CN116512837A - Control method and system for vehicle magneto-rheological damper integrating visual detection - Google Patents
Control method and system for vehicle magneto-rheological damper integrating visual detection Download PDFInfo
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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
- B60G17/0152—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 action on a particular type of suspension unit
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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
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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
- B60G17/0162—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 mainly during a motion involving steering operation, e.g. cornering, overtaking
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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
- B60G17/019—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 type of sensor or the arrangement thereof
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60G17/06—Characteristics of dampers, e.g. mechanical dampers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16F—SPRINGS; SHOCK-ABSORBERS; MEANS FOR DAMPING VIBRATION
- F16F9/00—Springs, vibration-dampers, shock-absorbers, or similarly-constructed movement-dampers using a fluid or the equivalent as damping medium
- F16F9/32—Details
- F16F9/53—Means for adjusting damping characteristics by varying fluid viscosity, e.g. electromagnetically
- F16F9/535—Magnetorheological [MR] fluid dampers
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- B60G2400/80—Exterior conditions
- B60G2400/82—Ground surface
- B60G2400/821—Uneven, rough road sensing affecting vehicle body vibration
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Abstract
The invention discloses a control method and a control system for a vehicle magneto-rheological damper integrating visual detection, wherein the system comprises a controllable power supply, a plurality of magneto-rheological dampers, a gesture sensor, a vehicle speed sensor, a vibration sensor, a camera, a controller and a cradle head; the method comprises the following steps: 1) judging the type of the generated road condition parameters, if the road condition parameters are not generated at the same time, entering the step 2), otherwise, entering the step 3); 2) Sequentially generating control signals according to the sequence of road condition parameter generation, and sequentially transmitting the control signals to a controllable power supply; 3) According to the sequence of the artificial driving road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the long-time road condition parameters, control signals are generated in sequence and transmitted to a controllable power supply; 4) The controllable power supply transmits current to each magnetorheological damper respectively, so that each magnetorheological damper is controlled to output damping force to the corresponding wheel. The invention can obviously improve the comfort, stability and safety of the vehicle in the running process.
Description
Technical Field
The invention relates to the field of vehicle control, in particular to a vehicle magneto-rheological damper control method and system integrating visual detection.
Background
The magnetorheological damper can be used for constructing an intelligent vehicle semi-active suspension with adjustable damping, and the magnetorheological fluid in the intelligent vehicle semi-active suspension is changed between Newtonian fluid with good fluidity and a quasi-solid state by regulating and controlling a magnetic field, so that the damping force of the magnetorheological damper is changed, and the transient controllability enables the intelligent vehicle semi-active suspension to have great application value in the field of vehicle vibration reduction. As a semi-active suspension control system, besides the performance of the magneto-rheological damping device, road condition parameter detection and control links for providing control parameter decision are also important. At present, a vehicle magneto-rheological system generally obtains motion parameters such as acceleration, speed, displacement and the like through a traditional acceleration sensor based on infrared, inductance, piezoelectricity and other principles, so as to realize open-loop or closed-loop control. The magnetorheological system used as the inductive load has larger response delay, and when the running speed of the vehicle is higher, the response of the traditional sensor means that a certain road condition happens, and the hysteresis response of the magnetorheological system can not timely process the road condition, so that the optimal effect is difficult to achieve. For example, when a vehicle passes through a raised deceleration strip at a high speed, the conventional solution needs to control the magnetorheological damper after the acceleration sensor detects that the signal generated by the deceleration strip is abnormal, and since the magnetorheological system generally has a response time of the order of ten milliseconds, the vehicle may have driven off the deceleration strip when it responds. Obviously, if the abnormal road condition in front of the vehicle (namely, pre-sensing) can be detected in advance, a sufficient pretreatment and preset control strategy can be provided for the magnetorheological system, so that the output damping force of the magnetorheological damper can be accurately and timely controlled, and a more ideal vehicle vibration reduction effect is realized.
The vision detection technology mainly utilizes computer vision based on artificial intelligence to accurately and rapidly extract the characteristics of a complex image. The traditional computer vision mainly adopts a characteristic method of manual design, has good extraction capability only aiming at single characteristics in a specific environment, has poor robustness and generalization capability, and is difficult to realize accurate recognition effect in practical application problems.
Disclosure of Invention
The invention aims to provide a vehicle magneto-rheological damper control system integrating visual detection, which comprises a controllable power supply, a plurality of magneto-rheological dampers, an attitude sensor, a vehicle speed sensor, a vibration sensor, a camera, a controller and a cradle head, wherein the controllable power supply is connected with the magneto-rheological dampers;
the controllable power supply transmits current to each magnetorheological damper respectively, so that each magnetorheological damper is controlled to respond and damping force is output to the corresponding wheel;
the magneto-rheological damper is arranged between the wheel and the frame;
the attitude sensor acquires the inclination angle of the vehicle body and the centripetal acceleration of the vehicle when turning, and transmits the inclination angle and the centripetal acceleration to the controller;
the vehicle speed sensor acquires the running speed of the vehicle and transmits the running speed to the controller;
the vibration sensor acquires vibration parameters transmitted to the vehicle body from the ground and transmits the vibration parameters to the controller;
The camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
the cradle head is arranged on the vehicle body and used for stabilizing the camera;
the controller is provided with an image processing network frame;
the image processing frame processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
after receiving data of the attitude sensor, the vehicle speed sensor and the vibration sensor, the controller processes the data to obtain artificial driving road condition parameters;
the controller stores a current intensity test value set of the vehicle model and the magneto-rheological damper model under different long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and artificial driving road condition parameters;
the controller searches a current intensity test value set, obtains current long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and current intensity values under artificial driving road condition parameters respectively, generates corresponding control signals, and transmits the control signals to the controllable power supply.
