CN107833464B - Driving behavior safety assessment method and storage medium - Google Patents
Driving behavior safety assessment method and storage medium Download PDFInfo
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Abstract
The invention discloses a driving behavior safety assessment method, which comprises the following steps: pre-storing road network data, wherein the road network data comprises road descriptive data and intersection signal lamp descriptive data; acquiring driving state data, wherein the driving state data comprises a driving person ID, a driving moment, a geographical position, a driving speed and a driving acceleration; dividing the driving state data into intersection data and non-intersection data according to the distance between a driver and an intersection; evaluating the driving behavior of the non-intersection according to the non-intersection data and the corresponding road network data; evaluating the driving behavior of the home socket according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time; and calculating the driving safety index according to the evaluation result, and providing data support for the optimized management of distribution service personnel.
Description
Technical Field
The invention relates to the technical field of data processing and analysis, in particular to a driving behavior safety assessment method and a storage medium.
Background
With the continuous progress of internet technology, the distribution service industry is greatly developed. At present, short-distance distribution service personnel mostly use electric bicycles, motorcycles, small automobiles and small trucks. The electric bicycle has the characteristics of high speed, low price, low energy consumption, convenience in riding and the like, so that the effective hardware support is provided for the distribution service industry, and the application range is wide. With the use of electric bicycles in large quantities, the defects of the electric bicycles in traffic safety are increasingly obvious, and the electric bicycles are two-wheeled vehicles, so that certain instability exists in the driving process of the electric bicycles, and the electric bicycles are subjected to speed pursuit by practitioners, so that higher traffic safety risks are caused. Therefore, the riding safety of the electric bicycle rider needs to be evaluated, so that the driving behavior safety of the electric bicycle rider can be judged and evaluated efficiently in real time, and the management and accident prevention of the rider can be completed in an auxiliary manner.
In the current society aiming at the riding safety research of the electric bicycle, on one hand, the self structure of the electric bicycle is taken as a research object, and the safety evaluation is completed through the self performance detection of the electric bicycle; on the other hand, the evaluation is carried out from multiple aspects such as the behavior of a riding person and the performance of the electric vehicle based on the experimental environment. However, in the practical application process, the performance of the electric bicycle is difficult to directly detect, and the electric bicycle mainly depends on manufacturers of the electric bicycles, and the riding environment of the electric bicycle is variable and difficult to carry out standardized evaluation.
In the prior art, methods for scoring the driving behavior of an automobile exist, but data acquisition is carried out by depending on automobile-mounted equipment and a professional sensor, so that the manufacturing cost of the automobile is very high, the acquired data has a lot of state information about the automobile, and the scoring process is very complex and is not timely.
Especially, currently, under the large sharing background, the vehicles and the passengers can be shared by multiple persons for use many times, and how to perform driving behavior safety assessment on different users is also necessary.
Therefore, how to directly acquire data and reliably and efficiently evaluate driving behaviors based on the data is a technical problem to be solved at present.
Disclosure of Invention
In view of the above, the present invention has been developed to provide a solution to, or at least partially solve, the above problems.
In one aspect of the present invention, there is provided a driving behavior safety evaluation method, including: pre-storing road network data, wherein the road network data comprises road descriptive data and intersection signal lamp descriptive data;
acquiring driving state data, wherein the driving state data comprises a driving person ID, a driving moment, a geographical position, a driving speed and a driving acceleration;
dividing the driving state data into intersection data and non-intersection data according to the distance between the driver and the nearest intersection;
evaluating the driving behavior of the non-intersection according to the non-intersection data and the corresponding road network data;
evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time;
and calculating the driving safety index according to the evaluation result.
Optionally, the method further includes:
and matching the acquired geographical position information with the pre-stored road network data to acquire matched road longitude and latitude mapping points as the geographical positions of the travelers after processing.
Optionally, the method of dividing the driving state data into intersection data and non-intersection data according to the distance between the driver and the nearest intersection includes:
presetting a first distance threshold Ld before an intersection and a second distance threshold Lb after the intersection;
calculating the distance between the geographic position of the driver and the nearest intersection;
and if the distance calculated before passing through the intersection is smaller than the threshold value Ld or the distance calculated after passing through the intersection is smaller than the threshold value Lb, dividing the driving state data corresponding to the geographic position of the driving person into intersection data, and otherwise, dividing the driving state data into non-intersection data.
