CN114647235B - Control method of drive-by-wire chassis, combined control system and server - Google Patents
Control method of drive-by-wire chassis, combined control system and server Download PDFInfo
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- CN114647235B CN114647235B CN202210565419.6A CN202210565419A CN114647235B CN 114647235 B CN114647235 B CN 114647235B CN 202210565419 A CN202210565419 A CN 202210565419A CN 114647235 B CN114647235 B CN 114647235B
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
- G05B23/0213—Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
Abstract
A control method, a combined control system and a server of a drive-by-wire chassis are applied to the control method of the drive-by-wire chassis of the server, and comprise the following steps: the method comprises the steps of acquiring vehicle data uploaded by a vehicle, wherein the vehicle data comprise an operation instruction of a driver, external environment information and vehicle posture information corresponding to the operation instruction; a strategy making step, namely obtaining an ideal execution strategy corresponding to vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle; strategy optimization, namely acquiring actual execution parameters uploaded by the vehicle, wherein the actual execution parameters correspond to vehicle data; optimizing an ideal execution strategy according to the actual execution parameters; the optimized ideal execution strategy is issued to a vehicle to obtain vehicle data through a server, the current operation instruction corresponding to the ideal execution strategy is obtained through a big data platform, and optimization is performed through actual execution parameters, so that the use experience of a client can be optimized in a state that the software of the whole vehicle does not need to be upgraded, and the service cost is reduced.
Description
Technical Field
The invention relates to a drive-by-wire chassis control technology, in particular to a control method of a drive-by-wire chassis, a combined control system and a server.
Background
In an intelligent automobile, a drive-by-wire technology is applied to an automobile chassis. The traditional executing mechanism for controlling the automobile chassis is mainly realized by a mechanical or hydraulic system, the drive-by-wire chassis acquires the operation information, the vehicle running information, the traffic environment information and the like of a driver through a vehicle sensor, converts the acquired information into electric signals and transmits the electric signals to a related chassis controller, and the controller adjusts and corrects the driving decision of the driver through the intention of the driver and the running posture of the vehicle and finally controls an executing device to work.
In the prior art, the control of the drive-by-wire chassis is mainly carried out by analyzing the information and the dynamic model of the whole vehicle, embedding the information and the dynamic model into a chassis control algorithm for analysis to obtain a control strategy, and then delivering the control strategy to an independent execution unit for working.
The prior art can not accurately identify the environment information outside the vehicle, and the cooperative control of the chassis still stays in the control of the drive-by-wire chassis on the basis of the sensor of the vehicle and the operation information of a driver; and the current emphasis on the redundant control of the drive-by-wire chassis is more than electrical redundancy and execution redundancy, and does not consider the cooperative control of historical data and vehicle road information under the background of big data.
Disclosure of Invention
The invention mainly solves the technical problem that the existing control method of the wire control chassis is lack of the problem of realizing combined control with a large data platform.
According to a first aspect, an embodiment provides a control method of a drive-by-wire chassis, applied to a server, including:
the method comprises the steps of acquiring vehicle data uploaded by a vehicle, wherein the vehicle data comprise an operation instruction of a driver, external environment information and vehicle posture information corresponding to the operation instruction;
a strategy making step, namely obtaining an ideal execution strategy corresponding to vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle;
strategy optimization, namely acquiring actual execution parameters uploaded by the vehicle, wherein the actual execution parameters correspond to vehicle data; optimizing an ideal execution strategy according to the actual execution parameters; and issuing the optimized ideal execution strategy to the vehicle.
According to a second aspect, an embodiment provides a control method of a drive-by-wire chassis, including:
the method comprises the steps of uploading data and obtaining vehicle data, wherein the vehicle data comprise an operation instruction of a driver, and external environment information and vehicle posture information corresponding to the operation instruction; uploading the vehicle data to a server;
a strategy obtaining step of obtaining an ideal execution strategy issued by a server;
a strategy executing step, namely obtaining a whole vehicle end executing strategy corresponding to the operation instruction according to the ideal executing strategy; controlling an execution unit to work according to a whole vehicle end execution strategy;
the strategy feedback step is to obtain the actual execution parameters of the execution strategy of the whole vehicle end corresponding to the execution unit; and uploading the actual execution parameters to a server, and repeating the strategy acquisition step.
