CN111332289B

CN111332289B - Vehicle operation environment data acquisition method and device and storage medium

Info

Publication number: CN111332289B
Application number: CN202010209201.8A
Authority: CN
Inventors: 侯琛
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-03-23
Filing date: 2020-03-23
Publication date: 2021-05-07
Anticipated expiration: 2040-03-23
Also published as: CN111332289A

Abstract

The application relates to a vehicle operation environment data acquisition method and device, computer equipment and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining road condition parameters, vehicle operation parameters and driving control parameters of an operating vehicle, obtaining operating accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, preset parameter standard values and pre-distributed parameter weight data, obtaining operating accident probability threshold values of operating road sections where the operating vehicle is located, sending operating environment data obtaining requests carrying positioning data when the operating accident probability is larger than the operating accident probability threshold values, and receiving operating environment data of the operating vehicle based on positioning data feedback. By adopting the method, the running vehicle can acquire the vehicle running environment data according to the actual situation, thereby avoiding useless data interaction and unnecessary data acquisition and improving the bandwidth utilization rate.

Description

Vehicle operation environment data acquisition method and device and storage medium

Technical Field

The application relates to the technical field of vehicle networking, in particular to a vehicle operation environment data acquisition method and device, computer equipment and a storage medium.

Background

With the development of the internet of vehicles technology, the interaction between the vehicle and the server plays an increasingly important role in vehicle driving control, for example, the vehicle is based on vehicle operating environment data, such as surrounding vehicle information, acquired from the server and fed back to the driver.

In the conventional technology, vehicles periodically request the server for vehicle operating environment data by polling to access the server, and this interactive mode requires that the vehicles periodically send requests to the server, but the data fed back by the server is not necessary data of the vehicles each time the requests are made, and the problem of serious bandwidth waste exists in the interactive process with the server.

Disclosure of Invention

In view of the above, it is necessary to provide a vehicle operating environment data acquisition method, apparatus, computer device, and storage medium capable of improving bandwidth utilization, in order to solve the technical problem of serious bandwidth waste.

A vehicle operating environment data acquisition method includes:

acquiring road condition parameters, vehicle operation parameters and driving control parameters of an operating vehicle;

obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, the preset standard values of all the parameters and pre-distributed parameter weight data;

acquiring an operation accident probability threshold value of an operation road section where an operation vehicle is located, and sending an operation environment data acquisition request carrying positioning data when the operation accident probability is greater than the operation accident probability threshold value;

and receiving the running environment data of the running vehicle based on the positioning data feedback.

A vehicle operating environment data acquisition apparatus, the apparatus comprising:

the parameter acquisition module is used for acquiring road condition parameters, vehicle operation parameters and driving control parameters of an operating vehicle;

the probability analysis module is used for obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, the preset standard values of all the parameters and pre-distributed parameter weight data;

the request sending module is used for obtaining an operation accident probability threshold value of an operation vehicle on an operation road section, and sending a vehicle operation environment data obtaining request when the operation accident probability is greater than the operation accident probability threshold value;

and the data receiving module is used for receiving the running environment data of the running vehicle based on the positioning data feedback.

A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

According to the vehicle operation environment data acquisition method, the vehicle operation parameter acquisition device, the computer equipment and the storage medium, the accident probability of the operating vehicle is analyzed by acquiring the road condition parameter, the vehicle operation parameter and the driving control parameter of the operating vehicle based on the parameter standard value and the parameter weight value data, and the time for sending the vehicle operation environment data acquisition request is further determined by comparing the accident probability threshold with the operation accident probability threshold of the operating road section, so that the operating vehicle can acquire the vehicle operation environment data according to the actual condition, useless data interaction and unnecessary data acquisition are avoided, and the bandwidth utilization rate is improved.

Drawings

FIG. 1 is a diagram of an exemplary implementation of a method for obtaining vehicle operating environment data;

FIG. 2 is a schematic flow chart diagram illustrating a method for obtaining vehicle operating environment data according to one embodiment;

FIG. 3 is a schematic flow chart diagram illustrating a vehicle operating environment data acquisition method according to still another embodiment;

FIG. 4 is a schematic flowchart of a vehicle operating environment data acquisition method according to another embodiment;

FIG. 5 is a schematic flowchart of a vehicle operating environment data acquisition method in yet another embodiment;

FIG. 6 is a schematic flowchart of a vehicle operating environment data acquisition method according to still another embodiment;

FIG. 7 is a schematic diagram of a scenario of a vehicle operating environment data acquisition method according to an embodiment;

FIG. 8 is a flowchart illustrating a vehicle operating environment data acquisition method according to another embodiment;

FIG. 9 is a block diagram showing the construction of a vehicle running environment data obtaining apparatus according to an embodiment;

FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further 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 vehicle operation environment data acquisition method provided by the application can be applied to the application environment shown in fig. 1. The vehicle 102 communicates with the server 104 via a network via a built-in processor. A processor in the vehicle 102 obtains road condition parameters, vehicle operating parameters, and driving control parameters for operating the vehicle. And obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, the preset standard values of all the parameters and the pre-distributed parameter weight data. And acquiring an operation accident probability threshold of an operation road section where the operation vehicle is located, and when the operation accident probability is greater than the operation accident probability threshold, sending an operation environment data acquisition request carrying positioning data to the server 104. The server 104 responds to the data acquisition request, determines running environment data of the running vehicle based on the positioning data, and feeds back the running environment data to a processor of the running vehicle. The vehicle 102 may be a motor vehicle of various sizes, and the server 104 may be implemented by a separate server or a server cluster composed of a plurality of servers.

