CN116587978A - Collision early warning method and system based on vehicle-mounted display screen - Google Patents
Collision early warning method and system based on vehicle-mounted display screen Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
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- B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
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Abstract
The application relates to the technical field of intelligent control of vehicles, in particular to a collision early warning method and a system of a vehicle-mounted display screen, wherein the method comprises the following steps: acquiring front road information and judging vehicles; responsive to the identification of the vehicle ahead, tracking and analyzing the vehicle to obtain relative travel data; performing collision risk assessment on the relative driving data based on the safety early warning model; if the rear-end collision risk exists, sending early warning information to the driver according to a preset early warning rule and recommending corresponding actions. According to the application, the early warning information can be sent to the driver and corresponding actions can be executed according to different driving environments, so that the safety of automobile driving is improved.
Description
Technical Field
The application relates to the technical field of intelligent control of vehicles, in particular to a collision early warning method and system of a vehicle-mounted display screen.
Background
At present, a method for preventing an automobile from collision is generally based on monitoring a front vehicle by a camera, but only plays a role of early warning, and cannot provide targeted protection according to actual driving conditions, so how to improve the safety of automobile driving is a problem to be solved by a person skilled in the art.
Disclosure of Invention
Aiming at the problem that the existing collision early-warning device of the automobile cannot provide targeted prevention according to actual running conditions, the application provides a collision early-warning method and system based on a vehicle-mounted display screen.
According to one aspect of the application, there is provided a collision early warning method based on a vehicle-mounted display screen, comprising:
acquiring front road information and judging vehicles;
responsive to the identification of the vehicle ahead, tracking and analyzing the vehicle to obtain relative travel data;
performing collision risk assessment on the relative driving data based on the safety early warning model;
if the rear-end collision risk exists, early warning information is sent to the driver according to a preset early warning rule, and corresponding actions are executed.
According to the technical scheme, after the front vehicle is identified, the relative running data are acquired, the collision risk assessment is carried out on the relative running data based on the safety early warning model, if the rear-end collision risk occurs, early warning information is sent to the driver according to the preset early warning rule, corresponding actions are executed, corresponding early warning and actions can be executed according to the real-time running condition, and the running safety of the vehicle is improved.
Preferably, acquiring the road information ahead and performing the vehicle judgment includes:
acquiring a characteristic image of a front object by using a front camera;
performing edge enhancement processing on the characteristic image by adopting a soble algorithm or a canny algorithm to obtain a horizontal edge and a vertical edge of a front object;
and judging the vehicle of the front object by using the edge constraint condition.
According to the technical scheme, after the characteristic image of the front object is acquired, the edge enhancement processing is performed by utilizing the matching algorithm so as to acquire the horizontal edge and the vertical edge of the front object, and the vehicle judgment is performed under the field edge constraint condition, so that the accuracy of the vehicle judgment can be improved.
Preferably, acquiring the road information ahead and performing the vehicle judgment includes:
acquiring a characteristic image of a front object by using a front camera;
and judging the vehicle on the basis of the deep learning model.
Through the technical scheme, after the characteristic image of the front object is acquired, the vehicle is judged by using the deep learning model, so that the vehicle judging efficiency can be improved, and the accuracy is improved.
Preferably, responsive to the identification of the vehicle ahead, tracking and analyzing the vehicle for relative travel data includes:
responsive to the identification of the preceding vehicle, tracking the preceding vehicle;
and acquiring image information of the front vehicle by using the front camera, and calculating a first vehicle distance between the current vehicle and the front vehicle based on the focal length of the front camera and preset parameters.
Through the technical scheme, the front-facing camera is utilized to acquire the front vehicle image information, and based on the focus of the front-facing camera and the preset parameters, the current vehicle and the distance between the front vehicle can acquire the position information of the current vehicle and the front vehicle in real time, so that a data basis is provided for the evaluation of collision risk.
Preferably, the collision risk assessment of the relative driving data based on the safety precaution model includes:
selecting a current road condition environment on a vehicle-mounted display screen;
the method comprises the steps of matching an early warning distance and an emergency braking distance based on the current road condition environment and the speed of a current vehicle;
judging the first vehicle distance by utilizing the early warning distance and the braking distance to determine the collision risk type of the current vehicle;
when the first vehicle distance is larger than the early warning distance, judging that no risk exists;
when the first distance is larger than the early warning distance and smaller than the emergency braking distance, judging that the first distance is a first-level risk;
and when the first vehicle distance is smaller than the emergency braking distance, determining that the vehicle is at secondary risk.