According to a third aspect, there is provided in one embodiment a joint control system for a drive-by-wire chassis, comprising: the system comprises a networked automobile, an external environment information module and a server;
the networked automobile comprises a vehicle acquisition module, a vehicle control module, a communication module and an execution unit;
the external environment information module is used for providing external environment information for the vehicle;
the communication module is used for communicating with the server;
the vehicle acquisition module is used for acquiring an operation instruction of a driver; the system is also used for collecting the dynamic information of the vehicle;
the vehicle control module is used for obtaining vehicle attitude information according to the dynamic information; the vehicle data acquisition unit is also used for acquiring vehicle data, wherein the vehicle data comprises an operation instruction, external environment information and vehicle posture information; uploading the vehicle data to a server; acquiring an ideal execution strategy issued by a server; obtaining a whole vehicle end execution strategy corresponding to the operation instruction according to the ideal execution strategy; controlling an execution unit to work according to a whole vehicle end execution strategy; acquiring actual execution parameters of an execution strategy of the whole vehicle end corresponding to the execution unit; uploading the actual execution parameters to a server;
the server is used for acquiring vehicle data uploaded by the networked automobiles; obtaining an ideal execution strategy corresponding to the vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle; acquiring actual execution parameters uploaded by a vehicle, wherein the actual execution parameters correspond to vehicle data; optimizing an ideal execution strategy according to the actual execution parameters; and issuing the optimized ideal execution strategy to the vehicle.
According to a fourth aspect, there is provided in an embodiment a server comprising:
a memory for storing a program;
a processor for implementing the method described in the first aspect by executing a program stored in the memory.
According to a fifth aspect, an embodiment provides a computer readable storage medium having a program stored thereon, the program being executable by a processor to implement the method described in the first aspect and the method described in the second aspect.
According to the control method, the combined control system and the server of the drive-by-wire chassis of the embodiment, the vehicle data is obtained through the server, the ideal execution strategy corresponding to the current operation instruction is obtained through the big data platform, and the actual execution parameters are optimized, so that the use experience of a client can be optimized in a state that the whole vehicle software is not required to be upgraded, and the service cost is reduced.
Drawings
FIG. 1 is a schematic structural diagram of a joint control system of a drive-by-wire chassis according to an embodiment;
FIG. 2 is a schematic structural diagram of a combined control system of a drive-by-wire chassis according to an embodiment;
FIG. 3 is a flow chart of a joint control method of a drive-by-wire chassis according to an embodiment;
FIG. 4 is a flow chart of a control method of a drive-by-wire chassis according to an embodiment;
fig. 5 is a flowchart of another control method of a drive-by-wire chassis according to an embodiment.
Reference numerals: 1-a server; 2-networked automobiles; 21-a vehicle control module; 22-vehicle acquisition module; 23-a communication module; 24-external environment information module; 25-execution unit.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted or replaced with other elements, materials, methods in different instances. In some instances, certain operations related to the present application have not been shown or described in detail in order to avoid obscuring the core of the present application from excessive description, and it is not necessary for those skilled in the art to describe these operations in detail, so that they may be fully understood from the description in the specification and the general knowledge in the art.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, the various steps or actions in the description of the methods may be transposed or transposed in order, as will be apparent to a person skilled in the art. Thus, the various sequences in the specification and drawings are for the purpose of clearly describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where a certain sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings).
In the existing chassis system, the vehicle data is estimated in an off-line manner, so as to control the execution unit 25 to work. This means that the correlation algorithm is determined at the factory and calculated off-line during the vehicle operation. The current automobile operation working conditions are complex and various, and various testing and calibration work is needed to meet the reasonability of the control algorithm of the wire-controlled chassis when the automobile leaves a factory, so that on one hand, the development period can be prolonged, and on the other hand, the complexity of the algorithm is also caused.
In the embodiment of the invention, a control method, a combined control system and a server of a wire-controlled chassis are provided by utilizing a vehicle-road cooperative technology (V2X) and a big data technology, chassis and external environment data are processed by utilizing cloud big data, an ideal execution strategy is obtained at a cloud, the fault state of a chassis device can be obtained through the final residual error comparison of actual execution parameters, a gain adjustment is carried out on a cooperative execution algorithm, the accuracy of chassis control is ensured, and the safety of the wire-controlled chassis at the end of a whole vehicle is ensured. On the other hand, the use experience of a client can be optimized in a state that the whole vehicle software does not need to be upgraded by perfecting a control algorithm of cloud big data, and the service cost is reduced.
The first embodiment is as follows:
referring to fig. 1 and fig. 2, the present embodiment provides a joint control system for a drive-by-wire chassis, including: the networked automobile, the external environment information module 24 and the server 1.