Further, the long-term road condition parameter is the road surface type of the vehicle running at the future time t1, including but not limited to high-grade asphalt, common asphalt, cement, sand and stone, muddy, grassy, snowy and icy;
the short-time road condition parameters comprise short-time road condition classification and corresponding depth information;
the short-term road conditions are classified into the type of road condition targets required to be crossed by the vehicle in the future t2 time and the distance between the road condition targets and the camera; t2 is less than t1; road condition target types include, but are not limited to, pits, raised pavement, well covers, deceleration strips, steps;
the medium-time road condition parameters are medium-time road condition types including, but not limited to, uphill, downhill and curved road;
the man-made driving road condition parameters include, but are not limited to, emergency starting, emergency braking, acceleration, deceleration and centripetal acceleration corresponding to the types of emergency turning driving conditions.
Further, after the controller obtains the current intensity value, the suggested current intensity gear under the current long-time road condition parameters and the middle-time road condition parameters is also provided for the driver;
at this time, the controllable power supply provides a current step switch;
the driver manually selects the current intensity gear suggested by the controller or selected by himself.
Further, the step of obtaining the control signal under the short-time Cheng Lukuang parameter includes:
a1 The controller calculates the advance distance between the road condition target and each magneto-rheological damper; the advance distance is equal to the sum of the distance between the road condition target and the camera and the distance between the camera and each magneto-rheological damper;
a2 Dividing the advance distance by the current vehicle speed to calculate the advance time from the road condition target to each magneto-rheological damper under the current vehicle speed;
a3 Subtracting the response time of the magnetorheological system from the lead time, and calculating to obtain the delay starting time of each magnetorheological damper;
a4 Acquiring the span size of the road condition object in the transverse direction of the road surface and the running deflection angle of the vehicle, and calculating the wheels which will cross the road condition object;
a5 Dividing the forward span size of the road condition target on the road surface by the current vehicle speed, and calculating to obtain the retention time of each magneto-rheological damper after delay starting;
a6 The controller searches a current intensity test value set to obtain a current intensity value under the current short-time road condition parameters, and generates a control signal according to the current intensity value, the lead time of the magnetorheological damper, the holding time of the magnetorheological damper after delay starting and the wheels crossing the road condition targets;
The controller transmits a control signal to the controllable power supply to enable the magnetorheological damper corresponding to the wheel crossing the road condition target to respond.
Further, when the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters are generated in a crossing way, corresponding control signals are generated in a crossing way, and then the controllable power supply is enabled to execute different control signals in a crossing way.
Further, when the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters are generated simultaneously, corresponding control signals are generated according to the priority order of the road condition parameters and transmitted to the controllable power supply; the road condition parameter priority order is: artificial driving road condition parameters, short-term Cheng Lukuang parameters, medium-term road condition parameters and long-term road condition parameters.
Further, each magnetorheological damper has a corresponding vibration sensor.
Further, the method for processing the road condition picture to obtain the distance between the road condition target and the camera in the road condition picture includes but is not limited to the following two methods:
b1 Obtaining a two-dimensional road condition picture and a three-dimensional road condition picture by using a binocular camera, and performing image processing on the two-dimensional road condition picture and the three-dimensional road condition picture to obtain the distance between a road condition target and the camera;
b2 The distance between the road condition target and the camera is obtained by utilizing the fusion depth information or the range radar in the depth camera.
Further, the step of processing the road condition picture in front of the vehicle and the distance between the road condition target and the camera by the image processing frame includes:
c1 Image preprocessing is carried out on road condition pictures in front of the vehicle, and the image preprocessing comprises frame number adjustment, resolution and sampling rate;
c2 According to the weight file, the video is processed again, so that the road surface feature discrimination and the geometric position marking are realized; methods of reprocessing video include, but are not limited to, deep convolutional network models.
A method of using the vehicle magnetorheological damper control system of fusion visual inspection, comprising the steps of:
1) Acquiring the inclination angle of the vehicle body and the centripetal acceleration of the vehicle during turning by using an attitude sensor, and transmitting the inclination angle and the centripetal acceleration to a controller;
acquiring the running speed of the vehicle by using a vehicle speed sensor and transmitting the running speed to a controller;
the vibration parameters transmitted to the vehicle body from the ground are acquired by utilizing a vibration sensor and transmitted to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
2) The image processing network frame of the controller processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
the controller processes data of the attitude sensor, the vehicle speed sensor and the vibration sensor to obtain artificial driving road condition parameters;
3) The controller judges the type of the generated road condition parameters, if the long-time road condition parameters, the short-time Cheng Lukuang parameters, the middle-time road condition parameters and the artificial driving road condition parameters are not generated at the same time, the step 4) is carried out, otherwise, the step 5) is carried out;
4) Sequentially generating control signals according to the sequence of generating the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters, sequentially transmitting the control signals to a controllable power supply, and jumping to the step 6);
5) Generating corresponding control signals according to the road condition parameter priority order, transmitting the control signals to a controllable power supply, and entering step 6); the method comprises the following specific steps:
5.1 The controller determines a current intensity value, a holding time after the magnetorheological damper is started and wheels to be controlled according to the current manual driving road condition parameters, generates control signals and transmits the control signals to the controllable power supply;
5.2 The controller determines a current intensity value, the lead time of the magneto-rheological damper, the holding time of the magneto-rheological damper after delay starting and wheels crossing the road condition target according to the current short-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.3 The controller determines a current intensity value according to the current middle-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.4 The controller determines a current intensity value according to the current long-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
6) And the controllable power supply respectively transmits current to each magnetorheological damper according to the sequence of receiving the control signals, so as to control each magnetorheological damper to respond and output damping force to the corresponding wheel.
