CN112977411A - Intelligent chassis control method and device - Google Patents
Intelligent chassis control method and device Download PDFInfo
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- CN112977411A CN112977411A CN202110391119.6A CN202110391119A CN112977411A CN 112977411 A CN112977411 A CN 112977411A CN 202110391119 A CN202110391119 A CN 202110391119A CN 112977411 A CN112977411 A CN 112977411A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
Abstract
The invention discloses an intelligent chassis control method and device, relating to the technical field of chassis control, improving driving safety according to a predictive control strategy of a chassis, and more intelligently meeting the driving demand control of a user, wherein the main technical scheme of the invention is as follows: receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of a target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle; simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information; searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not; and if so, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
Description
Technical Field
The invention relates to the technical field of chassis control, in particular to an intelligent chassis control method and device.
Background
The chassis is a combination of four parts of a transmission system, a running system, a steering system and a braking system on the vehicle, and is used for supporting and mounting a vehicle engine and all parts and assemblies thereof to form the integral shape of the vehicle and receive the power of the engine to enable the vehicle to move so as to ensure normal running. Meanwhile, a good chassis can ensure the life safety of a driver.
At present, the control of the chassis is still based on the factors such as the instant road condition encountered by the vehicle in the current driving process, the vehicle performance, the driver driving technique, etc., and the chassis is correspondingly controlled to execute the required operation in real time in order to meet the current driving safety traffic.
However, if a driving scene requiring emergency risk avoidance is encountered, the experience of the driver is insufficient, and the situation that the operation of the chassis is not timely influenced is inevitable, which will face unpredictable driving risks.
Disclosure of Invention
The invention provides an intelligent chassis control method and device, and mainly aims to obtain a chassis prediction control strategy by means of auxiliary analysis of a cloud server, so that a corresponding chassis control strategy is provided as far as possible before similar emergency road conditions are met, driving safety is improved, and driving requirement control of a user is met more intelligently.
In order to achieve the above purpose, the present invention mainly provides the following technical solutions:
the application provides an intelligent chassis control method in a first aspect, and the method comprises the following steps:
receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of a target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle;
simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information;
searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not;
and if so, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
In some variations of the first aspect of the present application, the method further comprises:
obtaining historical driving sample information, wherein the historical driving sample information at least comprises historical driving record information corresponding to different vehicle types, different vehicle performances and different driving road conditions;
acquiring vehicle attribute information and driving application scenes corresponding to different vehicle attribute information by analyzing the historical driving record information;
compiling a preset driving strategy according to the vehicle attribute information and driving application scenes corresponding to different vehicle attribute information;
and constructing a preset mapping relation among the vehicle attribute information, the driving application scene and the preset driving strategy according to the vehicle attribute information, the driving application scenes corresponding to different vehicle attribute information and the preset driving strategy.
In some modified embodiments of the first aspect of the present application, the finding whether a target driving strategy matching the attribute information of the target vehicle and the target driving application scenario exists includes:
forming a first data set by the attribute information of the target vehicle and the target driving application scene;
forming a second data set by the vehicle attribute information and the driving application scenes contained in each group of mapping relations;
respectively comparing the first data set with a plurality of second data sets to judge whether the same data sets exist or not;
if so, acquiring vehicle attribute information and a preset driving strategy corresponding to a driving application scene contained in the same data set by searching the preset mapping relation, and taking the vehicle attribute information and the preset driving strategy as a target driving strategy.
In some variations of the first aspect of the present application, if there is no identical data set, the method further comprises:
according to preset strategy rules, compiling a corresponding driving strategy according to the attribute information of the target vehicle and the target driving application scene;
respectively carrying out similarity comparison operation on the compiled driving strategy and a plurality of preset driving strategies to obtain a plurality of similarity values corresponding to the comparison operation;
obtaining a maximum similarity value from the plurality of similarity values;
and if the maximum similarity value is judged to be larger than a preset threshold value, correspondingly comparing the maximum similarity value with a preset driving strategy corresponding to the operation, and taking the preset driving strategy as a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene.
In some modified embodiments of the first aspect of the present application, if there is no target driving strategy matching the attribute information of the target vehicle and the target driving application scenario, the method further includes:
and sending alarm information to the target vehicle so that the target vehicle can calculate and analyze a corresponding chassis control strategy according to the current road surface information, the vehicle positioning information, the positioning corresponding map information, the current running environment information and the attribute information of the target vehicle.
