CN113393593A - Non-replaceable memory-saving driving recording system - Google Patents

Non-replaceable memory-saving driving recording system Download PDF

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CN113393593A
CN113393593A CN202110664684.5A CN202110664684A CN113393593A CN 113393593 A CN113393593 A CN 113393593A CN 202110664684 A CN202110664684 A CN 202110664684A CN 113393593 A CN113393593 A CN 113393593A
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data
module
road
driving
recorder
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乔宏哲
包林霞
沈梦娜
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Changzhou Vocational Institute of Mechatronic Technology
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Changzhou Vocational Institute of Mechatronic Technology
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841—Registering performance data
    • G07C5/085—Registering performance data using electronic data carriers
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions

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Abstract

The invention belongs to the technical field of automobiles, and particularly relates to a non-replaceable memory-saving driving recording system, which comprises a driving recorder and a cloud server connected with the driving recorder; the driving recorder is used for collecting driving images and sending real-time driving position data to the cloud server; the cloud server receives the driving position data, calculates a road danger index of the driving position, and sends the road danger index to the driving recorder; and the automobile data recorder adjusts and stores the video resolution according to the road danger index. The current position is determined through the automobile data recorder, the risk index of the current position is calculated through the cloud server, the resolution of the stored video is adjusted according to the risk index, the storage space is optimized, and the problem of seconds missing caused by the adoption of an alternative mode for saving the storage space is solved.

