CN108492398A - The method for early warning that drive automatically behavior based on accelerometer actively acquires - Google Patents
The method for early warning that drive automatically behavior based on accelerometer actively acquires Download PDFInfo
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- CN108492398A CN108492398A CN201810126781.7A CN201810126781A CN108492398A CN 108492398 A CN108492398 A CN 108492398A CN 201810126781 A CN201810126781 A CN 201810126781A CN 108492398 A CN108492398 A CN 108492398A
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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
- G07C5/08—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
- G07C5/0866—Registering performance data using electronic data carriers the electronic data carrier being a digital video recorder in combination with video camera
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/42—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation
- H04N19/423—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation characterised by memory arrangements
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Abstract
Drive automatically behavior monitoring method detects the video data just recorded when dangerous driving behavior before and after trigger point, very clear to driving behavior analysis, greatly mitigates the workload of analysis video, solves the problems, such as that new data covers legacy data well.
Description
Technical field
The present invention relates to drive automatically behavior monitoring fields.
Background technology
If to analyze driving behavior meeting, there are many inconvenient places using the video data of automobile data recorder.It goes first
Vehicle recorder be at any time record video data, if to be picked out from one big section of video dangerous driving behavior need spend compared with
The long time watches video, this needs to put into a large amount of manpowers to read through video from the beginning to the end to see, and inefficient.Its
It is secondary because automobile data recorder is record video data at any time, therefore a large amount of video file can be generated, the SD card note of a 32G
The duration of record 1080P videos does not exceed 10 hours, for driving duration far more than the number of front for 10 hours long-distance vehicles
According to will be fallen to cause data imperfect by subsequent data cover, and only this 32G data copy to PC just need it is half small
When.
Invention content
The technology actively acquired the present invention is based on the drive automatically behavior of accelerometer solves the above problem well.
Technical solution：
A kind of drive automatically behavior monitoring method, which is characterized in that system detectio to have it is anxious accelerate, it is anxious slow down, racing
The video data before and after trigger point is just recorded when curved dangerous driving behavior, it is very clear to driving behavior analysis, greatly mitigate
The workload of video is analyzed, solves the problems, such as that new data covers legacy data well.
Further, concrete methods of realizing is：Including dangerous driving detection algorithm (algorithm 1), the compression sound based on memory
Video cache algorithm (algorithm 2)；
The dangerous driving detection algorithm is：
Step 1. main control module reads acceleration information with 20Hz frequencies.
The horizontal reference of step 2. data is demarcated, and is tilted to adapt to three axis accelerometer installation, equipment reads accelerometer
Preceding ten second data demarcates accelerometer adaptively to install inclination.
After the calibration of step 3. horizontal reference, start to detect dangerous driving.For the calibrated data that receive first into
Row Kalman filtering keeps accelerating curve smoothened to remove interference noise.
Step 4. sends out dangerous driving early warning for the acceleration value more than threshold value, is transferred to the compression Video ＆ Audio based on memory
The step 3 of cache algorithm (algorithm 2).
The compression Video ＆ Audio cache algorithm of the memory is：
Step 1. reads camera initial data, reads audio PWM count evidence；
Step 2. is the data that the camera compressing original data of acquisition is h264 formats, for the first frame of acquisition per second
As I frames, remaining frame is put into memory queue as P frames, and 40 seconds data are preserved in queue, and overtime data remove queue；
Then the PWM audio datas elder generation boil down to aac formats of acquisition are stored in memory in an identical manner；
Step 3. responds dangerous driving early warning, and triggers video-with-audio recording, records 30 seconds before and after current point in time numbers
According to being stored in file system with mp4 formatted files.
The accelerometer tilts calibration, and algorithm is：
2.1. 10 seconds data are read first, and are cached；
2.2. these data are ranked up according to the value of vector；
2.3. remove the data of minimum and maximum 10% respectively, and average；
2.4. angle of the current average vector with accelerometer when horizontal positioned is calculated；
2.5. the data read below are corrected according to current average vector.
The system, including main control module, three axis accelerometer module (or there are three independent sensor constitute), take the photograph
As head module, the main control module includes CPU, memory module, memory again, wherein：Main control module is responsible for dispatching other
Module；Three axis accelerometer is used for detecting anxious acceleration, anxious deceleration, zig zag, is provided in main control module；Two cameras are adopted respectively
Collection front and driver's video, are provided in main control module.
The method of the present invention is very clear to driving behavior analysis, greatly mitigates the workload of analysis video, also well
Solve the problems, such as that new data covers legacy data.
Description of the drawings
Fig. 1 is dangerous driving detection algorithm flow chart.
Fig. 2 is that accelerometer tilts calibration algorithm flow chart.
Fig. 3 is the compression Video ＆ Audio cache algorithm based on memory.
Fig. 4 is EM equipment module figure.
Specific implementation mode
The technology actively acquired the present invention is based on the drive automatically behavior of accelerometer solves the prior art well
Problem.
First, the different records at any time with automobile data recorder, the technology are to have detected anxious acceleration, anxious deceleration, zig zag
Etc. dangerous driving behaviors can just record, and generate before and after video file record trigger point each 30 seconds every time totally one minute
Video data, it is very clear to driving behavior analysis, greatly mitigate the workload of analysis video.
