CN114750775A - Road vehicle anticollision warning system based on millimeter wave radar - Google Patents
Road vehicle anticollision warning system based on millimeter wave radar Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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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
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
- B60Q9/002—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for parking purposes, e.g. for warning the driver that his vehicle has contacted or is about to contact an obstacle
- B60Q9/004—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for parking purposes, e.g. for warning the driver that his vehicle has contacted or is about to contact an obstacle using wave sensors
- B60Q9/006—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for parking purposes, e.g. for warning the driver that his vehicle has contacted or is about to contact an obstacle using wave sensors using a distance sensor
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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
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
- 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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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/12—Messaging; Mailboxes; Announcements
- H04W4/14—Short messaging services, e.g. short message services [SMS] or unstructured supplementary service data [USSD]
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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Abstract
The invention belongs to the field of vehicle anti-collision systems, relates to a radar detection technology, and is used for solving the problem that the conventional road vehicle anti-collision warning system cannot perform dynamic early warning analysis during reversing, in particular to a road vehicle anti-collision warning system based on a millimeter wave radar, which comprises a processor, wherein the processor is in communication connection with a dynamic analysis module, an early warning module, a characteristic analysis module, a habit analysis module and a storage module, and the dynamic analysis module is used for performing anti-collision analysis on vehicles on a road and judging whether a main vehicle has collision risk; according to the invention, the dynamic analysis module can be used for carrying out dynamic anti-collision analysis on the vehicle when the vehicle runs and backs the vehicle and obtaining the collision coefficient of the analysis object, and whether the host vehicle has collision risk can be judged according to the numerical value of the collision coefficient, so that early warning is carried out on the host vehicle, the occurrence of collision accidents is prevented, and the safety of vehicle running and backing is improved.
Description
Technical Field
The invention belongs to the field of vehicle anti-collision systems, relates to radar detection technology, and particularly relates to a road vehicle anti-collision warning system based on a millimeter wave radar.
Background
With the mass popularization of private cars, more and more cars run on the road, the complexity of the road is more and more serious, and the running safety becomes an important subject.
The existing road vehicle anti-collision warning system can perform anti-collision warning on a dynamic vehicle or other objects when the vehicle runs, however, when the vehicle runs backwards, the existing anti-collision warning system can only perform anti-collision warning on static obstacles, and cannot perform anti-collision analysis on moving objects, such as electric vehicles, pedestrians and the like, so that drivers cannot be reminded in advance, and the safety of the road vehicle anti-collision warning system is low; in addition, the conventional road vehicle anti-collision warning system cannot analyze the driving habits of the driver according to the characteristics of each dangerous object in a dangerous state, so that the driver in dangerous driving cannot be dissuaded.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide a road vehicle anti-collision warning system based on a millimeter wave radar, which is used for solving the problem that the conventional road vehicle anti-collision warning system cannot perform dynamic early warning analysis when backing a car;
the technical problems to be solved by the invention are as follows: how to provide a road vehicle anti-collision warning system capable of carrying out dynamic early warning analysis in the process of backing a car.
The purpose of the invention can be realized by the following technical scheme:
a road vehicle anti-collision warning system based on a millimeter wave radar comprises a processor, wherein the processor is in communication connection with a dynamic analysis module, an early warning module, a characteristic analysis module, a habit analysis module and a storage module;
the early warning module comprises a digital audio player;
the dynamic analysis module is used for carrying out anti-collision analysis on vehicles on a road and judging whether a main vehicle has collision risks or not;
the characteristic analysis module is used for extracting the characteristics of an analysis object, wherein the analysis object is a dynamic target which is closest to the main vehicle; the characteristics of the analysis object comprise digital characteristics and color system characteristics;
the habit analysis module is used for analyzing the driving state of the host vehicle.
As a preferred embodiment of the present invention, the specific process of the dynamic analysis module performing collision avoidance analysis on the vehicle on the road includes: marking the vehicle subjected to collision avoidance analysis as a host vehicle; the method comprises the steps of obtaining the traveling speeds of a host vehicle and an analysis object, respectively marking the traveling speeds as a main speed ZS and an analysis speed FS, marking the distance value between the host vehicle and the analysis object as a JL, obtaining a collision coefficient PZ of the analysis object through numerical calculation of the main speed, the analysis speed and the distance value, obtaining a collision threshold PZmax through a storage module, comparing the collision coefficient PZ with the collision threshold PZmax, and judging whether the host vehicle has a collision risk or not through a comparison result.
