Vehicle driving behavior monitoring method and device

Abstract

The application provides a vehicle driving behavior monitoring method and device, and relates to the technical field of intelligent monitoring.The method comprises the following steps: acquiring positioning points corresponding to a driving track of a target vehicle, collecting vehicle sensor information of each positioning point, performing track segmentation on the driving track to obtain track segmentation information, and further determining the vehicle driving behavior on the basis of automatically segmenting the driving track of the target vehicle and according to the track segmentation information.The vehicle driving behavior monitoring method provided by the application improves the rationality and accuracy of track automatic segmentation, and can further determine the vehicle driving behavior according to the track segmentation information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent monitoring, in particular to a vehicle driving behavior monitoring method and device. BACKGROUND

[0002] The vehicle trajectory automatic cutting segmentation method is a key technology for processing continuous trajectory data, and is mainly used in the fields of data analysis, behavior recognition, path planning, etc. In the prior art, the trajectory segmentation method mainly includes: time interval segmentation method and time period segmentation method.

[0003] The time interval segmentation method: by setting a fixed time threshold, when the time difference between the adjacent two trajectory points exceeds the threshold, the trajectory cutting is performed between the two points, this method does not consider the vehicle abnormal situation, such as the vehicle adopting the backup power supply to upload the positioning information when the vehicle is off, the vehicle speed is 0 when the vehicle is parked;

[0004] The time period segmentation method: according to a specific time period (such as natural day, hour, etc.), the trajectory is segmented, if the trajectory crosses the set time period boundary, the cutting is performed at the boundary, this method is segmented fixedly, without combining the driving scene, and a complete driving trajectory is often divided into multiple segments.

[0005] The segmentation method in the prior art not only has low accuracy, but also does not involve the analysis of the vehicle driving behavior. SUMMARY

[0006] The present application provides a vehicle driving behavior monitoring method and device to solve the above problems existing in the prior art.

[0007] The present application provides a vehicle driving behavior monitoring method, comprising the following steps.

[0008] Obtaining the positioning points corresponding to the driving trajectory of the target vehicle, and collecting the vehicle sensor information of each positioning point;

[0009] Segmenting the driving trajectory according to the vehicle sensor information, and recording the trajectory segmentation information;

[0010] Determining the vehicle driving behavior according to all the trajectory segmentation information.

[0011] According to the vehicle driving behavior monitoring method provided by the present application, the vehicle sensor information includes vehicle speed and acc state information.

[0012] According to the vehicle driving behavior monitoring method provided by the present application, the segmenting the driving trajectory according to the vehicle sensor information comprises:

[0013] determining whether the current positioning point is the starting point of a new trajectory segment according to a time interval between a positioning time of the current positioning point and a last time with speed;

[0014] In a case where the time interval is greater than a preset threshold, the current positioning point is determined as the starting point of a new trajectory segment, and a last positioning point corresponding to the last time with speed is determined as the ending point of a last trajectory segment.

[0015] According to the vehicle driving behavior monitoring method provided by the application, the trajectory segment information is recorded, including:

[0016] The mileage summary, the effective speed summary, the effective speed number, the segment start time and the segment end time of the trajectory segment are recorded.

[0017] According to the vehicle driving behavior monitoring method provided by the application, the trajectory segment information is recorded, including:

[0018] The distance between two adjacent positioning points in the trajectory segment is calculated.

[0019] The distances between all adjacent positioning points are accumulated to obtain the mileage summary of the trajectory segment.

[0020] According to the vehicle driving behavior monitoring method provided by the application, the trajectory segment information is recorded, including:

[0021] The positioning validity and the speed validity of each positioning point in the trajectory segment are determined; the positioning point is valid in a case where the positioning point has corresponding position data and the acc state information is in an ignition state, and the speed of the positioning point is valid in a case where the speed of the positioning point is greater than zero.

[0022] The effective speed summary and the effective speed number are determined according to the speed corresponding to the valid positioning point and the number of the valid positioning points.

[0023] The application further provides a vehicle driving behavior monitoring device, including the following modules:

[0024] The acquisition module is used for acquiring the positioning points corresponding to the driving trajectory of the target vehicle, and collecting the vehicle sensor information of each positioning point.

[0025] The segment module is used for segmenting the driving trajectory according to the vehicle sensor information, and recording the trajectory segment information.

[0026] The determination module is used for determining the vehicle driving behavior according to all the trajectory segment information.

