CN115959095A - Active braking safety distance judgment method and system and active braking control method - Google Patents
Active braking safety distance judgment method and system and active braking control method Download PDFInfo
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Abstract
The invention relates to a method and a system for judging a safety distance of active braking and an active braking control method. The method comprises the following steps: collecting information of different types of pavements in different environments, and labeling the information to form a data set; training and testing the data set to generate a pavement recognition model; acquiring a current road surface image in real time, identifying the current road surface type in real time through a road surface identification model, and outputting a road surface identification result; when the active brake is triggered, the active brake system maps the road surface identification result into a friction coefficient; and calculating the safe braking distance according to the friction coefficient and the current speed of the vehicle. The invention obtains the current road image through the vehicle front-view camera based on the road identification model established in advance, identifies the current road type in real time according to the road identification model, maps the road identification result into a friction coefficient, calculates the safe braking distance according to the friction coefficient and the current vehicle speed of the vehicle, and carries out braking processing on the vehicle by combining the safe braking distance.
Description
Technical Field
The invention relates to the technical field of vehicle active braking, in particular to a method and a system for judging a safe distance of active braking and an active braking control method.
Background
At present, the popularization rate of automatic assistant driving in automobiles is higher and higher, and an active braking system is an important safety function of the automatic assistant driving. The active braking system is used for actively braking when a vehicle encounters a sudden danger or the distance between the vehicle and a front vehicle, a pedestrian or an obstacle is less than a safe distance, so that collision cannot be completely avoided, and the occurrence of collision accidents such as rear-end collision and the like can be reduced or avoided as far as possible.
Currently, most active braking technologies adopt the following schemes to judge the safety distance: firstly, a safe distance is calculated by the current vehicle speed, the deceleration of the safe distance is calculated according to PBC (Peak Braking Coefficient), namely a friction Coefficient measuring value (measured on a dry road according to GB/T26987-2011 regulation 6) of the tire and the road surface, the maximum deceleration can be generated, and then Braking is carried out through fixed Braking parameters; and on the basis of the scheme I, the distance of the front obstacle is judged in real time through a sensor, and the braking force is dynamically increased.
However, the friction coefficient in the first scheme is fixed and is a value measured by a dry road surface, and the brake may not be stopped if the dry road surface meets rainy or snowy weather or a slippery road surface. Because the frictional force between the tire and the road surface is reduced in rainy and snowy weather and wet and slippery road surfaces, the safety distance needs to be lengthened, if the safety distance is judged only according to the vehicle speed, the safety distance is short, vehicle collision can be caused, and the life safety of drivers and passengers is threatened.
The second scheme is optimized on the basis of the first scheme, but the real-time requirement on obstacle judgment is extremely high, and the sensitivity of the sensor and the calculation force of the controller are naturally high. On the one hand, the overall hardware cost is increased; on the other hand, the obstacle judgment inevitably causes time delay and misjudgment, the dynamic braking force adjustment lags behind the first-step judgment accuracy, and a higher collision risk still exists.
Disclosure of Invention
The invention provides an active braking safe distance judgment method, an active braking safe distance judgment system and an active braking control method, aiming at solving the problems that in the prior art, the safe distance is calculated through the current vehicle speed, the deceleration can generate a friction coefficient measuring value of the maximum deceleration according to a tire and a road surface, then braking is carried out through fixed braking parameters, and because the friction coefficient is fixed and is a value tested through a dry road surface, the vehicle cannot be braked when the vehicle encounters rainy and snowy weather or a wet and slippery road surface, the vehicle is collided, the life safety of drivers and passengers is threatened, and the like.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for judging the safe distance of active braking comprises the following steps:
acquiring a current road surface image in real time, identifying the current road surface type in real time through a road surface identification model, and outputting a road surface identification result;
when active braking is triggered, mapping the road surface recognition result into a friction coefficient;
and calculating the safe braking distance according to the friction coefficient and the current speed of the vehicle.
Further, as a preferred technical solution, before acquiring a current road image in real time, a road identification model needs to be established in advance, and the establishment of the road identification model includes:
respectively collecting a plurality of pieces of picture information of various road surface types, and labeling the picture information to form a data set;
and training and testing the data set to generate a road surface recognition model.
Further, as a preferred technical solution, the generation of the road surface identification model specifically includes:
the data set is divided into a training data set and a testing data set, the training data set is trained through a deep learning training model to obtain a training model, and the testing data set verifies the training model to generate a road surface recognition model.
