Cooperative control method and device for steering and braking of vehicle in emergency obstacle avoidance working condition

Abstract

This invention discloses a method and apparatus for coordinated control of steering and braking in emergency obstacle avoidance conditions of a vehicle. The method includes: responding to the vehicle triggering an emergency obstacle avoidance mode, calculating braking weight and steering weight based on vehicle speed and road adhesion coefficient; planning an obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle's obstacle avoidance, and calculating the target yaw moment required to track the obstacle avoidance trajectory; decomposing the target yaw moment into steering correction yaw moment and braking correction yaw moment based on the braking weight and steering weight; calculating the steering correction angle based on the steering correction yaw moment, and calculating the target braking moment for each wheel based on the braking correction yaw moment; controlling the vehicle's steering system to execute the steering correction angle, and controlling the braking system to execute the target braking moment, thereby achieving deep coordinated control of the steering and braking systems, improving the vehicle's driving stability and trajectory tracking accuracy in emergency obstacle avoidance conditions, and effectively enhancing the vehicle's active safety performance.

Description

Technical Field

[0001] This application relates to the field of vehicle control, specifically to a method and device for coordinated control of steering and braking in emergency obstacle avoidance situations of a vehicle. Background Technology

[0002] With the popularization of new energy vehicles and the development of autonomous driving assistance technologies, the active safety of vehicles in emergency obstacle avoidance situations is receiving increasing attention. The coordinated control of the chassis system, especially the coordination between the steering and braking systems, has become a key technology for improving the vehicle's extreme obstacle avoidance capabilities and stability. However, existing technologies still face many challenges in this area.

[0003] Traditional chassis control systems often employ a separate, independent control architecture. This system typically consists of an Electric Power Steering (EPS) controller, an Electronic Stability Program (ESP) / Anti-lock Braking System (ABS) controller, and a Vehicle Control Unit (VCU), with these controllers exchanging information via a CAN bus. In this architecture, the EPS primarily provides steering assistance based on signals from the steering wheel torque sensor, while the ESP independently calculates braking force distribution based on wheel speed and brake pedal signals. Both systems merely broadcast their own states on the bus, lacking proactive and predictive collaborative control requests. The main drawback of this architecture is its significant system response latency (typically 100-200ms). In a high-speed emergency obstacle avoidance scenario at 80km / h, this latency can cause the vehicle to travel an additional 2.2-4.4 meters, significantly increasing the risk of a collision.

[0004] Some existing technologies introduce a fixed-priority / discrete strategy selection architecture. These solutions add a cooperative control module (usually integrated into the VCU or domain controller) to the original architecture. By pre-setting fixed logic such as "braking priority" or "steering priority," they select a limited number of discrete strategy modes (e.g., pure braking, pure steering, braking + steering). The drawback of this approach is its inability to achieve fine-grained dynamic adjustment. When road condition parameters reach the boundary conditions of mode selection, the control strategy abruptly changes, leading to drastic changes in vehicle yaw moment and lateral acceleration, causing discontinuities in vehicle dynamic characteristics and seriously threatening stability during obstacle avoidance.

[0005] In related technologies, a unified optimization control (MPC) architecture has been proposed to achieve better control performance. This scheme employs a model predictive control (MPC) algorithm, treating steering and braking as a unified optimization problem to simultaneously output the optimal steering angle and braking torque. However, this method has a long single-step optimization time (50-100ms) and extremely high computational requirements for the ECU (>10 DMIPS). This makes it difficult to deploy this technology on a large scale on the low-cost, low-computing-power ECU hardware platform used in mass-produced vehicles, limiting its industrial application prospects.

[0006] Therefore, there is an urgent need for a steering and braking coordinated control method that can break down information silos between controllers, eliminate policy mode jumps, and achieve rapid response and smooth dynamic adjustment. Summary of the Invention

[0007] This application provides a method and device for coordinated control of steering and braking under emergency obstacle avoidance conditions of a vehicle. It can solve the technical problem in the prior art where the steering system and braking system are controlled independently, making it difficult to coordinate the distribution of braking torque and steering correction in emergency obstacle avoidance conditions, which leads to the obstacle avoidance trajectory deviating from the expected trajectory or vehicle instability.

[0008] In a first aspect, embodiments of this application provide a method for coordinated control of steering and braking under emergency obstacle avoidance conditions of a vehicle, the method comprising: In response to the vehicle triggering the emergency obstacle avoidance mode, braking weight and steering weight are calculated based on vehicle speed and road adhesion coefficient. Plan the obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle obstacle avoidance, and calculate the target yaw moment required to track the obstacle avoidance trajectory; Based on the braking weight and the steering weight, the target yaw moment is decomposed into steering-corrected yaw moment and braking-corrected yaw moment; The steering correction angle is calculated based on the steering correction yaw moment, and the target braking torque for each wheel is calculated based on the braking correction yaw moment. The vehicle's steering system is controlled to execute the steering correction angle, and the braking system is controlled to execute the target braking torque.

[0009] Secondly, embodiments of this application provide a coordinated control device for steering and braking under emergency obstacle avoidance conditions of a vehicle, the device comprising: The first calculation module is used to calculate braking weight and steering weight based on vehicle speed and road adhesion coefficient in response to the vehicle triggering emergency obstacle avoidance mode. The second calculation module is used to plan the obstacle avoidance trajectory based on the starting coordinates and ending coordinates of the vehicle obstacle avoidance, and to calculate the target yaw moment required to track the obstacle avoidance trajectory. The third calculation module is used to decompose the target yaw moment into steering-corrected yaw moment and braking-corrected yaw moment according to the braking weight and the steering weight. The fourth calculation module is used to calculate the steering correction angle based on the steering correction yaw moment, and to calculate the target braking torque of each wheel based on the braking correction yaw moment. The control module is used to control the vehicle's steering system to execute the steering correction angle and to control the braking system to execute the target braking torque.