[0027] The application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the vehicle driving behavior monitoring method according to any one of the above when executing the computer program.

[0028] The application further provides a non-transitory computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the vehicle driving behavior monitoring method according to any one of the above.

[0029] The application further provides a computer program product, comprising a computer program, wherein the computer program is executed by a processor to implement the vehicle driving behavior monitoring method according to any one of the above.

[0030] The application provides a vehicle driving behavior monitoring method, which comprises the following steps: obtaining a positioning point corresponding to a driving track of a target vehicle, collecting vehicle sensor information of each positioning point, performing track segmentation on the driving track, and obtaining track segmentation information, so as to improve the rationality and accuracy of automatic track segmentation; and then on the basis of accurately and reasonably segmenting the driving track of the target vehicle, further determining the vehicle driving behavior according to the track segmentation information, including that the vehicle is parked for a long time in a time period, and the driver drives how many times, the distance of each driving and the driving time interval in other time periods. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0032] Figure 1 is a flowchart of a vehicle driving behavior monitoring method provided by the present application.

[0033] Figure 2 is an algorithm implementation relationship and dependent step schematic diagram in a vehicle driving behavior monitoring method provided by the present application.

[0034] Figure 3 is a structural schematic diagram of a vehicle driving behavior monitoring device provided by the present application.

[0035] Figure 4 is a structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0036] For long-time trajectory recording, if as a whole, it often leads to too large data volume, which is difficult to effectively store, retrieve and analyze. Through segmentation, data management and processing flow can be simplified. Therefore, it is often necessary to simplify data management and processing flow through trajectory segmentation processing.

[0037] In the prior art, the time interval segmentation method does not consider vehicle abnormal conditions. For example, the vehicle adopts backup power to upload positioning information when the vehicle is off, and the vehicle is parked. The time interval segmentation method often segments fixedly, without combining the driving scene, which can split a complete driving trajectory into multiple segments.

[0038] In addition, the segmentation method in the prior art only considers the problem of low retrieval efficiency and poor rendering performance caused by too large data volume, without involving driver behavior analysis.

[0039] The vehicle driving behavior monitoring method provided by the present application judges the vehicle condition according to the vehicle sensor information, filters invalid positioning in the off (vehicle built-in battery uploads trajectory positioning data) and long-time parking scenes during segmentation. At the same time, the driver's behavior can also be analyzed, such as how many times the driver drives, the distance and time interval of each driving in a certain time period.

[0040] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the present application will be described clearly and completely in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] The vehicle driving behavior monitoring method and device of the present application will be described below. Figures 1 to 4 The vehicle driving behavior monitoring method and device of the present application will be described below.

[0042] The vehicle driving behavior monitoring method provided by the present application comprises obtaining vehicle trajectory data and vehicle sensor data. The sensor data is obtained to judge whether the vehicle is normally running, filter abnormal positioning information, and comprehensively judge whether the trajectory is segmented according to the vehicle driving condition and positioning information, which improves the rationality and accuracy of automatic trajectory segmentation. At the same time, the driver's behavior can also be analyzed, such as how many times the driver drives, the distance and time interval of each driving in a certain time period.

[0043] Figure 1 is a flowchart of a vehicle driving behavior monitoring method provided by the present application, as Figure 1 shown, the method comprises the following steps:

[0044] Step 101, acquiring a positioning point corresponding to a driving track of a target vehicle, and collecting vehicle sensor information of each positioning point.

[0045] In any driving track of the target vehicle, a plurality of positioning points are set, and the position information of the positioning points is acquired through a Beidou navigation system.

[0046] Optionally, the vehicle sensor information includes vehicle speed and acc state information.

[0047] Specifically, the vehicle speed information and the acc state of the vehicle (i.e., the vehicle is in an ignition state or an off state) are collected through the vehicle sensor, and whether the vehicle is running normally is determined according to the vehicle speed information and the acc state of the vehicle. For example, if the vehicle speed is not 0 and the vehicle is in an ignition state, it is determined that the vehicle is running normally; and if the vehicle speed is 0 and / or the vehicle is in an off state, it is determined that the vehicle is not running.

[0048] Step 102, segmenting the driving track according to the vehicle sensor information, and recording track segment information.

[0049] Optionally, the segmenting the driving track according to the vehicle sensor information comprises:

[0050] According to the time interval between the positioning time of the current positioning point and the last time with speed, it is determined whether the current positioning point is the starting point of a new track segment.