Through the technical scheme, the early warning distance and the emergency braking distance are matched based on the current road condition environment and the vehicle speed, the current first vehicle distance is judged, the corresponding risk grades are matched under different conditions, the risk assessment can be carried out on the running condition of the vehicle, and the running safety of the vehicle is improved.
Preferably, the preset early warning rule includes:
when the first-level risk is judged, the vehicle-mounted display screen releases the early warning signal, and the intensity of the early warning signal increases gradually along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary risk is judged.
Through the technical scheme, when the first-level risk is judged, the risk early warning is carried out on the driver through the release of the early warning signal, and when the second-level risk is judged, the danger is avoided through emergency braking, so that the occurrence of traffic accidents is avoided.
Preferably, the preceding vehicle includes a first vehicle traveling in parallel and a second vehicle positioned in front of the first vehicle, and tracking and analyzing the vehicles to obtain the relative traveling data in response to the identification of the preceding vehicle includes:
responsive to the identification of the preceding vehicle, tracking the preceding vehicle;
acquiring a first relative distance and a first relative speed of a first vehicle and a current vehicle by utilizing millimeter wave radar detection;
a second relative distance and a second relative speed of a second vehicle from the current vehicle are obtained using millimeter wave radar detection.
Through the technology, the millimeter wave radar detection is utilized to track and detect the first vehicle and the second vehicle in front respectively, so that the accident risk caused by the emergency can be known and prevented in advance.
Preferably, the collision risk assessment of the relative driving data based on the safety precaution model includes:
selecting a current road condition environment on a vehicle-mounted display screen;
calculating a first relative distance between the first vehicle and the second vehicle and a change rate of the first relative distance in real time based on the first vehicle distance and the second vehicle distance;
when the first relative distance is reduced to a preset safety distance and the change rate of the first relative distance is larger than a preset first change rate, determining that the first-level collision risk is generated;
and when the first relative distance is reduced to the preset safety distance and the change rate of the first relative distance is larger than the preset second change rate, judging that the second collision risk is generated.
Through the technical scheme, the first vehicle distance, the first relative distance and the change rate of the first relative distance are analyzed and calculated, so that the driving conditions of the first vehicle and the second vehicle can be tracked, and the occurrence of a chain traffic accident caused by a visual field blind area is avoided.
Preferably, the preset early warning rule includes:
when the first-level collision risk is judged, the vehicle-mounted display screen releases the early warning signal, outputs first relative information and first vehicle distance information in real time, and increases the intensity of the early warning signal along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary collision risk is judged.
Through the technical scheme, when the first-level collision risk is judged, the first relative information and the first vehicle distance information are output in real time through the vehicle-mounted display screen, so that a driver can master the running condition of the front vehicle to perform corresponding operation, and when the second-level collision risk is judged, the danger is avoided through emergency braking measures, and further the occurrence of traffic accidents is avoided.
According to another aspect of the present application, there is also provided a collision early warning system based on a vehicle-mounted display screen, including:
the vehicle judging module is used for acquiring the front road information and judging the vehicle;
the tracking analysis module is used for responding to the identification of the vehicles in front, tracking and analyzing the vehicles to obtain relative running data; the collision risk assessment module is used for carrying out collision risk assessment on the relative driving data based on the safety early warning model;
the execution module: and if the rear-end collision risk exists, sending early warning information to the driver according to a preset early warning rule and executing corresponding actions.
According to the technical scheme, after the vehicle judging module identifies the front vehicle, the relative running data are acquired through the tracking analysis module, the collision risk assessment module carries out collision risk assessment on the relative running data based on the safety early warning model, and the execution module sends early warning information to the driver according to the preset early warning rule and executes corresponding actions when rear-end collision risk occurs, so that corresponding early warning and actions can be executed according to real-time running conditions, and the running safety of the vehicle is improved.