[0010] The beneficial effects of the technical solutions provided in this application include: By responding to the vehicle triggering an emergency obstacle avoidance mode, braking and steering weights are calculated based on vehicle speed and road adhesion coefficient; an obstacle avoidance trajectory is planned based on the starting and ending coordinates of the vehicle's obstacle avoidance, and the target yaw moment required to track the obstacle avoidance trajectory is calculated; based on the braking and steering weights, the target yaw moment is decomposed into steering correction yaw moment and braking correction yaw moment; a steering correction angle is calculated based on the steering correction yaw moment, and a target braking moment for each wheel is calculated based on the braking correction yaw moment; the vehicle's steering system is controlled to execute the steering correction angle, and the braking system is controlled to execute the target braking moment. This solves the technical problem in related technologies where independent control or fixed allocation strategies of the steering and braking systems lead to deviation of the vehicle's obstacle avoidance trajectory or vehicle instability.

[0011] This application dynamically calculates the weight allocation of braking and steering by real-time monitoring of vehicle speed and road surface adhesion coefficient, enabling the vehicle to adapt to different road environments (such as wet or dry roads) and driving conditions. When the road surface adhesion coefficient is low, the braking weight can be appropriately reduced to avoid tire lock-up and instability, or the steering weight can be adjusted to ensure steering response; when the road surface adhesion coefficient is high, the braking system can be fully utilized to assist the steering system in obstacle avoidance. This dynamic weight allocation mechanism based on vehicle dynamics maximizes the utilization of tire force and effectively avoids the limitations of a single control strategy under extreme conditions.

[0012] Furthermore, by decomposing the target yaw moment into steering correction and braking correction, deep synergy between the steering and braking systems is achieved. The steering system is responsible for the main trajectory following, while the braking system provides additional yaw moment to enhance vehicle stability. The two complement each other, not only improving the success rate of emergency obstacle avoidance but also ensuring the vehicle's stable posture during obstacle avoidance, reducing the risk of rollover or fishtailing caused by violent maneuvers, and significantly improving the vehicle's active safety performance. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating an embodiment of the coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle according to this application. Figure 2 This is a schematic diagram of the overall system architecture of this application; Figure 3 This is a schematic diagram of the fuzzy logic weight allocation in this application; Figure 4 This is a schematic diagram of the obstacle avoidance trajectory planning in this application; Figure 5 This is a schematic diagram of the hierarchical collaborative control execution process of this application; Figure 6This is a schematic diagram of the stability closed-loop correction process for this application; Figure 7 This is a functional module diagram of an embodiment of the coordinated control device for steering and braking in emergency obstacle avoidance of a vehicle according to this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present application.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0016] In a first aspect, embodiments of this application provide a method for coordinated control of steering and braking under emergency obstacle avoidance conditions of a vehicle.

[0017] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the coordinated control method for steering and braking under emergency obstacle avoidance conditions for vehicles according to this application. Figure 1 As shown, the coordinated control method for steering and braking under emergency obstacle avoidance conditions includes: Step S1: In response to the vehicle triggering the emergency obstacle avoidance mode, calculate the braking weight and steering weight based on the vehicle speed and road adhesion coefficient.

[0018] Step S2: Plan the obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle obstacle avoidance, and calculate the target yaw moment required to track the obstacle avoidance trajectory.

[0019] Step S3: Based on the braking weight and the steering weight, decompose the target yaw moment into steering-corrected yaw moment and braking-corrected yaw moment.

[0020] Step S4: Calculate the steering correction angle based on the steering correction yaw moment, and calculate the target braking torque for each wheel based on the braking correction yaw moment.

[0021] Step S5: Control the vehicle's steering system to execute the steering correction angle, and control the braking system to execute the target braking torque.

[0022] This embodiment clarifies the complete quantitative mathematical mapping link of weight allocation, yaw moment decomposition, steering correction angle and braking torque calculation of each wheel through the above steps, ensuring the real-time performance and accuracy of collaborative control, and effectively improving the active safety performance of the vehicle in emergency obstacle avoidance scenarios.

[0023] As shown in Figure 2, the hardware system provided in this application embodiment adopts a layered architecture, including a perception layer, a control layer, and an execution layer. The layers interact with each other through an on-board communication bus.

[0024] The perception layer is used to collect road condition and vehicle status information, including forward-facing millimeter-wave radar, cameras, wheel speed sensors, a 6-axis inertial measurement unit (IMU), and a steering wheel angle sensor. The millimeter-wave radar has a detection range of ≥150m and a frequency of 20Hz; the camera can be a monocular or tricular camera with a resolution of 1280×720 and a frame rate of 30fps; the IMU has a frequency of 1000Hz, and the wheel speed and angle sensors have frequencies of 100Hz.

[0025] The controller layer includes functions for data fusion and state estimation. Specifically, it estimates the road adhesion coefficient, center of gravity sideslip angle, and yaw rate based on an extended Kalman filter (EKF), generates an obstacle avoidance trajectory using a fifth-order polynomial, calculates braking and steering weights using fuzzy logic, and calculates the target yaw moment using a low-level QR code and decomposes it into steering correction moment and braking correction moment. The lower-level controller calculates the steering correction angle and the target braking moment for each wheel, and integrates stability monitoring and correction functions.

[0026] The execution layer includes the electric power steering (EPS) motor, the electric motor braking unit, and the hydraulic braking unit (ESP). The EPS motor responds to commands to execute the steering correction angle, and the electric motor braking unit and the hydraulic braking unit work together to execute the target braking torque, thereby achieving coordinated control of steering and braking.

