Emergency obstacle avoidance control method and system for unmanned vehicle

Abstract

The invention discloses an emergency obstacle avoidance control method and system for an unmanned vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: accessing an unmanned vehicle control terminal, and receiving an obstacle avoidance planning path and obstacle avoidance control parameters outputted by an emergency obstacle avoidance control module; performing rollover risk identification according to the real-time state of the current vehicle through a rollover risk identification module, and outputting a rollover risk level label; and when the rollover risk level label is greater than a preset rollover risk level, activating a flexible control instruction, driving a flexible control module to perform flexible control, outputting a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters, and performing flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters. The technical problem that in the prior art, rollover or instability of the vehicle may be caused in the emergency obstacle avoidance process of the vehicle is solved, and the technical effects that it is ensured that the vehicle stably and safely avoids the obstacle in the obstacle avoidance process, and rollover is effectively prevented are achieved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to an emergency obstacle avoidance control method and system for an unmanned vehicle. Background Art

[0002] With the rapid development of autonomous driving technology, driverless vehicles are increasingly used in transportation, logistics and distribution. The driverless system uses sensors, cameras, lidar and other equipment to perceive the environment and make decisions in real time, significantly improving transportation efficiency and safety. However, with the complexity of the road environment and the diversification of driving scenarios, existing control methods face new challenges, especially in emergency obstacle avoidance. Traditional obstacle avoidance control methods often focus on path planning and speed control, but when faced with complex obstacles and sharp turns, the control accuracy and stability are often difficult to meet the requirements, which may cause vehicle instability, rollover or other safety hazards. Summary of the invention

[0003] The present application provides an emergency obstacle avoidance control method and system for an unmanned vehicle, which is used to solve the technical problem in the prior art that the vehicle may roll over or become unstable during the emergency obstacle avoidance process.

[0004] In view of the above problems, the present application provides an emergency obstacle avoidance control method and system for an unmanned vehicle.

[0005] In a first aspect of the present application, an emergency obstacle avoidance control method for an unmanned vehicle is provided, the method comprising: An unmanned vehicle control terminal is connected to the current vehicle; the unmanned vehicle control terminal receives the obstacle avoidance planning path and obstacle avoidance control parameters output by an emergency obstacle avoidance control module, the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; the obstacle avoidance planning path and the obstacle avoidance control parameters are input into a rollover risk identification module, the rollover risk identification module identifies the rollover risk according to the real-time status of the current vehicle, and outputs a rollover risk level label; when the rollover risk level label is greater than a preset rollover risk level, a flexible control instruction is activated; according to the flexible control instruction, a flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are output, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

[0006] The second aspect of the present application provides an emergency obstacle avoidance control system for an unmanned vehicle, the system comprising: A terminal connection unit, used to access the unmanned vehicle control terminal of the current vehicle; a data receiving unit, used for the unmanned vehicle control terminal to receive the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module, the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; a rollover risk identification unit, used to input the obstacle avoidance planning path and the obstacle avoidance control parameters into the rollover risk identification module, the rollover risk identification module performs rollover risk identification according to the real-time status of the current vehicle, and outputs a rollover risk level label; a control instruction activation unit, used to activate a flexible control instruction when the rollover risk level label is greater than a preset rollover risk level; an obstacle avoidance control unit, used to drive the flexible control module connected to the emergency obstacle avoidance control module to perform flexible control according to the flexible control instruction, output a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application is connected to an unmanned vehicle control terminal of the current vehicle; the unmanned vehicle control terminal receives an obstacle avoidance planning path and obstacle avoidance control parameters output by an emergency obstacle avoidance control module, the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; the obstacle avoidance planning path and the obstacle avoidance control parameters are input into a rollover risk identification module, the rollover risk identification module performs rollover risk identification according to the real-time status of the current vehicle, and outputs a rollover risk level label; when the rollover risk level label is greater than a preset rollover risk level, a flexible control instruction is activated; according to the flexible control instruction, a flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are output, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters. The present invention solves the technical problem in the prior art that the vehicle may roll over or become unstable during an emergency obstacle avoidance process. By connecting to the unmanned vehicle control terminal of the current vehicle, receiving the obstacle avoidance planning path and obstacle avoidance control parameters, the rollover risk of the path is evaluated and flexible control is performed, so as to achieve the technical effect of ensuring that the vehicle can smoothly and safely avoid obstacles and effectively prevent rollover during the obstacle avoidance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of an emergency obstacle avoidance control method for an unmanned vehicle provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an emergency obstacle avoidance control system for an unmanned vehicle provided in an embodiment of the present application.

[0010] Explanation of reference numerals: terminal connection unit 11 , data receiving unit 12 , rollover risk identification unit 13 , control instruction activation unit 14 , obstacle avoidance control unit 15 . DETAILED DESCRIPTION

[0011] The present application provides an emergency obstacle avoidance control method and system for an unmanned vehicle, aiming to solve the technical problem in the prior art that the vehicle may roll over or become unstable during the emergency obstacle avoidance process. By connecting to the unmanned vehicle control terminal of the current vehicle, receiving the obstacle avoidance planning path and obstacle avoidance control parameters, evaluating the rollover risk of the path and performing flexible control, the technical effect of ensuring that the vehicle can smoothly and safely avoid obstacles and effectively prevent rollover during the obstacle avoidance process is achieved.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides an emergency obstacle avoidance control method for an unmanned vehicle, the method comprising: Step S100: Access the unmanned vehicle control terminal of the current vehicle.