In some variant embodiments of the first aspect of the present application, the chassis control system comprises at least: the system comprises a steering control module, a stable control module, a suspension control module and four independent wheel control modules; and the chassis control system controls the steering control module, the stability control module, the suspension control module and the four independent wheel control modules to execute control operation according to the received prediction control strategy.
This application second aspect provides an intelligence chassis controlling means, the device includes:
the receiving unit is used for receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle;
the construction unit is used for simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information;
the searching unit is used for searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not;
and the feedback unit is used for feeding back the target driving strategy to the target vehicle if the target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists, so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
In some variations of the second aspect of the present application, the apparatus further comprises:
the method comprises the steps that an acquisition unit acquires historical driving sample information, wherein the historical driving sample information at least comprises historical driving record information corresponding to different vehicle types, different vehicle performances and different driving road conditions;
the obtaining unit is further configured to obtain vehicle attribute information and driving application scenes corresponding to different vehicle attribute information by analyzing the historical driving record information;
the compiling unit is used for compiling a preset driving strategy according to the vehicle attribute information and the driving application scenes corresponding to different vehicle attribute information;
and the establishing unit is used for establishing a preset mapping relation among the vehicle attribute information, the driving application scene and the preset driving strategy according to the vehicle attribute information, the driving application scenes corresponding to different vehicle attribute information and the preset driving strategy.
In some variations of the second aspect of the present application, the lookup unit comprises:
the composition module is used for composing the attribute information of the target vehicle and the target driving application scene into a first data set;
the composition module is further used for composing the vehicle attribute information and the driving application scenes contained in each group of mapping relations into a second data set;
the comparison module is used for respectively comparing the first data set with the plurality of second data sets and judging whether the same data sets exist or not;
and the obtaining module is used for obtaining the vehicle attribute information and the preset driving strategy corresponding to the driving application scene contained in the same data set as the target driving strategy by searching the preset mapping relation if the same data set is judged to exist.
In some variations of the second aspect of the present application, the lookup unit further comprises:
the compiling module is used for compiling a corresponding driving strategy according to the attribute information of the target vehicle and the target driving application scene according to a preset strategy rule if the same data set does not exist;
the comparison module is further used for respectively carrying out similarity comparison operation on the compiled driving strategy and a plurality of preset driving strategies to obtain a plurality of similarity values corresponding to the comparison operation;
the obtaining module is further configured to obtain a maximum similarity value from the plurality of similarity values;
and the determining module is used for comparing the maximum similarity value with a preset driving strategy corresponding to the operation if the maximum similarity value is judged to be larger than a preset threshold value, and using the preset driving strategy as a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene.
In some variations of the second aspect of the present application, the apparatus further comprises:
and the alarm unit is used for sending alarm information to the target vehicle so that the target vehicle can calculate and analyze a corresponding chassis control strategy according to the current road surface information, the vehicle positioning information, the positioning corresponding map information, the current running environment information and the attribute information of the target vehicle.
In some variant embodiments of the second aspect of the present application, the chassis control system comprises at least: the system comprises a steering control module, a stable control module, a suspension control module and four independent wheel control modules; and the chassis control system controls the steering control module, the stability control module, the suspension control module and the four independent wheel control modules to execute control operation according to the received prediction control strategy.
A third aspect of the present application provides a storage medium, which includes a stored program, wherein when the program runs, the apparatus on which the storage medium is located is controlled to execute the intelligent chassis control method as described above.
A fourth aspect of the present application provides an electronic device comprising at least one processor, and at least one memory, a bus, connected to the processor;
the processor and the memory complete mutual communication through the bus;
the processor is configured to call program instructions in the memory to perform the intelligent chassis control method as described above.
