阅读说明：本技术 车辆急转弯识别方法、装置、计算机设备和存储介质 (Vehicle sharp turn recognition method and device, computer equipment and storage medium ) 是由 黄婉刁 温煦 潘钟声 江勇 于 2019-09-24 设计创作，主要内容包括：本申请涉及一种车辆急转弯识别方法、装置、计算机设备和存储介质。所述方法包括：接收车辆的实时GPS数据；所述实时GPS数据包含接收时间、地面速度、航向角及定位状态；根据所述定位状态,判断所述实时GPS数据是否有效；若所述实时GPS数据有效,获取与所述接收时间相邻的历史GPS数据；根据所述实时GPS数据及所述历史GPS数据确定横向力系数；所述横向力系数与所述实时GPS数据的接收时间、所述历史GPS数据的接收时间、地面速度及航向角相关；若所述横向力系数大于或等于急转弯判定阈值,判定车辆处于急转弯状态。采用本方法的车载设备对安装位置及安装方式无硬性要求,进而可以提升车辆急转弯识别的准确性。(The application relates to a method and a device for identifying sharp turning of a vehicle, computer equipment and a storage medium. The method comprises the following steps: receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state; judging whether the real-time GPS data is valid or not according to the positioning state; if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time; determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle; and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state. The vehicle-mounted equipment adopting the method has no rigid requirements on the installation position and the installation mode, and the accuracy of identifying the sharp turn of the vehicle can be further improved.)

1. A vehicle sharp turn recognition method, characterized in that the method comprises:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

2. The method of claim 1, wherein the real-time GPS data further comprises a horizontal positioning factor; if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time, wherein the acquiring comprises the following steps:

judging whether the horizontal positioning factor is larger than a precision threshold value;

and if the horizontal positioning factor is smaller than or equal to the precision threshold, acquiring historical GPS data adjacent to the receiving time.

3. The method of claim 2, wherein obtaining historical GPS data adjacent to the time of receipt if the horizontal positioning factor is less than or equal to the accuracy threshold comprises:

sequentially judging whether the historical GPS data before the receiving time is valid;

and determining effective historical GPS data with the minimum time interval between the effective historical GPS data and the receiving time as historical GPS data adjacent to the receiving time.

4. The method of claim 1, determining a lateral force coefficient from the real-time GPS data and the historical GPS data, comprising:

judging whether the real-time GPS data and the historical GPS data meet preset vehicle sharp turn identification conditions or not; the vehicle sharp turn identification condition is a condition determined according to the receiving time and/or the course angle;

and if so, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

5. The method of claim 4, wherein the determining whether the real-time GPS data and the historical GPS data satisfy a preset vehicle sharp turn recognition condition comprises:

determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data;

judging whether the time interval is greater than a positioning time threshold value or not;

and if the time interval is smaller than or equal to the positioning time threshold, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition.

6. The method of claim 4, wherein said determining whether said real-time GPS data and said historical GPS data satisfy a predetermined vehicle sharp turn identification condition further comprises:

acquiring a course angle of the historical GPS data;

determining a course angle difference value between the course angle of the historical GPS data and the course angle of the real-time GPS data;

judging whether the course angle difference value is larger than an angle threshold value;

and if the difference value of the course angle is smaller than or equal to the angle threshold value, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition.

7. The method of claim 1, further comprising, prior to said determining a lateral force coefficient from said real-time GPS data and said historical GPS data:

judging whether the ground speed in the real-time GPS data is zero or not;

and if the ground speed in the real-time GPS data is not zero, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

8. The method of claim 1, further comprising, prior to determining a lateral force coefficient from the real-time GPS data and the historical GPS data:

judging whether the ground speed in the real-time GPS data is greater than a speed threshold value or not;

and if the ground speed in the real-time GPS data is greater than or equal to the speed threshold, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

9. The method of claim 1, wherein determining a lateral force coefficient from the real-time GPS data and the historical GPS data comprises:

acquiring the ground speed and the course angle of the historical GPS data;

determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data;

determining a course angle difference value between the course angle of the historical GPS data and the course angle of the real-time GPS data;

and calculating the transverse force coefficient according to the ground speed of the real-time GPS data, the time interval and the heading angle difference.

10. The method according to any one of claims 1 to 9, characterized in that the sharp turn determination threshold is 0.35.