The invention has the technical effects that the invention can realize the pre-perception of the road condition of the vehicle by utilizing the vision of the advanced computer without doubt, namely, the invention can acquire the target type (such as barriers, concave-convex road surfaces, curves, slopes and the like) and depth information (namely, the distance between the target object and the magnetorheological damper) of the road condition in advance, and can provide sufficient pre-processing time for the magnetorheological system by combining the motion parameters such as the speed, the posture and the like provided by the existing vehicle-mounted sensor, thereby realizing more ideal vehicle vibration reduction effect, obviously improving the comfort, the stability and the safety in the running process of the vehicle, and having important significance for the upgrade of the intelligent automobile industry.
Drawings
FIG. 1 is a block diagram of a vehicle magnetorheological damper control system incorporating visual inspection;
FIG. 2 is a system control block diagram;
FIG. 3 is a long-term road condition workflow diagram;
FIG. 4 is a short-term road condition control flow chart;
FIG. 5 is a flow chart of the medium-time road condition control;
FIG. 6 is a flow chart of the traffic control for the driver;
FIG. 7 is a priority control flow diagram;
FIG. 8 is a schematic diagram of an embodiment of a short-range road condition;
in the figure, 1-lane lines (curves/ramps), 2-pits, 3-vehicles, 4-cameras, 5-magneto-rheological dampers.
Detailed Description
The present invention is further described below with reference to examples, but it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. Various substitutions and alterations are made according to the ordinary skill and familiar means of the art without departing from the technical spirit of the invention, and all such substitutions and alterations are intended to be included in the scope of the invention.
Example 1:
referring to fig. 1 to 8, a vehicle magnetorheological damper control system integrating visual detection comprises a controllable power supply, a plurality of magnetorheological dampers 5, an attitude sensor, a vehicle speed sensor, a vibration sensor, a camera 4, a controller and a cradle head;
the controllable power supply transmits current to each magnetorheological damper respectively, so that each magnetorheological damper is controlled to respond and damping force is output to the corresponding wheel;
The magneto-rheological damper is arranged between the wheel and the frame;
the attitude sensor acquires the inclination angle of the vehicle body and the centripetal acceleration of the vehicle when turning, and transmits the inclination angle and the centripetal acceleration to the controller;
the vehicle speed sensor acquires the running speed of the vehicle and transmits the running speed to the controller;
the vibration sensor acquires vibration parameters transmitted to the vehicle body from the ground and transmits the vibration parameters to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
the cradle head is arranged on the vehicle body and used for stabilizing the camera;
the controller is provided with an image processing network frame; the image processing network framework may be a deep neural network framework.
The image processing frame processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
after receiving data of the attitude sensor, the vehicle speed sensor and the vibration sensor, the controller processes the data to obtain artificial driving road condition parameters;
The controller stores a current intensity test value set of the vehicle model and the magneto-rheological damper model under different long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and artificial driving road condition parameters;
the controller searches a current intensity test value set, obtains current long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and current intensity values under artificial driving road condition parameters respectively, generates corresponding control signals, and transmits the control signals to the controllable power supply.
The long-term road condition parameters are road surface types of vehicles running at the future time t1, and include, but are not limited to, high-grade asphalt paths, common asphalt paths, cement paths, gravel paths, muddy paths, grasslands, snowlands and ice lands;
the short-time road condition parameters comprise short-time road condition classification and corresponding depth information;
the short-term road conditions are classified into the type of road condition targets required to be crossed by the vehicle in the future t2 time and the distance between the road condition targets and the camera; t2 is less than t1; road condition target types include, but are not limited to, pits, raised pavement, well covers, deceleration strips, steps;
the medium-time road condition parameters are medium-time road condition types including, but not limited to, uphill, downhill and curved road;
The man-made driving road condition parameters include, but are not limited to, emergency starting, emergency braking, acceleration, deceleration and centripetal acceleration corresponding to the types of emergency turning driving conditions.
After the controller obtains the current intensity value, the controller also provides the suggested current intensity gear under the current long-time road condition parameters and the middle-time road condition parameters for the driver;
at this time, the controllable power supply provides a current step switch;
the driver manually selects the current intensity gear suggested by the controller or selected by himself.
The step of obtaining the control signal under the short-time Cheng Lukuang parameter comprises:
a1 The controller calculates the advance distance between the road condition target and each magneto-rheological damper; the advance distance is equal to the sum of the distance between the road condition target and the camera and the distance between the camera and each magneto-rheological damper;
a2 Dividing the advance distance by the current vehicle speed to calculate the advance time from the road condition target to each magneto-rheological damper under the current vehicle speed;
a3 Subtracting the response time of the magnetorheological system from the lead time, and calculating to obtain the delay starting time of each magnetorheological damper;
a4 Acquiring the span size of the road condition object in the transverse direction of the road surface and the running deflection angle of the vehicle, and calculating the wheels which will cross the road condition object;
a5 Dividing the forward span size of the road condition target on the road surface by the current vehicle speed, and calculating to obtain the retention time of each magneto-rheological damper after delay starting;
a6 The controller searches a current intensity test value set to obtain a current intensity value under the current short-time road condition parameters, and generates a control signal according to the current intensity value, the lead time of the magnetorheological damper, the holding time of the magnetorheological damper after delay starting and the wheels crossing the road condition targets;
the controller transmits a control signal to the controllable power supply to enable the magnetorheological damper corresponding to the wheel crossing the road condition target to respond.
When the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters are generated in a crossing way, corresponding control signals are generated in a crossing way, and then the controllable power supply can execute different control signals in a crossing way.
When the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters are generated simultaneously, corresponding control signals are generated according to the priority order of the road condition parameters and are transmitted to a controllable power supply; the road condition parameter priority order is: artificial driving road condition parameters, short-term Cheng Lukuang parameters, medium-term road condition parameters and long-term road condition parameters.
Each magnetorheological damper has a corresponding vibration sensor.