As a preferred embodiment of the present invention, the process of comparing the collision coefficient PZ with the collision threshold PZmax includes: if the collision coefficient WX is larger than or equal to a collision threshold value PZmax, judging that the host vehicle has a collision risk, marking an analysis object as a collision object, sending a collision early warning signal to a processor by a dynamic analysis module, sending the collision early warning signal to an early warning module and a feature analysis module by the processor after receiving the collision early warning signal, and controlling a digital audio player to play a pre-recorded collision early warning reminding voice after receiving the collision early warning signal by the early warning module; if the collision coefficient PZ is less than the collision threshold PZmax, it is determined that the host vehicle is not at risk of collision.
As a preferred embodiment of the present invention, a specific process of extracting the feature of the analysis object by the feature analysis module includes: shooting an image of an analysis object, marking the obtained image as an analysis image, analyzing the image by a feature extraction technology to obtain letters and numbers in the analysis image, marking a character string consisting of the extracted letters and numbers as a digital feature, amplifying the analysis image into a pixel grid image, the method comprises the steps of obtaining the gray value of each pixel grid in a pixel grid image through gray level conversion, dividing a gray level range of 0-255 into a plurality of gray level intervals, marking the number of the pixel grids with the gray value positioned in the gray level intervals as the containing quantity of the gray level intervals, marking the average value of the maximum gray value and the minimum gray value of the gray level interval with the highest containing quantity value as color system characteristics, sending the digital characteristics and the color system characteristics to a processor by a characteristic analysis module, and sending the digital characteristics and the color system characteristics to a danger analysis module after the processor receives the digital characteristics and the color system characteristics.
As a preferred embodiment of the present invention, the habit analyzing module analyzes the driving state of the host vehicle by a specific process including: acquiring the times of receiving collision early warning signals by the processor in L1 minutes, and marking the times as a danger value WX, wherein L1 is a constant quantity; acquiring a danger threshold WXmax through a storage module, and comparing the danger coefficient WX with the danger threshold WXmax: if the risk coefficient WX is smaller than the risk threshold WXmax, the driving state of the host vehicle is judged to be safe; if the risk coefficient WX is equal to or greater than the risk threshold WXmax, it is determined that the driving state of the host vehicle is dangerous, and the dangerous objects within L1 minutes are subjected to characteristic comparison: if the digital characteristics and the color system characteristics of the two dangerous objects are different, judging that the characteristics of the two dangerous objects are different; otherwise, the characteristics of the two dangerous objects are judged to be the same.
As a preferred embodiment of the present invention, the alignment process of the numerical characteristics includes: marking the character strings of the two numerical characteristics as a first string and a second string respectively, randomly selecting one character in the first string and marking the character as a marking character, and judging whether the character which is the same as the marking character exists in the second string:
if yes, marking the corresponding character as a matched character, deleting the marked character and the matched character from the first string and the second string respectively, and reselecting one character from the first string as the marked character for character comparison;
if not, reselecting a character from the first string as a mark character for character comparison;
until all characters in the first string are used as mark characters to complete character comparison, marking the sum of the number of the deleted characters in the first string and the second string as SC, marking the sum of the number of the characters in the first string and the second string as ZS, marking the ratio of SC to ZS as a deletion ratio, acquiring a deletion threshold value through a storage module, and comparing the deletion ratio with the deletion threshold value: and if the deletion ratio is larger than the deletion threshold value, judging that the digital characteristics of the two dangerous objects are the same, otherwise, judging that the digital characteristics of the two dangerous objects are different.
