CN116968493A - Suspension control method and device for automatic driving vehicle, vehicle and medium - Google Patents
Suspension control method and device for automatic driving vehicle, vehicle and medium Download PDFInfo
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- 239000000725 suspension Substances 0.000 title claims abstract description 121
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000011217 control strategy Methods 0.000 claims abstract description 306
- 238000011156 evaluation Methods 0.000 claims abstract description 67
- 238000010801 machine learning Methods 0.000 claims description 21
- 238000012549 training Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 description 15
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- 238000010586 diagram Methods 0.000 description 7
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
- B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
- B60G17/0182—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method involving parameter estimation, e.g. observer, Kalman filter
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Vehicle Body Suspensions (AREA)
Abstract
The invention discloses a suspension control method, a suspension control device, a suspension control vehicle and a suspension control medium for an automatic driving vehicle, wherein the method comprises the following steps: determining a driving environment of the vehicle in the driving environment database according to the environment information; determining a first control strategy in a preconfigured first functional logic set according to the driving environment; controlling the operation of the vehicle based on the first control strategy, and acquiring first vehicle data of the vehicle when the first control strategy is executed; determining a second control strategy corresponding to the driving environment according to the first vehicle data; comparing the first control strategy with the second control strategy to determine a first evaluation score; and determining a target control strategy according to the first evaluation score. The method can solve the problems of inaccurate suspension control and low efficiency of the automatic driving vehicle and ensure the driving safety of the vehicle.
Description
Technical Field
The present invention relates to the field of autopilot technology, and in particular, to a suspension control method and apparatus for an autopilot vehicle, a vehicle, and a medium.
Background
In the related art, suspension control of an automatic driving vehicle is an important component in the automatic driving technology, key parameters such as the posture, the acceleration, the road surface condition, the driving environment of the vehicle and the like of the vehicle need to be perceived, intelligent decisions are made based on the information, and the control strategy of suspension is switched according to different driving modes and scenes, however, in complex traffic scenes, precise control of the suspension of the vehicle is difficult to realize, so that the driving safety of the vehicle cannot be ensured.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a suspension control method, apparatus, vehicle, and medium for an autonomous vehicle that can achieve precise control of the suspension of the vehicle.
A suspension control method of an autonomous vehicle, comprising the steps of:
determining a driving environment database, acquiring environment information around a vehicle, and determining the driving environment of the vehicle in the driving environment database according to the environment information;
determining a first control strategy in a preconfigured first functional logic set according to the driving environment;
controlling the operation of the vehicle based on the first control strategy, and acquiring first vehicle data of the vehicle when the first control strategy is executed;
Determining a second control strategy corresponding to the driving environment according to the first vehicle data;
comparing the first control strategy with the second control strategy to determine a first evaluation score; the first evaluation score characterizes a similarity of the first control strategy and the second control strategy;
determining a target control strategy according to the first evaluation score; the target control strategy is used for controlling the suspension of the vehicle.
A suspension control apparatus of an autonomous vehicle, comprising the steps of:
the first determining module is used for determining a driving environment database, acquiring environment information around a vehicle and determining the driving environment of the vehicle in the driving environment database according to the environment information;
the second determining module is used for determining a first control strategy in a preconfigured first functional logic set according to the driving environment;
the acquisition module is used for controlling the operation of the vehicle based on the first control strategy and acquiring first vehicle data of the vehicle when the first control strategy is executed;
a third determining module, configured to determine a second control strategy corresponding to the driving environment according to the first vehicle data;
A fourth determining module, configured to compare the first control policy with the second control policy, and determine a first evaluation score; the first evaluation score characterizes a similarity of the first control strategy and the second control strategy;
a fifth determining module, configured to determine a target control policy according to the first evaluation score; the target control strategy is used for controlling the suspension of the vehicle.
An autonomous vehicle comprising a memory storing a computer program and a processor implementing the steps of the method for controlling the suspension of an autonomous vehicle when the processor executes the computer program.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the suspension control method of an autonomous vehicle described above.
According to the suspension control method, the suspension control device, the vehicle and the medium for the automatic driving vehicle, the driving environment of the vehicle is determined in the driving environment database through the environment information around the vehicle, the first control strategy corresponding to the driving environment is determined based on the first functional logic set, the second control strategy is determined by utilizing the vehicle data generated when the vehicle executes the first control strategy, and the target control strategy for controlling the vehicle suspension is generated by utilizing the first control strategy and the second control strategy, so that the vehicle suspension can be controlled in an effective and safe mode, and the efficiency and the accuracy of suspension control are improved.
