CN111640329A - Vehicle early warning method based on collision model - Google Patents
Vehicle early warning method based on collision model Download PDFInfo
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- CN111640329A CN111640329A CN202010467465.3A CN202010467465A CN111640329A CN 111640329 A CN111640329 A CN 111640329A CN 202010467465 A CN202010467465 A CN 202010467465A CN 111640329 A CN111640329 A CN 111640329A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
Abstract
The application relates to a vehicle early warning method based on a collision model, which realizes collision early warning by judging whether a far vehicle falls into the collision range of a main vehicle according to the collision model, is simple and effective to realize, avoids false warning and false missing warning caused by short-distance collision time and collision distance calculation and improves the warning efficiency; and the method has universal adaptability to any road condition and vehicle type.
Description
Technical Field
The application relates to the technical field of automotive electronics, in particular to a vehicle early warning method based on a collision model.
Background
The existing vehicle collision early warning method mostly adopts a mass center point to calculate collision time and collision distance for collision prediction, and the method is suitable for long-distance and high-speed targets; the method for obtaining the collision probability is complex in calculation by a small part of method for calculating the area overlapping rate according to the vehicle length and the vehicle width, and the calculation modes of the appearances of different vehicle types are slightly different, so that the method is mostly only applied to cross early warning of crossroads; in complex road conditions, particularly roundabouts, low-speed congestion road sections and the like, and vehicle early warning aiming at different vehicle types such as large trucks, cars, motorcycles and the like, the two calculation methods are not simple enough, the false alarm rate is high, the alarm efficiency is reduced, and the burden of a driver is increased.
Disclosure of Invention
In order to solve the technical problem, the application provides a vehicle early warning method based on a collision model, which is applied to an automobile electronic product, and is characterized in that the method comprises the following steps:
acquiring current remote vehicle data transmitted by a remote vehicle in real time;
establishing a collision model of the main vehicle and the remote vehicle according to the remote vehicle data;
obtaining a remote vehicle test coordinate and a main vehicle test coordinate according to the collision model, and judging whether the remote vehicle falls into the collision range of the main vehicle;
if yes, performing early warning processing;
otherwise, no processing is performed.
Optionally, the obtaining, in real time, current remote vehicle data transmitted from a remote vehicle includes:
and establishing communication connection with a remote vehicle and acquiring current remote vehicle data in real time through a V2X communication technology.
Optionally, the current vehicle-far data includes at least one or more of a length, a width, a vehicle speed, an acceleration, a position, an angular velocity, and a direction of the vehicle.
Optionally, the establishing a collision model of the host vehicle and the remote vehicle according to the remote vehicle data includes:
acquiring current remote vehicle data, calculating the current remote vehicle data and the remote vehicle data at the previous moment, and calculating remote vehicle average driving data of the remote vehicles;
acquiring current main vehicle data of a main vehicle, calculating the current main vehicle data and the main vehicle data at the previous moment, and calculating main vehicle average driving data of the main vehicle;
and obtaining a remote vehicle test coordinate according to the remote vehicle average driving data, and obtaining a main vehicle test coordinate according to the main vehicle average driving data.
Optionally, the obtaining, according to the remote average driving data, a displacement of the remote vehicle at the test time includes:
taking the current time position of the main vehicle as a coordinate center;
in the X-axis direction of the main vehicle, the vehicle is far awayIs displaced by the X axis
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
in the Y-axis direction of the primary carriage, the Y-axis of the remote carriage is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
and calculating the test coordinate of the remote vehicle after the test time is calculated according to the current coordinate of the remote vehicle, the X-axis displacement of the remote vehicle and the Y-axis displacement of the remote vehicle.
Optionally, the obtaining current host vehicle data of the host vehicle, calculating the current host vehicle data and host vehicle data at a previous time, and calculating host vehicle average driving data of the host vehicle includes:
taking the current time position of the main vehicle as a coordinate center;
in the X-axis direction of the subject, the X-axis of the subject is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, and T is the testThe time, a, is the average acceleration,
is the angular velocity;
in the Y-axis direction of the master, the Y-axis of the master is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
and calculating the test coordinate of the main vehicle after the test time is calculated according to the current coordinate of the main vehicle, the X-axis displacement of the main vehicle and the Y-axis displacement of the main vehicle.