In one embodiment, as shown in fig. 2, a vehicle operating environment data acquisition method is provided, which is described by taking a processor of the vehicle in fig. 1 as an example, and includes the following steps 210 to 240.

Step 210, obtaining road condition parameters, vehicle operation parameters and driving control parameters of the running vehicle.

The running vehicles refer to vehicles in running states, and with the development of the vehicle networking and the vehicle road cooperation network, running data of each vehicle is cooperated through the server in the running process of the vehicles, so that vehicle accidents can be effectively reduced or even avoided. In an embodiment, in a system formed by a vehicle networking network or a vehicle road cooperative network, the server may specifically be a cloud server, and the system may further include data acquisition devices disposed around a road, for example, a camera device having an image acquisition or video acquisition function, and the like.

In an embodiment, the road condition parameters, the vehicle operation parameters and the driving control parameters can be acquired through internal software installed in the vehicle or sensors arranged on the vehicle, so that data interaction with a server in the vehicle networking system is reduced, and the occupation of bandwidth is reduced.

Specifically, the road condition parameter refers to road condition data of a road section where the running vehicle is currently located. In one embodiment, the road condition parameters include one or a combination of more of lane width, road friction, road visibility, and vehicle density. The lane width refers to an actual width of a lane of a road section where the running vehicle is currently located, and the road friction refers to a friction coefficient between a road surface and a vehicle tire caused when the road surface humidity of the road is affected by weather conditions (such as sunny days, rainy days, snowy days, ice formation and the like). The road visibility refers to the visible distance of a running vehicle on a current road section when the visible distance of air is influenced by weather factors such as fog or haze. Vehicle density is a traffic parameter used to characterize the vehicle concentration on the road segment where the operating vehicle is currently located.

The vehicle operating parameters refer to vehicle condition data of the operating vehicle during operation. In an embodiment, the vehicle operating parameter comprises one or a combination of more of a vehicle type, a vehicle attitude, a vehicle speed, and a vehicle acceleration. The vehicle type is a parameter for describing the size of the vehicle, and the vehicle may be classified into a large-sized vehicle, a medium-sized vehicle, and a small-sized vehicle according to the length or weight of the vehicle. For example, in terms of the division according to the vehicle length, it is possible to divide a vehicle having a vehicle length of more than 6m into a large-sized vehicle, a vehicle having a vehicle length of more than 3.5m and not more than 6m into a medium-sized vehicle, and a vehicle having a vehicle length of not more than 3.5m into a small-sized vehicle. It is understood that in other embodiments, the classification category and classification standard of the vehicle type may be adjusted as needed, and are not limited herein. The vehicle attitude refers to a state of the vehicle during running. Such as normal driving, cornering, uphill or downhill. The vehicle speed refers to speed data of the vehicle during operation, and the acceleration of the vehicle refers to a rate of change of the speed of the vehicle during operation.

The driving control parameters refer to basic data of a current controller operating the vehicle. In an embodiment, the driving control parameter comprises one or a combination of two of a driving object reaction time and a duration driving time. The driving object is a driver for driving and controlling the vehicle, and it can be understood that the driver may be a real person, or a "robot" replaced by system automatic control, that is, corresponding to two different driving modes of manual driving and unmanned driving, the driving object reaction time is a time length for the driver to react to an unexpected situation.

And step 220, obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, the preset standard values of all the parameters and pre-distributed parameter weight data.

The preset standard value of each parameter refers to a reference value of each parameter obtained through data analysis under a standard condition, and the pre-distributed parameter weight data refers to proportion data distributed to each parameter based on the importance degree of each parameter. In an embodiment, the sum of the parameter weight data assigned to the respective parameters is 1.

In the embodiment, the operation accident probability of the running vehicle can be obtained by calculating the ratio of each parameter in the road condition parameters, the vehicle running parameters and the driving control parameters to the corresponding preset parameter standard value, then multiplying the ratio data by the corresponding parameter weight data, and finally accumulating the weight data calculation results of each parameter.