As a preferred embodiment of the present invention, the number of the dangerous objects with the same characteristics is marked as a dangerous number, the dangerous number with the largest numerical value is marked as a coincidence value CH, the ratio of the coincidence value CH to the dangerous value WX is marked as a coincidence ratio CB, and a coincidence threshold value CBmax is obtained through a storage module; compare the coincidence ratio CB to the coincidence threshold CBmax:
if the coincidence ratio CB is smaller than the coincidence threshold CBmax, judging that the driver of the main vehicle has dangerous driving habits, sending a dangerous driving early warning signal to a processor by a habit analysis module, sending the dangerous driving early warning signal to an early warning module after the processor receives the dangerous driving early warning signal, controlling a digital audio player to play a pre-recorded dangerous early warning reminding voice after the early warning module receives the dangerous driving early warning signal, and sending a dangerous driving persuasive message to a mobile phone terminal of the driver of the main vehicle by the processor after the driving behavior is finished;
if the coincidence ratio CB is larger than or equal to the coincidence threshold CBmax, the driver of the host vehicle is judged to have gambling driving habits, the habit analysis module sends a gambling driving early warning signal to the processor, the processor receives the gambling driving early warning signal and then sends the gambling driving early warning signal to the early warning module, the early warning module receives the gambling driving early warning signal and then controls the digital audio player to play a previously recorded gambling early warning reminding voice, and after the driving behaviors are finished, the processor sends a gambling driving persuasion message to the mobile phone terminal of the driver of the host vehicle.
The invention has the following beneficial effects:
1. the dynamic analysis module can perform dynamic anti-collision analysis on the vehicle when the vehicle runs and backs a car to obtain a collision coefficient of an analysis object, and can judge whether the main vehicle has collision risk or not according to the numerical value of the collision coefficient, so that the main vehicle is early warned in advance, collision accidents are prevented, and the safety of the vehicle running and backing a car is improved;
2. the analysis object with collision risk can be subjected to feature extraction and analysis through a feature analysis module, the features of the analysis object comprise digital features and color system features, whether dangerous driving behaviors exist in the host vehicle is judged through the feature overlap ratio of the analysis object within a certain time, and therefore the driving behaviors of the host vehicle are monitored through the features of the dangerous objects;
3. the number of times of collision early warning signals occurring in a certain time can be acquired through the habit analysis module, then whether the driving state of the host vehicle is safe or not is judged through the numerical value of the danger value, under the condition that the driving state is dangerous, factors causing the driving state to be dangerous are analyzed, whether the host vehicle driver has dangerous driving habits or not is judged through a characteristic comparison mode, and therefore dissuasion can be timely carried out on the host vehicle driver.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a system block diagram of the present invention as a whole;
FIG. 2 is a block diagram of a system according to a first embodiment of the present invention;
fig. 3 is a system block diagram of a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in figure 1, the road vehicle anti-collision warning system based on the millimeter wave radar comprises a processor, wherein the processor is in communication connection with a dynamic analysis module, an early warning module, a characteristic analysis module, a habit analysis module and a storage module. The early warning module includes a digital audio player, which is a device capable of storing, organizing and playing audio file formats, and is often called an MP3 player, and the storage module is used for data storage.
Example one
When the vehicle backs, current anticollision warning system only can carry out the anticollision early warning to static barrier, and to the object in the moving, for example electric motor car, pedestrian etc. can not carry out the anticollision analysis to it, consequently can't remind for the navigating mate in advance, leads to road vehicle anticollision warning system to have the potential safety hazard.