Optionally, the obtaining a far vehicle test coordinate and a main vehicle test coordinate according to the collision model, and determining whether the far vehicle falls into the collision range of the main vehicle includes:
establishing a main vehicle rectangular frame of the main vehicle according to the length, the width and the reserved area of the main vehicle, and establishing a far vehicle rectangular frame of a far vehicle according to the length, the width and the reserved area of the far vehicle;
according to the collision model, after the prediction time is obtained, the test coordinates of the main vehicle and the coordinates of four corner points of the rectangular frame of the main vehicle are obtained;
and judging whether any point of the far rectangular frame falls into the main rectangular frame or not according to a collision formula.
Optionally, the collision formula comprises:
wherein, (PointX, PointY) is a point coordinate on the remote vehicle; (RectX1, RectY1), (RectX2, RectY2), (RectX3, RectY3), (RectX4, and RectY4) respectively represent coordinate values of four corner points of the rectangular frame of the host vehicle.
Optionally, the determining the corner point of the far car rectangular frame according to the collision formula includes:
when a >0 and b >0 and c >0 and d >0 or a <0 and b <0 and c <0 and d <0, the point coordinates of the distant vehicle fall within the frame range of the host vehicle.
Optionally, the performing the early warning process includes:
and displaying the collision early warning information through an on-board central control display screen or playing the collision early warning information through a loudspeaker in the vehicle.
The vehicle early warning method based on the collision model has the advantages that: whether a far vehicle falls into the collision range of a main vehicle or not is judged according to a collision model to realize collision early warning, so that the method is simple and effective to realize, false alarm and missing alarm caused by early warning by calculating collision time and collision distance in a short distance are avoided, and the alarm efficiency is improved; and the method has universal adaptability to any road condition and vehicle type.
Drawings
FIG. 1 is a flow chart of a method according to an embodiment of the present application.
Fig. 2 is a flowchart of a collision model establishment according to an embodiment of the present application.
Fig. 3 is a first schematic diagram of a collision test according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a collision test according to an embodiment of the present application.
Detailed Description
The following detailed description of the preferred embodiments of the present application, taken in conjunction with the accompanying drawings, will make the advantages and features of the present application more readily appreciated by those skilled in the art, and thus will more clearly define the scope of the invention.
In an embodiment shown in fig. 1, the present application provides a vehicle early warning method based on a collision model, which is applied to an automotive electronic product, and is characterized in that the method includes:
100, acquiring current remote vehicle data transmitted by a remote vehicle in real time;
in step 100, the remote vehicle communication method establishes communication connection with a remote vehicle and obtains current remote vehicle data in real time through a V2X communication technology, wherein the current remote vehicle data comprise the length, width, vehicle speed, acceleration, position, angular speed and direction of the current vehicle.
200, establishing a collision model of the main vehicle and the remote vehicle according to the remote vehicle data;
in step 200, establishing a collision model of the main vehicle and the remote vehicle comprises acquiring current remote vehicle data, calculating the current remote vehicle data and the remote vehicle data at the previous moment, and calculating remote vehicle average driving data of the remote vehicle; acquiring current main vehicle data of a main vehicle, calculating the current main vehicle data and the main vehicle data at the previous moment, and calculating main vehicle average driving data of the main vehicle; and obtaining the coordinates of the remote vehicle at the testing time according to the average driving data of the remote vehicle, and obtaining the coordinates of the main vehicle at the testing time according to the average driving data of the main vehicle.
300, acquiring a remote vehicle test coordinate and a main vehicle test coordinate according to the collision model, and judging whether the remote vehicle falls into the collision range of the main vehicle;
in step 300, obtaining the test coordinates of the far vehicle and the test coordinates of the main vehicle according to the collision model, and judging whether the far vehicle falls into the collision range of the main vehicle, wherein the step comprises the steps of establishing a main vehicle rectangular frame of the main vehicle according to the length, the width and the reserved area of the main vehicle, and establishing a far vehicle rectangular frame of the far vehicle according to the length, the width and the reserved area of the far vehicle; according to the collision model, after the prediction time is obtained, the test coordinates of the main vehicle and the coordinates of four corner points of the rectangular frame of the main vehicle are obtained; and judging whether any point of the far rectangular frame falls into the main rectangular frame or not according to a collision formula.
