CN115123284B - Vehicle braking distance dynamic early warning system based on traffic Internet of Things - Google Patents
Vehicle braking distance dynamic early warning system based on traffic Internet of Things Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- 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
- B60Q9/008—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
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
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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
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/18—Braking system
- B60W2510/184—Brake temperature, e.g. of fluid, pads or discs
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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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
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
- B60W2554/802—Longitudinal distance
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Abstract
The invention relates to the technical field of vehicle anti-collision, in particular to a vehicle braking distance dynamic early warning system based on the traffic internet of things, which is actually a vehicle anti-collision system. The system comprises: the data acquisition module is used for acquiring data such as the speed of the vehicle, the distance between the vehicle and the front vehicle, the depth of a brake pedal, the temperature of a brake pad, a braking duration sequence and the like; the data processing module is used for analyzing the data acquired by the data acquisition module and matching the vehicle; and the vehicle early warning module is used for carrying out early warning on a driver by utilizing the obtained braking distance pairs of the two vehicles in the vehicle matching pair and the distance between the vehicle and the front vehicle. According to the invention, the influence of the temperature of the brake pad on the braking effect is considered, and meanwhile, the analysis is carried out by combining with other data of the vehicle running, so that the early warning can be accurately carried out on the vehicle driver, meanwhile, the data of the vehicle can be collected in real time based on the traffic Internet of things, the early warning can be carried out on the vehicle in real time, and the sufficient reaction time is reserved for the driver.
Description
Technical Field
The invention relates to the technical field of vehicle anti-collision, in particular to a vehicle braking distance dynamic early warning system based on traffic internet of things.
Background
Automobiles are becoming more and more popular in life of people and become important riding tools, but as the number of automobiles is increasing, the accidents of vehicle collision are increasing, and the reasons for the accidents are that the braking is not timely due to the lagged reaction speed of people, so that people cannot take emergency measures, and once the accidents occur, personal and property safety loss which is difficult to estimate is caused. Meanwhile, the temperature of the brake pad has great influence on the braking effect when the vehicle brakes, and too high or too low temperature of the brake pad can lead to the braking effect not reaching the expected value, thereby causing accidents.
In the prior art, the early warning of the braking of a plurality of vehicles is carried out when the distance between the vehicles and the front vehicles reaches a certain threshold value, and then the early warning is judged and carried out by combining the road conditions of the vehicles in time, but the connection between the temperature of the most important brake pad and the braking effect in the braking process of the vehicles is not considered; moreover, the early warning effect is not achieved during early warning, the response time reserved for the driver is too short, and accidents cannot be well prevented.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a vehicle braking distance dynamic early warning system based on the traffic internet of things, which adopts the following technical scheme: the embodiment of the invention provides a vehicle braking distance dynamic early warning system based on traffic Internet of things. The system comprises: the data acquisition module is used for acquiring a speed sequence of the vehicle, a distance sequence between the vehicle and a front vehicle, a depth sequence of a brake pedal and a temperature sequence of a brake pad at a preset sampling frequency; meanwhile, a braking duration sequence is obtained according to the duration of each time the brake pedal is stepped down to return to the original position;
the data processing module is used for obtaining a driving habit index by utilizing fluctuation degrees of the speed sequence, the depth sequence and the temperature sequence; obtaining a braking condition vector according to the depth sequence and the braking duration sequence; obtaining a matching coefficient between vehicles based on the similarity of the braking situation vectors, the correlation of the speed sequence variation trend, the driving habit index difference value and the similarity of the temperature sequences between the vehicles; matching the vehicles by using the matching coefficients to obtain vehicle matching pairs;
the vehicle early warning module is used for obtaining a braking deceleration sequence by utilizing the speed sequences of two vehicles in the vehicle matching pair; obtaining braking deceleration lines corresponding to various temperatures according to depth sequences of two vehicles in the matching pair of the vehicles corresponding to each temperature in the temperature sequence and all elements in the braking deceleration sequence; acquiring the deceleration of the vehicle at the current moment based on a brake deceleration straight line corresponding to the temperature of a brake pad at the current moment of the vehicle in the vehicle matching pair and a preset brake pedal depth; and pre-warning the driver based on the braking distance of the vehicle in the vehicle matching pair obtained by using the deceleration and the speed at the current moment.