Optionally, the method of dividing the driving state data into intersection data and non-intersection data according to the distance between the driver and the intersection includes:
presetting a first distance threshold Ld and a second distance threshold Ld1, wherein Ld > Ld 1;
calculating the distance between the geographic position of the driver and the nearest intersection;
if the calculated distance is less than the threshold value Ld, dividing the driving state data corresponding to the geographic position of the driver into intersection data;
if the calculated distance is greater than the threshold value Ld1, the driving state data corresponding to the geographical position of the person is classified as non-intersection data.
Optionally, the non-intersection driving behavior is evaluated according to the non-intersection data and the corresponding road network data, and the method specifically includes:
a speed stability indicator is calculated from the travel speed.
Optionally, the non-intersection driving behavior is evaluated according to the non-intersection data and the corresponding road network data, and the method specifically includes:
and calculating a speed jerk index according to the running acceleration.
Optionally, the non-intersection driving behavior is evaluated according to the non-intersection data and the corresponding road network data, and the method specifically includes:
a speed overspeed indicator is calculated from the travel speed and the road descriptive data.
Optionally, the method for evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time specifically includes:
and judging whether a red light running behavior exists according to the acquired matching vector of the geographic position of the running person and the road network data and the real-time acquired red light period of the intersection.
Optionally, the method for evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time specifically includes:
and judging whether an intersection accelerated driving behavior exists or not according to the intersection driving acceleration and the intersection signal lamp state data acquired in real time.
Optionally, the method for evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time specifically includes:
and judging whether a sharp steering behavior exists according to the acquired matching vector and acceleration vector of the geographic position of the driver and the road network data.
Optionally, the method further includes:
and calculating the behavior index of the intersection according to the judgment result.
Optionally, the steps are as follows: calculating a driving safety index according to the evaluation result, specifically comprising:
and for the single driving process, calculating the driving safety index according to the weighting of each index.
Optionally, the steps are as follows: calculating a driving safety index according to the evaluation result, specifically comprising:
and for multiple driving processes, carrying out weighted calculation on the single driving safety index according to the travel time period to obtain the total driving safety index.
Optionally, the method comprises the steps of: acquiring the driving state data, and then: and extracting effective driving state data from the acquired driving state data, specifically including abnormal data filtering and/or extracting data larger than a preset driving speed.
Optionally, the driving behavior safety evaluation method is an electric vehicle driving behavior safety evaluation method.
Optionally, the driving state data is acquired by a mobile terminal application.
The present invention provides a storage medium for storing a computer program for performing the method as described above.
The invention provides a server comprising a memory, a processor and a computer program stored on said memory, which computer program, when executed by the processor, carries out the steps of the method as set forth above.
The present invention provides a hand-held terminal comprising a memory, a processor and a computer program stored on said memory, which computer program, when being executed by the processor, carries out the steps of the method as set forth above.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
the foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 shows a flow diagram of an evaluation method according to one embodiment of the invention;
FIG. 2 illustrates a non-intersection driving behavior coverage area division schematic;
fig. 3 shows a schematic diagram of intersection driving behavior coverage area division.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The driving behavior safety assessment method is based on real-time detection data of the handheld mobile terminal (including a mobile phone), real-time signal lamp state data and cloud-stored road network data to evaluate the driving behavior safety. The invention specifically provides a driving behavior safety evaluation method, as shown in fig. 1, the method comprises the following steps:
s1, pre-storing road network data, wherein the road network data comprises road descriptive data and intersection signal lamp descriptive data;
s2, acquiring driving state data, wherein the driving state data comprises a driving person ID, a driving moment, a geographical position, a driving speed and a driving acceleration;
s3, dividing the driving state data into intersection data and non-intersection data according to the distance between the driver and the intersection;
s4, evaluating the driving behavior of the non-intersection according to the non-intersection data and the corresponding road network data;
s5, evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time;
and S6, calculating a driving safety index according to the evaluation result.