Step 230, acquiring an operation accident probability threshold of an operation road section where the operation vehicle is located, and sending an operation environment data acquisition request carrying positioning data when the operation accident probability is greater than the operation accident probability threshold;

the operation accident probability refers to the ratio of the number of times of operation accidents on the current road section to the traffic flow data on the current road section. In one embodiment, the specific data of the operation accident probability may be data published after data statistics by a traffic management department, and software that can interact with a server corresponding to the published data of the traffic management department is installed on the vehicle, so that the operation accident probability of each operation section can be obtained. Specifically, data of the traffic management department is generally periodically updated, the vehicle system is correspondingly periodically updated with the operation accident probability threshold of each operation road section, and when the vehicle runs, the processor on the vehicle can determine the current operation road section based on the positioning data, so as to read the operation accident probability threshold of the operation road section which is stored in advance.

Compared with the traditional polling server access mode, the data acquisition efficiency can be improved, the running vehicles can request the road condition information from the server according to the actual conditions, and the bandwidth utilization rate is improved.

And step 240, receiving the running environment data of the running vehicle based on the positioning data feedback.

And when receiving the operating environment data acquisition request, the server extracts the positioning information carried by the operating environment data acquisition request, and determines the operating environment data around the operating vehicle based on the positioning information. The operating environment data may specifically include stationary objects, moving vehicles, traffic indication signals, and the like within a preset safety range, and specifically may be data divided into three types of objects, where the first type of object is a stationary object that may collide with the vehicle, the second type of object is an object that does not collide with the vehicle at this time but is traffic signal data that may cause pressure on traveling of the vehicle, and the third type of object is a moving object such as a running vehicle.

In an embodiment, the preset safety range may specifically include a range of N meters at both sides of the vehicle and in front of and behind the vehicle. Stationary objects include, among other things, non-moving objects on and around a roadway, such as stationary vehicles, road edges, etc., that may pose a potential threat to the operation of a moving vehicle. The traffic signal data may be a fixed driving sign or a warning from another vehicle, such as a brake or the like of a preceding vehicle or a turn signal.

According to the vehicle operation environment data acquisition method, the accident probability of the operating vehicle is analyzed by acquiring the road condition parameters, the vehicle operation parameters and the driving control parameters of the operating vehicle based on the parameter standard values and the parameter weight data, and the time for sending the vehicle operation environment data acquisition request is further determined by comparing the accident probability threshold with the operation accident probability threshold of the operating road section, so that the operating vehicle can request the operation environment data of the operating vehicle according to the actual condition, useless data interaction and unnecessary data acquisition are avoided, and the bandwidth utilization rate is improved.

In one embodiment, as shown in fig. 3, after receiving the vehicle operating environment data based on the positioning data feedback, steps 310 to 340 are further included.

In step 310, vehicle data of the environmental vehicle in the vehicle operation environment data is extracted.

And 320, obtaining collision energy data of the running vehicle and the environmental vehicle according to the vehicle data of the environmental vehicle and the vehicle running parameters of the running vehicle.

Step 330, obtaining standard collision energy data obtained based on historical data analysis, and determining the collision probability of the running vehicle and the environmental vehicle according to the ratio of the collision energy data to the standard collision energy data.

And 340, generating a collision early warning message when the collision probability is greater than a preset collision probability threshold value.

The collision process between the two objects is actually the change of energy of the two objects, and it can be understood that based on the speed, the quality and other factors of the objects, the energy change when the collision between the two objects is assumed can be calculated, and then whether the calculation result of the energy change between the two objects reaches the energy of the critical value of the real collision between the two objects is judged, and further whether the two objects can collide and how large the probability of the collision is.

The collision energy data is energy variation corresponding to a potential collision possibility (assumed to be a collision) between the running vehicle and another environmental object calculated based on environmental factors due to influence of the environmental data. Whether a collision will occur may be determined based on whether the calculated energy change reaches the energy value of the standard collision energy data. Furthermore, the collision probability of the running vehicle and the environmental object can be determined according to the ratio of the collision energy data to the standard collision energy data, the value of the standard collision energy data is the value of the vehicle at the collision critical point, and the specific data can be obtained based on historical data or through analysis of ideal critical conditions.

Specifically, during the operation of the vehicle, when it is assumed that it collides with another object, the energy change thereof may be based on the gravitational field theory model (corresponding to the first term on the right side of equation (1), whose basic expression F ═ G × M₁×M₂)/R²) Spring potential energy model (corresponding to the second term on the right side of formula (1), and its basic expression is U-1/2 kx²) Doppler effect model (corresponding equation (2), basic expression f thereof_oAnalysis was performed based on (fs × v)/(v ± vs)).

The specific analysis process is as follows:

in the above calculation formula, the first term on the right side of formula (1) is collision energy data caused by a collision with the host vehicle by an object which is stationary and may collide with the host vehicle, and the second term on the right side is collision energy data caused by traffic signal data which is stationary and may not collide with the host vehicle but may give pressure to the traveling of the host vehicle. The formula (2) is collision energy data of the moving object to the vehicle.