Referring to fig. 2, the dynamic analysis module is used for performing collision avoidance analysis on vehicles on a road: marking a vehicle subjected to collision avoidance analysis as a host vehicle, acquiring a dynamic target closest to the host vehicle and marking the dynamic target as an analysis object, wherein the dynamic target comprises an automobile, an electric vehicle, a bicycle and a pedestrian; the method comprises the steps of obtaining the traveling speeds of a host vehicle and an analysis object, marking the traveling speeds as a main speed ZS and an analysis speed FS respectively, marking the distance value of the host vehicle and the analysis object as a JL, collecting the distance value by a millimeter wave radar, wherein the millimeter wave radar is a radar working in a millimeter wave band for detection, generally, the frequency range of millimeter waves is 30-300 GHz, and the wavelength of the millimeter waves is between the microwave and centimeter waves, so that the millimeter wave radar has some advantages of a microwave radar and a photoelectric radar. Obtaining a collision coefficient PZ of an analysis object by a formula PZ ═ α 1 × ZS + α 2 × FS)/(α 3 × JL, where the collision coefficient is a possibility of collision with the analysis object when a host vehicle continues to travel according to a current state, and a larger value of the collision coefficient indicates a higher possibility of collision with the analysis object when the host vehicle continues to travel according to the current state, where α 1, α 2, and α 3 are proportional coefficients, and α 3 > α 2 > α 1 > 0; acquiring a collision threshold PZmax through a storage module, and comparing the collision coefficient PZ with the collision threshold PZmax: if the collision coefficient WX is larger than or equal to a collision threshold value PZmax, judging that the host vehicle has a collision risk, marking an analysis object as a collision object, sending a collision early warning signal to a processor by a dynamic analysis module, sending the collision early warning signal to an early warning module and a characteristic analysis module by the processor after receiving the collision early warning signal, and controlling a digital audio player to play a pre-recorded collision early warning reminding voice after receiving the collision early warning signal by the early warning module; if the collision coefficient PZ is less than the collision threshold PZmax, it is determined that the host vehicle is not at risk of collision. It should be noted that, the dynamic analysis module includes a driving state and a reversing state when specifically operating, and the values of the collision threshold corresponding to the driving state and the reversing state are different, and it can be understood that the collision threshold of the driving state is much higher than the collision threshold of the reversing state, and the values of the collision thresholds of the driving state and the reversing state are set by the administrator through simulation experiments; the dynamic anti-collision early warning analysis can be carried out on the main vehicle during running and reversing through the switching of the working modes of the dynamic analysis module, the dynamic anti-collision analysis is carried out on the main vehicle during running and reversing, the collision coefficient of an analysis object is obtained, whether the main vehicle has collision risk or not can be judged through the numerical value of the collision coefficient, and therefore the early warning is carried out on the main vehicle in advance, the occurrence of collision accidents is prevented, and the running and reversing safety of the vehicle is improved.
Example two
For a driver of a host vehicle with dangerous driving habits, the omnibearing dynamic early warning analysis system cannot restrict the driving behavior of the driver, but may become more frequent due to a safe driving environment manufactured by a more comprehensive anti-collision early warning function, so that if the driving behavior of the driver of the host vehicle is not analyzed and restricted, the dynamic anti-collision early warning analysis system may be counterproductive, and the occurrence probability of traffic safety accidents is increased.
Referring to fig. 2, after receiving the collision warning signal, the feature analysis module extracts features of the analysis object: the method comprises the steps of shooting an analysis object, marking the obtained image as an analysis image, analyzing the image by a feature extraction technology to obtain letters and numbers in the analysis image, marking a character string consisting of the extracted letters and numbers as a digital feature, amplifying the analysis image into a pixel grid image, obtaining gray values of all pixel grids in the pixel grid image by gray level conversion, dividing a gray range of 0-255 into a plurality of gray level intervals, marking the number of the pixel grids with the gray values positioned in the gray level intervals as the containing quantity of the gray level intervals, marking the average value of the maximum gray level and the minimum gray level of the gray level interval with the highest containing quantity value as a color system feature, sending the digital feature and the color system feature to a processor by a feature analysis module, sending the digital feature and the color system feature to a danger analysis module after receiving the digital feature and the color system feature by the processor, the digital characteristics and the color system characteristics of the analysis object are collected, so that whether the analysis object with collision early warning for multiple times in a short time is the same analysis object is judged, if yes, the fact that the driver of the host vehicle and the driver of the analysis object possibly have the behavior of gambling is shown, the driver of the host vehicle needs to be cooled down in time, and safety accidents caused by uncontrolled emotion are prevented; if not, it is said that the driver of the host vehicle may have a habit of dangerous driving, and it is necessary to advise the driver after the driving behavior is over.
