CN115880926A - Variable speed limit control method and device based on driving style and computer equipment - Google Patents
Variable speed limit control method and device based on driving style and computer equipment Download PDFInfo
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Abstract
The application relates to a variable speed limit control method, device, computer equipment, storage medium and computer program product based on driving style. The method comprises the following steps: acquiring first driving risk information before the vehicle executes the speed limit control information on the target road section and second driving risk information after the vehicle executes the speed limit control information; obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information; according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, the speed limit control information of the vehicle is updated iteratively to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section. The method can improve the speed limiting effect of each vehicle on the road.
Description
Technical Field
The application relates to the technical field of internet of things, in particular to a variable speed limit control method and device based on a driving style, computer equipment, storage media and a computer program product.
Background
With the popularization of motor vehicles and the increase of transportation demands, the driving speed of a driver needs to be limited to ensure the driving safety of the driver. At present, a static speed control method is mainly adopted in the highway safety control technology, and the static speed control method cannot provide reasonable speed limit guidance for drivers aiming at dynamic and variable road traffic environments.
In the conventional art, the safety interval between the vehicles is always detected to remind the driver of speed reduction and speed limit, however, if the driving speed of the driver is too high, the speed cannot be reduced in time, vehicle collision is caused, and the speed limit effect of the vehicles on the road is poor.
Disclosure of Invention
In view of the above, it is necessary to provide a driving style-based variable speed limit control method, apparatus, computer device, computer readable storage medium and computer program product capable of improving the speed limit effect of vehicles on a road, in view of the above technical problems.
In a first aspect, the present application provides a variable speed limit control method based on a driving style. The method comprises the following steps:
acquiring first driving risk information before a vehicle executes speed limit control information on a target road section and second driving risk information after the vehicle executes the speed limit control information;
obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
In one embodiment, acquiring first driving risk information of a vehicle before speed limit control information is executed on a target road section comprises the following steps:
obtaining a first relative driving risk before the vehicle executes the speed limit control information according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a first observation time period before the vehicle executes the speed limit control information;
and obtaining the first driving risk information according to the expectation of the first relative driving risk.
In one embodiment, obtaining the first driving risk information according to the expectation of the first relative driving risk includes:
obtaining the driving risk expectation of the vehicle before executing the speed limit control information on the target road section according to the lane changing risk mean value of the vehicle in the first observation time period and the driving risk expectation of the vehicle in the per-person observation time period;
obtaining the expectation of the dangerous behavior duration before the vehicle executes the speed limit control information on the target road section according to the following risk average value of the vehicle in the first observation period and the expectation of the dangerous behavior duration of the vehicle in the per-person observation period;
obtaining the expectation of the historical average driving risk of the vehicles at the target road section according to the historical average vehicle lane changing risk average value of the target road section and the average historical driving risk expectation of the vehicles per capita at the target road section;
obtaining the expectation of the historical vehicle average dangerous behavior duration of the target road section according to the historical vehicle average following risk average of the target road section and the expectation of the average human historical vehicle average dangerous behavior duration of the target road section;
and obtaining first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, and the historical driving risk average expectation and the historical dangerous behavior duration expectation of the target road section.
In one embodiment, before obtaining the second driving risk information after the vehicle executes the speed limit control information, the method further includes:
judging whether the first driving risk information of the vehicle is larger than a preset driving risk threshold value or not;
when the first driving risk information of the vehicle is larger than the preset driving risk threshold value, the speed limit control information is sent to the vehicle; and the speed limit control information is used for indicating the vehicle to execute corresponding speed limit information.
In one embodiment, the obtaining of the second driving risk information after the vehicle executes the speed limit control information comprises:
obtaining a second relative driving risk after the vehicle executes the speed limit control information according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a second observation time period after the vehicle executes the speed limit control information;
and obtaining second driving risk information according to the expectation of the second relative driving risk.
In one embodiment, obtaining the risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information includes:
summing differences between first driving risk information and second driving risk information of all vehicles on the target road section to obtain a risk change sum of all vehicles on the target road section;
and obtaining the risk change information of the speed limit control information according to the risk change sum and the number of vehicles of the target road section.
In one embodiment, iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information, and the update termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road segment includes:
determining the driving style type of the vehicle according to the longitudinal speed, the transverse speed, the acceleration and deceleration of the vehicle and the driving style type in the driving style clustering result;
taking the risk change information and the speed dispersion loss information as target functions, and performing particle swarm optimization on the speed limiting control information of the vehicle to obtain updated speed limiting control information;
and when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming that the updated speed limit control information is the target speed limit control information.
In one embodiment, before determining the driving style type to which the vehicle belongs according to the longitudinal speed, the lateral speed, the acceleration, deceleration and the driving style type in the driving style clustering result, the method further comprises:
according to the longitudinal speed of each vehicle on the target road section, carrying out following style clustering on each vehicle on the target road section to obtain a clustering result of the following style of each vehicle;
according to the transverse speed of each vehicle on the target road section, carrying out lane changing style clustering on the clustering results of the following styles of the vehicles to obtain the clustering results of the following styles and lane changing styles of the vehicles;
and dividing the clustering results of the following and lane changing styles of each vehicle according to a preset threshold value of acceleration and deceleration to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle as the driving style clustering results.
In one embodiment, the speed limit control information comprises initial speed limit information, an initial speed change period and an initial speed change gradient;
the target speed limit control information comprises target speed limit information, a target speed change period and a target speed change gradient.
In a second aspect, the application also provides a variable speed limit control device based on the driving style. The device comprises:
the driving risk acquisition module is used for acquiring first driving risk information before the speed limit control information is executed by a vehicle on a target road section and second driving risk information after the speed limit control information is executed by the vehicle;
the risk change acquisition module is used for acquiring risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
the speed limit control updating module is used for iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:
acquiring first driving risk information before a vehicle executes speed limit control information on a target road section and second driving risk information after the vehicle executes the speed limit control information;
obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, carrying out iterative update on the speed limit control information of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring first driving risk information before a vehicle executes speed limit control information on a target road section and second driving risk information after the vehicle executes the speed limit control information;
obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, carrying out iterative update on the speed limit control information of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed dispersion loss information is obtained by calculation based on the speed standard deviation of the vehicle on the target road section.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:
acquiring first driving risk information before a vehicle executes speed limit control information on a target road section and second driving risk information after the vehicle executes the speed limit control information;
obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, carrying out iterative update on the speed limit control information of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
According to the variable speed limit control method, the variable speed limit control device, the computer equipment, the storage medium and the computer program product based on the driving style, the first driving risk information before the speed limit control information is executed by the vehicle on the target road section and the second driving risk information after the speed limit control information is executed by the vehicle are obtained; obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information; according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, the speed limit control information of the vehicle is updated iteratively to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section. By adopting the method, speed limit guidance can be carried out according to different driving style types of vehicles, risk change information is used as a reward value of the iterative update process of speed limit control information, speed discrete loss information is used as a correction factor of the iterative update process, the problem of large speed discreteness among vehicles caused by individualized variable speed limit among vehicles is solved, the robustness of the method is high, and the speed limit effect of each vehicle on a road is improved.