The method for processing the road condition picture to obtain the distance between the road condition target and the camera in the road condition picture comprises, but is not limited to, the following two methods:
b1 Obtaining a two-dimensional road condition picture and a three-dimensional road condition picture by using a binocular camera, and performing image processing on the two-dimensional road condition picture and the three-dimensional road condition picture to obtain the distance between a road condition target and the camera;
b2 The distance between the road condition target and the camera is obtained by utilizing the fusion depth information or the range radar in the depth camera.
The step of processing the road condition picture in front of the vehicle and the distance between the road condition target and the camera by the image processing frame comprises the following steps:
c1 Image preprocessing is carried out on road condition pictures in front of the vehicle, and the image preprocessing comprises frame number adjustment, resolution and sampling rate;
c2 According to the weight file, the video is processed again, so that the road surface feature discrimination and the geometric position marking are realized; methods of reprocessing video include, but are not limited to, deep convolutional network models.
A method of using the vehicle magnetorheological damper control system of fusion visual inspection, comprising the steps of:
1) Acquiring the inclination angle of the vehicle body and the centripetal acceleration of the vehicle during turning by using an attitude sensor, and transmitting the inclination angle and the centripetal acceleration to a controller;
Acquiring the running speed of the vehicle by using a vehicle speed sensor and transmitting the running speed to a controller;
the vibration parameters transmitted to the vehicle body from the ground are acquired by utilizing a vibration sensor and transmitted to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
2) The image processing network frame of the controller processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
the controller processes data of the attitude sensor, the vehicle speed sensor and the vibration sensor to obtain artificial driving road condition parameters;
3) The controller judges the type of the generated road condition parameters, if the long-time road condition parameters, the short-time Cheng Lukuang parameters, the middle-time road condition parameters and the artificial driving road condition parameters are not generated at the same time, the step 4) is carried out, otherwise, the step 5) is carried out;
4) Sequentially generating control signals according to the sequence of generating the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters, sequentially transmitting the control signals to a controllable power supply, and jumping to the step 6);
5) Generating corresponding control signals according to the road condition parameter priority order, transmitting the control signals to a controllable power supply, and entering step 6); the method comprises the following specific steps:
5.1 The controller determines a current intensity value, a holding time after the magnetorheological damper is started and wheels to be controlled according to the current manual driving road condition parameters, generates control signals and transmits the control signals to the controllable power supply;
5.2 The controller determines a current intensity value, the lead time of the magneto-rheological damper, the holding time of the magneto-rheological damper after delay starting and wheels crossing the road condition target according to the current short-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.3 The controller determines a current intensity value according to the current middle-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.4 The controller determines a current intensity value according to the current long-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
6) And the controllable power supply respectively transmits current to each magnetorheological damper according to the sequence of receiving the control signals, so as to control each magnetorheological damper to respond and output damping force to the corresponding wheel.
Example 2:
A vehicle magneto-rheological damper control system based on fusion visual detection comprises a controllable power supply, a magneto-rheological damper, an attitude sensor, a vehicle speed sensor, a vibration sensor, a camera, a cradle head, a controller, a sensor and the like, wherein the controllable power supply, the magneto-rheological damper, the attitude sensor, the vehicle speed sensor, the vibration sensor, the camera, the cradle head, the controller, the sensor and the like are shown in figure 1.
The controllable power supply is used for generating constant or waveform current to the magneto-rheological damper; further, the controller obtains output current feedback of the controllable power supply to form a closed loop current control.
The magneto-rheological damper is arranged between the wheels and the frame (in addition, a spring element) and can be arranged on the front wheels, the rear wheels or all the wheels according to actual requirements.
The attitude sensor is used for acquiring the inclination angle of the vehicle body and the centripetal acceleration of the vehicle during turning; the system can also directly read the existing attitude sensor information of the vehicle-mounted system.
The vehicle speed sensor is used for acquiring the running speed of the vehicle; the system can also directly read the existing vehicle speed sensor information of the vehicle-mounted system.
The vibration sensor is used for acquiring vibration parameters transmitted to the vehicle body from the ground, such as a vibration acceleration sensor, a displacement sensor and the like; more preferably, each magnetorheological damper has a corresponding vibration sensor.
The camera is used for acquiring road condition pictures in front of the vehicle, and the distance between a target object in the road condition pictures and the camera can be acquired by two methods: one method is by two-dimensional to three-dimensional image processing (e.g., binocular camera), and the other is to blend depth information or range radar (e.g., depth camera) in the camera.
The cradle head is arranged on the vehicle body and used for stabilizing the camera so as to acquire a stable picture.
The controller is provided with a deep convolution network framework and is used for processing camera image data and acquiring road condition type parameters; the controller also processes related data of the attitude sensor, the vehicle speed sensor and the vibration sensor; the controller processes control signals required by different working conditions and sends the control signals to the controllable power supply; further, the controller that processes the camera and sensor signals may be separate from the controller that sends to the controllable power supply and processes the control signals required for different operating conditions.
The road condition type parameter and the corresponding control method thereof specifically comprise the following steps:
the first road condition type parameter is a long-term road condition classification (i.e., road surface type traveled by the vehicle over a longer period of time), including, but not limited to, high-grade asphalt, general asphalt, cement, gravel, mud, grass, snow, ice, etc.; the long-term road condition classification is obtained by camera data or by auxiliary verification of a vibration sensor; the controller automatically sends corresponding control signals to the controllable power supply, and the controllable power supply controls each magnetorheological damper to make the same response, specifically according to the specific vehicle type and the corresponding current intensity of the magnetorheological damper after various road tests; the controller can also send road condition classification prompt to the driver (voice or information display), and the controllable power supply provides a current step switch, and the driver manually selects the current intensity gear suggested by the controller or selected by the driver. Fig. 3 illustrates a workflow embodiment.