The external environment information module 24 is used for providing external environment information to the vehicle; or, it may also be used to provide external environment information to the server 1. As shown in fig. 1, the external environment information module 24 may be integrated on the internet automobile 2; as shown in fig. 2, it is also possible to transmit the external environment information to the vehicle by communicating with the communication module 23 of the internet vehicle 2, or to transmit the external environment information to the big data platform (i.e., the server 1) through the network, independently of the internet vehicle 2.
The external environment information may include at least one of position information of the vehicle, road block information, and traffic condition information. In the specific implementation, the traffic condition information may be obtained in a networking manner, the roadblock information may be obtained in a radar or camera manner, and the vehicle position information and the surrounding environment information may be obtained in a GPS positioning manner, but the implementation is not limited to the above-mentioned manner, and the surrounding environment information may also be not limited to the above-mentioned three types of information.
The networked automobile 2 comprises a vehicle acquisition module 22, a vehicle control module 21, a communication module 23 and an execution unit 25. The vehicle with the vehicle-road cooperation technology can be called an internet vehicle or an intelligent internet vehicle, and can carry out data communication with the outside through a 4G or 5G mobile network.
The communication module 23 is used for communicating with the server 1; the communication module 23 may include a wireless communication module 23, or include a wireless communication module as well as a wired communication module. Because the existing big data platform is realized by depending on the server 1 with large calculation capacity, the server 1 has large volume and large power consumption, and needs to process data of a plurality of vehicles at the same time, and is not suitable for being placed in the vehicles, and at the moment, the vehicles need to communicate with the server 1 in a wireless communication mode. However, for some special scenarios, the server 1 may be installed in a vehicle, or a miniaturized server may be used to implement data processing for a single vehicle, and at this time, the server may communicate with the vehicle by wired communication.
The vehicle acquisition module 22 is used for acquiring an operation instruction of a driver; but also for collecting vehicle dynamics information. For example, the vehicle collection module 22 may include various sensors for measuring vehicle control information such as steering wheel torque and angular velocity, brake pedal depth, accelerator pedal depth, etc. to generate the driver's operating instructions. The vehicle dynamics information such as yaw velocity, longitudinal acceleration, lateral acceleration, longitudinal vehicle speed, mass center slip angle, road surface peak adhesion coefficient and the like can also be measured. The vehicle attitude information includes at least one vehicle dynamics information of yaw rate, longitudinal acceleration, lateral acceleration, longitudinal vehicle speed, centroid slip angle, and road surface peak adhesion coefficient.
The vehicle control module 21 is used for obtaining vehicle attitude information according to the dynamics information; specifically, a dynamic model of the vehicle is established through dynamic information to obtain vehicle attitude information.
The vehicle control module 21 acquires the operation instruction, the external environment information, and the vehicle posture information, and uploads the whole to the server 1 as vehicle data. The vehicle control module 21 acquires an ideal execution strategy issued by the server 1; and obtaining a whole vehicle end execution strategy corresponding to the operation instruction according to the ideal execution strategy. The vehicle control module 21 controls the execution unit 25 to operate according to the vehicle end execution strategy. The vehicle control module 21 obtains actual execution parameters of the execution unit 25 corresponding to the whole vehicle-end execution strategy, and uploads the actual execution parameters to the server 1. Wherein the operating command may include at least one of a steering command, a braking command, a driving command, and a suspension command.
The actuator unit 25 may include at least one of a brake unit, a steering unit, a driving unit, and a suspension unit. The vehicle end execution strategy includes execution parameters corresponding to the execution units 25, and the vehicle end execution strategy is consistent with the ideal execution strategy. The ideal execution strategy includes the execution parameters of all execution units, and the vehicle control module 21 may modify the current vehicle-end execution strategy of the vehicle according to the ideal execution strategy. For example, the execution parameters of the brake unit may include a brake opening degree and a motor brake current; the execution parameters of the steering unit may include steering angle and steering angle rate; the execution parameters of the driving unit can comprise the throttle depth and the current of the driving motor; the implementation parameters of the suspension unit may include stiffness and damping pressure.
The server 1 is used for acquiring vehicle data uploaded by the networked automobile 2; and obtaining an ideal execution strategy corresponding to the vehicle data according to the vehicle data. And issuing the ideal execution strategy to the vehicle. And acquiring actual execution parameters uploaded by the vehicle, wherein the actual execution parameters correspond to the vehicle data. And optimizing the ideal execution strategy according to the actual execution parameters. And issuing the optimized ideal execution strategy to the vehicle. If necessary, the vehicle data can be processed, for example, data cleaning including vacant assignment, error value removal, cross check and the like can be performed, and any existing data cleaning mode of a large data platform can be referred to.