Drawings
FIG. 1 is a diagram of an application environment of a driving style based variable speed limit control method in one embodiment;
FIG. 2 is a schematic flow chart illustrating a driving style-based variable speed limit control method according to one embodiment;
FIG. 3 is a schematic diagram of sending target speed limit control information to vehicles on a road in one embodiment;
FIG. 4 is a flowchart illustrating a first driving risk information step before a vehicle executes speed limit control information on a target road segment in one embodiment;
FIG. 5 is a schematic diagram illustrating particle swarm optimization for speed limit control information of a vehicle in one embodiment;
FIG. 6 is a schematic diagram of clustering driving style types in one embodiment;
FIG. 7 is a flowchart illustrating a driving style-based variable speed limit control method according to another embodiment;
FIG. 8 is a flowchart illustrating a driving style based variable speed limit control method in yet another embodiment;
fig. 9 is a schematic diagram of an iterative update process of target speed limit control information in one embodiment;
FIG. 10 is a block diagram illustrating an exemplary configuration of a variable speed limit control apparatus based on a driving style;
FIG. 11 is a diagram illustrating an internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The variable speed limit control method based on the driving style provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the vehicle data collection device 104 via a network. The data storage system may store data that the terminal 102 needs to process, and the data storage system may also be integrated on a server, or may be placed on a cloud or other network server. The vehicle data acquisition device 104 is used for acquiring the position and the driving track data of the vehicle on the road and sending the data to the terminal 102. The terminal 102 acquires first driving risk information before the vehicle executes the speed limit control information on the target road section and second driving risk information after the vehicle executes the speed limit control information; obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information; iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers. The vehicle data collection device 104 may be, but is not limited to, a millimeter wave radar.
In one embodiment, as shown in fig. 2, a driving style-based variable speed limit control method is provided, which is illustrated by being applied to the terminal in fig. 1, it is to be understood that the method can also be applied to a server, and can also be applied to a system comprising the terminal and the server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step S201, first driving risk information before the vehicle executes the speed limit control information on the target road section and second driving risk information after the vehicle executes the speed limit control information are obtained.
Specifically, the vehicle data acquisition device acquires vehicle trajectory data of lane levels of each vehicle on a road in real time and transmits the vehicle trajectory data to the terminal. The frequency of the collection and data transmission of the vehicle data acquisition equipment is at least millisecond level, the positioning precision of the position of the vehicle is at least decimeter level, and the vehicle data acquisition equipment can monitor the position and the speed of the vehicle under all-weather and various meteorological conditions. Carrying out data preprocessing on the vehicle track data, and extracting track characteristic parameters such as speed, acceleration change rate, transverse offset, headway, lane change frequency and the like of the vehicle; and obtaining the driving risk information of the vehicle in each continuous time period according to the track characteristic parameters of the vehicle on the target road section acquired in real time.
Further, first driving risk information of the vehicle in a preset time period before execution is calculated according to the track characteristic parameter and the historical risk mean value of the vehicle before execution of the speed limit control information, and second driving risk information of the vehicle in the preset time period after execution is calculated according to the track characteristic parameter and the historical risk mean value of the vehicle after execution of the speed limit control information after the vehicle executes the received speed limit control information.
And S202, obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information.
The first driving risk information and the second driving risk information respectively refer to driving risks of the vehicle before and after the speed limit control information is executed.
The risk change information refers to the change condition of the influence of the speed limit control information on the driving risk of the vehicle.
Specifically, the terminal obtains unit risk change information of the vehicle according to the difference between the first driving risk information and the second driving risk information of the vehicle; and then obtaining the risk change information of the speed limit control information according to the unit risk change information of all vehicles executing the same speed limit control information on the road.
Step S203, according to the risk change information, the speed discrete loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle, the speed limit control information of the vehicle is updated iteratively to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section.
The driving style of the vehicle refers to a driving style of the driver when driving the vehicle. The driving style comprises a following style, a lane changing style, an acceleration and deceleration style and the like.
The speed limit control information is initialization setting information for performing variable speed limit guidance for each vehicle.
Specifically, the terminal clusters the acquired vehicle track data of each vehicle on the target road section in advance to obtain a driving style clustering result. Determining a parameter value interval to which vehicle track data of the vehicle belongs according to the parameter value interval of each driving style type in the driving style clustering result to obtain a driving style type corresponding to the vehicle; further obtaining an update termination condition corresponding to the speed limit control information of the vehicle according to the preset update termination condition of each driving style type; and taking the risk change information and the speed dispersion loss information of the speed limit control information as an objective function, iteratively updating the speed limit control information of the vehicle based on an optimization algorithm, and terminating the iterative updating of the speed limit control information when an updating termination condition corresponding to the speed limit control information of the vehicle is met to obtain the target speed limit control information of the vehicle on a target road section.
The optimization algorithm can be a particle swarm optimization algorithm, a simulated annealing algorithm, a genetic algorithm and an ant colony algorithm.
In practical applications, the speed dispersion loss information is labeled as σ v This can be obtained by the following formula:
wherein σ i Representing the speed standard deviation of the vehicle i on the target road section, and n represents the number of vehicles on the target road section.
Fig. 3 is a schematic diagram of transmitting target speed limit control information to vehicles on a Road, and as shown in fig. 3, the target speed limit control information is transmitted to a vehicle-mounted terminal of a corresponding vehicle or a user terminal of a driver through a Road Side Unit (RSU), so that targeted speed limit guidance is performed on vehicles of different driving styles. It should be noted that, in addition to updating the target speed limit control information on line, the steps S201 to S203 may be performed in an off-line environment in advance to obtain target speed limit control information of different driving styles, and a variable speed limit policy library is generated according to the target speed limit control information of different driving styles; when a vehicle runs to a target road section, acquiring vehicle track data of the vehicle and sending the vehicle track data to a terminal (or a server), wherein the terminal obtains the driving style type of the vehicle according to the vehicle track data of the vehicle and obtains driving risk information of the vehicle; and when the driving risk information of the vehicle is greater than a preset driving risk threshold value, sending target speed limit control information matched with the driving style type of the vehicle in the variable speed limit strategy library to a road side unit, and forwarding the target speed limit control information to the vehicle by the road side unit.
