Vehicle transverse and longitudinal cooperative control method, system, equipment and medium

Abstract

The invention provides a vehicle transverse and longitudinal cooperative control method, system and device and a medium, and belongs to the technical field of intelligent driving. The method comprises the steps that vehicle real-time operation parameters are obtained through a drive-by-wire chassis domain controller; calculating a vehicle stability margin based on the parameters, and constructing an instability risk field model to calculate an instability risk value; the performance boundary of the actuator is dynamically corrected based on the stability margin and the instability risk value, and a dynamic performance ellipsoid constraint is generated; taking tracking of an upper-layer instruction as a target, taking a dynamic efficiency ellipsoid and vehicle dynamics as constraints, and solving through mixed integer programming to obtain an optimization control instruction; and fault-tolerant processing is performed on the optimization instruction based on the health state of the actuator, and a final instruction is generated and distributed to the transverse and longitudinal actuators. According to the method, through cooperation of instability risk prediction, dynamic boundary constraint, intelligent instruction optimization and health state fault tolerance, the handling stability and active safety of the vehicle under the complex working condition are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent driving technology, and more specifically relates to a method, system, device and medium for vehicle lateral and longitudinal coordinated control. Background Technology

[0002] With the deepening application of intelligent driving technology in the commercial vehicle sector, vehicle operating scenarios are becoming increasingly complex, encompassing various conditions such as high-speed cruising, urban congestion, mountain road curves, and even low-friction surfaces. These complex and variable conditions place extremely high demands on the dynamic coordination and close cooperation between the vehicle's lateral and longitudinal control actuators. Currently, most commercial vehicle lateral and longitudinal control systems adopt an independently designed, hierarchical architecture. While this decoupling approach is feasible under normal operating conditions, the pursuit of single-dimensional control precision often overlooks the inherent strong coupling relationship between lateral and longitudinal dynamics, potentially leading to conflicts in control commands at the underlying execution level.

[0003] Specifically, existing technical solutions have several significant drawbacks. First, traditional chassis domain controllers or actuators are typically viewed as passive command execution units, only responsible for converting and transmitting commands issued by the upper-level intelligent driving controller. They lack the inherent ability to assess and correct whether the commands themselves are reasonable, feasible, or even safe under the current dynamic state of the vehicle. This is equivalent to placing all responsibility for safety boundary judgments forward to the upper-level planning layer, while the planning layer model often struggles to accurately cover all vehicle physical limits in real time.

[0004] Secondly, the fragmented design of the lateral and longitudinal control systems is the core cause of the risk. In emergency or extreme conditions, such as when it is necessary to simultaneously steer to avoid obstacles and decelerate, the independent lateral controller may request a large steering input, while the longitudinal controller may request emergency braking. If the two are not coordinated, the resulting combined dynamic effects can easily exceed the adhesion limits between the tires and the ground, directly leading to vehicle skidding, fishtailing, and other instability phenomena, seriously threatening driving safety.

[0005] Furthermore, many control algorithms employ oversimplified vehicle dynamics models to reduce computational complexity, neglecting key coupling factors such as lateral and longitudinal acceleration, tire lateral slip characteristics, and load transfer. This simplification has little impact at low speeds or during smooth operation, but at high speeds, on steep curves, or on low-adhesion surfaces, model errors will be amplified, leading to a significant decrease in control accuracy and even generating erroneous control commands, inducing instability.

[0006] Finally, control strategies with fixed parameters or rules are difficult to adapt to complex and ever-changing driving scenarios. Faced with a combination of challenges such as continuous sharp curves, dynamic traffic participants, and sudden changes in road surface adhesion conditions, systems lacking coordination and adaptive capabilities often respond slowly or make suboptimal decisions, limiting the safe and reliable operation of intelligent driving vehicles in a wider range of more demanding scenarios. Summary of the Invention

[0007] To address the above problems, the present invention aims to provide a vehicle lateral and longitudinal cooperative control method, system, device, and medium. By constructing an instability risk field and dynamic performance ellipsoidal constraints, and combining mixed integer programming and actuator health state models, the chassis domain controller can proactively predict risks, dynamically limit control boundaries, intelligently correct infeasible commands, and smoothly tolerate faults. This significantly improves the active safety and handling stability of vehicles under complex operating conditions and broadens the safe operation boundaries of intelligent driving systems.