The habit analysis module is used for analyzing the driving state of the host vehicle: acquiring the times of receiving collision early warning signals by the processor in the L1 minutes, and marking the times as a danger value WX, wherein L1 is a quantity constant; acquiring a danger threshold WXmax through a storage module, and comparing the danger coefficient WX with the danger threshold WXmax: if the risk coefficient WX is smaller than the risk threshold WXmax, judging that the driving state of the host vehicle is safe; if the risk coefficient WX is greater than or equal to the risk threshold WXmax, it is determined that the driving state of the host vehicle is dangerous, and at this time, it is necessary to analyze the cause of the driving state being dangerous, where there are two causes, generally speaking, the cause is that the mood of the driver of the host vehicle is difficult to control due to some factors, the emotion of the driver of the host vehicle is "proportional" to the racing vehicle on the road, the driver of the host vehicle has habits of dangerous driving, and the manner of determining the cause is a feature comparison, and the dangerous objects within L1 minutes are subjected to feature comparison: if the digital characteristics and the color system characteristics of the two dangerous objects are different, judging that the characteristics of the two dangerous objects are different; otherwise, judging that the characteristics of the two dangerous objects are the same; the method comprises the steps of marking the number of dangerous objects with the same characteristics as a dangerous number, marking the dangerous number with the largest value as a coincidence value CH, marking the ratio of the coincidence value CH to the dangerous value WX as a coincidence ratio CB, wherein the coincidence ratio is a numerical value representing the frequency of collision early warning of a main vehicle and the same analysis object in a certain time, and the higher the numerical value of the coincidence ratio is, the higher the frequency of collision early warning of the main vehicle and the same analysis object in the certain time is, namely, the higher the possibility that the reason of causing the driving state to be dangerous is the first reason is. Acquiring a coincidence threshold value CBmax through a storage module; comparing the coincidence ratio CB with the coincidence threshold CBmax: if the coincidence ratio CB is smaller than the coincidence threshold CBmax, judging that the driver of the main vehicle has dangerous driving habits, sending a dangerous driving early warning signal to a processor by a habit analysis module, sending the dangerous driving early warning signal to an early warning module after the processor receives the dangerous driving early warning signal, controlling a digital audio player to play a pre-recorded dangerous early warning reminding voice after the early warning module receives the dangerous driving early warning signal, and sending a dangerous driving persuasive message to a mobile phone terminal of the driver of the main vehicle by the processor after the driving behavior is finished; if the coincidence ratio CB is more than or equal to the coincidence threshold CBmax, the driver of the host vehicle is judged to have gambling driving habits, the habit analysis module sends a gambling driving early warning signal to the processor, the processor receives the gambling driving early warning signal and then sends the gambling driving early warning signal to the early warning module, the early warning module receives the gambling driving early warning signal and then controls the digital audio player to play a gambling early warning reminding voice which is recorded in advance, after the driving behavior is finished, the processor sends a gambling driving advising short message to the mobile phone terminal of the driver of the main vehicle, whether the driving state of the host vehicle is safe or not is judged according to the numerical value of the danger value, and when the driving state is dangerous, and analyzing the factors causing the driving state to be dangerous, and judging whether the driver of the host vehicle has dangerous driving habits or not in a characteristic comparison mode, so that the driver of the host vehicle can be dissuaded in time.
Obtaining the digital characteristics and color system characteristics of two dangerous objects with characteristic comparison, marking the character strings of the two digital characteristics as a first string and a second string respectively, randomly selecting one character in the first string and marking as a marked character, judging whether the character which is the same as the marked character exists in the second string, if so, marking the corresponding character as a matched character, deleting the marked character and the matched character from the first string and the second string respectively, and reselecting one character in the first string as the marked character for character comparison; if not, reselecting a character from the first string as a mark character for character comparison; until all characters in the first string are used as mark characters to complete character comparison, marking the sum of the number of the deleted characters in the first string and the second string as SC, marking the sum of the number of the characters in the first string and the second string as ZS, marking the ratio of SC to ZS as a deletion ratio, acquiring a deletion threshold value through a storage module, and comparing the deletion ratio with the deletion threshold value: and if the deletion ratio is larger than the deletion threshold value, judging that the digital characteristics of the two dangerous objects are the same, otherwise, judging that the digital characteristics of the two dangerous objects are different.