Preferably, the obtaining a braking duration sequence according to the duration of each depression of the brake pedal to return to the original position includes: and collecting the time length of returning to the original position after each time the brake pedal is stepped on, and forming a brake time length sequence according to the time sequence by the obtained time length.
Preferably, the obtaining the driving habit index includes: the variances of the speed sequence, the depth sequence and the temperature sequence are inversely related to the driving habit index.
Preferably, the obtaining a braking condition vector according to the depth sequence and the braking duration sequence includes: obtaining the depth of a corresponding brake pedal of each brake duration in the depth sequence in the brake duration sequence; the sum of the depths of the corresponding brake pedals in the depth sequence of each brake duration forms a sequence, and the sequence is recorded as an accumulated brake depth sequence; a braking condition vector is constructed based on the braking duration sequence and the accumulated braking depth sequence.
Preferably, the matching coefficient between the vehicles is:
wherein,representing a matching coefficient between vehicle a and vehicle B; />A braking situation vector representing the vehicle a,a braking condition vector representing the vehicle B; />Representing the speed sequence of vehicle a, +.>Representing a speed sequence of vehicle B; />Index indicating driving habit of vehicle a, +.>A driving habit index indicating a vehicle B; />The degree of difference between the temperature sequence of the vehicle A and the temperature sequence of the vehicle B obtained by using a DTW algorithm is shown; />Representing the temperature sequence of vehicle A, +.>A temperature sequence of the vehicle B is shown.
Preferably, the matching the vehicles by using the matching coefficient, and obtaining the vehicle matching pair includes: obtaining sample distances between vehicles according to the matching coefficients between the vehicles, wherein the matching coefficients and the sample distances between the vehicles form a negative correlation; and pairing the vehicles based on the sample distance between the vehicles by using a K-M algorithm to obtain a vehicle pairing pair.
Preferably, the obtaining a braking deceleration sequence using a speed sequence of two vehicles in a vehicle match pair comprises: the ratio of the adjacent element difference value to the preset sampling frequency in the speed sequence of the two vehicles in the vehicle matching pair forms a braking deceleration sequence of the vehicle matching pair.
Preferably, the obtaining the braking deceleration lines for the various temperatures includes: the temperatures of the plurality of brake pads in the temperature sequence of two vehicles in the vehicle matching pair can be divided into a plurality of temperatures; and obtaining all corresponding elements in the depth sequences and the braking deceleration sequences of the two vehicles at corresponding moments of a plurality of temperatures in each temperature, and obtaining all elements by using each temperature to obtain a braking deceleration straight line corresponding to each temperature by straight line fitting.
Preferably, the early warning of the driver based on the braking distance of the vehicle in the vehicle matching pair obtained by using the deceleration and the speed at the current moment comprises: and the braking distance is the distance traveled when the vehicle in the vehicle matching pair brakes at the current moment corresponding to the deceleration, and when the braking distance is greater than or equal to the distance between the vehicle and the front vehicle, the vehicle driver is warned.
The embodiment of the invention has at least the following beneficial effects: the early warning system is in fact a vehicle collision avoidance system. According to the invention, the braking deceleration straight line of the vehicle at each brake pad temperature is obtained through analysis of the speed of the vehicle in the running process, the distance from the front vehicle, the temperature of the brake pad and the depth of the brake pedal being stepped on; and then obtaining braking deceleration with a preset braking pedal depth so as to obtain the braking distance of the vehicle, and carrying out early warning according to the braking distance and the distance between the vehicle and the front vehicle. According to the invention, the influence of the temperature of the brake pad on the braking effect is considered, and meanwhile, the analysis is carried out by combining with other data of the vehicle running, so that the early warning can be accurately carried out on the vehicle driver, meanwhile, the data of the vehicle can be collected in real time based on the traffic Internet of things, the early warning can be carried out on the vehicle in real time, and the sufficient reaction time is reserved for the driver.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a vehicle braking distance dynamic early warning system based on traffic Internet of things.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of a vehicle braking distance dynamic early warning system based on the traffic internet of things according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment, and the detailed description of the specific implementation, the structure, the characteristics and the effects thereof is as follows. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of a vehicle braking distance dynamic early warning system based on the traffic Internet of things, which is specifically described below with reference to the accompanying drawings.