11. A vehicle sharp turn recognition device, characterized in that the device comprises:

the data receiving module is used for receiving and receiving real-time GPS data of the vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

the validity verification module is used for judging whether the real-time GPS data is valid or not according to the positioning state;

the historical data acquisition module is used for acquiring historical GPS data adjacent to the receiving time if the real-time GPS data is valid;

the transverse force coefficient determining module is used for determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and the sharp turn judging module is used for judging that the vehicle is in a sharp turn if the transverse force coefficient is greater than or equal to a sharp turn judging threshold value.

Technical Field

The present application relates to the field of vehicle data processing technologies, and in particular, to a method and an apparatus for identifying a sharp turn of a vehicle, a computer device, and a storage medium.

Background

With the development of economy and the improvement of automobile technology, more and more people select automobiles as travel tools, and the related traffic accidents are increased along with the improvement of road traffic conditions and the increase of the number of automobiles. When the vehicle makes a sharp turn, the vehicle is likely to cause dangers such as rollover, runaway and the like, and further traffic accidents are caused.

Therefore, the recognition of the sharp turning of the vehicle can provide a data basis for the analysis of the driving habits of the driver and also provide judgment factors for whether the accident occurs and the reason of the accident. At present, the collection and analysis of sharp turn data of a vehicle is to calculate the lateral acceleration of the vehicle by arranging an acceleration sensor in the vehicle and analyze whether the vehicle makes a sharp turn according to the lateral acceleration.

However, in the conventional vehicle sharp turn recognition process, the vehicle sharp turn recognition device needs to be accurately fixed in a direction and a position to calculate the lateral acceleration after the vehicle advancing direction and the gravity acceleration of the vehicle are removed, if the position has deviation, the lateral acceleration is directly influenced, and the magnitude of the acceleration directly causes inaccurate vehicle sharp turn recognition.

Disclosure of Invention

In view of the above, it is necessary to provide a vehicle sharp turn recognition method, apparatus, computer device and storage medium with high accuracy.

In a first aspect, an embodiment of the present invention provides a method for identifying a sharp turn of a vehicle, where the method includes:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

In one embodiment of the present application, the real-time GPS data further comprises a horizontal positioning factor; if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time, wherein the acquiring comprises the following steps:

judging whether the horizontal positioning factor is larger than a precision threshold value;

and if the horizontal positioning factor is smaller than or equal to the precision threshold, acquiring historical GPS data adjacent to the receiving time.

In one embodiment of the present application, if the horizontal positioning factor is less than or equal to the accuracy threshold, acquiring historical GPS data adjacent to the receiving time includes:

sequentially judging whether the historical GPS data before the receiving time is valid;

and determining effective historical GPS data with the minimum time interval between the effective historical GPS data and the receiving time as historical GPS data adjacent to the receiving time.

In one embodiment of the present application, determining a lateral force coefficient from the real-time GPS data and the historical GPS data comprises:

judging whether the real-time GPS data and the historical GPS data meet preset vehicle sharp turn identification conditions or not; the vehicle sharp turn identification condition is a condition determined according to the receiving time and/or the course angle;

and if so, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

In an embodiment of the present application, the determining whether the real-time GPS data and the historical GPS data satisfy a preset vehicle sharp turn recognition condition includes:

determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data;

judging whether the time interval is greater than a positioning time threshold value or not;

and if the time interval is smaller than or equal to the positioning time threshold, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition. In an embodiment of the present application, the determining whether the real-time GPS data and the historical GPS data satisfy a preset vehicle sharp turn recognition condition further includes:

acquiring a course angle of the historical GPS data;

determining a course angle difference value between the course angle of the historical GPS data and the course angle of the real-time GPS data;

judging whether the course angle difference value is larger than an angle threshold value;

and if the difference value of the course angle is smaller than or equal to the angle threshold value, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition. In one embodiment of the present application, before determining the lateral force coefficient according to the real-time GPS data and the historical GPS data, the method further includes:

judging whether the ground speed in the real-time GPS data is zero or not;

and if the ground speed in the real-time GPS data is not zero, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

In one embodiment of the present application, before determining the lateral force coefficient according to the real-time GPS data and the historical GPS data, the method further includes:

judging whether the ground speed in the real-time GPS data is greater than a speed threshold value or not;

and if the ground speed in the real-time GPS data is greater than the speed threshold, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data.

In one embodiment of the present application, determining a lateral force coefficient from the real-time GPS data and the historical GPS data comprises:

acquiring the ground speed and the course angle of the historical GPS data;

determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data;

determining a course angle difference value between the course angle of the historical GPS data and the course angle of the real-time GPS data;

and calculating the transverse force coefficient according to the ground speed of the real-time GPS data, the time interval and the heading angle difference.