In summary, the application comprises at least one of the following beneficial technical effects:
1. through real-time tracking and analysis of the front vehicle, corresponding early warning and actions can be executed according to real-time driving conditions, and the driving safety of the vehicle is improved;
2. the method has the advantages that the early warning distance and the emergency braking distance are matched based on the current road condition environment and the vehicle speed, the current first vehicle distance is judged, the corresponding risk grades are matched under different conditions, risk assessment can be carried out on the running condition of the vehicle, and the running safety of the vehicle is improved;
3. after the driving data of the first vehicle and the second vehicle in front are acquired, the driving conditions of the first vehicle and the second vehicle can be tracked by analyzing and calculating the first vehicle distance, the first relative distance and the change rate of the first relative distance, so that the occurrence of a chain traffic accident caused by a visual field blind area is avoided.
Drawings
Fig. 1 is a flow chart of a collision early warning method based on a vehicle-mounted display screen according to an embodiment of the application.
Fig. 2 is a flowchart of a method for determining a vehicle according to an embodiment of the application.
Fig. 3 is a flowchart of a method for acquiring relative driving data according to an embodiment of the application.
FIG. 4 is a flowchart of a collision risk assessment method according to an embodiment of the application.
Fig. 5 is a flowchart of a method for acquiring relative driving data according to another embodiment of the present application.
FIG. 6 is a flowchart of a collision risk assessment method according to another embodiment of the present application.
Fig. 7 is a schematic structural diagram of a collision warning device based on a vehicle-mounted display screen according to an embodiment of the application.
Detailed Description
The objects, technical solutions and advantages of the present application will become more apparent hereinafter, and the present application will be further described in detail by referring to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Fig. 1 is a flow chart of a collision early warning method based on a vehicle-mounted display screen according to an embodiment of the application.
Referring to fig. 1, the application provides a collision early warning method based on a vehicle-mounted display screen, which comprises the following steps:
s102, acquiring front road information and judging a vehicle.
In order to acquire road information in front of a vehicle, a front camera may be used to acquire a feature image of a front object, and the feature image is analyzed to realize vehicle judgment, where the front vehicle judgment generally refers to a vehicle that is in the same lane and running in the same direction as the current vehicle.
Further, the method of analyzing the feature image includes, but is not limited to, a method of using edge features as constraint conditions, a method of matching based on templates, a method of detecting judgment based on symmetry, and a method of judging based on texture features.
Image texture refers to a structure consisting of a large number or more or slightly similar texels or modes. The image texture is described by roughness, the size of which is related to the spatial repetition period of the local structure, with large roughness for periods and small roughness for periods, wherein the fractal dimension is an important parameter for estimating the surface roughness. The principle of detecting a vehicle in front by using a fractal dimension calculation method based on Brownian motion is as follows: for some natural objects, typically less than 1.6; the fractal dimension of the edges of artifacts such as automobiles is large and is larger than 2.0, and for horizontal edges and vertical edges, the fractal dimensions in the horizontal direction and the vertical direction are equal to 0, and the possible vehicle areas in the images can be determined by using the three fractal dimension parameters.
In one embodiment, acquiring the forward road information and making a vehicle determination includes: the method comprises the steps of obtaining a color image of a front object, carrying out initial segmentation on the color image to obtain a plurality of segmented areas, predicting each segmented area by adopting a streamer method or kalman filtering, drawing a motion track of each area, then carrying out area combination according to two principles of area adjacency and estimation similarity, and judging by adopting two principles of minimum length and minimum reliability test of the motion track of the area in order to improve the accuracy of a moving target.
S104, in response to the identification of the front vehicle, tracking and analyzing the vehicle to obtain relative driving data.
Generally, after a preceding vehicle is identified, in order to understand the real-time driving situation of the vehicle and ensure safe driving, real-time tracking analysis is required to be performed on the vehicle to obtain relative driving data corresponding to the current vehicle driving, such as: relative vehicle speed and distance.
S106, performing collision risk assessment on the relative driving data based on the safety early warning model.
After the relative running data are obtained, the relative running data are analyzed and judged based on the safety early warning model, for example, the vehicle distance is judged, and when the vehicle distance is too close, the driving risk exists; the relative vehicle speed is determined, for example, when the relative vehicle speed is continuously increased and the vehicle distance is continuously decreased, the corresponding driving risk is higher, so that data support is provided for executing corresponding operation.
S108, if the rear-end collision risk exists, sending early warning information to the driver according to a preset early warning rule and executing corresponding actions.
Based on the current risk condition, early warning information is sent to a driver according to a preset early warning rule, and corresponding actions are executed, such as: inputting an early warning audio signal, and vibrating a driving seat or a steering wheel; and closing windows and skylights, starting an automatic braking system, etc.