400, if yes, performing early warning processing;
in step 400, the performing of the warning process includes displaying the collision warning information through an onboard central control display screen or playing the collision warning information through an in-vehicle speaker.
500, otherwise, not processing;
in step 500, if the distant vehicle has no risk of collision with the main vehicle through the collision model, the current distant vehicle data of the distant vehicle is subjected to packet loss processing.
According to the vehicle early warning method based on the collision model, whether a far vehicle falls into the collision range of a main vehicle or not is judged according to the collision model to realize collision early warning, so that the method is simple and effective to realize, false alarm and false failure caused by early warning by calculating collision time and collision distance in a short distance are avoided, and the warning efficiency is improved; and the method has universal adaptability to any road condition and vehicle type.
In some embodiments, the obtaining of the current remote data from the remote vehicles in real time includes:
and establishing communication connection with a remote vehicle and acquiring current remote vehicle data in real time through a V2X communication technology. In the present embodiment, V2X is the exchange of information from vehicle to outside. The Internet of vehicles establishes a new automobile technology development direction by integrating a Global Positioning System (GPS) navigation technology, an automobile-to-automobile communication technology, a wireless communication technology and a remote sensing technology, and realizes the compatibility of manual driving and automatic driving. According to the method, the main vehicle and the surrounding remote vehicles are connected through a V2X communication technology, and the current remote vehicle data of the remote vehicles are obtained in real time, wherein the current remote vehicle data at least comprise one or more of the length, the width, the vehicle speed, the acceleration, the position, the angular speed and the direction of the vehicle. In the present embodiment, the current vehicle-distant data includes the length, width, vehicle speed, acceleration, position, angular velocity, and direction of the current vehicle.
In some embodiments, referring to fig. 2, establishing a collision model of the host vehicle and the remote vehicle based on the remote vehicle data comprises:
210, obtaining current remote vehicle data, calculating the current remote vehicle data and the remote vehicle data at the previous moment, and calculating remote vehicle average driving data of the remote vehicles;
in step 210, the vehicle of the present application obtains data of a remote vehicle in real time, wherein the average driving data of the remote vehicle can be averaged by the remote vehicle data of the current time and the remote vehicle data of the previous time; the remote average driving data can also be averaged with a plurality of previous remote data by the remote data at the current moment. In this embodiment, the time interval between the vehicle distant data at the current time and the vehicle distant data at the previous time is 0.2 seconds.
220, acquiring current main vehicle data of the main vehicle, calculating the current main vehicle data and the main vehicle data at the previous moment, and calculating the main vehicle average driving data of the main vehicle;
in step 220, the vehicle of the present application acquires data of the host vehicle in real time through the CAN bus, wherein the average driving data of the host vehicle CAN be averaged by the host vehicle data at the current time and the remote vehicle data at the previous time; the average host vehicle driving data may also be averaged with a plurality of previous host vehicle data by host vehicle data at the current time. In the present embodiment, the host time interval between the host data of the current time and the host time of the previous time is 0.2 seconds.
And 230, obtaining the remote vehicle test coordinates according to the remote vehicle average driving data, and obtaining the main vehicle test coordinates according to the main vehicle average driving data.
In step 230, the remote vehicle acquires real-time test coordinates of the remote vehicle according to the average driving data; and the main vehicle obtains real-time test coordinates of the main vehicle according to the average driving data.
In an implementation manner of the above embodiment, obtaining the displacement of the far vehicle at the test time according to the average driving data of the far vehicle includes:
taking the current time position of the main vehicle as a coordinate center;
in the direction of the main vehicle's X-axis, the X-axis of the remote vehicle is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the average angular velocity; in this embodiment, v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data with the average angular speed as the far vehicle through the far vehicle data at the current moment and the far vehicle data at the previous moment;or obtaining an average value by the remote vehicle data at the current moment and a plurality of previous remote vehicle data. In this embodiment, the remote vehicle can predict the displacement of the remote vehicle in the X-axis direction after the time T in real time; after the prediction time T is tested, the displacement in the X-axis direction of the vehicle far away after the second test time may be further tested. Here, the predicted time T may be 0.2 seconds.