In one embodiment of the present application, after the determining that the vehicle is in a sharp turning state, the method further includes:

saving the real-time GPS data and the corresponding transverse force coefficient as a sharp turn record;

and sending the sharp turning record to a server so that the server pushes the sharp turning record to a terminal device, or generating a driving report according to the sharp turning record.

In one embodiment of the present application, the sharp turn determination threshold is 0.35.

In a second aspect, an embodiment of the present invention provides a vehicle sharp turn recognition apparatus, including:

the data receiving module is used for receiving and receiving real-time GPS data of the vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

the validity verification module is used for judging whether the real-time GPS data is valid or not according to the positioning state;

the historical data acquisition module is used for acquiring historical GPS data adjacent to the receiving time if the real-time GPS data is valid;

the transverse force coefficient determining module is used for determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle; and the sharp turn judging module is used for judging that the vehicle is in a sharp turn if the transverse force coefficient is greater than or equal to a sharp turn judging threshold value.

In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the following steps when executing the computer program:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

The vehicle sharp turn identification method, the vehicle sharp turn identification device, the computer equipment and the storage medium receive real-time GPS data of the vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state; judging whether the real-time GPS data is valid or not according to the positioning state; if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time; determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle; and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state. According to the method for identifying the sharp turn of the vehicle, provided by the embodiment of the application, the real-time transverse force coefficient of the vehicle is obtained by receiving the GPS data and combining the real-time GPS data and the historical GPS data, and whether the vehicle is in the sharp turn state is determined according to the relation between the transverse force coefficient and the sharp turn judgment threshold value. Because the GPS data is adopted, the acquired vehicle motion data does not need to consider the forward acceleration and the gravitational acceleration of the vehicle, so that the vehicle-mounted equipment has no hard requirements on the mounting position and the mounting mode, the accuracy of identifying the sharp turn of the vehicle can be further ensured, and the accuracy of identifying the sharp turn of the vehicle can be further ensured by judging the validity of the GPS data.

Drawings

FIG. 1 is an implementation environment diagram of a method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 2 is a flow chart of a method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 3 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 4 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 5 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 6 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 7 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 8 is a schematic diagram of a lateral force coefficient calculation provided in an embodiment of the present application;

FIG. 9 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

FIG. 10 is a flow chart of another method for identifying a sharp turn of a vehicle according to an embodiment of the present application;

fig. 11 is a block diagram of a vehicle sharp turn recognition device according to an embodiment of the present application;

FIG. 12 is a block diagram of another vehicle sharp turn recognition device provided in the embodiments of the present application;

fig. 13 is a block diagram of a computer device according to an embodiment of the present application.

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 method for recognizing the sharp turn of the vehicle can be applied to the application environment shown in fig. 1. In the application environment shown in fig. 1, a vehicle end 101 and a server 102 may be included. The vehicle end 101 may be a terminal on a vehicle for controlling the operation of the vehicle, and is also commonly referred to as an on-board center control platform. The vehicle terminal 101 may include an in-vehicle device therein. The vehicle end 101 can acquire various driving data of the vehicle through the vehicle-mounted equipment. The vehicle-mounted device may be, but is not limited to, various OBD devices, vehicle-mounted smart devices, personal computers, notebook computers, smart phones, and tablet computers, and the server 103 may be implemented by an independent server or a server cluster formed by a plurality of servers.

Referring to fig. 2, it shows a method for identifying a sharp turn of a vehicle according to the present embodiment, which is described by taking the method as an example applied to the vehicle end in fig. 1, and includes the following steps:

step 202, receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state.

In one embodiment of the application, a GPS receiver is arranged in the vehicle-mounted device, and the vehicle side acquires real-time GPS data of the vehicle through the vehicle-mounted device.

In one embodiment of the present application, the GPS receiver in the present application may collect longitude and latitude information, reception time, positioning status, number of satellites used, HDOP horizontal positioning factor, VDOP vertical accuracy factor, altitude, ground speed, and heading angle information of the vehicle, and transmit them as GPS data to the vehicle end 101 through the NEMA protocol.

In one embodiment of the present application, the GPS data is received by the vehicle end 101 at a fixed receiving frequency, and when the receiving frequency is 1hz, it indicates that the vehicle end can receive one GPS data of the vehicle every second.

And step 204, judging whether the real-time GPS data is effective or not according to the positioning state.

In an embodiment of the present application, when receiving the GPS data, the vehicle end 101 parses the GPS data, extracts the positioning status information therein, executes the subsequent steps when the positioning status information indicates that the GPS is valid, and terminates the vehicle sharp turn recognition step and waits for the next GPS data when the positioning information indicates that the GPS is invalid.

