Vehicle obstacle avoidance control method based on active control of vehicle body posture and damping optimization

Abstract

The application provides a vehicle obstacle avoidance control method based on active control of a vehicle body posture and optimization of damping, comprising the following steps: step 1, collecting road environment data of a road where a current vehicle is located in real time, and analyzing the road environment data to obtain real-time road conditions; step 2, planning a safe obstacle avoidance path according to the real-time road conditions, determining a target vehicle body posture trajectory based on the safe obstacle avoidance path, and adaptively matching optimal damping parameters of a semi-active suspension according to the target vehicle body posture trajectory; and step 3, constructing a posture-damping cooperative obstacle avoidance strategy according to the adaptive matching result, and controlling the vehicle to perform an obstacle avoidance operation according to the posture-damping cooperative obstacle avoidance strategy. The optimal distribution of damping force is directly and intelligently driven, so that the intelligence, stability and safety of the controlled vehicle in obstacle avoidance are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle obstacle avoidance control technology, and in particular to a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. Background Technology

[0002] Currently, in the modern traffic environment, the number of vehicles on the road is constantly increasing, and traffic conditions are becoming increasingly complex. Many traffic accidents are caused by vehicles failing to avoid obstacles in a timely and effective manner during driving. For example, on urban roads, obstacles such as pedestrians, vehicles, or road construction areas that suddenly appear require vehicles to have the ability to avoid obstacles quickly and safely. Moreover, with the development of the automobile consumer market, consumers have increasingly higher requirements for driving comfort. During vehicle operation, uneven road surfaces (such as potholes and bumps) and vehicle obstacle avoidance maneuvers will cause vibrations and changes in vehicle body posture.

[0003] However, many existing vehicle obstacle avoidance technologies mainly focus on path planning, using sensors to detect obstacles and plan a driving path to avoid them; but they neglect the vehicle's body attitude control during the obstacle avoidance process; for example, when the vehicle suddenly turns to avoid an obstacle, the body may tilt significantly, which not only affects the vehicle's stability but may also cause the vehicle to deviate from the planned obstacle avoidance path; moreover, obstacle avoidance and damping control in existing technologies often cannot work together, which greatly reduces the safety and stability of the control process.

[0004] Therefore, in order to overcome the above-mentioned technical problems, the present invention provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. Summary of the Invention

[0005] This invention provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. By collecting road environment data, real-time road conditions can be effectively obtained, providing effective data reference for planning safe obstacle avoidance paths. The target vehicle posture trajectory is effectively determined through the safe obstacle avoidance path, thereby achieving effective matching of optimal damping parameters. The posture-damping cooperative obstacle avoidance strategy is effectively constructed based on the matching results, thereby achieving the optimal allocation of damping force to directly and intelligently drive the posture control target, which greatly improves the intelligence, stability and safety of vehicle obstacle avoidance control.

[0006] A vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization includes:

[0007] Step 1: Collect real-time road environment data of the road where the vehicle is currently located, and analyze the road environment data to obtain real-time road conditions;

[0008] Step 2: Plan a safe obstacle avoidance path based on real-time road conditions, determine the target vehicle body attitude trajectory based on the safe obstacle avoidance path, and adaptively match the optimal damping parameters of the semi-active suspension based on the target vehicle body attitude trajectory.

[0009] Step 3: Construct an attitude-damping cooperative obstacle avoidance strategy based on the adaptive matching results, and control the vehicle to perform obstacle avoidance operations based on the attitude-damping cooperative obstacle avoidance strategy.

[0010] Preferably, a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization includes, in step 1, real-time acquisition of road environment data of the road where the vehicle is currently located, and analysis of the road environment data to obtain real-time road conditions, including:

[0011] Acquire multimodal acquisition devices and collect sub-road environment datasets corresponding to each acquisition device on the road where the vehicle is currently located;

[0012] The sub-road environment datasets corresponding to each acquisition device are sorted according to time order, and the sub-road environment datasets are aligned in the time dimension according to the sorting results.

[0013] Based on the alignment results, the sub-road environment datasets are read and analyzed to determine the sub-road environment characteristics of each sub-road environment data output.

[0014] By integrating the characteristics of the sub-road environment, real-time traffic conditions can be obtained.

[0015] Preferably, a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization reads and analyzes the sub-road environment datasets according to the alignment results, and determines the sub-road environment characteristics of the output sub-road environment data, including:

[0016] Obtain the data type corresponding to the environment dataset of each sub-road;

[0017] Input the data type into the preset analysis mode library for matching to determine the target data analysis method corresponding to each sub-road environment dataset;

[0018] The corresponding sub-road environment dataset is analyzed according to the target data analysis method, and the corresponding sub-road environment characteristics are output based on the analysis results.