Drawings
FIG. 1 is a schematic diagram of a system architecture for suspension control of an autonomous vehicle in one embodiment;
FIG. 2 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
FIG. 3 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
FIG. 4 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
FIG. 5 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
FIG. 6 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
FIG. 7 is a flow chart of a method of suspension control of an autonomous vehicle in one embodiment;
fig. 8 is a block diagram showing a configuration of a suspension control apparatus of an autonomous vehicle according to an embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Before explaining the embodiments of the present application in detail, a suspension control strategy in the related art will be briefly described.
The suspension control of an autonomous vehicle is an important component in the automatic driving technology, and is mainly used for providing higher-level suspension control so as to adapt the vehicle to different driving conditions and road surface conditions, thereby improving the driving safety, comfort and performance.
An autonomous vehicle is able to make suspension control strategies based on key parameters such as vehicle attitude, acceleration, road surface conditions, vehicle driving environment, etc., by sensing these information. However, suspension control of autonomous vehicles still presents some problems and challenges. One of them is how to achieve accurate suspension control under complex road conditions, urban roads and different road conditions bring about many variables, such as potholes, bumps and different road friction coefficients, which affect the performance of the suspension system, so advanced algorithms and control strategies need to be developed to adapt to different road conditions and provide accurate suspension control strategies.
Another challenge is that an autonomous vehicle needs to make decisions on suspension adjustment in a short time and generate corresponding control command execution to ensure stability and ride comfort of the vehicle, during which safe driving of the vehicle is critical, and thus achieving efficient and safe suspension control is another challenge to be addressed.
Based on the above, the embodiment of the application can determine the target control strategy in an effective and safe manner through the first control strategy and the second control strategy according to the current driving condition, thereby realizing the suspension control of the vehicle and overcoming the defects in the related art.
As shown in fig. 1, the suspension control method of the autonomous vehicle provided by the application can be implemented by the system architecture shown in fig. 1. Wherein the vehicle-mounted computer 101, the environment recognition module 102 and the control module 103 constitute a suspension control system.
In this system, the environment recognition module 102 may include an environment sensor (camera, lidar, ultrasonic radar, etc.), a vehicle body acceleration sensor, a suspension displacement sensor, a tire deformation sensor. The environment recognition module 102 can collect environment data around the vehicle, the environment recognition module 102 is connected with the vehicle-mounted computer 101 through the wire harness 21, and the environment data collected by the environment recognition module 102 can be transmitted to the vehicle-mounted computer 101 through the wire harness 21.
The vehicle-mounted computer 101 can process the environmental data, determine the current driving environment of the autonomous vehicle, and generate a first control vehicle for controlling the vehicle suspension using a first set of function logic configured in advance.
The vehicle-mounted computer 101 is connected with the control module 103 through the wire harness 22, the first control strategy is transmitted to the control module 103, and the control module 103 controls the suspension of the vehicle according to the first control strategy. The control module can comprise an electronic control type suspension system, an active driving comfort control system, an active electromagnetic induction suspension system, an electronic stabilization program and the like.
The vehicle-mounted computer 101 determines a second control strategy by using vehicle data generated by executing the first control strategy by the automatic driving vehicle, analyzes, evaluates and compares the first control strategy and the second control strategy to obtain a first evaluation score, and determines a target control strategy by using the first evaluation score.
The vehicle-mounted computer 101 transmits the target control strategy to the control module 103 through the wire harness 22, and the control module 103 controls the suspension of the vehicle, so that the efficiency and safety of suspension control can be improved.
The following is a detailed description of implementation details of the technical solution of the embodiment of the present application.
In one embodiment, as shown in fig. 2, a suspension control method of an autonomous vehicle is provided, and the suspension control method of an autonomous vehicle is described as an example in which the method is applied to the suspension control system of an autonomous vehicle in fig. 1, and may include the steps of:
Step S201, a driving environment database is determined, and environmental information around the vehicle is acquired, and the driving environment of the vehicle is determined in the driving environment database according to the environmental information.