[0015] In the embodiment of the present application, the unmanned vehicle control terminal of the current vehicle is accessed through the communication bus or wireless network interface of the current vehicle. The unmanned vehicle control terminal is the core control system of the vehicle, responsible for coordinating and managing all driving tasks of the vehicle. It integrates a computing platform, control algorithms, and communication interfaces to process data from sensors and execute decision commands, and ultimately realizes unmanned driving functions by controlling the vehicle's driving, steering, acceleration, braking, and other systems.

[0016] Step S200: The unmanned vehicle control terminal receives the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module, wherein the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module.

[0017] In an embodiment of the present application, the unmanned vehicle control terminal receives the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module. This process involves the collaborative work of multiple modules to ensure that the vehicle can safely and stably avoid obstacles and continue driving.

[0018] Among them, the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module. The task of the obstacle avoidance path planning module is to plan a safe obstacle avoidance path based on the environmental information around the vehicle. This module uses environmental data from vehicle sensors (such as lidar, camera, radar, etc.) to perform path planning through the A* algorithm. Specifically, sensors such as lidar scan the environment around the vehicle and collect the location information of obstacles. Based on this data, the path planning module converts the environmental information into a grid map or topology map, where each grid or node represents a location, and obstacles and free areas occupy different nodes respectively. Subsequently, the A* algorithm evaluates the cost of each node and selects the optimal path through heuristic search to ensure that the vehicle can reach the target in the shortest time while avoiding obstacles. Finally, the obstacle avoidance path planning module outputs an obstacle avoidance planning path, which is a safe path that avoids obstacles and guides the vehicle to reach the target smoothly from the current position.

[0019] The obstacle avoidance control analysis module generates actual control instructions based on the planned path to ensure that the vehicle follows the planned path and maintains stability when avoiding obstacles. This module adopts a rule-based control method to generate control parameters based on real-time sensor data and preset obstacle avoidance rules. Specifically, first, the obstacle avoidance control analysis module uses the data collected by the sensor to determine the location of surrounding obstacles and identify obstacles in front, on the side, and behind. If the distance of the obstacle in front is less than the set threshold (for example, 2 meters), a "slow down" or "stop" instruction will be issued; if the obstacle on the left or right is closer, a "turn right" or "turn left" command will be output to avoid the obstacle. In emergency situations, "maximum steering angle" and "minimum speed" commands will be output according to the rules to avoid obstacles. The key to this process lies in the setting of rules, which determine the obstacle avoidance behavior based on the relative position and distance between the vehicle and the obstacle. Finally, based on these real-time data and judgments, the obstacle avoidance control analysis module generates specific obstacle avoidance control parameters, including steering angle, braking force, acceleration, etc., which will guide the vehicle to avoid obstacles.

[0020] Through the obstacle avoidance path planning module and obstacle avoidance control analysis module, the obstacle avoidance planning path and obstacle avoidance control parameters are output and input into the unmanned vehicle control terminal.

[0021] Step S300: inputting the obstacle avoidance planning path and the obstacle avoidance control parameters into a rollover risk identification module, and the rollover risk identification module performs rollover risk identification according to the real-time status of the current vehicle and outputs a rollover risk level label.

[0022] In an embodiment of the present application, the rollover risk identification module realizes dynamic assessment of rollover risk through a pre-embedded identification model. The obstacle avoidance planning path and obstacle avoidance control parameters are used as input, and the input data is calculated by a support vector machine. The model performs a comprehensive analysis based on the real-time status of the vehicle (such as speed, lateral acceleration, steering angle, etc.), evaluates whether the current operation will cause rollover, and outputs a rollover risk level label. The label quantifies the risk level and is used to guide subsequent flexible obstacle avoidance control to ensure the safety of the vehicle.

[0023] Furthermore, in the method provided in the embodiment of the application, the rollover risk identification module is embedded with a classification identification model obtained by decision boundary learning through support vector machine training, and the expression is as follows: ; in, is the rollover risk level of the current input path x, n is the sample data size, is the Lagrange multiplier in the support vector machine, indicating that each training sample The weight of For training samples The real rollover risk level label, It is used to calculate the input sample x and the training sample The kernel function of the similarity between them; , Used to measure input samples x and training samples The difference between is the bandwidth parameter of the kernel function, which controls the influence range of the similarity metric. A bias term for adjusting the classification boundary position.

[0024] In an embodiment of the present application, the rollover risk identification module is embedded with a classification identification model obtained by decision boundary learning through support vector machine training.

[0025] In the support vector machine (SVM) model, the purpose of the rollover risk identification module is to evaluate the rollover risk based on the real-time status of the vehicle by inputting the obstacle avoidance planning path and obstacle avoidance control parameters into the SVM model, and output the rollover risk level label.

[0026] Specifically, the input data includes the real-time status of the vehicle, obstacle avoidance path planning, and control parameters. These data are preprocessed and converted into feature vectors suitable for SVM model processing. Each input sample Contains a set of features, such as vehicle speed, acceleration, steering angle, lateral acceleration, wheel rotation, etc., which are crucial for predicting rollover risk. Each input sample Each is given a label , indicating the rollover risk level of the sample. The labels are divided into four levels: level 1, level 2, level 3, and level 4, representing different levels from low risk to high risk. These labels are obtained based on the annotations in historical data or simulated training data sets, where level 1 represents low risk and level 4 represents high risk.