[0027] In this embodiment, the controller layer fuses data from millimeter-wave radar and a visual camera, outputting the relative distance d_rel, relative velocity v_rel, and width W_obs of the obstacle, and calculates the collision time: TTC = d_rel / v_rel. When the collision time TTC ≤ 1.5s and the lateral obstacle avoidance space is available (e.g., the width of the adjacent lane ≥ 2.5m), the emergency obstacle avoidance condition is determined to be met, and the emergency obstacle avoidance mode is triggered.

[0028] After triggering the emergency obstacle avoidance mode, the controller layer uses the Extended Kalman Filter (EKF) algorithm based on the vehicle's two-degree-of-freedom dynamics model to estimate the vehicle's state in real time, including the road adhesion coefficient μ (estimated range 0.1~1.2, accuracy ±0.05), the center of gravity sideslip angle β, and the actual yaw rate ω_z_act. These state parameters will serve as the basic input data for subsequent calculations of braking weights, steering weights, and target yaw moment, ensuring that the control strategy can adapt to the current road adhesion conditions and vehicle dynamics, thereby improving the accuracy of cooperative control.

[0029] In one embodiment, such as Figure 3 As shown, in step S1, in response to the vehicle triggering the emergency obstacle avoidance mode, braking and steering weights are calculated based on vehicle speed and road surface adhesion coefficient. This is specifically implemented by constructing a dual-input single-output fuzzy logic controller. This controller can dynamically allocate steering and braking weights according to the vehicle's real-time driving conditions to ensure vehicle stability under extreme conditions. Specific steps include: Step S101: Determine the membership degree of the vehicle speed to a preset fuzzy subset of vehicle speeds, wherein the fuzzy subset of vehicle speeds includes low-speed, medium-speed and high-speed fuzzy subsets, and there is a vehicle speed overlap region between adjacent fuzzy subsets of vehicle speeds.

[0030] First, the vehicle's current speed *v* is obtained. Since the vehicle's driving state is continuous, rigidly defining speed boundaries can easily lead to abrupt changes in control weights (e.g., using 40 km / h as the boundary between low and medium speeds results in drastically different control strategies at 39 km / h and 41 km / h), thus affecting driving smoothness. This application employs a fuzzy control method, based on a preset triangular membership function, mapping vehicle speed to three fuzzy subsets of vehicle speed: The low-speed fuzzy subset S has a membership function domain of [0, 40] km / h; the medium-speed fuzzy subset M has a membership function domain of [30, 80] km / h; and the high-speed fuzzy subset H has a membership function domain of [70, 120+] km / h. There is overlap between adjacent subsets; for example, speeds of 30-40 km / h belong to both low-speed and medium-speed categories. When the vehicle speed is 35 km / h, by querying the membership function, it can be calculated that the membership degree belonging to the low-speed fuzzy subset is 0.6, and the membership degree belonging to the medium-speed fuzzy subset is 0.4. This partial membership characteristic ensures that the subsequently calculated weights change continuously, avoiding abrupt changes in control commands.

[0031] Step S102: Determine the membership degree of the road surface adhesion coefficient to a preset fuzzy subset of road surface adhesion coefficients, wherein the fuzzy subset of road surface adhesion coefficients includes low adhesion, medium adhesion and high adhesion fuzzy subsets, and there is an overlapping area of ​​road surface adhesion coefficients between adjacent fuzzy subsets of road surface adhesion coefficients.

[0032] Specifically, the adhesion coefficient μ of the current road surface is obtained. Similar to the processing of vehicle speed v, the adhesion coefficient is also fuzzified into three fuzzy subsets of road surface adhesion coefficients, including: low adhesion fuzzy subset L: domain of [0.1, 0.35]; medium adhesion fuzzy subset M: domain of [0.3, 0.7]; and high adhesion fuzzy subset H: domain of [0.65, 1.2].

[0033] Similarly, adjacent subsets are set to overlap. For example, when the road surface adhesion coefficient μ=0.32, it is neither completely low adhesion nor completely medium adhesion, but rather a transitional state between the two. This design allows the vehicle to smoothly transition the weights on roads with different adhesion coefficients, without abrupt changes in braking / steering force.

[0034] Step S103: Based on the membership degree of the vehicle speed and the membership degree of the road surface adhesion coefficient, query the preset fuzzy rule table to perform reasoning and obtain the fuzzy output set of braking weight. The fuzzy rule table defines the combination conditions of the fuzzy subset of vehicle speed and the fuzzy subset of road surface adhesion coefficient and the mapping relationship between the fuzzy subset of braking weight.

[0035] In this embodiment, fuzzy inference can employ the Mamdani-type inference method. The fuzzy rule table is constructed by the intersection of three fuzzy subsets of vehicle speed and three fuzzy subsets of road surface adhesion coefficient, forming a 3×3 rule base containing a total of 9 fuzzy rules, comprehensively covering all combinations of vehicle speed and road surface adhesion coefficient. The fuzzy rule table is formulated based on vehicle dynamics characteristics and extensive simulation test calibration, aiming to balance obstacle avoidance efficiency and vehicle stability.

[0036] For example, a specific example of fuzzy rule logic is as follows: High-speed, low-adhesion driving condition (v=H, μ=L): Insufficient road surface adhesion leads to easy brake instability. The rule is set as: IF v=HAND μ=L THEN Braking weight W_b = S (0.25), meaning the braking weight is relatively small, with steering correction as the primary method. High-speed, high-adhesion condition (v=H, μ=H): Sufficient road surface adhesion and high braking efficiency. The rule is set as: IF v=HAND μ=H THEN Braking weight W_b = B (0.75), that is, the braking weight is relatively large, and braking correction is the main focus.

[0037] Medium-speed, medium-weighted operating condition (v=M, μ=M): Steering and braking capabilities are balanced. The rule is set as: IF v=M AND μ=MTHEN Braking weight W_b= M (0.50), that is, the braking weight is relatively moderate, and a cooperative balance strategy is adopted.