The steps of the method may be implemented by a computer application program capable of running on a processor of the server, the computer program being stored in a memory of the server, or some or all of the steps may be implemented by running a client application program on a processor of the handheld mobile terminal, the client application program being stored in a memory of the mobile terminal.
In step S1, the road network data is structured data used to describe the connection relationship between roads and the longitude and latitude information of roads, and the data provides the specific longitude and latitude position, road trend, road complexity and other information of each intersection, and can be specifically divided into two parts, namely road descriptive data and intersection signal light descriptive data. The road descriptive data comprises fields such as road ID, road longitude and latitude sequences, road passing direction, road length, road lane number and the like; the descriptive data of the signal lamps at the intersections comprise fields of signal lamp ID, signal lamp longitude and latitude, signal lamp phase, corresponding road ID of the signal lamp phase pair and the like. The road network data may be stored in advance instead of the real-time data.
In step S2, the real-time driving state data may be provided by a handheld mobile terminal (including a mobile phone), and specifically, may be periodically uploaded by a mobile phone APP (business-related APP used by a practitioner). Current APP just can upload the personnel's of riding concrete position, lets the customer know the concrete position of commodity circulation. In the invention, the mobile phone detects the position (longitude and latitude information) of a driver, the time, the driving speed and the driving acceleration of the driver to form collected data, and the APP installed on the mobile phone collects the collected data and uploads the data to the server. The driving state information may be expressed in the form of a vector:
wherein
Indicates that the riding person i is at the time tjThe corresponding formal state data vector is then used,
respectively represents the longitude and latitude information of the driving personnel at the moment,
and respectively shows the speed and the acceleration information of the driver at the moment.
Further matrix forms of the available driving state data are as follows:
where n represents the number of sample points. For the condition that partial speed data is missing, calculation and supplement can be carried out according to longitude and latitude data, and the calculation is as follows:
where length (#) represents a mapping formula for Euclidean distance calculation according to the longitude and latitude between two points, tthAnd (3) judging a threshold value for the continuity of the longitude and latitude tracks, namely judging that the time interval between the current and the last two sampling points is smaller than the threshold value, and considering that the tracks are continuous, wherein the value can be 3min (which can be determined according to specific conditions). Similarly, for missing acceleration data, the following can be calculated:
the signal lamp state data is data indicating real-time states of the signal lamps, and can be used for judging the passing state of a specific phase at a specific intersection, for example, the specific phase has a straight-going phase from east to west and a left-turning phase from east to south. The signal lamp state data can be generally obtained through a signal lamp related interface, and can also be obtained through internet mobile data calculation, and a specific calculation method is not a key point of the invention, and therefore, detailed description is not needed.
The driving behavior safety evaluation method is used for evaluating the driving behavior safety by processing and analyzing the data, can be completed by using widely-carried intelligent mobile terminals on the aspect of data acquisition, does not need to specially install special equipment on a vehicle, hardly needs to spend any extra hardware cost, and is easy to implement and popularize.
As a specific implementation, the real-time data needs to be preprocessed to ensure the reliability and validity of the data. Firstly, abnormal data filtering and data dividing are carried out on the driving state data.
The abnormal data filtering is to filter data which does not conform to actual conditions in the collected driving state data, the abnormal data is usually caused by reasons such as abnormal detector and data transmission error, and the filtering of the data is beneficial to ensuring the reliability of the data and the effectiveness of subsequent processing. When the driving behavior safety assessment method is used for electric vehicle riding, the data detected by the mobile phone is included
Recording and filtering of data of (a), wherein vthWhich represents the maximum speed that can be reached by the electric bicycle (which can be set according to the actual situation).
The data division means dividing the complete riding track of the electric bicycle into a riding stage and a service providing stage, wherein the riding stage refers to a stage in which a riding person rides the electric bicycle to finish position movement, namely a normal riding behavior generation stage; and in the service providing stage, namely the riding personnel reach the position of a service demander to provide service for the required personnel, and the mobile phone detection data does not correspond to the riding behavior of the electric bicycle because the position transfer is completed in the stage. According to the method, the complete mobile phone detection data is divided according to the position and speed change of the starting and ending stage of riding, and the behavior data of the riding stage is only used as the object of subsequent research. A certain time interval before and after the starting and stopping time of riding can be set, and the riding behavior far away from the starting and stopping time is taken as the behavior of the riding stage. Through data division, effective driving state data can be stripped.