[0019] Preferably, a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization includes, in step 2, planning a safe obstacle avoidance path according to real-time road conditions, including:

[0020] Read real-time traffic conditions and determine the distribution characteristics of road obstacles, the characteristic attributes of road obstacles, and road characteristic attributes based on the real-time traffic conditions;

[0021] The characteristic attributes of obstacles include: dynamic obstacles and static obstacles;

[0022] The real-time location trajectory data of dynamic obstacles is determined based on the distribution characteristics of road obstacles. At the same time, the location data of static obstacles is determined based on the distribution characteristics of road obstacles.

[0023] Determine the first real-time path of the vehicle based on road feature attributes;

[0024] The first real-time path is segmented based on the real-time position trajectory data of dynamic obstacles to obtain a first local obstacle avoidance path set. At the same time, the first real-time path is segmented based on the real-time position trajectory of static obstacles to obtain a second local obstacle avoidance path set.

[0025] The first local obstacle avoidance path set is matched with the second local obstacle avoidance path set, and the first local obstacle avoidance path set and the second local obstacle avoidance path set are fused according to the matching result. At the same time, the first fusion result is fused with the first real-time path to obtain the second real-time path.

[0026] The second real-time path is used as the planning result for the safe obstacle avoidance path.

[0027] Preferably, a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization includes: firstly fusing a first local obstacle avoidance path set and a second local obstacle avoidance path set according to the matching result; and secondly fusing the first fusion result with a first real-time path to obtain a second real-time path, comprising:

[0028] Obtain similar paths between the first obstacle avoidance path set and the second obstacle avoidance path set, remove similar paths from either the first obstacle avoidance path set or the second obstacle avoidance path set, and associate the first obstacle avoidance path set and the second obstacle avoidance path set based on the removal results to complete the first fusion of the first obstacle avoidance path set and the second obstacle avoidance path set.

[0029] The first real-time path is matched with the first fusion result to determine the path segments that do not intersect with the first real-time path and the first fusion result; the path segments that do not intersect are supplemented in the first fusion result, and the second real-time path is obtained based on the supplemented result.

[0030] Preferably, in a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization, step 2, determining the target vehicle posture trajectory based on a safe obstacle avoidance path, includes:

[0031] Read the safe obstacle avoidance path and determine the path feature set of the safe obstacle avoidance path;

[0032] Read the vehicle model of the current vehicle and input the vehicle model of the current vehicle into the preset model library for comparison to determine the vehicle shape characteristics of the current vehicle;

[0033] The path feature set of the safe obstacle avoidance path and the vehicle shape features of the current vehicle are input into the preset historical vehicle posture library for matching.

[0034] Output the target vehicle body posture set based on the matching results;

[0035] The safe obstacle avoidance path is associated with and mapped to the target vehicle body posture set to generate the target vehicle body posture trajectory.

[0036] Preferably, a vehicle obstacle avoidance control method based on active vehicle attitude control and damping optimization associates and maps a safe obstacle avoidance path with a set of target vehicle attitudes to generate a target vehicle attitude trajectory, including:

[0037] A first simulation is performed on a preset computer based on the vehicle model and vehicle shape to obtain a simulated vehicle. At the same time, a second simulation is performed on a preset computer based on the path feature set to obtain a simulated path.

[0038] The simulated vehicle runs along the simulated path according to the target vehicle body posture set. At the same time, the correspondence between the path feature set and the target vehicle body posture set is recorded based on the simulation results.

[0039] Based on the correspondence, the safe obstacle avoidance path is associated and mapped with the target vehicle body posture set to generate the target vehicle body posture trajectory.

[0040] Preferably, a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization includes, in step 2, adaptively matching the optimal damping parameters of the semi-active suspension according to the target vehicle posture trajectory, including:

[0041] The target vehicle body attitude trajectory is obtained and then decomposed to obtain the target vehicle body attitude in each time frame.

[0042] The target vehicle posture at each time frame is analyzed to obtain the corresponding steering angle and the corresponding vehicle roll angle. At the same time, the real-time road conditions are analyzed to obtain the road surface friction coefficient of the road where the vehicle is currently located.

[0043] Based on safe driving standards, the steering angle and vehicle roll angle are analyzed to determine the probability of wheel liftoff risk of the current vehicle under the target body posture. Combined with the road friction coefficient, the optimal balance damping parameters are determined when the current vehicle is driving stably under the probability of wheel liftoff risk.

[0044] The optimal balance damping parameters corresponding to the target vehicle body posture in each time frame are summarized, and the optimal damping parameters of the semi-active suspension are adaptively matched based on the summary results.

