CN116229723A - Intelligent traffic information data analysis management system - Google Patents
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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Abstract
The invention relates to the technical field of vehicle position adjustment, in particular to an intelligent traffic information data analysis and management system. The system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring the driving direction of a vehicle at the current position, the signal range of the vehicle and the lane line in the signal range; the driving trend acquisition module is used for determining the form trend of the vehicle in each driving direction; the angle deviation value acquisition module is used for determining an angle deviation value of the vehicle; the vehicle position prediction module is used for adjusting the position sequence of the vehicle to obtain a predicted position sequence of the vehicle. According to the invention, through predicting and analyzing the state of the current vehicle and the characteristics of the vehicle in the running process, the noise in the real observed data value is removed, and the characteristic of more accurate data for prediction is achieved.
Description
Technical Field
The invention relates to the technical field of vehicle position adjustment, in particular to an intelligent traffic information data analysis and management system.
Background
Intelligent traffic is an indispensable important infrastructure and one of the future development trends. The intelligent traffic system has the characteristics of automation, high efficiency, safety and the like, and becomes a bright spot and an important research and development direction in the modern traffic field. The application range of intelligent traffic will be wider and wider, and the prospect will be wider. Intelligent transportation will meet the ever-increasing travel demands of people. The urban process is faster and faster, and the intelligent traffic system can reduce traffic jam, improve the road utilization rate and ensure that urban traffic is smoother. And it is a primary task to ensure road safety while providing convenience to people. The intelligent traffic system can greatly reduce the occurrence rate of traffic accidents and can also carry out fine management on road traffic.
In the intelligent driving scene, vehicles are connected with the road intelligent terminal to mutually transmit data, so that the driving direction is judged. However, in the actual running process, mutual interference exists among vehicle signals, so that the result of the traditional Markov prediction directly through the measured value is easy to deviate, and further the running state of the vehicle is caused to deviate, and finally the traffic safety is influenced.
Disclosure of Invention
In order to solve the technical problem that the result of Markov prediction directly through a measured value is easy to deviate, the invention aims to provide an intelligent traffic information data analysis and management system, which adopts the following technical scheme:
the data acquisition module is used for acquiring the driving direction of the vehicle at the current position, the signal range of the vehicle and the lane line in the signal range;
the driving trend acquisition module is used for taking a line segment which is parallel to the lane line and bisects the signal range as a lane center line and taking the shortest line segment from the vehicle to the lane center line as a deviation line; determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line;
the angle deviation value acquisition module is used for determining the angle deviation value of the vehicle according to the drivable direction corresponding to the maximum value of the driving trend of the vehicle and the driving direction from the last position to the current position;
the vehicle position prediction module is used for screening the angle deviation value of the vehicle to obtain a low angle deviation value sequence; and adjusting the position sequence of the vehicle according to the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain a predicted position sequence of the vehicle.
Preferably, the determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line includes:
acquiring the current signal intensity of a vehicle, taking the included angle between the drivable direction of the vehicle and a deviation line as a deviation angle, taking the product of the deviation angle and the length of the deviation line as a first initial trend when the deviation angle of the vehicle belongs to a preset deviation range, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of a preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the normalized value of the ratio of the numerator and the denominator as the driving trend; when the deviation angle of the vehicle does not belong to the preset deviation range, taking the product of the deviation angle and the length of the deviation line as a first initial trend, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of the preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the negative number of the normalized value of the ratio of the numerator and the denominator as the running trend of the vehicle in the running direction.
Preferably, the determining the angle deviation value of the vehicle according to the drivable direction corresponding to the maximum value of the driving trend of the vehicle and the driving direction from the previous position to the current position includes:
and taking the included angle value of the drivable direction corresponding to the maximum value of the driving trend and the driving direction from the last position to the current position as an angle deviation value.
Preferably, the screening the angle deviation value of the vehicle to obtain a low angle deviation value sequence includes:
ordering the angle deviation values of the vehicles in order from small to large to obtain corresponding angle deviation value sequences; and sequentially selecting angle deviation values from the angle deviation value sequence from front to back until the termination selection condition is met, stopping selecting the angle deviation values from the angle deviation value sequence, and constructing a corresponding low angle deviation value sequence by the selected angle deviation values.