[0051] In the case where the time interval is greater than a preset threshold, the current positioning point is determined as the starting point of a new track segment, and the end point of the last track segment is determined as the positioning point corresponding to the last time with speed.

[0052] Specifically, in the embodiment of the application, the threshold of the time interval between the positioning time of the current positioning point and the last time with speed is set to 10 minutes (in other scenarios or embodiments, the threshold can also be set to 20 minutes, 40 minutes or other arbitrary values, and in the embodiment of the application, the threshold is set to 10 minutes as an example).

[0053] Suppose the positioning time of the current positioning point of the target vehicle is 10:00, and the last time with speed (i.e., speed greater than zero) of the target vehicle is 9:30, then the time interval is 30 minutes, which is greater than the threshold (10 minutes). At this time, the current positioning point (i.e., the positioning point corresponding to 10:00) can be taken as the starting point of a new driving track segment, and the positioning point corresponding to 9:30 can be taken as the positioning point corresponding to the last time with speed (i.e., speed greater than zero).

[0054] Optionally, the recording of the track segment information comprises:

[0055] a mileage summary of the track segment, a valid speed summary, a valid speed number, a segment start time and a segment end time.

[0056] Specifically, the track segment information includes a mileage summary, a valid speed summary, a valid speed number, a segment start time and a segment end time.

[0057] Suppose the positioning time of the current positioning point of the target vehicle is 10:00, and the last time with speed (i.e. speed greater than zero) of the target vehicle is 9:30, which is greater than the threshold (10 minutes), then the positioning point corresponding to 10:00 is the starting point of a new track segment, and the segment start time is 10:00, and the positioning point corresponding to 9:30 is the positioning point corresponding to the last time with speed (i.e. speed greater than zero), and 9:30 is the segment end time of the last segment.

[0058] Optionally, the track segment information includes:

[0059] The positioning validity and speed validity of each positioning point in the track segment are determined; the positioning point is valid if there is corresponding position data and the acc state information is the ignition state, and the speed is valid if the corresponding speed of the positioning point is greater than zero;

[0060] The valid speed summary and the valid speed number are determined according to the speed corresponding to the valid positioning point and the number of valid positioning points.

[0061] Specifically, if the current positioning speed is greater than zero, the valid speed summary is accumulated with the current positioning speed, and the valid speed number is accumulated by 1; the last time with speed (positioning speed greater than zero) is the positioning time corresponding to the last time with speed.

[0062] Suppose the positioning time of the current positioning point of the target vehicle is 10:00, and the corresponding position data of the positioning point is A square (the position data can also be B street or C building or other positions as long as the position with valid latitude and longitude coordinates), and the speed is 20 kilometers per hour (the speed can also be 10 kilometers per hour or 30 kilometers per hour or other values as long as it is greater than 0), which indicates that the positioning of the target vehicle at 10:00 is valid, and the speed is valid, at this time the positioning point (i.e. A square) corresponding to 10:00 is recorded as a valid positioning point, the total number of valid positioning points is added by 1, and the valid speed summary is determined; the speed corresponding to 10:00 is the valid speed (i.e. 20 kilometers per hour), and the total number of valid speeds is added by 1, thereby determining the valid speed number.

[0063] Optionally, the track segment information includes:

[0064] The distance between two adjacent positioning points in the track segment is calculated;

[0065] And all the adjacent positioning between the distance is accumulated, the mileage summary of the trajectory segment is obtained.

[0066] Specifically, a plurality of sub-segments are contained in a trajectory segment, and each sub-segment contains a plurality of vehicle positioning coordinates. For example, four vehicle positioning coordinates are contained in a sub-segment, i.e. positioning 1 (initial positioning), positioning 2, positioning 3 and positioning 4. When calculating the mileage of the sub-segment, positioning 1 is set as the first coordinate, and then whether positioning 2 can be used as the second coordinate is determined according to the time interval between the positioning time of positioning 2 and the positioning time of positioning 1 (i.e. the last time with speed).

[0067] Suppose the threshold of the time interval is 10 minutes.