Further, as a preferred technical scheme, the pavement types include dry pavement, wet and slippery pavement and ice and snow pavement;
the dry pavement refers to the condition that the pavement is not wet, seeped water, accumulated snow and frozen;
the wet and slippery road surface refers to the condition that the road surface is wet and seeped water;
the ice and snow road surface refers to the condition of ice and snow on the road surface.
Further, as a preferred technical solution, in the step of executing, before mapping the road surface identification result to a friction coefficient when the active brake is triggered, the following steps are also required to be executed:
and establishing a mapping relation table between the road surface type and the friction coefficient.
Further, as a preferred technical solution, mapping the road surface identification result into a friction coefficient specifically includes:
when the active brake is triggered, the active brake system maps the road surface identification result into a friction coefficient in a mode of looking up a mapping relation table.
Further, as a preferred technical solution, the method further comprises: and judging whether the friction coefficient is smaller than the PBC national standard specified value or not, and if so, adopting the PBC national standard specified value for the friction coefficient.
Further, as a preferred technical solution, the safe braking distance is calculated by the following formula:
S＝V*V/2gμ；
where S denotes a safe braking distance, V denotes a current vehicle speed, g =9.8m/S2, and μ denotes a friction coefficient.
An active braking safety distance judgment system, comprising:
the image acquisition module is used for acquiring images of different types of pavements in different environments;
a calculation module: the road surface recognition model is generated according to the acquired image information of different types of road surfaces in different environments;
the image real-time acquisition module: the system is arranged in front of the vehicle and used for acquiring a road surface image in front of the vehicle in real time;
a detection module: the road surface recognition system is used for recognizing the current road surface type in real time through a road surface recognition model according to a road surface image in front of a vehicle collected in real time and outputting a road surface recognition result;
an active braking module: and mapping the road surface identification result into a friction coefficient, calculating a safe braking distance according to the friction coefficient and the current vehicle speed of the vehicle, and outputting a braking instruction according to the safe braking distance.
An active braking control method comprises the following steps:
obtaining a safe braking distance by the active braking safe distance judging method;
and the active braking system outputs a braking instruction according to the safe braking distance so as to realize braking treatment on the vehicle.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention uses the sensing capability of the vehicle forward-looking camera based on the road surface identification model established in advance, obtains the current road surface image through the vehicle forward-looking camera, identifies the current road surface type in real time according to the road surface identification model to output the road surface identification result, maps the road surface identification result into a friction coefficient, and calculates the safe braking distance according to the friction coefficient and the current vehicle speed of the vehicle, thereby leading the active braking buffer distance to be longer, and braking the vehicle by combining the safe braking distance to ensure the personal safety of drivers and passengers.
Drawings
Fig. 1 is a flowchart of steps of a method for determining an active braking safety distance according to embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of a road surface identification model establishing process of the active braking safe distance determining method provided in embodiment 2 of the present invention.
Fig. 3 is a block diagram of a structure of an active braking safe distance determining system according to embodiment 3 of the present invention.
Fig. 4 is a flowchart of steps of an active braking control method according to embodiment 4 of the present invention.
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted; the same or similar reference numerals correspond to the same or similar parts; the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand for those skilled in the art and will therefore make the scope of the invention more clearly defined. .
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it is to be understood that the terms "upper", "lower", "left", "right", "top", "bottom", "inner", "outer", and the like, if any, are used in the orientations and positional relationships indicated in the drawings only for the convenience of describing the present invention and simplifying the description, but not for indicating or implying that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore the terms describing the positional relationships in the drawings are used for illustrative purposes only and are not to be construed as limiting the present patent.
Furthermore, if the terms "first," "second," and the like are used for descriptive purposes only, they are used for mainly distinguishing different devices, elements or components (the specific types and configurations may be the same or different), and they are not used for indicating or implying relative importance or quantity among the devices, elements or components, but are not to be construed as indicating or implying relative importance.
Example 1
The embodiment discloses an active braking safety distance judgment method for solving the problems that in the prior art, the safety distance is calculated through the current vehicle speed, the deceleration can generate a friction coefficient measured value of the maximum deceleration according to a tire and a road surface, then braking is carried out through a fixed braking parameter, and because the friction coefficient is fixed and is a value tested through a dry road surface, the vehicle cannot be braked when meeting rainy and snowy weather or wet and slippery road surfaces, vehicle collision is caused, the life safety of a driver and passengers is threatened, and the like.