M_a、M_bAnd M_jThe virtual masses are calculated based on the actual mass and the velocity of the object itself, and are respectively the virtual masses of the object a, the object b, and the object j, wherein the object a is a stationary object, the object b is a moving vehicle, and the object j is a host vehicle. r is_ajRefers to the displacement between object a and object j. r is_bjRefers to the displacement between object b and object j. R_a、R_b、R_jIs a parameter reflecting the road conditions of the object a, the object b and the object jThe number, which is related to the friction force of the object and the road, the gradient of the road, the camber of the road and the visibility of the road, is obtained based on the four parameters. DR (digital radiography)_jIs a parameter reflecting the responsiveness of the controller of object j, DR_bIs a parameter reflecting the responsiveness of the controller of the object b, DR_jAnd DR_bAssociated with the driving object reaction time and the duration of driving. D is lane width, G is gravitational constant, K₁、K₂、K₃Is a constant, in particular, K₁May be 3, or other values close to 3, such as 2.8, 3.3, etc., so as to satisfy K in the first term on the right side of the formula (1)₁-1 ≈ 2 to satisfy the basic expression F ═ G × M of the gravitational field theory model₁×M₂)/R². In the same way, K₂Can be 1 or other values close to 1 to satisfy K in the right-hand item 2 of the formula (1)₂+1 ≈ 2, and basically satisfies the basic expression U ═ 1/2kx of the spring potential energy model². In the same way, K₃The value of (A) may be 340 (light speed 340m/s), or other values close to 340, so as to substantially satisfy the basic expression f of the Doppler effect model_o＝(fs*v)/(v±vs)，V_bRefers to the speed of travel of the object. θ is the angle between the two objects, specifically the angle in the direction of the velocity between object b and object j.

Taking equation (2) as an example, when the traveling speed of object b and object j is higher, the distance between the objects is smaller, and the corresponding M is larger_a、M_bThe larger the value of (A), r_bjThe smaller the value of (b), the larger the final calculated value thereof, and the larger the collision energy representing the object b and the object j.

Through the calculation result of the formula (1), collision energy data of an environmental object which is not in motion in the environment and the running vehicle can be determined.

Through the calculation result of the formula (2), collision energy data of the environmental vehicle in the running state in the environment and the running vehicle can be determined.

By accumulating the calculation results of the above two formulas, collision energy data between the environment formed by the respective environmental objects in the environment and the running vehicle can be obtained, for example, 6 environmental vehicles are present within the safe distance of the running vehicle, but the running speed of each vehicle is relatively slow, and the probability of collision between each environmental vehicle and the running vehicle is relatively small by the above formula (2).

And determining the collision probability of the running vehicle and the environment vehicle by calculating the ratio of the collision energy data of the running vehicle and the environment vehicle to the standard collision energy data. By comparing the collision probability with the set collision probability threshold value, when the collision probability is greater than the preset collision probability threshold value, the collision early warning message is generated, the condition that the driver is frequently prompted of the vehicle driving risk can be avoided, the false alarm rate is reduced, and the user experience is improved.

In one embodiment, as shown in fig. 4, the road condition parameters include at least one of lane width, road friction, road visibility, and vehicle density. Taking the road condition parameters including lane width, road friction, road visibility and vehicle density as an example, obtaining the road condition parameters, vehicle operation parameters and driving control parameters of the running vehicle includes steps 410 to 440.

Step 410, acquiring image data acquired by the running vehicle at the current position.

And step 420, obtaining the width of the current lane of the running vehicle according to the image data, obtaining the road surface state and the visible distance of the current position of the running vehicle, and determining the road friction corresponding to the road surface state and the road visibility corresponding to the visible distance.

And step 430, identifying the number of vehicles in the image data to obtain the vehicle density.

In step 440, vehicle operating parameters and driving control parameters of the operating vehicle are obtained.

The road surface state includes a dry road surface state, a wet road surface state and an ice and snow road surface state. In other embodiments, because the road surface dryness degrees on the sunny day and the cloudy day are different, the dry road surface state can be further subdivided into a dry road surface state corresponding to the sunny day and a dry road surface state corresponding to the cloudy day, and the ice and snow road surface state can be subdivided into an accumulated snow road surface state and an frozen road surface state.

The image data collected by the running vehicle can be collected by a camera device arranged on the vehicle. The number of the camera devices can be multiple, and the camera devices can be arranged at different positions of the vehicle according to parameter acquisition requirements. For example, the data collecting direction of the camera for collecting the lane road surface is different from the data collecting direction of the camera for collecting the visible distance or the vehicle data, and the data can be collected by different camera devices, and the corresponding data can be obtained through different data analysis. In other embodiments, image data acquisition can be performed by a rotary camera device with controllable acquisition direction.

In one embodiment, the road condition parameters further include a road type, which is a type of a current road on which the vehicle is located, and specifically, the road type may be divided according to a traffic volume, divided according to a task, a function, and a flow rate of the road, and divided into five types, i.e., an expressway, a first-class highway, a second-class highway, a third-class highway, and a fourth-class highway.