The ideal execution strategy is calculated through the big data platform, and the actual execution parameters are obtained for optimization, so that the whole vehicle end execution strategy is optimized, the development period of the vehicle can be shortened, the complexity of an algorithm carried by the vehicle is reduced, the execution strategy redundancy is realized, and the safety and stability of the chassis are improved. The use experience of the customer is optimized in the state that the whole vehicle software is not required to be upgraded, and the service cost is reduced.
Example two:
referring to fig. 3 to 5, the present embodiment provides a joint control method for a drive-by-wire chassis, which is executed by a server 1 and an internet vehicle 2 in a joint control manner. As shown in fig. 2 and 4, the present embodiment also provides a control method of a drive-by-wire chassis executed by the server 1; as shown in fig. 3 and 5, the present embodiment also provides a control method of a drive-by-wire chassis executed by the internet-connected vehicle 2.
The following is a description of a specific process of the joint control method performed by the joint control system, as shown in fig. 3, including the following steps:
the data uploading step, the vehicle control module 21 obtains vehicle data, and the vehicle data comprises an operation instruction of a driver, and external environment information and vehicle posture information corresponding to the operation instruction; the vehicle control module 21 uploads the vehicle data to the server 1 through the communication module 23.
And a data acquisition step, wherein the server 1 acquires vehicle data uploaded by a vehicle. The vehicle data is then purged.
A strategy making step, namely obtaining an ideal execution strategy corresponding to vehicle data by the server 1 according to the vehicle data; and issuing the ideal execution strategy to the vehicle. Wherein the ideal execution strategy comprises at least one of brake execution parameters, driving execution parameters, steering execution parameters and suspension execution parameters.
In practical applications, the policy making step may include:
step 101, the server 1 identifies the current driving condition of the vehicle according to the vehicle data. The operating mode of going can drive speed operating mode and driving environment work, for example, to driving environment operating mode, can obtain the vehicle in city, suburb or high-speed through locating information and traffic condition information, and can also obtain the highway section of blocking up or unobstructed highway section. The driving speed working condition can be correspondingly determined through the current vehicle speed data. The driving conditions may also include mileage conditions, representing the amount of oil or electricity remaining.
Step 102: the server 1 obtains a decision model corresponding to the driving condition, and combines vehicle data to obtain an ideal execution strategy. Each decision model is provided with a plurality of corresponding target items, and the target items can comprise at least two of driving safety, execution response time, vehicle energy consumption, operation stability and driving comfort; the priority ranking between a plurality of target items of every two decision models is different, and the driving safety is always the first priority.
Specifically, the influencing factors of the driving safety of the vehicle may include controller stability, turning rate and angle, brake responsiveness, and the like; the influencing factors of the vehicle handling stability can include vehicle modal frequency; driving comfort may include vehicle angle, angular velocity, etc.; the response time may include an actuator network, controller software, etc.; energy consumption may include acceleration, wind resistance, vehicle speed, and the like.
For example, when the driving condition is urban high-speed driving, the priority is vehicle driving safety > vehicle handling stability, driving comfort > response time > energy consumption. And when the driving working condition is urban low-speed driving and is a low-mileage working condition, the vehicle driving safety is greater than the energy consumption and is greater than other target items.
In practical embodiment, a decision factor of each target is established, a weight is set for each target according to decision preference, the targets are changed into single targets, and if the fact that the corner angular speed is required to be adjusted to be larger (such as larger than 30 degrees/s) to ensure the driving safety of the vehicle (such as the fact that an obstacle exists in the front or emergency braking is required) is recognized, the braking is required to be completed within a shorter time and a shorter distance (such as the braking distance of 100km/h-0 is less than or equal to 35 m). Meanwhile, as for driving comfort, the stability of the vehicle corner angle and the braking speed is ensured, and the comfortable experience of customers is better; for energy consumption, smooth braking is low for energy consumption, but in order to ensure safety, angular velocity and braking distance are guaranteed to be prioritized.
For example, different calculation functions are provided for different driving conditions corresponding to one target item, and the specific adjustment of each execution parameter in different calculation functions is different. For example, in a working condition, a target item X1 is associated with a plurality of execution parameters (e.g., a execution parameter, B execution parameter, etc.) of a plurality of execution units, and X1= a1 × a + B1 × B + …; another target term X2= a2 a + B2B + …. Wherein, a1 × a represents the adjustment or reference amount of the execution parameter a1 in the current calculation function.