Preferably, the termination selection condition is:
calculating a standard deviation corresponding to the selected angle deviation value; and stopping the selection of the angle deviation value when the absolute value of the difference between the last selected angle deviation value and the last and last selected angle deviation value is larger than the standard deviation of the preset multiple.
Preferably, the adjusting the position sequence of the vehicle according to the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain the predicted position sequence of the vehicle includes:
selecting any one angle deviation value in the low angle deviation value sequence as a target deviation value;
taking the position corresponding to the target deviation value as a target position; for a position located before a target position in a position sequence of a vehicle, adjusting a running direction to a running direction corresponding to a running trend maximum value of the previous position without changing a moving path length from the previous position to the target position, and obtaining an adjusted position; and for the position located at the position behind the target position in the position sequence of the vehicle, adjusting the running direction to the running direction corresponding to the maximum value of the running trend of the target position without changing the moving path length from the target position to the position behind the target position, obtaining the adjusted position, and obtaining the predicted position sequence of the vehicle according to the adjusted position.
The embodiment of the invention has at least the following beneficial effects:
the invention relates to the technical field of vehicle position adjustment. The system firstly acquires the driving trend of the vehicle through the driving trend acquisition module, and the driving trend can reflect the movement trend of the vehicle through analyzing the driving direction of the vehicle and the corresponding deviation line. The angle deviation value of the vehicle is obtained through the angle deviation value obtaining module, the angle deviation value is obtained through comparison between the actual direction of the vehicle and the direction corresponding to the maximum value of the driving trend, the angle deviation value can reflect the deviation degree of the vehicle, and the larger the angle deviation value is, the larger the probability that the vehicle deviates from a normal driving path is. Finally, a low-angle deviation value sequence is obtained through the vehicle position prediction module, the angle deviation value in the low-angle deviation value sequence is data with lower error, so that the position sequence of the vehicle is adjusted through the positions corresponding to the angle deviation values in the low-angle deviation value sequence in one step, a more accurate predicted position sequence of the vehicle is obtained, namely, the prediction analysis on the state of the current vehicle and the characteristics of the vehicle in the running process is realized, the noise in the real vehicle data is removed, the obtained predicted position sequence is more accurate in the follow-up process, the problem that deviation is easy to occur when a predicted result is obtained directly through measured values is solved, and the accuracy of position prediction is improved.
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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 system block diagram of an intelligent traffic information data analysis management system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of signal ranges of a vehicle entrance system unit according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific implementation, structure, features and effects of an intelligent traffic information data analysis and management system according to the present invention with reference to the accompanying drawings and preferred embodiments. 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 embodiment of the invention provides a specific implementation method of an intelligent traffic information data analysis and management system, which is suitable for an intelligent traffic scene. In this scenario, the vehicle may fall within the signal range of a certain system unit as long as it is traveling on the road. The method aims to solve the technical problem that the result of Markov prediction directly through the measured value is easy to deviate. Compared with the traditional method for directly carrying out the Markov prediction according to the vehicle predicted value, the method has the advantages that the noise position in the Markov prediction process participates in calculation together, so that the error is overlarge, the accuracy of the prediction is interfered, and the accuracy of managing the vehicle by using traffic information data is reduced. According to the invention, through the current vehicle state and the lane line division, the prediction analysis is performed by combining the characteristics of the vehicle in the running process, so that the noise in the observed value is removed, and the characteristic of more accurate data for prediction is achieved.
The following specifically describes a specific scheme of the intelligent traffic information data analysis management system provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an intelligent traffic information data analysis management system according to an embodiment of the present invention is shown, where the system includes the following modules:
the data acquisition module 10 is used for acquiring the driving direction of the vehicle at the current position, the signal range where the vehicle is located and the lane lines in the signal range.
The intelligent traffic contacts the vehicles in the coverage signal range through the installed wireless interaction equipment, transmits the information of the current road section to the vehicles, guides the vehicles to stably advance, but has complex actual traffic conditions and complex data communication conditions between the vehicles and the system units, noise interference exists in the vehicle information data received by the system units, and errors exist in vehicle position judgment directly through the data reported by the vehicles.