[0027] During the training phase, the support vector machine learns the optimal decision boundary by inputting the training data into an optimization process. The decision boundary is a hyperplane that separates data points of different categories. The goal of the support vector machine is to find the maximum margin hyperplane so that data points of different categories are separated as much as possible. This process involves calculating the coefficients of each sample point, especially the important support vector. Each training sample The contribution to the decision boundary is given by the coefficient These coefficients are obtained through optimization algorithms during the training process of SVM. Their role is to determine which samples are support vectors. Support vectors are samples closest to the decision boundary and have the greatest impact on the classification decision. Through training, SVM will automatically adjust The value of maximises the impact of the support vector on the decision boundary while maximising the margin of the boundary.

[0028] In order to process nonlinearly separable data, SVM introduces kernel function. The kernel function maps the input data to a high-dimensional feature space so that a linear segmentation surface can be found in the space to separate data of different categories. In this step, SVM uses Gaussian kernel function to calculate the similarity between each sample and the training sample. is the input sample x and the training sample The Euclidean distance between is a parameter that controls the influence of the similarity measure. Determines the bandwidth or scale of the kernel function and affects the similarity measure between sample points. It can be optimized by cross-validation method to select a value that best suits the current data set. Generally speaking, smaller A value of will make the similarity measure more limited, considering only very close samples; while a larger A value of will make the similarity measure broader, taking into account more sample points. , which can improve the generalization ability and classification effect of the model.

[0029] Through the optimization algorithm, the support vector machine solves the optimal coefficients , and determine the parameters of the kernel function , thereby obtaining the best decision boundary. The goal of this process is to maximize the interval between samples and minimize the classification error. The optimized coefficients and kernel function parameters This ensures that the model can accurately classify new input samples.

[0030] Finally, the trained support vector machine model can classify new input data. For new obstacle avoidance planning paths and control parameters, the rollover risk level label is obtained through the classification recognition model.

[0031] Furthermore, in the method provided in the embodiment of the application, the training samples are obtained by simulating the vehicle dynamics model, and further include: Construct a vehicle dynamics model; define path segment feature samples, vehicle center of gravity structure samples and vehicle real-time status samples, wherein the path segment feature samples include path length, turning radius and ground friction coefficient, the vehicle center of gravity structure samples are vehicle geometry data and center of gravity height, and the vehicle real-time status samples include lateral acceleration, vehicle speed and steering angle; the vehicle dynamics model simulates the path segment feature samples, vehicle center of gravity structure samples and vehicle real-time status samples, and outputs a simulation data set and a rollover risk level label corresponding to the simulation data set; outputs a training sample according to the simulation data set and the rollover risk level label corresponding to the simulation data set.

[0032] In the embodiment of the present application, a vehicle dynamics model is first constructed. The model is based on the vehicle's dynamics equations and kinematics equations and is used to simulate the dynamic response of the vehicle under different driving conditions. The real-time data obtained by the vehicle sensor is used as input, including lateral acceleration, vehicle speed, steering angle, etc., combined with the dynamic balance equations (in It represents the moment generated by the vehicle due to lateral acceleration or turning. It indicates the force acting on the wheels when the vehicle is turning or moving sideways. represents the height of the vehicle's center of gravity) and the lateral acceleration formula (where v is the vehicle speed and R is the turning radius) calculates the vehicle's lateral acceleration and roll moment. This model can accurately simulate the vehicle's rollover risk indicators under different working conditions.

[0033] Next, we define the path segment feature samples, vehicle center of gravity structure samples, and vehicle real-time status samples. The path segment feature samples include path length, turning radius, and ground friction coefficient. The path length is usually directly read from historical driving data and represents the distance traveled by the vehicle; the turning radius is calculated from the historical records of the vehicle's steering angle and speed; the ground friction coefficient is estimated from the road surface information and historical sensor data, or set according to the data provided by the road condition database (such as 0.7-0.8 for dry asphalt and 0.1-0.3 for wet roads).

[0034] The vehicle center of gravity structure samples are vehicle geometry data and center of gravity height. The vehicle geometry data is obtained through the vehicle design parameters or vehicle specification information in the historical database; the center of gravity height is obtained through static test data or extracted from vehicle data in the historical database.

[0035] The real-time vehicle status samples include lateral acceleration, vehicle speed and steering angle. The lateral acceleration is extracted through the historical data of the IMU (Inertial Measurement Unit), the vehicle speed is obtained through the historical data recorded by the wheel speed sensor, and the steering angle is obtained through the historical data of the steering wheel angle sensor.

[0036] Next, the path segment feature samples, vehicle center of gravity structure samples, and vehicle real-time status samples are input into the constructed vehicle dynamics model, and the dynamic response of the vehicle is simulated through numerical simulation. The dynamic model uses the input data to calculate indicators such as lateral force, roll moment, and lateral acceleration to reflect the dynamic behavior of the vehicle in different path segments and driving conditions. For example, when the vehicle passes a sharp turn at high speed, the model calculates the vehicle's roll moment based on the input turning radius, vehicle speed, and lateral acceleration to assess the possibility of rollover. Through these simulations, a dynamic response data set of the vehicle under various driving conditions is generated.