After the front vehicles are identified, relative running data are acquired, collision risk assessment is carried out on the relative running data based on a safety early warning model, if rear-end collision risk occurs, early warning information is sent to a driver according to preset early warning rules, corresponding actions are executed, corresponding early warning and actions can be executed according to real-time running conditions, and running safety of the vehicles is improved.
Fig. 2 is a flowchart of a method for determining a vehicle according to an embodiment of the application.
Referring to fig. 2, acquiring the road information ahead, and making a vehicle determination includes:
s202, acquiring a characteristic image of a front object by using a front camera;
s204, performing edge enhancement processing on the characteristic image by adopting a soft algorithm or a canny algorithm, and obtaining the horizontal edge and the vertical edge of the front object.
And filtering for multiple times in the vehicle identification process, wherein prior knowledge such as the edge characteristics of the vehicle is used as constraint conditions each time, the horizontal and vertical edges obtained through the soble algorithm are divided into a plurality of 8 connected edge areas based on the latest principle, and then the edges belonging to the vehicle are segmented from the background by using the prior knowledge of the vehicle structure as constraint conditions.
S204, judging the vehicle of the front object by utilizing the edge constraint condition.
The method comprises the steps of further processing the 8 connected edges by adopting a rank filter, retaining the edge area close to the vehicle, filtering the edge area far away from the vehicle, and removing noise by adopting a hough conversion linear filter. The remaining edge regions are constrained under the vehicle aspect ratio based condition to create rectangular regions, thereby identifying the vehicle region.
Further, after the vehicle is detected, tracking the vehicle, specifically including: in the method for tracking the vehicle by using the obtained vehicle information, when the current image is processed, three criteria of error square sum SSD based on Euclidean distance, edge density and pixel density in the vehicle area are adopted to match possible vehicle areas in sequence, so that the vehicle in front is detected.
In one embodiment, a vehicle clustering algorithm is employed to track the vehicles ahead.
By employing clustered algorithms (e.g., K-means and mean shift clustering algorithms), the run time of the tracking algorithm can be reduced, which will ensure that only vehicles in front closest to the host vehicle are tracked.
After the characteristic image of the front object is acquired, edge enhancement processing is performed by utilizing a matching algorithm to acquire the horizontal edge and the vertical edge of the front object, and vehicle judgment is performed under the field edge constraint condition, so that the accuracy of vehicle judgment can be improved.
Further, acquiring the front road information and judging the vehicle includes:
acquiring a characteristic image of a front object by using a front camera;
and judging the vehicle on the basis of the deep learning model.
The method further comprises the following steps before the vehicle judgment is carried out on the characteristic image based on the deep learning model: training the acquired vehicle image dataset based on a convolutional neural network to obtain a trained model; visualizing a layer structure inside the model; optimizing the model by adjusting each network parameter and layer structure; and performing experimental test on the trained model, and detecting the deep learning model by using pictures and videos so as to enable the picture recognition rate, the detection speed and the tracking precision of the deep learning model to reach preset standards.
After the feature images of the front object are acquired, the vehicle is judged by using the deep learning model, so that the vehicle judgment efficiency can be improved, and the accuracy can be improved.
Fig. 3 is a flowchart of a method for acquiring relative driving data according to an embodiment of the application.
Referring to fig. 3, in response to the identification of a vehicle ahead, tracking and analyzing the vehicle for relative travel data includes: s302, responding to the identification of the front vehicle, and tracking the front vehicle;
wherein, after the front vehicles are identified, the front vehicles can be tracked by utilizing the trained deep learning model.
S304, acquiring image information of the front vehicle by using the front camera, and calculating a first vehicle distance between the current vehicle and the front vehicle based on the focal length of the front camera and preset parameters.
Calculating the first vehicle distance between the current vehicle and the front vehicle based on the distance between the front cameras and the preset distance specifically comprises the following steps:
road plane assumption; i.e. assuming that the road in the forward view is in one plane;
the optical axis of the camera is parallel to the ground, namely the median value is kept unchanged in the 3D space;
the relationship between a point P (X, Y, Z) on the road and a corresponding point P (X, Y) on the plane image is as follows:
at infinity, the point of the road surface in the image corresponds to the center point of the image, i.e. when the camera optical axis is parallel to the ground and the road is substantially planar, the vanishing point of the road is at the origin of the image plane and the road surface area is at the lower half of the image plane.