By the technical scheme, the technical scheme provided by the invention at least has the following advantages:
the invention provides an intelligent chassis control method and device, communication between a target vehicle and a cloud server is set up in advance, then current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle in running are uploaded to the cloud server side, a corresponding target driving application scene is constructed through analysis and simulation of the cloud server, a target driving strategy matched with the attribute information and the target driving application scene of the target vehicle is further searched, and the cloud server feeds the target driving strategy back to the target vehicle, so that the target vehicle generates a corresponding chassis prediction control strategy according to the target driving strategy and performs prediction control on a chassis control system according to the chassis prediction control strategy. Compared with the prior art, the chassis control device solves the technical problems that the existing chassis control operation is not timely and the driving risk exists. According to the invention, the prediction control strategy of the chassis is acquired by means of auxiliary analysis of the cloud server, so that the corresponding chassis control strategy is provided as far as possible before similar emergency road conditions are met, the driving safety is improved, and the driving demand control of a user is more intelligently met.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of an intelligent chassis control method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another intelligent chassis control method provided by the embodiment of the invention;
fig. 3 is a block diagram of an intelligent chassis control device according to an embodiment of the present invention;
fig. 4 is a block diagram of another intelligent chassis control device according to an embodiment of the present invention;
fig. 5 is an electronic device for intelligent chassis control according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The embodiment of the invention provides an intelligent chassis control method, as shown in fig. 1, the method is to build communication between a target vehicle and a cloud server in advance, so as to obtain a chassis prediction control strategy by means of analysis of the cloud server, and the following specific steps are provided for the embodiment of the invention:
101. and receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle.
It should be noted that, in the embodiment of the present invention, communication between the traveling vehicle and the cloud server is established in advance, the following step 101 and step 104 are executed by the cloud server, and in addition, in order to ensure the communication quality between the target vehicle and the cloud server, the embodiment of the present invention may be, but is not limited to, adopting 5G communication, thereby overcoming the problem of poor network communication caused by complex road conditions (such as mountains and tunnels). Embodiments of the present invention use the word "target vehicle" simply to clearly refer to a traveling vehicle that currently needs to acquire a predictive control strategy for controlling chassis operation.
In the embodiment of the invention, for a plurality of running vehicles connected with a cloud server, the current road surface information, the vehicle positioning information, the positioning corresponding map information, the current running environment information and the attribute information of the target vehicle are acquired on the running vehicle side.
For obtaining the current road surface information, specifically, data information such as a road surface geometric shape, a road surface roughness, a road surface adhesion coefficient, a road surface gradient, a road surface flatness, a road friction coefficient and the like can be obtained by using a radar and a camera.
For obtaining vehicle positioning information and positioning corresponding map information, the method can be specifically realized by means of Beidou and GPS positioning technologies, and can be combined with a high-precision map, so that road condition positions in the map where the running vehicles are located and road information around the running vehicles, such as mountain roads, bridges, mini-forests and the like, can be obtained.
For obtaining the current driving environment information, specifically, the related data information may be obtained by means of camera shooting or a vehicle-mounted computer, and it should be noted that the driving environment information at least includes: weather environment information (such as sunny days, smoke and rain, snow, fog) and traffic sign environment information (such as road signs, pedestrians, traffic signals).
For obtaining the attribute information of the vehicle, the attribute information at least comprises the model of the vehicle, the sprung mass, the unsprung mass, the suspension stiffness, the suspension damping, the tire equivalent stiffness, the tire equivalent damping, the current running speed, the turning angle, the pitching angle, the side tilting angle, the yaw angle, the suspension acceleration, the suspension displacement and the like.
For a target vehicle, after the traveling vehicle side acquires the data information, the data information is uploaded to the cloud server side.
102. And simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information.
In the embodiment of the invention, the cloud server has a related operation function, and then a plurality of corresponding target driving application scenes can be simulated and constructed in advance according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information of the target vehicle.
It should be noted that driving application scenarios that can be derived from different current road information, different vehicle positions, and current driving environment information are also rich and diverse, and various application scenarios can be pre-constructed by using a mathematical modeling method in the embodiment of the present invention.
103. And searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not.
In the embodiment of the invention, the preset driving strategies for dealing with different driving application scenes according to different vehicle types can be stored in the cloud server side in advance, and then on the basis, the cloud server searches whether the driving strategies which can be matched exist or not by analyzing the attribute information of the target vehicle and the target driving application scenes.
It should be noted that the driving strategy according to the embodiment of the present invention is relative to the future road conditions and driving environments, such as: if it is known from the target vehicle position that 100 meters will enter the mountain lane in the future and the current weather environment is clear and a turning speed limit sign will appear 50 meters in the future, then the driving strategy may correspond to a driving strategy starting at least 120 meters.
104. And if the target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
In the embodiment of the invention, for a target vehicle, after the cloud server side acquires the corresponding target driving strategy, the target driving strategy is fed back and issued to the target vehicle side, so that the target vehicle controller receives the driving strategy, and corrects and generates the chassis prediction control strategy required in real time by using the driving strategy as basic operation along with the continuous running of the running vehicle, and then the target vehicle controller sends the prediction control strategy to the chassis control system to implement the corresponding prediction control operation.