The vehicle is communicated with the first system unit only, the first system unit receives vehicle sending data for analysis, and the signal coverage corresponding to the system unit is called as the signal coverage in the following step. The vehicle then exits the first system unit and enters the subsequent system unit. Referring to fig. 2, fig. 2 is a schematic diagram of a signal range of a vehicle driving into a system unit. When the vehicle travels to the overlapping position of the signal ranges corresponding to the two system units, the system unit which receives the vehicle signal first is used as the main control unit, and the signal range of the other system unit is temporarily ignored.
Since the vehicle is traveling with a moving speed and the traveling direction is controlled by the driver, the current vehicle speed of the acquired vehicle and the positioning data between the vehicle and the system unit are transmitted as signals to the system unit.
When the system unit receives the signal sent by the vehicle, the vehicle positioning data are placed in the sample space, and the current position of the vehicle is obtained.
The real driving direction of the vehicle at the current position is obtained by using the shaft rotation speed sensor, and the lane line on the driving road can be obtained by using the camera connected with the management system. It should be noted that, the method for acquiring the driving direction of the vehicle and the lane line on the driving road is a well-known technique for those skilled in the art, and in other embodiments, the method may also be acquired by other manners, which will not be described herein.
The driving trend obtaining module 20 is configured to take a line segment parallel to the lane line and bisecting the signal range as a lane center line, and take a shortest line segment from the vehicle to the lane center line as a deviation line; and determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line.
When the vehicle just enters the signal range of the system unit, the vehicle is located at a position far from the system unit, so that the data transmission path is far, and the possibility of generating a positioning numerical error due to interference is higher. As the vehicle moves gradually, the distance between the vehicle and the system unit changes, and the closer the system unit is to the vehicle, the shorter the transmission path required by data transmission is, the shorter the transmission time length is, and the lower the possibility of data interference is. The method and the device obtain the high-precision prediction track by processing the collected vehicle return data.
The signal range of the system equipment can be divided into a lane areas by the lane lines on the prior ground, the middle part of the lane areas where the vehicle runs belongs to the normal condition, and when the positioning data deviate, the signal range indicates that the vehicle is changing the running direction or the noise interference occurs to the positioning data.
Firstly, judging the position of a vehicle in a lane area, and taking a line segment which is parallel to a lane line and bisects a signal range as a lane center line; taking the shortest line segment from the vehicle to the center line of the lane as a deviation line; and determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line. The shortest line segment of the vehicle and the lane center line is a perpendicular line segment perpendicular to the lane center line with the vehicle as an end point, and the perpendicular line segment is the shortest line segment of the vehicle and the lane center line, namely the deviation line corresponding to the vehicle, and the length of the deviation line is obtained while the deviation line is obtained.
And analyzing the current position of the vehicle and the vehicle information data obtained by the current communication data to obtain the movement trend of the current vehicle in each driving direction. It should be noted that, the drivable direction is a direction in which the vehicle can subsequently travel at the current position, each position corresponds to a plurality of drivable directions, n is greater than or equal to 2 in the embodiment of the present invention, and in other embodiments, the practitioner may set the drivable direction corresponding to each position according to the time situation, for example, the range of the drivable direction of the vehicle may be set to [0,180], and in the embodiment of the present invention, the drivable direction is not limited to a specific angle value any more.
Determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line, and specifically:
acquiring the current signal intensity of a vehicle, taking the included angle between the drivable direction of the vehicle and a deviation line as a deviation angle, taking the product of the deviation angle and the length of the deviation line as a first initial trend when the deviation angle of the vehicle belongs to a preset deviation range, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of a preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the normalized value of the ratio of the numerator and the denominator as the driving trend; when the deviation angle of the vehicle does not belong to the preset deviation range, taking the product of the deviation angle and the length of the deviation line as a first initial trend, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of the preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the negative number of the normalized value of the ratio of the numerator and the denominator as the driving trend. It should be noted that the current signal strength is the signal strength of the vehicle signal received by the system unit, each vehicle has a corresponding current signal strength, and the signal strength is the data obtained by the direct receiving and processing of the system unit, which is not described in detail.
The calculation formula of the driving trend is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the driving trend corresponding to the ith driving direction;
the included angle between the driving direction of the vehicle and the deviation line is the deviation angle of the vehicle;
the deviation range is preset;
is a normalization function;
for vehicles
The preset change multiplying power of the signal intensity after moving to the ith driving direction;
for vehicles
Is the current signal strength of (a);
length of the offset line for the vehicle;
is a preset adjustment coefficient.