The image acquisition based on a single front camera (for example) is to project a three-dimensional scene onto a two-position image plane (CCD photosensitive matrix surface) of a CCD camera, and the projection transformation can be measured by using a monocular vision ranging model based on the principle of small-hole imaging, P1 (x) 0 ，y 0 ) The calculation formula of the longitudinal distance Z between the P point of the vehicle and the center of the lens of the front camera of the running vehicle is as follows:
h is the distance between the center of the camera lens and the driving road surface, and f is the distance between the center of the lens and P1.
The front vehicle image information is acquired by the front camera, and the position information of the current vehicle and the front vehicle can be acquired in real time based on the focus of the front camera and the preset parameters of the distance between the current vehicle and the front vehicle, so that a data base is provided for the evaluation of collision risk.
FIG. 4 is a flowchart of a collision risk assessment method according to an embodiment of the application.
Referring to fig. 4, collision risk assessment of relative travel data based on a safety precaution model includes:
s402, selecting a current road condition environment on a vehicle-mounted display screen;
the road condition environment here refers to the running road condition of the current vehicle: such as: the collision risk assessment can be applied to different driving road conditions due to the quality of road surface conditions, the climatic conditions and the geographic environment of driving and the braking performance of vehicles.
S406, the early warning distance and the emergency braking distance are matched based on the current road condition environment and the speed of the current vehicle.
It will be appreciated that the pre-warning distance means that the risk of a relative collision is small at this time, and the emergency braking distance means that the distance between the current vehicle and the vehicle in front is already close to the dangerous value, and an emergency braking measure needs to be taken.
The vehicle matching early warning distance and the emergency braking distance can be obtained based on a deep learning model, a driver is subjected to multiple tests to obtain a large amount of data, and regression analysis is performed on the obtained data to obtain the matching early warning distance and the emergency braking distance.
S408, performing the first distance by using the pre-warning distance and the braking distance
Judging to determine the collision risk type of the current vehicle;
s410, judging that no risk exists when the first vehicle distance is larger than the early warning distance;
s412, when the first distance is larger than the early warning distance and smaller than the emergency braking distance, determining that the first distance is a first-level risk;
and S414, judging that the vehicle is at the second risk when the first vehicle distance is smaller than the emergency braking distance.
The vehicle running situation can be subjected to risk assessment based on the fact that the current road condition environment and the vehicle speed are matched with the early warning distance and the emergency braking distance, the current first vehicle distance is judged, and corresponding risk grades are matched with different situations, so that the vehicle running safety is improved.
Further, the preset early warning rule includes:
when the first-level risk is judged, the vehicle-mounted display screen releases the early warning signal, and the intensity of the early warning signal increases gradually along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary risk is judged.
The emergency braking measures include controlling the automatic braking system of the current vehicle to perform emergency braking, and automatic emergency closing of the windows and the sunroof and automatic tightening of the safety belt.
When the first-level risk is judged, the risk early warning is carried out on the driver through the release of the early warning signal, and when the second-level risk is judged, the danger is avoided through emergency braking, so that the occurrence of traffic accidents is avoided.
Fig. 5 is a flowchart of a method for acquiring relative driving data according to another embodiment of the present application.
Referring to fig. 5, a front vehicle includes a first vehicle traveling side by side and a second vehicle located in front of the first vehicle, and tracking and analyzing the vehicles in response to identification of the front vehicle to obtain relative traveling data includes:
s502, tracking the front vehicle in response to the identification of the front vehicle;
s504, acquiring a first relative distance and a first relative speed between a first vehicle and a current vehicle by millimeter wave radar detection;
s506, acquiring a second relative distance and a second relative speed between the second vehicle and the current vehicle by utilizing millimeter wave radar detection.
The millimeter wave radar sensor measures a first relative distance based on a TOF principle (Time Of Flight), namely, measures by utilizing the Time difference between a reflected wave and a transmitted wave; the relative velocity can be measured by the frequency difference between the echo and the transmitted wave, or by differentiating the tracked position of the preceding vehicle to obtain the velocity of the preceding vehicle.