Specifically, when the GPS Data is known by a positioning status field in Geographic location information (GLL) or Recommended positioning information (RMC) of NEMA protocol, when the field is a, the GPS Data of this time is a valid positioning, and when the field is V, the GPS Data of this time is an invalid positioning.

In step 206, if the real-time GPS data is valid, historical GPS data adjacent to the receiving time is obtained.

In an embodiment of the present application, when the GPS data received this time is determined to be valid, a previous GPS data is further acquired as historical GPS data, where the historical GPS data is a previous GPS data adjacent to the current real-time GPS data.

In one embodiment of the present application, the historical GPS data is also valid, that is, if a GPS data adjacent to the real-time GPS data receiving time is invalid, the acquisition of the earlier GPS data is required until a GPS data is found to be in a valid positioning state.

Step 208, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the lateral force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle.

In one embodiment of the present application, the vehicle end calculates the lateral force coefficient according to the receiving time interval of the real-time GPS data and the historical GPS data, the navigation angle difference, and the ground speed of the real-time GPS data, wherein the lateral force coefficient is positively correlated to the navigation angle difference and the ground speed of the real-time GPS data, and the lateral force coefficient is negatively correlated to the receiving time interval.

And step 210, if the lateral force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

In one embodiment of the present application, the greater the lateral force coefficient, the greater the probability that indicates that the vehicle is in a sharp turn state, the more intense the driver who rides the vehicle will be and the more uncomfortable the passenger will be, and therefore, by setting a sharp turn determination threshold, when the lateral force coefficient is greater than or equal to the sharp turn determination threshold, it is determined that the vehicle is in a sharp turn state. Through actual vehicle test, the larger the transverse force coefficient is, the larger the influence on a driver and passengers is, and when the transverse force coefficient reaches 0.35, the driver can be tense, and the passengers feel uncomfortable. Thus, in a preferred embodiment, the lateral force coefficient is set to 0.35.

In the method for identifying the sharp turn of the vehicle, the real-time GPS data of the vehicle is received; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state; judging whether the real-time GPS data is valid or not according to the positioning state; if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time; determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle; and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state. According to the method for identifying the sharp turn of the vehicle, provided by the embodiment of the application, the real-time transverse force coefficient of the vehicle is obtained by receiving the GPS data and combining the real-time GPS data and the historical GPS data, and whether the vehicle is in the sharp turn state is determined according to the relation between the transverse force coefficient and the sharp turn judgment threshold value. Because the GPS data is adopted, the acquired vehicle motion data does not need to consider the forward acceleration and the gravitational acceleration of the vehicle, so that the vehicle-mounted equipment has no hard requirements on the mounting position and the mounting mode, the accuracy of identifying the sharp turn of the vehicle can be further ensured, and the accuracy of identifying the sharp turn of the vehicle can be further ensured by judging the validity of the GPS data.

Referring to fig. 3, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, the real-time GPS data further includes a horizontal positioning factor, and the step 206 may specifically include the following steps:

step 302, determine whether the horizontal localization factor is greater than the accuracy threshold.

In step 304, if the horizontal positioning factor is less than or equal to the accuracy threshold, historical GPS data adjacent to the receiving time is obtained.

In an embodiment of the present application, when the real-time GPS data is determined to be valid, a horizontal positioning factor is further extracted from the GPS data, and the smaller the horizontal positioning factor is, the higher the positioning accuracy of the GPS data in the horizontal direction is, so that it needs to be further determined whether the horizontal positioning factor is greater than a precision threshold, and if the horizontal positioning factor is less than or equal to the precision threshold, the GPS data at this time meets the precision requirement, so as to obtain historical GPS data adjacent to the receiving time. If the horizontal positioning factor is larger than the precision threshold value, the GPS data does not meet the precision requirement, the vehicle sharp turn identification step is stopped, and the next GPS data is waited.

In one embodiment of the present application, the accuracy threshold is set to 2.5.

In one embodiment of the present application, the range of the horizontal localization factor is 0.5 to 99.9, and when the analyzed horizontal localization factor is not within the range, the GPS data is determined to be invalid data, the vehicle sharp turn recognition step is stopped, and the next GPS data is waited.