In the embodiment of the invention, the preset deviation range is as follows
The value of the preset adjustment coefficient is an infinitesimal value, and in other embodiments, the value may be adjusted by an implementer according to actual situations, and the sum of the first initial trend and the preset adjustment coefficient is taken as a denominator to avoid the situation that the denominator is 0. It should be noted that, the preset change rate of the signal strength is determined by a priori proportion, for example, the closer the vehicle and the unit are, the less the signal propagation attenuation is, the stronger the signal is, and the preset change rate is a priori amount of how many times the signal is enhanced, for example, by one meter.
In the calculation formula of the driving trend, when the deviation angle of the vehicle belongs to the preset deviation range, the vehicle is represented as advancing in the advancing direction of the lane area, and when the deviation angle of the vehicle does not belong to the preset deviation range, the vehicle is reflected to possibly appear driving actions such as reversing or turning. In the driving trend
The larger the signal strength is, the more the signal strength is increased after the vehicle moves to the ith driving direction, and the higher the stability of signal transmission between the vehicle and the system unit is. First initial trend
The deviation angle is the product of the deviation angle and the length of the deviation line, and when the deviation angle is larger, the higher the deviation length is, the higher the degree of deviation from the current lane area is, and the more the vehicle deviates from the driving trend of moving towards the center of the lane after moving towards the ith driving direction. As can be seen from the common general knowledge of normal driving, a vehicle running normally rarely backs up in the center of a road and changes at the same timeThe lane area is also changed during traveling.
By calculating the driving trend X in each direction, the current position of the vehicle is obtained
Is the driving trend of each driving direction of the center. And the running trend of the vehicle at the current moment can be obtained by processing the collected vehicle signal data.
The angle deviation value obtaining module 30 is configured to determine an angle deviation value of the vehicle according to a drivable direction corresponding to a maximum value of a driving trend of the vehicle and a driving direction from a previous position to a current position.
The system unit and the vehicle equipment interfere when in information communication, the vehicle moves in the current system unit, the difference calculation and calibration of other data can be carried out through high-quality transmission data with small difference between the measured value and the predicted value, and the position change condition in the current system unit is synthesized and transmitted to the next system unit, so that a more accurate moving direction prediction method is obtained.
In the running process of the vehicle, the vehicle-mounted equipment is continuously communicated with the system unit, the system unit is matched with the movement trend direction through more obtained vehicle data measured values, the influence of noise interference on the data is obtained, and the noise is specifically removed in the follow-up prediction, so that the follow-up measured values are more accurate.
And acquiring each position of the vehicle from the signal range of the entering vehicle to the current position, and acquiring the driving trend of the vehicle from the signal range of the entering vehicle to the current position in different driving directions corresponding to each position. Wherein, each position corresponds to a plurality of running directions, and each running direction corresponds to one running trend, namely, each position corresponds to a plurality of running trends.
Further, the angle deviation value of the vehicle is determined according to the drivable direction corresponding to the maximum value of the driving trend of the vehicle and the driving direction from the last position to the current position. And adjusting the predicted position of the subsequent vehicle according to the angle deviation value of the vehicle.
Specific: and taking the included angle value of the drivable direction corresponding to the maximum value of the driving trend of the vehicle and the driving direction from the last position to the current position as an angle deviation value.
A vehicle position prediction module 40, configured to screen the angle deviation value of the vehicle to obtain a low angle deviation value sequence; and adjusting the position sequence of the vehicle according to the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain a predicted position sequence of the vehicle.
Further, the more true predicted value is judged by comprehensively correcting the slightly deviated part and the excessively deviated part in the angle deviation value. And selecting the position of the minimum angle deviation value corresponding to the current vehicle, wherein the position of the minimum angle deviation value is used as the position where the difference between the observed value and the predicted data is minimum, and represents the minimum interference in the data transmission process, and the measured data is the truest.
And (3) taking the value of the angle deviation value, acquiring a part of low-difference positions of the vehicle and the transmission data of the system unit, and selecting too much or too little low-error data which cannot be acquired in a sufficient quantity for error judgment and correction. And judging the range of the interference source according to the distribution condition of the low-error position, and correcting the interference according to the characteristics of the low-error position.