[0037] By analyzing the dynamic response indicators in the simulation data set and combining the preset rollover risk level standards, the corresponding rollover risk level labels are generated. The risk level is graded according to key parameters such as lateral acceleration. For example, when the lateral acceleration is <0.3g, it is marked as low risk (level 1), 0.3g-0.5g is marked as medium-low risk (level 2), 0.5g-0.7g is marked as medium-high risk (level 3), and >0.7g is marked as high risk (level 4). These labels are used to characterize the risk of vehicle rollover under different conditions.

[0038] Finally, the simulated data set and the rollover risk level labels corresponding to the simulated data set are integrated and output as training samples.

[0039] Step S400: When the rollover risk level label is greater than a preset rollover risk level, the flexible control instruction is activated.

[0040] In the embodiment of the present application, the rollover risk level label is compared with the preset rollover risk level, and when the rollover risk level label is greater than the preset rollover risk level, the flexible control instruction is activated. The preset rollover risk level is set in advance, for example, it can be set to level 2.

[0041] Step S500: According to the flexible control instruction, the flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are outputted; the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

[0042] In an embodiment of the present application, when the flexible control instruction is activated, the flexible control module connected to the emergency obstacle avoidance control module is driven to control and generate a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters. By initializing the variational probability distribution and setting the variational lower bound, these parameters are continuously adjusted during the calculation process until the similarity between the initialized variational probability distribution and the preset posterior probability distribution is greater than the preset value. Finally, by maximizing the variational lower bound output, a flexible probability distribution that meets the optimization goal is obtained, thereby obtaining an accurate obstacle avoidance planning path and flexible obstacle avoidance control parameters. The obstacle avoidance planning path and flexible obstacle avoidance control parameters are passed to the unmanned vehicle control terminal, which performs flexible obstacle avoidance control on the vehicle according to these paths and parameters to ensure that the vehicle can safely avoid obstacles and avoid rollover.

[0043] Furthermore, in the method provided in the embodiment of the application, the flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control according to the flexible control instruction, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are output, and the method also includes: Initialize the variational probability distribution; set a variational lower bound, the variational lower bound includes a log-likelihood expectation and an entropy of the variational probability distribution, wherein the log-likelihood expectation is a joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameters obtained by integrating the variational probability distribution; adjust the parameters of the initialized variational probability distribution by maximizing the variational lower bound until the similarity between the initialized variational probability distribution and the posterior probability distribution is greater than a preset similarity, and output the current variational probability distribution as a flexible probability distribution, wherein the posterior probability distribution is a preset true probability distribution; output a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters using the flexible probability distribution.

[0044] In the embodiment of the present application, the variational probability distribution is first initialized by a high-dimensional space variational inference algorithm. At this stage, a simple probability distribution, such as a Gaussian distribution, is selected as a preliminary variational probability distribution form. The initial parameters of the variational probability distribution (such as mean and covariance matrix) are determined by the real-time state data of the vehicle (such as vehicle speed, steering angle, location of environmental obstacles, etc.). The purpose of this step is to provide an initial estimate for subsequent optimization, although the initial distribution may not fully reflect the actual driving environment.

[0045] Next, set the variational lower bound as the optimization objective function. The variational lower bound consists of two parts: the log-likelihood expectation and the entropy of the variational probability distribution. The log-likelihood expectation is used to measure the joint probability distribution of the current obstacle avoidance path and control parameters under the variational probability distribution. Specifically, by integrating the variational probability distribution, the probability distribution associated with the obstacle avoidance path and control parameters is calculated. For example, if the vehicle is making a sharp turn, environmental data provided by sensors (such as lidar) will help determine the optimal obstacle avoidance path and control parameters. Entropy ensures that the distribution is not too concentrated by measuring the uncertainty of the variational probability distribution, which helps avoid the emergence of local optimal solutions.

[0046] The variational probability distribution is then optimized by maximizing the variational lower bound. To accomplish this optimization, an optimization algorithm such as gradient descent or natural gradient descent is used to adjust parameters such as the mean and covariance of the variational distribution so that the variational probability distribution gradually approaches the posterior probability distribution. During the optimization process, the variational probability distribution is continuously adjusted, and the likelihood expectation part is updated using real-time sensor data (such as vehicle speed, lateral acceleration, and steering angle). At the same time, the entropy part is optimized to enhance the diversity and robustness of the distribution. Through multiple iterations, the similarity between the variational probability distribution and the posterior probability distribution is greater than the preset similarity. For example, if the goal is to make the obstacle avoidance path with a turning radius of 10 meters highly consistent with the optimal path in reality, then after optimization, the variational probability distribution will approach the actual situation.

[0047] When the optimization is completed, the current variational probability distribution is regarded as the final flexible probability distribution. This flexible probability distribution contains the optimal distribution of path planning and control parameters, which can effectively reflect the dynamic behavior of the vehicle in a complex environment. At this point, when performing obstacle avoidance, the vehicle can generate a safe and efficient obstacle avoidance planning path and control parameters based on the flexible probability distribution.