[0038] The remaining rules follow the same principle. During the inference process, the system substitutes the input membership degree into the fuzzy rule table, performs calculations through the inference engine, and finally obtains a fuzzy output set of braking weights containing multiple possible values.

[0039] To ensure real-time response capability under emergency obstacle avoidance conditions, the working cycle of the fuzzy logic controller in this embodiment is set to 20ms, that is, the weight coefficient is updated once every 20ms.

[0040] Step S104: Defuzzify the fuzzy output set of the braking weights using the centroid method to obtain the braking weights.

[0041] It is worth noting that since the controller internally calculates fuzzy quantities, while braking and steering require specific digital signals, defuzzification is necessary. This application preferably uses the centroid method to calculate the abscissa value corresponding to the fuzzy output set, with the formula: W_b = Σ(μ_i·w_i) / Σ(μ_i). Here, i represents the index of the output fuzzy subset participating in rule aggregation.

[0042] For example, in actual calculation, this is equivalent to taking a weighted average of all activated rule outputs. For instance, if the membership degree of rule output "S(0.25)" is 0.4 and the membership degree of "M(0.5)" is 0.6, then the final W_b = 0.25 × 0.4 + 0.5 × 0.6 = 0.4.

[0043] The advantage of using the center-of-gravity method is that it smoothly transforms the rule outputs with overlapping steps into a continuous value between 0 and 1, effectively avoiding step-like jumps in control weights at operating condition switching points, thus ensuring the smoothness of the control process.

[0044] Step S105: Calculate the steering weight based on the braking weight. The sum of the braking weight and the steering weight is 1, and the steering weight W_s = 1 - W_b.

[0045] In one embodiment, combined with Figure 4 As shown, step S2 involves planning the obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle's obstacle avoidance and calculating the target yaw moment required to track the obstacle avoidance trajectory. Specifically, this includes the following steps: Step S201: Obtain the vehicle's current pose as the starting coordinates, and determine the ending coordinates based on the obstacle position and target lane information.

[0046] In this embodiment, a lane coordinate system is established with the vehicle's current position as the origin, the vehicle's forward direction as the x-axis, and the lateral direction perpendicular to the forward direction as the y-axis.

[0047] The starting coordinates are (x=0, y=0, y'=0, y''=0), where y=0 indicates that the vehicle is in the center of the lane, y'=0 indicates that there is no initial lateral velocity, and y''=0 indicates that there is no initial lateral acceleration. Therefore, the starting coordinates indicate that the vehicle is located in the center of the current lane and has no initial lateral displacement.

[0048] The endpoint coordinates are (X=D_avoid, y=Y_target, y'=0, y''=0), where the lateral position Y_target is determined based on the obstacle width W_obs, with a safety margin of 1.0m introduced, i.e., Y_target=W_obs+1.0m, to ensure sufficient lateral safety distance between the vehicle and the obstacle; the longitudinal position D_avoid is dynamically calculated based on the current vehicle speed and obstacle distance to ensure the vehicle completes lane change before collision. In the boundary conditions, y=Ytarget represents the completion of lane change, and y'=0 and y''=0 respectively indicate that the lateral velocity and lateral acceleration at the endpoint are both zero, to ensure the vehicle smoothly returns to straight-line driving.

[0049] Step S202: Using the starting point coordinates and the ending point coordinates as boundary conditions, construct a fifth-order polynomial obstacle avoidance trajectory function: y(x) = a0+ a1x + a2x² + a3x³ + a4x 4 + a5x 5 In the formula, y(x) represents the lateral displacement as a function of the longitudinal displacement.

[0050] This application introduces higher-order constraints using a fifth-order polynomial, and the coefficient a0 can be uniquely determined by solving the system of equations. a5. This polynomial can satisfy a total of 6 boundary conditions for the start and end points (position, first derivative, and second derivative are all zero), thus achieving smooth convergence of the trajectory.

[0051] Step S203: Based on the obstacle avoidance trajectory function, generate a reference state sequence of the target centroid sideslip angle and the target yaw rate.

[0052] By performing differential operations on the trajectory function, the target yaw rate ω_z_ref and the target centroid sideslip angle β_ref, which reflect the vehicle's stability, can be obtained.

[0053] Preferably, this application employs a preview control strategy: every 20ms, a set of reference state sequences for the next 10 steps (i.e., the next 200ms) is generated, including the target yaw rate ω_z_ref(t) and the target centroid sideslip angle β_ref(t). This mechanism enables the controller to anticipate path changes in advance, allowing for earlier braking or steering intervention.

[0054] Step S204: Calculate the target yaw moment based on the reference state sequence using the Linear Quadratic Regulator (LQR) algorithm. This torque is applied to the subsequent distribution of steering and braking systems.

[0055] Furthermore, in combination Figure 5 As shown, step S3, which involves decomposing the target yaw moment into a steering-corrected yaw moment and a braking-corrected yaw moment based on the braking weight and the steering weight, includes: multiplying the target yaw moment by the braking weight to calculate the braking-corrected yaw moment. The steering correction yaw moment is calculated by multiplying the target yaw moment by the steering weight. .

[0056] In one embodiment, the step of calculating the steering correction angle based on the steering correction yaw moment includes: calculating an initial steering correction angle based on the vehicle's two-degree-of-freedom dynamics model, the steering correction yaw moment, the current vehicle speed, the vehicle mass, the front wheel lateral stiffness, and the distance from the center of gravity to the front axle; and limiting the initial steering correction angle within a preset safety angle threshold range to obtain the final steering correction angle.