The embodiment of the invention provides an intelligent chassis control method, communication between a target vehicle and a cloud server is set up in advance, then current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle in running are uploaded to the cloud server side, a corresponding target driving application scene is constructed through analysis and simulation of the cloud server, a target driving strategy matched with the attribute information and the target driving application scene of the target vehicle is further searched, and the cloud server feeds the target driving strategy back to the target vehicle, so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy. Compared with the prior art, the chassis control device solves the technical problems that the existing chassis control operation is not timely and the driving risk exists. According to the embodiment of the invention, the prediction control strategy of the chassis is acquired by means of auxiliary analysis of the cloud server, so that the corresponding chassis control strategy is provided as far as possible before similar emergency road conditions are met, the driving safety is improved, and the driving requirement control of a user is more intelligently met.
In order to explain the above embodiments in more detail, an embodiment of the present invention further provides another intelligent chassis control method, where an execution subject of the method is a cloud server, and as shown in fig. 2, the embodiment of the present invention provides the following specific steps:
201. historical driving sample information is obtained, and the historical driving sample information at least comprises historical driving record information corresponding to different vehicle types, different vehicle performances and different driving road conditions.
For historical driving sample information, more abundant and various historical driving record information is acquired from data dimensions of different vehicle types, different vehicle performances, different driving road conditions and the like, and the abundant and various historical driving record information is taken as a data sample, so that a cloud server can conveniently analyze the data sample by utilizing related operations to construct a preset mapping relation among vehicle attribute information, a driving application scene and a preset driving strategy.
In the embodiment of the present invention, the cloud server has a related computing capability, and in combination with step 201 and step 204, a preset mapping relationship between the vehicle attribute information, the driving application scenario, and the preset driving strategy is pre-established at the cloud server side.
202. And acquiring vehicle attribute information and driving application scenes corresponding to different vehicle attribute information by analyzing historical driving record information.
The embodiment of the invention utilizes a large amount of abundant and various historical driving record information as a data sample to analyze, analyze and perform related operation so as to summarize vehicle attribute information of different vehicle types and driving application scenes correspondingly constructed by the different vehicle attribute information.
203. And compiling a preset driving strategy according to the vehicle attribute information and the driving application scenes corresponding to different vehicle attribute information.
In the embodiment of the present invention, because the vehicle attribute information is different and the driving application scenarios are different, the required driving strategies are also different, so the step is mainly to compile corresponding preset driving strategies according to the historical data (the vehicle attribute information obtained in step 202 is different and the driving application scenarios are different).
204. And constructing a preset mapping relation among the vehicle attribute information, the driving application scene and the preset driving strategy according to the vehicle attribute information, the driving application scenes corresponding to different vehicle attribute information and the preset driving strategy.
In this way, in the embodiment of the invention, the preset mapping relation among the constructed vehicle attribute information, the driving application scene and the preset driving strategy is finally obtained on the cloud server side by related analysis and operation and by using historical sample data.
205. And receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle.
In the embodiment of the present invention, please refer to step 101 for the statement of this step, which is not described herein again.
206. And simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information.
In the embodiment of the present invention, please refer to step 102 for the statement of this step, which is not described herein again.
207. And searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not.
In the embodiment of the present invention, the target driving strategy matched with the target vehicle is further searched according to the preset mapping relationship among the vehicle attribute information, the driving application scenario and the preset driving strategy, which is constructed in steps 201 and 204, and the specific statement is as follows:
first, attribute information of a target vehicle and a target driving application scene are combined into a first data set. Forming a second data set by the vehicle attribute information and the driving application scenes contained in each group of mapping relations;
secondly, the first data set and the plurality of second data sets are respectively compared to judge whether the same data sets exist or not. If yes, vehicle attribute information and a preset driving strategy corresponding to the driving application scene contained in the same data set are obtained by searching a preset mapping relation and are used as a target driving strategy.