In other embodiments, the road condition parameters further include lane curvature, lane gradient, and the like, corresponding to the vehicle attitude. The road camber measuring method comprises the steps of measuring road camber, measuring according to hardware arranged on a running vehicle, and specifically providing three methods, wherein the first method is to install an angle measuring sensor at a steering wheel or a front wheel, measuring the rotating angle of the steering wheel or the front wheel, the second method is to install a camera above the steering wheel, shooting the rotating angle of the steering wheel, the third method is to respectively install a rotating speed sensor on two wheels of a rear wheel, and measuring the rotating speed difference of an inner wheel and an outer wheel to obtain the variable quantity of the prop camber. The method can be used for measuring the road gradient (sinking and ascending), can be used for measuring by mounting a gyroscope (gravity acceleration sensor) or a level sensor or a probe locator in the vehicle, and can be used for determining road condition parameters such as lane curvature, lane gradient and the like based on the vehicle posture, so that the analysis of collision energy data can be conveniently carried out.

Each road condition parameter can be obtained through measurement and analysis, but under the ideal condition, each road condition parameter has a corresponding standard value.

Specifically, the standard value b of the lane width: on a specific road type, the standard value is the width that the road should have according to the state regulation.

Standard value c of road friction force: the road friction can be divided into four conditions, namely road friction in sunny days, road friction in rainy days, road friction in snowy days and road friction in icing days, and the friction coefficients corresponding to different weather conditions can be obtained through friction analysis between the road surface and vehicle tires in different weather conditions in historical data. The road friction coefficients in these four weather conditions are represented by u1, u2, u3 and u4, respectively, and the standard value of the road friction force may be taken as (u1+ u2+ u3+ u 4)/4.

Standard value s of road gradient: on a specific road, the standard value of the road gradient is taken as the road gradient that the country specifies for that road. Road camber standard value q: on a specific road, the standard value of the road curvature is taken as the road curvature of the road specified by the country. Standard value d of vehicle density: the distance between vehicles on the same lane should be a safety distance on the road prescribed by the country, and the vehicles on different lanes should not occupy the opposite lane. The number of vehicles on a lane within a distance (e.g., the number of vehicles per 100 meters of a road segment) is a standard density with the above requirements satisfied.

Standard value L of road visibility: according to the influence degree on the traffic, the visible distance is classified into 1 grade according to the visible distance, specifically, 1 grade is the visible distance of more than 200m and less than or equal to 500m, 2 grade is the visible distance of more than 100m and less than or equal to 200m, 3 grade is the visible distance of more than 50m and less than or equal to 100m, and 4 grade is the visible distance of less than or equal to 50 m. According to the grade corresponding to the visible distance obtained by actual measurement and analysis, if the L is actually measured_rAt levels 1, 2, 3, and 4, then L takes the upper bound of the corresponding range, 500, 200, 100, and 50.

In one embodiment, the acquiring the road surface state and the visible distance of the current position of the running vehicle comprises acquiring positioning data of the running vehicle, acquiring weather data corresponding to the position of the positioning data, and determining the road surface state and the visible distance according to the weather data.

In another embodiment, obtaining the road surface condition and the visible distance of the current position of the running vehicle comprises determining the type of the road surface condition and the visible distance through recognition and analysis of the image data according to the image data.

The road surface state includes a dry road surface state, a wet road surface state and an ice and snow road surface state. Specifically, the road surface condition may be determined based on the weather type. For example, the road surface is dry in sunny days or cloudy days, the road surface is wet in rain, the road surface is snow in snow, the road surface is frozen when the temperature reaches zero and the water is accumulated, and the like.

In the embodiment, the road friction can be specifically divided into four conditions, namely the road friction of a dry road surface, the friction of a wet and slippery road surface, the friction of an icy road surface and the friction of a snow road surface, and the friction coefficients corresponding to different weathers can be obtained through friction analysis between the road surface and vehicle tires in historical data under different weathers. In other embodiments, the friction force corresponding to the dry road surface can be further expanded and refined into two different conditions according to two typical weathers, namely sunny weather or cloudy weather, and the friction force corresponding to the specific weather can be obtained according to experiments.

In an embodiment, the road surface state can be determined by acquiring a road surface picture through a vehicle-mounted camera, and can also be determined according to a weather type in weather data, and the road friction force can be obtained based on the determined road surface state. The road visibility distance is similar to the road surface state. When the road visible distance is measured, the visibility data in weather forecast data issued by a meteorological department is accessed into a vehicle-mounted computer to be directly read and acquired, and the image data in the front view of a vehicle is shot by a vehicle-mounted camera and is acquired by analyzing the image.

It can be understood that the lane width, the vehicle type, the road visibility, the road friction and the vehicle density can be obtained by shooting and analyzing the road condition information with the slow change degree and the long duration through the camera in the vehicle.

In one embodiment, as shown in FIG. 5, the vehicle operating parameters include at least one of vehicle type, vehicle attitude, vehicle speed, and vehicle acceleration.

