CN116653975A - Vehicle stability control method based on road surface recognition - Google Patents
Vehicle stability control method based on road surface recognition Download PDFInfo
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- CN116653975A CN116653975A CN202310642475.XA CN202310642475A CN116653975A CN 116653975 A CN116653975 A CN 116653975A CN 202310642475 A CN202310642475 A CN 202310642475A CN 116653975 A CN116653975 A CN 116653975A
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- 238000011217 control strategy Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000003287 optical effect Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 5
- 238000003709 image segmentation Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 5
- 230000005484 gravity Effects 0.000 claims description 3
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- 238000005259 measurement Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000011218 segmentation Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/064—Degree of grip
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/068—Road friction coefficient
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0097—Predicting future conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Regulating Braking Force (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a vehicle stability control method based on road surface identification, which comprises the following steps: road surface information in the running process of a vehicle is collected: including road friction coefficient, road roughness, road temperature, etc.; processing the acquired road surface information, and identifying the type of the current driving road surface: including dry road surfaces, wet road surfaces, ponding road surfaces, frozen road surfaces, snow covered road surfaces, and the like; according to the identified road surface type, predicting the motion state of the vehicle, and dynamically adjusting the vehicle stability control strategy: comprises the steps of adjusting ABS working parameters, adjusting TCS working parameters, adjusting ESP working parameters and the like; and applying the adjusted vehicle stability control strategy to the running process of the vehicle to realize dynamic adjustment of the running stability of the vehicle. The invention can identify the type of the road surface on which the vehicle is currently running through real-time acquisition and processing of the road surface information, and improves the adaptability of the vehicle stability control method.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a vehicle stability control method based on road surface identification.
Background
The type of the road surface on which the vehicle is currently running can be identified through real-time acquisition and processing of the road surface information, and vehicle stability control is an important research direction in the current vehicle control technology. During the running of a vehicle, the road surface conditions have a crucial influence on the stability of the vehicle. For example, when a vehicle encounters conditions such as wet road, uneven road surface, steep road surface and the like during running, phenomena such as sideslip, slipping, out of control and the like can occur, and running safety is seriously affected.
Currently, a number of vehicle stability control methods have been proposed and applied to actual vehicles. Among them, a control method based on a vehicle interior sensor is a common method, for example, measuring a vehicle state by using a vehicle speed sensor, a steering angle sensor, an acceleration sensor, and the like, and maintaining vehicle stability by controlling braking force, steering force, and the like. However, these methods neglect the influence of the road surface condition on the stability of the vehicle, so that under complex road surface conditions, the control accuracy and robustness are limited, and it is difficult to meet the actual demands. For this reason, researchers have begun to explore a vehicle stability control method based on road surface recognition. This method calculates a control command by acquiring road surface information such as a road surface friction coefficient, a road surface height, etc., and predicting a motion state of the vehicle such as a slip angle, a roll angle, etc., to maintain stability of the vehicle. The method can control the vehicle more accurately, improve the control precision and the robustness, and adapt to the vehicle control requirements under different road conditions.
In recent years, with the continuous improvement of sensor technology and computer processing capability, a vehicle stability control method based on road surface recognition has been attracting attention. Researchers have adopted various pavement recognition methods, such as image processing, laser radar, millimeter wave radar, etc., to obtain pavement information. The motion state of the vehicle is predicted based on methods such as machine learning and deep learning. These methods find application in practical vehicles and achieve good results.