[0045] Preferably, a vehicle obstacle avoidance control method based on active vehicle attitude control and damping optimization includes, in step 3, constructing an attitude-damping cooperative obstacle avoidance strategy based on the adaptive matching results, including:

[0046] Read the adaptive matching results and determine the optimal damping parameters of several vehicle postures and matching in the target vehicle posture trajectory of the adaptive matching results;

[0047] Several attitude-optimal damping parameter data sets are constructed based on the vehicle body attitude and the matched optimal damping parameters;

[0048] The vehicle body attitude and optimal damping parameter data sets are learned, and the cooperative relationship between vehicle body attitude and optimal damping parameters is determined based on the learning results.

[0049] Construct a posture-damping cooperative obstacle avoidance strategy based on the cooperative relationship.

[0050] Preferably, a vehicle obstacle avoidance control method based on active vehicle attitude control and damping optimization controls the vehicle to perform obstacle avoidance operations according to an attitude-damping cooperative obstacle avoidance strategy, including:

[0051] Based on the attitude-damping cooperative obstacle avoidance strategy, a set of control instructions for vehicle obstacle avoidance control is generated, and the real-time vehicle attitude data of the road where the vehicle is located is monitored in real time.

[0052] The command triggering conditions for real-time vehicle attitude data are determined based on the attitude-damping cooperative obstacle avoidance strategy. When the command triggering conditions are met, the corresponding control command is initiated to control the current vehicle to perform obstacle avoidance operations.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] By collecting road environment data, real-time road conditions can be effectively obtained, thus providing effective data reference for planning safe obstacle avoidance paths. The target vehicle's attitude trajectory can be effectively determined through the safe obstacle avoidance path, thereby achieving effective matching of optimal damping parameters. The matching results can be used to effectively construct an attitude-damping collaborative obstacle avoidance strategy, thereby achieving the optimal distribution of damping force to directly and intelligently drive the attitude control target, which greatly improves the intelligence, stability and safety of vehicle obstacle avoidance.

[0055] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0056] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 This is a flowchart of a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization in an embodiment of the present invention.

[0059] Figure 2 This is a flowchart of step 1 in a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to Embodiment 2 of the present invention;

[0060] Figure 3 This is a flowchart of step 2 in a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to Embodiment 6 of the present invention. Detailed Implementation

[0061] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0062] Example 1:

[0063] This embodiment provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization, such as... Figure 1 As shown, it includes:

[0064] Step 1: Collect real-time road environment data of the road where the vehicle is currently located, and analyze the road environment data to obtain real-time road conditions;

[0065] Step 2: Plan a safe obstacle avoidance path based on real-time road conditions, determine the target vehicle body attitude trajectory based on the safe obstacle avoidance path, and adaptively match the optimal damping parameters of the semi-active suspension based on the target vehicle body attitude trajectory.

[0066] Step 3: Construct an attitude-damping cooperative obstacle avoidance strategy based on the adaptive matching results, and control the vehicle to perform obstacle avoidance operations based on the attitude-damping cooperative obstacle avoidance strategy.

[0067] In this embodiment, the road environment data may include, but is not limited to, obstacles, puddles, road unevenness, and real-time vehicle density data. The data can be collected using methods such as cameras and millimeter-wave radar.

[0068] In this embodiment, real-time traffic conditions refer to the road path of the current vehicle, the location information of dynamic obstacles, and the location information of static obstacles.

[0069] In this embodiment, the target vehicle body posture is determined based on the safe obstacle avoidance path, which is the tilt, yaw, and other postures of the vehicle body determined according to the safe obstacle avoidance path.

[0070] In this embodiment, the optimal damping parameters of the semi-active suspension are adaptively matched according to the target vehicle body attitude trajectory. The semi-active suspension system can adjust the vehicle's suspension characteristics according to different damping parameters. For example, after the target vehicle body attitude trajectory is determined, the system will calculate the most suitable damping parameters through an algorithm based on factors such as vehicle speed, steering angle, and road surface excitation, so as to achieve effective control of the vehicle body attitude. For example, when the vehicle is making an emergency turn to avoid an obstacle, a larger damping is required to suppress the body roll.

[0071] The working principle and beneficial effects of the above technical solution are as follows: by collecting road environment data, real-time road conditions can be effectively obtained, thus providing effective data reference for planning safe obstacle avoidance paths. The target vehicle attitude trajectory can be effectively determined through the safe obstacle avoidance path, thereby achieving effective matching of optimal damping parameters. The attitude-damping cooperative obstacle avoidance strategy can be effectively constructed through the matching results, thereby achieving the optimal distribution of damping force by directly and intelligently driving the attitude control target, which greatly improves the intelligence, stability and safety of vehicle obstacle avoidance.