In the Y-axis direction of the primary carriage, the Y-axis of the remote carriage is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity; in this embodiment, v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data with the average angular speed as the far vehicle through the far vehicle data at the current moment and the far vehicle data at the previous moment; or obtaining an average value by the remote vehicle data at the current moment and a plurality of previous remote vehicle data. In this embodiment, the remote vehicle can predict the displacement of the remote vehicle in the Y-axis direction after the time T in real time; after the prediction time T is tested, the displacement in the X-axis direction of the vehicle far away after the second test time may be further tested. Here, the predicted time T may be 0.2 seconds.
And calculating the current coordinate of the remote vehicle, the X-axis displacement of the remote vehicle and the Y-axis displacement of the remote vehicle, and then testing the coordinate of the remote vehicle after the testing time is calculated. In this embodiment, the remote vehicle test coordinate can be obtained by adding the current coordinate to the X-axis displacement and the Y-axis displacement of the remote vehicle.
In one implementation of the above-described embodiment, obtaining current host vehicle data of the host vehicle, calculating the current host vehicle data and host vehicle data at a previous time, and calculating host vehicle average driving data of the host vehicle, includes:
taking the current time position of the main vehicle as a coordinate center;
in the X-axis direction of the subject, the X-axis of the subject is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity; in this embodiment, v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data of the host vehicle by the host vehicle data at the current time and the host vehicle data at the previous time, wherein the average angular velocity is the average driving data of the host vehicle; or an average value is obtained by the host vehicle data at the current time and a plurality of previous host vehicle data. In this embodiment, the host vehicle can predict the displacement of the host vehicle in the X-axis direction after the time T in real time; the displacement in the X-axis direction of the subject after the second test time may be further tested after the prediction time T test. Here, the predicted time T may be 0.2 seconds.
In the Y-axis direction of the master, the Y-axis of the master is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity; in the present embodiment, it is preferred that,v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data of the host vehicle by the host vehicle data at the current time and the host vehicle data at the previous time, wherein the average angular velocity is the average driving data of the host vehicle; or an average value is obtained by the host vehicle data at the current time and a plurality of previous host vehicle data. In this embodiment, the host vehicle can predict the displacement of the host vehicle in the X-axis direction after the time T in real time; the displacement in the X-axis direction of the subject after the second test time may be further tested after the prediction time T test. Here, the predicted time T may be 0.2 seconds.
The current coordinate of the main vehicle, the X-axis displacement of the main vehicle and the Y-axis displacement of the main vehicle, and the test coordinate of the main vehicle after the test time is calculated. In this embodiment, the current coordinates are added to the X-axis displacement and the Y-axis displacement of the host vehicle to obtain the test coordinates of the host vehicle.
In some embodiments, referring to fig. 3-4, obtaining the test coordinates of the remote vehicle and the test coordinates of the host vehicle according to the collision model, and determining whether the remote vehicle falls within the collision range of the host vehicle includes:
establishing a main vehicle rectangular frame of the main vehicle according to the length, the width and the reserved area of the main vehicle, and establishing a far vehicle rectangular frame of a far vehicle according to the length, the width and the reserved area of the far vehicle; in this embodiment, a reserved position is set for further preventing the vehicle from colliding, and the test safety and the driving safety are improved. In the embodiment, the reserved area is 10-20cm added to the length and the width of the original vehicle to form a main vehicle rectangular frame or a far vehicle rectangular frame.