In the method for identifying the sharp turn of the vehicle, whether a horizontal positioning factor is larger than an accuracy threshold value is judged; if the horizontal positioning factor is less than or equal to the accuracy threshold, historical GPS data adjacent to the receiving time is obtained. By further acquiring the horizontal positioning factor in the GPS data and comparing the horizontal positioning factor with the precision threshold value, when the precision requirement is not met, the vehicle sharp turn recognition step is stopped, so that the wrong judgment of the sharp turn state caused by the inaccuracy of the GPS data can be avoided, and the accuracy of vehicle sharp turn recognition is ensured.

Referring to fig. 4, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the foregoing embodiment, the step 304 may specifically include the following steps:

step 402, sequentially judging whether the historical GPS data before the receiving time is valid.

In step 404, the valid historical GPS data with the smallest time interval with the receiving time is determined as the historical GPS data adjacent to the receiving time.

In an embodiment of the present application, a vehicle end receives and stores GPS data of a vehicle, and a plurality of GPS data exist before the time of receiving the real-time GPS data, and in this embodiment, the vehicle end sequentially determines whether each GPS data is valid according to the time of receiving each GPS data, that is, first determines whether a GPS data before the real-time GPS data is valid, and if valid, takes the GPS data as the historical GPS data, and if invalid, determines whether the next GPS data is valid, and so on, until a valid GPS data is obtained and taken as the historical GPS data.

In another embodiment of the present application, the vehicle side extracts GPS data within a period of time before the real-time GPS data, determines whether each GPS data is valid, and determines valid historical GPS data having a minimum time interval with the reception time of the real-time GPS data as historical GPS data adjacent to the reception time according to the reception time of each obtained valid GPS data.

In the method for identifying the sharp turn of the vehicle, the accuracy of input data can be ensured by acquiring the effective historical GPS data of the vehicle and determining the transverse force coefficient according to the real-time GPS data and the historical GPS data, so that the real transverse force coefficient can be obtained, and the accuracy of identifying the sharp turn of the vehicle is ensured.

The present embodiment also provides another vehicle sharp turn recognition method that can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, the step 208 may specifically include the following steps:

judging whether the real-time GPS data and the historical GPS data meet preset vehicle sharp turn identification conditions or not; the vehicle sharp turn identification condition is a condition determined according to the receiving time and/or the heading angle; and if so, executing the step of determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In one embodiment of the present application, the determination of the vehicle sharp turn identification condition is required on the obtained real-time GPS data and the historical GPS data before calculating the lateral force coefficient to complete the sharp turn detection. By the method of the embodiment, the problem of wrong judgment of the sharp turning state caused by overlarge time interval between the real-time GPS data and the historical GPS data and/or large difference value of the course angle can be solved, and the accuracy of identifying the sharp turning of the vehicle is ensured.

Referring to fig. 5, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the above embodiment, the step of determining whether the real-time GPS data and the historical GPS data satisfy the preset vehicle sharp turn recognition condition may specifically include the following steps:

step 502, a time interval between the time of receipt of the historical GPS data and the time of receipt of the real-time GPS data is determined.

Step 504, determine whether the time interval is greater than the positioning time threshold.

In step 506, if the time interval is less than or equal to the positioning time threshold, it is determined that the real-time GPS data and the historical GPS data satisfy the vehicle sharp turn recognition condition.

In an embodiment of the present application, if the time interval between the receiving time of the historical GPS data and the receiving time of the real-time GPS data is too large, which indicates that the current positioning condition is poor and the accuracy of vehicle sharp turn identification cannot be ensured, the vehicle sharp turn identification step is terminated and the next real-time GPS data is waited when the time interval is greater than the positioning time threshold. In a preferred embodiment, the time to locate threshold may be set at 3 seconds.

In the method for identifying the sharp turn of the vehicle, the positioning time threshold is set, and the condition for identifying the sharp turn of the vehicle is added to the real-time GPS data and the historical GPS data, so that the problem of wrong judgment of the sharp turn state caused by overlarge time interval between the real-time GPS data and the historical GPS data can be solved, and the accuracy of identifying the sharp turn of the vehicle is ensured.

Referring to fig. 6, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the above embodiment, the step of determining whether the real-time GPS data and the historical GPS data satisfy the preset vehicle sharp turn recognition condition may specifically include the following steps:

step 602, a course angle of historical GPS data is obtained.

Step 604, determining a course angle difference between the course angle of the historical GPS data and the course angle of the real-time GPS data.

Step 606, determine whether the heading angle difference is greater than the angle threshold.

And step 608, if the difference value of the heading angle is smaller than or equal to the angle threshold value, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition.