[0072] Example 2:

[0073] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization, such as... Figure 2 As shown, in step 1, real-time road environment data of the road where the vehicle is currently located is collected and analyzed to obtain real-time road conditions, including:

[0074] S101: Acquire multimodal acquisition devices and collect the sub-road environment datasets corresponding to each acquisition device in the road where the current vehicle is located;

[0075] S102: Sort the sub-road environment datasets corresponding to each acquisition device according to the time order, and align the sub-road environment datasets in the time dimension according to the sorting results;

[0076] S103: Based on the alignment results, read and analyze each sub-road environment dataset to determine the sub-road environment characteristics of each sub-road environment data output;

[0077] S104: Integrate the environmental characteristics of sub-roads to obtain real-time traffic conditions.

[0078] In this embodiment, multimodal acquisition devices refer to data acquisition devices corresponding to different data types, including cameras and millimeter radar devices.

[0079] In this embodiment, the sub-road environment dataset refers to the collection of road environment data collected by different acquisition devices.

[0080] In this embodiment, aligning the sub-road environment datasets in the time dimension according to the sorting results means aligning different sub-road environment datasets according to the collection time, that is, summarizing and associating sub-road environment data of different categories at the same time.

[0081] In this embodiment, the sub-road environment features refer to the specific road conditions corresponding to different sub-road environment data, such as the width of the road and the distribution of obstacles in the road.

[0082] The beneficial effects of the above technical solution are: by collecting sub-road environment datasets in the road through multimodal acquisition equipment, and processing and analyzing the collected sub-road environment datasets, the corresponding sub-road environment characteristics can be determined, and then the sub-road environment characteristics can be integrated to achieve accurate and effective determination of the real-time road conditions of the current road, providing reliable data support for vehicle obstacle avoidance control.

[0083] Example 3:

[0084] Building upon Example 2, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. It analyzes and reads the data sets of each sub-road environment according to the alignment results, determining the sub-road environment characteristics output by each sub-road environment data set, including:

[0085] Obtain the data type corresponding to the environment dataset of each sub-road;

[0086] Input the data type into the preset analysis mode library for matching to determine the target data analysis method corresponding to each sub-road environment dataset;

[0087] The corresponding sub-road environment dataset is analyzed according to the target data analysis method, and the corresponding sub-road environment characteristics are output based on the analysis results.

[0088] In this embodiment, the data type refers to the data modality corresponding to the sub-road environment dataset, such as image data and digital signal data.

[0089] In this embodiment, the preset analysis mode library is pre-set and contains a variety of different types of data analysis methods.

[0090] In this embodiment, the target data analysis method refers to the data analysis method applicable to the current sub-road environment data, and is a component in the preset analysis mode library.

[0091] The beneficial effects of the above technical solution are: by determining the data type corresponding to each sub-road environment dataset, the preset analysis mode library can be called according to the data type for analysis, thereby achieving accurate and effective determination of the sub-road environment characteristics, ensuring the accuracy and reliability of the final real-time road conditions, and also providing convenience for vehicle obstacle avoidance control.

[0092] Example 4:

[0093] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. In step 2, a safe obstacle avoidance path is planned according to real-time road conditions, including:

[0094] Read real-time traffic conditions and determine the distribution characteristics of road obstacles, the characteristic attributes of road obstacles, and road characteristic attributes based on the real-time traffic conditions;

[0095] The characteristic attributes of obstacles include: dynamic obstacles and static obstacles;

[0096] The real-time location trajectory data of dynamic obstacles is determined based on the distribution characteristics of road obstacles. At the same time, the location data of static obstacles is determined based on the distribution characteristics of road obstacles.

[0097] Determine the first real-time path of the vehicle based on road feature attributes;

[0098] The first real-time path is segmented based on the real-time position trajectory data of dynamic obstacles to obtain a first local obstacle avoidance path set. At the same time, the first real-time path is segmented based on the real-time position trajectory of static obstacles to obtain a second local obstacle avoidance path set.

[0099] The first local obstacle avoidance path set is matched with the second local obstacle avoidance path set, and the first local obstacle avoidance path set and the second local obstacle avoidance path set are fused according to the matching result. At the same time, the first fusion result is fused with the first real-time path to obtain the second real-time path.

[0100] The second real-time path is used as the planning result for the safe obstacle avoidance path.

[0101] In this embodiment, road characteristic attributes refer to the width of the road, the degree of curvature of the road, or the shape of the road, etc.

[0102] In this embodiment, real-time location trajectory data refers to the actual location data of a dynamic obstacle at different times.

[0103] In this embodiment, the first real-time path refers to the driving path corresponding to the current vehicle when it is driving on the road.