The existing vehicle stability control method mainly comprises the following steps: electronic Stability Program (ESP), traction Control System (TCS), antilock Braking System (ABS), etc. Although the method can improve the running stability of the vehicle to a certain extent, the control method is usually passive control, the vehicle is often interfered when the vehicle is unstable, and the method has certain limitation on the adaptability due to lack of adjustment of different road conditions. For this purpose, a vehicle stability control method based on road surface recognition is proposed.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a vehicle stability control method based on road surface identification, which solves the problems that: the method for controlling the stability of the vehicle has low response speed and aims at the problem of lack of adjustment of different roads, and the method can identify the type of the road on which the vehicle is currently running by collecting and processing the road information in real time, so that the adaptability of the method for controlling the stability of the vehicle is improved, and the control strategy for the stability of the vehicle is dynamically adjusted according to the identified type of the road, so that the running stability of the vehicle under the condition of complex roads is improved.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: a vehicle stability control method based on road surface recognition, the vehicle stability control method comprising the steps of:
step one, collecting pavement information in the running process of a vehicle: including road friction coefficient, road roughness, road temperature, etc.;
step two, processing the acquired road surface information, and identifying the type of the current driving road surface: including dry road surfaces, wet road surfaces, ponding road surfaces, frozen road surfaces, snow covered road surfaces, and the like;
step three, predicting the motion state of the vehicle according to the identified road surface type, and dynamically adjusting the vehicle stability control strategy: comprises the steps of adjusting ABS working parameters, adjusting TCS working parameters, adjusting ESP working parameters and the like;
and step four, the adjusted vehicle stability control strategy is applied to the vehicle running process, so that the dynamic adjustment of the vehicle running stability is realized.
In a further preferred mode of the present invention, the method for acquiring the road surface information in the first step is that the sensor such as a camera and a laser radar acquires the road surface image or the height information, and the data processing device simply classifies and processes the acquired information data.
As a further preferable mode of the present invention, the road surface friction coefficient in the first step is detected by a slip ratio method or a laser ranging method.
As a further preferable mode of the present invention, the slip ratio method has a calculation formula as follows:
μ＝(tanα-tanβ)/(1+tanα×tanβ)
where μ is the road surface friction coefficient, α is the slip angle, and β is the slip angle. It should be noted that the measurement of the slip angle and the slip angle requires special equipment such as gyroscopes, accelerometers, etc., and needs to be performed during actual vehicle travel.
As a further preferable mode of the present invention, the calculation formula of the laser ranging method is as follows:
road surface height h=h-L
Wherein H is the height of the laser beam, L is the contact position of the laser beam and the road surface;
the calculation formula of the road friction coefficient is as follows: mu= (W-N)/(w+n) ×tan θ
Wherein W is the gravity component of the wheel perpendicular to the road surface, N is the supporting force component of the road surface to the wheel perpendicular to the road surface, and θ is the friction angle between the wheel and the road surface.
As a further preferable mode of the present invention, the road surface information processing step in the step two includes a method of object detection, image segmentation, feature extraction, optical flow method, machine learning, and the like to extract the road surface information.
As a further preferable mode of the present invention, the method of predicting the motion state of the vehicle in the third step is a state estimation method based on sensor data or a state prediction method based on a physical model.
(III) beneficial effects
The invention provides a vehicle stability control method based on road surface identification. The beneficial effects are as follows:
1. the invention can identify the type of the road surface on which the vehicle is currently running through real-time acquisition and processing of the road surface information, and improves the adaptability of the vehicle stability control method.
2. According to the identified road surface type, the vehicle stability control strategy is dynamically adjusted, and the running stability of the vehicle under the complex road surface condition is improved.
3. Compared with the existing stability control method, the method can better adapt to various road surface conditions and improve the running safety of the vehicle.
Drawings
Fig. 1 is a schematic diagram of a vehicle stability control method based on road surface recognition according to the present invention.
Detailed Description
A vehicle stability control method based on road surface recognition, the vehicle stability control method comprising the steps of:
step one, collecting pavement information in the running process of a vehicle: including road friction coefficient, road roughness, road temperature, etc.;
step two, processing the acquired road surface information, and identifying the type of the current driving road surface: including dry road surfaces, wet road surfaces, ponding road surfaces, frozen road surfaces, snow covered road surfaces, and the like;
step three, predicting the motion state of the vehicle according to the identified road surface type, and dynamically adjusting the vehicle stability control strategy: comprises the steps of adjusting ABS working parameters, adjusting TCS working parameters, adjusting ESP working parameters and the like;
and step four, the adjusted vehicle stability control strategy is applied to the vehicle running process, so that the dynamic adjustment of the vehicle running stability is realized.
In a further improvement, the method for collecting the road surface information in the first step is that road surface images or height information are obtained through sensors such as cameras and laser radars, and collected information data are simply classified and processed through data processing equipment, so that the system can process the information rapidly and efficiently.