The second road condition type parameter is a short-time Cheng Lukuang classification (namely, road condition target type which the vehicle needs to cross in a very short time span) and depth information (namely, distance between the target object and the camera) thereof, and the second road condition type parameter comprises barriers such as pits, raised road surfaces, well covers, deceleration strips, steps and the like; the short-time road condition classification is obtained by camera data; the controller calculates the advance distance between the road condition target and each magneto-rheological damper, specifically, the distance between the road condition target acquired by the camera and the distance between the known camera and each magneto-rheological damper is added, and then the advance distance is divided by the current vehicle speed acquired by the vehicle speed sensor, so that the advance time between the road condition target and each magneto-rheological damper under the current vehicle speed is obtained; the controller sends corresponding control signals to the controllable power supply, and the controllable power supply controls each magnetorheological damper to respond differently, specifically according to the specific vehicle type and the corresponding current intensity of the magnetorheological damper after various road surfaces and vehicle speed tests; subtracting the response time of the magnetorheological system from the lead time to obtain the delay starting time of each magnetorheological damper; further, the controller also obtains the span size of the road condition target in the forward direction of the road surface, and the forward span size is divided by the current vehicle speed obtained by the vehicle speed sensor, namely the holding time of each magneto-rheological damper after the delayed starting; further, the controller also obtains the span size of the road condition object in the transverse direction of the road surface, and calculates the wheel which will cross the road condition object through the vehicle running deflection angle obtained by the attitude sensor, thereby controlling the magnetorheological damper corresponding to the wheel to respond correspondingly, but not responding to the magnetorheological damper corresponding to the wheel which crosses the road condition object; further, if the vehicle is in a variable speed motion, the controller considers the influence of the running acceleration when calculating the lead time, the holding time and the crossing wheel; further, if the delayed start time is less than 0, no response is made. Fig. 4 illustrates a workflow embodiment.
The third type of road condition type parameters are middle-time road condition classification, including ascending, descending, curved road and the like; the medium-time road condition classification is acquired by camera data or by auxiliary verification of an attitude sensor; the controller automatically sends corresponding control signals to the controllable power supply, and the controllable power supply controls each magnetorheological damper to make the same response, specifically according to the specific vehicle type and the corresponding current intensity of the magnetorheological damper after various road tests; the controller can also send road condition classification prompt to the driver (voice or information display), and the controllable power supply provides a current step switch, and the driver manually selects the current intensity gear suggested by the controller or selected by the driver. Fig. 5 illustrates a workflow embodiment. (note: this type of control method is practically the same as the first type)
The fourth type of road condition type parameters are the manual driving road conditions such as emergency starting, emergency braking, emergency turning and the like, and the working condition parameters (acceleration, deceleration and centripetal acceleration) are acquired by an attitude sensor and a vehicle speed sensor; the controller automatically sends corresponding control signals to the controllable power supply, and the controllable power supply controls all the magnetorheological dampers to make the same response, specifically according to the specific vehicle type and the corresponding current intensity of the magnetorheological dampers after various road tests. Fig. 6 illustrates a workflow embodiment.
When the above-mentioned various road condition type parameters cross and take place, the correspondent control method is cross-implemented; when the four road condition type parameters occur simultaneously, a corresponding control method is implemented according to the priority of 'fourth class > second class > third class > first class'. Fig. 7 illustrates a workflow embodiment.
The above applies the current intensity on the standby road condition beauty parameters: in addition to applying a step current, a gradually rising waveform current is also possible; when the applied current is removed, it is also a gradually decreasing waveform current.
The control system is a multi-input multi-output strong coupling system, and input parameters and output parameters are mutually influenced, wherein the multi-input system comprises characteristic information and distance information obtained by a camera for collecting road surfaces, speed information collected by a speed sensor, information collected by a vibration sensor, vehicle state information collected by a posture sensor and the like; the multiple outputs comprise a plurality of independent magnetorheological dampers which can be respectively controlled to output corresponding damping forces.
After the road surface type parameters are identified, the core control idea is classification control, and different control strategies are adopted for different road conditions. For example, different control strategies are adopted for different road condition targets when Cheng Lukuang is short; for example, when the vehicle passes through the deceleration strip, the magnetorheological damper arranged on the front wheel can be firstly actuated, and then the magnetorheological damper arranged on the rear wheel can be actuated, so that better comfort is obtained, and the magnetorheological damper is not actuated simultaneously; for another example, when a vehicle passes through a damaged road pit, the magnetorheological damper on the wheel passing through the pit is selected to act, so that better comfort is obtained, and the magnetorheological damper does not act.
When the depth convolution network algorithm is used for carrying out feature recognition on road surface condition information, the control method mainly comprises the following steps of 1, selecting a picture set containing an object to be detected in advance for labeling training, and generating a corresponding weight file; 2. video information transmitted by a camera is read, and image preprocessing is carried out on the video information, wherein the image preprocessing comprises frame number adjustment, resolution, sampling rate and the like;
3. and (3) processing the video again by using the deep convolution network model according to the weight file to realize pavement characteristic discrimination and geometric position labeling.
Example 3:
aiming at the classification of the second short-term Cheng Lukuang, a control method and a system diagram of a vehicle magneto-rheological damper integrating visual detection are provided, and a schematic diagram is shown in fig. 8. When a vehicle 3 loaded with four magneto-rheological dampers runs on a road surface with a lane line 1, pits 2 appear on the road surface in front, the type, the advance distance and the span size of the target are identified through a road condition visual detection technology, wheels which pre-pass through the pits are calculated in advance, the advance time of the wheels which want to pass through the pits is obtained, and the response time of a magneto-rheological system is subtracted, so that the delay starting time is obtained; when the time-lapse starting time passes, the controller sends control signals to the controllable power supply, and the controllable power supply adjusts the damping force of the magnetorheological damper 5 corresponding to the tire, and the magnetorheological damper provides better vibration reduction effect when the wheel passes through the pit, so that better riding experience is realized.