In an embodiment of the application, during the running process of the vehicle, the course angle does not change greatly in a short time, so that if the course angle difference value between the course angle of the historical GPS data and the course angle of the real-time GPS data is too large, it indicates that the GPS data received by the vehicle is inaccurate, cannot represent a real running state, and cannot ensure the accuracy of vehicle sharp turn identification, and therefore when the heading angle difference value is greater than an angle threshold value, the vehicle sharp turn identification step is terminated, and the next real-time GPS data is waited. In a preferred embodiment, the angle threshold may be set at 60 degrees.

In the method for identifying the sharp turn of the vehicle, the angle threshold is set, and the condition for identifying the sharp turn of the vehicle is added to the real-time GPS data and the historical GPS data, so that the problem of wrong judgment of the sharp turn state caused by overlarge angle threshold between the real-time GPS data and the historical GPS data can be solved, and the accuracy of identifying the sharp turn of the vehicle is ensured.

The present embodiment also provides another vehicle sharp turn recognition method that can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, before the step 208, the following steps may be further included:

judging whether the ground speed in the real-time GPS data is zero or not; and if the ground speed in the real-time GPS data is not zero, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In one embodiment of the present application, whether the vehicle is in a relatively stationary state or not can be determined by detecting whether the ground speed in the real-time GPS data is zero or not, and whether the real-time GPS data is abnormal or not can also be determined. When the ground speed in the real-time GPS data is zero, it indicates that the vehicle is in a relatively stationary state or the GPS data is abnormal, and therefore it is necessary to terminate the vehicle sharp turn recognition step and wait for the next real-time GPS data. And if the ground speed in the real-time GPS data is not zero, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In the method for identifying the sharp turn of the vehicle, whether the ground speed in the real-time GPS data is zero or not is detected, and when the ground speed in the real-time GPS data is not zero, the transverse force coefficient is determined according to the real-time GPS data and historical GPS data. The problem of wrong judgment of the sharp turning state caused by turning around when the vehicle is relatively static is solved, and the problem of wrong judgment of the sharp turning state caused by abnormal GPS data is also solved. The accuracy of vehicle sharp turn recognition is improved.

The present embodiment also provides another vehicle sharp turn recognition method that can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, before the step 208, the following steps may be further included:

judging whether the ground speed in the real-time GPS data is greater than a speed threshold value or not; and if the ground speed in the real-time GPS data is greater than or equal to the speed threshold, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In one embodiment of the present application, it may be determined whether the vehicle satisfies a speed condition for sharp turn detection by detecting whether the ground speed in the real-time GPS data is greater than or equal to a speed threshold. When the ground speed in the real-time GPS data is less than the speed threshold, it indicates that the vehicle is in a low-speed running state, and a sharp turn does not occur, so that it is necessary to terminate the vehicle sharp turn recognition step and wait for the next real-time GPS data. And if the speed is larger than or equal to the speed threshold, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In the method for identifying the sharp turn of the vehicle, whether the ground speed in the real-time GPS data is greater than or equal to the speed threshold or not is detected, and when the ground speed in the real-time GPS data is greater than or equal to the speed threshold, the transverse force coefficient is determined according to the real-time GPS data and historical GPS data. The waste of vehicle-side computing resources caused by the identification step of still performing a sharp turning state when the vehicle is at a low speed is avoided.

Referring to fig. 7, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, the step 202 may specifically include the following steps:

step 702, acquiring the ground speed and the course angle of historical GPS data.

Step 704, a time interval between the time of receipt of the historical GPS data and the time of receipt of the real-time GPS data is determined.

Step 706, a course angle difference between the course angle of the historical GPS data and the course angle of the real-time GPS data is determined.

Step 708, calculating a lateral force coefficient based on the ground speed, the time interval, and the heading angle difference of the real-time GPS data.

In one embodiment of the present application, as shown in FIG. 8, the lateral force coefficient is used to represent the ratio of the centrifugal force to the gravitational force received by the vehicle, as follows:

where φ represents the transverse force coefficient, F_XRepresenting the centrifugal force to which the vehicle is subjected, F_ZIndicating the gravitational force to which the vehicle is subjected. Wherein, F_X＝mωυ。

Where ω represents angular velocity, rad/s, and υ represents linear velocity, and m/s, m represents vehicle mass. F_ZMg, where g denotes acceleration of gravity in m/s²。

Then:

where Δ θ represents an angle change amount, and Δ t represents a time change amount.

Then:

therefore, the temperature of the molten metal is controlled,

in the method for identifying the sharp turn of the vehicle, provided by the embodiment of the application, the transverse force coefficient is calculated through the ground speed, the time interval and the course angle difference value of the real-time GPS data, so that more accurate transverse force coefficient can be obtained, and the accuracy of identifying the sharp turn of the vehicle is ensured.