In the variable speed limit control method based on the driving style, first driving risk information before the speed limit control information is executed by the vehicle on the target road section and second driving risk information after the speed limit control information is executed by the vehicle are obtained; obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information; iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section. By adopting the method, speed limit guidance can be carried out according to different driving style types of vehicles, risk change information is used as a reward value of the iterative update process of speed limit control information, speed discrete loss information is used as a correction factor of the iterative update process, the problem of large speed discreteness among vehicles caused by individualized variable speed limit among vehicles is solved, the robustness of the method is high, and the speed limit effect of each vehicle on a road is improved.
In one embodiment, as shown in fig. 4, in step S201, the first driving risk information before the vehicle executes the speed limit control information on the target road segment is acquired, which specifically includes the following contents:
step S401, obtaining a first relative driving risk before the speed limit control information is executed by the vehicle according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration in a first observation time period before the speed limit control information is executed by the vehicle.
Wherein, the first relative driving risk refers to the relative driving risk of the vehicle before executing the speed limit control information.
Wherein relative driving risk refers to the quasi-relative driving risk exposure of the vehicle. And the relative driving risk is obtained by comparing the current relative risk exposure estimation with the historical exposure estimation mean value through a statistical model.
Specifically, according to the vehicle head position information of the vehicle, the instantaneous speed of the vehicle and the length of the vehicle body in the vehicle track data, and the vehicle head position information of the front vehicle and the instantaneous speed of the front vehicle of the vehicle, the collision time of the vehicle relative to the front vehicle is obtained; when the collision time is smaller than a preset time threshold, the vehicle is considered to have dangerous behaviors; and taking the time length of the dangerous behavior existing in the observation period as the dangerous behavior time length.
In practical applications, the dangerous behavior can be obtained by the following formula:
wherein, TTC i Representing the collision time of the vehicle relative to the front vehicle at the moment t, and X is the position of the head of the vehicle because the vehicle position acquired by data is the position of the head of the vehicle i (t) indicates the head position of the vehicle at time i, X h (t) the head position of the vehicle h ahead of the vehicle at time i, l h Showing the body length, V, of the preceding vehicle h i (t) represents the instantaneous speed of the vehicle, V, at time t h (t) represents the instantaneous speed of the vehicle h ahead of time t, 2.0s represents a preset time threshold, and the preset time threshold may be any value other than 2.0 s.
Further, the terminal divides the product of the driving risk of the vehicle in the first observation time period before the vehicle executes the speed limit control information and the dangerous behavior duration before the vehicle executes the speed limit control information by the product of the historical vehicle average driving risk of the target road section and the historical vehicle average dangerous behavior duration of the target road section to obtain a first relative driving risk before the vehicle executes the speed limit control information.
In practical application, the relative driving risk may be a first relative driving risk or a second relative driving risk; marking relative driving risks as
This can be obtained by the following formula:
wherein, F M And F C Respectively representing the historical driving risk of the vehicle in the target road section and the driving risk of the vehicle in the vehicle observation time period, N M And N C The average dangerous behavior duration of the historical vehicles in the target road section and the dangerous behavior duration of the vehicles in the vehicle observation time period are respectively.
It should be noted that the vehicle observation period may be a first observation period or a second observation period, the first relative driving risk is obtained by calculating corresponding data in the first observation period, and the second relative driving risk is obtained by calculating corresponding data in the second observation period.
Step S402, obtaining first driving risk information according to the expectation of the first relative driving risk.
Specifically, the terminal performs expected processing on the first relative driving risk to obtain first driving risk information of the vehicle.
In practical application, the driving risk information may be first driving risk information or second driving risk information; marking driving risk information as Q i This can be obtained by the following formula:
in this embodiment, the terminal compares the driving risk and the dangerous behavior duration in the first observation period before the speed-limiting control information is executed by the vehicle with the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration in the target road section to obtain a first relative driving risk before the speed-limiting control information is executed by the vehicle, and then obtains the first driving risk information according to the expectation of the first relative driving risk, so that the driving risk before the speed-limiting control information is executed by the vehicle can be reasonably obtained.
In an embodiment, in step S302, the first driving risk information is obtained according to the expectation of the first relative driving risk, which specifically includes the following contents: obtaining driving risk expectation of the vehicle before executing speed limit control information on a target road section according to the lane changing risk mean value of the vehicle in the first observation time period and the driving risk expectation of the vehicle in the per-person observation time period; obtaining the expectation of the dangerous behavior duration before the vehicle executes the speed limit control information on the target road section according to the following risk average value of the vehicle in the first observation period and the expectation of the dangerous behavior duration of the vehicle in the human observation period; obtaining the expectation of the historical average driving risk of the vehicles at the target road section according to the historical average vehicle lane changing risk mean value of the target road section and the average historical driving risk expectation of the vehicles per capita at the target road section; obtaining an expectation of the time length of the average dangerous behaviors of the historical vehicles of the target road section according to the average following risk value of the historical vehicles of the target road section and the expectation of the time length of the average dangerous behaviors of the historical vehicles of the target road section; and obtaining first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, and the historical driving risk average expectation and the historical dangerous behavior duration expectation of the target road section.
The lane change risk refers to the proportion of the time length of dangerous behaviors of the vehicle in the lane change process. The following risk refers to the proportion of the time that the vehicle is in dangerous behavior during the following process.
Specifically, the terminal multiplies the lane change risk mean value of the vehicle in the first observation period and the driving risk expectation of the vehicle in the per-person observation period to obtain the driving risk expectation of the vehicle before the speed limit control information is executed on the target road section; multiplying the following risk mean value of the vehicle in the first observation period and the dangerous behavior duration expectation of the vehicle in the human-average observation period to obtain the dangerous behavior duration expectation of the vehicle before the speed limit control information is executed on the target road section; multiplying the historical vehicle average lane change risk average value of the target road section and the average human historical vehicle driving risk expectation of the target road section to obtain the historical vehicle average driving risk expectation of the target road section; multiplying the historical vehicle following risk average value of the target road section and the expectation of the average human-vehicle dangerous behavior duration of the target road section to obtain the expectation of the average historical vehicle dangerous behavior duration of the target road section; and finally, obtaining first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, and the historical driving risk average expectation and the historical dangerous behavior duration expectation of the target road section.
In practical application, due to driving risk information
The following formula can be obtained:
suppose that:
E(F C )＝Q C E(E C )
E(F M )＝Q M E(E M )
wherein Q is C Representing the mean value of the risk of lane change of the vehicle during the observation period of the vehicle, E C Representing the driving risk of the vehicle in the observation period of the average person, P C Mean value of the risk of following a vehicle during a vehicle observation period, E C * Representing the duration of dangerous behaviour of the vehicle in the human-observer interval, Q M Mean value of historical vehicle lane change risk representing target road section, E M Representing the per-person historical per-vehicle driving risk of the target road section, P M Mean value of historical vehicle-to-vehicle following risk representing target road section, E M * And representing the time length of the man-to-all historical vehicle-to-all dangerous behavior of the target road section.