And then, correcting the geographic position information acquired in real time by using road network data. And matching the acquired geographical position information with the pre-stored road network data to acquire matched road longitude and latitude mapping points as the geographical positions of the travelers after processing. The road matching means that longitude and latitude sampling points in data collected by a mobile phone are matched with corresponding road IDs, and longitude and latitude mapping points of the matched roads are calculated. The road matching work can be completed by the current relatively common road matching algorithm or can be completed by calling the road matching service provided by the current internet platform.
The driving behavior index is calculated from multiple aspects by taking the effective driving state data as the basis through the steps S3, S4 and S5, so that the driving behavior is embodied comprehensively.
Because the embodiment of the driving safety depends on the driving habits of the drivers and the traffic environment of the drivers, the safety index calculation is carried out from the non-intersection driving behaviors and the intersection driving behaviors respectively. The non-intersection driving behavior represents the driving behavior embodied by a driver in the driving process on a normal road (not approaching an intersection), and the intersection driving behavior represents the driving behavior embodied by the driver in the process of passing through the intersection.
The following description will be made by taking an electric bicycle as an example.
According to the distance between the electric bicycle and the intersection, dividing the non-intersection riding behaviors and the intersection riding behaviors, as shown in fig. 2, presetting a distance threshold value Ld, dividing the behaviors when the distance between the electric bicycle and the nearest intersection is greater than Ld into the non-intersection behaviors, and dividing the behaviors which are at the upstream and downstream distances of the intersection into the Ld and the behaviors which are within the Ld into the intersection riding behaviors.
If necessary, the range covered by the non-intersection riding behavior and the range covered by the intersection riding behavior can be overlapped to a certain extent, as shown in fig. 3. Presetting a first distance threshold Ld and a second distance threshold Ld1, wherein Ld > Ld 1; calculating the distance between the geographical position of the rider and the nearest intersection; if the calculated distance is less than the threshold value Ld, dividing the driving state data corresponding to the geographic position of the driver into intersection data; if the calculated distance is greater than the threshold value Ld1, the driving state data corresponding to the geographical position of the rider is classified as non-intersection data.
Evaluating the driving behaviors of the non-intersection according to the non-intersection data and the corresponding road network data, and specifically comprising the following steps of: calculating a speed stability index according to the running speed; calculating a speed jerk index according to the driving acceleration; a speed overspeed indicator is calculated from the travel speed and the road descriptive data.
The non-intersection riding behavior evaluation is based on the non-intersection riding data, and index evaluation is performed on the aspects of speed stability, rapid acceleration and deceleration, overspeed behavior and the like through processing of the part of riding data.
a) Speed stability index assessment
The riding stability refers to the ability of a riding person to maintain stable speed and stable speed change process in the riding process, the stability of the speed reflects the sensitivity and the adjusting ability of the driving person to the driving speed, and the stability of the speed change process reflects the driving anticipation ability and the driving habit of the driving person.
Suppose that the speed variation vector of the non-intersection riding behavior of the rider i is as follows:
wherein
Representing the detected riding speed at time tj. The speed stability indicator may be defined as follows:
wherein g (-) represents a high-pass filtering process aiming at the time sequence, and can be realized by adopting forms of Gaussian filtering, wavelet filtering and the like; f (-) represents data for processed high frequency velocity fluctuations
The process of treatment can be calculated by statistics
The variance form of the vector is implemented, i.e. the meaning is
Wherein N represents the number of sampling points, u represents the mean value of all sampling points, D represents a reference value which can be set according to actual conditions, max (x, y) represents the smaller value between x and y, and the index value αa∈[0,1]And the larger the value is, the better the riding safety is.
b) Evaluation of index of rapid acceleration and deceleration behavior
The rapid acceleration and deceleration behavior refers to a short-time strong acceleration and deceleration behavior generated by a riding person in the riding process, and the rapid acceleration and deceleration behavior may be caused by the influence of a traffic environment (for example, handling an accident), on the one hand, or by the driving behavior of the riding person (rapid acceleration) or the driving misjudgment (for example, rapid deceleration caused by not finding a pedestrian in time) on the other hand. The rapid acceleration and deceleration behavior of the rider easily causes certain sudden influence on subsequent drivers, and the rapid acceleration and deceleration behavior is a great traffic hidden trouble, so that the rapid acceleration and deceleration behavior is an important index for evaluating the riding safety.