[0104] In this embodiment, the first segmentation operation refers to dividing the first real-time path using the real-time position trajectory data of dynamic obstacles.

[0105] In this embodiment, the first local obstacle avoidance path set refers to the driving path obtained by dividing the first real-time path through the real-time position trajectory data of the dynamic obstacle, which can avoid the dynamic obstacle at different times.

[0106] In this embodiment, the second segmentation operation refers to dividing the first real-time path by the real-time position trajectory of static obstacles.

[0107] In this embodiment, the second local obstacle avoidance path set refers to the driving path obtained by dividing the first real-time path through the real-time position trajectory data of static obstacles, which can avoid static obstacles at different times.

[0108] In this embodiment, the first fusion refers to associating the first local obstacle avoidance path set with the second local obstacle avoidance path set to obtain a complete obstacle avoidance path.

[0109] In this embodiment, the second fusion refers to associating the first fusion result with the first real-time path. In this embodiment, the second real-time path refers to the driving path of the vehicle on the road, enabling it to avoid both static and dynamic obstacles.

[0110] The beneficial effects of the above technical solution are as follows: By analyzing real-time road conditions, the distribution characteristics of road obstacles, the characteristic attributes of road obstacles, and the characteristic attributes of roads can be effectively determined, thereby effectively determining the location of obstacles. Secondly, the first real-time path of the current vehicle is determined based on the road characteristic attributes, and the first real-time path is analyzed based on the location of obstacles, thereby determining the obstacle avoidance path. This ensures the safety and reliability of the final safe obstacle avoidance path, thereby ensuring the accuracy of vehicle obstacle avoidance control and ensuring vehicle driving safety.

[0111] Example 5:

[0112] Based on Example 4, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. According to the matching result, a first local obstacle avoidance path set and a second local obstacle avoidance path set are first fused. Simultaneously, the first fusion result is second fused with a first real-time path to obtain a second real-time path, including:

[0113] Obtain similar paths between the first obstacle avoidance path set and the second obstacle avoidance path set, remove similar paths from either the first obstacle avoidance path set or the second obstacle avoidance path set, and associate the first obstacle avoidance path set and the second obstacle avoidance path set based on the removal results to complete the first fusion of the first obstacle avoidance path set and the second obstacle avoidance path set.

[0114] The first real-time path is matched with the first fusion result to determine the path segments that do not intersect with the first real-time path and the first fusion result; the path segments that do not intersect are supplemented in the first fusion result, and the second real-time path is obtained based on the supplemented result.

[0115] The beneficial effects of the above technical solution are as follows: by selectively eliminating similar paths from the first obstacle avoidance path set and the second obstacle avoidance path set, and then associating the first obstacle avoidance path set and the second obstacle avoidance path set based on the elimination result, the fusion effect of the first obstacle avoidance path set and the second obstacle avoidance path set is ensured, and duplicate fusion is avoided. At the same time, by parsing the fusion result through the first real-time path, the fusion result of the first obstacle avoidance path set and the second obstacle avoidance path set is effectively supplemented, thereby enabling the effective determination of the second real-time path.

[0116] Example 6:

[0117] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization, such as... Figure 3 As shown, in step 2, determining the target vehicle's attitude trajectory based on the safe obstacle avoidance path includes:

[0118] S201: Read the safe obstacle avoidance path and determine the path feature set of the safe obstacle avoidance path;

[0119] S202: Read the vehicle model of the current vehicle and input the vehicle model of the current vehicle into the preset model library for comparison to determine the vehicle shape characteristics of the current vehicle;

[0120] S203: Input the path feature set of the safe obstacle avoidance path and the vehicle shape features of the current vehicle into the preset historical vehicle posture library for matching;

[0121] S204: Output the target vehicle body posture set based on the matching results;

[0122] S205: Associate and map the safe obstacle avoidance path with the target vehicle body posture set to generate the target vehicle body posture trajectory.

[0123] In this embodiment, the path feature set refers to the key location points corresponding to the vehicle when it is driving to avoid obstacles, which are determined after reading the safe obstacle avoidance path.

[0124] In this embodiment, the preset model library is pre-set and used to store vehicle shape features corresponding to different vehicle models. The vehicle shape features include the vehicle's exterior outline, etc.

[0125] In this embodiment, the preset historical vehicle posture library is pre-set and used to store the vehicle postures of different models of vehicles under the corresponding safe obstacle avoidance paths.

[0126] In this embodiment, the target vehicle posture set refers to the vehicle posture that the current vehicle will exhibit under the current safe obstacle avoidance path.