Here, there is a certain correlation between the vehicle suspension control and the driving environment, and the vehicle suspension control strategy is affected by the difference of the driving environment, such as the occurrence of a depression in the road, jolt and different road surface friction coefficients. For example, on highways, drivers often pursue stability and comfort, and thus suspension control strategies may adjust the stiffness and damping of the suspension system to provide better stability and reduce roll of the vehicle. Based on this, it is necessary to perform an evaluation of the driving environment so that the driving environment of the vehicle can be understood as comprehensively as possible and a reliable guidance is provided for the suspension control strategy selected and executed by the vehicle.
The driving environment in which the vehicle is located can be determined by collecting environmental information around the vehicle. In practical application, the sensor and the external equipment on the vehicle can be utilized to collect and sense the environmental information. The following are common environmental information and the manner in which the environmental information is obtained:
(1) And a video camera. The cameras installed on the vehicles can capture images of roads and surrounding environments, and can identify information such as road marks, traffic signs, intersections, other vehicles and the like through image processing and computer vision technologies.
(2) And (5) laser radar. The lidar may generate a three-dimensional point cloud image of the surrounding environment of the vehicle by emitting a laser beam and measuring its return time, and by analyzing the point cloud data, surrounding vehicles, pedestrians, obstacles, etc. may be detected and tracked.
(3) And (5) radar. Radar can detect the position and speed of surrounding objects by emitting radio waves and measuring their return time, and radar on vehicles can be used to detect and track other vehicles, pedestrians, obstacles, etc.
(4) An ultrasonic sensor. The ultrasonic sensor may measure the distance and position of objects around the vehicle for detecting obstacles near the vehicle.
(5) A vehicle body acceleration sensor. The vehicle body acceleration sensor can change acceleration and posture of the vehicle in the running process. Such information may be used to determine the running state of the vehicle and the road surface condition.
(6) Suspension displacement sensor. Suspension displacement sensors can measure displacement and deformation of the suspension system. This information can be used to determine the operating state of the suspension system and adjust suspension parameters.
(7) A tyre deformation sensor. The tire deformation sensor may measure the deformation and pressure of the tire and may be used to determine the state of the tire and the road surface condition.
The sensors can be used for data fusion and processing through a sensing system of the vehicle so as to acquire environment information of the vehicle, and corresponding driving environments can be determined in a driving database through analyzing and interpreting the environment information, wherein the driving environments can comprise traffic signs, traffic lights, lane information and the like, and are of great importance to suspension control of the automatic driving vehicle.
In determining the driving environment of the vehicle, the collected environmental information may be compared with the features corresponding to the driving environments stored in the driving environment database, so that a probability value obtained by comparing the collected environmental information with each driving environment may be obtained, and the probability value may be used to describe a matching probability of the collected environmental information with the compared driving environments, where a higher probability value indicates a higher probability that the collected environmental information belongs to the compared driving environment.
In practical application, the probability value may be compared with a preset threshold, and when the probability value is higher than the preset threshold, it may be determined that the vehicle is determined to be in the compared driving environment, thereby completing the determination of the driving environment of the vehicle. When there are a plurality of probability values greater than the preset threshold value, the driving environment corresponding to the highest probability value may be determined as the driving environment of the vehicle.
In practical application, the vehicle can also be downloaded from the server periodically to update the data in the driving environment library, so that the accuracy of the data in the driving database can be further improved, the driving environment of the vehicle can be accurately determined, and the driving safety is improved.
Step S202, determining a first control strategy in a preconfigured first functional logic set according to driving environment.
Here, after determining the driving environment, the first control strategy for the vehicle suspension control adapted to the driving environment of the vehicle can be determined according to the driving environment of the vehicle, so that the suspension control of the vehicle can be made to conform to the current driving environment of the vehicle, and for example, in the case of full load, the hardness and damping of the vehicle can be increased to support a larger load and keep the vehicle body stable.
It should be noted that the first control strategy is not a simple digital or analog control command, for example, a vehicle turns right, but a detailed control strategy, for example, a vehicle suspension control strategy including a lateral component and a longitudinal component, and the lateral component and the longitudinal component in the suspension control strategy are briefly described below:
the lateral component includes lateral stability control and steering response control, wherein the lateral stability control adjusts the stiffness and damping of the suspension system by tuning to reduce roll of the vehicle body and provide better steering stability. Steering response control adjusts the stiffness and damping of the suspension system to reduce roll of the vehicle body and provide better steering stability.