It should be noted that the vehicle observation time period may be a first observation time period or a second observation time period, the first driving risk information is obtained by calculation through corresponding data in the first observation time period, and the second driving risk information is obtained by calculation through corresponding data in the second observation time period.
The following formula can be obtained:
in the embodiment, the terminal obtains the first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, the historical vehicle average driving risk expectation and the historical vehicle average dangerous behavior duration expectation of the target road section, so that the driving risk before the speed limit control information is executed on the vehicle is reasonably evaluated, and the accuracy of the subsequent speed limit control information updating process is improved.
In one embodiment, before obtaining the second driving risk information after the vehicle executes the speed limit control information, the method further comprises the following steps: judging whether the first driving risk information of the vehicle is larger than a preset driving risk threshold value or not; when the first driving risk information of the vehicle is larger than a preset driving risk threshold value, sending speed limit control information to the vehicle; the speed limit control information is used for indicating the vehicle to execute corresponding speed limit information.
The driving risk threshold is used for judging whether the speed limit control information, the target speed limit control information obtained after updating the speed limit control information, the information obtained after updating the target speed limit control information and the like are sent to the corresponding vehicle. The driving risk threshold may be 3.0.
Specifically, the terminal calculates first driving risk information of the vehicle before speed limit control information is executed according to vehicle track data transmitted by the vehicle data acquisition equipment in real time; judging whether the first driving risk information of the vehicle is larger than a preset driving risk threshold value or not; and when the first driving risk information of the vehicle is greater than the preset driving risk threshold value, the speed limit control information is sent to the vehicle so as to indicate the vehicle to execute the speed limit information in the speed limit control information.
In the embodiment, the terminal calculates the driving risk of the vehicle according to the vehicle track data transmitted by the vehicle data acquisition equipment in real time, and when the driving risk of the vehicle is greater than a preset vehicle risk threshold value, the speed limit control information is sent to the vehicle so as to remind a driver of limiting the speed, so that the driving safety of the driver is improved.
In an embodiment, in step S201, the second driving risk information after the vehicle executes the speed limit control information is acquired, which specifically includes the following contents: obtaining a second relative driving risk after the speed limit control information is executed by the vehicle according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section and the driving risk and the dangerous behavior duration of the vehicle in a second observation period after the speed limit control information is executed by the vehicle; and obtaining second driving risk information according to the expectation of the second relative driving risk.
Specifically, the terminal divides the product of the driving risk of the vehicle in the second observation period after the vehicle executes the speed limit control information and the dangerous behavior duration before the vehicle executes the speed limit control information by the product of the historical vehicle average driving risk of the target road section and the historical vehicle average dangerous behavior duration of the target road section to obtain a second relative driving risk of the vehicle before the vehicle executes the speed limit control information. And carrying out expected processing on the second relative driving risk to obtain second driving risk information of the vehicle.
The second relative driving risk calculation method is similar to the relative driving risk calculation method shown in the embodiment of step S301
The calculation modes are the same, but the specific values of the parameters are different; second traveling craneThe risk information is based on the risk information ≥ based on the risk information shown in the embodiment of step S302 above>
The calculation methods are the same, but the specific values of the parameters are different.
In this embodiment, the terminal compares the driving risk and the dangerous behavior duration in the second observation period after the speed limit control information is executed by the vehicle with the historical average driving risk and the historical average dangerous behavior duration of the vehicle in the target road section to obtain a second relative driving risk before the speed limit control information is executed by the vehicle, and then obtains second driving risk information according to the expectation of the second relative driving risk, so as to reasonably obtain the driving risk after the speed limit control information is executed by the vehicle.
In an embodiment, in step S202, the risk change information of the speed limit control information is obtained according to a difference between the first driving risk information and the second driving risk information, and the method specifically includes the following steps: summing differences between the first driving risk information and the second driving risk information of all vehicles on the target road section to obtain a risk change sum of all vehicles on the target road section; and obtaining the risk change information of the speed limit control information according to the risk change sum and the number of vehicles in the target road section.
Specifically, the terminal can obtain unit risk change information of the vehicle according to the difference between the first driving risk information and the second driving risk information of the vehicle, and then the terminal obtains the unit risk change information of the vehicle executing the same speed limit control information on the target road section; summing unit risk change information of vehicles executing the same speed limit control information on the target road section to obtain the risk change sum of the vehicles executing the same speed limit control information on the target road section; and averaging the risk change sum according to the number of vehicles in the target road section to obtain the risk change information of the speed limit control information.
In practical application, the unit risk variation information of the vehicle is marked as E (mu) i ) This can be obtained by the following formula:
wherein i represents the ith vehicle on the target road segment,
indicates a first vehicle risk information, based on the status of the vehicle, of the i-th vehicle>
And second driving risk information of the ith vehicle is represented.
Marking risk change information of the speed limit control information as E (mu) p ) This can be obtained by the following formula:
where n represents the number of vehicles in the target link.
In this embodiment, the terminal sums the differences between the first driving risk information and the second driving risk information of all vehicles executing the same speed limit control information on the target road segment to obtain the total risk change of all vehicles executing the same speed limit control information on the target road segment, and obtains the risk change information of the speed limit control information according to the total risk change and the number of vehicles on the target road segment, so that the reasonable evaluation of the risk change information is realized, the speed limit control information is favorably updated according to the risk change information in the following process, the speed limit effect of each vehicle on the road is improved, and the driving safety of a driver is improved.
In an embodiment, the step S203 specifically includes the following steps: determining the driving style type of the vehicle according to the longitudinal speed, the transverse speed, the acceleration and deceleration of the vehicle and the driving style type in the driving style clustering result; taking risk change information and speed dispersion loss information as objective functions, and performing particle swarm optimization on the speed limiting control information of the vehicle to obtain updated speed limiting control information; and when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming that the updated speed limit control information is the target speed limit control information.
For example, the updating termination conditions of different driving style types are different, taking the target road segment as an expressway as an example, where:
(1) Lane change aggressive following conservative type: the lane change is limited and the lane control is carried out in the variable speed limit guiding process of the target speed limit control information, so that the high-risk lane change of a driver is avoided; (2) the following drive is changed into a conservative type: setting that the real-time speed limit value in the target speed limit control information cannot be lower than the running speed of the vehicle minus 15km/h, and avoiding the risk generated in the following process of a driver; (3) following drive changing lane excitation type: the method comprises the steps that in the variable speed limit guiding process of target speed limit control information, a vehicle is limited to change lanes and lane control is carried out, and meanwhile, a real-time speed limit value cannot be lower than the running speed of the vehicle minus 15km/h; (4) follow conservative switch conservative types: without limitation; (5) acceleration and deceleration aggressive type: the variable speed limit gradient of the limited vehicle cannot exceed 5km/h and the speed change period cannot exceed 2min in the variable speed limit guiding process of the target speed limit control information, so that the collision risk of the driver on the front vehicle and the rear vehicle caused by rapid acceleration and rapid deceleration is avoided; (6) conservative type of acceleration and deceleration: no optimal limit condition is set for the gradient of the speed change and the period of the speed change of the vehicle.