[0008] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a vehicle lateral and longitudinal cooperative control method, including: The vehicle's real-time operating parameters are obtained through the vehicle's drive-by-wire chassis domain controller; Based on the operating parameters, the vehicle stability margin is calculated, and an instability risk field model is constructed to calculate the instability risk value that characterizes the current instability probability of the vehicle. Based on the stability margin and instability risk value, the performance boundary of the actuator is dynamically corrected to generate a dynamic performance ellipsoid constraint. With the goal of tracking the instructions of the upper-level controller and satisfying the dynamic performance ellipsoidal constraints and vehicle dynamics constraints, the optimized control instructions are obtained by solving a mixed integer programming problem. Based on the actuator health status, the optimized control instructions are processed to generate the final control instructions; The final control command is assigned to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

[0009] In an optional implementation, obtaining the vehicle's real-time operating parameters through the vehicle's drive-by-wire chassis domain controller includes: The vehicle's center of gravity sideslip angle is obtained through the vehicle's drive-by-wire chassis domain controller. Side slip angle change rate Angular velocity ω, angular acceleration ω longitudinal acceleration lateral acceleration .

[0010] In an optional implementation, the step of calculating the vehicle stability margin based on the operating parameters, constructing an instability risk field model, and calculating the instability risk value characterizing the current probability of vehicle instability includes: Define the six-dimensional motion phase plane state vector of the vehicle. ; The stability margin SM is calculated based on the six-dimensional motion phase plane state vector, and the calculation formula is as follows:

[0011] in, This is the extreme sideslip angle. To achieve the maximum stable yaw rate, The coefficient of friction of the road surface;

[0012] Where L is the wheelbase and K is the understeer gradient. This refers to the front wheel steering angle; An instability risk field model is constructed, and the instability risk value R of the current state is calculated based on the current motion state vector represented by the six-dimensional motion phase plane state vector. Its expression is as follows:

[0013] in, This is the current motion state vector. This is a characteristic point of instability. Risk weights are dynamically related to the stability margin SM. This represents the risk diffusion coefficient.

[0014] In an optional implementation, the step of dynamically correcting the actuator's performance boundary based on the stability margin and instability risk value to generate a dynamic performance ellipsoid constraint includes: Establish with longitudinal force F x and lateral force F y The ellipsoid equation for the actuator capability as a variable:

[0015] in, For longitudinal force boundary, For the lateral force boundary, This is the longitudinal force scaling factor. This is the scaling factor for the lateral force. Based on the stability margin and instability risk value, the longitudinal force scaling factor is corrected using the following formula. and lateral force scaling factor :

[0016]

[0017] Using the scaling factor and The actuator capability ellipsoid equation is dynamically scaled and its shape biased to generate a dynamic performance ellipsoid constraint.

[0018] In an optional implementation, the step of obtaining optimized control commands by solving mixed-integer programming, with the goal of tracking upper-level controller commands and satisfying the dynamic performance ellipsoid constraints and vehicle dynamics constraints, includes: An objective function is constructed to minimize the deviation between the optimized instruction and the upper-level instruction, as well as the control risk. The objective function is:

[0019] in, To optimize the control command vector, The upper-level raw instruction is Q, where Q is the instruction follow-weight matrix. For risk penalties based on vehicle status, As a risk penalty coefficient, To control the rate of change of quantity; Constraints are set, including vehicle dynamics differential equations, tire adhesion elliptic constraints, and dynamic performance ellipsoidal constraints; the objective function is solved under these constraints to obtain the optimized control command vector. .

