CN105957345A - Processing method for vehicle driving data - Google Patents
Processing method for vehicle driving data Download PDFInfo
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- CN105957345A CN105957345A CN201610403920.7A CN201610403920A CN105957345A CN 105957345 A CN105957345 A CN 105957345A CN 201610403920 A CN201610403920 A CN 201610403920A CN 105957345 A CN105957345 A CN 105957345A
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- 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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- 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
- G08G1/0125—Traffic data processing
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- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
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Abstract
The invention discloses a processing method for vehicle driving data. The method comprises: an instrument panel template database is established according to a vehicle model; instrument panel image information during a vehicle driving process, image information in front of a driven vehicle, and vehicle state information are obtained in real time; a vehicle state is monitored; if the vehicle state is abnormal, the instrument panel image information is extracted and is compared with the instrument panel template database, vehicle velocity data and instrument panel indication signal data are identified, an image and driving data are generated by combining other information, gathering and storage are carried out, and alarm information is sent out; and if the vehicle state is determined to be a traffic accident state, driving data of an involved vehicle are extracted and an accident happening process is restored. According to the invention, an event occurring in a driving process is associated with a corresponding instrument panel image; and the image data can be used as visual evidences and can be used for regulation breaking determination or accident liability judgment.
Description
Technical field
The present invention relates to vehicle and travel record and driving conditions reduction technique field, be specifically related to vehicle operation data processing method.
Background technology
When automobile generation specific event, accident or fault, record specific event, the reason of analysis accident or fault need to obtain travelling data, but these travelling datas obtain being difficult to afterwards.
Driver is when driving, and main focus is three aspects: dead ahead visual angle, instrumental panel, the transport condition (such as sound, vibrations etc.) of well perception vehicle.In existing system, seldom have and will carry out collecting preservation with the information of this three aspect, and reduce for driving conditions.And the instruction information of instrumental panel is more and the most general, a lot of drivers can only debate and know conventional instruction data, often do not have concept maybe can ignore these promptings to special fault cues.
The most existing vehicle is commonly used GPS alignment system and is positioned, but owing to the positioning precision of GPS is relatively low, gps signal existence and stability problem in vehicle motor process so that the continuity of data, reliability are not high enough.
By collection electronic data as reference more than in existing scheme, and electronic data can not become evidence intuitively；Owing to the kind model of vehicle is different, can cannot preserve complete evidence when accident or happenstance occur with the transport condition of accurate recording vehicle, the state of vehicle at that time cannot be completely reproduced up currently without a kind of simple common apparatus.
Summary of the invention
Goal of the invention: for the deficiencies in the prior art, the present invention provides a kind of vehicle operation data processing method, it is possible to realize reappearing driving conditions afterwards.
Technical scheme: vehicle operation data processing method of the present invention, including following method:
(1) instrumental panel template database is set up according to automobile model；
(2) obtaining the instrumental panel image information in vehicle travel process in real time, vehicle travels image information and the car status information in front；
(3) monitoring vehicle-state, if finding, vehicle strange happening part is abnormal state: extraction apparatus dial plate image information and instrumental panel template database phase comparison, identify vehicle speed data and instrumental panel indication signal data, travel the image information in front in conjunction with vehicle, car status information generates the image with digital watermarking and travelling data, data after extraction process carry out collecting preservation, and to vehicle driver's alert；
(4) monitoring vehicle-state, if finding, vehicle strange happening part is vehicle accident: determine crucial 2, vehicle in vehicle accident, extract instrumental panel image information and the car status information of crucial vehicle, utilize the histogram analysis in data mining, determine strange happening part material time point, on the basis of material time point, extract the image information that in vehicle accident, in material time point, involved vehicle travels front and carry out extracting and matching feature points based on SIFT algorithm, retrodict out the travelling data of each vehicle before having an accident, recovery accident panoramic view, reduction accident generating process.
Improving technique scheme further, the information of described instrumental panel template data library storage includes instrumental panel picture, the figure of each indication signal, position, indicating mode and instruction implication in instrumental panel.
Further, described instrumental panel image information supports at least one instrumental panel video camera shooting acquisition on seat by being fixed on steering wheel；Described vehicle is travelled the image information in front and is obtained by the forward direction visual angle photographic head shooting on vehicle；Described car status information is by the one or more acquisitions in the angular-rate sensor being arranged on vehicle, shaking sensor, acceleration transducer, gyro sensor, speed pulse sensor.
Further, the instrumental panel image information obtained in vehicle travel process in real time in described step (2) comprises the steps:
(21) read pending instrumental panel image, be translated into bianry image；
(22) area in bianry image is removed too small, it is possible to certainly for non-pointer and the region of scale；
(23) position finder hand of dial, expands white portion, removes unrelated parameter；
(24) search connected region border, retain image simultaneously, in case labelling below；
(25) the largest connected region of pointer in all connected regions is found out；
(26) extract pointer angle with Radon algorithm, obtain velocity information by the scale template of storage in instrumental panel template database；
(27) deduct the connected region figure in largest connected region, identify R, neutral gear N, automatic catch D, the parking P information of falling back, determine gear information；
(28) store non-image with data form after extracting critical data, reduce the pressure of storage image.