Description

Non-replaceable memory-saving driving recording system
Technical Field
The invention belongs to the technical field of automobiles, and particularly relates to a non-replaceable memory-saving driving recording system.
Background
The automobile data recorder can record video images and sound of the whole automobile running process, and can provide evidence for judging the responsibility of traffic accidents. Because the files usually have a large memory and often cause insufficient space of the memory card, most automobile data recorders can automatically replace the earliest recorded video segment with the latest recorded video segment in time sequence after the memory card is full, so that the latest video segment can be completely stored. But here also a common problem is involved-missing seconds. Many recorders with poor performance will miss seconds between two video segments and cannot store them. The reason is that the machine cannot record a video while storing the previous video, and only after the previous video is stored, the machine can record the video, so that several seconds are missed in the process, and the video cannot be recorded and stored. Although only a few seconds, if a traffic accident occurs within a few seconds, it cannot be verified, resulting in serious loss and consequences.
In view of the above, there is a need for a non-replaceable memory-saving driving recording system.
Disclosure of Invention
The invention aims to provide a non-replaceable memory-saving driving recording system to solve the technical problem of seconds leakage in the process of saving storage space.
In order to solve the technical problem, the invention provides a non-replaceable memory-saving driving recording system, which comprises a driving recorder and a cloud server connected with the driving recorder; the driving recorder is used for collecting driving images and sending real-time driving position data to the cloud server; the cloud server receives the driving position data, calculates a road danger index of the driving position, and sends the road danger index to the driving recorder; and the automobile data recorder adjusts and stores the video resolution according to the road danger index.
Further, the automobile data recorder comprises an image sensor module, an FPGA module, a storage module, a GPS module, a communication module and a control module; wherein the GPS module is adapted to determine a current location of the vehicle; the image sensor module is used for collecting driving images; the communication module is suitable for receiving a road risk index calculated by a cloud server; the control module is in telecommunication connection with the FPGA module, the communication module and the storage module, is suitable for adjusting the data reading mode of the FPGA module according to the road danger index received by the communication module, and stores data into the storage module so as to store videos with different resolutions according to different danger indexes.
Further, the cloud server comprises a data acquisition module, which is used for acquiring traffic flow data, traffic flow type data, road attributes and accident conditions through road video monitoring, and dividing traffic flow grades, vehicle complexity grades and road grades of each road section in each time period; the accident algorithm model calculation module is used for constructing an accident algorithm model according to the traffic flow grade, the vehicle complexity grade and the road grade of each road section in each time period; the risk index calculation module is used for calculating the road risk index of the current road section according to the traffic flow data, the traffic flow type data, the road attribute and the accident algorithm model of the current road section; and the transmission module is used for transmitting the risk index to the communication module of the automobile data recorder.
Further, the accident algorithm model calculation module is adapted to establish a data vector x ═ x (x)(1),x(2),x(3)) And the coefficient vector w ═ w (w)(1)，w(2)，w(3)) And using a soft space SVM to solve the classification hyperplane with the maximum geometric space, and representing the problem as a constrained optimization problem
Figure BDA0003116838300000021
S.t yi(w.xi+b)≥1-ξi
ξiN is equal to or greater than 0i ═ 1, 2,. N; wherein x isiFor training ith data vector x ═ x (x)(1),x(2),x(3))，x(1)Is the traffic class, x(1)＝1、2……5，x(2)For the vehicle complexity level, x(2)＝1、2……5；x(3)Is road grade, x(3)＝1、2……5；w(j)For the corresponding feature x(j)The value of j is 1, 2 and 3; c is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset, yiIs corresponding to xiClass mark of whether accidents occur, wherein the number of accidents which do not occur is marked as 1, the number of accidents which occur is marked as-1, and N is the number of training data.
Further, the accident algorithm model calculation module is adapted to use the KKT condition to solve an optimal solution of the optimal classification hyperplane and the coefficient vector, that is, a normal vector of the optimal classification hyperplane:
Figure BDA0003116838300000031
wherein alpha isi *Is the ith element of the solution to the dual problem in the lagrange multiplier vector.
Furthermore, the accident algorithm model calculation module is suitable for projecting the data without accidents on the normal vector w to obtain a data mean value
Figure BDA0003116838300000032
Mean value of data obtained after projection of accident data on normal vector w
Figure BDA0003116838300000033
And according to muA、μBConstruction of an Accident Algorithm model
Figure BDA0003116838300000034
Wherein,
Figure BDA0003116838300000035
NAis a category yiNumber of samples equal to 1, NBIs a category yi-1 sample number; zC＝w*xC，xCAnd D is a risk index of the current road section time period data vector, the value range is 0-1, and the larger the value of D is, the higher the accident probability is.
Further, the risk index calculation module is suitable for substituting the data vector of the current road section time period into the accident algorithm model to calculate the risk index D of the current road section time period.
Furthermore, the automobile data recorder also comprises a human-computer interaction module connected with the control module; the human-computer interaction module is suitable for setting a risk index threshold K; the control module is suitable for controlling the FPGA module to continuously read out the video data when D is larger than or equal to K and controlling the FPGA module to read out the video data at intervals when D is smaller than K.
The method has the advantages that the current position is determined through the automobile data recorder, the risk degree index of the current position is calculated through the cloud server, the resolution ratio of the stored video is adjusted according to the risk degree index, the storage space is optimized, and the problem of second leakage caused by the adoption of an alternative mode for saving the storage space is solved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic block diagram of a driving recording system according to the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1, embodiment 1 provides a non-replaceable memory-saving driving recording system, which may include a driving recorder and a cloud server connected to the driving recorder; the driving recorder is used for collecting driving images and sending real-time driving position data to the cloud server; the cloud server receives the driving position data, calculates a road danger index of the driving position, and sends the road danger index to the driving recorder; the automobile data recorder adjusts and stores the video resolution according to the road danger index, and particularly, the high resolution is adopted when the road danger index is high, and the high resolution is adopted when the road danger index is low.