The utility model provides a road vehicle anticollision warning system based on millimeter wave radar, during operation, dynamic analysis module is used for carrying out collision avoidance analysis and judging whether there is the collision risk host vehicle to the vehicle on the road, when having the collision risk, adopts the characteristic analysis module to extract the characteristic of analysis object to the driving behavior and the driving habit of host vehicle are analyzed and are persuaded through the mode that the characteristic is compared, supervise navigating mate.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula PZ ═ α 1 × ZS + α 2 × FS)/(α 3 × JL); collecting multiple groups of sample data and setting a corresponding collision coefficient for each group of sample data by a person skilled in the art; substituting the set collision coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are 1.15, 1.25 and 2.95 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding collision coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the collision coefficient is inversely proportional to the value of the distance value.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. A road vehicle anti-collision warning system based on a millimeter wave radar comprises a processor, and is characterized in that the processor is in communication connection with a dynamic analysis module, an early warning module, a characteristic analysis module, a habit analysis module and a storage module;
the early warning module comprises a digital audio player;
the dynamic analysis module is used for carrying out anti-collision analysis on vehicles on the road and judging whether a main vehicle has collision risks or not;
the characteristic analysis module is used for extracting the characteristics of an analysis object, and the analysis object is a dynamic target which is closest to the main vehicle; the characteristics of the analysis object comprise digital characteristics and color system characteristics;
the habit analysis module is used for analyzing the driving state of the host vehicle.
2. The millimeter wave radar-based road vehicle anti-collision warning system as claimed in claim 1, wherein the specific process of the dynamic analysis module for performing anti-collision analysis on the vehicles on the road comprises: marking the vehicle subjected to collision avoidance analysis as a host vehicle; the method comprises the steps of obtaining the traveling speeds of a host vehicle and an analysis object, respectively marking the traveling speeds as a main speed ZS and an analysis speed FS, marking the distance value between the host vehicle and the analysis object as a JL, obtaining a collision coefficient PZ of the analysis object through numerical calculation of the main speed, the analysis speed and the distance value, obtaining a collision threshold PZmax through a storage module, comparing the collision coefficient PZ with the collision threshold PZmax, and judging whether the host vehicle has a collision risk or not through a comparison result.
3. The millimeter wave radar-based road vehicle anti-collision warning system according to claim 2, wherein the comparison process of the collision coefficient PZ and the collision threshold value PZmax comprises: if the collision coefficient WX is larger than or equal to a collision threshold value PZmax, judging that the host vehicle has a collision risk, marking an analysis object as a collision object, sending a collision early warning signal to a processor by a dynamic analysis module, sending the collision early warning signal to an early warning module and a feature analysis module by the processor after receiving the collision early warning signal, and controlling a digital audio player to play a pre-recorded collision early warning reminding voice after receiving the collision early warning signal by the early warning module; if the collision coefficient PZ is less than the collision threshold PZmax, it is determined that the host vehicle is not at risk of collision.
4. The millimeter wave radar-based road vehicle anti-collision warning system according to claim 2, wherein the specific process of extracting the features of the analysis object by the feature analysis module comprises the following steps: shooting an image of an analysis object, marking the obtained image as an analysis image, analyzing the image by a feature extraction technology to obtain letters and numbers in the analysis image, marking a character string consisting of the extracted letters and numbers as a digital feature, amplifying the analysis image into a pixel grid image, the method comprises the steps of obtaining the gray value of each pixel grid in a pixel grid image through gray level conversion, dividing a gray level range of 0-255 into a plurality of gray level intervals, marking the number of the pixel grids with the gray value positioned in the gray level intervals as the containing quantity of the gray level intervals, marking the average value of the maximum gray value and the minimum gray value of the gray level interval with the highest containing quantity value as color system characteristics, sending the digital characteristics and the color system characteristics to a processor by a characteristic analysis module, and sending the digital characteristics and the color system characteristics to a danger analysis module after the processor receives the digital characteristics and the color system characteristics.
5. The millimeter wave radar-based road vehicle collision avoidance warning system of claim 4, wherein the habit analysis module analyzes the driving state of the host vehicle by a specific process comprising: acquiring the times of receiving collision early warning signals by the processor in the L1 minutes, and marking the times as a danger value WX, wherein L1 is a quantity constant; acquiring a danger threshold WXmax through a storage module, and comparing the danger coefficient WX with the danger threshold WXmax: if the risk coefficient WX is smaller than the risk threshold WXmax, the driving state of the host vehicle is judged to be safe; if the risk coefficient WX is equal to or greater than the risk threshold WXmax, it is determined that the driving state of the host vehicle is a risk, and the risk objects within L1 minutes are subjected to a characteristic comparison: if the digital characteristics and the color system characteristics of the two dangerous objects are different, judging that the characteristics of the two dangerous objects are different; otherwise, the characteristics of the two dangerous objects are judged to be the same.