[0020] In an optional implementation, the step of performing fault-tolerant processing on the optimized control instructions based on the actuator health status to generate final control instructions includes: Based on the cumulative working time of the actuator and number of work cycles The actuator health status (SOH) is calculated using the following formula:

[0021] Where MTBF is the mean time between failures. The rated lifespan cycle count; When the actuator health status (SOH) falls below a set threshold, a degradation mode is triggered, and the control vector is optimized using a smooth transition function. Attenuation is performed to generate the final control command. The calculation method is as follows:

[0022]

[0023] Where K is the smooth transition coefficient. Preset security restriction instructions; When the actuator health status (SOH) is lower than the set threshold, the optimized control command will be used as the final control command.

[0024] In an optional implementation, the risk weight The dynamic relationship with the stability margin SM is as follows: ;in, The preset weighting coefficient is the one corresponding to the i-th unstable feature point.

[0025] Secondly, embodiments of this application also provide a vehicle lateral and longitudinal cooperative control system, including: The parameter acquisition module is used to acquire the vehicle's real-time operating parameters through the vehicle's drive-by-wire chassis domain controller; The stability assessment and risk prediction module is used to calculate the vehicle stability margin based on the operating parameters, construct an instability risk field model, and calculate the instability risk value that characterizes the current instability probability of the vehicle. The dynamic capability boundary correction module is used to dynamically correct the performance boundary of the actuator based on the stability margin and the instability risk value, and generate dynamic performance ellipsoidal constraints. The optimized command correction module is used to obtain optimized control commands by means of mixed integer programming, with the goal of tracking the commands of the upper controller and satisfying the dynamic performance ellipsoid constraints and vehicle dynamics constraints. A multi-mode fault-tolerant control module is used to perform fault-tolerant processing on the optimized control instructions based on the actuator health status, and generate the final control instructions; The instruction allocation and execution module is used to allocate the final control instructions to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

[0026] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the vehicle lateral and longitudinal cooperative control method as described in any of the above.

[0027] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the vehicle lateral and longitudinal cooperative control method as described in any of the above claims.

[0028] As can be seen from the above technical solutions, the present invention has the following advantages: The vehicle lateral and longitudinal cooperative control method provided in this application achieves advanced prediction and quantification of vehicle instability trends by constructing a two-layer evaluation model that integrates stability margin and instability risk field. Then, a dynamic efficiency ellipsoid is used to map the evaluation results into executable control boundaries in real time. Mixed-integer programming is used to accurately follow upper-level instructions while ensuring stability. Finally, fault-tolerant arbitration is performed based on actuator health status to form a decision-making closed loop. This method enables the chassis domain controller to possess cerebellum-like autonomous evaluation, prediction, and correction capabilities, fundamentally solving the problem of insufficient dynamic coordination in lateral and longitudinal decoupled control. It significantly improves vehicle handling stability and driving safety boundaries under complex operating conditions, and broadens the operational scenarios of intelligent driving systems.

[0029] This application constructs a risk field model that integrates real-time motion state and instability characteristics, and dynamically adjusts the weights based on stability margin, achieving advanced quantitative prediction of vehicle instability trends. This enables the system to identify high-risk areas before physical instability occurs and intervene in control in advance, fundamentally changing the passive situation of traditional stability control that only triggers after instability occurs, and greatly improving active safety capabilities.

[0030] This application maps stability states and risk values ​​to dynamic performance ellipsoidal constraints, enabling the actuator's force output boundary to be adjusted smoothly and in real time according to vehicle status and road conditions. Under low adhesion or extreme conditions, the system can automatically shrink the longitudinal force boundary and appropriately relax the lateral force tolerance, prioritizing directional stability, thereby maximizing the utilization of tire-road adhesion potential within physical limits and achieving an optimal balance between safety and performance.

[0031] This application embeds a dynamic performance ellipsoid as a hard constraint into a mixed-integer programming problem, enabling the chassis domain controller to autonomously evaluate the feasibility of upper-level instructions. When an instruction exceeds the safety boundary, it intelligently solves for the optimal approximate instruction within the physically realizable range, rather than simply passing it or brute-force truncation. This endows the controller with a cerebellum-like instruction evaluation and correction function, making up for the safety gap caused by the decoupling of decision-making and control in traditional architectures.