[0048] Output the flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters with flexible probability distribution. Generate the flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters of the vehicle through flexible probability distribution. For example, suppose the vehicle is facing an obstacle and has a high risk of rollover. In this case, the optimal obstacle avoidance path and control strategy are calculated based on the flexible probability distribution. For example, in front of an obstacle, you may choose to avoid it by the right side, and at the same time, according to factors such as turning radius, vehicle speed, and ground friction, generate a suitable steering angle (e.g. 20 degrees), braking force (e.g. 50%), and acceleration control (e.g. reducing acceleration by 10%). Adjust the vehicle's motion behavior in real time according to these flexible control parameters to ensure that the obstacle avoidance process is both safe and smooth, and to avoid rollover or other unstable behaviors.

[0049] Ultimately, the vehicle executes these flexible obstacle avoidance control instructions through the unmanned vehicle control terminal and adjusts its motion trajectory in real time to ensure that the vehicle can safely avoid obstacles and maintain stability.

[0050] Furthermore, in the method provided in the embodiment of the application, the setting of the variational lower bound further includes: According to the flexible control module, a high-dimensional mapping is performed on the real-time state of the current vehicle to output a high-dimensional state space; based on the high-dimensional state space, the joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameters is updated to optimize the set variational lower bound.

[0051] In the embodiment of the present application, the flexible control module first maps the vehicle's real-time state to a multi-dimensional state space by high-dimensional mapping, and maps the vehicle's state data (such as vehicle speed, lateral acceleration, steering angle, etc.) and surrounding environment data (such as obstacle position, ground friction coefficient, etc.). This step is achieved through sensor data (such as lidar, IMU sensor, camera, etc.), ensuring that the vehicle's current motion state and various dynamic factors of the external environment can be captured, generating a high-dimensional state space that contains all key factors related to obstacle avoidance decisions.

[0052] Based on the obtained high-dimensional state space, the joint probability distribution of the obstacle avoidance planning path and the obstacle avoidance control parameters is then updated. Through the Bayesian inference method, each state in the high-dimensional state space is used to calculate the joint probability distribution of the obstacle avoidance path and the control parameters, and the obstacle avoidance strategy is updated. This process adjusts the obstacle avoidance path through real-time sensor data and environmental information to ensure that the vehicle can flexibly respond to obstacles in a dynamic environment, while optimizing the control parameters to maintain the stability of the vehicle.

[0053] Finally, variational inference is used to optimize the set variational lower bound. The variational lower bound is an objective function used to measure the difference between the variational distribution and the true posterior distribution. It contains two parts: the log-likelihood expectation and the entropy of the variational probability distribution. By adjusting the parameters of the variational distribution (such as the mean and covariance matrix), the variational lower bound is continuously maximized to optimize the choice of obstacle avoidance path and control parameters. Finally, the setting of the variational lower bound is completed, which provides an optimized theoretical basis for the subsequent path planning and control parameter generation. Furthermore, the method provided in the application embodiment also includes: The flexible control module also includes a stop interval control module; after outputting the flexible obstacle avoidance control parameter, the flexible obstacle avoidance control parameter is subjected to stop interval processing according to a preset interval of the stop interval control module to obtain the processed flexible obstacle avoidance control parameter.

[0054] In an embodiment of the present application, the flexible control module also includes a stop interval control module. The function of this module is to smoothly adjust the flexible obstacle avoidance control parameters through preset time intervals after generating these parameters. Specifically, after the obstacle avoidance control parameters of the vehicle (such as steering angle, braking force or acceleration) are calculated by the flexible control module, the stop interval control module processes these control parameters according to the preset intervals. For example, assuming that the steering angle needs to be adjusted from 0 degrees to 30 degrees, the stop interval control module will not immediately make the steering angle jump to 30 degrees, but will gradually complete the change of the steering angle according to the set interval (for example, adjusting 5 degrees every 100 milliseconds). This not only prevents the vehicle from making violent movements in a short period of time, but also effectively reduces the impact of dynamic adjustment on vehicle stability and reduces the risk of rollover or loss of control due to violent operations.

[0055] Through this point-stop interval processing, the flexible control module optimizes each flexible obstacle avoidance control parameter (such as braking force and acceleration) in stages, making the vehicle's obstacle avoidance operation smoother and safer. Finally, the point-stop interval control module outputs the processed flexible obstacle avoidance control parameters, which are smoothly adjusted to ensure that the vehicle maintains stability during obstacle avoidance in a dynamic environment, reduce the risks caused by sudden changes, and provide safe and reliable control input for subsequent control execution.

[0056] Furthermore, in the method provided in the embodiment of the application, after outputting the obstacle avoidance planning path and the obstacle avoidance control parameters, the method further includes: A path response length analysis is performed on the obstacle avoidance planning path. If the current path response length is less than a preset path response length, the flexible control module performs full-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs full-stage flexible obstacle avoidance control parameters. If the path response length is greater than or equal to the preset path response length, the obstacle avoidance planning path is segmented to obtain multiple segments of obstacle avoidance planning paths. The flexible control module performs multi-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs stage-by-stage flexible obstacle avoidance control parameters.

[0057] In the embodiment of the present application, the path response length of the obstacle avoidance planning path is first analyzed. Specifically, the path response length of the obstacle avoidance planning path is first determined, which refers to the time or distance required for the vehicle to execute the obstacle avoidance path from the current state. Different flexible control strategies are adopted based on the comparison between the path response length and the preset path response length.