[0068] When the time interval between the positioning time of positioning 2 and the positioning time of positioning 1 is less than or equal to 10 minutes:

[0069] If positioning 2 is valid, positioning 2 is used as the second coordinate, at this time, the distance between positioning 1 and positioning 2 and other data information are calculated, and then positioning 2 is reset as the first coordinate;

[0070] Continue to determine whether positioning 3 is set as the second coordinate by the above method, if yes, calculate the distance between positioning 2 and positioning 3;

[0071] If positioning 3 has speed, the speed is accumulated, and the number of speed is accumulated.

[0072] When the time interval between the positioning time of positioning 2 and the positioning time of positioning 1 is greater than 10 minutes:

[0073] If there is no participation in calculation before and after positioning 1, it is an isolated point, the start time and the end time of the segment are the positioning time of positioning 1, and the mileage of the segment is 0.

[0074] When the time interval between the positioning time of positioning 3 and the positioning time of positioning 2 is greater than 10 minutes:

[0075] If positioning 3 is valid, the coordinate and the positioning time of positioning 2 are the end coordinate and the end time of the previous segment, and the coordinate of positioning 3 is the start coordinate of the next segment;

[0076] If positioning 3 has speed, the positioning time of positioning 3 is the start time of the next segment.

[0077] In this way, a plurality of mileage values can be obtained by calculation, and the mileage summary of the plurality of sub-segments in the trajectory segment can be obtained by accumulation.

[0078] The vehicle driving behavior monitoring method provided by the application judges the vehicle condition according to vehicle sensor information, filters invalid positioning in scenarios such as engine stall (positioning data on the built-in battery of the vehicle) or long-time parking, and avoids the driving track from being segmented multiple times due to invalid positioning. For example, positioning data when the vehicle is stalled or parked is concentrated in one position, resulting in meaningless segmentation. By filtering meaningless segmentation, the accuracy of segmenting the driving track of the vehicle is improved.

[0079] The vehicle driving behavior monitoring method provided by the application is flexible in segmentation, judges the vehicle condition according to vehicle sensor information (including vehicle speed and acc state information), and segments in combination with specific driving scenarios, so that a complete driving track is not divided into multiple segments, and the rationality of segmenting the driving track of the target vehicle is improved.

[0080] Based on any of the above embodiments, the actual calculation process involved in the track segmentation process in the embodiments of the application is described below. Figure 2 is a schematic diagram of the algorithm implementation relationship dependent steps in the vehicle driving behavior monitoring method provided by the application, as shown in Figure 2 .

[0081] First, the initialization value is constructed.

[0082] Mileage summary: mileageSum is 0 (driving mileage of each segmented track).

[0083] Effective speed summary: speedSum is 0 (driving effective speed cumulative summary of each segmented track).

[0084] Effective speed count: speedCount is 0 (driving effective speed count summary of each segmented track).

[0085] First coordinate: (Lat1, Lng1) (latitude coordinate and longitude coordinate of the start of the positioning of the segmentation).

[0086] Second coordinate: (Lat2, Lng2) (latitude coordinate and longitude coordinate of the end of the positioning of the segmentation).

[0087] Segmentation start time: beginTime.

[0088] Segmentation end time: splitSpeedTime.

[0089] Last speed time: lastSpeedTime.

[0090] Secondly, a batch of positioning points of the track is taken in a loop, and the track segmentation information is recorded, mainly including the driving mileage and average speed of the segmentation.

[0091] The distance between the first and second coordinates of a segment is calculated as follows:

[0092] Given the positions of two points P1 (start of positioning) and P2 (end of positioning) on the Earth's surface, the latitude and longitude coordinates of P1 are (Lat1, Lng1), and the latitude and longitude coordinates of P2 are (Lat2, Lng2).

[0093] The great-circle distance d (in meters) between these two points can be calculated by the following steps:

[0094] (1) Convert the latitude and longitude from degrees to radians, where the radian corresponding to the latitude coordinate lat1 of P1, the radian corresponding to the latitude coordinate lat2 of P2, is the difference in radians between the longitude coordinate lng2 of P2 and the longitude coordinate lng1 of P1.

[0095] = lat1 × , = lat2 × , = (lng2 - lng1) ×

[0096] (2) Calculate the intermediate value a:

[0097] a = sin( ) ⋅ sin( ) + cos( ) ⋅ cos( ) ⋅ cos( )

[0098] (3) Calculate the angular distance θ (in radians) between P1 and P2:

[0099] θ = arccos(min(a, 1))

[0100] (4) Finally, calculate the distance d (in meters) between P1 and P2 by multiplying the angular distance by the average radius of the Earth R:

[0101] d = θ ⋅ R

[0102] where: R is the average radius of the Earth (approximately 6371000 meters); arccos is the inverse cosine function; min(a, 1) ensures that the argument of the arccos function does not exceed 1, which is to avoid problems due to rounding errors in numerical calculations.