[0032] This application introduces an actuator health state model and designs a smooth transition degradation strategy. When actuator performance degrades, control commands can decay smoothly to a safe mode without disturbance, avoiding control abrupt changes or complete failure due to component failure or performance degradation. This significantly enhances the durability, reliability, and fault tolerance of the entire control system, making it particularly suitable for the long-cycle, high-load operation requirements of commercial vehicles.

[0033] This application organically integrates multiple core functions, such as multi-source information fusion, stability assessment, risk prediction, constraint optimization, and fault tolerance management, into a unified control framework and domain controller platform. This not only improves the overall performance of horizontal and vertical collaborative control but also enables intelligent driving systems to be safely extended to more complex operating scenarios such as low-adhesion surfaces, emergency obstacle avoidance, and extreme maneuvering, providing key technical support for the commercialization of high-level autonomous driving. Attached Figure Description

[0034] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating the vehicle lateral and longitudinal cooperative control method provided in this application.

[0036] Figure 2 This is a schematic diagram of the vehicle lateral and longitudinal cooperative control system provided in this application.

[0037] Figure 3 A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0038] The various embodiments of this disclosure will be described more fully in the detailed steps of the vehicle lateral and longitudinal cooperative control method described below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0039] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 The diagram shown is a flowchart of a vehicle lateral and longitudinal cooperative control method in a specific embodiment. The method includes: S1: Obtain the vehicle's real-time operating parameters through the vehicle's drive-by-wire chassis domain controller.

[0042] In a specific implementation, the vehicle's center of gravity sideslip angle is obtained through the vehicle's drive-by-wire chassis domain controller. Side slip angle change rate Angular velocity ω, angular acceleration ω longitudinal acceleration lateral acceleration .

[0043] For example, the longitudinal acceleration of a vehicle is obtained through an inertial measurement unit (IMU). lateral acceleration And the yaw rate ω; by integrating wheel speed signals and IMU data, the vehicle's center of gravity sideslip angle is estimated using a state observer (such as a Kalman filter). and its rate of change In addition, yaw acceleration These parameters can be obtained through differential yaw rate signals or dedicated sensors. Together, they constitute the original set of states describing the vehicle's planar motion.

[0044] S2: Calculate the vehicle stability margin based on the operating parameters, construct an instability risk field model, and calculate the instability risk value that characterizes the current probability of vehicle instability.

[0045] In a specific implementation, the six-dimensional motion phase plane state vector of the vehicle is first defined. This vector comprehensively characterizes the instantaneous motion state of the vehicle in the phase plane from multiple dimensions, providing a standardized input for stability analysis.

[0046] Then, the stability margin SM is calculated based on the six-dimensional motion phase plane state vector, and the calculation formula is as follows:

[0047] in, This is the extreme sideslip angle. To achieve the maximum stable yaw rate, The road surface friction coefficient is denoted by SM. The stability margin range at the current moment is (0, 1), with smaller values ​​indicating closer proximity to instability. For example, when the SM value is close to 1, it indicates that the vehicle is in a highly stable region; when the SM value decreases (e.g., below 0.5), it indicates that the vehicle is approaching the instability boundary. This step standardizes the raw sensor data into stability quantification values ​​in the range of 0-1, providing a unified input for subsequent decision-making.

[0048]

[0049] Where L is the wheelbase and K is the understeer gradient. This refers to the front wheel steering angle; At this point, an instability risk field model is constructed, and the instability risk value R of the current state is calculated based on the current motion state vector represented by the six-dimensional motion phase plane state vector. Its expression is as follows:

[0050] in, This is the current motion state vector. This is a characteristic point of instability. Risk weights are dynamically related to the stability margin SM. is the risk diffusion coefficient. The probability of instability risk (0, 1) of the vehicle at its current position in the phase plane.

[0051] In the above formula, risk weight The dynamic relationship with the stability margin SM is as follows: ;in, The preset weighting coefficient is the one corresponding to the i-th unstable feature point.