Referring to fig. 9, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above. On the basis of the embodiment shown in fig. 2, after step 210, the following steps may be further included:

step 902, saving real-time GPS data and corresponding lateral force coefficients as a sharp turn record.

And step 904, sending the sharp turning record to the server so that the server pushes the sharp turning record to the terminal equipment or generates a driving report according to the sharp turning record.

In one embodiment of the present application, after determining that the vehicle is in a sharp turn state according to the real-time GPS data, the real-time GPS data and the corresponding lateral force coefficient are saved as a sharp turn record. The information can be sent to the server immediately or a plurality of stored sharp turn records can be sent to the server together when the updating time is up. So that the server pushes the sharp turn record to a preset terminal device or generates a driving report according to the sharp turn record.

In the method for identifying the sharp turn of the vehicle, the real-time GPS data and the corresponding force coefficient are used as the sharp turn record and sent to the server, so that the server can further generate a driving report according to the sharp turn record, or the driving report is timely pushed to the terminal equipment to remind a driver of paying attention to driving, and the applicability of the method is improved.

Referring to fig. 10, a flow chart of another vehicle sharp turn recognition method provided by the present embodiment is shown, which can be applied to the vehicle end 101 in the implementation environment described above.

The method comprises the following steps:

step 1002, receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state.

And 1004, judging whether the real-time GPS data is effective according to the positioning state. If yes, go to step 1006, otherwise go to step 1026.

In step 1006, it is determined whether the horizontal localization factor is greater than the accuracy threshold. If yes, go to step 1008, otherwise go to step 1026.

Step 1008, obtaining historical GPS data adjacent to the receiving time, determining a time interval between the receiving time of the historical GPS data and the receiving time of the real-time GPS data, and determining a course angle difference value between a course angle of the historical GPS data and a course angle of the real-time GPS data.

Step 1010, determine whether the time interval is greater than the positioning time threshold. If yes, go to step 1026, otherwise go to step 1012.

Step 1012, determine whether the heading angle difference is greater than the angle threshold. If so, go to step 1026, otherwise go to step 1014.

Step 1014, determine if the ground speed is zero. If so, go to step 1026, otherwise, go to step 1016.

Step 1016, determine if the ground speed is greater than a speed threshold. If yes, go to step 1018, otherwise go to step 1026.

Step 1018, determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the lateral force coefficient is related to the time interval, ground speed in the real-time GPS data and the heading angle difference.

Step 1020, it is determined whether the lateral force coefficient is greater than or equal to a sharp turn determination threshold. If yes, go to step 1022, otherwise go to step 1024.

In step 1022, it is determined that the vehicle is in a sharp turning state.

Step 1024, determine that the vehicle is not in a sharp turn state.

Step 1026, end.

In an embodiment of the present application, for the steps 1004 to 1016, the relative order between the steps may be adjusted according to different implementation scenarios, and one or more of the steps may also be omitted.

According to the method for identifying the sharp turn of the vehicle, provided by the embodiment of the application, the real-time transverse force coefficient of the vehicle is obtained by receiving the GPS data and combining the real-time GPS data and the historical GPS data, and whether the vehicle is in the sharp turn state is determined according to the relation between the transverse force coefficient and the sharp turn judgment threshold value. Because the GPS data is adopted, the acquired vehicle motion data does not need to consider the forward acceleration and the gravity acceleration of the vehicle, so that the vehicle-mounted terminal has no hard requirements on the mounting position and the mounting mode, the accuracy of identifying the sharp turn of the vehicle can be further ensured, and the accuracy of identifying the sharp turn of the vehicle can be further ensured by judging the validity of the GPS data and the relation between the running state data of each vehicle and the preset threshold value.

It should be understood that, although the steps in the above-described flowcharts 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 a portion of the steps in the above-described flowcharts may include multiple sub-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 sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.

Referring to fig. 11, a block diagram of a vehicle sharp turn recognition device 1100 according to an embodiment of the present application is shown. As shown in fig. 11, the vehicle sharp turn recognition device 1100 may include: the data receiving module 1101, the validity verifying module 1102, the historical data obtaining module 1103, the lateral force coefficient determining module 1104 and the sharp turn determining module 1105, wherein:

the data receiving module 1101 is configured to receive real-time GPS data of a vehicle. The real-time GPS data comprises receiving time, ground speed, course angle and positioning state.

And the validity verification module 1102 is configured to determine whether the real-time GPS data is valid according to the positioning status.