The longitudinal component includes a vibration damping control and a load adjusting control, wherein the vibration damping control adjusts damping of the suspension system according to the unevenness of the road surface and the longitudinal acceleration of the vehicle to provide a better vibration damping effect and riding comfort. The load adjustment control is to adjust the stiffness and damping of the suspension system to provide better suspension adjustment and stability depending on the load and passenger carrying conditions of the vehicle.
Step S203 controls the operation of the vehicle based on the first control strategy, and acquires first vehicle data of the vehicle when the first control strategy is executed.
In practical application, the vehicle can convert the first control strategy into an actual suspension control command and execute the actual suspension control command, so that the vehicle suspension can be controlled based on the first control strategy.
In the process of the vehicle executing the first control strategy, first vehicle data generated by the vehicle when executing the first control strategy can be acquired, wherein the first vehicle data can comprise the posture, the acceleration and the rotation speed of the vehicle, the displacement and the stroke of a suspension system, the rigidity of a suspension damper and the like.
In this embodiment, the first vehicle data provides real-time data regarding the vehicle state, environmental conditions, and behavior, and may be used to monitor the movement of the vehicle during execution of the first control strategy, thereby evaluating the effect of the execution of the first control strategy and making necessary adjustments to the first control strategy.
In practical application, after the first vehicle data is acquired, the first vehicle data can be uploaded to the server, so that the server can synchronously learn the first vehicle data uploaded by all vehicles, and accurate driving environment data can be continuously enriched and provided.
Step S204, determining a second control strategy corresponding to the driving environment according to the first vehicle data.
Here, by analyzing the first vehicle data, the execution condition of the first control strategy can be determined, and then the first control strategy is adjusted according to the first vehicle data, so that the suspension control of the vehicle more accords with the driving environment of the vehicle. Likewise, the second control strategy here is not a simple digital or analog control command, but a detailed control strategy, which may be, for example, a vehicle suspension control strategy that includes a lateral component and a longitudinal component.
It should be noted that, the second control strategy is formulated based on the real-time data and the driving environment, so the second control strategy may be adjusted according to different factors, and is generated according to the individual requirements of the vehicle.
In one embodiment, FIG. 3 is a flow chart diagram of a method of suspension control of an automatic vehicle.
Step S301, inputting the first vehicle data into the set machine learning framework for training, so as to obtain a second functional logic set.
After the first vehicle data is acquired, the first vehicle data is input into a set machine learning framework, which can learn any function of the suspension system through the input first vehicle data, thereby obtaining a corresponding second functional logic set.
The machine learning framework is set to be a machine learning framework specially used for simulating and controlling driving behaviors, the behaviors of a driver can be learned and predicted through a machine learning algorithm to generate a second functional logic set, and more intelligent and self-adaptive driving learning can be realized, so that a vehicle can make more accurate suspension control decisions according to actual conditions.
In practical applications, by taking the first vehicle data as input, processing and analyzing the input first vehicle data by setting a machine learning framework, meaningful features can be extracted from the first vehicle data, the features can help a machine learning algorithm understand modes and correlations in the data, and then the features are trained and modeled by using the machine learning algorithm, such as deep learning, decision trees, support vector machines and the like, so that a second functional logic set can be finally generated.
The second set of functional logic is trained using a large amount of vehicle data, which allows finer suspension control of the vehicle and better accommodates variations in conditions and environments.
Step S302, determining a second control strategy corresponding to the driving environment in the second functional logic set.
Here, in the second set of functional logic, the corresponding second control strategy is determined for the driving environment. Likewise, the second control strategy here is not a simple digital or analog control command, but a detailed control strategy, which may be, for example, a vehicle suspension control strategy that includes a lateral component and a longitudinal component.
It should be noted that, the second control strategy is determined based on the second set of functional logic, and can be adaptively selected according to actual data of the vehicle, so as to cope with more complex and variable driving situations, and is generated according to individual requirements of the vehicle.
Step S205, comparing the first control strategy with the second control strategy to determine a first evaluation score.
Here, comparing the first control strategy with the second control strategy involves comparing differences in parameters, behaviors, conditions, etc. of the first control strategy with the second control strategy, thereby determining a first evaluation score, wherein the first evaluation score is capable of determining a similarity between the first control strategy and the second control strategy, and when the first evaluation score is higher, it indicates that the first control strategy is more similar to the second control strategy.