Examples
The main application scene of the invention is as follows: in the running process of vehicles of the same vehicle type, various data of the vehicles are collected, the data are transmitted to the traffic vehicle network, the data are analyzed to obtain the braking distance of each moment of the vehicles, and early warning is carried out on vehicle drivers according to the braking distance of each vehicle to prevent the vehicles from accident.
Referring to fig. 1, a block diagram of a vehicle braking distance dynamic early warning system based on traffic internet of things according to an embodiment of the present invention is shown, and the method includes the following modules:
and a data acquisition module: the method comprises the steps of obtaining a speed sequence of a vehicle, a distance sequence of the vehicle and a front vehicle, a depth sequence of a brake pedal and a temperature sequence of a brake pad at a preset sampling frequency; meanwhile, a braking duration sequence is obtained according to the duration of each depression of the brake pedal to return to the original position.
Firstly, in the running process of a vehicle, due to different driving habits of drivers, some drivers like to brake at a distance shorter than a front vehicle, and some people like to release an accelerator pedal at a distance longer so as to enable the vehicle to slide; some drivers prefer frequent stepping on the accelerator and brake pedals; thus, the service condition and the service time of the brake pedal are greatly different due to different driving habits.
Collecting distance sequence between vehicle and preceding vehicle during runningN represents vehicles of the same vehicle type and different vehicles. The preset sampling frequency is 0.1s, the sampling can be carried out for a long time, and the millimeter wave is installed in front of the vehicleAnd a radar for reading radar data, wherein the distance between the radar and the preceding vehicle in the sequence is 400 meters when no object is detected in front of 200 because the distance measurement range of the millimeter wave radar is about 200 meters.
Further, the instantaneous speed of the vehicle is acquired at a preset sampling frequency to obtain a speed sequence of the vehicleThe acquisition mode is that the vehicle-mounted ECU directly reads the data and can record the data for a long time.
Then, the depth of the depression of the brake pedal of the vehicle is acquired at a preset sampling frequency to obtain a depth sequence of the brake pedalThe method comprises the steps of carrying out a first treatment on the surface of the The acquisition mode is that a distance sensor is arranged below the brake pedal, and the stepping depth of the brake pedal is acquired.
Then, when the vehicle brakes, the temperature of the brake pad can be rapidly increased due to friction, and during normal running, the temperature can be reduced again due to air cooling, so that the temperature of the brake pad is ensured not to overheat. Frequent braking is susceptible to thermal decay of braking performance.
An infrared temperature sensor is arranged at each brake pad, only the braking state of the left front wheel is discussed in the embodiment, the processing modes of other wheels are the same, the acquisition is also carried out at a preset sampling frequency, the temperature sequence of the brake pad in the running process of the vehicle is obtained, and the temperature sequence is recorded as。
Finally, collecting a sequence of braking duration of the brake pedal, wherein the braking duration is the duration when the brake pedal is stepped on each time and then returns to the original position, and obtaining the sequence of the braking duration of the brake pedal。
The data processing module is used for obtaining a driving habit index by utilizing fluctuation degrees of the speed sequence, the depth sequence and the temperature sequence; obtaining a braking condition vector according to the depth sequence and the braking duration sequence; obtaining a matching coefficient between vehicles based on the similarity of the braking situation vectors, the correlation of the speed sequence variation trend, the driving habit index difference value and the similarity of the temperature sequences between the vehicles; and matching the vehicles by using the matching coefficients to obtain a vehicle matching pair.