[0057] For example, in this step, the abstract yaw moment requirement is transformed into a specific steering wheel angle command. This application uses a two-degree-of-freedom vehicle model for approximate solution and derives the steering correction angle. While ensuring computational accuracy, it also meets real-time requirements:

[0058] in, Indicates the steering correction angle. This represents the steering correction yaw moment, where v is the vehicle speed (m / s) and m is the vehicle mass (kg). Front wheel lateral stiffness (N / rad, typical value 80000-120000). This is the distance from the center of gravity to the front axle.

[0059] Excessive steering correction angle may cause driver panic, vehicle instability, or exceed the physical limits of the actuators; therefore, a safety limit is required for the steering correction angle. In this embodiment, the safety angle threshold range is... The processing logic is as follows: if Δδ>3 The output will be 3. If Δδ < 3 Then output 3 If it is within the range, it will be directly output as the final steering correction angle.

[0060] Furthermore, the step of calculating the target braking torque of each wheel based on the braking correction yaw moment includes: calculating the initial target braking torque of each wheel based on the load ratio distribution according to the braking correction yaw moment, the effective rolling radius of the wheel, the wheel track, the braking system efficiency, and the vertical load of each wheel; calculating the maximum permissible braking torque of each wheel according to the road adhesion coefficient, the vertical load of each wheel, and the effective rolling radius of the wheel; and limiting the initial target braking torque of each wheel within the corresponding maximum permissible braking torque range to obtain the final target braking torque of each wheel.

[0061] For example, in order to maximize tire adhesion and ensure braking stability, this application adopts a distribution strategy based on vertical load ratio, calculated as follows:

[0062]

[0063]

[0064]

[0065] in, The target braking torque for the left front wheel, The target braking torque for the right front wheel. The target braking torque for the left rear wheel, The target braking torque for the right rear wheel.

[0066] In one embodiment, after calculating the target braking torque, the control braking system executes the target braking torque, specifically including the following steps: Step S301: Calculate the sum of the target braking torques of all wheels to obtain the total required braking torque T_demand = ∑T_bi, where T_bi are the target braking torques of the left front wheel, right front wheel, left rear wheel and right rear wheel, respectively.

[0067] Step S302: Determine the maximum regenerative braking torque T_regen_max that is currently available for the motor based on the state of charge (SOC) of the vehicle's power battery, the motor speed, and the motor temperature.

[0068] In this embodiment, a pre-stored motor external characteristic pulse spectrum (Map) can be queried in real time. Using SOC, speed, and temperature as indexes, a lookup table can be used to obtain the maximum negative torque that the motor can safely provide under the current operating conditions. The response time of this lookup calculation process is less than 1ms, ensuring the real-time intervention of regenerative braking and avoiding hardware damage caused by battery overcharging, motor overspeeding, or overheating.

[0069] Step S303: Compare the total required braking torque T_demand with the maximum regenerative braking torque T_regen_max.

[0070] Step S304: If the total required braking torque T_demand is less than or equal to the maximum regenerative braking torque T_regen_max, then control the motor to output the regenerative braking torque of the total required braking torque to achieve 100% regenerative braking with a response time of <10ms, maximizing the utilization of recovered energy while ensuring obstacle avoidance stability.

[0071] Step S305: If the total required braking torque T_demand is greater than the maximum regenerative braking torque T_regen_max, then control the motor to output the maximum regenerative braking torque, and control the hydraulic braking system to output the hydraulic braking torque equal to the difference between the total required braking torque and the maximum regenerative braking torque. This strategy prioritizes utilizing the motor's regenerative braking capacity, then uses the hydraulic system to supplement the remaining braking force, achieving a synergistic superposition of full-load regenerative braking and hydraulic braking. The regenerative braking command response time is less than 10ms, and the hydraulic braking pressure build-up response time is less than 30ms, ensuring the synchronization and smoothness of the combined braking.

[0072] This embodiment, through a regenerative braking priority strategy, can maximize the use of motor regenerative braking while meeting the obstacle avoidance yaw torque requirements, thus significantly reducing energy loss.

[0073] Furthermore, such as Figure 6 As shown, the control braking system executes the target braking torque and also includes stability closed-loop monitoring and correction, specifically including the following steps: Step S401: Calculate the absolute value of the yaw rate deviation between the target yaw rate and the actual yaw rate of the vehicle, e=|ω_z_act-ω_z_ref|, where ω_z_act is the actual yaw rate and ω_z_ref is the target yaw rate.

[0074] Step S502: If the absolute value of the yaw rate deviation is greater than a preset first deviation threshold and the duration exceeds a preset first duration, then stability correction control is triggered.

[0075] For example, in this embodiment, the first deviation threshold is set to 0.05 rad / s, and the first duration is set to 40 ms. When the absolute value of the yaw rate deviation e is detected to be greater than 0.05 rad / s and the duration t is greater than 40 ms, it is determined that the vehicle is in a state of slight instability or trajectory deviation, and stability correction control is immediately triggered to prevent the deviation from further increasing.

[0076] Step S403: When the stability correction control is triggered, if the actual yaw rate ω_z_act is less than the target yaw rate ω_z_ref, then understeer is determined, and the braking system is controlled to apply an additional corrective braking torque to the outer rear wheel of the steering wheel, using this torque to make the front of the vehicle turn inward.

[0077] Step S404: If the actual yaw rate ω_z_act is greater than the target yaw rate ω_z_ref, then it is determined that the steering is oversteer, and the braking system is controlled to apply an additional corrective braking torque to the outer front wheel of the steering wheel to suppress the rear of the vehicle from sliding.

[0078] The additional corrected braking torque is calculated using a proportional-integral control algorithm based on the yaw rate deviation. The calculation method is as follows: ΔT_b = k_p·e(t) + k_i·∫e(t)dt In the formula, ΔT_b is the additional corrected braking torque, and e=ωz_ref ωz_act represents the yaw rate deviation. To improve control robustness, the proportional coefficient Kp is adaptively adjusted with vehicle speed, ranging from 0.5 to 2.0, while Ki is set to a range of 0.1 to 0.5, balancing response speed and steady-state accuracy.