In the embodiment of the present invention, the multiple preset driving strategies of the cloud server just store the conditions matching the same attribute information of the target vehicle and the target driving application scenario, and then find the required conditions of the preset driving strategies. However, according to the comparison operation, if the same data set (i.e. the same attribute information of the target vehicle and the same target driving application scenario) does not exist, then further, the following method may be adopted to determine the preset driving policy corresponding to the target vehicle, which is specifically stated as follows:
firstly, according to preset strategy rules, a corresponding driving strategy is compiled according to the attribute information of the target vehicle and the target driving application scene. It should be noted that, in the embodiment of the present invention, the cloud server has the capabilities of correlation analysis and calculation, so that the corresponding driving strategy is compiled through correlation calculation according to the attribute information of the target vehicle corresponding to the target vehicle and the target driving application scenario. The programmed driving strategy is calculated in real time.
And secondly, respectively carrying out similarity comparison operation on the compiled driving strategy and a plurality of preset driving strategies to obtain a plurality of similarity values corresponding to the comparison operation. The maximum similarity value is obtained from the plurality of similarity values.
It should be noted that, in the embodiment of the present invention, the preset mapping relationship stored in the cloud server and the preset driving strategy existing in the relationship are obtained by performing analysis and related operations according to a large amount, rich and diverse historical sample data, so that the data reliability of the preset mapping relationship and the preset driving strategy is higher. Therefore, the embodiment of the invention utilizes the actual calculation to compile the function of comparing the similarity between the driving strategy and the preset driving strategy, and under the condition that the completely matched preset driving strategy is not found actually or actually, the embodiment of the invention can select to find the preset driving strategy which is most similar and matched as far as possible, thereby ensuring that the target driving strategy which is finally issued to the target vehicle is reliable in data.
And finally, if the maximum similarity value is judged to be larger than the preset threshold value, the corresponding preset driving strategy of the maximum similarity value is correspondingly compared and operated to serve as the target driving strategy matched with the attribute information of the target vehicle and the target driving application scene.
208a, if a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
For example, for different target driving strategies, a corresponding preset trigger distance may be matched in advance, where the preset trigger distance is a distance (for example, 20 meters) before reaching a certain specified position to trigger the target driving strategy, and in general, the size of the preset trigger distance is set to be mainly related to a current driving speed of a current vehicle.
It should be noted that, the chassis control system is mainly implemented by a chassis controller for specific control, and the chassis control system at least includes: the system comprises a steering control module, a stability control module, a suspension control module and four independent wheel control modules.
The chassis controller controls the steering control module, the stability control module and the suspension control module according to the received corresponding predictive control strategy, and as the modules are controlled, the four independent wheel control modules correspondingly control the wheels to work independently, and specifically, the detailed explanation of the realized control process is as follows:
and the at least four acceleration sensors are connected with the chassis controller, are respectively arranged on the four wheel modules below the vehicle body, and are used for detecting the instantaneous acceleration or the instantaneous speed of the wheel modules and sending the instantaneous acceleration or the instantaneous speed to the chassis controller.
And the at least four displacement sensors are connected with the chassis controller, are respectively arranged on the four wheel modules below the vehicle body, and are used for detecting the self stroke of the wheel modules after adjustment and sending the self stroke to the chassis controller.
And the at least four gyroscopes are connected with the chassis controller, are respectively arranged on the four wheel modules below the vehicle body, and are used for detecting the pitch angle, the side-tilting angle and the yaw angle of the wheel modules and sending the pitch angle, the side-tilting angle and the yaw angle to the chassis controller.
Four independent wheel control modules: the four wheels are designed as independent modules, each having four independent steering components for driving, braking, steering, and suspension.
For example, although the four wheel designs are independent of each other, the four control components of driving, braking, steering and suspension have certain following performance, in order to prevent the influence on the normal running of the vehicle, a difference threshold value of four independent wheel control modules is set, and when the threshold value is exceeded, a warning or emergency braking is given. For example, when steering, the left front wheel is steered by 90 degrees, and the right front wheel is steered by 0 degrees; when the suspension is controlled, the left front wheel suspension is lifted up by 10cm, the right front wheel suspension is lowered down by 20cm and the like, which all affect the safety of the vehicle.
A steering control module: the steering motor, the torque sensor and the resistance motor are included; the steering motor is connected with the steering control module, the steering motor is connected with the torque sensor through a first coupler, the torque sensor is connected with the resistance motor through a second coupler, and the resistance motor is connected with the real-time processor; the steering motor is used for assisting in completing steering operation according to the control of the steering control module; the torque sensor is used for measuring resistance torque generated by the resistance motor and sending the resistance torque to the real-time processor.