The method for judging the safety distance of active braking disclosed by the embodiment, as shown in fig. 1, includes the following steps:
s101, acquiring a current road surface image in real time, identifying the type of the current road surface in real time through a road surface identification model, and outputting a road surface identification result.
In the present embodiment, the current road surface image is extracted from a video image captured by a front-view camera disposed in front of the vehicle, and a road surface recognition model is established in advance to recognize the current road surface image captured by the front-view camera, and the road surface type includes a dry road surface, a wet road surface, or an icy or snowy road surface.
Wherein, the dry road surface means the condition that the road surface is not wet, seeper, snow and ice; the wet and slippery road surface refers to the condition that the road surface is wet and seeped water; the ice and snow road surface refers to the condition of ice and snow on the road surface.
Therefore, the method specifically comprises the following steps: during the driving process of the vehicle, the front-view camera collects a current road surface image in real time, the collected current road surface image is identified through the road surface identification model, so that the current road surface type, namely a dry road surface, a wet and slippery road surface or an ice and snow road surface, is identified in real time, and a road surface identification result is output.
In the step, if the current road surface type does not need to be identified, the road surface is directly processed according to the dry road surface, and a road surface identification result is output.
And S102, when the active brake is triggered, the active brake system maps the road surface identification result into a friction coefficient.
Before this step is performed, a mapping table between the road surface type and the friction coefficient needs to be established.
In the present embodiment, the correspondence relationship between the road surface recognition result and the friction coefficient is shown in the following table:
| road surface type | Coefficient of friction mu |
| Dry pavement | 0.8 |
| Wet and slippery road surface | 0.4 |
| Ice and snow road surface | 0.2 |
When the active brake is triggered, the active brake system maps the road surface identification result into a friction coefficient in a mapping relation table checking mode.
Meanwhile, in the step, whether the friction coefficient is smaller than a PBC national standard specified value or not needs to be judged, if yes, the PBC national standard specified value is adopted as the friction coefficient, and if not, the currently output friction coefficient is adopted.
And S103, calculating a safe braking distance according to the friction coefficient and the current speed of the vehicle, and outputting the safe braking distance.
In this step, the safety braking distance is calculated by the following formula:
S＝V*V/2gμ；
where S denotes a safe braking distance, V denotes a current vehicle speed, g =9.8m/S2, and μ denotes a friction coefficient.
In this embodiment, after the safe braking distance is calculated, the safe braking distance is output to inform the active braking system.
This example is illustrated by:
based on the road surface identification model, assuming that the identified current road surface type is a dry road surface, 0.8 is output according to a mapping relation table between the road surface type and the friction coefficient, that is, the value of the friction coefficient under the dry road surface is 0.8, and the safe braking distance of the dry road surface is calculated to be about 17.7m according to a calculation formula of the safe braking distance S = V V/2g mu in combination with the current vehicle speed of 60km/h, so that the braking distance is 17.7m.
In the same calculation process, assuming that the identified current road surface type is a wet road surface, 0.2 is output according to a mapping relation table between the road surface type and the friction coefficient, that is, the value of the friction coefficient under the wet road surface is 0.2, and the safe braking distance of the wet road surface is calculated to be about 71m according to the calculation formula of the safe braking distance S = V/2g μ in combination with the current vehicle speed of 60km/h, so that the braking distance is 71m. If an obstacle is detected in front of the vehicle and emergency braking is needed, the vehicle needs to enter a braking state when the distance from the obstacle is larger than 71m, so that the vehicle is ensured to be braked.
Example 2
The embodiment discloses a method for judging the safety distance of active braking, and further discloses a process for establishing a road surface identification model on the basis of the embodiment 1.
Referring to fig. 2, a flow of establishing a road surface identification model in the method for determining a safe distance of active braking according to the embodiment of the present application is shown in the figure.
In this embodiment, as shown in fig. 2, the process of establishing the road surface identification model includes the following steps:
s201, collecting a plurality of pieces of picture information of various road surface types respectively, and labeling the picture information to form a data set.
This step can be understood as:
a plurality of pictures of various types of road surfaces under different environments are collected in advance and marked to form a data set.
For example, the road surface types include a dry road surface, a wet road surface and an ice and snow road surface, and a plurality of road surface pictures of each type are collected and then marked as follows, wherein the dry road surface is marked as 1, the wet road surface is marked as 2, and the ice and snow road surface is marked as 3.