[0052] S3: Based on the stability margin and instability risk value, dynamically correct the performance boundary of the actuator and generate dynamic performance ellipsoid constraints.

[0053] In a specific implementation, firstly, a longitudinal force F is established. x and lateral force F y The ellipsoid equation for the actuator capability as a variable:

[0054] in, For longitudinal force boundary, For the lateral force boundary, This is the longitudinal force scaling factor. This is the scaling factor for the lateral force. .

[0055] According to the actuator capability ellipsoid equation, when SM and the risk field model R are input into the dynamic performance ellipsoid equation, when SM decreases, the ellipsoid size is reduced proportionally, forcibly limiting the actuator output capability and avoiding the control command from exacerbating the risk of instability; the higher the risk value, the ellipsoid shape is biased towards the safe direction (e.g., the longitudinal force allowable range is reduced, and the lateral force range is appropriately widened to prioritize directional stability).

[0056] Then, based on the stability margin and the instability risk value, the longitudinal force scaling factor is corrected using the following formula. and lateral force scaling factor :

[0057]

[0058] As can be seen from the above formula, when SM < 0.3, it can be considered as high risk, f(SM) ≈ 0.5, that is, the maximum longitudinal force is halved; when SM > 0.7, it can be considered as safe, f(SM) ≈ 1.0, that is, the calibration capability is maintained.

[0059] When the instability risk value R=0, g=1.0, which means the lateral force is at its full capacity; when the risk value R=1, g=0.7, which means the upper limit of the lateral force is reduced by 30%.

[0060] Finally, using the scaling factor and The actuator capability ellipsoid equation is dynamically scaled and its shape biased to generate a dynamic performance ellipsoid constraint.

[0061] For example, when there is high SM and low risk: the ellipsoid is full, allowing for aggressive control; when there is low SM and high risk: the ellipsoid is flattened, shrinking longitudinally and moderately widening laterally, prioritizing the maintenance of stability.

[0062] S4: With the goal of tracking the upper-level controller's instructions and satisfying the dynamic performance ellipsoidal constraints and vehicle dynamics constraints, the optimized control instructions are obtained by solving a mixed-integer programming problem.

[0063] In a specific implementation, an objective function is constructed to minimize the deviation between the optimized instruction and the upper-level instruction, as well as the control risk. The objective function is:

[0064] in, To optimize the control command vector, The upper-level raw instruction is Q, where Q is the instruction follow-weight matrix. For risk penalties based on vehicle status, As a risk penalty coefficient, To control the rate of change of quantity; for example, when the vehicle approaches the sideslip boundary, The weights are automatically increased, forcing the optimizer to prioritize reducing lateral acceleration.

[0065] Then, constraints are set, including vehicle dynamics differential equations, tire adhesion ellipse constraints, and dynamic performance ellipsoid constraints.

[0066] For example, the constraints include:

[0067] in, , Here, m represents the lateral stiffness of the front and rear axle tires, and m is the total vehicle mass. The distance from the center of mass to the front and rear axles. Let μ be the yaw moment of inertia, μ be the road adhesion coefficient, and Fz be the tire vertical load.

[0068] The ellipsoid equations are transformed into quadratic constraints in the MIP:

[0069] This constraint ensures that the solution results are always within the physically realizable range.

[0070] Finally, the objective function is solved under the constraints to obtain the optimized control command vector. .

[0071] S5: Based on the actuator health status, perform fault-tolerant processing on the optimized control instructions to generate the final control instructions.

[0072] In a specific implementation, the cumulative working time of the actuator is used as the basis. and number of work cycles The actuator health status (SOH) is calculated using the following formula:

[0073] Where MTBF is the mean time between failures. This refers to the rated lifespan cycle count. When the actuator health status (SOH) falls below 0.7, a degradation mode is triggered, and the control vector is optimized using a smooth transition function. Attenuation is performed to generate the final control command. The calculation method is as follows:

[0074]

[0075] Where K is the smooth transition coefficient. These are preset security restriction commands.