Then the weights of different target terms are different for different decision models, e.g., target term X1 is weighted to Q1, target term X2 is weighted to Q2, and so on, and finally Q1+ Q2+ … Qn =100%. At this time, corresponding to the a execution parameter, the adjustment amount a = a1 × Q1+ a2 × Q2+ … + an × Qn of the a execution parameter is finally obtained.
Since the ideal execution strategy is obtained on a large data platform, the complex calculation analysis does not influence the normal running of the vehicle. The application is not focused on how to specify specific parameters of the decision model, but is to propose that a plurality of decision models are used for dealing with various working conditions so as to correspondingly generate different ideal execution strategies. Taking braking as an example, the braking distance corresponding to 100km/h-0 can be 35m in suburban areas, 35m in high speed and 30m in overhead according to different driving environment conditions.
And establishing priority sequencing for each target under different user working conditions by adopting a multi-target optimization decision model, wherein the final purpose of using the multi-target optimization model is to obtain an optimal compromise solution and meet the requirements of customers under different working conditions.
In the strategy acquiring step, the vehicle control module 21 acquires the ideal execution strategy issued by the server 1 through the communication module 23.
In the strategy execution step, the vehicle control module 21 obtains a whole vehicle end execution strategy corresponding to the operation instruction according to the ideal execution strategy; the vehicle control module 21 controls the execution unit 25 to operate according to the vehicle end execution strategy.
In practical application, because the vehicles are not necessarily networked in real time in the driving process, the ideal execution strategy is not necessarily acquired by the vehicles in real time, and at the moment, the vehicles still respond to the operation command according to the original control strategy, and after the ideal execution strategy is received, the control strategy of the vehicles is optimized.
In practical applications, the policy executing step may further include:
the vehicle control module 21 determines whether there is an execution parameter that needs to be corrected between the current ideal execution strategy and the current full vehicle end execution strategy.
If the execution parameter needing to be modified exists, whether the execution unit 25 to which the execution parameter needing to be modified belongs can be modified immediately is judged. The instant modification can be carried out according to the actual situation by the preset rule without a unique standard.
If the vehicle-mounted terminal can be modified immediately, updating the vehicle-mounted terminal execution strategy according to the current ideal execution strategy; if the current vehicle end can not be modified immediately, maintaining the current vehicle end execution strategy; and obtaining a whole vehicle end execution strategy according to the current ideal execution strategy at preset time. The preset time may be modified when the vehicle is refueled or charged, parked for waiting, or driven at a low speed.
Because the chassis control is related to the safety of the vehicle, in order to ensure the safety of the vehicle under the condition of vehicle network lag, the chassis cannot wait for the execution strategy of the big data platform to be sent to the operation. Therefore, in this step, the execution strategy of the vehicle end maintains the offline control logic of the original chassis control unit, the external environment sensing module and the ideal big data execution strategy are not considered, only the ideal execution strategy of the big data platform at the last moment is considered, the left wheel turning speed abnormality is detected at the last moment, and the correction is performed at the current moment.
Strategy feedback, namely the vehicle control module 21 acquires actual execution parameters of the execution strategy of the whole vehicle end corresponding to the execution unit 25 through the vehicle acquisition module 22; the vehicle control module 21 uploads the actual execution parameters to the server 1 through the communication module 23, and repeats the policy acquisition step.
Specifically, after each execution unit 25 executes the corresponding execution parameter according to the execution parameter of the vehicle-end execution policy, the corresponding actual execution parameter is generated. For example, the server 1 obtains, according to the analysis of the operation instruction, that the user needs to turn by 30 °, the execution parameter corresponding to the whole vehicle end execution strategy is 30 ° of turning, but the actual angle after turning is 28 °, in order to achieve the actual target of 30 ° of turning, the execution parameter needs to be 32 ° or other degrees, and finally, one obtained execution parameter corresponds to the actual turning angle of 30 °.
Strategy optimization, namely, acquiring actual execution parameters uploaded by a vehicle by a server 1, wherein the actual execution parameters correspond to vehicle data; the server 1 optimizes an ideal execution strategy according to the actual execution parameters; and issuing the optimized ideal execution strategy to the vehicle.
In practical applications, optimizing the ideal execution strategy according to the actual execution parameters may include:
setting a failure threshold value V2 and a modification threshold value V1 for each execution unit 25 of the vehicle; the execution parameter of the execution unit 25 corresponding to the ideal execution policy is defined as S1, and the execution parameter of the execution unit 25 corresponding to the actual execution parameter is defined as S2.
If | S1-S2| < V1, then no modification is required to execute the parameter S1. Where | S1-S2| can be defined as a residual.
And if V1 is less than or equal to | S1-S2| is less than or equal to V2, making S1= S1+ Delta S, and enabling Delta S to be a preset modification increment so as to realize optimization of the ideal execution strategy.