Suppose that the acceleration change vector of the non-intersection riding behavior of the rider i is as follows:
wherein
Representing the acceleration detected at time tj. The finger for rapid acceleration and decelerationThe criteria may be defined as follows:
wherein DbThe reference value can be set according to actual conditions,
whether the function belongs to the rapid acceleration and deceleration judging function or not is shown, and the expression is as follows:
wherein a isthThe acceleration and deceleration threshold can be set according to actual conditions.
Index value αb∈[0,1]And the larger the value is, the better the riding safety is.
c) Overspeed behavior index evaluation
The overspeed behavior means that the riding speed of a riding person exceeds the running limit (or other specified speed limits) of a running road, the higher running speed brings greater safety risk due to the poorer stability of the electric bicycle, and the overspeed behavior reflects the driving concept and habit of the riding person to a certain extent, so that the overspeed behavior is an important index for evaluating the riding safety.
Suppose that the speed variation vector of the non-intersection riding behavior of the rider i is as follows:
wherein
Represents the time tjThe detected riding speed. The overspeed behavior index may be defined as follows:
wherein L isiIndicating the total length of the ride of the rider in non-intersection behavior,
the total distance of the speeding of the rider is represented, h' (v) represents a judging function of whether speeding is involved, and the expression is as follows:
wherein v isthThe speed threshold value can be set according to actual conditions.
Index value αc∈[0,1]And the larger the value is, the better the riding safety is.
Evaluating the driving behavior of the intersection according to the intersection data, the corresponding road network data and the intersection signal lamp state data acquired in real time, and specifically comprising the following steps of: judging whether a red light running behavior exists or not according to the acquired matching vector of the geographic position of the running person and road network data and the real-time acquired red light period of the intersection; judging whether an intersection accelerated driving behavior exists or not according to the intersection driving acceleration and the intersection signal lamp state data acquired in real time; and judging whether a sharp steering behavior exists according to the acquired matching vector and acceleration vector of the geographic position of the driver and the road network data. And finally, integrating the judgment results to calculate the behavior index of the intersection.
The intersection riding behavior evaluation is based on the intersection riding data, and the intersection riding data is processed to evaluate the abnormal behaviors such as red light running behavior, accelerated driving behavior and sharp turning behavior.
a) Red light running behavior judgment
The red light running behavior refers to the behavior that the riding personnel does not obey the dispatching of traffic lights at the intersection and passes through the intersection in the red light period. The intersection provides traffic service for multi-directional vehicles at the same time, so that the driving behavior which is not subjected to signal lamp scheduling easily conflicts with the driving behaviors of the vehicles in other directions, and traffic accidents are caused, and the red light running behavior is used as an index for safety evaluation.