Taking the vehicle operation parameters including the vehicle type, the vehicle attitude, the vehicle speed, and the vehicle acceleration as an example, acquiring the road condition parameter, the vehicle operation parameter, and the driving control parameter of the operating vehicle includes steps 510 to 540.

And step 510, reading vehicle scale data prestored in the running vehicle, and determining the vehicle type.

And step 520, reading the attitude sensing data collected by the running vehicle, and determining the attitude of the vehicle.

Step 530, reading the vehicle speed of the running vehicle, and obtaining the vehicle acceleration according to the vehicle speed.

And 540, acquiring road condition parameters and driving control parameters of the running vehicle.

The vehicle type is determined based on vehicle scale data such as the length of the vehicle body, the weight of the vehicle body, and the like. The vehicle scale data may be stored in a vehicle memory system, or may be obtained based on collected measurement data of the vehicle.

The vehicle attitude comprises attitude sensing data collected by sensing equipment such as a rotating speed sensor, a gyroscope, a horizontal sensor or a probe locator and the like and is used for representing the attitude of a running vehicle, and specifically, the vehicle attitude comprises at least one of balance, turning, ascending and descending.

Vehicle speed v_rCan be directly obtained through the dial plate in the automobile. Acceleration a of vehicle_rThe vehicle acceleration can be obtained through measurement of a gyroscope accelerometer, and can also be obtained through calculation of the change rate of the vehicle speed in a period of time.

Standard value m of vehicle type: the proportion of each type of vehicle in the target area is counted, and the vehicles are classified into three types according to the mass or the length of the vehicle body, which are respectively marked as p1, p2 and p 3. Three types of vehicles are respectively marked as m1, m2 and m3 (the mass or the length of each type of vehicle meeting the national standard is specified, so the mass or the length of each type of vehicle is known, if the mass of each type of vehicle specified by the country is not a specific numerical value, but a range, the median of the range is taken), and the expected standard value m of the mass or the length is calculated to be p1 m1+ p2 m2+ p3 m 3.

Vehicle speed standard value v and vehicle acceleration standard value a: on a particular road, the standard value for vehicle speed is the legal speed on that road and the standard value for vehicle acceleration is the legal acceleration on that road.

The vehicle attitude is converted into road grade data and road curvature data, and the standard value corresponding to the corresponding vehicle attitude can be converted into the standard values of road grade and road curvature.

In one embodiment, as shown in fig. 6, the driving control parameter includes at least one of a reaction time and a duration driving time of the driving object. Taking the driving control parameters including the driving object reaction time and the continuous driving time as an example, acquiring the road condition parameter of the running vehicle, the vehicle running parameter, and the driving control parameter includes steps 610 to 630.

And step 610, acquiring the latest starting recording time of the running vehicle to obtain the continuous driving time.

And step 620, acquiring the identity information of the driving object, reading historical driving data of the driving object according to the identity information, and determining the reaction time of the driving object.

Step 630, road condition parameters of the running vehicle and vehicle running parameters are obtained.

Duration of driving time t_rMay be determined by the time difference between the latest start recording time of the operating vehicle and the current time. The criterion value t of the duration time may be obtained by dividing the duration time into a plurality of levels, depending on the level of the duration time,the maximum value of the grade is taken as a standard value of the continuous driving time.

In the embodiment, the driving modes comprise manual driving and unmanned driving, the driving objects comprise a robot and an actual 'human', and the reaction time f of the driving objects of the robot is_rMay be determined by the data transmission rate or data processing time for various sensors inside the vehicle. For actual people, the reaction time of the driving object is the average reaction time f of the driver in the historical driving data_rFor example, each reaction time in the history data is 0.1 to 0.5 seconds (the reaction time of the driving target obtained by the averaging process is 0.3 seconds), and in the embodiment, the reaction time of the driving target may be slightly adjusted according to the actual situation. For example, before the vehicle departs, the in-vehicle camera may capture the driver's characteristics, and then combine the traffic control department's history data with the public security department's identification card data, such as whether the driver has a license, whether the driver has a history of violations, whether the driver has a history of drunk driving, whether its age matches the type of vehicle being driven. If the driver is abnormal, for example, drunk driving is delayed from normal by 0.1 to 0.5 seconds, the specific delay time can be obtained by analyzing the delay time in the historical driving data of the driving object through the historical driving data of the historical driving data. The standard value f of the reaction time of the driving object can be obtained by analyzing the reaction time of the driver in normal driving through a large number of data samples.