If the absolute value of S1-S2 is more than V2, an abnormal alarm signal is sent to the vehicle, and S1 is still used as an execution parameter. That is, the modification cannot be made at this time, and the control is performed in accordance with the original execution parameter.
The execution parameters of each execution unit can be correspondingly set with fault threshold values and modified threshold values, so that closed-loop adjustment can be formed until all execution parameters of an ideal execution strategy corresponding to one vehicle data are completed.
That is to say, the fault state of the chassis can be obtained by comparing the ideal execution strategy with the residual error of the actual execution parameter, and the gain adjustment can be performed on the ideal execution strategy, so that the accuracy of chassis control is ensured, and the safety of the whole vehicle end linear control chassis is ensured.
Specifically, still taking the steering angle of 30 ° as an example, in the ideal execution strategy obtained by the first analysis of the server 1, the steering execution parameter S1 is 30 °, the actual execution parameter S2 is 28 °, the modification threshold V1 is 1 °, the failure threshold V2 is 3 °, and the preset modification increment Δ S is 1 °. Then, the steering execution parameter S1 is updated to 30 ° +1 °, a new ideal execution strategy is formed, and the new ideal execution strategy is issued to the vehicle, and the vehicle is waited for the next feedback of the actual execution parameter corresponding to the steering of 30 ° for further determination. For example, when the actual execution parameter of the vehicle is 25 °, a warning signal is given to the vehicle abnormality so that the driver knows that the current steering execution unit is abnormal.
Further, when the steering execution parameter determined in the ideal execution strategy is 30 °, if the vehicle can modify the whole vehicle end execution strategy in real time, it is determined whether the whole vehicle end execution strategy needs to be modified, for example, the steering execution parameter of the current whole vehicle end execution strategy is 29 °, the steering execution parameter of the whole vehicle end execution strategy is modified to 30 °, the actual execution parameter is obtained to be 28 °, and the actual execution parameter is fed back to the big data platform. And the large data platform calculates to obtain the steering execution parameter of the ideal execution strategy again as 31 degrees, and then sends the steering execution parameter to the vehicle, the vehicle modifies the steering execution parameter of the whole vehicle end execution strategy into 31 degrees, and obtains the actual execution parameter again as 29 degrees. At the moment, the residual error is 1 degree, the big data platform can judge the steering requirement of the current operation instruction and can not modify the steering requirement. But can also be further modified, for example, the preset modification increment is changed to 0.5 degrees, and the loop is repeated, so that the decision model of the ideal execution strategy of the large data platform is continuously optimized, and finally the execution strategy modification of the drive-by-wire chassis is realized.
If the vehicle-end execution strategy cannot be modified immediately, after modification is completed within the preset time, the modified steering execution parameters of the whole vehicle-end execution strategy are executed next time, corresponding actual execution parameters are obtained, and the actual execution parameters are uploaded to a big data platform.
Therefore, the optimized ideal execution strategy is used as a redundancy cooperation strategy, the chassis of the vehicle can be cooperatively controlled by utilizing historical data and vehicle path information, a big data redundancy cooperation strategy is added on the basis of the original electric redundancy and redundancy execution technology, system fault judgment and driving condition identification can be carried out on a big data platform, and the failure mode and the operation reliability of the drive-by-wire chassis can be predicted in advance; the safety of the automobile chassis is further improved, and the robustness of braking, steering and suspension of the automobile is improved.
According to the joint control method and the joint control system, due to the fact that data analysis of a large data platform is used, a redundancy cooperation strategy is provided on the basis that the whole vehicle is not changed, and safety of the whole vehicle in the field of chassis is improved. In addition, the mode of cloud big data can reduce calibration and debugging work in the early stage, and upgrade iteration of the chassis algorithm is carried out in real time under different use conditions of clients in the later-stage use process.
The specific process description of the control method executed by the server or the networked automobile is already described in the above-mentioned combined control method, and has the same technical effect, and the description is not repeated here.
The embodiment also provides a server, which comprises a memory and a processor.
A memory for storing a program.
The processor, configured to execute the program stored in the memory to implement the portion executed by the server in the method described in the second embodiment, has the same technical effect, and the description is not repeated here.