First, a driving habit index of a driver of each vehicle is obtained:
wherein,a driving habit index indicating a driver of the nth vehicle; />Variance representing vehicle speed sequence, +.>Representing a depth sequence of the brake pedal of the vehicle, +.>The variance of the temperature sequence of the vehicle brake pad is shown. />Has a value range of [0,1 ]]The meaning of this value is that the variances of the velocity sequence, the depth sequence and the temperature sequence are multiplied, when the multiplied value is larger the +.>The closer the value is to 0, the larger the sequence fluctuation is, the more violent driving is caused in the driving process, the speed is changed frequently, the more frequent braking is caused, and the braking degree is suddenly increased or reduced. Conversely->The closer to 1, the smoother the driving habit of the driver.
Further, the situation of the vehicles when the vehicles run is analyzed based on the driving habits of the drivers of the vehicles, and the vehicles with the same driving habits are paired; because of different driving habits, driving data of each vehicle needs to be analyzed and calculated to obtain a matching coefficient between the vehicles, and the vehicles are paired based on the matching coefficient.
The braking time length sequence is acquired in the data acquisition moduleDepth sequence->Calculating the sum of the depths of the brake pedals corresponding to each braking duration when braking is performed each time, for example, when the brake pedal is first depressed, the pedals return to the original positions, and the sum of the depths of the brake pedals braked at this time is the sum of the first 10 elements in the depth sequence after 1s of the brake pedal is passed in the middle; obtaining the sum of the depths of the brake pedals corresponding to each brake duration in a brake duration sequence to form a sequence, and recording the sequence as an accumulated brake depth sequence +.>The method comprises the steps of carrying out a first treatment on the surface of the Vehicle-based brake duration sequence>And cumulative brake depth sequence->Constructing a vector which reflects the braking situation of the vehicle and is denoted as the braking situation vector +.>The vector contains two reflecting vehiclesTwo vectors of braking conditions when the vehicle is braked.
Then, a matching coefficient between the respective vehicles is obtained based on the braking condition vector, the speed sequence, the driving habit index, and the temperature sequence between the respective vehicles:
wherein,representing a matching coefficient between vehicle a and vehicle B; />A braking situation vector representing the vehicle a,a braking condition vector representing the vehicle B; />Representing the speed sequence of vehicle a, +.>Representing a speed sequence of vehicle B; />Index indicating driving habit of vehicle a, +.>A driving habit index indicating a vehicle B; />The degree of difference between the temperature sequence of the vehicle A and the temperature sequence of the vehicle B obtained by using a DTW algorithm is shown; />Indicating vehicleTemperature sequence of vehicle A,/->A temperature sequence of the vehicle B is shown.
The cosine similarity of the braking condition vector between two vehicles is shown in the first term molecule, the absolute value of the difference value of two vector modes is shown in the denominator, and when the sequence values in the two vectors are closer, the term is closer to 1; the second term is the pearson correlation coefficient of the speed sequence between two trucks, and when the change trend of the speed sequence between two vehicles is more similar, the value is more similar to 1, which means that the driving habits are more similar; the third term is the difference in driving habit index between two vehicles, when the driving habits of two trucks are similar, the closer this term is to 1, meaning the closer the change in braking condition is; the fourth term is the dynamic time warping value of the temperature sequence of the brake pad between two vehicles, the closer this value is to 1 when the elements in the two sequences are more similar, which also means that the braking conditions during driving are closer. Matching coefficientHas a value range of [0,1 ]]When various data indicators between two vehicles are close, the value is closer to 1, and conversely, the value is closer to 0.
And finally, matching the vehicles based on the matching coefficients among the vehicles by using a K-M algorithm. The specific process is as follows: converting a matching coefficient W between vehicles into a sample distance between vehiclesAnd matching by using a K-M algorithm based on the sample distance between the vehicles to obtain a vehicle matching pair.