In one embodiment, the millimeter wave sensors of the present vehicle include a short range millimeter wave radar sensor, a medium range millimeter wave radar sensor, and a long range millimeter wave radar sensor; wherein, 24GHZ radar sensor can carry out the distance measurement within 60 meters, 77GHZ radar sensor MRR can realize the distance measurement about 100 meters, 77GHZ radar sensor LRR can realize the distance measurement more than 200 meters.
In the above embodiment, the millimeter wave radar detection is used to track and detect the first vehicle and the second vehicle in front, respectively, so that the accident risk caused by the emergency can be known and prevented in advance.
FIG. 6 is a flowchart of a collision risk assessment method according to another embodiment of the present application.
Referring to fig. 6, collision risk assessment of relative travel data based on a safety precaution model includes:
s602, selecting a current road condition environment on a vehicle-mounted display screen;
s604, calculating a first relative distance between the first vehicle and the second vehicle and a change rate of the first relative distance in real time based on the first vehicle distance and the second vehicle distance;
s606, when the first relative distance is reduced to a preset safety distance and the change rate of the first relative distance is larger than a preset first change rate, determining that the first-level collision risk is generated;
when the first relative distance is reduced to the preset safety distance, the distance between the two vehicles in front is continuously reduced, the speed of the distance change is continuously increased, and the two vehicles in front have potential risks of collision.
S608, when the first relative distance is reduced to the preset safety distance and the change rate of the first relative distance is larger than the preset second change rate, determining that the second collision risk is generated.
When the first relative distance is reduced to the preset safety distance, the distance between the two vehicles in front is continuously reduced, the distance between the two vehicles in front is rapidly reduced, and the collision risk of the two vehicles in front is rapidly increased.
By analyzing and calculating the first vehicle distance, the first relative distance and the change rate of the first relative distance, the driving conditions of the first vehicle and the second vehicle can be tracked, and the occurrence of chain traffic accidents caused by visual field blind areas is avoided.
Further, the preset early warning rule includes:
when the first-level collision risk is judged, the vehicle-mounted display screen releases the early warning signal, outputs first relative information and first vehicle distance information in real time, and increases the intensity of the early warning signal along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary collision risk is judged.
If the collision risk of the two front vehicles suddenly increases, an accident that the two front vehicles possibly collide with each other is caused, and emergency measures are executed, wherein the emergency braking measures comprise controlling an automatic braking system of the current vehicle to perform emergency braking, and automatically closing the windows and the sunroof in an emergency manner and automatically tightening the safety belt.
When the first-level collision risk is judged, the first relative information and the first vehicle distance information are output in real time through the vehicle-mounted display screen, so that a driver can master the running condition of the front vehicle to perform corresponding operation, and when the second-level collision risk is judged, the danger is avoided through emergency braking measures, and further the occurrence of traffic accidents is avoided.
Fig. 7 is a schematic structural diagram of a collision warning device based on a vehicle-mounted display screen according to an embodiment of the application.
Referring to fig. 7, according to another aspect of the present application, there is also provided a collision early warning system based on a vehicle-mounted display screen, including:
a vehicle judging module 71 for acquiring the road information in front and judging the vehicle;
a tracking analysis module 72 for tracking and analyzing the vehicle to obtain relative travel data in response to the identification of the vehicle in front; a collision risk assessment module 73 for performing collision risk assessment on the relative running data based on the safety precaution model;
execution module 74: and if the rear-end collision risk exists, sending early warning information to the driver according to a preset early warning rule and executing corresponding actions.
After the vehicle judging module 71 identifies the front vehicle, the relative running data is acquired 72 through the tracking analysis module, the collision risk assessment module 73 carries out collision risk assessment on the relative running data based on the safety early warning model, and the executing module 74 sends early warning information to the driver according to a preset early warning rule and executes corresponding actions when the rear-end collision risk occurs, so that corresponding early warning and actions can be executed according to real-time running conditions, and the running safety of the vehicle is improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. The collision early warning method based on the vehicle-mounted display screen is characterized by comprising the following steps of:
acquiring front road information and judging vehicles;
responsive to the identification of a preceding vehicle, tracking and analyzing the vehicle to obtain relative travel data;
performing collision risk assessment on the relative driving data based on a safety early warning model;
if the rear-end collision risk exists, early warning information is sent to the driver according to a preset early warning rule, and corresponding actions are executed.