Secondly, this technology is not real-time record video, if can remember by the SD card that 30 minutes trigger 32G for primary is driven
More than the data of two weeks, the workload for mitigating access evidence also solves the problems, such as that new data covers legacy data well for record.
One, the drive automatically behavior active collecting device based on accelerometer, as shown in figure 4, including main control module, three
Axis accelerometer module (or there are three independent sensors to constitute), camera module, the main control module include again
CPU, memory module, memory, wherein：Main control module is responsible for dispatching other modules；Three axis accelerometer is used for detecting anxious add
Speed, anxious deceleration, zig zag, are provided in main control module；Two cameras acquire front and driver's video respectively, are provided in master control
Module.
Two, main algorithm includes：Dangerous driving detection algorithm (algorithm 1), the compression Video ＆ Audio caching based on memory are calculated
Method (algorithm 2).
As shown in Figure 1, dangerous driving detection algorithm.
Step 1. main control module reads acceleration information with 20Hz frequencies.
The horizontal reference of step 2. data is demarcated, and is tilted to adapt to three axis accelerometer installation, equipment reads accelerometer
Preceding ten second data demarcates accelerometer adaptively to install inclination.Referring to flow chart 2, accelerometer tilts calibration algorithm
For：
2.1. 10 seconds data are read first, and are cached；
2.2. these data are ranked up according to the value of vector；
2.3. remove the data of minimum and maximum 10% respectively, and average；
2.4. angle of the current average vector with accelerometer when horizontal positioned is calculated；
2.5. the data read below are corrected according to current average vector.
After the calibration of step 3. horizontal reference, start to detect dangerous driving.For the calibrated data that receive first into
Row Kalman filtering keeps accelerating curve smoothened to remove interference noise.
Step 4. sends out dangerous driving early warning for the acceleration value more than threshold value, is transferred to the compression Video ＆ Audio based on memory
The step 3 of cache algorithm (algorithm 2).
As shown in figure 3, in the compression Video ＆ Audio cache algorithm of memory：
Step 1. reads camera initial data, reads audio PWM count evidence.
Step 2. is the data that the camera compressing original data of acquisition is h264 formats, for the first frame of acquisition per second
As I frames, remaining frame is put into memory queue as P frames, and 40 seconds data are preserved in queue, and overtime data remove queue.
Then the PWM audio datas elder generation boil down to aac formats of acquisition are stored in memory in an identical manner.
Step 3. responds dangerous driving early warning, and triggers video-with-audio recording, records 30 seconds before and after current point in time numbers
According to being stored in file system (SD card) with mp4 formatted files.
Claims (4)
1. a kind of drive automatically behavior monitoring method, which is characterized in that system detectio to have it is anxious accelerate, it is anxious slow down, zig zag
The video data before and after trigger point is just recorded when dangerous driving behavior.
2. drive automatically behavior monitoring method as described in claim 1, which is characterized in that concrete methods of realizing is：Including
Dangerous driving detection algorithm (algorithm 1), the compression Video ＆ Audio cache algorithm (algorithm 2) based on memory；
The dangerous driving detection algorithm is：
Step 1. main control module reads acceleration information with 20Hz frequencies.
The horizontal reference of step 2. data is demarcated, and is tilted to adapt to three axis accelerometer installation, equipment is read ten before accelerometer
Second data demarcates accelerometer adaptively to install inclination.
After the calibration of step 3. horizontal reference, start to detect dangerous driving.The calibrated data received are blocked first
Kalman Filtering keeps accelerating curve smoothened to remove interference noise.
Step 4. sends out dangerous driving early warning for the acceleration value more than threshold value, is transferred to the compression Video ＆ Audio caching based on memory
The step 3 of algorithm (algorithm 2).
The compression Video ＆ Audio cache algorithm of the memory is：
Step 1. reads camera initial data, reads audio PWM count evidence；
Step 2. is the data that the camera compressing original data of acquisition is h264 formats, for the first frame conduct of acquisition per second
I frames, remaining frame are put into memory queue as P frames, and 40 seconds data are preserved in queue, and overtime data remove queue；Obtaining
Then the PWM audio datas elder generation boil down to aac formats taken store in memory in an identical manner；
Step 3. responds dangerous driving early warning, and triggers video-with-audio recording, records 30 seconds before and after current point in time data, with
Mp4 formatted files are stored in file system.
3. drive automatically behavior monitoring method as claimed in claim 2, which is characterized in that the accelerometer tilts mark
Fixed, algorithm is：
2.1. 10 seconds data are read first, and are cached；
2.2. these data are ranked up according to the value of vector；
2.3. remove the data of minimum and maximum 10% respectively, and average；
2.4. angle of the current average vector with accelerometer when horizontal positioned is calculated；
2.5. the data read below are corrected according to current average vector.
4. drive automatically behavior monitoring method as described in claim 1 or 2, which is characterized in that the system, including master
Module, three axis accelerometer module (or there are three independent sensors to constitute), camera module are controlled, the main control module is again
Include CPU, memory module, memory, wherein：Main control module is responsible for dispatching other modules；Three axis accelerometer is used for examining
Anxious acceleration, anxious deceleration, zig zag are surveyed, main control module is provided in；Two cameras acquire front and driver's video respectively, provide
In main control module.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109709956A (en) * | 2018-12-26 | 2019-05-03 | 同济大学 | A kind of automatic driving vehicle speed control multiple-objection optimization with algorithm of speeding |
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Application publication date: 20180904 |