[0058] When the path response length is less than the preset path response length, it indicates that the vehicle can complete the obstacle avoidance operation in a shorter time. In this case, the same method as above is used to perform full-stage flexible control on the entire obstacle avoidance path and control parameters. Specifically, the variational probability distribution is first initialized, and the variational lower bound is set as the optimization target, and the joint probability distribution of the obstacle avoidance path and control parameters is calculated. By maximizing the variational lower bound, the parameters of the variational probability distribution are optimized so that they gradually approach the true posterior probability distribution. This process ensures that all control parameters of the entire obstacle avoidance path can be globally optimized to meet the needs of rapid obstacle avoidance on shorter paths. Finally, the full-stage-flexible obstacle avoidance control parameters are output, which can ensure that the vehicle completes the obstacle avoidance operation smoothly and avoids obstacles in a shorter time.

[0059] When the path response length is greater than or equal to the preset path response length, it means that the obstacle avoidance path is longer and the vehicle needs more time or a greater distance to complete the obstacle avoidance. In this case, the obstacle avoidance path is segmented, and the long path is divided into multiple smaller segments, and the length or time of each path is roughly equal. This process divides the path into multiple segments so that each path can be optimized independently, thereby improving the accuracy and stability of the control. Next, the obstacle avoidance planning path and the obstacle avoidance control parameters are subjected to multi-stage flexible control, that is, each path segment is flexibly controlled separately, and the control parameters of each path segment (such as steering angle, acceleration, braking force, etc.) are optimized independently. Through the variational inference method, the optimal control parameters are calculated for each path segment to ensure that the vehicle can smoothly avoid obstacles at each stage and maintain good driving stability. Finally, the staged-flexible obstacle avoidance control parameters are output, which can independently optimize the control behavior in each path segment to ensure the stable obstacle avoidance of the vehicle in a complex environment.

[0060] Through path response length analysis, the appropriate control strategy is selected according to different path complexities. In the case of a short path, full-stage flexible control is used to optimize the control parameters of the entire path; in the case of a long path, segmented processing is adopted, and multi-stage flexible control is used to optimize the control parameters of each path segment. Ultimately, the vehicle can avoid obstacles smoothly and safely in different obstacle avoidance scenarios through these optimized control parameters, ensuring that the obstacle avoidance process is both efficient and stable.

[0061] Furthermore, in the method provided in the embodiment of the application, after the obstacle avoidance planning path is segmented to obtain multiple obstacle avoidance planning paths, the method further includes: The rollover risk identification module identifies the rollover risk of each obstacle avoidance planning path in the multiple obstacle avoidance planning paths according to the real-time status of the current vehicle, and outputs multiple rollover risk level labels corresponding to the multiple obstacle avoidance planning paths; identifies the obstacle avoidance planning paths whose multiple rollover risk level labels are greater than the preset rollover risk level; and performs separate flexible control on each identified obstacle avoidance planning path according to the flexible control module.

[0062] In an embodiment of the present application, after the obstacle avoidance planning path is segmented to obtain multiple obstacle avoidance planning paths, the rollover risk identification module performs rollover risk identification on each obstacle avoidance planning path in the multiple obstacle avoidance planning paths according to the real-time status of the current vehicle. Specifically, the rollover risk identification module performs rollover risk identification on each obstacle avoidance planning path in the multiple obstacle avoidance planning paths according to the real-time status of the current vehicle through an embedded classification recognition model, and obtains multiple rollover risk level labels corresponding to the multiple obstacle avoidance planning paths.

[0063] Next, the multiple rollover risk level labels corresponding to the obtained multiple obstacle avoidance planning paths are compared with the preset rollover risk level, and the obstacle avoidance planning paths with a rollover risk level greater than the preset rollover risk level among the multiple rollover risk level labels are identified.

[0064] Finally, the flexible control module performs individual flexible control on each marked obstacle avoidance planning path using the same method as described above, ensuring that the vehicle can avoid obstacles smoothly and safely during the obstacle avoidance process, and avoiding rollover or other unsafe behaviors caused by the complexity or instability of the path.

[0065] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application is connected to an unmanned vehicle control terminal of the current vehicle; the unmanned vehicle control terminal receives an obstacle avoidance planning path and obstacle avoidance control parameters output by an emergency obstacle avoidance control module, the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; the obstacle avoidance planning path and the obstacle avoidance control parameters are input into a rollover risk identification module, the rollover risk identification module performs rollover risk identification according to the real-time status of the current vehicle, and outputs a rollover risk level label; when the rollover risk level label is greater than a preset rollover risk level, a flexible control instruction is activated; according to the flexible control instruction, a flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are output, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters. The present invention solves the technical problem in the prior art that the vehicle may roll over or become unstable during an emergency obstacle avoidance process. By connecting to the unmanned vehicle control terminal of the current vehicle, receiving the obstacle avoidance planning path and obstacle avoidance control parameters, the rollover risk of the path is evaluated and flexible control is performed, so as to achieve the technical effect of ensuring that the vehicle can smoothly and safely avoid obstacles and effectively prevent rollover during the obstacle avoidance process.