[0076] In addition, when the actuator health status (SOH) is not lower than the set threshold, the control commands will be directly optimized. As the final control command .

[0077] S6: Distribute the final control command to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control. In a specific implementation, based on the final instruction vector (Typically includes vehicle-level commands such as target longitudinal force and target yaw moment), and combined with the current vehicle state and pre-calibrated control distribution strategy, calculates the specific commands for each independent actuator. For example, the target longitudinal force is distributed to the front / rear axle drive motors and the braking force of the four wheels; the target yaw moment is distributed to the difference in driving force or braking force between the left and right wheels (torque vector control), and combined with the front wheel steering angle to achieve true lateral and longitudinal coordination.

[0078] After the command is sent to each actuator ECU via the bus, the controller continuously monitors the actual response of each actuator (such as the actual torque of the motor and the actual braking pressure) and compares it with the expected command. This feedback information is used for closed-loop control on the one hand, and to update the actuator state model on the other hand, forming a complete "cerebellum-like" autonomous control closed loop from perception, decision-making, control to execution and then perception again. In this way, the dynamic behavior of the vehicle is always kept within a safe and controllable range under various complex operating conditions and system states.

[0079] In this embodiment, by constructing a quantitative evaluation model of stability margin and instability risk field, early perception and warning of vehicle instability trends are achieved. A dynamic efficiency ellipsoid is used to transform the stability state into an adaptive constraint on actuator force output in real time, ensuring that control commands always remain within the dynamic safety boundary. Using a mixed-integer programming algorithm, the optimal tracking solution for upper-level commands is intelligently solved while strictly adhering to the aforementioned safety constraints and vehicle dynamics. Simultaneously, smooth command arbitration is performed based on actuator health status, ensuring control continuity when component performance degrades. The synergistic effect of these techniques enables the chassis domain controller to proactively protect against risks, dynamically adapt control capabilities, and robustly respond to internal performance changes, thereby significantly improving vehicle handling stability and active safety levels in various complex driving scenarios and system states.

[0080] like Figure 2 As shown, the following are embodiments of the vehicle lateral and longitudinal cooperative control system provided in this disclosure. This system and the vehicle lateral and longitudinal cooperative control methods in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the vehicle lateral and longitudinal cooperative control system, please refer to the embodiments of the above vehicle lateral and longitudinal cooperative control methods.

[0081] A vehicle lateral and longitudinal cooperative control system includes: The parameter acquisition module is used to acquire the vehicle's real-time operating parameters through the vehicle's drive-by-wire chassis domain controller.

[0082] The stability assessment and risk prediction module is used to calculate the vehicle stability margin based on the operating parameters, construct an instability risk field model, and calculate the instability risk value that characterizes the current probability of vehicle instability.

[0083] The dynamic capability boundary correction module is used to dynamically correct the performance boundary of the actuator based on the stability margin and the instability risk value, and generate dynamic performance ellipsoidal constraints.

[0084] The optimized command correction module is used to obtain optimized control commands by means of mixed integer programming, with the goal of tracking the commands of the upper controller and satisfying the dynamic performance ellipsoid constraints and vehicle dynamics constraints.

[0085] The multi-mode fault-tolerant control module is used to perform fault-tolerant processing on the optimized control instructions based on the actuator health status, and generate the final control instructions.

[0086] The instruction allocation and execution module is used to allocate the final control instructions to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

[0087] The vehicle lateral and longitudinal cooperative control system provided in this embodiment achieves advanced quantitative prediction of stability risks by constructing an instability risk field model, and uses a dynamic efficiency ellipsoid to map the risk state to an executable control boundary in real time. Then, it intelligently corrects upper-level instructions while ensuring stability through mixed integer programming, and finally makes smooth fault-tolerant decisions by combining the health status of the actuators. This enables the chassis domain controller to have a "cerebellum-like" decision-making ability of autonomous evaluation, prediction and correction, thereby significantly improving the vehicle's handling stability and active safety under complex working conditions, effectively making up for the shortcomings of traditional lateral and longitudinal decoupled control, and broadening the safe operation boundary of the intelligent driving system.