In one embodiment, FIG. 4 is a flow chart diagram of a method of suspension control of an automatic vehicle.
Step S401, determining a similarity of each control parameter between the first control strategy and the second control strategy.
Step S402, determining a first evaluation score according to the similarity of each control parameter and a preset weight coefficient corresponding to each control parameter.
The comparison matrix Y is assumed for performing an evaluation of the control strategy, wherein key parameters and thresholds of the comparison matrix Y may be preset.
Assume that the comparison matrix Y has n parameters, each with n thresholds:
assuming that each threshold score weight is w, then w is:
thereby can get a comparison with
Wherein w is k Refers to the kth weight, satisfies w k ≥0，k＝1，2，...，n 1 *n 2 . Comparison factorIs the minimum deviation of the control strategy evaluation.
Assume that the control parameters included in the first control strategy are:
assume that the control parameters included in the second control strategy are:
comparing the similarity of the first control strategy and the second control strategy to obtain a first evaluation score
vec(T)＝vec(t 1 )-vec(t 2 )
When (when)The target control strategy may be derived by combining the first control strategy and a second control strategy, wherein the second control strategy is an optimization of the first control strategy being executed by the vehicle.
Step S206, determining a target control strategy according to the first evaluation score.
Here, it is determined whether or not the first control strategy and the second control strategy need to be combined according to the first evaluation score, thereby obtaining a corresponding target control strategy.
In practical application, after the target control strategy is determined, the target control strategy can be converted into a corresponding control instruction, so that the control module responds to the control instruction, and suspension control of the vehicle is realized. In this embodiment, the control module may include an electronically controlled suspension system, an active ride comfort control system, an active electromagnetic induction suspension system, an electronic stability program, and the like. Wherein, the liquid crystal display device comprises a liquid crystal display device,
an electronically controlled suspension system is an advanced suspension system that automatically adjusts suspension stiffness and height in response to control commands to provide better ride comfort and handling performance;
an active ride comfort control system is a system that provides better ride comfort by adjusting the suspension system and other related systems of the vehicle, and provides a more comfortable driving and riding experience by automatically adjusting suspension stiffness, shock absorber and seat adjustment, etc. in response to control commands to minimize bump and vibration of the vehicle and passengers.
The active electromagnetic induction suspension system is an advanced suspension system, and the active control of the vehicle suspension system is realized by utilizing the electromagnetic induction principle through responding to a control command.
The electronic stability program is a dynamic stability control system for vehicles that can detect potential runaway conditions of the vehicle, such as sideslip, stall, etc., and reduce the risk of runaway by adjusting braking force and engine torque in response to control commands.
In this embodiment, the determined target control policy has two cases, where the first case is that the target control policy is the first control policy, that is, the first control policy will be continuously executed; the second case is where the target control strategy is a combination of the first control strategy and the second control strategy.
It will be appreciated that the first control strategy and the second control strategy take into account the suspension control of the vehicle from both longitudinal and transverse directions respectively, and that a combination of both results in a more comprehensive suspension control decision that can take into account the overall motion state of the vehicle. In addition, in the actual running process of the vehicle, a plurality of complex driving environments can be met, and the capability of coping with complex driving scenes can be improved by integrating the first control strategy and the second control strategy to generate the target control strategy, so that the reliability of suspension control of the automatic driving vehicle is improved, and the vehicle can be ensured to run safely.
In one embodiment, the first control strategy and the second control strategy are combined to obtain the target control strategy in case the first evaluation score is greater than a first preset threshold.
Here, the calculated first evaluation score is compared with a preset threshold value. This threshold may be set according to specific requirements for determining whether a combining strategy is required. Under the condition that the first evaluation score is larger than a first preset threshold value, the first control strategy and the second control strategy are similar, and because the second control strategy is obtained by using vehicle data generated in the process of executing the first control strategy, the second control strategy can better determine the execution effect of the first control strategy and the complexity of a driving environment, namely, the second control strategy is the optimization of the executing first control strategy, and based on the optimization, the first control strategy and the second control strategy are combined to obtain a target control strategy, so that the vehicle suspension is controlled through the target control strategy, and the more control effect can be achieved.