Obtaining a test coordinate of the main vehicle and coordinates of four corner points of a rectangular frame of the main vehicle after the prediction time is obtained according to the collision model;
in the present embodiment, the master test coordinates are obtained by: taking the current time position of the main vehicle as a coordinate center; in the X-axis direction of the subject, the X-axis of the subject is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity; in this embodiment, v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data of the host vehicle by the host vehicle data at the current time and the host vehicle data at the previous time, wherein the average angular velocity is the average driving data of the host vehicle; or an average value is obtained by the host vehicle data at the current time and a plurality of previous host vehicle data. In this embodiment, the host vehicle can predict the displacement of the host vehicle in the X-axis direction after the time T in real time; the displacement in the X-axis direction of the subject after the second test time may be further tested after the prediction time T test. Here, the predicted time T may be 0.2 seconds. In the Y-axis direction of the master, the Y-axis of the master is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity; in this embodiment, v is the average velocity, T is the test time, a is the average acceleration,
obtaining average value of the average driving data of the host vehicle by the host vehicle data at the current time and the host vehicle data at the previous time, wherein the average angular velocity is the average driving data of the host vehicle; or an average value is obtained by the host vehicle data at the current time and a plurality of previous host vehicle data.In this embodiment, the host vehicle can predict the displacement of the host vehicle in the X-axis direction after the time T in real time; the displacement in the X-axis direction of the subject after the second test time may be further tested after the prediction time T test. Here, the predicted time T may be 0.2 seconds. The current coordinate of the main vehicle, the X-axis displacement of the main vehicle and the Y-axis displacement of the main vehicle, and the test coordinate of the main vehicle after the test time is calculated. In this embodiment, the current coordinates are added to the X-axis displacement and the Y-axis displacement of the host vehicle to obtain the test coordinates of the host vehicle.
In the embodiment, coordinates of four corner points of the rectangular frame of the main vehicle are calculated according to the test coordinates of the main vehicle, the length, the width and the reserved area of the vehicle.
And judging whether any point of the far rectangular frame falls into the main rectangular frame or not according to a collision formula.
In one implementation of the above embodiment, the collision formula includes:
wherein, (PointX, PointY) is a point coordinate on the remote vehicle; (RectX1, RectY1), (RectX2, RectY2), (RectX3, RectY3), (RectX4, and RectY4) respectively represent coordinate values of four corner points of the rectangular frame of the host vehicle.
According to the collision formula, the method for judging the angular point of the rectangular frame of the remote vehicle comprises the following steps:
referring to fig. 4, when a >0 and b >0 and c >0 and d >0 or a <0 and b <0 and c <0 and d <0, then Point is within rectangular frame Rect, i.e. there is an overlap in the areas of the primary and remote rectangular frames; the point coordinates of the remote vehicle fall into the parking space frame range of the main vehicle; then the collision risk is indicated, and the early warning is needed to be carried out on the owner of the main vehicle. According to the method and the device, remote vehicle data are obtained in real time, a collision model is established or updated, and remote vehicle test coordinates and main vehicle test coordinates are output through the collision model, so that the collision test of the main vehicle and the remote vehicle is realized. By the method, the main vehicle and the plurality of remote vehicles can be synchronously subjected to collision early warning test so as to ensure the driving safety.
In some embodiments, the pre-warning process comprises: and displaying the collision early warning information through an on-board central control display screen or playing the collision early warning information through a loudspeaker in the vehicle. The method and the system for warning the driver in the vehicle-mounted terminal send out early warning in advance and inform the driver of safety.
According to the vehicle early warning method based on the collision model, whether a far vehicle falls into the collision range of a main vehicle or not is judged according to the collision model to realize collision early warning, so that the method is simple and effective to realize, false alarm and false failure caused by early warning by calculating collision time and collision distance in a short distance are avoided, and the warning efficiency is improved; and the method has universal adaptability to any road condition and vehicle type.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present application within the knowledge of those skilled in the art.
Claims (10)
1. A vehicle early warning method based on a collision model is applied to an automobile electronic product, and is characterized by comprising the following steps:
acquiring current remote vehicle data transmitted by a remote vehicle in real time;
establishing a collision model of the main vehicle and the remote vehicle according to the remote vehicle data;
obtaining a remote vehicle test coordinate and a main vehicle test coordinate according to the collision model, and judging whether the remote vehicle falls into the collision range of the main vehicle;
if yes, performing early warning processing; otherwise, no processing is performed.
2. The vehicle early warning method based on the collision model as claimed in claim 1, wherein the obtaining of the current vehicle-far data transmitted from the vehicle-far in real time comprises:
and establishing communication connection with a remote vehicle and acquiring current remote vehicle data in real time through a V2X communication technology.
3. The vehicle early warning method based on the collision model as claimed in claim 2, wherein the current vehicle far data at least comprises one or more of length, width, vehicle speed, acceleration, position, angular speed and direction of the vehicle.