Firstly, a low-error position is obtained, and in the embodiment of the invention, the angle deviation value reflects the error of the data transmitted by the vehicle and the system unit, so that the angle deviation value of the vehicle is further screened to obtain a low-angle deviation value sequence, and the position corresponding to the angle deviation value in the low-angle deviation value sequence is the low-error position.
The angle deviation value of the vehicle is screened to obtain a low angle deviation value sequence, and the low angle deviation value sequence is specifically: ordering the angle deviation values of the vehicles in order from small to large to obtain corresponding angle deviation value sequences; and sequentially selecting angle deviation values from the angle deviation value sequence from front to back until the termination selection condition is met, stopping selecting the angle deviation values from the angle deviation value sequence, and constructing a corresponding low angle deviation value sequence by the selected angle deviation values. Wherein, the termination selection condition is: calculating a standard deviation corresponding to the selected angle deviation value; and stopping the selection of the angle deviation value when the absolute value of the difference between the last selected angle deviation value and the last and last selected angle deviation value is larger than the standard deviation of the preset multiple. In the embodiment of the present invention, the preset multiple has a value of 2, and in other embodiments, the practitioner can adjust the value according to the actual situation.
That is, in the angular deviation value sequence formed by the angular deviation values of the vehicle, the minimum value of the angular deviation values is calculated
Initially, the minimum value of the angle deviation values except the minimum value is sequentially selected
Until occurrence of
When the selection is stopped, the selection is performed
As a sequence of low angle deviation values.
And correcting and judging the error position, and adjusting the position sequence of the vehicle through the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain a predicted position sequence. The sequence of positions is recorded as
Wherein
For a vehicle to drive into the first position within the signal range where the vehicle is located,
a second position within the signal range where the vehicle is located;
is the nth position in the signal range where the vehicle is driven into. The running trend sequence of each running direction under each position is
Wherein, the method comprises the steps of, wherein,
a driving trend sequence for the nth position in the signal range of the vehicle driving into the vehicle;
the running trend of the 1 st running direction corresponding to the nth position in the signal range of the vehicle entrance;
the driving trend of the 2 nd driving direction corresponding to the nth position in the signal range of the vehicle entrance vehicle;
the driving trend of the ith driving direction corresponding to the nth position in the signal range of the vehicle entrance vehicle is adopted.
Specific: selecting any one angle deviation value in the low angle deviation value sequence as a target deviation value; taking the position corresponding to the target deviation value as a target position; for a position located before a target position in a position sequence of a vehicle, adjusting a running direction to a running direction corresponding to a running trend maximum value of the previous position without changing a moving path length from the previous position to the target position, and obtaining an adjusted position; and for the position located at the rear position of the target position in the position sequence of the vehicle, the travel direction is adjusted to the travel direction corresponding to the maximum travel trend of the target position without changing the length of the travel path from the target position to the rear position, the adjusted position is obtained, and the predicted position sequence is obtained according to the adjusted position. That is, the position before and after the target position in the position sequence is adjusted, and the adjusted sequence is used as the predicted position sequence.
For example, the number of the cells to be processed,
is the first in the low angle deviation value sequenceEight positions corresponding to the angular deviation value sequences
As the target position, the target position
The previous position in the sequence of angle deviation values
And the latter position
By the target position
For the previous position
And the latter position
And updating.
Wherein when the target position
Is the previous position of (a)
And the latter position
When belonging to the low angle deviation value sequence, the method does not relate to the previous position
And the latter position
And (5) adjusting. For example when the latter position
Belonging to a low-angle deviation value sequence, then only the previous position
Make adjustments without taking the latter position
And (5) adjusting.
In the process of adjusting the position sequence, the position where a certain angle deviation value c may occur is located between two target positions in the position sequence, and in this case, the absolute value of the difference between the angle deviation values of the two target positions and the angle deviation value c is obtained, and the position corresponding to the angle deviation value c is adjusted according to the target position corresponding to the smaller value of the two absolute values of the difference. For example
,
Representative position
Whose deviation is smaller, the data points with smaller deviation are selected for updating, and the data is more credible.
And updating the previous position and the next position corresponding to the updated target position into the position sequence until the sequence judgment is completed, so as to obtain an updated predicted position sequence.
And conveying the updated predicted position sequence to a Markov network for training the network. And updating the predicted position sequence, transmitting the predicted position sequence to a Markov algorithm, generating a corresponding state transition matrix, and calculating and outputting the vehicle moving direction at the next moment through the algorithm. And processing the predicted position sequence according to the updated predicted position sequence through a Markov algorithm to obtain the moving direction of the vehicle at the next moment.