Those skilled in the art will appreciate that all or part of the functions of the methods in the above embodiments may be implemented by hardware, or may be implemented by a computer program. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For a person skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (9)
1. A control method of a drive-by-wire chassis is applied to a server and is characterized by comprising the following steps:
the method comprises the steps of obtaining vehicle data uploaded by a vehicle, wherein the vehicle data comprise an operation instruction of a driver, and external environment information and vehicle posture information corresponding to the operation instruction;
a strategy formulation step, namely obtaining an ideal execution strategy corresponding to the vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle;
strategy optimization, namely acquiring actual execution parameters uploaded by a vehicle, wherein the actual execution parameters correspond to the vehicle data; optimizing the ideal execution strategy according to the actual execution parameters; issuing the optimized ideal execution strategy to the vehicle;
wherein optimizing the ideal execution strategy according to the actual execution parameters comprises:
setting a fault threshold value V2 and a modification threshold value V1 corresponding to each execution unit of the vehicle; defining the execution parameter of the ideal execution strategy corresponding to the execution unit as S1, and the execution parameter of the actual execution parameter corresponding to the execution unit as S2;
if the absolute value of S1-S2 is less than V1, the execution parameter S1 does not need to be modified;
if V1 is less than or equal to | S1-S2| is less than or equal to V2, making S1= S1+ Δ S, and Δ S be a preset modification increment so as to realize optimization of the ideal execution strategy;
and if the absolute value of S1-S2 is more than V2, sending an abnormal alarm signal to the vehicle.
2. The control method according to claim 1, wherein the policy making step includes:
identifying the running condition of the current vehicle according to the vehicle data;
obtaining a decision model corresponding to the driving condition, and obtaining an ideal execution strategy by combining the vehicle data;
each decision model is provided with a plurality of corresponding target items, and the target items comprise at least two of driving safety, execution response time, vehicle energy consumption, operation stability and driving comfort; the priority ranking between the target items of every two decision models is different, and the driving safety is always the first priority.
3. The control method according to claim 1, wherein the external environment information includes at least one of position information of a vehicle, roadblock information, and traffic condition information; and/or the operating command comprises at least one of a steering command, a braking command, a driving command and a suspension command; and/or the vehicle attitude information comprises at least one vehicle dynamics information of yaw velocity, longitudinal acceleration, lateral acceleration, longitudinal vehicle speed, mass center slip angle and road surface peak adhesion coefficient.
4. The control method of claim 1, wherein the desired actuation strategy includes at least one of a brake actuation parameter, a drive actuation parameter, a steering actuation parameter, and a suspension actuation parameter.
5. A control method of a drive-by-wire chassis is characterized by comprising the following steps:
the method comprises the steps of uploading data and obtaining vehicle data, wherein the vehicle data comprise an operation instruction of a driver, and external environment information and vehicle posture information corresponding to the operation instruction; uploading the vehicle data to a server;
the method comprises the steps of obtaining vehicle data uploaded by a vehicle, wherein the vehicle data comprise an operation instruction of a driver, and external environment information and vehicle posture information corresponding to the operation instruction;
a strategy making step, namely obtaining an ideal execution strategy corresponding to the vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle; a strategy obtaining step of obtaining an ideal execution strategy issued by the server;
a strategy executing step, namely obtaining a whole vehicle end executing strategy corresponding to the operation instruction according to the ideal executing strategy; controlling an execution unit to work according to the whole vehicle end execution strategy;
strategy feedback step, obtaining the actual execution parameters of the execution strategy of the whole vehicle end corresponding to the execution unit; uploading the actual execution parameters to the server, and repeating the strategy acquisition step;
strategy optimization, namely acquiring actual execution parameters uploaded by a vehicle, wherein the actual execution parameters correspond to the vehicle data; optimizing the ideal execution strategy according to the actual execution parameters; issuing the optimized ideal execution strategy to the vehicle;
wherein optimizing the ideal execution strategy according to the actual execution parameters comprises:
setting a fault threshold value V2 and a modification threshold value V1 corresponding to each execution unit of the vehicle; defining the execution parameter of the ideal execution strategy corresponding to the execution unit as S1, and the execution parameter of the actual execution parameter corresponding to the execution unit as S2;
if the absolute value of S1-S2 is less than V1, the execution parameter S1 is not required to be modified;
if V1 is more than or equal to | S1-S2| is more than or equal to V2, making S1= S1+ Δ S, and Δ S be a preset modification increment so as to realize optimization of the ideal execution strategy;
and if the absolute value of S1-S2 is more than V2, sending an abnormal alarm signal to the vehicle.