In this embodiment, a dry road surface means a condition that the road surface is not wet, seeped water, snow or ice; the wet and slippery road surface refers to the condition that the road surface is wet and seeped water; the ice and snow road surface refers to the condition of ice and snow on the road surface. The road surface type is a conventional road surface state, and the standard is artificially distinguished during collection.
And S202, carrying out training test on the data set to generate a road surface recognition model.
The method specifically comprises the following steps:
the data set is divided into a training data set and a testing data set, the training data set is trained through a deep learning training model to obtain a training model, and the testing data set verifies the training model to generate a road surface recognition model.
In this embodiment, the deep learning training model may adopt a ResNet-50 network, or may adopt another training model, which is not specifically limited in this embodiment, and may implement a model training function.
In this embodiment, the training data set is trained through the deep learning training model, and then the trained training model is verified through the test data set, so as to improve the precision of the training model, generate the road surface recognition model, and further improve the road surface recognition precision.
In this embodiment, the trained training model is verified by the test data set, and if the accuracy is lower than 96%, the parameters of the deep learning training model, that is, the parameters of the ResNet-50 network, are adjusted to retrain the training data set.
In this embodiment, the setting of the accuracy to 96% is only an example, and if the accuracy requirement is high, the accuracy can be set to 98%, but whether the parameters of the ResNet-50 network need to be optimized, retrained, and determined according to the accuracy requirement is required.
This example is illustrated by:
taking two weather pavements as an example, including a wet and slippery pavement and a dry pavement, 10000 pictures are respectively collected on each pavement in an urban road and marked as the wet and slippery pavement or the dry pavement to form a data set, and the proportion of a training data set and a test data set which divide the data set is 8.
And building a ResNet-50 deep learning network on the server, and filling the training data set into the ResNet-50 deep learning network for training. And after the training is finished, verifying the precision through the test data set, and if the precision is lower than 96%, adjusting the ResNet-50 deep learning network parameters to retrain until the precision is met, thereby generating the road surface recognition model.
Example 3
The embodiment discloses an active braking safety distance judgment system, which adopts the active braking safety distance judgment method of embodiment 1 to calculate the safety braking distance according to the road surface condition, so that the active braking buffer distance is longer, and the personal safety of drivers and passengers is guaranteed.
Referring to fig. 3, a block diagram of a structure of an active braking safety distance determining system according to an embodiment of the present application is shown.
The active braking safe distance determining system disclosed in this embodiment, as shown in fig. 3, includes: the system comprises an image acquisition module 301, a calculation module 302, an image real-time acquisition module 303, a detection module 304 and an active braking module 305.
In this embodiment, the image acquisition module 301 is used to acquire images of different types of road surfaces in different environments.
The calculation module 302 is configured to generate a road surface identification model according to the acquired image information of different types of road surfaces in different environments. The generation process of the road surface identification model specifically refers to embodiment 2, and the description of this embodiment is not repeated.
The image real-time acquisition module 303 is arranged in front of the vehicle, and specifically is a vehicle front-view camera for acquiring a road image in front of the vehicle in real time.
The detection module 304 is configured to identify a current road type in real time through the road identification model according to a road image in front of the vehicle collected in real time, and output a road identification result.
The active braking module 305 maps the road surface recognition result into a friction coefficient, calculates a safe braking distance according to the friction coefficient and the current vehicle speed of the vehicle, and outputs a braking instruction according to the safe braking distance, so that the vehicle is braked, and the personal safety of drivers and passengers is guaranteed.
In the present embodiment, the road surface types include a dry road surface, a wet road surface, and an icy and snowy road surface. Wherein, the dry road surface means the condition that the road surface is not wet, seeper, snow and ice; the wet and slippery road surface refers to the condition that the road surface is wet and seeped water; the ice and snow road surface refers to the condition of ice and snow on the road surface. The road surface type is a conventional road surface state, and the standard is artificially distinguished during collection.
In this embodiment, the calculation process of the safe braking distance is referred to as embodiment 1, and the description of this embodiment is not repeated.
Example 4
The embodiment discloses an active braking control method, which is based on the safe braking distance calculated by the active braking safe distance judgment method in the embodiment 1 according to the road surface condition, so as to realize active braking control, thereby enabling the active braking buffer distance to be longer and ensuring the personal safety of drivers and passengers.
As shown in fig. 4, the active braking control method disclosed in this embodiment includes the following steps:
s401, obtaining a safe braking distance through the method for judging the safe braking distance in the embodiment 1, and then outputting the safe braking distance to an active braking system.
S402, the active braking system outputs a braking instruction according to the safe braking distance so as to brake the vehicle, so that the vehicle is braked, and the personal safety of drivers and passengers is guaranteed.