And processing and identifying the image of the abnormal area, and visually displaying the quality information of the current road.
After the moving direction of the vehicle at the next moment is obtained, the signal range of the vehicle moving into the next system unit is determined through the prior system unit distribution position, the obtained predicted position sequence is transmitted to the next system unit through the system unit corresponding to the signal range of the current vehicle, so that the purpose of more intelligently serving the vehicle is achieved, and the intelligent degree of traffic data analysis is improved.
In summary, compared with the traditional method for directly carrying out the Markov prediction according to the vehicle predicted value, the method provided by the invention has the advantages that the error caused by the calculation of the noise position in the Markov prediction process is overlarge, the accuracy of the interference prediction is reduced, and the accuracy of managing the vehicle by using the traffic information data is reduced. According to the invention, through the current vehicle state and the lane line division, the prediction analysis is performed by combining the characteristics of the vehicle in the running process, so that the noise in the observed value is removed, and the characteristic of more accurate data for prediction is achieved.
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. The processes depicted in the accompanying drawings 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.
Claims (6)
1. An intelligent traffic information data analysis and management system is characterized by comprising the following modules:
the data acquisition module is used for acquiring the driving direction of the vehicle at the current position, the signal range of the vehicle and the lane line in the signal range;
the driving trend acquisition module is used for taking a line segment which is parallel to the lane line and bisects the signal range as a lane center line and taking the shortest line segment from the vehicle to the lane center line as a deviation line; determining the driving trend of the vehicle according to the driving direction of the vehicle, the included angle of the deviation line and the length of the deviation line;
the angle deviation value acquisition module is used for determining the angle deviation value of the vehicle according to the drivable direction corresponding to the maximum value of the driving trend of the vehicle and the driving direction from the last position to the current position;
the vehicle position prediction module is used for screening the angle deviation value of the vehicle to obtain a low angle deviation value sequence; and adjusting the position sequence of the vehicle according to the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain a predicted position sequence of the vehicle.
2. The intelligent traffic information data analysis and management system according to claim 1, wherein the determining the driving tendency of the vehicle according to the driving direction of the vehicle and the angle of the deviation line and the length of the deviation line comprises:
acquiring the current signal intensity of a vehicle, taking the included angle between the drivable direction of the vehicle and a deviation line as a deviation angle, taking the product of the deviation angle and the length of the deviation line as a first initial trend when the deviation angle of the vehicle belongs to a preset deviation range, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of a preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the normalized value of the ratio of the numerator and the denominator as the driving trend; when the deviation angle of the vehicle does not belong to the preset deviation range, taking the product of the deviation angle and the length of the deviation line as a first initial trend, taking the sum of the first initial trend and a preset adjustment coefficient as a denominator, taking the product of the preset change multiplying power and the current signal intensity of the vehicle as a numerator, and taking the negative number of the normalized value of the ratio of the numerator and the denominator as the running trend of the vehicle in the running direction.
3. The intelligent traffic information data analysis and management system according to claim 1, wherein the determining the angular deviation value of the vehicle according to the drivable direction corresponding to the maximum value of the driving tendency of the vehicle and the driving direction from the last position to the current position comprises:
and taking the included angle value of the drivable direction corresponding to the maximum value of the driving trend and the driving direction from the last position to the current position as an angle deviation value.
4. The intelligent traffic information data analysis and management system according to claim 1, wherein the screening the angle deviation value of the vehicle to obtain the low angle deviation value sequence includes:
ordering the angle deviation values of the vehicles in order from small to large to obtain corresponding angle deviation value sequences; and sequentially selecting angle deviation values from the angle deviation value sequence from front to back until the termination selection condition is met, stopping selecting the angle deviation values from the angle deviation value sequence, and constructing a corresponding low angle deviation value sequence by the selected angle deviation values.
5. The intelligent traffic information data analysis and management system according to claim 4, wherein the termination selection condition is:
calculating a standard deviation corresponding to the selected angle deviation value; and stopping the selection of the angle deviation value when the absolute value of the difference between the last selected angle deviation value and the last and last selected angle deviation value is larger than the standard deviation of the preset multiple.