The vehicle early warning module is used for obtaining a braking deceleration sequence by utilizing the speed sequences of two vehicles in the vehicle matching pair; obtaining braking deceleration lines corresponding to various temperatures according to depth sequences of two vehicles in the matching pair of the vehicles corresponding to each temperature in the temperature sequence and all elements in the braking deceleration sequence; acquiring the deceleration of the vehicle at the current moment based on a brake deceleration straight line corresponding to the temperature of a brake pad at the current moment of the vehicle in the vehicle matching pair and a preset brake pedal depth; and pre-warning the driver based on the braking distance of the vehicle in the vehicle matching pair obtained by using the deceleration and the speed at the current moment.
First, a speed sequence of the vehicle is obtained in the data acquisition module, and a braking deceleration sequence is obtained by using the speed sequences of two vehicles in a vehicle matching pairBraking deceleration sequence->Element->Wherein, the method comprises the steps of, wherein,for a preset sampling frequency, +.>And->Is the speed sequence +.>Two adjacent elements of (a); at the same time 0 is supplemented at the first element in the braking deceleration sequence, so that the braking deceleration sequence +.>Depth sequence to brake pedal->Is the same length.
The temperature sequence of the brake pad has different temperatures and a plurality of temperatures, each temperature can correspond to a plurality of temperatures, and each temperature corresponds toA plurality of temperatures corresponding to a plurality of different moments, and a braking deceleration sequence is performed at different moments corresponding to each temperatureDepth sequence to brake pedal->Is determined by the corresponding braking deceleration sequence corresponding to the temperature of each brake pad +.>Depth sequence to brake pedal->The relationship between brake deceleration and depth of brake pedal at each brake pad temperature.
For example, when a temperature of a brake pad of a matched pair vehicle isWhen such a temperature is found, i.e. +.>At a corresponding plurality of different moments in the temperature sequence of the brake pad, the braking deceleration sequence of the vehicle at these plurality of different moments is found simultaneously +.>Depth sequence to brake pedal->All the elements corresponding are treated in this way for both vehicles in a matched pair of vehicles, when the temperature of the brake pad is obtained>Time-dependent control of two vehiclesDynamic deceleration sequence->Depth sequence to brake pedal->All elements of (1) constitute a comprehensive sequence +.>，Indicating the temperature of the brake pad as +.>When the brake pedal is depressed by a depth of +.>The corresponding braking deceleration of the vehicle is。
Then, according to the obtainedThe elements in the corresponding integrated sequence E are fitted to a straight line by using a least square method, wherein the straight line is in a rectangular coordinate system, the abscissa is the depth h of a brake pedal, the ordinate is the deceleration a of a vehicle during braking, and meanwhile, the slope k of the straight line is obtained, and the equation of the straight line is ∈>. And evaluating the fitting degree of the straight line by using the ordinate of the point on the straight line and the mean square error of the actual deceleration when the straight line is fitted:
wherein,a straight line fitting degree evaluation value is represented; />Represents the ordinate of the point on the straight line, +.>Representing the actual deceleration of the actual vehicle during braking; />Indicating the number of actual decelerations.
Setting an evaluation threshold value TH, preferably, the value of TH in the embodiment is 0.2, and when S is greater than the evaluation threshold value TH, removing more discrete elements in the integrated sequence E to re-fit the straight line; finally, the temperature of the vehicle in the matched pair of the vehicle at the brake pad is obtainedThe braking deceleration line at that time.
So far, the braking deceleration straight line of each vehicle of the same vehicle type in the traffic internet of things at the moment under the temperature of all brake pads of the vehicle in the matching pair can be obtained
Finally, the brake deceleration of the vehicle is different from the brake deceleration of the same brake pedal depth under different brake pad temperatures by acquiring the brake deceleration straight line, and the brake distance of the vehicle can be increased due to the fact that the temperature of the brake pad is too high or too low. Obtaining a braking deceleration straight line corresponding to the temperature of a brake pad at the current moment of a vehicle in a vehicle matching pair, and presetting the depth of a brake pedal to beObtaining the deceleration +.f at the present moment by using the brake deceleration line corresponding to the brake pad temperature obtained at the present moment>The method comprises the steps of carrying out a first treatment on the surface of the Obtaining a braking distance of the vehicle in the vehicle matching pair:
wherein V represents the speed of the vehicle in the matching pair of the vehicle at the current moment and is obtained from a speed sequence of the vehicle; x represents the braking distance of the vehicle.