[0066] Embodiment 2, based on the same inventive concept as the emergency obstacle avoidance control method for the unmanned vehicle in the above embodiment, Figure 2 As shown, the present application provides an emergency obstacle avoidance control system for an unmanned vehicle, and the system and method embodiments in the present application are based on the same inventive concept. The system includes: The terminal connection unit 11 is used to access the unmanned vehicle control terminal of the current vehicle; the data receiving unit 12 is used for the unmanned vehicle control terminal to receive the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module, and the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; the rollover risk identification unit 13 is used to input the obstacle avoidance planning path and the obstacle avoidance control parameters into the rollover risk identification module, and the rollover risk identification module performs rollover risk identification according to the real-time state of the current vehicle and outputs a rollover risk level label; the control instruction activation unit 14 is used to activate the flexible control instruction when the rollover risk level label is greater than the preset rollover risk level; the obstacle avoidance control unit 15 is used to drive the flexible control module connected to the emergency obstacle avoidance control module to perform flexible control according to the flexible control instruction, output the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

[0067] Furthermore, the system is also used to implement the following functions: A path response length analysis is performed on the obstacle avoidance planning path. If the current path response length is less than a preset path response length, the flexible control module performs full-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs full-stage flexible obstacle avoidance control parameters. If the path response length is greater than or equal to the preset path response length, the obstacle avoidance planning path is segmented to obtain multiple segments of obstacle avoidance planning paths. The flexible control module performs multi-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs stage-by-stage flexible obstacle avoidance control parameters.

[0068] Furthermore, the system is also used to implement the following functions: The rollover risk identification module identifies the rollover risk of each obstacle avoidance planning path in the multiple obstacle avoidance planning paths according to the real-time status of the current vehicle, and outputs multiple rollover risk level labels corresponding to the multiple obstacle avoidance planning paths; identifies the obstacle avoidance planning paths whose multiple rollover risk level labels are greater than the preset rollover risk level; and performs separate flexible control on each identified obstacle avoidance planning path according to the flexible control module.

[0069] Furthermore, the system is also used to implement the following functions: ; in, is the rollover risk level of the current input path x, n is the sample data size, is the Lagrange multiplier in the support vector machine, indicating that each training sample The weight of For training samples The real rollover risk level label, It is used to calculate the input sample x and the training sample The kernel function of the similarity between them; , Used to measure input samples x and training samples The difference between is the bandwidth parameter of the kernel function, which controls the influence range of the similarity metric. A bias term for adjusting the classification boundary position.

[0070] Furthermore, the system is also used to implement the following functions: Construct a vehicle dynamics model; define path segment feature samples, vehicle center of gravity structure samples and vehicle real-time status samples, wherein the path segment feature samples include path length, turning radius and ground friction coefficient, the vehicle center of gravity structure samples are vehicle geometry data and center of gravity height, and the vehicle real-time status samples include lateral acceleration, vehicle speed and steering angle; the vehicle dynamics model simulates the path segment feature samples, vehicle center of gravity structure samples and vehicle real-time status samples, and outputs a simulation data set and a rollover risk level label corresponding to the simulation data set; outputs a training sample according to the simulation data set and the rollover risk level label corresponding to the simulation data set.

[0071] Furthermore, the system is also used to implement the following functions: Initialize the variational probability distribution; set a variational lower bound, the variational lower bound includes a log-likelihood expectation and an entropy of the variational probability distribution, wherein the log-likelihood expectation is a joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameters obtained by integrating the variational probability distribution; adjust the parameters of the initialized variational probability distribution by maximizing the variational lower bound until the similarity between the initialized variational probability distribution and the posterior probability distribution is greater than a preset similarity, and output the current variational probability distribution as a flexible probability distribution, wherein the posterior probability distribution is a preset true probability distribution; output a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters using the flexible probability distribution.

[0072] Furthermore, the system is also used to implement the following functions: According to the flexible control module, a high-dimensional mapping is performed on the real-time state of the current vehicle to output a high-dimensional state space; based on the high-dimensional state space, the joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameters is updated to optimize the set variational lower bound.

[0073] Furthermore, the system is also used to implement the following functions: The flexible control module also includes a stop interval control module; after outputting the flexible obstacle avoidance control parameter, the flexible obstacle avoidance control parameter is subjected to stop interval processing according to a preset interval of the stop interval control module to obtain the processed flexible obstacle avoidance control parameter.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0076] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An emergency obstacle avoidance control method for an unmanned vehicle, characterized in that: The method comprises: Access the unmanned vehicle control terminal of the current vehicle; The unmanned vehicle control terminal receives the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module, wherein the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; The obstacle avoidance planning path and the obstacle avoidance control parameters are input into a rollover risk identification module, and the rollover risk identification module performs rollover risk identification according to the real-time state of the current vehicle and outputs a rollover risk level label; When the rollover risk level label is greater than a preset rollover risk level, activating a flexible control instruction; According to the flexible control instruction, the flexible control module connected to the emergency obstacle avoidance control module is driven to perform flexible control, and a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are output. The unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

2. The method for controlling an unmanned vehicle to avoid an obstacle in an emergency according to claim 1, wherein: After outputting the obstacle avoidance planning path and obstacle avoidance control parameters, the method further includes: Performing a path response length analysis on the obstacle avoidance planning path, if the current path response length is less than a preset path response length, the flexible control module performs full-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs full-stage flexible obstacle avoidance control parameters; If the path response length is greater than or equal to the preset path response length, the obstacle avoidance planning path is segmented to obtain multiple obstacle avoidance planning paths, and the flexible control module performs multi-stage flexible control on the obstacle avoidance planning path and the obstacle avoidance control parameters, and outputs the staged-flexible obstacle avoidance control parameters.