Specifically, according to the parameter value intervals of each driving style type in the driving style clustering result, determining the parameter value intervals to which the longitudinal speed, the transverse speed and the acceleration and deceleration of the vehicle belong to obtain the driving style type corresponding to the vehicle; taking the difference value of the risk change information and the speed dispersion loss information of the speed limit control information as a target function, and performing particle swarm optimization on the speed limit control information of the vehicle to obtain updated speed limit control information; the speed limit control information of the vehicle can be iteratively updated by using other optimization algorithms besides the particle swarm optimization mode. And when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming that the updated speed limit control information is the target speed limit control information.
It should be noted that, in addition to performing particle swarm optimization on the speed limit control information of the vehicle, simulated annealing may be performed on the speed limit control information of the vehicle, ant colony processing may also be performed on the speed limit control information of the vehicle, and certainly, iterative updating may also be performed on the speed limit control information of the vehicle through other optimization algorithms.
Fig. 5 is a schematic diagram of particle swarm optimization performed on speed limit control information of a vehicle. As shown in fig. 5, risk change information E (μ) p ) And velocity dispersion loss information σ v The difference value of (a) is used as a target optimization function and is input into the particle swarm optimization algorithm model. Initializing a particle swarm, and initializing the speed and the position of the particles by setting the swarm size to be N. And setting a fitness function, and calculating the fitness of each particle through the fitness function. And respectively storing the optimal position and the optimal fitness undergone by each particle and the optimal position and the optimal fitness of the population.
And comparing the fitness of each particle with the fitness Pi of the historical optimal point, and if the fitness of the particle is superior to the historical optimal point, taking the current position as the historical optimal position of the particle, and simultaneously, taking the fitness of the particle as the historical optimal fitness so as to search the individual optimal.
The position and velocity of the particles are updated according to the following:
wherein the acceleration factor c 1 Adjusting the maximum step size of the individual optimum position flight, c 2 And adjusting the maximum step length of the global optimal position flight. If the acceleration factor value is too large, the particles can fly out of the target area rapidly, and if the acceleration factor value is too small, the particles can reachThe target area is too slow. Suitably c 1 And c 2 The value can improve the convergence rate of the particle swarm algorithm and avoid falling into local optimization. c. C 1 And c 2 The value is [0,4 ]]Meanwhile, the present embodiment is set to 2.
Representing the influence of the speed left by the previous generation of the particle on the flight behavior of the particle, and being regarded as the inertia of the particle; the second section->
The method is characterized in that the method comprises the following steps that (1) individual cognition is carried out, particles are searched towards the direction of the optimal solution searched by the particles, and the learning of the particles on the past experience of the particles is represented; the third section->
The method belongs to social cognition, and the particles learn from other particles in a population for reference and represent the approval of the particles on the overall search condition of the population.
The adaptive value corresponding to the historical optimal position of each particle and the population optimal fitness P g And comparing, and if the comparison is better, updating the historical optimal position of the population to adapt to the history so as to find the global optimal.
If the updating termination condition is met, the updating is terminated, otherwise, the updating steps of the positions and the speeds of the particles are circulated, and the obtained optimal positions of the particles are used as target speed limit control information.
In the embodiment, the risk change information is used as the reward value of the iterative update process of the speed limit control information, and the speed discrete loss information is used as the correction factor of the iterative update process, so that the problem of high speed discreteness among vehicles caused by individualized variable speed limit among the vehicles is solved, the speed limit effect of each vehicle on a road is improved, and the driving safety of a driver is improved.
In one embodiment, before determining the driving style type to which the vehicle belongs according to the longitudinal speed, the lateral speed, the acceleration, deceleration and the driving style type in the driving style clustering result, the method further comprises: according to the longitudinal speed of each vehicle on the target road section, carrying out following style clustering on each vehicle on the target road section to obtain a clustering result of the following style of each vehicle; according to the transverse speed of each vehicle on the target road section, lane changing style clustering is carried out on the clustering results of the following style of each vehicle, and the clustering results of the following style and the lane changing style of each vehicle are obtained; and dividing the clustering results of the following and lane changing styles of each vehicle according to a preset threshold value of acceleration and deceleration to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle as the driving style clustering results.
The clustering mode may be K-means clustering, or other clustering modes.
Wherein, the longitudinal speed refers to a characteristic parameter involved in the following process of the vehicle; the longitudinal speed comprises a transverse speed absolute value mean value, a transverse speed standard deviation, a transverse acceleration absolute value mean value and a transverse acceleration standard deviation.
Wherein, the transverse speed refers to a characteristic parameter related to the vehicle in the lane changing process; the transverse speed comprises a transverse speed absolute value mean value, a transverse speed standard deviation, a transverse acceleration absolute value mean value and a transverse acceleration standard deviation.
Specifically, fig. 6 is a schematic diagram of clustering driving style types, as shown in fig. 6, extracting trajectory feature parameters based on real-time vehicle trajectory data includes: the average value of absolute values of transverse speed, the standard deviation of transverse and longitudinal speeds, the maximum value of longitudinal speed, the average value of absolute values of transverse and longitudinal accelerations, the standard deviation of transverse and longitudinal accelerations, the average value of absolute values of transverse and longitudinal Jerk, the standard deviation of transverse and longitudinal Jerk, the average value of headway time, the standard deviation of headway time, and the frequency of lane change.
According to the Z-score method, carrying out abnormal value identification on the track characteristic parameters to obtain abnormal values higher than a parameter threshold value, and deleting the abnormal values; wherein the parameter threshold may be 2.0. And carrying out normalization processing on each track characteristic parameter, and normalizing the track characteristic parameters to the range of [ -1,1 ].
Based on a clustering method, clustering the following characteristic parameters of each vehicle on a target road section, namely clustering the longitudinal speed standard deviation, the longitudinal speed maximum value, the longitudinal acceleration absolute value mean value, the longitudinal acceleration standard deviation, the longitudinal Jerk absolute value mean value, the longitudinal Jerk standard deviation, the headway mean value and the headway standard deviation to obtain 2 types of following driving excitation and following conservation driving style, and using the types as the clustering result of the following style of each vehicle.
On the basis of the 2 types of the driving style of the follow-up driving and the conservative follow-up driving, the clustering result of the follow-up style of each vehicle is subjected to lane change style clustering according to the lane change characteristic parameters of each vehicle on a target road section, namely, the driving style types are divided into 4 types of lane change driving conservative follow-up driving, lane change aggressive follow-up driving and lane change conservative follow-up driving of each vehicle according to the transverse speed absolute value mean value, the transverse speed standard deviation, the transverse acceleration absolute value mean value, the transverse acceleration standard deviation, the transverse Jerk absolute value mean value, the transverse Jerk standard deviation, the transverse deviation absolute value mean value, the transverse deviation standard deviation and the lane change frequency.