A historical data obtaining module 1103, configured to obtain historical GPS data adjacent to the receiving time if the real-time GPS data is valid.

And a transverse force coefficient determining module 1104, configured to determine a transverse force coefficient according to the real-time GPS data and the historical GPS data. The lateral force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle.

The sharp turn determination module 1105 is configured to determine that the vehicle is in a sharp turn if the lateral force coefficient is greater than or equal to the sharp turn determination threshold.

In an embodiment of the present application, the real-time GPS data further includes a horizontal positioning factor, and the historical data acquiring module 1103 is specifically configured to: if the real-time GPS data is valid, judging whether the horizontal positioning factor is greater than a precision threshold value; if the horizontal positioning factor is less than or equal to the accuracy threshold, historical GPS data adjacent to the receiving time is obtained.

In an embodiment of the application, the historical data obtaining module 1103 is further specifically configured to: sequentially judging whether historical GPS data before the receiving time is valid; and determining effective historical GPS data with the minimum time interval between the effective historical GPS data and the receiving time as historical GPS data adjacent to the receiving time.

In an embodiment of the present application, the lateral force coefficient determining module 1104 is specifically configured to: judging whether the real-time GPS data and the historical GPS data meet preset vehicle sharp turn identification conditions or not; the vehicle sharp turn identification condition is a condition determined according to the receiving time and/or the course angle; and if so, executing the step of determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In an embodiment of the present application, the lateral force coefficient determining module 1104 is specifically further configured to: determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data; judging whether the time interval is greater than a positioning time threshold value or not; and if the time interval is smaller than or equal to the positioning time threshold, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition.

In an embodiment of the present application, the lateral force coefficient determining module 1104 is specifically further configured to: acquiring a course angle of historical GPS data; determining a course angle difference value between a course angle of historical GPS data and a course angle of real-time GPS data; judging whether the course angle difference is larger than an angle threshold value; and if the difference value of the course angle is smaller than or equal to an angle threshold value, determining that the real-time GPS data and the historical GPS data meet the vehicle sharp turn identification condition.

In an embodiment of the present application, the lateral force coefficient determining module 1104 is specifically further configured to: acquiring the ground speed and the course angle of historical GPS data; determining a time interval between a time of receipt of the historical GPS data and a time of receipt of the real-time GPS data; determining a course angle difference value between a course angle of historical GPS data and a course angle of real-time GPS data; and calculating the transverse force coefficient according to the ground speed, the time interval and the course angle difference value of the real-time GPS data.

Referring to fig. 12, a block diagram of a vehicle sharp turn recognition device 1200 according to an embodiment of the present application is shown. As shown in fig. 12, the vehicle sharp turn recognition device 1200 may include, in addition to the modules included in the vehicle sharp turn recognition device 1100, optionally: a ground speed determination module 1106 and a push module 1107. Wherein:

in an embodiment of the present application, the ground speed determination module 1106 is configured to determine whether the ground speed in the real-time GPS data is zero; and if the ground speed in the real-time GPS data is not zero, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In an embodiment of the present application, the ground speed determination module 1106 is configured to determine whether the ground speed in the real-time GPS data is greater than a speed threshold; and if the ground speed in the real-time GPS data is greater than the speed threshold, determining the transverse force coefficient according to the real-time GPS data and the historical GPS data.

In an embodiment of the present application, the pushing module 1107 is configured to store real-time GPS data and corresponding lateral force coefficients as a sharp turn record; and sending the sharp turning record to the server so that the server pushes the sharp turning record to the terminal equipment or generates a driving report according to the sharp turning record.

For specific limitations of the vehicle sharp turn recognition device, reference may be made to the above limitations of the vehicle sharp turn recognition method, which are not described herein again. The various modules in the vehicle sharp turn recognition 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 terminal, and its internal structure diagram may be as shown in fig. 13. The computer device includes a processor, a memory, a network 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 network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle sharp turn recognition 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. 13 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 provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:

receiving real-time GPS data of a vehicle; the real-time GPS data comprises receiving time, ground speed, course angle and positioning state;

judging whether the real-time GPS data is valid or not according to the positioning state;

if the real-time GPS data is valid, acquiring historical GPS data adjacent to the receiving time;

determining a transverse force coefficient according to the real-time GPS data and the historical GPS data; the transverse force coefficient is related to the receiving time of the real-time GPS data, the receiving time of the historical GPS data, the ground speed and the course angle;

and if the transverse force coefficient is larger than or equal to the sharp turning judgment threshold value, judging that the vehicle is in a sharp turning state.

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 may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

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-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 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.