[0127] The working principle and beneficial effects of the above technical solution are as follows: by determining the path feature set of the safe obstacle avoidance path and matching the current vehicle shape features with the historical vehicle body posture database, the target vehicle body posture set of the current vehicle can be accurately determined. Then, through correlation mapping, the target vehicle body posture trajectory can be effectively obtained, which effectively ensures the accuracy, objectivity and effectiveness of obtaining the target vehicle body posture trajectory, thereby providing reliable data support for determining the optimal damping parameters.

[0128] Example 7:

[0129] Building upon Example 6, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. It associates and maps safe obstacle avoidance paths with a set of target vehicle postures to generate a target vehicle posture trajectory, including:

[0130] A first simulation is performed on a preset computer based on the vehicle model and vehicle shape to obtain a simulated vehicle. At the same time, a second simulation is performed on a preset computer based on the path feature set to obtain a simulated path.

[0131] The simulated vehicle runs along the simulated path according to the target vehicle body posture set. At the same time, the correspondence between the path feature set and the target vehicle body posture set is recorded based on the simulation results.

[0132] Based on the correspondence, the safe obstacle avoidance path is associated and mapped with the target vehicle body posture set to generate the target vehicle body posture trajectory.

[0133] In this embodiment, the preset computer is pre-configured.

[0134] In this embodiment, the first simulation refers to simulating the vehicle state in a computer based on the vehicle model and vehicle shape state. The simulated vehicle is the virtual vehicle obtained after simulation in a preset computer.

[0135] In this embodiment, the first simulation refers to simulating the safe obstacle avoidance path in a preset computer based on the path feature set, wherein the simulated path is the virtual path obtained after the simulation.

[0136] The working principle and beneficial effects of the above technical solution are as follows: by performing the first simulation and the second model in a preset computer, the correspondence between the path feature set and the target vehicle posture set can be effectively recorded, thereby ensuring the accuracy of the association mapping and the objectivity of generating the target vehicle posture trajectory.

[0137] Example 8:

[0138] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization. In step 2, the optimal damping parameters of the semi-active suspension are adaptively matched according to the target vehicle posture trajectory, including:

[0139] The target vehicle body attitude trajectory is obtained and then decomposed to obtain the target vehicle body attitude in each time frame.

[0140] The target vehicle posture at each time frame is analyzed to obtain the corresponding steering angle and the corresponding vehicle roll angle. At the same time, the real-time road conditions are analyzed to obtain the road surface friction coefficient of the road where the vehicle is currently located.

[0141] Based on safe driving standards, the steering angle and vehicle roll angle are analyzed to determine the probability of wheel liftoff risk of the current vehicle under the target body posture. Combined with the road friction coefficient, the optimal balance damping parameters are determined when the current vehicle is driving stably under the probability of wheel liftoff risk.

[0142] The optimal balance damping parameters corresponding to the target vehicle body posture in each time frame are summarized, and the optimal damping parameters of the semi-active suspension are adaptively matched based on the summary results.

[0143] In this embodiment, the target vehicle posture refers to the vehicle posture at different times obtained by decomposing the target vehicle posture trajectory according to time information.

[0144] In this embodiment, the road surface friction coefficient refers to the roughness of the road surface, that is, the degree of impact on the vehicle when it is in motion.

[0145] In this embodiment, the safe driving standard is known in advance and is used to characterize the maximum steering angle and vehicle roll angle corresponding to the vehicle's ability to safely avoid obstacles while driving.

[0146] In this embodiment, the probability of wheel liftoff risk refers to the probability that the wheels may lift off the ground when the vehicle is in the target body posture.

[0147] In this embodiment, the optimal balance damping parameter refers to the best balance damping parameter that can overcome the risk of wheel liftoff.

[0148] The beneficial effects of the above technical solution are as follows: by decomposing and analyzing the target vehicle body posture trajectory, the steering angle and vehicle roll angle corresponding to each target vehicle body posture can be effectively determined. At the same time, by combining real-time road conditions to analyze the probability of wheel liftoff risk of the current vehicle under the target vehicle body posture, the optimal balance damping parameters for stable driving can be determined, ensuring the reliability of the optimal damping parameters obtained by the final adaptive matching, and ensuring the driving safety of the vehicle.

[0149] Example 9:

[0150] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle attitude control and damping optimization. In step 3, an attitude-damping cooperative obstacle avoidance strategy is constructed based on the adaptive matching results, including:

[0151] Read the adaptive matching results and determine the optimal damping parameters of several vehicle postures and matching in the target vehicle posture trajectory of the adaptive matching results;

[0152] Several attitude-optimal damping parameter data sets are constructed based on the vehicle body attitude and the matched optimal damping parameters;

[0153] The vehicle body attitude and optimal damping parameter data sets are learned, and the cooperative relationship between vehicle body attitude and optimal damping parameters is determined based on the learning results.