Then, the steering mechanism can be steered like a common vehicle (four-wheel steering, front wheel steering, crab steering and the like), and can also be steered to different directions simultaneously, namely any direction of the four wheels can be independently rotated, and the turning radius can be reduced to zero. When the vehicle turns in a narrow space, the vehicle is required to have higher maneuverability depending on the size of the turning radius, so the maneuverability is higher, and the turning radius is small, and in this case, the reverse phase steering is adopted.
Illustratively, when the vehicle needs to be parked laterally or steered in situ, or under some special working conditions and special driving environments, the steering at any angle within 360 degrees of the wheels can be controlled according to driving requirements, corresponding functions are realized, and the maneuvering flexibility of the vehicle is improved.
A stability control module: road surface roughness is too big, and the automobile body unstability produces multi freedom and rocks, and the automobile body barycenter effect has great transverse force and longitudinal force, takes place to heel or pitch motion, can lead to the vehicle to turn on one's side. When the environment of the field road is severe, the vehicle is easy to turn over under the control of over-bending steering due to the overhigh gravity center of the vehicle body. When the vehicle is detected to be about to roll, the controller calculates and sends an instruction to the actuator to control one side air spring (or hydro-pneumatic spring) to generate active actuating force, so that anti-roll moment is generated, and the vehicle is prevented from rolling over. When the impact is detected, firstly, the height of the vehicle body is quickly reduced, and meanwhile, the air spring (or the hydro-pneumatic spring) on the other side is controlled to generate corresponding active actuating force, so that the active anti-rollover function is realized. If the two sides of the road are raised, when a large obstacle is in front of the road, the vehicle chassis is low and cannot pass smoothly, and the vehicle chassis can be improved through the active suspension, so that the vehicle can pass the obstacle smoothly, and the passing performance of the vehicle is improved; when a severe road such as rain, snow and the like is encountered, in order to prevent slippage and improve the tire adhesion, the stability control module is controlled by combining road friction coefficients (four wheels are respectively given by a cloud server) to improve the vehicle stability;
a suspension control module: the suspension is an active suspension, and comprises an active air suspension or an active oil-gas suspension, and the suspension rigidity and the damping can be adjusted. If the suspension is an air suspension (the air suspension is an active suspension with an additional air chamber), the suspension control module is used for sending the air spring temperature and pressure sent by the air spring temperature sensor and the air spring pressure sensor and the information collected by the vehicle displacement sensor and the vehicle body vertical acceleration sensor to the controller, generating a driving signal of an air spring electromagnetic valve after calculation, implementing air inflation and deflation of an air bag, adjusting the rigidity and damping of the suspension in time and ensuring the vehicle to run.
As described above, the sensor signal processing provided by the embodiment of the present invention is performed through the kalman filter.
208b, if the target driving strategy matched with the attribute information of the target vehicle and the target driving application scene does not exist, alarm information is sent to the target vehicle, so that the target vehicle can calculate and analyze the corresponding chassis control strategy according to the current road surface information, the vehicle positioning information, the positioning corresponding map information, the current driving environment information and the attribute information of the target vehicle.
In the embodiment of the invention, the alarm information has the following functions: the prompt target vehicle controller can make a response as soon as possible, a prediction operation strategy for a chassis control system is obtained through analysis and calculation, and meanwhile, corresponding driving operation is prompted for a driver indirectly in time.
Further, as an implementation of the method shown in fig. 1 and fig. 2, an embodiment of the present invention provides an intelligent chassis control device. The embodiment of the apparatus corresponds to the embodiment of the method, and for convenience of reading, details in the embodiment of the apparatus are not repeated one by one, but it should be clear that the apparatus in the embodiment can correspondingly implement all the contents in the embodiment of the method. The device is applied to the auxiliary analysis and acquisition of the predictive control strategy for the chassis by means of the cloud server, and specifically as shown in fig. 3, the device comprises:
a receiving unit 31, configured to receive current road information, vehicle positioning information, positioning corresponding map information, current driving environment information, and attribute information of a target vehicle, where the target vehicle is driving;
the construction unit 32 is configured to simulate and construct a target driving application scene according to the current road information, the vehicle positioning information, the positioning corresponding map information, and the current driving environment information;
the searching unit 33 is configured to search whether a target driving strategy matching the attribute information of the target vehicle and the target driving application scenario exists;
and the feedback unit 34 is configured to, if a target driving strategy matching the attribute information of the target vehicle and the target driving application scenario exists, feed back the target driving strategy to the target vehicle, so that the target vehicle generates a corresponding chassis prediction control strategy according to the target driving strategy and performs prediction control on a chassis control system according to the chassis prediction control strategy.