Example 4:
a deep convolution network in a vehicle magneto-rheological damper control method integrating road condition visual detection is used for identifying and detecting road condition types, a single-step (one-stage) target detection algorithm based on bounding box regression is adopted, such as SSD, YOLO and the like, the algorithm does not need a regional suggestion stage, category probability and position coordinates of a target are directly generated by the network, a final detection result can be directly obtained through single detection, and good detection speed and precision are achieved.
Example 5:
the vehicle magneto-rheological damper control system integrating visual detection comprises a controllable power supply, a plurality of magneto-rheological dampers, an attitude sensor, a vehicle speed sensor, a vibration sensor, a camera, a controller and a cradle head;
the controllable power supply transmits current to each magnetorheological damper respectively, so that each magnetorheological damper is controlled to respond and damping force is output to the corresponding wheel;
the magneto-rheological damper is arranged between the wheel and the frame;
the attitude sensor acquires the inclination angle of the vehicle body and the centripetal acceleration of the vehicle when turning, and transmits the inclination angle and the centripetal acceleration to the controller;
the vehicle speed sensor acquires the running speed of the vehicle and transmits the running speed to the controller;
The vibration sensor acquires vibration parameters transmitted to the vehicle body from the ground and transmits the vibration parameters to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
the cradle head is arranged on the vehicle body and used for stabilizing the camera;
the controller is provided with an image processing network frame;
the image processing frame processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
after receiving data of the attitude sensor, the vehicle speed sensor and the vibration sensor, the controller processes the data to obtain artificial driving road condition parameters;
the controller stores a current intensity test value set of the vehicle model and the magneto-rheological damper model under different long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and artificial driving road condition parameters;
the controller searches a current intensity test value set, obtains current long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and current intensity values under artificial driving road condition parameters respectively, generates corresponding control signals, and transmits the control signals to the controllable power supply.
Example 6:
the main content of the vehicle magnetorheological damper control system integrating visual detection is as shown in the embodiment 5, wherein the long-term road condition parameter is the road surface type of the vehicle running at the time t1 in the future, and the vehicle magnetorheological damper control system comprises, but is not limited to, high-grade asphalt paths, general asphalt paths, cement paths, gravel paths, muddy paths, grasslands, snowlands and ice lands;
the short-time road condition parameters comprise short-time road condition classification and corresponding depth information;
the short-term road conditions are classified into the type of road condition targets required to be crossed by the vehicle in the future t2 time and the distance between the road condition targets and the camera; t2 is less than t1; road condition target types include, but are not limited to, pits, raised pavement, well covers, deceleration strips, steps;
the medium-time road condition parameters are medium-time road condition types including, but not limited to, uphill, downhill and curved road;
the man-made driving road condition parameters include, but are not limited to, emergency starting, emergency braking, acceleration, deceleration and centripetal acceleration corresponding to the types of emergency turning driving conditions.
Example 7:
the main content of the vehicle magneto-rheological damper control system integrating visual detection is shown in the embodiment 5, wherein after the controller obtains the current intensity value, the suggested current intensity gear under the current long-time road condition parameters and the medium-time road condition parameters is also provided for a driver;
At this time, the controllable power supply provides a current step switch;
the driver manually selects the current intensity gear suggested by the controller or selected by himself.
Example 8:
the main content of the vehicle magnetorheological damper control system integrating visual detection is as shown in embodiment 5, wherein the step of obtaining the control signal under the short-time road condition parameters comprises the following steps:
a1 The controller calculates the advance distance between the road condition target and each magneto-rheological damper; the advance distance is equal to the sum of the distance between the road condition target and the camera and the distance between the camera and each magneto-rheological damper;
a2 Dividing the advance distance by the current vehicle speed to calculate the advance time from the road condition target to each magneto-rheological damper under the current vehicle speed;
a3 Subtracting the response time of the magnetorheological system from the lead time, and calculating to obtain the delay starting time of each magnetorheological damper;
a4 Acquiring the span size of the road condition object in the transverse direction of the road surface and the running deflection angle of the vehicle, and calculating the wheels which will cross the road condition object;
a5 Dividing the forward span size of the road condition target on the road surface by the current vehicle speed, and calculating to obtain the retention time of each magneto-rheological damper after delay starting;
a6 The controller searches a current intensity test value set to obtain a current intensity value under the current short-time road condition parameters, and generates a control signal according to the current intensity value, the lead time of the magnetorheological damper, the holding time of the magnetorheological damper after delay starting and the wheels crossing the road condition targets;
The controller transmits a control signal to the controllable power supply to enable the magnetorheological damper corresponding to the wheel crossing the road condition target to respond.
Example 9:
the main content of the vehicle magneto-rheological damper control system integrating visual detection is shown in the embodiment 5, wherein when the long-time road condition parameter, the short-time Cheng Lukuang parameter, the medium-time road condition parameter and the artificial driving road condition parameter are generated in a crossing way, corresponding control signals are generated in a crossing way, and further different control signals are executed by the controllable power supply in a crossing way.
Example 10:
the main content of the vehicle magneto-rheological damper control system integrating visual detection is shown in the embodiment 5, wherein when the long-time road condition parameter, the short-time Cheng Lukuang parameter, the middle-time road condition parameter and the artificial driving road condition parameter are generated simultaneously, corresponding control signals are generated according to the priority order of the road condition parameters and are transmitted to a controllable power supply; the road condition parameter priority order is: artificial driving road condition parameters, short-term Cheng Lukuang parameters, medium-term road condition parameters and long-term road condition parameters.
Example 11:
the main content of the vehicle magnetorheological damper control system integrating visual detection is as shown in the embodiment 5, wherein each magnetorheological damper is provided with a corresponding vibration sensor.