On the basis of the clustering results of the following and lane changing styles, the terminal further divides the driving style type of which the original acceleration and deceleration of the vehicle is greater than the threshold value of the preset acceleration and deceleration into acceleration and deceleration aggressive types; the driving style type of which the acceleration and deceleration is always less than or equal to a preset threshold value of the acceleration and deceleration is further divided into a conservative acceleration and deceleration type; wherein the preset threshold value of the acceleration and deceleration comprises 2m/s 2 (ii) a Finally, 8 types of driving styles are obtained, including: lane change rapid and lane change conservative (acceleration and deceleration rapid) type, lane change rapid and lane change rapid (acceleration and deceleration rapid) type, lane change conservative (acceleration and deceleration rapid) type, lane change rapid and lane change conservative (acceleration and deceleration conservative) type. The 8 types of driving style types are used as the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle, namely the driving style clustering results.
In the embodiment, the terminal firstly carries out following style clustering on each vehicle on the target road section according to the following characteristic parameters of each vehicle on the target road section to obtain the clustering result of the following style of each vehicle; then according to the lane change characteristic parameters of all vehicles on the target road section, lane change style clustering is carried out on the following style clustering results of all vehicles to obtain the following and lane change style clustering results of all vehicles; and finally, according to a preset threshold value of acceleration and deceleration, dividing the clustering results of the following and lane changing styles of each vehicle to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle, wherein the clustering results are used as the driving style clustering results, so that the driving styles of drivers of the vehicles are accurately classified, and further the speed-limiting guidance can be performed according to different driving style types of the vehicles, thereby improving the speed-limiting effect of each vehicle on the road.
In one embodiment, the speed limit control information includes initial speed limit information, an initial speed change period, and an initial speed change gradient; the target speed limit control information includes target speed limit information, a target speed change period, and a target speed change gradient.
Wherein, the speed limit information refers to the speed limit of the vehicle. The speed change period refers to a time period limit for the vehicle speed to reach the speed limit information. The speed change gradient comprises a time gradient and a speed gradient, wherein the speed gradient refers to the change gradient for limiting the speed of the vehicle and is used for instructing a driver to reduce the speed to speed limit information according to the speed gradient; the time gradient refers to a gradient of change in the limit speed gradient over time, for instructing the driver to adjust the speed gradient in accordance with the time gradient.
Specifically, the speed limit control information refers to speed limit information obtained by initializing and setting speed limit control information of each vehicle by the terminal. Namely, the initial speed limit information, the initial speed change period and the initial speed change gradient are respectively initialized. For example, the initial speed limit information is set to 100km/h, the initial speed change period is set to 1min, and the initial speed change gradient is set to 5km/h. The target speed limit control information refers to that the terminal performs iterative optimization on the speed limit control information of the vehicle to obtain the speed limit information.
For example, assuming that the current speed of the vehicle a is 100km/h, the target speed limit information is 80km/h, the target speed change period is 2min, the speed gradient in the target speed change gradient is 10km/h, and the time gradient in the target speed change gradient is 1min, the vehicle a needs to reduce the vehicle speed from 100km/h to 80km/h within 2min, but the vehicle a does not suddenly reduce the speed to 80km/h within tens of seconds, even seconds, which is disadvantageous to the safety of the driver, therefore, the present embodiment sets the target speed change gradient to guide the driver of the vehicle a to reduce the vehicle speed from 100km/h to 80km/h within 2min according to the gradient of reducing 10km/h per 1min, so as to improve the driving safety of the driver.
In this embodiment, the speed limit control information and the target speed limit control information include speed limit information, speed change period, and speed change gradient multi-level speed change information, so that the driver can be guided to perform reasonable speed change more comprehensively, and the safety of the driver in the speed change process is improved.
In one embodiment, as shown in fig. 7, another driving style-based variable speed limit control method is provided, which is exemplified by applying the method to a terminal, and includes the following steps:
step S701, obtaining a first relative driving risk before the speed limit control information is executed by the vehicle according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section and the driving risk and the dangerous behavior duration of the vehicle in a first observation time period before the speed limit control information is executed by the vehicle.
Step S702, obtaining first driving risk information according to the expectation of the first relative driving risk.
Step S703, determining whether the first driving risk information of the vehicle is greater than a preset driving risk threshold.
Step S704, when the first driving risk information of the vehicle is larger than a preset driving risk threshold value, sending speed limit control information to the vehicle; the speed limit control information is used for indicating the vehicle to execute corresponding speed limit information.
Step S705, obtaining a second relative driving risk after the vehicle executes the speed limit control information according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a second observation time period after the vehicle executes the speed limit control information.
And step S706, obtaining second driving risk information according to the expectation of the second relative driving risk.
Step S707, summing differences between the first driving risk information and the second driving risk information of all vehicles on the target road section to obtain a risk change sum of all vehicles on the target road section; and obtaining the risk change information of the speed limit control information according to the risk change sum and the number of vehicles in the target road section.
And step S708, according to the longitudinal speed of each vehicle on the target road section, performing following style clustering on each vehicle on the target road section to obtain a clustering result of the following style of each vehicle.
And step S709, according to the transverse speed of each vehicle on the target road section, performing lane changing style clustering on the clustering result of the following style of each vehicle to obtain the clustering result of the following and lane changing styles of each vehicle.
And step S710, dividing the clustering results of the following and lane changing styles of each vehicle according to a preset threshold value of acceleration and deceleration to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle as the driving style clustering results.
And step S711, determining the driving style type of the vehicle according to the longitudinal speed, the transverse speed, the acceleration and deceleration of the vehicle and the driving style type in the driving style clustering result.
And step S712, performing particle swarm optimization on the speed limiting control information of the vehicle by taking the risk change information and the speed dispersion loss information as objective functions to obtain updated speed limiting control information.
And step S713, when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming that the updated speed limit control information is the target speed limit control information.
The variable speed-limiting control method based on the driving style can not only conduct speed-limiting guidance aiming at different driving style types of vehicles, but also take risk change information as a reward value of an iterative updating process of speed-limiting control information and take speed discrete loss information as a correction factor of the iterative updating process, so that the problem of large speed discreteness among vehicles caused by individualized variable speed limiting among the vehicles is solved, the robustness of the method is high, and the speed-limiting effect of each vehicle on a road is improved.
In order to more clearly illustrate the driving style-based variable speed limit control method provided by the embodiment of the present disclosure, the driving style-based variable speed limit control method is specifically described below with a specific embodiment. In one embodiment, as shown in fig. 8, another driving style-based variable speed limit control method is provided, which includes the following specific steps:
(1) Collecting traffic data: the high-precision vehicle position sensor based on the millimeter wave radar acquires vehicle track data of each vehicle on a road, and performs side-end data processing on the vehicle track data to obtain processed data.