In practical application, the combination of the first control strategy and the second control strategy may be selected according to specific situations and requirements, and the following may be implemented:
(1) Fusion strategy. The first control strategy and the second control strategy are fused to comprehensively utilize the advantages of the two control strategies, and in practical application, the output of the two control strategies can be combined through weighted average, logic operation or other modes to obtain the target control strategy.
(2) Hierarchical policies. The first control strategy is used as a main strategy, and the second control strategy is used as an auxiliary strategy. The primary strategy is responsible for the primary control tasks and the secondary strategy provides additional support and adjustment when needed. The relationship of the first control strategy and the second control strategy may be managed by priority, weight, or other means.
In the present embodiment, the operation effect of the first control strategy is evaluated by using the first evaluation score, and the first control strategy executed by the vehicle can be finely adjusted according to the driving environment of the vehicle, so that the suspension of the vehicle can be controlled more accurately and safely.
In one embodiment, FIG. 5 is a flow chart diagram of a method of suspension control of an autonomous vehicle.
In step S501, the second vehicle data is input to the set machine learning framework for training, so as to obtain the third functional logic set.
In step S502, a third control strategy corresponding to the driving environment is determined in the third functional logic set.
Step S503, determining the target control strategy according to the first control strategy, the second control strategy and the third control strategy.
In this embodiment, when the first evaluation score is less than or equal to the first preset threshold, it indicates that there is a large deviation between the first control strategy and the second control strategy, and further, the second vehicle data needs to be further introduced for learning. Wherein the second vehicle data characterizes data pertaining to the second set of functional logic, including data obtained during generation of the second control strategy based on the second set of functional logic, and data obtained during comparison of the second control strategy with the first control strategy.
The second vehicle data is input into the set machine learning framework such that the set machine learning framework is capable of learning the second vehicle data to generate a third set of functional logic.
After the third set of functional logic is acquired using machine learning, a third control strategy corresponding to the driving situation of the vehicle is determined using the third set of functional logic. Here, the third control strategy is not a simple digital or analog control command, such as a vehicle suspension damping increase or decrease, but a detailed control strategy, such as a vehicle suspension control strategy that includes a lateral component and a longitudinal component.
After the third control strategy is determined, the target control strategy is finally generated by analyzing, evaluating and comparing the first control strategy, the second control strategy and the third control strategy. In the present embodiment, the suspension control safety of the vehicle is further improved by providing three sets of functional logic and three control strategies based on the same driving situation.
In one embodiment, FIG. 6 is a flow chart diagram of a method of suspension control of an automatic vehicle.
And step S601, comparing the first control strategy, the second control strategy and the third control strategy to determine a second evaluation score of the three items.
In step S602, the first control strategy, the second control strategy and the third control strategy are combined to obtain the target control strategy when the second evaluation scores of at least two items are greater than the first preset threshold.
Here, comparing and analyzing the first control strategy, the second control strategy, and the third control strategy can result in a second evaluation score of three termsAnd->Wherein, the liquid crystal display device comprises a liquid crystal display device,
second evaluation scoreIs obtained by comparing and analyzing the first control strategy with the third control strategy, and the second evaluation score +.>Can be used to describe the similarity between the first control strategy and the third control strategy.
Second evaluation scoreIs obtained by comparing and analyzing a second control strategy with a third control strategy, a second evaluation score +.>Can be used to describe the similarity between the second control strategy and the third control strategy.
Second evaluation scoreIs obtained by comparing and analyzing the first control strategy, the second control strategy and the third control strategy together, and the second evaluation score +.>Can be used to describe the similarity between the first control strategy, the second control strategy, and the third control strategy.
Of course, the specific calculation manner of the second evaluation score may be the specific calculation manner of the first evaluation score.
Upon determining a second evaluation scoreAnd->Thereafter, a second evaluation score is specified +.>And->And under the condition that at least two items of the control strategy are larger than a first set threshold value, combining the first control strategy, the second control strategy and the third control strategy to obtain a target control strategy.
The combination of the first control strategy, the second control strategy and the third control strategy can also refer to the combination of the first control strategy and the second control strategy.
In one embodiment, FIG. 7 is a flow chart diagram of a method of suspension control of an automatic vehicle.
In step S701, the third vehicle data is input to the set machine learning framework for training, and a fourth functional logic set is obtained.