6. The intelligent traffic information data analysis and management system according to claim 1, wherein the adjusting the position sequence of the vehicle according to the positions corresponding to the angle deviation values in the low angle deviation value sequence to obtain the predicted position sequence of the vehicle comprises:
selecting any one angle deviation value in the low angle deviation value sequence as a target deviation value;
taking the position corresponding to the target deviation value as a target position; for a position located before a target position in a position sequence of a vehicle, adjusting a running direction to a running direction corresponding to a running trend maximum value of the previous position without changing a moving path length from the previous position to the target position, and obtaining an adjusted position; and for the position located at the position behind the target position in the position sequence of the vehicle, adjusting the running direction to the running direction corresponding to the maximum value of the running trend of the target position without changing the moving path length from the target position to the position behind the target position, obtaining the adjusted position, and obtaining the predicted position sequence of the vehicle according to the adjusted position.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200922816A (en) * | 2007-11-30 | 2009-06-01 | Automotive Res & Amp Testing Ct | Method and device for detecting the lane deviation of vehicle |
| JP2013190965A (en) * | 2012-03-13 | 2013-09-26 | Nissan Motor Co Ltd | Road information preparation and distribution device, in-vehicle device, road information preparation and distribution system, road information preparation and distribution method |
| CN108082185A (en) * | 2017-03-30 | 2018-05-29 | 长城汽车股份有限公司 | A kind of travel control method of vehicle, device and vehicle |
| CN112233417A (en) * | 2020-09-17 | 2021-01-15 | 新石器慧义知行智驰(北京)科技有限公司 | Vehicle track prediction method, control device and unmanned vehicle |
| CN112733270A (en) * | 2021-01-08 | 2021-04-30 | 浙江大学 | System and method for predicting vehicle running track and evaluating risk degree of track deviation |
| CN113538919A (en) * | 2019-03-11 | 2021-10-22 | 百度在线网络技术（北京）有限公司 | Lane departure recognition method, apparatus, device, and storage medium |
| WO2022104790A1 (en) * | 2020-11-23 | 2022-05-27 | 深圳元戎启行科技有限公司 | Travel trajectory determining method and apparatus, computer device, and storage medium |
| WO2022104925A1 (en) * | 2020-11-23 | 2022-05-27 | 重庆长安汽车股份有限公司 | Redundancy control method for automatic driving system, automatic driving system, automobile, controller, and computer readable storage medium |
| CN114802235A (en) * | 2022-05-16 | 2022-07-29 | 中国第一汽车股份有限公司 | Vehicle control method and device, computer-readable storage medium and electronic equipment |
-
2023
- 2023-05-06 CN CN202310498092.XA patent/CN116229723B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW200922816A (en) * | 2007-11-30 | 2009-06-01 | Automotive Res & Amp Testing Ct | Method and device for detecting the lane deviation of vehicle |
| JP2013190965A (en) * | 2012-03-13 | 2013-09-26 | Nissan Motor Co Ltd | Road information preparation and distribution device, in-vehicle device, road information preparation and distribution system, road information preparation and distribution method |
| CN108082185A (en) * | 2017-03-30 | 2018-05-29 | 长城汽车股份有限公司 | A kind of travel control method of vehicle, device and vehicle |
| CN113538919A (en) * | 2019-03-11 | 2021-10-22 | 百度在线网络技术（北京）有限公司 | Lane departure recognition method, apparatus, device, and storage medium |
| CN112233417A (en) * | 2020-09-17 | 2021-01-15 | 新石器慧义知行智驰(北京)科技有限公司 | Vehicle track prediction method, control device and unmanned vehicle |
| WO2022104790A1 (en) * | 2020-11-23 | 2022-05-27 | 深圳元戎启行科技有限公司 | Travel trajectory determining method and apparatus, computer device, and storage medium |
| WO2022104925A1 (en) * | 2020-11-23 | 2022-05-27 | 重庆长安汽车股份有限公司 | Redundancy control method for automatic driving system, automatic driving system, automobile, controller, and computer readable storage medium |
| CN112733270A (en) * | 2021-01-08 | 2021-04-30 | 浙江大学 | System and method for predicting vehicle running track and evaluating risk degree of track deviation |
| CN114802235A (en) * | 2022-05-16 | 2022-07-29 | 中国第一汽车股份有限公司 | Vehicle control method and device, computer-readable storage medium and electronic equipment |
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