Example 12:
the main content of the vehicle magnetorheological damper control system integrating visual detection is shown in embodiment 5, wherein the method for processing the road condition picture to obtain the distance between the road condition target and the camera in the road condition picture comprises, but is not limited to, the following two methods:
b1 Obtaining a two-dimensional road condition picture and a three-dimensional road condition picture by using a binocular camera, and performing image processing on the two-dimensional road condition picture and the three-dimensional road condition picture to obtain the distance between a road condition target and the camera;
b2 The distance between the road condition target and the camera is obtained by utilizing the fusion depth information or the range radar in the depth camera.
Example 13:
the main content of the vehicle magnetorheological damper control system integrating visual detection is as shown in embodiment 5, wherein the step of processing the road condition picture in front of the vehicle and the distance between the road condition target and the camera by the image processing frame comprises the following steps:
c1 Image preprocessing is carried out on road condition pictures in front of the vehicle, and the image preprocessing comprises frame number adjustment, resolution and sampling rate;
c2 According to the weight file, the video is processed again, so that the road surface feature discrimination and the geometric position marking are realized; methods of reprocessing video include, but are not limited to, deep convolutional network models.
Example 14:
a method of using the vehicle magnetorheological damper control system of fusion visual inspection, comprising the steps of:
1) Acquiring the inclination angle of the vehicle body and the centripetal acceleration of the vehicle during turning by using an attitude sensor, and transmitting the inclination angle and the centripetal acceleration to a controller;
acquiring the running speed of the vehicle by using a vehicle speed sensor and transmitting the running speed to a controller;
the vibration parameters transmitted to the vehicle body from the ground are acquired by utilizing a vibration sensor and transmitted to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
2) The image processing network frame of the controller processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
the controller processes data of the attitude sensor, the vehicle speed sensor and the vibration sensor to obtain artificial driving road condition parameters;
3) The controller judges the type of the generated road condition parameters, if the long-time road condition parameters, the short-time Cheng Lukuang parameters, the middle-time road condition parameters and the artificial driving road condition parameters are not generated at the same time, the step 4) is carried out, otherwise, the step 5) is carried out;
4) Sequentially generating control signals according to the sequence of generating the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters, sequentially transmitting the control signals to a controllable power supply, and jumping to the step 6);
5) Generating corresponding control signals according to the road condition parameter priority order, transmitting the control signals to a controllable power supply, and entering step 6); the method comprises the following specific steps:
5.1 The controller determines a current intensity value, a holding time after the magnetorheological damper is started and wheels to be controlled according to the current manual driving road condition parameters, generates control signals and transmits the control signals to the controllable power supply;
5.2 The controller determines a current intensity value, the lead time of the magneto-rheological damper, the holding time of the magneto-rheological damper after delay starting and wheels crossing the road condition target according to the current short-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.3 The controller determines a current intensity value according to the current middle-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.4 The controller determines a current intensity value according to the current long-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
The order of generating the corresponding control signals is determined according to the road condition parameter priority, and the priority is as follows: if two, three or four road condition parameters are generated simultaneously, two, three or four of the above steps are executed according to the priority order of the road condition parameters.
For example, when the artificial driving road condition parameter and the long-term road condition parameter are generated simultaneously, step 5.1) is executed first, step 5.4) is executed later, and finally step 6) is skipped.
6) And the controllable power supply respectively transmits current to each magnetorheological damper according to the sequence of receiving the control signals, so as to control each magnetorheological damper to respond and output damping force to the corresponding wheel.
Claims (10)
1. The vehicle magnetorheological damper control system integrating visual detection is characterized by comprising a controllable power supply, a plurality of magnetorheological dampers, an attitude sensor, a vehicle speed sensor, a vibration sensor, a camera, a controller and a cradle head.
The controllable power supply transmits current to each magnetorheological damper respectively, so that each magnetorheological damper is controlled to respond and damping force is output to the corresponding wheel;
the magneto-rheological damper is arranged between the wheel and the frame;
The attitude sensor acquires the inclination angle of the vehicle body and the centripetal acceleration of the vehicle when turning, and transmits the inclination angle and the centripetal acceleration to the controller;
the vehicle speed sensor acquires the running speed of the vehicle and transmits the running speed to the controller;
the vibration sensor acquires vibration parameters transmitted to the vehicle body from the ground and transmits the vibration parameters to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
the cradle head is arranged on the vehicle body and used for stabilizing the camera;
the controller is provided with an image processing network frame;
the image processing frame processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
after receiving data of the attitude sensor, the vehicle speed sensor and the vibration sensor, the controller processes the data to obtain artificial driving road condition parameters;
the controller stores a current intensity test value set of the vehicle model and the magneto-rheological damper model under different long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and artificial driving road condition parameters;
The controller searches a current intensity test value set, obtains current long-time road condition parameters, short-time Cheng Lukuang parameters, medium-time road condition parameters and current intensity values under artificial driving road condition parameters respectively, generates corresponding control signals, and transmits the control signals to the controllable power supply.
2. The vehicle magnetorheological damper control system of claim 1, wherein the long-term road condition parameter is a road surface type of the vehicle traveling at a future time t1, including but not limited to high grade asphalt, general asphalt, cement, gravel, mud, grass, snow, ice;
the short-time road condition parameters comprise short-time road condition classification and corresponding depth information;
the short-term road conditions are classified into the type of road condition targets required to be crossed by the vehicle in the future t2 time and the distance between the road condition targets and the camera; t2 is less than t1; road condition target types include, but are not limited to, pits, raised pavement, well covers, deceleration strips, steps;
the medium-time road condition parameters are medium-time road condition types including, but not limited to, uphill, downhill and curved road;
the man-made driving road condition parameters include, but are not limited to, emergency starting, emergency braking, acceleration, deceleration and centripetal acceleration corresponding to the types of emergency turning driving conditions.
3. The vehicle magnetorheological damper control system integrating visual inspection according to claim 1, wherein the controller further provides the driver with a recommended current intensity gear under the current long-time road condition parameter and the medium-time road condition parameter after acquiring the current intensity value;
at this time, the controllable power supply provides a current step switch;
the driver manually selects the current intensity gear suggested by the controller or selected by himself.