[0154] Construct a posture-damping cooperative obstacle avoidance strategy based on the cooperative relationship.

[0155] In this embodiment, the cooperative relationship is used to characterize the correlation or action parameter between the vehicle body posture and the optimal damping parameter.

[0156] The working principle and beneficial effects of the above technical solution are as follows: by constructing several attitude-optimal damping parameter data sets, the cooperative relationship between vehicle body attitude and optimal damping parameters can be effectively determined through learning, thereby accurately constructing an attitude-damping cooperative obstacle avoidance strategy, providing effective data support for realizing the optimal allocation of intelligent drive damping force and obstacle avoidance intelligence.

[0157] Example 10:

[0158] Based on Example 1, this example provides a vehicle obstacle avoidance control method based on active vehicle attitude control and damping optimization. The method controls the vehicle to perform obstacle avoidance operations according to an attitude-damping cooperative obstacle avoidance strategy, including:

[0159] Based on the attitude-damping cooperative obstacle avoidance strategy, a set of control instructions for vehicle obstacle avoidance control is generated, and the real-time vehicle attitude data of the road where the vehicle is located is monitored in real time.

[0160] The command triggering conditions for real-time vehicle attitude data are determined based on the attitude-damping cooperative obstacle avoidance strategy. When the command triggering conditions are met, the corresponding control command is initiated to control the current vehicle to perform obstacle avoidance operations.

[0161] In this embodiment, real-time vehicle posture data refers to the real-time vehicle posture corresponding to the current road.

[0162] In this embodiment, the command triggering condition refers to the condition that needs to be met when the vehicle performs obstacle avoidance control, such as detecting the distance between the obstacle and the vehicle.

[0163] The beneficial effects of the above technical solution are: by generating a control command set for vehicle obstacle avoidance control through the attitude-damping cooperative obstacle avoidance strategy, the vehicle can perform obstacle avoidance operation by controlling the current vehicle through the control command set when the vehicle attitude meets the command triggering conditions, thereby improving the accuracy of vehicle obstacle avoidance control and improving the vehicle's safe driving coefficient.

[0164] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization, characterized in that, include: Step 1: Collect real-time road environment data of the road where the vehicle is currently located, and analyze the road environment data to obtain real-time road conditions; Step 2: Plan a safe obstacle avoidance path based on real-time road conditions, determine the target vehicle body attitude trajectory based on the safe obstacle avoidance path, and adaptively match the optimal damping parameters of the semi-active suspension based on the target vehicle body attitude trajectory. Step 3: Construct an attitude-damping cooperative obstacle avoidance strategy based on the adaptive matching results, and control the vehicle to perform obstacle avoidance operations based on the attitude-damping cooperative obstacle avoidance strategy; In step 2, the target vehicle's attitude trajectory is determined based on the safe obstacle avoidance path, including: Read the safe obstacle avoidance path and determine the path feature set of the safe obstacle avoidance path; Read the vehicle model of the current vehicle and input the vehicle model of the current vehicle into the preset model library for comparison to determine the vehicle shape characteristics of the current vehicle; The path feature set of the safe obstacle avoidance path and the vehicle shape features of the current vehicle are input into the preset historical vehicle posture library for matching. Output the target vehicle body posture set based on the matching results; The safe obstacle avoidance path is associated with and mapped to the target vehicle body posture set to generate the target vehicle body posture trajectory.

2. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, In step 1, real-time road environment data of the road where the vehicle is currently located is collected and analyzed to obtain real-time road conditions, including: Acquire multimodal acquisition devices and collect sub-road environment datasets corresponding to each acquisition device on the road where the vehicle is currently located; The sub-road environment datasets corresponding to each acquisition device are sorted according to time order, and the sub-road environment datasets are aligned in the time dimension according to the sorting results. Based on the alignment results, the sub-road environment datasets are read and analyzed to determine the sub-road environment characteristics of each sub-road environment data output. By integrating the characteristics of the sub-road environment, real-time traffic conditions can be obtained.

3. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 2, characterized in that, Based on the alignment results, the environmental datasets of each sub-road were read and analyzed to determine the sub-road environmental characteristics output by each sub-road environmental dataset, including: Obtain the data type corresponding to the environment dataset of each sub-road; Input the data type into the preset analysis mode library for matching to determine the target data analysis method corresponding to each sub-road environment dataset; The corresponding sub-road environment dataset is analyzed according to the target data analysis method, and the corresponding sub-road environment characteristics are output based on the analysis results.

4. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, Step 2 involves planning a safe obstacle avoidance path based on real-time traffic conditions, including: Read real-time traffic conditions and determine the distribution characteristics of road obstacles, the characteristic attributes of road obstacles, and road characteristic attributes based on the real-time traffic conditions; The characteristic attributes of obstacles include: dynamic obstacles and static obstacles; The real-time location trajectory data of dynamic obstacles is determined based on the distribution characteristics of road obstacles. At the same time, the location data of static obstacles is determined based on the distribution characteristics of road obstacles. Determine the first real-time path of the vehicle based on road feature attributes; The first real-time path is segmented based on the real-time position trajectory data of dynamic obstacles to obtain a first local obstacle avoidance path set. At the same time, the first real-time path is segmented based on the real-time position trajectory of static obstacles to obtain a second local obstacle avoidance path set. The first local obstacle avoidance path set is matched with the second local obstacle avoidance path set, and the first local obstacle avoidance path set and the second local obstacle avoidance path set are fused according to the matching result. At the same time, the first fusion result is fused with the first real-time path to obtain the second real-time path. The second real-time path is used as the planning result for the safe obstacle avoidance path.

5. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 4, characterized in that, Based on the matching results, the first local obstacle avoidance path set and the second local obstacle avoidance path set are first fused together. Simultaneously, the first fusion result is second fused together with the first real-time path to obtain the second real-time path, including: Obtain similar paths between the first obstacle avoidance path set and the second obstacle avoidance path set, remove similar paths from either the first obstacle avoidance path set or the second obstacle avoidance path set, and associate the first obstacle avoidance path set and the second obstacle avoidance path set based on the removal results to complete the first fusion of the first obstacle avoidance path set and the second obstacle avoidance path set. The first real-time path is matched with the first fusion result to determine the path segments that do not intersect with the first real-time path and the first fusion result; the path segments that do not intersect are supplemented in the first fusion result, and the second real-time path is obtained based on the supplemented result.

6. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, The safe obstacle avoidance path is associated and mapped with the target vehicle body posture set to generate the target vehicle body posture trajectory, including: A first simulation is performed on a preset computer based on the vehicle model and vehicle shape to obtain a simulated vehicle. At the same time, a second simulation is performed on a preset computer based on the path feature set to obtain a simulated path. The simulated vehicle runs along the simulated path according to the target vehicle body posture set. At the same time, the correspondence between the path feature set and the target vehicle body posture set is recorded based on the simulation results. Based on the correspondence, the safe obstacle avoidance path is associated and mapped with the target vehicle body posture set to generate the target vehicle body posture trajectory.

7. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, In step 2, the optimal damping parameters of the semi-active suspension are adaptively matched based on the target vehicle body attitude trajectory, including: The target vehicle body attitude trajectory is obtained and then decomposed to obtain the target vehicle body attitude in each time frame. The target vehicle posture at each time frame is analyzed to obtain the corresponding steering angle and the corresponding vehicle roll angle. At the same time, the real-time road conditions are analyzed to obtain the road surface friction coefficient of the road where the vehicle is currently located. Based on safe driving standards, the steering angle and vehicle roll angle are analyzed to determine the probability of wheel liftoff risk of the current vehicle under the target body posture. Combined with the road friction coefficient, the optimal balance damping parameters are determined when the current vehicle is driving stably under the probability of wheel liftoff risk. The optimal balance damping parameters corresponding to the target vehicle body posture in each time frame are summarized, and the optimal damping parameters of the semi-active suspension are adaptively matched based on the summary results.

8. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, In step 3, an attitude-damping cooperative obstacle avoidance strategy is constructed based on the adaptive matching results, including: Read the adaptive matching results and determine the optimal damping parameters of several vehicle postures and matching in the target vehicle posture trajectory of the adaptive matching results; Several attitude-optimal damping parameter data sets are constructed based on the vehicle body attitude and the matched optimal damping parameters; The vehicle body attitude and optimal damping parameter data sets are learned, and the cooperative relationship between vehicle body attitude and optimal damping parameters is determined based on the learning results. Construct a posture-damping cooperative obstacle avoidance strategy based on the cooperative relationship.

9. The vehicle obstacle avoidance control method based on active vehicle posture control and damping optimization according to claim 1, characterized in that, The vehicle is controlled to perform obstacle avoidance operations based on the attitude-damping cooperative obstacle avoidance strategy, including: Based on the attitude-damping cooperative obstacle avoidance strategy, a set of control instructions for vehicle obstacle avoidance control is generated, and the real-time vehicle attitude data of the road where the vehicle is located is monitored in real time. The command triggering conditions for real-time vehicle attitude data are determined based on the attitude-damping cooperative obstacle avoidance strategy. When the command triggering conditions are met, the corresponding control command is initiated to control the current vehicle to perform obstacle avoidance operations.

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