(2) Data processing: and the high-precision vehicle position sensor transmits the processed data to a terminal (or a server), and the terminal (or the server) performs cloud data processing on the processed data to obtain track characteristic parameters.
(3) And (3) speed limit control: and judging the driving style of the driver according to the track characteristic parameters to obtain the driving style type of the driver, determining target speed limit control information based on the driving style type of the driver, and generating a variable speed limit control strategy library according to the target speed limit control information.
(4) And releasing speed limit control information: and sending the target speed limit control information matched with the driving style type of the driver in the variable speed limit control library to a vehicle-mounted terminal of the vehicle or a user terminal of the driver through the RSU.
Specifically, real-time track data of a vehicle is utilized, the driving style of a driver is analyzed by the vehicle track, the driving style is divided into 8 types, driving risk judgment and evaluation criteria are constructed, a driving risk continuous evaluation method is established by combining a driving behavior change rule based on a quasi-relative risk exposure analysis method, an optimal variable speed limit control strategy is searched by adopting an improved particle swarm optimization algorithm according to the driving style of different drivers, the driving risk is used as an actual reward value, and road speed discreteness is used as a correction factor, and finally a variable speed limit control scheme adaptive to each driving style is established to form a control scheme strategy library.
Further, fig. 9 is a schematic diagram of an iterative update process of the target speed limit control information. As shown in fig. 9, the iterative update process of the target speed limit control information may be regarded as a closed-loop optimization process, and the terminal uses vehicle trajectory data of the vehicle as an input quantity, uses risk change information and speed dispersion loss information of the speed limit control information as control quantities, and performs judgment by the controller to update the speed limit control information to obtain updated speed limit control information corresponding to the driving style type; the updated speed limit control information is issued to a vehicle-mounted terminal or a user terminal through an RSU; acquiring risk change information and speed discrete loss information before and after the vehicle executes the updated speed limit control information through a feedback element, calculating a difference value between the driving risk information and a driving risk threshold value through the feedback element, and if the difference value is a positive number, continuously judging through a controller until the driving risk information is lower than the driving risk threshold value; updating the updated speed limit control information according to risk change information and speed dispersion loss information before and after the updated speed limit control information; and circulating the processes until the updating termination condition is reached, and outputting the target speed limit control information. The controlled object in fig. 9 refers to relevant parameters in the speed limit control information, such as target speed limit information, a target speed change period, and a target speed change gradient.
In the embodiment, vehicle track data are acquired through millimeter wave radar detection equipment fixed on the road side, real-time radar data are adopted, the detection precision is high, the detection speed is high, and the real-time perception of vehicle position information can be realized through distributed calculation; analyzing the driving style of a driver through vehicle track data, extracting track parameters such as speed, acceleration change rate, transverse offset, headway, lane change frequency and the like, dividing the driving style into 8 classes by utilizing a clustering method, and carrying out targeted speed limit guidance for each driving style; aiming at the driving styles of different drivers, the driving risk is used as an actual reward value, the speed dispersion of the road section is used as a correction factor, the improved particle swarm optimization algorithm is adopted to carry out iterative updating to obtain target speed limit control information, and finally a variable speed limit strategy library suitable for each driving style is established, so that the problem that the speed dispersion among individual vehicles is large due to personalized variable speed limit control is solved.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a driving style-based variable speed limit control device for realizing the driving style-based variable speed limit control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in one or more driving style-based variable speed limit control device embodiments provided below can be referred to the limitations on the driving style-based variable speed limit control method in the above, and details are not repeated herein.
In one embodiment, as shown in fig. 10, there is provided a driving style-based variable speed limit control apparatus 1000 including: driving risk obtaining module 1001, risk change obtaining module 1002 and speed limit control updating module 1003, wherein:
the driving risk obtaining module 1001 is configured to obtain first driving risk information before the vehicle executes the speed limit control information on the target road segment, and second driving risk information after the vehicle executes the speed limit control information.
The risk change obtaining module 1002 is configured to obtain risk change information of the speed limit control information according to a difference between the first driving risk information and the second driving risk information.
The speed limit control updating module 1003 is used for iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; the speed dispersion loss information is calculated based on the standard deviation of the speed of the vehicle on the target road section.
In one embodiment, the risk change obtaining module 1002 is further configured to obtain a first relative driving risk before the speed limit control information is executed by the vehicle according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road segment, and the driving risk and the dangerous behavior duration in a first observation period before the speed limit control information is executed by the vehicle; and obtaining first driving risk information according to the expectation of the first relative driving risk.
In one embodiment, the variable speed-limiting control device 1000 based on driving style further includes a first risk obtaining module, configured to obtain an expectation of driving risk of the vehicle before the vehicle executes the speed-limiting control information on the target road segment according to a lane-changing risk mean value of the vehicle in a first observation period and a driving risk expectation of the vehicle in a per-person observation period; obtaining an expectation of dangerous behavior duration before the vehicle executes speed limit control information on a target road section according to the following risk average value of the vehicle in a first observation time period and the expectation of the dangerous behavior duration of the vehicle in a human observation time period; obtaining the expectation of the historical vehicle average driving risk of the target road section according to the historical vehicle average lane changing risk mean value of the target road section and the average historical vehicle average driving risk expectation of the target road section; obtaining the expectation of the time length of the historical vehicle average dangerous behavior of the target road section according to the historical vehicle average following risk average value of the target road section and the expectation of the time length of the historical vehicle average dangerous behavior of the target road section; and obtaining first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, and the historical driving risk average expectation and the historical dangerous behavior duration expectation of the target road section.
In one embodiment, the variable speed-limiting control device 1000 based on driving style further includes a speed-limiting information issuing module, configured to determine whether the first driving risk information of the vehicle is greater than a preset driving risk threshold; when the first driving risk information of the vehicle is larger than a preset driving risk threshold value, sending speed limit control information to the vehicle; the speed limit control information is used for indicating the vehicle to execute corresponding speed limit information.
In one embodiment, the driving risk obtaining module 1001 is further configured to obtain a second relative driving risk after the speed limit control information is executed by the vehicle according to the historical average driving risk of the vehicle and the historical average dangerous behavior duration of the vehicle in the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a second observation period after the speed limit control information is executed by the vehicle; and obtaining second driving risk information according to the expectation of the second relative driving risk.
In one embodiment, the risk change obtaining module 1002 is further configured to sum differences between the first driving risk information and the second driving risk information of all vehicles on the target road segment to obtain a total risk change of all vehicles on the target road segment; and obtaining the risk change information of the speed limit control information according to the risk change sum and the number of vehicles in the target road section.