[0088] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0089] The vehicle lateral and longitudinal cooperative control method provided in this application embodiment can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0090] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0091] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0092] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0093] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0094] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0095] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0096] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0097] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0098] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0099] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0100] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0101] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0102] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0103] The aforementioned electronic equipment realizes the vehicle lateral and longitudinal cooperative control method of this application by constructing an instability risk field model to quantitatively predict stability risk, using dynamic efficiency ellipsoid constraints to map risk as a controllable boundary in real time, using mixed integer programming to intelligently optimize control commands within safety constraints, and combining actuator health state model to achieve smooth fault-tolerant control. This achieves the beneficial effects of enabling the chassis domain controller to have autonomous evaluation and correction capabilities, significantly improving the vehicle's handling stability and active safety under complex working conditions, effectively making up for the shortcomings of traditional lateral and longitudinal decoupling control, and broadening the safe operation boundary of intelligent driving system.

[0104] The storage medium provided in this application stores a program product capable of implementing a vehicle lateral and longitudinal coordinated control method.

[0105] Vehicle lateral and longitudinal cooperative control methods include: The vehicle's real-time operating parameters are obtained through the vehicle's drive-by-wire chassis domain controller; Based on the operating parameters, the vehicle stability margin is calculated, and an instability risk field model is constructed to calculate the instability risk value that characterizes the current instability probability of the vehicle. Based on the stability margin and instability risk value, the performance boundary of the actuator is dynamically corrected to generate a dynamic performance ellipsoid constraint. With the goal of tracking the instructions of the upper-level controller and satisfying the dynamic performance ellipsoidal constraints and vehicle dynamics constraints, the optimized control instructions are obtained by solving a mixed integer programming problem. Based on the actuator health status, the optimized control instructions are processed to generate the final control instructions; The final control command is assigned to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

[0106] In some possible implementations, the vehicle lateral and longitudinal cooperative control method of this disclosure can be implemented as a program product including program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0107] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0108] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for coordinated lateral and longitudinal control of a vehicle, characterized in that, include: The vehicle's real-time operating parameters are obtained through the vehicle's drive-by-wire chassis domain controller; Based on the operating parameters, the vehicle stability margin is calculated, and an instability risk field model is constructed to calculate the instability risk value that characterizes the current instability probability of the vehicle. Based on the stability margin and instability risk value, the performance boundary of the actuator is dynamically corrected to generate a dynamic performance ellipsoid constraint. With the goal of tracking the instructions of the upper-level controller and satisfying the dynamic performance ellipsoidal constraints and vehicle dynamics constraints, the optimized control instructions are obtained by solving a mixed integer programming problem. Based on the actuator health status, the optimized control instructions are processed to generate the final control instructions; The final control command is assigned to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

2. The vehicle lateral and longitudinal cooperative control method according to claim 1, characterized in that, The process of obtaining real-time operating parameters of the vehicle through the vehicle's drive-by-wire chassis domain controller includes: The vehicle's center of gravity sideslip angle is obtained through the vehicle's drive-by-wire chassis domain controller. Side slip angle change rate Angular velocity ω, angular acceleration ω longitudinal acceleration lateral acceleration .

3. The vehicle lateral and longitudinal cooperative control method according to claim 2, characterized in that, The process of calculating the vehicle stability margin based on the operating parameters and constructing an instability risk field model to calculate the instability risk value characterizing the current probability of vehicle instability includes: Define the six-dimensional motion phase plane state vector of the vehicle. ; The stability margin SM is calculated based on the six-dimensional motion phase plane state vector, and the calculation formula is as follows: in, This is the extreme sideslip angle. To achieve the maximum stable yaw rate, The coefficient of friction of the road surface; Where L is the wheelbase and K is the understeer gradient. This refers to the front wheel steering angle; An instability risk field model is constructed, and the instability risk value R of the current state is calculated based on the current motion state vector represented by the six-dimensional motion phase plane state vector. Its expression is as follows: in, This is the current motion state vector. This is a characteristic point of instability. Risk weights are dynamically related to the stability margin SM. This represents the risk diffusion coefficient.