3. The method for controlling an unmanned vehicle to avoid obstacles in an emergency as claimed in claim 2, characterized in that: After the obstacle avoidance planning path is segmented to obtain multiple obstacle avoidance planning paths, the method includes: The rollover risk identification module performs rollover risk identification on each of the multiple obstacle avoidance planning paths according to the real-time status of the current vehicle, and outputs multiple rollover risk level labels corresponding to the multiple obstacle avoidance planning paths; Identifying obstacle avoidance planning paths whose rollover risk level labels are greater than the preset rollover risk level; Each marked obstacle avoidance planning path is independently and flexibly controlled according to the flexible control module.

4. The method for emergency obstacle avoidance control of an unmanned vehicle according to claim 1, characterized in that: The rollover risk identification module is embedded with a classification recognition model obtained by decision boundary learning through support vector machine training, and the expression is as follows: ； in, is the rollover risk level of the current input path x, n is the sample data size, is the Lagrange multiplier in the support vector machine, indicating that each training sample The weight of For training samples The real rollover risk level label, It is used to calculate the input sample x and the training sample The kernel function of the similarity between them; , Used to measure input samples x and training samples The difference between is the bandwidth parameter of the kernel function, which controls the influence range of the similarity metric. A bias term for adjusting the classification boundary position.

5. The method for controlling an unmanned vehicle to avoid obstacles in an emergency according to claim 4, characterized in that: The training samples are obtained by simulating the vehicle dynamics model, and the method includes: Construct vehicle dynamics models; Define path segment feature samples, vehicle center of gravity structure samples and vehicle real-time status samples, wherein the path segment feature samples include path length, turning radius and ground friction coefficient, the vehicle center of gravity structure samples are vehicle geometry data and center of gravity height, and the vehicle real-time status samples include lateral acceleration, vehicle speed and steering angle; The vehicle dynamics model simulates the path segment feature samples, the vehicle center of gravity structure samples and the vehicle real-time state samples, and outputs a simulated data set and a rollover risk level label corresponding to the simulated data set; Output training samples according to the simulated data set and the rollover risk level labels corresponding to the simulated data set.

6. The method for controlling an unmanned vehicle to avoid obstacles in an emergency according to claim 1, wherein: The method includes: driving a flexible control module connected to the emergency obstacle avoidance control module to perform flexible control according to the flexible control instruction, and outputting a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters. Initialize the variational probability distribution; Setting a variational lower bound, the variational lower bound includes a log-likelihood expectation and an entropy of a variational probability distribution, wherein the log-likelihood expectation is a joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameter obtained by integrating the variational probability distribution; By maximizing the variational lower bound, the parameters of the initialized variational probability distribution are adjusted until the similarity between the initialized variational probability distribution and the posterior probability distribution is greater than a preset similarity, and the current variational probability distribution is output as a flexible probability distribution, wherein the posterior probability distribution is a preset true probability distribution; The flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters are outputted according to the flexible probability distribution.

7. The method for controlling an unmanned vehicle to avoid obstacles in an emergency according to claim 6, wherein: The method for setting the variational lower bound further includes: Performing high-dimensional mapping on the real-time state of the current vehicle according to the flexible control module, and outputting a high-dimensional state space; Based on the high-dimensional state space, the joint probability distribution corresponding to the obstacle avoidance planning path and the obstacle avoidance control parameter is updated to optimize the set variational lower bound.

8. The method for controlling an unmanned vehicle to avoid obstacles in an emergency according to claim 6, wherein: The flexible control module also includes a stop interval control module; After the flexible obstacle avoidance control parameter is output, the flexible obstacle avoidance control parameter is subjected to a point-stop interval processing according to a preset interval of the point-stop interval control module to obtain the processed flexible obstacle avoidance control parameter.

9. An emergency obstacle avoidance control system for an unmanned vehicle, characterized in that: The system comprises: A terminal connection unit, used to access the unmanned vehicle control terminal of the current vehicle; A data receiving unit, used for the unmanned vehicle control terminal to receive the obstacle avoidance planning path and obstacle avoidance control parameters output by the emergency obstacle avoidance control module, wherein the emergency obstacle avoidance control module includes an obstacle avoidance path planning module and an obstacle avoidance control analysis module; A rollover risk identification unit, used for inputting the obstacle avoidance planning path and the obstacle avoidance control parameters into a rollover risk identification module, wherein the rollover risk identification module performs rollover risk identification according to the real-time status of the current vehicle and outputs a rollover risk level label; A control instruction activation unit, used for activating a flexible control instruction when the rollover risk level label is greater than a preset rollover risk level; An obstacle avoidance control unit is used to drive the flexible control module connected to the emergency obstacle avoidance control module to perform flexible control according to the flexible control instruction, output a flexible obstacle avoidance planning path and flexible obstacle avoidance control parameters, and the unmanned vehicle control terminal performs flexible obstacle avoidance control on the current vehicle according to the flexible obstacle avoidance planning path and the flexible obstacle avoidance control parameters.

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