of the rear axle; For the braking torque of each wheel, The effective rolling radius of the wheel; The state transition equation is expressed as follows: in, For discrete time steps, This encapsulates the discrete state transition function of the continuous-time vehicle dynamics differential equation. Let k be the system state vector at step k. This is the control vector for the k-th step; S5-4: Based on each prediction step k, according to the predicted target vehicle state and the predicted status of the following vehicle Calculate the instantaneous damage index corresponding to the prediction step size, the expression is: in, The instantaneous damage index, To predict collision points Vulnerability coefficient of the corresponding region The normal relative velocity, Let the velocity vector of the following vehicle be the velocity vector at the k-th prediction step. Let be the longitudinal velocity vector of the target vehicle at the k-th prediction step; The unit normal vector of the contact surface; S5-5: Taking the vehicle state x(t) at the current sampling time t as the initial state, a finite time domain open-loop optimization problem is preset, including discrete dynamic constraints, actuator amplitude constraints, actuator rate constraints, pitch attitude constraints, tire adhesion circle constraints, forward safety distance constraints, and control time domain external constraints. The nonlinear programming solver is called to solve the problem online to obtain the optimal control sequence. S5-6: In the next sampling period, repeat the above steps to perform adaptive rolling optimization control.

6. The method for actively controlling the rear-end collision posture of a vehicle based on a vehicle damage assessment model according to claim 5, characterized in that: S6 includes: S6-1: Send the first control quantity of the optimal control sequence to the vehicle's steer-by-wire system and brake-by-wire system; S6-2: The steer-by-wire system executes the front wheel steering angle command to control the vehicle yaw angle; the brake-by-wire system executes the four-wheel independent braking torque command to control the vehicle pitch angle and longitudinal speed, achieving coordinated control of the vehicle's yaw and pitch multi-degree-of-freedom attitude.

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