Further, the road friction coefficient in the first step is detected by a slip ratio method and a laser ranging method.
Further improved, the slip ratio method has the following calculation formula:
μ＝(tanα-tanβ)/(1+tanα×tanβ)
where μ is the road surface friction coefficient, α is the slip angle, and β is the slip angle. It should be noted that the measurement of the slip angle and the slip angle requires special equipment such as gyroscopes, accelerometers, etc., and needs to be performed during actual vehicle travel.
Further improved, the calculation formula of the laser ranging method is as follows:
road surface height h=h-L
Wherein H is the height of the laser beam, L is the contact position of the laser beam and the road surface;
the calculation formula of the road friction coefficient is as follows: mu= (W-N)/(w+n) ×tan θ
Wherein W is the gravity component of the wheel perpendicular to the road surface, N is the supporting force component of the road surface to the wheel perpendicular to the road surface, and θ is the friction angle between the wheel and the road surface.
Further improved, the pavement information processing step in the step two comprises the steps of target detection, image segmentation, feature extraction, optical flow method, machine learning and other methods to extract pavement information, so that the efficiency and accuracy of road condition identification on the pavement surface are improved, the response speed of a control system is improved, the requirement of real-time adjustment of vehicles is met, and the running stability of the vehicles is improved.
Further, the object detection is to detect an object in a road surface image, such as traffic sign, pedestrian, and the like, and common object detection methods include a Haar feature classifier, a convolutional neural network, and the like.
Further, the image segmentation is to segment the road surface image into different parts so as to analyze the road surface condition of different positions more accurately, and common image segmentation methods include threshold-based segmentation, edge-based segmentation, region-based segmentation, and the like.
Further, the feature extraction is to extract important feature information, such as road color, texture, shape, etc., from the road surface image, and a common feature extraction method includes SIFT, SURF, HOG, etc.
Further, the optical flow method is to analyze pixel changes between continuous frames to judge the motion state of the road surface and the friction coefficient of the road surface, and common optical flow methods include Lucas-Kanade optical flow method, horn-Schunck optical flow method and the like.
Further improved, the machine learning is a road surface recognition method which has been widely applied in recent years, and the automatic classification and recognition of road surface images are realized by training a deep neural network. Common deep learning methods include Convolutional Neural Networks (CNNs), recurrent Neural Networks (RNNs), and the like.
The method for predicting the motion state of the vehicle in the third step is a state estimation method based on sensor data or a state prediction method based on a physical model, and the more realistic data model is utilized for simulation, so that the vehicle is ensured to more accord with a prediction result in the actual running process, and meanwhile, the predicted motion state of the vehicle is used for controlling the vehicle in advance, so that the vehicle can be controlled stably more efficiently.
Examples
In the running process of the vehicle, road surface information is acquired through a sensor arranged at a chassis of the vehicle, wherein the road surface information comprises a road surface friction coefficient, road surface roughness, road surface temperature and the like, the road surface friction coefficient in the actual running process of the vehicle is calculated through a calculation formula mu= (tan alpha-tan beta)/(1+tan alpha×tan beta) of a slip ratio method, important characteristic information such as road surface color, texture and shape is extracted from road surface images, the acquired road surface information is transmitted to a vehicle-mounted controller for processing, the type of the road surface which is currently running is identified, factors which interfere with the stability of the vehicle in the running process of the current vehicle are predicted according to the type of the road surface which is currently running, and the factors which interfere with the stability of the vehicle in the running process of the current vehicle are pre-aimed by adopting corresponding measures, and the vehicle-mounted controller dynamically adjusts the vehicle stability control strategy, so that the running stability of the vehicle on different types of the road surfaces is effectively improved; for example, when an icy road is identified, the onboard controller may lower the brake pressure threshold of the ABS, increase the traction limit of the TCS, and decrease the magnitude of the intervention of the ESP to accommodate the driving conditions of the icy road; when the ponding road surface is identified, the vehicle-mounted controller can increase the braking pressure threshold value of the ABS, reduce the traction limit of the TCS, and adjust the intervention strategy of the ESP at the same time so as to reduce the sideslip risk of the vehicle on the ponding road surface; when the snow covered road surface is identified, the vehicle-mounted controller can reduce the brake pressure threshold value of the ABS, enhance the traction limitation of the TCS, and adjust the intervention strategy of the ESP at the same time so as to reduce the sideslip risk of the vehicle on the snow covered road surface; when the dry road surface is identified, the vehicle-mounted controller can improve the brake pressure threshold value of the ABS, reduce the traction limit of the TCS and improve the intervention amplitude of the ESP so as to adapt to the running condition of the dry road surface and improve the running speed and stability of the vehicle; and the adjusted vehicle stability control strategy is applied to the vehicle running process in real time, so that the dynamic adjustment of the vehicle running stability is realized.