Meanwhile, the distance d between the vehicle at the current moment and the front vehicle is obtained, and if the braking distance X between the vehicle at the current moment and the front vehicle is larger than or equal to the distance d between the vehicle at the current moment and the front vehicle, the driver of the vehicle is warned, so that the driver takes measures in advance, and accidents are prevented.
According to the method, when the vehicles are pre-warned, the data of the vehicles in the matched pairs are analyzed and simulated in the traffic internet of things to obtain the braking distances of different conditions, the vehicles in the matched pairs are pre-warned according to the braking distances, meanwhile, real-time detection can be achieved, driving safety is guaranteed, and vehicle collision is prevented.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (2)
1. The utility model provides a vehicle braking distance dynamic early warning system based on traffic thing networking which characterized in that, this system includes: the data acquisition module is used for acquiring a speed sequence of a vehicle of the same vehicle type, a distance sequence of the vehicle and a front vehicle, a depth sequence of a brake pedal and a temperature sequence of a brake pad at a preset sampling frequency; meanwhile, a braking duration sequence is obtained according to the duration of each time the brake pedal is stepped down to return to the original position;
the data processing module is used for obtaining a driving habit index by utilizing fluctuation degrees of the speed sequence, the depth sequence and the temperature sequence; obtaining a braking condition vector according to the depth sequence and the braking duration sequence; obtaining a matching coefficient between vehicles based on the similarity of the braking situation vectors, the correlation of the speed sequence variation trend, the driving habit index difference value and the similarity of the temperature sequences between the vehicles; matching the vehicles by using the matching coefficients to obtain vehicle matching pairs; the matching coefficients among the vehicles are as follows:
wherein,representing a matching coefficient between vehicle a and vehicle B; />A braking situation vector representing vehicle a +.>A braking condition vector representing the vehicle B; />Representing the speed sequence of vehicle a, +.>Representing a speed sequence of vehicle B; />Index indicating driving habit of vehicle a, +.>A driving habit index indicating a vehicle B; />The degree of difference between the temperature sequence of the vehicle A and the temperature sequence of the vehicle B obtained by using a DTW algorithm is shown; />Representing the temperature sequence of vehicle A, +.>A temperature sequence representing the vehicle B;
the vehicle early warning module is used for obtaining a braking deceleration sequence by utilizing the speed sequences of two vehicles in the vehicle matching pair; obtaining braking deceleration lines corresponding to various temperatures according to depth sequences of two vehicles in the matching pair of the vehicles corresponding to each temperature in the temperature sequence and all elements in the braking deceleration sequence; acquiring the deceleration of the vehicle at the current moment based on a brake deceleration straight line corresponding to the temperature of a brake pad at the current moment of the vehicle in the vehicle matching pair and a preset brake pedal depth; early warning is carried out on a driver based on a braking distance of the vehicle in the vehicle matching pair, which is obtained by utilizing the deceleration and the speed at the current moment;
the obtaining a braking deceleration sequence using a speed sequence of two vehicles in a vehicle match pair comprises: the ratio of the adjacent element difference value to the preset sampling frequency in the speed sequence of two vehicles in the vehicle matching pair forms a braking deceleration sequence of the vehicle matching pair;
the obtaining of the braking deceleration lines corresponding to the various temperatures comprises the following steps: the temperatures of the plurality of brake pads in the temperature sequence of two vehicles in the vehicle matching pair can be divided into a plurality of temperatures; obtaining all corresponding elements in the depth sequences and the braking deceleration sequences of the two vehicles at corresponding moments of a plurality of temperatures in each temperature, and performing linear fitting on all the obtained elements by using each temperature to obtain braking deceleration lines corresponding to each temperature;