In one embodiment, the speed limit control updating module 1003 is further configured to determine a driving style type to which the vehicle belongs according to the longitudinal speed, the lateral speed, the acceleration/deceleration of the vehicle and the driving style type in the driving style clustering result; taking the risk change information and the speed dispersion loss information as target functions, and performing particle swarm optimization on the speed limit control information of the vehicle to obtain updated speed limit control information; and when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming the updated speed limit control information as the target speed limit control information.
In one embodiment, the driving style-based variable speed-limiting control device 1000 further includes a driving style clustering module, configured to perform following style clustering on each vehicle on the target road segment according to a longitudinal speed of each vehicle on the target road segment, so as to obtain a clustering result of the following style of each vehicle; according to the transverse speed of each vehicle on the target road section, lane changing style clustering is carried out on the clustering results of the following style of each vehicle, and the clustering results of the following style and the lane changing style of each vehicle are obtained; and dividing the clustering results of the following and lane changing styles of each vehicle according to a preset threshold value of acceleration and deceleration to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle as the driving style clustering results.
In one embodiment, the variable speed limit control device 1000 based on driving style further includes speed limit control information and target speed limit control information; the speed limit control information comprises initial speed limit information, an initial speed change period and an initial speed change gradient; the target speed limit control information includes target speed limit information, a target speed change period, and a target speed change gradient.
The various modules in the driving style based variable speed limit control apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 11. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a variable speed limit control method based on a driving style. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 11 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, carries out the steps in the method embodiments described above.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (13)
1. A variable speed limit control method based on driving style is characterized by comprising the following steps:
acquiring first driving risk information before a vehicle executes speed limit control information on a target road section and second driving risk information after the vehicle executes the speed limit control information;
obtaining risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
according to the risk change information, the speed dispersion loss information of the speed limit control information and the update termination condition matched with the driving style type of the vehicle, carrying out iterative update on the speed limit control information of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
2. The method of claim 1, wherein the obtaining of the first driving risk information of the vehicle before the speed limit control information is executed on the target road segment comprises:
obtaining a first relative driving risk before the vehicle executes the speed limit control information according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a first observation time period before the vehicle executes the speed limit control information;
and obtaining the first driving risk information according to the expectation of the first relative driving risk.
3. The method of claim 2, wherein said deriving the first driving risk information based on the first relative driving risk expectation comprises:
obtaining driving risk expectation of the vehicle before executing the speed limit control information on a target road section according to the lane changing risk mean value of the vehicle in the first observation time period and the driving risk expectation of the vehicle in the per-person observation time period;
obtaining the expectation of the dangerous behavior duration before the vehicle executes the speed limit control information on the target road section according to the following risk average value of the vehicle in the first observation period and the expectation of the dangerous behavior duration of the vehicle in the per-person observation period;
obtaining the expectation of the historical average driving risk of the vehicles at the target road section according to the historical average vehicle lane changing risk average value of the target road section and the average historical driving risk expectation of the vehicles per capita at the target road section;
obtaining the expectation of the historical vehicle average dangerous behavior duration of the target road section according to the historical vehicle average following risk average of the target road section and the expectation of the average human historical vehicle average dangerous behavior duration of the target road section;
and obtaining first driving risk information of the vehicle according to the driving risk expectation and the dangerous behavior duration expectation of the vehicle, and the historical driving risk average expectation and the historical dangerous behavior duration expectation of the target road section.
4. The method according to claim 1, characterized by, before acquiring second driving risk information after the vehicle executes the speed limit control information, further comprising:
judging whether the first driving risk information of the vehicle is larger than a preset driving risk threshold value or not;
when the first driving risk information of the vehicle is larger than the preset driving risk threshold value, the speed limit control information is sent to the vehicle; and the speed limit control information is used for indicating the vehicle to execute corresponding speed limit information.
5. The method according to claim 1, wherein the obtaining of the second driving risk information after the vehicle executes the speed limit control information comprises:
obtaining a second relative driving risk after the vehicle executes the speed limit control information according to the historical vehicle average driving risk and the historical vehicle average dangerous behavior duration of the target road section, and the driving risk and the dangerous behavior duration of the vehicle in a second observation time period after the vehicle executes the speed limit control information;
and obtaining second driving risk information according to the expectation of the second relative driving risk.
6. The method according to claim 1, wherein the obtaining the risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information comprises:
summing differences between first driving risk information and second driving risk information of all vehicles on the target road section to obtain a risk change sum of all vehicles on the target road section;
and obtaining the risk change information of the speed limit control information according to the risk change sum and the number of vehicles in the target road section.
7. The method of claim 1, wherein the iteratively updating the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information, and the update termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road segment comprises:
determining the driving style type of the vehicle according to the longitudinal speed, the transverse speed, the acceleration and deceleration of the vehicle and the driving style type in the driving style clustering result;
taking the risk change information and the speed dispersion loss information as target functions, and performing particle swarm optimization on the speed limiting control information of the vehicle to obtain updated speed limiting control information;
and when the updated speed limit control information meets the update termination condition matched with the driving style type of the vehicle, confirming that the updated speed limit control information is the target speed limit control information.
8. The method according to claim 7, before determining the driving style type to which the vehicle belongs according to the longitudinal speed, the lateral speed, and the driving style type in the addition-subtraction speed and driving style clustering result of the vehicle, further comprising:
according to the longitudinal speed of each vehicle on the target road section, carrying out following style clustering on each vehicle on the target road section to obtain a clustering result of the following style of each vehicle;
according to the transverse speed of each vehicle on the target road section, lane changing style clustering is carried out on the clustering results of the following style of each vehicle, and the clustering results of the following style and the lane changing style of each vehicle are obtained;
and dividing the clustering results of the following and lane changing styles of each vehicle according to a preset threshold value of acceleration and deceleration to obtain the clustering results of the following, lane changing and acceleration and deceleration styles of each vehicle as the driving style clustering results.
9. The method of claim 1, wherein the speed limit control information includes initial speed limit information, an initial speed change period, and an initial speed change gradient;
the target speed limit control information comprises target speed limit information, a target speed change period and a target speed change gradient.
10. A variable speed limit control apparatus based on a driving style, characterized in that the apparatus comprises:
the driving risk acquiring module is used for acquiring first driving risk information before the speed limit control information is executed by a vehicle on a target road section and second driving risk information after the speed limit control information is executed by the vehicle;
the risk change acquisition module is used for acquiring risk change information of the speed limit control information according to the difference between the first driving risk information and the second driving risk information;
the speed limit control updating module is used for carrying out iterative updating on the speed limit control information of the vehicle according to the risk change information, the speed dispersion loss information of the speed limit control information and the updating termination condition matched with the driving style type of the vehicle to obtain the target speed limit control information of the vehicle on the target road section; and the speed discrete loss information is calculated based on the speed standard deviation of the vehicle on the target road section.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 9 when executing the computer program.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 9.
13. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 9 when executed by a processor.
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