6. The millimeter wave radar-based road vehicle anti-collision warning system according to claim 5, wherein the comparison process of the numerical characteristics comprises: marking the character strings of the two numerical characteristics as a first string and a second string respectively, randomly selecting one character in the first string and marking the character as a marking character, and judging whether the character which is the same as the marking character exists in the second string:
if yes, marking the corresponding character as a matched character, deleting the marked character and the matched character from the first string and the second string respectively, and reselecting one character from the first string as the marked character for character comparison;
if not, reselecting a character from the first string as a mark character for character comparison;
until all characters in the first string are used as mark characters to complete character comparison, marking the sum of the number of the deleted characters in the first string and the second string as SC, marking the sum of the number of the characters in the first string and the second string as ZS, marking the ratio of SC to ZS as a deletion ratio, acquiring a deletion threshold value through a storage module, and comparing the deletion ratio with the deletion threshold value: and if the deletion ratio is larger than the deletion threshold value, judging that the digital characteristics of the two dangerous objects are the same, otherwise, judging that the digital characteristics of the two dangerous objects are different.
7. The millimeter wave radar-based road vehicle anti-collision warning system according to claim 5, characterized in that the number of dangerous objects with the same characteristics is marked as a dangerous number, the dangerous number with the largest value is marked as a coincidence value CH, the ratio of the coincidence value CH to the dangerous value WX is marked as a coincidence ratio CB, and a coincidence threshold value CBmax is obtained through a storage module; compare the coincidence ratio CB to the coincidence threshold CBmax:
if the coincidence ratio CB is smaller than the coincidence threshold CBmax, judging that a driver of the main vehicle has dangerous driving habits, sending a dangerous driving early warning signal to a processor by a habit analysis module, sending the dangerous driving early warning signal to an early warning module by the processor after receiving the dangerous driving early warning signal, controlling a digital audio player to play a pre-recorded dangerous early warning reminding voice by the early warning module after receiving the dangerous driving early warning signal, and sending a dangerous driving persuasion short message to a mobile phone terminal of the driver of the main vehicle by the processor after the driving behavior is finished;
if the coincidence ratio CB is larger than or equal to the coincidence threshold CBmax, the driver of the host vehicle is judged to have gambling driving habits, the habit analysis module sends a gambling driving early warning signal to the processor, the processor receives the gambling driving early warning signal and then sends the gambling driving early warning signal to the early warning module, the early warning module receives the gambling driving early warning signal and then controls the digital audio player to play a previously recorded gambling early warning reminding voice, and after the driving behaviors are finished, the processor sends a gambling driving persuasion message to the mobile phone terminal of the driver of the host vehicle.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115497302A (en) * | 2022-11-22 | 2022-12-20 | 山东世运专用汽车有限公司 | Vehicle safety anti-collision early warning system based on data analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104756487A (en) * | 2012-10-31 | 2015-07-01 | 歌乐株式会社 | Image processing system and image processing method |
| CN106553655A (en) * | 2016-12-02 | 2017-04-05 | 深圳地平线机器人科技有限公司 | Hazardous vehicles detection method and system and the vehicle including the system |
| US20190259284A1 (en) * | 2018-02-20 | 2019-08-22 | Krishna Khadloya | Pedestrian detection for vehicle driving assistance |
| US10583780B1 (en) * | 2018-12-20 | 2020-03-10 | Honda Motor Co., Ltd. | Vehicle collision warning system and method |
-
2022
- 2022-03-24 CN CN202210296655.2A patent/CN114750775B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104756487A (en) * | 2012-10-31 | 2015-07-01 | 歌乐株式会社 | Image processing system and image processing method |
| CN106553655A (en) * | 2016-12-02 | 2017-04-05 | 深圳地平线机器人科技有限公司 | Hazardous vehicles detection method and system and the vehicle including the system |
| US20190259284A1 (en) * | 2018-02-20 | 2019-08-22 | Krishna Khadloya | Pedestrian detection for vehicle driving assistance |
| US10583780B1 (en) * | 2018-12-20 | 2020-03-10 | Honda Motor Co., Ltd. | Vehicle collision warning system and method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115497302A (en) * | 2022-11-22 | 2022-12-20 | 山东世运专用汽车有限公司 | Vehicle safety anti-collision early warning system based on data analysis |
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