The system for recording vehicle driving provided by this embodiment determines the current position through vehicle event data recorder, calculates the risk index of current position through cloud server to adjust vehicle event data recorder storage video resolution according to the risk index, can effectively optimize vehicle event data recorder's storage space, solve the often full problem of memory card and can not appear leaking the second phenomenon.
In this embodiment, the cloud server includes a data acquisition module, configured to acquire traffic data, traffic type data, road attributes, and accident conditions through road video monitoring, and divide traffic grades, vehicle complexity grades, and road grades in each time period of each road segment; the accident algorithm model calculation module is used for constructing an accident algorithm model according to the traffic flow grade, the vehicle complexity grade and the road grade of each road section in each time period; the risk index calculation module is used for calculating the road risk index of the current road section according to the traffic flow data, the traffic flow type data, the road attribute and the accident algorithm model of the current road section; and the transmission module is used for transmitting the risk index to the communication module of the automobile data recorder.
In this embodiment, the accident algorithm model calculation module is adapted to establish a data direction x ═ x (x)(1),x(2),x(3)) Coefficient vectorw＝(w(1)，w(2)，w(3)) And using a soft space SVM to solve the classification hyperplane with the maximum geometric space, and representing the problem as a constrained optimization problem
Figure BDA0003116838300000051
S.t yi(w.xi+b)≥1-ξi
ξiN is equal to or greater than 0i ═ 1, 2,. N; wherein x isiFor training ith data vector x ═ x (x)(1),x(2),x(3))，x(1)Is the traffic class, x(1)＝1、2……5，x(2)For the vehicle complexity level, x(2)＝1、2……5；x(3)Is road grade, x(3)＝1、2……5；w(j)For the corresponding feature x(j)The value of j is 1, 2 and 3; c is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is the offset yiIs corresponding to xiClass mark of whether accidents occur, wherein the number of accidents which do not occur is marked as 1, the number of accidents which occur is marked as-1, and N is the number of training data.
In this embodiment, the accident algorithm model calculation module is adapted to use a KKT condition, that is, a carodon-kun-tak condition, to solve an optimal solution of the optimal classification hyperplane and the coefficient vector, that is, a normal vector of the optimal classification hyperplane:
Figure BDA0003116838300000061
wherein alpha isi *Is the ith element of the solution to the dual problem in the lagrange multiplier vector.
In this embodiment, the accident algorithm model calculation module is adapted to project the data without accidents on the normal vector w to obtain the data mean
Figure BDA0003116838300000062
Mean value of data obtained after projection of accident data on normal vector w
Figure BDA0003116838300000063
And according to muA、μBConstruction of an Accident Algorithm model
Figure BDA0003116838300000064
Wherein,
Figure BDA0003116838300000065
NAis a category yiNumber of samples equal to 1, NBIs a category yi-1 sample number; zC＝w*xC，xCAnd D is a risk index of the current road section time period data vector, the value range is 0-1, and the larger the value of D is, the higher the accident probability is.
In this embodiment, the risk index calculation module is adapted to substitute the data vector of the current road segment time period into the accident algorithm model to calculate the risk index D of the current road segment time period.
In summary, when the system for recording a vehicle event provided by this embodiment is used, the algorithm model calculation module of the cloud server is used to construct the accident algorithm model, the vehicle event recorder is used to obtain the current position, the risk index calculation module of the cloud server is used to calculate the risk index of the current position, and the vehicle event recorder selects the resolution of the stored video according to the risk index of the current position, so as to optimize the storage space.
Example 2
On the basis of embodiment 1, in this embodiment 2, the automobile data recorder may include an image sensor module, an FPGA module, a storage module, a GPS module, a communication module, and a control module; wherein the GPS module is adapted to determine a current location of the vehicle; the image sensor module is used for collecting driving images; the communication module is suitable for receiving a road risk index calculated by a cloud server; the control module is in telecommunication connection with the FPGA module, the communication module and the storage module, is suitable for adjusting the data reading mode of the FPGA module according to the road danger index received by the communication module, and stores data into the storage module so as to store videos with different resolutions according to different danger indexes. Optionally, pixels of the image sensor module are 1920x1080, the FPGA module is provided with a sequential control circuit and a dual port ARM, when the road risk index is high, video data of 1920x1080 pixels are continuously read out from data written into the dual port RAM of the image sensor module to more clearly know the surrounding situation, and when the road risk index is low, video data of 1280x720 pixels are read out at intervals from data written into the dual port RAM of the image sensor module to save a storage space and ensure that the license plate number is clearly seen. The image sensor module may employ, but is not limited to, IMX307, the control module may employ, but is not limited to, an ARM processor, the memory module may employ, but is not limited to, AT24C128, the GPS module may employ, but is not limited to, G28U8FDTTL, and the communication module may employ, but is not limited to, a 4G module.
In this embodiment, in order to set a threshold value for the self-help risk index, the automobile data recorder further includes a human-computer interaction module connected to the control module; the human-computer interaction module is suitable for setting a risk index threshold K; the control module is suitable for controlling the FPGA module to continuously read out the video data when D is larger than or equal to K and controlling the FPGA module to read out the video data at intervals when D is smaller than K. The human-computer interaction module can adopt but is not limited to RK 3510A.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (8)