[0079] The calculated additional corrected braking torque ΔTb is superimposed with the target braking torque Tdemand. While applying total braking to the corresponding wheels, asymmetrical differential braking is applied to specific wheels on the outer side of the steering wheel. The resulting additional yaw moment is used to counteract the vehicle's instability tendency, thereby ensuring braking strength while accurately correcting yaw motion and ensuring that the vehicle stably tracks the obstacle avoidance trajectory.

[0080] Step S405: If the absolute value of the yaw rate deviation is less than or equal to a preset second deviation threshold, and the duration exceeds a preset second duration, then the stability correction control is terminated. In this embodiment, the second deviation threshold is 0.02, and the second duration is 100ms.

[0081] This embodiment introduces a stability closed-loop monitoring and correction mechanism, which can effectively eliminate trajectory tracking deviations caused by road surface disturbances or model errors, and significantly improve dynamic stability during emergency obstacle avoidance.

[0082] As a preferred implementation, the system also introduces a human-machine co-driving arbitration mechanism when executing the steering correction angle and the target braking torque, so as to balance the safety of autonomous driving and the takeover rights of human driving.

[0083] First, the steering wheel angle δ_drive input by the driver and the input torque applied to the steering wheel are obtained; the steering wheel angle δ_drive is superimposed with the steering correction angle Δδ to obtain the active steering target angle δ_act = δ_driver + Δδ; the steering system is controlled to output the active steering target angle at an intervention rate not greater than a preset limit rate. In this embodiment, the intervention rate is ≤50° / s to avoid the system steering intervention being too abrupt.

[0084] This embodiment also includes driver takeover arbitration logic. When the driver inputs torque greater than a preset torque threshold (e.g., >5 Nm), it is determined to be a driver takeover request. In response to the driver takeover request, the steering correction angle is gradually reduced according to a preset attenuation coefficient until the steering correction angle is zero. This smooth exit strategy respects the driver's priority while avoiding vehicle yaw oscillations caused by abrupt switching of control.

[0085] The coordinated control method for steering and braking under emergency obstacle avoidance conditions provided in this embodiment constructs a complete closed-loop link of adaptive weight generation, coordinated decomposition of yaw moment, and precise actuator allocation. This solves the problem of disconnect between decision-making and execution in traditional control, ensuring seamless connection from environmental perception to chassis response. Based on the dynamic allocation of wheel vertical load Fzi, and real-time linkage with braking weight Wb, the method maximizes the utilization of tire adhesion elliptical characteristics, significantly improving lateral stability while ensuring braking efficiency. Through a regenerative braking priority strategy, the contribution rate of regenerative braking is increased to over 40%, achieving a 5 to 8-fold improvement compared to the 5%-8% contribution rate in existing technologies, effectively balancing active safety and energy economy. Simulation and real-vehicle verification show that the overall obstacle avoidance success rate of this method reaches over 96%, reducing the obstacle avoidance failure rate to approximately 30% (i.e., a reduction of approximately 70%) compared to the 80%-85% of existing technologies, significantly improving driving safety under extreme conditions.

[0086] Secondly, embodiments of this application also provide a coordinated control device for steering and braking under emergency obstacle avoidance conditions of a vehicle, which can be configured in the controller layer.

[0087] In one embodiment, reference is made to Figure 7 , Figure 7 This is a functional module diagram of an embodiment of the coordinated control device for steering and braking under emergency obstacle avoidance conditions of a vehicle, as described in this application. Figure 7 As shown, the coordinated control device for steering and braking during vehicle emergency obstacle avoidance includes: The first calculation module is used to calculate braking weight and steering weight based on vehicle speed and road adhesion coefficient in response to the vehicle triggering emergency obstacle avoidance mode. The second calculation module is used to plan the obstacle avoidance trajectory based on the starting coordinates and ending coordinates of the vehicle obstacle avoidance, and to calculate the target yaw moment required to track the obstacle avoidance trajectory. The third calculation module is used to decompose the target yaw moment into steering-corrected yaw moment and braking-corrected yaw moment according to the braking weight and the steering weight. The fourth calculation module is used to calculate the steering correction angle based on the steering correction yaw moment, and to calculate the target braking torque of each wheel based on the braking correction yaw moment. The control module is used to control the vehicle's steering system to execute the steering correction angle and to control the braking system to execute the target braking torque.

[0088] Furthermore, in one embodiment, the first computing module is further configured to: Determine the membership degree of the vehicle speed to a preset fuzzy subset of vehicle speeds, wherein the fuzzy subset of vehicle speeds includes low-speed, medium-speed, and high-speed fuzzy subsets, and there is an overlapping area of ​​vehicle speeds between adjacent fuzzy subsets of vehicle speeds. The membership degree of the road surface adhesion coefficient to a preset fuzzy subset of road surface adhesion coefficients is determined, wherein the fuzzy subset of road surface adhesion coefficients includes low adhesion, medium adhesion and high adhesion fuzzy subsets, and there is an overlapping area of ​​road surface adhesion coefficients between adjacent fuzzy subsets of road surface adhesion coefficients. Based on the membership degree of the vehicle speed and the membership degree of the road surface adhesion coefficient, a preset fuzzy rule table is queried for reasoning to obtain a fuzzy output set of braking weights. The fuzzy rule table defines the combination conditions of the fuzzy subset of vehicle speed and the fuzzy subset of road surface adhesion coefficient and the mapping relationship between the fuzzy subset of braking weights. The braking weights are obtained by defuzzifying the fuzzy output set of the braking weights using the centroid method. The steering weight is calculated based on the braking weight, and the sum of the braking weight and the steering weight is 1.