4. The vehicle early warning method based on the collision model as claimed in claim 1, wherein the establishing the collision model of the main vehicle and the far vehicle according to the far vehicle data comprises:
acquiring current remote vehicle data, calculating the current remote vehicle data and the remote vehicle data at the previous moment, and calculating remote vehicle average driving data of the remote vehicles;
acquiring current main vehicle data of a main vehicle, calculating the current main vehicle data and the main vehicle data at the previous moment, and calculating main vehicle average driving data of the main vehicle;
and obtaining a remote vehicle test coordinate according to the remote vehicle average driving data, and obtaining a main vehicle test coordinate according to the main vehicle average driving data.
5. The vehicle early warning method based on the collision model as claimed in claim 4, wherein the obtaining the displacement of the far vehicle at the test time according to the average driving data of the far vehicle comprises:
taking the current time position of the main vehicle as a coordinate center;
in the direction of the main vehicle's X-axis, the X-axis of the remote vehicle is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
in the Y-axis direction of the primary carriage, the Y-axis of the remote carriage is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
and calculating the test coordinate of the remote vehicle after the test time is calculated according to the current coordinate of the remote vehicle, the X-axis displacement of the remote vehicle and the Y-axis displacement of the remote vehicle.
6. The method as claimed in claim 4, wherein the obtaining current host data of the host vehicle, calculating the current host vehicle data and the host vehicle data at the previous time, and calculating the average driving data of the host vehicle comprises:
taking the current time position of the main vehicle as a coordinate center;
in the X-axis direction of the subject, the X-axis of the subject is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
in the Y-axis direction of the master, the Y-axis of the master is displaced through
Performing calculation, wherein x (n-1) is the displacement of the current moment, v is the average speed, T is the test time, a is the average acceleration,
is the angular velocity;
and calculating the test coordinate of the main vehicle after the test time is calculated according to the current coordinate of the main vehicle, the X-axis displacement of the main vehicle and the Y-axis displacement of the main vehicle.
7. The vehicle early warning method based on the collision model as claimed in claim 4, wherein the obtaining of the remote vehicle test coordinates and the host vehicle test coordinates according to the collision model to determine whether the remote vehicle falls into the collision range of the host vehicle comprises:
establishing a main vehicle rectangular frame of the main vehicle according to the length, the width and the reserved area of the main vehicle, and establishing a far vehicle rectangular frame of a far vehicle according to the length, the width and the reserved area of the far vehicle;
according to the collision model, after the prediction time is obtained, the test coordinates of the main vehicle and the coordinates of four corner points of the rectangular frame of the main vehicle are obtained;
and judging whether any point coordinate of the far rectangular frame falls into the main rectangular frame or not according to a collision formula.
8. The collision model-based vehicle early warning method according to claim 7, wherein the collision formula comprises:
wherein, (PointX, PointY) is a point coordinate on the remote vehicle; (RectX1, RectY1), (RectX2, RectY2), (RectX3, RectY3), (RectX4, and RectY4) respectively represent coordinate values of four corner points of the rectangular frame of the host vehicle.
9. The vehicle early warning method based on the collision model as claimed in claim 8, wherein the determining the corner points of the rectangular frame of the far vehicle according to the collision formula comprises:
when a >0 and b >0 and c >0 and d >0 or a <0 and b <0 and c <0 and d <0, the point coordinates of the distant vehicle fall within the frame range of the host vehicle.
10. The vehicle early warning method based on the collision model as claimed in claim 1, wherein the performing early warning process comprises:
and displaying the collision early warning information through an on-board central control display screen or playing the collision early warning information through a loudspeaker in the vehicle.
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| CN113190909A (en) * | 2021-05-21 | 2021-07-30 | 杭州群核信息技术有限公司 | Method, device and storage medium for determining position reasonability of target object |
| CN113602200A (en) * | 2021-08-02 | 2021-11-05 | 岚图汽车科技有限公司 | Automobile console storage box control system and control method and automobile |
| WO2023185255A1 (en) * | 2022-03-31 | 2023-10-05 | 中兴通讯股份有限公司 | Collision early warning method, vehicle-mounted terminal and storage medium |
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