1. A non-replaceable, memory-efficient system for driving recording, comprising:
the system comprises an automobile data recorder and a cloud server connected with the automobile data recorder; wherein
The driving recorder is used for collecting driving images and sending real-time driving position data to the cloud server;
the cloud server is used for receiving the driving position data, calculating a road danger index of the driving position, and sending the road danger index to the driving recorder;
and the automobile data recorder adjusts and stores the video resolution according to the road danger index.
2. The system of claim 1,
the vehicle event data recorder includes:
the device comprises an image sensor module, an FPGA module, a storage module, a GPS module, a communication module and a control module; wherein
The GPS module is suitable for determining the current position of the vehicle;
the image sensor module is used for collecting driving images;
the communication module is suitable for receiving a road risk index calculated by a cloud server;
the control module is in telecommunication connection with the FPGA module, the communication module and the storage module, is suitable for adjusting the data reading mode of the FPGA module according to the road danger index received by the communication module, and stores data into the storage module so as to store videos with different resolutions according to different danger indexes.
3. The system of claim 1,
the cloud server includes:
the data acquisition module is used for acquiring traffic flow data, traffic flow type data, road attributes and accident conditions through road video monitoring, and dividing traffic flow grades, vehicle complexity grades and road grades of all road sections in all time periods;
the accident algorithm model calculation module is used for constructing an accident algorithm model according to the traffic flow grade, the vehicle complexity grade and the road grade of each road section in each time period;
the risk index calculation module is used for calculating the road risk index of the current road section according to the traffic flow data, the traffic flow type data, the road attribute and the accident algorithm model of the current road section;
and the transmission module is used for transmitting the risk index to the communication module of the automobile data recorder.
4. The system of claim 3,
the accident algorithm model calculation module is adapted to establish a data vector x ═ x (x)(1),x(2),x(3)) And the coefficient vector w ═ w (w)(1)，w(2)，w(3)) And using a soft space SVM to solve the classification hyperplane with the maximum geometric space, and representing the problem as a constrained optimization problem
Figure FDA0003116838290000021
S.t yi(w.xi+b)≥1-ξi
ξi≥0i＝1，2，...N；
Wherein x isiFor training ith data vector x ═ x (x)(1),x(2),x(3))，x(1)Is the traffic class, x(1)＝1、2……5，x(2)For the vehicle complexity level, x(2)＝1、2……5；x(3)Is road grade, x(3)＝1、2……5；w(j)For the corresponding feature x(j)The value of j is 1, 2 and 3; c is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset, yiIs corresponding to xiClass mark of whether accidents occur, wherein the number of accidents which do not occur is marked as 1, the number of accidents which occur is marked as-1, and N is the number of training data.
5. The system of claim 4,
the accident algorithm model calculation module is adapted to makeAnd (3) solving the optimal solution of the optimal classification hyperplane and the coefficient vector by using a KKT condition, namely obtaining a normal vector of the optimal classification hyperplane:
Figure FDA0003116838290000022
wherein alpha isi *Is the ith element of the solution to the dual problem in the lagrange multiplier vector.
6. The system of claim 5,
the accident algorithm model calculation module is suitable for projecting the data without accidents on a normal vector w to obtain a data mean value
Figure FDA0003116838290000031
Mean value of data obtained after projection of accident data on normal vector w
Figure FDA0003116838290000032
And
according to muA、μBConstruction of an Accident Algorithm model
Figure FDA0003116838290000033
Wherein,
Figure FDA0003116838290000034
NAis a category yiNumber of samples equal to 1, NBIs a category yi-1 sample number; zC＝w*xC，xCAnd D is a risk index of the current road section time period data vector, the value range is 0-1, and the larger the value of D is, the higher the accident probability is.
7. The system of claim 6,
and the risk index calculation module is suitable for substituting the data vector of the current road section time segment into the accident algorithm model to calculate the risk index D of the current road section time segment.
8. The system of claim 2,
the automobile data recorder also comprises a human-computer interaction module connected with the control module;
the human-computer interaction module is suitable for setting a risk index threshold K;
the control module is suitable for controlling the FPGA module to continuously read out the video data when D is larger than or equal to K and controlling the FPGA module to read out the video data at intervals when D is smaller than K.
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Application publication date: 20210914