The application also provides an application scene, and the vehicle operation environment data acquisition method is applied to the application scene. Specifically, the vehicle operation environment data acquisition method is applied to the application scene as follows:

a processor in a vehicle control system of a running vehicle acquires various parameter types of the running vehicle, determines required parameter types, and the required parameter types are specifically divided into three categories, namely a road condition parameter, a vehicle running parameter and a driving control parameter, wherein the road condition parameter comprises lane width b_rRoad friction c_rRoad visibility L_rAnd vehicle density d_rThe vehicle operation parameter comprises a vehicle type m_rVehicle attitude(road gradient s)_rCurve q of road_r) Vehicle speed v_rAnd vehicle acceleration a_rThe driving control parameter includes a driving object reaction time f_rAnd duration of driving time t_r。

The standard values of the parameters are stored in the control system of the running vehicle, and the actual measured values of the parameters can be obtained through the arranged sensors, internal software and the like. Acquiring parameter weight data divided based on the proportion of the factors in historical operation accident data, wherein the divided parameter weight data are w1, w2, w3, w4, w5, w6, w7, w8, w9, w10 and w11 respectively meet the condition that the parameter weight data sum is 1, namely w1+ w2+ w3+ w4+ w5+ w6+ w7+ w8+ w9+ w10+ w11 is 1, and the vehicle obtains various parameter values and standard values through internal software or sensors, wherein the parameter values are recorded as b software or sensors respectively_r、c_r、L_r、d_r、m_r、s_r、q_r、v_r、a_r、f_r、t_rThe standard values are b, c, L, d, m, s, q, v, a, f and t, respectively.

Calculating the weight sum of each parameter:

and if the weight sum W is larger than or equal to the threshold value T, the vehicle requests the running environment data of the running vehicle from the server, otherwise, the running environment data is not requested.

As shown in fig. 7, the vehicle 1 is used as the host vehicle, and the probability of collision between another vehicle and the host vehicle is calculated based on the operating environment data requested to be obtained from the server. Specifically, the driving risk is calculated using the received operating environment data (the operating environment data includes information such as the position, vehicle speed, and acceleration of the vehicle other than the vehicle).

Calculating the driving risk: calculating the vehicle speed (including the magnitude and the direction) of the vehicle and other vehicles, the vehicle type, a gravitational field theory model brought into the physics field, a spring potential energy model and a Doppler effect model (the specific parameters are the formula (1) and the formula (2)) to obtain collision energy data between the vehicles, and dividing the collision energy data by the standard collision strength to obtain the collision probability; and obtaining the collision risk of the calculation of the vehicle and other vehicles.

In one embodiment, vehicle 1 represents a host vehicle: the probability of collision that vehicle 2 brings to vehicle 1 is 0.79, the probability of collision that vehicle 3 brings to vehicle 1 is 0.69, the probability of collision that vehicle 4 brings to vehicle 1 is 0.58, the probability of collision that vehicle 5 brings to vehicle 1 is 0.49, the probability of collision that vehicle 6 brings to vehicle 1 is 0.38, and the probability of collision that vehicle 7 brings to vehicle 1 is 0.29. Assuming that the collision probability threshold is 0.7, the driver is prompted to pay attention to the vehicle 2 to avoid collision therewith.

In one embodiment, as shown in FIG. 8, a vehicle operating environment data acquisition method is provided, the method comprising steps 802 through 832.

Step 802, acquiring image data acquired by a running vehicle at a current position.

And step 804, obtaining the width of the current lane of the running vehicle according to the image data.

Step 806, identifying and analyzing the image data according to the image data to determine the road surface state and the visible distance.

And 808, identifying the number of vehicles in the image data to obtain the vehicle density.

And step 810, reading the pre-stored vehicle scale data of the running vehicle, and determining the vehicle type.

And step 812, reading the attitude sensing data collected by the running vehicle, and determining the attitude of the vehicle.

And 814, reading the vehicle speed of the running vehicle, and obtaining the vehicle acceleration according to the vehicle speed.

And step 816, acquiring the latest starting recording time of the running vehicle to obtain the continuous driving time.

Step 818, obtaining the identity information of the driving object, reading the historical driving data of the driving object according to the identity information, and determining the reaction time of the driving object.

And 820, obtaining the operation accident probability of the running vehicle according to the parameters, the preset standard values of the parameters and the pre-distributed parameter weight data.

Step 822, acquiring an operation accident probability threshold of an operation road section where the operation vehicle is located, and when the operation accident probability is greater than the operation accident probability threshold, sending an operation environment data acquisition request carrying positioning data.

At step 824, operating environment data for the operating vehicle based on the positioning data feedback is received.

In step 826, vehicle data of the environmental vehicle in the vehicle operation environment data is extracted.

And step 828, obtaining collision energy data of the running vehicle and the environmental vehicle according to the vehicle data of the environmental vehicle and the vehicle running parameters of the running vehicle.

Step 830, standard collision energy data obtained based on historical data analysis is obtained, and the collision probability between the running vehicle and the environmental vehicle is determined according to the ratio of the collision energy data to the standard collision energy data.

And step 832, generating a collision early warning message when the collision probability is greater than a preset collision probability threshold value.

It should be understood that although the various steps in the flowcharts of fig. 2-6, 8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order 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 some of the steps in fig. 2-6 and 8 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 order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternatively with other steps or at least some of the other steps or stages.