The method is based on signal lamp state data, specific red light period on a travel phase corresponding to a riding person can be obtained, and by taking intersection k as an example, the start time of the red light period corresponding to the riding behavior of the intersection is assumed to be
And an end time of
The corresponding intersection riding behavior detection data road matching vector is
Wherein
Indicates that the riding person i is at tjThe road ID of the road where the moment is located is assumed that the set of influence road sections at the phase upstream of the signal lamp is RuIn the downstream set RdThen, the red light running judgment function can be defined as follows:
namely, when detecting that the riding personnel passes through the intersection in the red light period, judging that the riding personnel runs the red light, namely
b) Intersection accelerated driving behavior judgment
The intersection accelerated driving behavior means that a riding person does not take a deceleration measure at the intersection, but accelerates to drive into the intersection, and generally occurs under the condition that the riding person robs the intersection before a red light. Due to the complex traffic environment at the intersection, the electric bicycle has poor stability, and certain safe wind exists in the rapid acceleration driving behaviorAnd (5) risking. Taking intersection k as an example, assume that the start time of the green light cycle corresponding to the intersection riding behavior is
And an end time of
The corresponding intersection riding behavior acceleration vector is
Wherein
Indicates that the riding person i is at tjThe acceleration value at the moment can be defined as follows according to the judgment function of the accelerated driving-in behavior on the basis of detecting that the riding personnel pass through the intersection:
namely, when detecting that the riding personnel has the behavior of accelerating to pass through the intersection before the end of the green light, judging that the riding personnel has the behavior of accelerating to enter, namely
c) Determination of sharp turning behavior
The rapid turning behavior of the intersection means that the riding personnel does not obviously decelerate in the turning process of the intersection, and the turning operation is carried out at a higher speed. Because the stability of the electric bicycle is poor, a certain safety risk exists in the fast steering behavior.
Taking intersection k as an example, assume that the start time of the green light cycle corresponding to the intersection riding behavior is
And an end time of
The corresponding intersection riding behavior road matching vector and acceleration vector are
Wherein
Indicates that the riding person i is at tjThe link ID of the link on which the time is located,
indicates that the riding person i is at tjTime and speed, assuming the signal lamp phase upstream influencing road section set is RuDownstream steering set is RdMeanwhile, the average riding speed in the period of green light is counted to be
The sudden steering behavior determination function is as follows
Namely, when the riding person is detected to turn at a higher speed in the green light period, the riding person is judged to have a sharp turning behavior, namely the riding person is judged to have the sharp turning behavior
d) Intersection behavior index assessment
Based on the above discriminant function, it can be further determined that after the rider passes through a plurality of intersections, the intersection behavior evaluation index can be defined as follows:
where K represents the number of intersections that are passed through within the evaluation time period.
The index value beta belongs to [0,1], and the larger the value is, the better the riding safety is.
Calculating a driving safety index according to the evaluation result, specifically comprising: for a single driving process, calculating a driving safety index according to the weighting of each index; and for multiple driving processes, carrying out weighted calculation on the single driving safety index according to the travel time period to obtain the total driving safety index.
The safety embodied in the single dispatching process of the riding personnel and the multiple dispatching process of the riding personnel is evaluated, and the following implementation mode can be specifically adopted:
(1) single riding process
The riding data time span is short in the single dispatch process, and the index value α can be detected by using the mobile phone to detect the dataaαbαcβ, and then evaluating the single-ride safety index of the riding personnel in a weighted summation form, wherein the expression is as follows:
wherein x1,x2,x3,x4Representing weight coefficients
(2) Multiple riding process
The multiple riding processes cover riding records in different time periods and reflect riding habits of a riding person in different time periods, so that in the riding safety evaluation of the riding person through multiple riding data, the riding time period can be divided into three time periods, namely a peak time period (such as 7:00-9:00,17:00-20:00), a night time period (such as 20:00-06:00) and other time periods, and corresponding safety index values α in the three time periods are respectively calculatedaαbαcβ, obtaining the riding safety index at the peak time period by weighting
Riding safety index in night time
Riding safety index in other periods
The final resulting composite safety assessment index is as follows:
by calculating the index, the safety of the driver can be directly reflected on the basis of integrating the driving behaviors of all aspects, and the driving safety degree of the driver can be directly reflected.
The above embodiments of the present invention are mainly described by taking an electric bicycle as an example, and actually, the present invention is not only applicable to electric bicycles, but also applicable to other vehicles such as automobiles (including passenger cars and trucks), and is applicable to all scenes where the safety of driving behaviors of drivers needs to be evaluated, and is particularly more applicable to a situation where two driving vehicles in a service industry are used by multiple service personnel and a situation where vehicles are shared.
The invention provides a rider safety riding index system, which evaluates the riding safety coefficient of riders from different aspects, provides data support for the optimization management of riders and is beneficial to improving the service level of short-distance distribution business.