Further, as shown in fig. 4, the apparatus further includes:
the obtaining unit 35 obtains historical driving sample information, where the historical driving sample information at least includes historical driving record information corresponding to different vehicle types, different vehicle performances, and different driving road conditions;
the obtaining unit 35 is further configured to obtain vehicle attribute information and driving application scenes corresponding to different pieces of vehicle attribute information by analyzing the historical driving record information;
the compiling unit 36 is configured to compile a preset driving strategy according to the vehicle attribute information and driving application scenes corresponding to different vehicle attribute information;
the establishing unit 37 is configured to establish a preset mapping relationship among the vehicle attribute information, the driving application scenario, and the preset driving strategy according to the vehicle attribute information, the driving application scenarios corresponding to different vehicle attribute information, and the preset driving strategy.
Further, as shown in fig. 4, the search unit 33 includes:
a composition module 331, configured to combine the attribute information of the target vehicle and the target driving application scenario into a first data set;
the composition module 331 is further configured to combine the vehicle attribute information and the driving application scene included in each group of mapping relationships into a second data set;
a comparing module 332, configured to compare the first data set with a plurality of the second data sets, respectively, to determine whether the same data set exists;
an obtaining module 333, configured to, if it is determined that the same data set exists, obtain, by searching for the preset mapping relationship, vehicle attribute information and a preset driving policy corresponding to a driving application scenario included in the same data set, and use the obtained information as a target driving policy.
Further, as shown in fig. 4, the search unit 33 further includes:
the compiling module 334 is configured to compile a corresponding driving strategy according to the attribute information of the target vehicle and the target driving application scenario according to a preset strategy rule if the same data set does not exist;
the comparison module 332 is further configured to perform similarity comparison operations on the compiled driving strategies and a plurality of preset driving strategies respectively to obtain a plurality of similarity values corresponding to the comparison operations;
the obtaining module 333, configured to obtain a maximum similarity value from the multiple similarity values;
a determining module 335, configured to compare the maximum similarity value with a preset driving policy corresponding to the maximum similarity value if it is determined that the maximum similarity value is greater than a preset threshold, and use the preset driving policy as a target driving policy matched with the attribute information of the target vehicle and the target driving application scenario.
Further, as shown in fig. 4, the apparatus further includes:
and the alarm unit 38 is configured to send alarm information to the target vehicle, so that the target vehicle calculates and analyzes a corresponding chassis control strategy according to the current road information, the vehicle positioning information, the positioning corresponding map information, the current driving environment information, and the attribute information of the target vehicle.
Further, as shown in fig. 4, the chassis control system at least includes: the system comprises a steering control module, a stability control module, a suspension control module, four independent wheel control modules and four independent wheel control modules; and the chassis control system controls the steering control module, the stability control module, the suspension control module and the four independent wheel control modules to execute control operation according to the received prediction control strategy.
The intelligent chassis control device comprises a processor and a memory, wherein the receiving unit, the constructing unit, the searching unit, the feedback unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the prediction control strategy of the chassis is obtained by adjusting the kernel parameters through the auxiliary analysis of the cloud server, so that the corresponding chassis control strategy is provided as far as possible before similar emergency road conditions are met, the driving safety is improved, and the driving requirement control of a user is more intelligently met.
An embodiment of the present invention provides a storage medium on which a program is stored, the program implementing the intelligent chassis control method when executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the intelligent chassis control method is executed when the program runs.
An embodiment of the present invention provides an electronic device 40, as shown in fig. 5, the device includes at least one processor 401, and at least one memory 402 and a bus 403 connected to the processor 401; the processor 401 and the memory 402 complete communication with each other through the bus 403; the processor 401 is used to call program instructions in the memory 402 to perform the intelligent chassis control method described above.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
an intelligent chassis control method, the method comprising: receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of a target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle; simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information; searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not; and if so, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An intelligent chassis control method, characterized in that the method comprises:
receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of a target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle;
simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information;
searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not;
and if so, feeding the target driving strategy back to the target vehicle so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
2. The method of claim 1, further comprising:
obtaining historical driving sample information, wherein the historical driving sample information at least comprises historical driving record information corresponding to different vehicle types, different vehicle performances and different driving road conditions;
acquiring vehicle attribute information and driving application scenes corresponding to different vehicle attribute information by analyzing the historical driving record information;
compiling a preset driving strategy according to the vehicle attribute information and driving application scenes corresponding to different vehicle attribute information;
and constructing a preset mapping relation among the vehicle attribute information, the driving application scene and the preset driving strategy according to the vehicle attribute information, the driving application scenes corresponding to different vehicle attribute information and the preset driving strategy.