[0089] Furthermore, in one embodiment, the second computing module is also used for: The vehicle's current pose is used as the starting coordinates, and the ending coordinates are determined based on the obstacle positions and target lane information. Using the starting point coordinates and the ending point coordinates as boundary conditions, a fifth-order polynomial obstacle avoidance trajectory function is constructed. Based on the obstacle avoidance trajectory function, a reference state sequence of the target's centroid sideslip angle and target yaw rate is generated; The target yaw moment is calculated based on the reference state sequence using a linear quadratic regulator algorithm.

[0090] Furthermore, in one embodiment, the third computing module is also used for: The braking correction yaw moment is calculated by multiplying the target yaw moment by the braking weight. The steering correction yaw moment is calculated by multiplying the target yaw moment by the steering weight.

[0091] Furthermore, in one embodiment, the fourth computing module is also used for: Based on the vehicle's two-degree-of-freedom dynamics model, the initial steering correction angle is calculated according to the steering correction yaw moment, current vehicle speed, vehicle mass, front wheel lateral stiffness, and distance from the center of gravity to the front axle. The initial steering correction angle is limited to a preset safe angle threshold range to obtain the final steering correction angle.

[0092] Furthermore, in one embodiment, the fourth computing module is also used for: Based on the braking correction yaw moment, effective wheel rolling radius, wheel track, braking system efficiency, and vertical load on each wheel, the initial target braking torque of each wheel is calculated based on the load ratio distribution. Based on the road surface adhesion coefficient, the vertical load of each wheel, and the effective rolling radius of the wheel, calculate the maximum allowable braking torque of each wheel; The initial target braking torque of each wheel is limited to the corresponding maximum allowable braking torque range to obtain the final target braking torque of each wheel.

[0093] Furthermore, in one embodiment, the control module is also used for: Calculate the sum of the target braking torques for all wheels to obtain the total required braking torque; The maximum regenerative braking torque currently available for the motor is determined based on the state of charge of the vehicle's power battery, the motor speed, and the motor temperature. Compare the total required braking torque with the maximum regenerative braking torque; If the total required braking torque is less than or equal to the maximum regenerative braking torque, then control the motor to output the regenerative braking torque of the total required braking torque; If the total required braking torque is greater than the maximum regenerative braking torque, then the motor is controlled to output the maximum regenerative braking torque, and the hydraulic braking system is controlled to output the hydraulic braking torque equal to the difference between the total required braking torque and the maximum regenerative braking torque.

[0094] Furthermore, in one embodiment, the control module is also used for: Calculate the absolute value of the yaw rate deviation between the target yaw rate and the actual yaw rate of the vehicle; If the absolute value of the yaw rate deviation is greater than a preset first deviation threshold and the duration exceeds a preset first duration, then stability correction control is triggered. When the stability correction control is triggered, if the actual yaw rate is less than the target yaw rate, the braking system is controlled to apply an additional corrective braking torque to the outer rear wheel of the steering wheel. The additional corrective braking torque is calculated based on the yaw rate deviation using a proportional-integral control algorithm. If the actual yaw rate is greater than the target yaw rate, then the braking system is controlled to apply an additional corrective braking torque to the outer front wheel. If the absolute value of the yaw rate deviation is less than or equal to a preset second deviation threshold, and the duration exceeds a preset second duration, then the stability correction control is terminated.

[0095] Furthermore, in one embodiment, the control module is also used for: It acquires the steering wheel angle input by the driver and the input torque applied to the steering wheel; The steering wheel angle and the steering correction angle are superimposed to obtain the active steering target angle; The steering system is controlled to output the active steering target angle at an intervention rate not exceeding a preset limit rate; When the input torque is greater than a preset torque threshold, it is determined to be a driver takeover request; In response to the driver takeover request, the steering correction angle is gradually reduced according to a preset attenuation coefficient until the steering correction angle is zero.

[0096] The functions of each module in the above-mentioned vehicle emergency obstacle avoidance coordination control device correspond to the steps in the above-mentioned vehicle emergency obstacle avoidance coordination control method embodiment, and their functions and implementation processes will not be described in detail here.

[0097] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0098] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0099] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0100] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0101] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0102] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0103] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for coordinated control of steering and braking under emergency obstacle avoidance conditions of a vehicle, characterized in that, The method for coordinated control of steering and braking under emergency obstacle avoidance conditions includes: In response to the vehicle triggering the emergency obstacle avoidance mode, braking weight and steering weight are calculated based on vehicle speed and road adhesion coefficient. Plan the obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle obstacle avoidance, and calculate the target yaw moment required to track the obstacle avoidance trajectory; Based on the braking weight and the steering weight, the target yaw moment is decomposed into steering-corrected yaw moment and braking-corrected yaw moment; The steering correction angle is calculated based on the steering correction yaw moment, and the target braking torque for each wheel is calculated based on the braking correction yaw moment. The vehicle's steering system is controlled to execute the steering correction angle, and the braking system is controlled to execute the target braking torque.

2. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The calculation of braking weight and steering weight based on vehicle speed and road surface adhesion coefficient includes: Determine the membership degree of the vehicle speed to a preset fuzzy subset of vehicle speeds, wherein the fuzzy subset of vehicle speeds includes low-speed, medium-speed, and high-speed fuzzy subsets, and there is an overlapping area of ​​vehicle speeds between adjacent fuzzy subsets of vehicle speeds. The membership degree of the road surface adhesion coefficient to a preset fuzzy subset of road surface adhesion coefficients is determined, wherein the fuzzy subset of road surface adhesion coefficients includes low adhesion, medium adhesion and high adhesion fuzzy subsets, and there is an overlapping area of ​​road surface adhesion coefficients between adjacent fuzzy subsets of road surface adhesion coefficients. Based on the membership degree of the vehicle speed and the membership degree of the road surface adhesion coefficient, a preset fuzzy rule table is queried for reasoning to obtain a fuzzy output set of braking weights. The fuzzy rule table defines the combination conditions of the fuzzy subset of vehicle speed and the fuzzy subset of road surface adhesion coefficient and the mapping relationship between the fuzzy subset of braking weights. The braking weights are obtained by defuzzifying the fuzzy output set of the braking weights using the centroid method. The steering weight is calculated based on the braking weight, and the sum of the braking weight and the steering weight is 1.

3. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The process of planning an obstacle avoidance trajectory based on the starting and ending coordinates of the vehicle obstacle avoidance, and calculating the target yaw moment required to track the obstacle avoidance trajectory, includes: The vehicle's current pose is used as the starting coordinates, and the ending coordinates are determined based on the obstacle positions and target lane information. Using the starting point coordinates and the ending point coordinates as boundary conditions, a fifth-order polynomial obstacle avoidance trajectory function is constructed. Based on the obstacle avoidance trajectory function, a reference state sequence of the target's centroid sideslip angle and target yaw rate is generated; The target yaw moment is calculated based on the reference state sequence using a linear quadratic regulator algorithm.

4. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The step of decomposing the target yaw moment into steering-corrected yaw moment and braking-corrected yaw moment based on the braking weight and the steering weight includes: The braking correction yaw moment is calculated by multiplying the target yaw moment by the braking weight. The steering correction yaw moment is calculated by multiplying the target yaw moment by the steering weight.

5. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The calculation of the steering correction angle based on the steering correction yaw moment includes: Based on the vehicle's two-degree-of-freedom dynamics model, the initial steering correction angle is calculated according to the steering correction yaw moment, current vehicle speed, vehicle mass, front wheel lateral stiffness, and distance from the center of gravity to the front axle. The initial steering correction angle is limited to a preset safe angle threshold range to obtain the final steering correction angle.

6. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The calculation of the target braking torque for each wheel based on the braking correction yaw moment includes: Based on the braking correction yaw moment, effective wheel rolling radius, wheel track, braking system efficiency, and vertical load on each wheel, the initial target braking torque of each wheel is calculated based on the load ratio distribution. Based on the road surface adhesion coefficient, the vertical load of each wheel, and the effective rolling radius of the wheel, calculate the maximum allowable braking torque of each wheel; The initial target braking torque of each wheel is limited to the corresponding maximum allowable braking torque range to obtain the final target braking torque of each wheel.

7. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The control braking system executes the target braking torque, including: Calculate the sum of the target braking torques for all wheels to obtain the total required braking torque; The maximum regenerative braking torque currently available for the motor is determined based on the state of charge of the vehicle's power battery, the motor speed, and the motor temperature. Compare the total required braking torque with the maximum regenerative braking torque; If the total required braking torque is less than or equal to the maximum regenerative braking torque, then control the motor to output the regenerative braking torque of the total required braking torque; If the total required braking torque is greater than the maximum regenerative braking torque, then the motor is controlled to output the maximum regenerative braking torque, and the hydraulic braking system is controlled to output the hydraulic braking torque equal to the difference between the total required braking torque and the maximum regenerative braking torque.

8. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The control braking system for executing the target braking torque further includes: Calculate the absolute value of the yaw rate deviation between the target yaw rate and the actual yaw rate of the vehicle; If the absolute value of the yaw rate deviation is greater than a preset first deviation threshold and the duration exceeds a preset first duration, then stability correction control is triggered. When the stability correction control is triggered, if the actual yaw rate is less than the target yaw rate, the braking system is controlled to apply an additional corrective braking torque to the outer rear wheel of the steering wheel. The additional corrective braking torque is calculated based on the yaw rate deviation using a proportional-integral control algorithm. If the actual yaw rate is greater than the target yaw rate, then the braking system is controlled to apply an additional corrective braking torque to the outer front wheel. If the absolute value of the yaw rate deviation is less than or equal to a preset second deviation threshold, and the duration exceeds a preset second duration, then the stability correction control is terminated.

9. The coordinated control method for steering and braking under emergency obstacle avoidance conditions of a vehicle as described in claim 1, characterized in that, The steering system controlling the vehicle executes the steering correction angle, and further includes: It acquires the steering wheel angle input by the driver and the input torque applied to the steering wheel; The steering wheel angle and the steering correction angle are superimposed to obtain the active steering target angle; The steering system is controlled to output the active steering target angle at an intervention rate not exceeding a preset limit rate; When the input torque is greater than a preset torque threshold, it is determined to be a driver takeover request; In response to the driver takeover request, the steering correction angle is gradually reduced according to a preset attenuation coefficient until the steering correction angle is zero.

10. A coordinated control device for steering and braking during emergency obstacle avoidance of a vehicle, characterized in that, The coordinated control device for steering and braking during vehicle emergency obstacle avoidance includes: The first calculation module is used to calculate braking weight and steering weight based on vehicle speed and road adhesion coefficient in response to the vehicle triggering emergency obstacle avoidance mode. The second calculation module is used to plan the obstacle avoidance trajectory based on the starting coordinates and ending coordinates of the vehicle obstacle avoidance, and to calculate the target yaw moment required to track the obstacle avoidance trajectory. The third calculation module is used to decompose the target yaw moment into steering-corrected yaw moment and braking-corrected yaw moment according to the braking weight and the steering weight. The fourth calculation module is used to calculate the steering correction angle based on the steering correction yaw moment, and to calculate the target braking torque of each wheel based on the braking correction yaw moment. The control module is used to control the vehicle's steering system to execute the steering correction angle and to control the braking system to execute the target braking torque.

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