6. The control method of claim 5, wherein the policy execution step further comprises:
judging whether an execution parameter needing to be corrected exists between the current ideal execution strategy and the current finished vehicle end execution strategy;
if the execution parameters needing to be corrected exist, judging whether the execution unit to which the execution parameters needing to be corrected belong can be modified immediately;
if the vehicle-mounted terminal can be modified instantly, obtaining the whole vehicle-mounted terminal execution strategy according to the current ideal execution strategy;
if the current vehicle end execution strategy cannot be modified immediately, the current vehicle end execution strategy is kept; and updating the whole vehicle end execution strategy according to the current ideal execution strategy at preset time.
7. A joint control system for a drive-by-wire chassis, comprising: the system comprises a networked automobile, an external environment information module and a server;
the internet automobile comprises a vehicle acquisition module, a vehicle control module, a communication module and an execution unit;
the external environment information module is used for providing external environment information for the vehicle;
the communication module is used for communicating with the server;
the vehicle acquisition module is used for acquiring an operation instruction of a driver; the system is also used for collecting the dynamic information of the vehicle;
the vehicle control module is used for obtaining vehicle attitude information according to the dynamics information; the vehicle data acquisition unit is further used for acquiring vehicle data, wherein the vehicle data comprises the operation instruction, external environment information and vehicle posture information; uploading the vehicle data to a server; acquiring an ideal execution strategy issued by the server; obtaining a whole vehicle end execution strategy corresponding to the operation instruction according to the ideal execution strategy; controlling the execution unit to work according to the whole vehicle end execution strategy; acquiring actual execution parameters of the execution strategy of the whole vehicle end corresponding to the execution unit; uploading the actual execution parameters to the server;
the server is used for acquiring vehicle data uploaded by the networked automobiles; obtaining an ideal execution strategy corresponding to the vehicle data according to the vehicle data; issuing the ideal execution strategy to the vehicle; acquiring actual execution parameters uploaded by a vehicle, wherein the actual execution parameters correspond to the vehicle data; optimizing the ideal execution strategy according to the actual execution parameters; issuing the optimized ideal execution strategy to the vehicle;
wherein optimizing the ideal execution strategy according to the actual execution parameters comprises:
setting a fault threshold value V2 and a modification threshold value V1 corresponding to each execution unit of the vehicle; defining the execution parameter of the ideal execution strategy corresponding to the execution unit as S1, and the execution parameter of the actual execution parameter corresponding to the execution unit as S2;
if the absolute value of S1-S2 is less than V1, the execution parameter S1 is not required to be modified;
if V1 is less than or equal to | S1-S2| is less than or equal to V2, making S1= S1+ Δ S, and Δ S be a preset modification increment so as to realize optimization of the ideal execution strategy;
and if the absolute value of S1-S2 is more than V2, sending an abnormal alarm signal to the vehicle.
8. A server, comprising:
a memory for storing a program;
a processor for implementing the method of any one of claims 1-4 by executing a program stored by the memory.
9. A computer-readable storage medium, characterized in that the medium has stored thereon a program which is executable by a processor to implement the method according to any one of claims 1-6.
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| CN107380161B (en) * | 2017-07-18 | 2018-03-09 | 吉林大学 | A kind of active steering control device for aiding in driver to realize desired ride track |
| CN109085837B (en) * | 2018-08-30 | 2023-03-24 | 阿波罗智能技术(北京)有限公司 | Vehicle control method, vehicle control device, computer equipment and storage medium |
| CN109435961B (en) * | 2018-11-13 | 2020-10-02 | 常熟理工学院 | Driver characteristic-based full-line control electric automobile chassis coordination control method |
| CN110626340B (en) * | 2019-09-30 | 2023-07-11 | 南京航空航天大学 | Intelligent automobile path tracking control system and method based on wolf algorithm |
| CN110979304B (en) * | 2019-12-24 | 2023-03-31 | 大连理工大学 | Moment distribution method of vehicle under variable attachment working condition |
| CN112572327B (en) * | 2020-12-23 | 2022-04-05 | 南京航空航天大学 | Internet connection type intelligent line control chassis domain control device and control method thereof |
| CN112874502B (en) * | 2021-03-01 | 2022-07-12 | 南京航空航天大学 | Wire control chassis information physical system in intelligent traffic environment and control method |
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| CN113581278B (en) * | 2021-07-20 | 2023-03-28 | 浙江万安科技股份有限公司 | Multi-mode wire control chassis system and control method thereof |
| CN113777918A (en) * | 2021-07-28 | 2021-12-10 | 张金宁 | Automobile intelligent drive-by-wire chassis control method of digital twin architecture |
| CN113682308A (en) * | 2021-08-27 | 2021-11-23 | 北京理工大学深圳汽车研究院(电动车辆国家工程实验室深圳研究院) | Centralized control system for platform development wire-controlled chassis system |
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