[0103] The average speed avgSpeed between the first and second coordinates of a segment is calculated as follows (rounded to two decimal places):

[0104] avgSpeed = Math.round(AvgSpeed * 100.0) / 100.0

[0105] Based on any of the above embodiments, before recording the trajectory segment information, it is also necessary to determine whether the trajectory segment information is a starting point.

[0106] For example, the target vehicle is always stationary, and starts for the first time at 10:00 (the speed at 10:00 is greater than 0), so the positioning point corresponding to 10:00 can be taken as the starting point of a new trajectory segment, and the effective positioning and effective speed corresponding to 10:00 are the first data, indicating that it is the first data of this batch of trajectory positioning. At this time, the mileage summary, effective speed summary, effective speed number, segment start time, segment end time are initialized, the segment start time is taken as 10:00, that is, the subsequent driving trajectory of the target vehicle is segmented from 10:00, and the trajectory segment information is recorded.

[0107] Step 103, determining the vehicle driving behavior according to all the trajectory segment information.

[0108] Based on the accurate and reasonable segmentation of the trajectory information of the target vehicle, the vehicle driving behavior is further determined based on the segmentation result of the trajectory information. For example, the vehicle is parked for a long time in a certain time period, and the driver drives how many times, the distance and time interval of each driving in other time periods, etc.

[0109] The vehicle driving behavior monitoring method provided by the application acquires vehicle trajectory data and vehicle sensor data, judges whether the vehicle is normally running, and filters abnormal or invalid positioning information according to the driving condition and positioning information of the vehicle, comprehensively judges whether the driving trajectory needs to be segmented, and improves the rationality and accuracy of automatic trajectory segmentation. Then, based on the accurate and reasonable segmentation of the trajectory information of the target vehicle, the vehicle driving behavior is further determined based on the segmentation result of the trajectory information, including that the vehicle is parked for a long time in a certain time period, and the driver drives how many times, the distance and driving time interval of each driving in other time periods, etc.

[0110] A vehicle driving behavior monitoring device provided by the application is described below, and the vehicle driving behavior monitoring device described below can be correspondingly referred to the vehicle driving behavior monitoring method described above.

[0111] Based on any of the above embodiments, Figure 3 is a structural schematic diagram of a vehicle driving behavior monitoring device provided by the application, like Figure 3As shown, the embodiment of the present application provides a vehicle driving behavior monitoring device, comprising an acquisition module 301, a segmentation module 302 and a determination module 303, wherein:

[0112] The acquisition module 301 is configured to acquire positioning points corresponding to a driving trajectory of a target vehicle, and collect vehicle sensor information of each positioning point; the segmentation module 302 is configured to segment the driving trajectory according to the vehicle sensor information, and record trajectory segmentation information; and the determination module 303 is configured to determine a vehicle driving behavior according to all the trajectory segmentation information.

[0113] The vehicle driving behavior monitoring device provided by the present application can judge whether the vehicle is running normally by acquiring vehicle trajectory data and vehicle sensor data, filter abnormal or invalid positioning information according to vehicle driving conditions and positioning information, comprehensively judge whether the driving trajectory needs to be segmented, and improve the rationality and accuracy of automatic trajectory segmentation; then, on the basis of accurately and reasonably segmenting the trajectory information of the target vehicle, the vehicle driving behavior is further determined based on the segmentation result of the trajectory information, including that the vehicle is parked for a long time in a time period, and the driver drives how many times, the distance and driving time interval of each driving in other time periods.

[0114] Figure 4 An example of an entity structure diagram of an electronic device is shown in FIG. 1. Figure 4 As shown, the electronic device can include a processor 410, a communications interface 420, a memory 430 and a communications bus 440, wherein the processor 410, the communications interface 420 and the memory 430 can communicate with each other through the communications bus 440. The processor 410 can call the logical instructions in the memory 430 to execute a vehicle driving behavior monitoring method, which comprises:

[0115] Acquiring positioning points corresponding to a driving trajectory of a target vehicle, and collecting vehicle sensor information of each positioning point;

[0116] Segmenting the driving trajectory according to the vehicle sensor information, and recording trajectory segmentation information;

[0117] Determining a vehicle driving behavior according to all the trajectory segmentation information.