the obtaining the driving habit index includes: the variances of the speed sequence, the depth sequence and the temperature sequence are in negative correlation with the driving habit index;
the obtaining the braking condition vector according to the depth sequence and the braking duration sequence comprises the following steps: obtaining the depth of a corresponding brake pedal of each brake duration in the depth sequence in the brake duration sequence; the sum of the depths of the corresponding brake pedals in the depth sequence of each brake duration forms a sequence, and the sequence is recorded as an accumulated brake depth sequence; constructing a braking condition vector based on the braking duration sequence and the accumulated braking depth sequence;
the matching of the vehicles by using the matching coefficient, the obtaining of the vehicle matching pair comprises: obtaining sample distances between vehicles according to the matching coefficients between the vehicles, wherein the matching coefficients and the sample distances between the vehicles form a negative correlation; pairing vehicles based on sample distances among the vehicles by using a K-M algorithm to obtain vehicle pairing pairs;
the pre-warning the driver based on the braking distance of the vehicle in the vehicle matching pair obtained by utilizing the deceleration and the speed at the current moment comprises the following steps: and the braking distance is the distance traveled when the vehicle in the vehicle matching pair brakes at the current moment corresponding to the deceleration, and when the braking distance is greater than or equal to the distance between the vehicle and the front vehicle, the vehicle driver is warned.
2. The vehicle braking distance dynamic early warning system based on the traffic internet of things according to claim 1, wherein the obtaining a braking duration sequence according to a duration of each depression of a brake pedal to return to a home position comprises: and collecting the time length of returning to the original position after each time the brake pedal is stepped on, and forming a brake time length sequence according to the time sequence by the obtained time length.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5684473A (en) * | 1994-03-25 | 1997-11-04 | Nippondenso Co., Ltd. | Measuring apparatus for detecting distance between vehicles and related warning system |
| CN206049656U (en) * | 2016-08-24 | 2017-03-29 | 宝沃汽车（中国）有限公司 | The detecting system and vehicle of vehicle brake efficiency |
| CN108891350A (en) * | 2018-07-27 | 2018-11-27 | 武汉理工大学 | A kind of rear-end collision prevention early warning system and method based on front truck operator brake habit |
| CN113165613A (en) * | 2018-11-20 | 2021-07-23 | 克诺尔轨道车辆***有限公司 | Method for determining a braking distance |
| CN114228727A (en) * | 2021-12-21 | 2022-03-25 | 湖北文理学院 | Vehicle driving safety evaluation method, device, equipment and storage medium |
| CN114526930A (en) * | 2022-03-09 | 2022-05-24 | 河南职业技术学院 | Intelligent network connection automobile fault detection method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011001365A1 (en) * | 2011-03-17 | 2012-09-20 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Braking device and braking method for a motor vehicle |
| US9254824B2 (en) * | 2013-12-30 | 2016-02-09 | Automotive Research & Testing Center | Adaptive anti-collision method for vehicle |
-
2022
- 2022-08-30 CN CN202211050981.1A patent/CN115123284B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5684473A (en) * | 1994-03-25 | 1997-11-04 | Nippondenso Co., Ltd. | Measuring apparatus for detecting distance between vehicles and related warning system |
| CN206049656U (en) * | 2016-08-24 | 2017-03-29 | 宝沃汽车（中国）有限公司 | The detecting system and vehicle of vehicle brake efficiency |
| CN108891350A (en) * | 2018-07-27 | 2018-11-27 | 武汉理工大学 | A kind of rear-end collision prevention early warning system and method based on front truck operator brake habit |
| CN113165613A (en) * | 2018-11-20 | 2021-07-23 | 克诺尔轨道车辆***有限公司 | Method for determining a braking distance |
| CN114228727A (en) * | 2021-12-21 | 2022-03-25 | 湖北文理学院 | Vehicle driving safety evaluation method, device, equipment and storage medium |
| CN114526930A (en) * | 2022-03-09 | 2022-05-24 | 河南职业技术学院 | Intelligent network connection automobile fault detection method and system |
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