In step S702, a fourth control strategy corresponding to the driving environment is determined in the fourth functional logic set, and the fourth control strategy is determined as the target control strategy.
In the present embodiment, in the case of introducing the third control strategy, if there are no second evaluation scores of at least two of the three second evaluation scores that are greater than the first preset threshold, it is necessary to learn third vehicle data by using the set machine learning framework, thereby outputting a fourth functional logic set, wherein the third vehicle data is data related to the third functional logic set, including data obtained in the process of generating the third control strategy based on the third functional logic set, and data obtained in the process of comparing the first control strategy, the second control strategy, and the third control strategy.
After the fourth set of functional logic is output by setting the machine learning framework, a fourth control strategy corresponding to the driving environment is determined using the fourth set of functional logic, wherein the fourth control strategy is to be the target control strategy. In practical applications, the fourth control strategy enables the vehicle to be operated in a safe state, i.e. the fourth control strategy is a driving control strategy in a safe form.
In the above embodiment, the driving environment of the vehicle is determined by using the environmental information around the vehicle, the first control strategy and the second control strategy are determined by the driving environment of the vehicle, and the target control strategy is determined by using the first control strategy and the second control strategy, so that the first control strategy and the second control strategy are integrated to obtain a comprehensive control strategy about the suspension of the vehicle, thereby coping with the complex driving environment and ensuring that the vehicle can run safely.
In one embodiment, there is provided a suspension control apparatus of an autonomous vehicle, referring to fig. 8, the suspension control apparatus 800 of the autonomous vehicle may include: a first determination module 801, a second determination module 802, an acquisition module 803, a third determination module 804, a fourth determination module 805, and a fifth determination module 806.
The first determining module 801 is configured to determine a driving environment database, obtain environmental information around a vehicle, and determine a driving environment of the vehicle in the driving environment database according to the environmental information; the second determining module 802 is configured to determine, according to the driving environment, a first control strategy in a first set of preconfigured functional logics; the obtaining module 803 is configured to control operation of the vehicle based on the first control policy, and obtain first vehicle data of the vehicle when the first control policy is executed; the third determining module 804 is configured to determine, according to the first vehicle data, a second control policy corresponding to the driving environment; the fourth determining module 805 is configured to compare the first control strategy and the second control strategy to determine a first evaluation score; the first evaluation score characterizes a similarity of the first control strategy and the second control strategy; a fifth determining module 806 is configured to determine a target control policy according to the first evaluation score; the target control strategy is used for controlling the suspension of the vehicle.
In one embodiment, the third determining module 804 is specifically configured to input the first vehicle data into a set machine learning framework for training to obtain a second functional logic set; and determining a second control strategy corresponding to the driving environment in the second functional logic set.
In one embodiment, in a case where the first evaluation score is greater than a first preset threshold, the fifth determining module 806 is specifically configured to combine the first control strategy and the second control strategy to obtain the target control strategy.
In one embodiment, in the case that the first evaluation score is less than or equal to the first preset threshold, the fifth determining module 806 is further configured to input the second vehicle data to the set machine learning framework for training, to obtain a third functional logic set; the second vehicle data characterizes data related to the second set of functional logic; determining a third control strategy corresponding to the driving environment in the third functional logic set; and determining the target control strategy according to the first control strategy, the second control strategy and the third control strategy.
Further, the fifth determining module 806 is specifically configured to compare the first control policy, the second control policy, and the third control policy to determine a second evaluation score of the three items; the third evaluation score includes a similarity of the first control strategy and the third control strategy, a similarity of the second control strategy and the third control strategy, a similarity of the first control strategy, the second control strategy and the third control strategy; and combining the first control strategy, the second control strategy and the third control strategy to obtain the target control strategy under the condition that the second evaluation scores of at least two items are larger than the first preset threshold value.
In one embodiment, in the case where at least two of the second evaluation scores are less than or equal to the first preset threshold, the fifth determining module 806 is further configured to input third vehicle data into the set machine learning framework for training, to obtain a fourth functional logic set; the third vehicle data characterizes data associated with the third functional logic; determining a fourth control strategy corresponding to the driving environment in the fourth functional logic set, and determining the fourth control strategy as the target control strategy; the fourth control strategy is used for controlling the vehicle to run in a safe state.
In one embodiment, the fifth determining module 806 is specifically configured to determine a similarity of each control parameter between the first control strategy and the second control strategy; and determining the first evaluation score according to the similarity of each control parameter and a preset weight coefficient corresponding to each control parameter.