The process of obtaining the safe braking distance in this embodiment refers to embodiment 1, and the description of this embodiment is not repeated.
The specific implementation steps of this embodiment are also referred to in embodiment 1, and this embodiment is not repeatedly described.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. This need not be, nor should it be exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A method for judging the safe distance of active braking is characterized by comprising the following steps:
acquiring a current road surface image in real time, identifying the current road surface type in real time through a road surface identification model, and outputting a road surface identification result;
when active braking is triggered, mapping the road surface recognition result into a friction coefficient;
and calculating the safe braking distance according to the friction coefficient and the current speed of the vehicle.
2. The method for judging the safety distance of the active brake according to claim 1, wherein a road surface identification model needs to be established in advance before a current road surface image is collected in real time, and the establishment of the road surface identification model comprises the following steps:
respectively collecting a plurality of pieces of picture information of various road surface types, and labeling the picture information to form a data set;
and training and testing the data set to generate a road surface recognition model.
3. The method for determining the safe distance for active braking according to claim 2, wherein the generation of the road surface identification model specifically comprises:
the data set is divided into a training data set and a testing data set, the training data set is trained through a deep learning training model to obtain a training model, and the testing data set verifies the training model to generate a road surface recognition model.
4. The method as claimed in claim 2, wherein the road surface type includes dry road surface, wet road surface, and ice and snow road surface;
the dry pavement refers to the condition that the pavement is not wet, seeped water, accumulated snow and frozen;
the wet and slippery road surface refers to the condition that the road surface is wet and seeped water;
the ice and snow road surface refers to the condition of ice and snow on the road surface.
5. The method as claimed in claim 1, wherein before the step of performing, when the active brake is triggered, mapping the road surface identification result to the friction coefficient, the following steps are further performed:
and establishing a mapping relation table between the road surface type and the friction coefficient.
6. The active braking safe distance judgment method according to claim 5, wherein mapping the road surface identification result to a friction coefficient specifically comprises:
when the active brake is triggered, the active brake system maps the road surface identification result into a friction coefficient in a mapping relation table checking mode.
7. The method as claimed in claim 6, further comprising: and judging whether the friction coefficient is smaller than the PBC national standard specified value, if so, adopting the PBC national standard specified value for the friction coefficient.
8. The method for determining the safe distance for active braking according to claim 1, wherein the safe braking distance is calculated by the following formula:
S＝V*V/2gμ；
where S denotes a safe braking distance, V denotes a current vehicle speed, g =9.8m/S2, and μ denotes a friction coefficient.
9. An active braking safety distance judgment system, comprising:
the image acquisition module is used for acquiring images of different types of road surfaces in different environments;
a calculation module: the road surface recognition model is generated according to the acquired image information of different types of road surfaces in different environments;
the image real-time acquisition module: the system is arranged in front of the vehicle and used for acquiring a road surface image in front of the vehicle in real time;
a detection module: the road surface recognition system is used for recognizing the current road surface type in real time through a road surface recognition model according to a road surface image in front of a vehicle collected in real time and outputting a road surface recognition result;
an active braking module: and mapping the road surface identification result into a friction coefficient, calculating a safe braking distance according to the friction coefficient and the current vehicle speed of the vehicle, and outputting a braking instruction according to the safe braking distance.
10. An active braking control method is characterized by comprising the following steps:
obtaining a safe braking distance by the method for judging the safe braking distance of the active braking according to any one of claims 1 to 8;
and the active braking system outputs a braking instruction according to the safe braking distance so as to realize braking treatment on the vehicle.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116653975A (en) * | 2023-06-01 | 2023-08-29 | 盐城工学院 | Vehicle stability control method based on road surface recognition |
| CN117465394A (en) * | 2023-12-28 | 2024-01-30 | 深圳市开心电子有限公司 | Control method and system for emergency braking of electric vehicle |
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2023
- 2023-01-13 CN CN202310087528.6A patent/CN115959095A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116653975A (en) * | 2023-06-01 | 2023-08-29 | 盐城工学院 | Vehicle stability control method based on road surface recognition |
| CN117465394A (en) * | 2023-12-28 | 2024-01-30 | 深圳市开心电子有限公司 | Control method and system for emergency braking of electric vehicle |
| CN117465394B (en) * | 2023-12-28 | 2024-04-16 | 深圳市开心电子有限公司 | Control method and system for emergency braking of electric vehicle |
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