The technical scheme provided in the embodiment of the application at least has the following technical effects or advantages:
the safety of the driving behaviors of the driver can be evaluated, so that data support and basis can be provided for the optimized management of the driver (riding), and the improvement of the service level of the distribution service industry is facilitated.
The algorithms and processes, analyses provided herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.
Claims (12)
1. A driving behavior safety assessment method is characterized in that a client application program runs on a processor of a handheld mobile terminal to realize partial steps or all the steps, and the method comprises the following steps:
pre-storing road network data, wherein the road network data comprises road descriptive data and intersection signal lamp descriptive data;
acquiring driving state data through an intelligent mobile terminal, wherein the driving state data comprises a driving person ID, a driving moment, a geographical position, a driving speed and a driving acceleration;
dividing the driving state data into intersection data and non-intersection data according to the distance between the driver and the nearest intersection;
calculating a speed stability index according to the driving speed information in the non-intersection data, calculating a speed jerk index according to the driving acceleration in the non-intersection data, and calculating a speed overspeed index according to the driving speed in the non-intersection data and the road descriptive data;
judging whether a red light running behavior exists according to a matching vector of the geographic position of a driving person and road network data obtained from intersection data and an intersection red light period obtained in real time, judging whether an intersection accelerated driving behavior exists according to a driving acceleration in the intersection data and intersection signal light state data obtained in real time, and judging whether an urgent steering behavior exists according to the matching vector and the acceleration vector of the geographic position of the driving person and the road network data obtained from the intersection data;
and calculating the driving safety index according to each index and each behavior judgment result.
2. The method of claim 1, further comprising:
and matching the acquired geographical position information with the pre-stored road network data to acquire matched road longitude and latitude mapping points as the geographical positions of the travelers after processing.
3. The method according to claim 1 or 2, wherein the dividing of the driving state data into intersection data and non-intersection data according to the distance of the driver from the intersection includes:
presetting a first distance threshold Ld before an intersection and a second distance threshold Lb after the intersection;
calculating the distance between the geographic position of the driver and the nearest intersection;
and if the distance calculated before passing through the intersection is smaller than the threshold value Ld or the distance calculated after passing through the intersection is smaller than the threshold value Lb, dividing the driving state data corresponding to the geographic position of the driving person into intersection data, and otherwise, dividing the driving state data into non-intersection data.
4. The method according to claim 1 or 2, wherein the dividing of the driving state data into intersection data and non-intersection data according to the distance between the driver and the nearest intersection includes:
presetting a first distance threshold Ld before an intersection and a second distance threshold Ld1 before the intersection, wherein Ld > Ld 1;
calculating the distance between the geographical position of the driver before the intersection and the nearest intersection;
if the calculated distance is less than the threshold value Ld, dividing the driving state data corresponding to the geographic position of the driver into intersection data;
if the calculated distance is greater than the threshold value Ld1, the driving state data corresponding to the geographical position of the person is classified as non-intersection data.
5. The method of claim 1, further comprising:
and calculating the behavior index of the intersection according to the judgment result.
6. The method of claim 1, further characterized by the steps of: calculating a driving safety index according to each index and each behavior judgment result, which specifically comprises the following steps:
and for the single driving process, calculating the driving safety index in a weighting mode according to each index and the behavior judgment result.
7. The method according to claim 1, wherein calculating the driving safety index according to each index and each behavior determination result specifically comprises:
and for multiple driving processes, carrying out weighted calculation on the single driving safety index according to the travel time period to obtain the total driving safety index.
8. The method of claim 1, further characterized by the step of: acquiring the driving state data, and then: and extracting effective driving state data from the acquired driving state data, specifically including abnormal data filtering and/or extracting data larger than a preset driving speed.
9. The method of claim 1, used for evaluation of electric vehicle ride behavior.
10. A storage medium for storing a computer program for performing the method of any one of claims 1-9.
11. A server, characterized in that the server comprises a memory, a processor and a computer program stored on said memory, which computer program, when being executed by the processor, carries out the steps of the method according to any one of claims 1-9.
12. A hand-held terminal, characterized in that the terminal comprises a memory, a processor and a computer program stored on said memory, which computer program, when being executed by the processor, carries out the steps of the method as claimed in any one of the claims 1-9.
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