The specific limitation regarding the suspension control apparatus of the autonomous vehicle may be referred to the limitation of the suspension control method of the autonomous vehicle hereinabove, and will not be described in detail herein. The respective modules in the suspension control apparatus of the autonomous vehicle described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A suspension control method of an autonomous vehicle, comprising:
determining a driving environment database, acquiring environment information around a vehicle, and determining the driving environment of the vehicle in the driving environment database according to the environment information;
determining a first control strategy in a preconfigured first functional logic set according to the driving environment;
Controlling the operation of the vehicle based on the first control strategy, and acquiring first vehicle data of the vehicle when the first control strategy is executed;
determining a second control strategy corresponding to the driving environment according to the first vehicle data;
comparing the first control strategy with the second control strategy to determine a first evaluation score; the first evaluation score characterizes a similarity of the first control strategy and the second control strategy;
determining a target control strategy according to the first evaluation score; the target control strategy is used for controlling the suspension of the vehicle.
2. The method of claim 1, wherein the determining a second control strategy corresponding to the driving environment based on the first vehicle data comprises:
inputting the first vehicle data into a set machine learning framework for training to obtain a second functional logic set;
and determining a second control strategy corresponding to the driving environment in the second functional logic set.
3. The method of claim 2, wherein determining a target control strategy based on the first evaluation score comprises:
And combining the first control strategy and the second control strategy to obtain the target control strategy under the condition that the first evaluation score is larger than a first preset threshold value.
4. The method according to claim 3, wherein, in the case where the first evaluation score is equal to or less than a first preset threshold, the determining a target control strategy according to the first evaluation score includes:
inputting second vehicle data into a set machine learning framework for training to obtain a third functional logic set; the second vehicle data characterizes data related to the second set of functional logic;
determining a third control strategy corresponding to the driving environment in the third functional logic set;
and determining the target control strategy according to the first control strategy, the second control strategy and the third control strategy.
5. The method of claim 4, wherein the determining the target control strategy based on the first control strategy, the second control strategy, and the third control strategy comprises:
comparing the first control strategy, the second control strategy and the third control strategy to determine a second evaluation score of the three items; the third evaluation score includes a similarity of the first control strategy and the third control strategy, a similarity of the second control strategy and the third control strategy, a similarity of the first control strategy, the second control strategy and the third control strategy;
And combining the first control strategy, the second control strategy and the third control strategy to obtain the target control strategy under the condition that the second evaluation scores of at least two items are larger than the first preset threshold value.
6. The method according to claim 5, wherein in case at least two of the second evaluation scores are smaller than or equal to the first preset threshold value, the method further comprises:
inputting third vehicle data into the set machine learning framework for training to obtain a fourth functional logic set; the third vehicle data characterizes data associated with the third functional logic;
determining a fourth control strategy corresponding to the driving environment in the fourth functional logic set, and determining the fourth control strategy as the target control strategy; the fourth control strategy is used for controlling the vehicle to run in a safe state.
7. The method of claim 1, wherein the comparing the first control strategy to the second control strategy to determine the first evaluation score comprises:
determining a similarity of each control parameter between the first control strategy and the second control strategy;
And determining the first evaluation score according to the similarity of each control parameter and a preset weight coefficient corresponding to each control parameter.
8. A suspension control apparatus for an autonomous vehicle, comprising:
the first determining module is used for determining a driving environment database, acquiring environment information around a vehicle and determining the driving environment of the vehicle in the driving environment database according to the environment information;
the second determining module is used for determining a first control strategy in a preconfigured first functional logic set according to the driving environment;
the acquisition module is used for controlling the operation of the vehicle based on the first control strategy and acquiring first vehicle data of the vehicle when the first control strategy is executed;
a third determining module, configured to determine a second control strategy corresponding to the driving environment according to the first vehicle data;
a fourth determining module, configured to compare the first control policy with the second control policy, and determine a first evaluation score; the first evaluation score characterizes a similarity of the first control strategy and the second control strategy;
A fifth determining module, configured to determine a target control policy according to the first evaluation score; the target control strategy is used for controlling the suspension of the vehicle.
9. An autonomous vehicle comprising a memory and a processor, the memory having a computer program, the processor implementing the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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