3. The method of claim 2, wherein the finding whether the target driving strategy matching the attribute information of the target vehicle and the target driving application scenario exists comprises:
forming a first data set by the attribute information of the target vehicle and the target driving application scene;
forming a second data set by the vehicle attribute information and the driving application scenes contained in each group of mapping relations;
respectively comparing the first data set with a plurality of second data sets to judge whether the same data sets exist or not;
if so, acquiring vehicle attribute information and a preset driving strategy corresponding to a driving application scene contained in the same data set by searching the preset mapping relation, and taking the vehicle attribute information and the preset driving strategy as a target driving strategy.
4. The method of claim 3, wherein if there is no identical data set, the method further comprises:
according to preset strategy rules, compiling a corresponding driving strategy according to the attribute information of the target vehicle and the target driving application scene;
respectively carrying out similarity comparison operation on the compiled driving strategy and a plurality of preset driving strategies to obtain a plurality of similarity values corresponding to the comparison operation;
obtaining a maximum similarity value from the plurality of similarity values;
and if the maximum similarity value is judged to be larger than a preset threshold value, correspondingly comparing the maximum similarity value with a preset driving strategy corresponding to the operation, and taking the preset driving strategy as a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene.
5. The method of claim 1, wherein if there is no target driving strategy matching the attribute information of the target vehicle and the target driving application scenario, the method further comprises:
and sending alarm information to the target vehicle so that the target vehicle can calculate and analyze a corresponding chassis control strategy according to the current road surface information, the vehicle positioning information, the positioning corresponding map information, the current running environment information and the attribute information of the target vehicle.
6. The method of claim 1, wherein the chassis control system comprises at least: the system comprises a steering control module, a stable control module, a suspension control module and four independent wheel control modules; and the chassis control system controls the steering control module, the stability control module, the suspension control module and the four independent wheel control modules to execute control operation according to the received prediction control strategy.
7. An intelligent chassis control apparatus, the apparatus comprising:
the receiving unit is used for receiving current road surface information, vehicle positioning information, positioning corresponding map information, current driving environment information and attribute information of the target vehicle, wherein the current road surface information, the vehicle positioning information and the positioning corresponding map information are used for driving the target vehicle;
the construction unit is used for simulating and constructing a target driving application scene according to the current road surface information, the vehicle positioning information, the positioning corresponding map information and the current driving environment information;
the searching unit is used for searching whether a target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists or not;
and the feedback unit is used for feeding back the target driving strategy to the target vehicle if the target driving strategy matched with the attribute information of the target vehicle and the target driving application scene exists, so that the target vehicle can generate a corresponding chassis prediction control strategy according to the target driving strategy and implement prediction control on a chassis control system according to the chassis prediction control strategy.
8. The apparatus of claim 7, further comprising:
the method comprises the steps that an acquisition unit acquires historical driving sample information, wherein the historical driving sample information at least comprises historical driving record information corresponding to different vehicle types, different vehicle performances and different driving road conditions;
the obtaining unit is further configured to obtain vehicle attribute information and driving application scenes corresponding to different vehicle attribute information by analyzing the historical driving record information;
the compiling unit is used for compiling a preset driving strategy according to the vehicle attribute information and the driving application scenes corresponding to different vehicle attribute information;
and the establishing unit is used for establishing a preset mapping relation among the vehicle attribute information, the driving application scene and the preset driving strategy according to the vehicle attribute information, the driving application scenes corresponding to different vehicle attribute information and the preset driving strategy.
9. A storage medium comprising a stored program, wherein the apparatus on which the storage medium is located is controlled to perform the intelligent chassis control method according to any one of claims 1-6 when the program is executed.
10. An electronic device, comprising at least one processor, and at least one memory, bus connected to the processor;
the processor and the memory complete mutual communication through the bus;
the processor is configured to invoke program instructions in the memory to perform the intelligent chassis control method of any of claims 1-6.
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