In one embodiment, as shown in fig. 9, there is provided a vehicle operating environment data acquisition apparatus 900, which may be a part of a computer device using a software module or a hardware module, or a combination of the two, and specifically includes: a parameter obtaining module 910, a probability analysis module 920, a request sending module 930, and a data receiving module 940, wherein:

the parameter acquiring module 910 is configured to acquire a road condition parameter of a running vehicle, a vehicle running parameter, and a driving control parameter.

And the probability analysis module 920 is configured to obtain the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, the preset standard values of the parameters, and the pre-assigned parameter weight data.

The request sending module 930 is configured to obtain an operation accident probability threshold of an operation road section where the operation vehicle is located, and send an operation environment data obtaining request carrying the positioning data when the operation accident probability is greater than the operation accident probability threshold.

And a data receiving module 940 for receiving the operating environment data of the operating vehicle based on the positioning data feedback.

In one embodiment, the parameter obtaining module is further used for obtaining image data acquired by the running vehicle at the current position; according to the image data, obtaining the width of a lane where the running vehicle is currently located, obtaining the road surface state and the visible distance of the current position of the running vehicle, and determining the road friction corresponding to the road surface state and the road visibility corresponding to the visible distance; and identifying the number of vehicles in the image data to obtain the vehicle density.

In one embodiment, the parameter obtaining module is further configured to obtain positioning data of a running vehicle, obtain weather data corresponding to a position where the positioning data is located, determine a road surface state and a visible distance according to the weather data, or determine the road surface state and the visible distance through identification and analysis of image data according to the weather data.

For specific limitations of the vehicle operating environment data acquisition device, reference may be made to the above limitations of the vehicle operating environment data acquisition method, which are not described in detail herein. The modules in the vehicle operating environment data acquisition device can be wholly or partially realized by software, hardware and a combination 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 computer device on a vehicle, the internal structure of which may be as shown in fig. 10. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by 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, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a vehicle operating environment data acquisition method. 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. 10 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 one 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, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

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, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. 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 technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above examples 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 invention. 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 patent shall be subject to the appended claims.

Claims

1. A vehicle operating environment data acquisition method, characterized in that the method comprises:

obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, preset standard values of all parameters and pre-distributed parameter weight data;

acquiring an operation accident probability threshold value of an operation road section where the operation vehicle is located, and sending an operation environment data acquisition request carrying positioning data when the operation accident probability is greater than the operation accident probability threshold value;

2. The method of claim 1, further comprising, after said receiving vehicle operating environment data based on said positioning data feedback:

extracting vehicle data of the environmental vehicle from the vehicle operation environment data;

obtaining collision energy data of the running vehicle and the environment vehicle according to the vehicle data of the environment vehicle and vehicle running parameters of the running vehicle;

acquiring standard collision energy data obtained based on historical data analysis, and determining the collision probability of the running vehicle and the environmental vehicle according to the ratio of the collision energy data to the standard collision energy data;

and when the collision probability is greater than a preset collision probability threshold value, generating a collision early warning message.

3. The method of claim 1, wherein the road condition parameters include at least one of lane width, road friction, road visibility, and vehicle density.

4. The method of claim 1, wherein the road condition parameters include lane width, road friction, road visibility, and vehicle density;

the acquisition process of the road condition parameters of the running vehicle comprises the following steps:

acquiring image data acquired by the running vehicle at the current position;

according to the image data, obtaining the width of a lane where the running vehicle is located currently, obtaining the road surface state and the visible distance of the current position of the running vehicle, and determining the road friction corresponding to the road surface state and the road visibility corresponding to the visible distance, wherein the road surface state comprises a dry road surface state, a wet road surface state and a snow and ice road surface state;

and identifying the number of vehicles in the image data to obtain the vehicle density.

5. The method according to claim 4, wherein the obtaining of the road surface state and the visible distance of the current position of the running vehicle comprises any one of:

acquiring positioning data of the running vehicle, acquiring weather data corresponding to the position of the positioning data, and determining a road surface state and a visible distance according to the weather data;

and determining the road surface state and the visible distance by identifying and analyzing the image data according to the image data.

6. The method according to claim 1, wherein the driving control parameter includes at least one of a reaction time of a driving object and a duration of driving, wherein the reaction time of the driving object is obtained by acquiring identity information of the driving object, and reading and analyzing historical driving data of the driving object according to the identity information.

7. The method of claim 1, wherein the vehicle operating parameter comprises at least one of a vehicle type, a vehicle attitude, a vehicle speed, and a vehicle acceleration.

8. A vehicle operating environment data acquisition apparatus, characterized in that the apparatus comprises:

the probability analysis module is used for obtaining the operation accident probability of the operating vehicle according to the road condition parameters, the vehicle operation parameters, the driving control parameters, preset standard values of all parameters and pre-distributed parameter weight data;

the request sending module is used for obtaining an operation accident probability threshold value of an operation road section where the operation vehicle is located, and sending an operation environment data obtaining request carrying positioning data when the operation accident probability is greater than the operation accident probability threshold value;

and the data receiving module is used for receiving the running environment data of the running vehicle fed back based on the positioning data.

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.