[0118] In addition, the logic instructions in the memory 430 described above can be implemented in the form of a software function unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0119] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the vehicle driving behavior monitoring method provided by the above-mentioned methods, which comprises:

[0120] obtaining the positioning points corresponding to the driving trajectory of the target vehicle, and collecting the vehicle sensor information of each positioning point;

[0121] segmenting the driving trajectory according to the vehicle sensor information, and recording the trajectory segmentation information;

[0122] determining the vehicle driving behavior according to all the trajectory segmentation information.

[0123] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the vehicle driving behavior monitoring method provided by the above-mentioned methods, which comprises:

[0124] obtaining the positioning points corresponding to the driving trajectory of the target vehicle, and collecting the vehicle sensor information of each positioning point;

[0125] segmenting the driving trajectory according to the vehicle sensor information, and recording the trajectory segmentation information;

[0126] determining the vehicle driving behavior according to all the trajectory segmentation information.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0128] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0129] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0130] It should also be noted that the terms "target," "first," and "second," etc., used in the present invention are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "first" and "second" generally distinguish objects of the same type, and do not limit the number of objects. For example, the first object can be one or more.

[0131] The term "plurality" in the present application refers to two or more, and other quantifiers are similar.

[0132] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A vehicle driving behavior monitoring method characterized by, The method comprises: obtaining positioning points corresponding to a driving track of a target vehicle, and collecting vehicle sensor information of each positioning point; segmenting the driving track according to the vehicle sensor information, and recording track segment information; determining a driving behavior of the vehicle according to all the track segment information; the vehicle sensor information comprises vehicle speed and ACC state information; the segmenting the driving track according to the vehicle sensor information comprises: determining whether a current positioning point is a starting point of a new track segment according to a time interval between a positioning time of the current positioning point and a last time with speed; in a case where the time interval is greater than a preset threshold, determining that the current positioning point is the starting point of the new track segment, and determining that an ending point of a previous track segment is a positioning point corresponding to the last time with speed; the recording the track segment information comprises: determining positioning validity and speed validity of each positioning point in the track segment; a positioning point is valid in positioning if the positioning point has corresponding position data and the ACC state information is an ignition state, and a positioning point is valid in speed if the corresponding speed of the positioning point is greater than zero; determining valid speed summary and valid speed number according to the speed corresponding to the valid positioning point and the number of the valid positioning points.

2. The vehicle driving behavior monitoring method according to claim 1, characterized by, the recording the track segment information comprises: recording mileage summary, valid speed summary, valid speed number, segment start time and segment end time of the track segment.

3. The vehicle driving behavior monitoring method according to claim 1, characterized by, the recording the track segment information comprises: calculating a distance between two adjacent positioning points in the track segment; and adding up distances between all adjacent positioning points to obtain mileage summary of the track segment.

4. A vehicle driving behavior monitoring apparatus characterized by comprising: The method comprises: an obtaining module, configured to obtain positioning points corresponding to a driving track of a target vehicle, and collect vehicle sensor information of each positioning point; a segmenting module, configured to segment the driving track according to the vehicle sensor information, and record track segment information; a determining module, configured to determine a driving behavior of the vehicle according to all the track segment information; the vehicle sensor information comprises vehicle speed and ACC state information; the segmenting the driving track according to the vehicle sensor information comprises: determining whether a current positioning point is a starting point of a new track segment according to a time interval between a positioning time of the current positioning point and a last time with speed; in a case where the time interval is greater than a preset threshold, determining that the current positioning point is the starting point of the new track segment, and determining that an ending point of a previous track segment is a positioning point corresponding to the last time with speed; the recording the track segment information comprises: determining positioning validity and speed validity of each positioning point in the track segment; a positioning point is valid in positioning if the positioning point has corresponding position data and the ACC state information is an ignition state, and a positioning point is valid in speed if the corresponding speed of the positioning point is greater than zero; determining valid speed summary and valid speed number according to the speed corresponding to the valid positioning point and the number of the valid positioning points.

5. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor implements the vehicle driving behavior monitoring method according to any one of claims 1 to 3 when executing the computer program.

6. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the vehicle driving behavior monitoring method according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the vehicle driving behavior monitoring method according to any one of claims 1 to 3.

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