The invention can identify the type of the road surface on which the vehicle is currently running through real-time acquisition and processing of road surface information, improves the adaptability of the vehicle stability control method, dynamically adjusts the vehicle stability control strategy according to the identified type of the road surface, improves the running stability of the vehicle under complex road surface conditions, and better adapts to various road surface conditions and improves the running safety of the vehicle compared with the existing stability control method.
In the description of the present invention, it should be understood that the terms "coaxial," "bottom," "one end," "top," "middle," "another end," "upper," "one side," "top," "inner," "front," "center," "two ends," etc. indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," "third," "fourth," 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, whereby features defining "first," "second," "third," "fourth" may explicitly or implicitly include at least one such feature.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "configured," "connected," "secured," "screwed," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intermediaries, or in communication with each other or in interaction with each other, unless explicitly defined otherwise, the meaning of the terms described above in this application will be understood by those of ordinary skill in the art in view of the specific circumstances.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A vehicle stability control method based on road surface identification is characterized in that: the vehicle stability control method includes the steps of:
step one, collecting pavement information in the running process of a vehicle: including road friction coefficient, road roughness, road temperature, etc.;
step two, processing the acquired road surface information, and identifying the type of the current driving road surface: including dry road surfaces, wet road surfaces, ponding road surfaces, frozen road surfaces, snow covered road surfaces, and the like;
step three, predicting the motion state of the vehicle according to the identified road surface type, and dynamically adjusting the vehicle stability control strategy: comprises the steps of adjusting ABS working parameters, adjusting TCS working parameters, adjusting ESP working parameters and the like;
and step four, the adjusted vehicle stability control strategy is applied to the vehicle running process, so that the dynamic adjustment of the vehicle running stability is realized.
2. The vehicle stability control method based on road surface recognition according to claim 1, characterized in that: the method for collecting the road surface information in the first step is that the sensors such as cameras and laser radars are used for obtaining road surface images or height information, and the data processing equipment is used for simply classifying and processing the collected information data.
3. The vehicle stability control method based on road surface recognition according to claim 1, characterized in that: and step one, detecting the friction coefficient of the road surface by a slip ratio method and a laser ranging method.
4. A vehicle stability control method based on road surface recognition according to claim 3, characterized in that: the slip ratio method has the following calculation formula:
μ＝(tanα-tanβ)/(1+tanα×tanβ)
where μ is the road surface friction coefficient, α is the slip angle, and β is the slip angle. It should be noted that the measurement of the slip angle and the slip angle requires special equipment such as gyroscopes, accelerometers, etc., and needs to be performed during actual vehicle travel.
5. A vehicle stability control method based on road surface recognition according to claim 3, characterized in that: the calculation formula of the laser ranging method is as follows:
road surface height h=h-L
Wherein H is the height of the laser beam, L is the contact position of the laser beam and the road surface;
the calculation formula of the road friction coefficient is as follows: mu= (W-N)/(w+n) ×tan θ
Wherein W is the gravity component of the wheel perpendicular to the road surface, N is the supporting force component of the road surface to the wheel perpendicular to the road surface, and θ is the friction angle between the wheel and the road surface.
6. The vehicle stability control method based on road surface recognition according to claim 1, characterized in that: the pavement information processing step in the second step comprises the steps of target detection, image segmentation, feature extraction, an optical flow method, machine learning and other methods for extracting pavement information.
7. The vehicle stability control method based on road surface recognition according to claim 1, characterized in that: the method for predicting the motion state of the vehicle in the third step is a state estimation method based on sensor data or a state prediction method based on a physical model.
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Cited By (1)
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