4. The vehicle magnetorheological damper control system of claim 1, wherein the step of obtaining the control signal under the short-range road condition parameters comprises:
1) The controller calculates the advance distance between the road condition target and each magneto-rheological damper; the advance distance is equal to the sum of the distance between the road condition target and the camera and the distance between the camera and each magneto-rheological damper;
2) Dividing the advance distance by the current vehicle speed, and calculating the advance time from the road condition target to each magneto-rheological damper under the current vehicle speed;
3) Subtracting the response time of the magnetorheological system from the lead time, and calculating to obtain the delay starting time of each magnetorheological damper;
4) Acquiring the span size of a road condition target in the transverse direction of a road surface and the running deflection angle of a vehicle, and calculating wheels which will cross the road condition target;
5) Dividing the forward span size of the road condition target on the road surface by the current vehicle speed, and calculating to obtain the retention time of each magneto-rheological damper after delay starting;
6) The controller searches a current intensity test value set to obtain a current intensity value under the current short-time road condition parameter, and generates a control signal according to the current intensity value, the lead time of the magnetorheological damper, the holding time of the magnetorheological damper after delay starting and the wheels crossing the road condition target;
the controller transmits a control signal to the controllable power supply to enable the magnetorheological damper corresponding to the wheel crossing the road condition target to respond.
5. The vehicle magnetorheological damper control system integrating visual inspection according to claim 1, wherein when the long-term road condition parameter, the short-term Cheng Lukuang parameter, the medium-term road condition parameter and the artificial driving road condition parameter are generated in a crossing manner, corresponding control signals are generated in a crossing manner, so that the controllable power supplies can execute different control signals in a crossing manner.
6. The vehicle magnetorheological damper control system integrating visual inspection according to claim 1, wherein when the long-term road condition parameter, the short-term Cheng Lukuang parameter, the medium-term road condition parameter and the artificial driving road condition parameter are generated simultaneously, corresponding control signals are generated according to the priority order of the road condition parameters and transmitted to the controllable power supply; the road condition parameter priority order is: artificial driving road condition parameters, short-term Cheng Lukuang parameters, medium-term road condition parameters and long-term road condition parameters.
7. The vehicle magnetorheological damper control system of claim 1, wherein each magnetorheological damper has a corresponding vibration sensor.
8. The vehicle magnetorheological damper control system of claim 1, wherein the method for processing the road condition picture to obtain the distance between the road condition target and the camera in the road condition picture comprises, but is not limited to, the following two methods:
1) Obtaining a two-dimensional road condition picture and a three-dimensional road condition picture by using a binocular camera, and performing image processing on the two-dimensional road condition picture and the three-dimensional road condition picture to obtain the distance between a road condition target and the camera;
2) And obtaining the distance between the road condition target and the camera by using the fusion depth information or the range radar in the depth camera.
9. The vehicle magnetorheological damper control system of claim 1, wherein the image processing frame processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera comprises:
1) Image preprocessing is carried out on road condition pictures in front of a vehicle, wherein the image preprocessing comprises frame number adjustment, resolution and sampling rate;
2) According to the weight file, the video is processed again, so that the road surface feature discrimination and the geometric position marking are realized; methods of reprocessing video include, but are not limited to, deep convolutional network models.
10. A method of using a vehicle magnetorheological damper control system incorporating visual inspection according to any one of claims 1 to 9, comprising the steps of:
1) Acquiring the inclination angle of the vehicle body and the centripetal acceleration of the vehicle during turning by using an attitude sensor, and transmitting the inclination angle and the centripetal acceleration to a controller;
acquiring the running speed of the vehicle by using a vehicle speed sensor and transmitting the running speed to a controller;
the vibration parameters transmitted to the vehicle body from the ground are acquired by utilizing a vibration sensor and transmitted to the controller;
the camera acquires a road condition picture in front of the vehicle, and the controller processes the road condition picture to obtain the road condition type in the road condition picture and the distance between a road condition target and the camera;
2) The image processing network frame of the controller processes the road condition picture in front of the vehicle and the distance between the road condition target and the camera to obtain one or more of long-time road condition parameters, short-time Cheng Lukuang parameters and medium-time road condition parameters;
the controller processes data of the attitude sensor, the vehicle speed sensor and the vibration sensor to obtain artificial driving road condition parameters;
3) The controller judges the type of the generated road condition parameters, if the long-time road condition parameters, the short-time Cheng Lukuang parameters, the middle-time road condition parameters and the artificial driving road condition parameters are not generated at the same time, the step 4) is carried out, otherwise, the step 5) is carried out;
4) Sequentially generating control signals according to the sequence of generating the long-time road condition parameters, the short-time Cheng Lukuang parameters, the medium-time road condition parameters and the artificial driving road condition parameters, sequentially transmitting the control signals to a controllable power supply, and jumping to the step 6);
5) Generating corresponding control signals according to the road condition parameter priority order, transmitting the control signals to a controllable power supply, and entering step 6); the method comprises the following specific steps:
5.1 The controller determines a current intensity value, a holding time after the magnetorheological damper is started and wheels to be controlled according to the current manual driving road condition parameters, generates control signals and transmits the control signals to the controllable power supply;
5.2 The controller determines a current intensity value, the lead time of the magneto-rheological damper, the holding time of the magneto-rheological damper after delay starting and wheels crossing the road condition target according to the current short-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.3 The controller determines a current intensity value according to the current middle-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
5.4 The controller determines a current intensity value according to the current long-time road condition parameters, generates a control signal and transmits the control signal to the controllable power supply;
6) And the controllable power supply respectively transmits current to each magnetorheological damper according to the sequence of receiving the control signals, so as to control each magnetorheological damper to respond and output damping force to the corresponding wheel.
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