4. The vehicle lateral and longitudinal cooperative control method according to claim 3, characterized in that, The process of dynamically correcting the actuator's performance boundary based on the stability margin and instability risk value to generate a dynamic performance ellipsoid constraint includes: Establish with longitudinal force F x and lateral force F y The ellipsoid equation for the actuator capability as a variable: in, For longitudinal force boundary, For the lateral force boundary, This is the longitudinal force scaling factor. This is the scaling factor for the lateral force. Based on the stability margin and instability risk value, the longitudinal force scaling factor is corrected using the following formula. and lateral force scaling factor : Using the scaling factor and The actuator capability ellipsoid equation is dynamically scaled and its shape biased to generate a dynamic performance ellipsoid constraint.

5. The vehicle lateral and longitudinal cooperative control method according to claim 4, characterized in that, The process of obtaining optimized control commands through mixed-integer programming, with the goal of tracking commands from the upper-level controller and satisfying the dynamic performance ellipsoid constraints and vehicle dynamics constraints, includes: An objective function is constructed to minimize the deviation between the optimized instruction and the upper-level instruction, as well as the control risk. The objective function is: in, To optimize the control command vector, The upper-level raw instruction is Q, where Q is the instruction follow-weight matrix. For risk penalties based on vehicle status, As a risk penalty coefficient, To control the rate of change of quantity; Constraints are set, including vehicle dynamics differential equations, tire adhesion elliptic constraints, and dynamic performance ellipsoidal constraints; the objective function is solved under these constraints to obtain the optimized control command vector. .

6. The vehicle lateral and longitudinal cooperative control method according to claim 5, characterized in that, The step of performing fault-tolerant processing on the optimized control instructions based on the actuator health status to generate the final control instructions includes: Based on the cumulative working time of the actuator and number of work cycles The actuator health status (SOH) is calculated using the following formula: Where MTBF is the mean time between failures. This refers to the rated lifespan cycle count. When the actuator health status (SOH) falls below a set threshold, a degradation mode is triggered, and the control vector is optimized using a smooth transition function. Attenuation is performed to generate the final control command. The calculation method is as follows: Where K is the smooth transition coefficient. Preset security restriction instructions; When the actuator health status (SOH) is lower than the set threshold, the optimized control command will be used as the final control command.

7. The vehicle lateral and longitudinal cooperative control method according to claim 3, characterized in that, The risk weight The dynamic relationship with the stability margin SM is as follows: ;in, The preset weighting coefficient is the one corresponding to the i-th unstable feature point.

8. A vehicle lateral and longitudinal cooperative control system, characterized in that, The system employs the vehicle lateral and longitudinal cooperative control method as described in any one of claims 1 to 7; The system includes: The parameter acquisition module is used to acquire the vehicle's real-time operating parameters through the vehicle's drive-by-wire chassis domain controller; The stability assessment and risk prediction module is used to calculate the vehicle stability margin based on the operating parameters, construct an instability risk field model, and calculate the instability risk value that characterizes the current instability probability of the vehicle. The dynamic capability boundary correction module is used to dynamically correct the performance boundary of the actuator based on the stability margin and the instability risk value, and generate dynamic performance ellipsoidal constraints. The optimized command correction module is used to obtain optimized control commands by means of mixed integer programming, with the goal of tracking the commands of the upper controller and satisfying the dynamic performance ellipsoid constraints and vehicle dynamics constraints. A multi-mode fault-tolerant control module is used to perform fault-tolerant processing on the optimized control instructions based on the actuator health status, and generate the final control instructions; The instruction allocation and execution module is used to allocate the final control instructions to the longitudinal and lateral actuators of the vehicle to achieve coordinated longitudinal and lateral control.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the vehicle lateral and longitudinal cooperative control method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle lateral and longitudinal cooperative control method as described in any one of claims 1 to 7.

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