2. The collision warning method according to claim 1, wherein the acquiring the forward road information and the vehicle determination include:
acquiring a characteristic image of a front object by using a front camera;
performing edge enhancement processing on the characteristic image by adopting a soble algorithm or a canny algorithm to obtain a horizontal edge and a vertical edge of the front object;
and judging the vehicle of the front object by using the edge constraint condition.
3. The collision warning method according to claim 1, wherein the acquiring the forward road information and the vehicle determination include:
acquiring a characteristic image of a front object by using a front camera;
and judging the vehicle on the characteristic image based on the deep learning model.
4. The collision warning method of claim 1, wherein the tracking and analyzing the vehicle to obtain relative travel data in response to the identification of the vehicle ahead comprises:
responsive to the identification of the preceding vehicle, tracking the preceding vehicle;
and acquiring image information of the front vehicle by using a front camera, and calculating a first vehicle distance between the current vehicle and the front vehicle based on the focal length of the front camera and preset parameters.
5. The collision warning method of claim 4, wherein the collision risk assessment of the relative travel data based on a safety warning model comprises:
selecting a current road condition environment on a vehicle-mounted display screen;
the early warning distance and the emergency braking distance are matched based on the current road condition environment and the speed of the current vehicle;
judging the first vehicle distance by utilizing the early warning distance and the braking distance to determine the collision risk type of the current vehicle;
when the first distance is larger than the early warning distance, judging that the risk is not generated;
when the first vehicle distance is larger than the early warning distance and smaller than the emergency braking distance, judging that the first vehicle distance is a first-level risk;
and when the first vehicle distance is smaller than the emergency braking distance, judging that the vehicle is at secondary risk.
6. The collision warning method according to claim 5, wherein the preset warning rule includes:
when the first-level risk is judged, the vehicle-mounted display screen releases an early warning signal, and the intensity of the early warning signal increases gradually along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary risk is judged.
7. The collision warning method of claim 1, wherein the front vehicle comprises a first vehicle traveling side by side and a second vehicle positioned in front of the first vehicle, and wherein tracking and analyzing the vehicles to obtain the relative traveling data in response to the identification of the front vehicle comprises:
responsive to the identification of the preceding vehicle, tracking the preceding vehicle;
acquiring a first relative distance and a first relative speed between the first vehicle and the current vehicle by millimeter wave radar detection;
and acquiring a second relative distance and a second relative speed of the second vehicle and the current vehicle by utilizing millimeter wave radar detection.
8. The collision warning method of claim 7, wherein the collision risk assessment of the relative travel data based on a safety warning model comprises:
selecting a current road condition environment on a vehicle-mounted display screen;
calculating a first relative distance between the first vehicle and the second vehicle and a change rate of the first relative distance in real time based on the first vehicle distance and the second vehicle distance;
when the first relative distance is reduced to a preset safety distance and the change rate of the first relative distance is larger than a preset first change rate, determining that the first-level collision risk is generated;
and when the first relative distance is reduced to a preset safety distance and the change rate of the first relative distance is larger than a preset second change rate, judging that the first relative distance is at the risk of secondary collision.
9. The collision warning method according to claim 8, wherein the preset warning rule includes:
when the first-level collision risk is judged, the vehicle-mounted display screen releases an early warning signal, the first relative information and the first vehicle distance information are output in real time, and the strength of the early warning signal increases gradually along with the reduction of the first vehicle distance;
and executing emergency braking measures when the secondary collision risk is judged.
10. The utility model provides a collision early warning system based on-vehicle display screen which characterized in that includes:
the vehicle judging module is used for acquiring the front road information and judging the vehicle;
the tracking analysis module is used for responding to the identification of the vehicles in front, tracking and analyzing the vehicles to obtain relative running data;
the collision risk assessment module is used for carrying out collision risk assessment on the relative driving data based on a safety early warning model;
the execution module: and if the rear-end collision risk exists, sending early warning information to the driver according to a preset early warning rule and executing corresponding actions.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117163055A (en) * | 2023-09-08 | 2023-12-05 | 苏州宇洽科技有限公司 | Display console based on vehicle-mounted control |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117163055A (en) * | 2023-09-08 | 2023-12-05 | 苏州宇洽科技有限公司 | Display console based on vehicle-mounted control |
| CN117163055B (en) * | 2023-09-08 | 2024-05-07 | 苏州宇洽科技有限公司 | Display console based on vehicle-mounted control |
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