Further, described digital watermarking comprises instrumental panel analytical data, onboard sensor data, shooting time labelling.
Further, in described step (4), the method for reduction accident generating process is as follows:
First, extract and vehicle accident relates to the travelling data of thing vehicle and collects；
Second, by the instrumental panel image information in the travelling data of each vehicle, vehicle travels the image information in front and car status information determines that each vehicle has an accident the time point of correspondence；
3rd, on the basis of the time point that each vehicle has an accident, the wheelpath of each vehicle before having an accident of retrodicting out, show the vehicle-mounted image on corresponding time point, the process that reduction accident occurs simultaneously；
(31) be distributed by the rectangular histogram approximate data in data mining, Decision Tree Inductive analyzes triggered time point；
(32) to relating to image information I in two crucial vehicle traveling fronts in thing vehicle1(x,y)、I2(x, y), carries out extracting and matching feature points based on SIFT algorithm, recovery accident panoramic view: use Gaussian convolution computing to build metric space, difference of Gaussian function D (x, y, σ) is by calculating with two of constant multiplication factor k adjacent scalogram aberrations:
D (x, y, σ)=(G (x, y, k σ)-G (x, y, σ)) * I (x, y)=L (x, y, k σ)-L (x, y, σ) (1)
Initial pictures is through progressively Gaussian convolution computing, obtain a series of metric space, i.e. Gauss yardstick (DOG) space, spatial extrema point detection key point is made up of the Local Extremum in DOG space, middle test point compares with 9 × 2 points, 26 points that 8 consecutive points of yardstick are corresponding with neighbouring yardstick with it, to guarantee extreme point all to be detected at metric space and two dimensional image space totally；
The direction distribution of extreme point: (x, y), (x, y) (x, formula y) is with direction to calculate its gradient modulus value m for each sampled point L
Characteristic point describes the generation of son: rotated to by coordinate axes on the gradient direction of characteristic point, guarantee rotational invariance, 16 son points are generally used to describe a characteristic point, then the gradient accumulated value in 8 directions in every height point is calculated, obtain characteristic point and describe the characteristic vector of son, for 4*4*8=128 dimensional vector；Obtained characteristic vector has invariable rotary shape so I can be extracted respectively1And I2Characteristic vector in two width images carries out Feature Points Matching, is spliced into a width panoramic view；
4th, the process occurred according to the accident of on-the-spot reduction and simulation modules exhibit determines the responsibility of accident.
Beneficial effect: compared with prior art, advantages of the present invention:
1, event in driving conditions being connected with corresponding instrumental panel image, view data can be as evidence intuitively, for judgement, accident responsibility judgement violating the regulations etc.；
2, by the analysis to instrumental panel image, can be with the indication signal data of identifier dial plate, when failures are detected, can prompting driver promptly and accurately；
3, pass through on-the-spot reduction and simulation module and reappear driving conditions accurately, intuitively, can be used for scene of a traffic accident reduction and quickly locate fix duty, vehicle trouble investigation and reproduction, vehicle peccancy evidence obtaining.
Accompanying drawing explanation
Fig. 1 is the hardware layout figure of the present invention；
Fig. 2 is flow chart of data processing figure of the present invention；
Fig. 3 is accident reduction process flow chart.
Detailed description of the invention
Below technical solution of the present invention is described in detail.
Embodiment 1: as shown in Figure 1, size and structure according to meter panel of motor vehicle support at steering wheel 5 and arrange instrumental panel video camera 2 on seat, when instrumental panel is wider or during more dispersed and distributed, one instrumental panel video camera cannot shoot complete instrumental panel image, and multiple instrumental panel video camera 2 can be used to shoot the instrumental panel image of different piece respectively；Vehicle is installed forward direction visual angle photographic head 4；nullVehicle arranges angular-rate sensor 6、Shaking sensor 7、Acceleration transducer 8、Gyro sensor 9、Speed pulse sensor 10，Angular-rate sensor 6 is arranged on the steering wheel 5 of vehicle，For detecting the rotational angle of steering wheel 5，Shaking sensor 7 is for detecting the transport condition of vehicle and abnormal shake、Seismism，The acceleration information of acceleration transducer 8 collection vehicle，The data that gyro sensor 9 collection vehicle turns to，Speed pulse sensor 10 collection vehicle road speed data，The position installation data processing module 3 of instrumental panel video camera 2 and on-the-spot reduction and simulation module on vehicle，Data processing module is loaded with instrumental panel template database and is provided with wireless communication module and alarm module，Instrumental panel template database is set up according to automobile model，The information of storage includes instrumental panel picture，The figure of each indication signal in instrumental panel、Position、Indicating mode and instruction implication，Wireless communication module is for communicating with mobile network base station or traffic control system base station，Data processing module obtains the present road information of base station offer (such as speed limit restricted driving information by wireless communication module，Front road section traffic volume accident or the information of road congestion) give driver，And upload vehicular events data to base station and put on record，Event includes breaking rules and regulations、Accident、Catastrophic failure such as vehicle casts anchor warning.
Running data processing method is carried out based on above-mentioned setting:
(1) instrumental panel template database is set up according to automobile model；
(2) obtaining the instrumental panel image information in vehicle travel process in real time, vehicle travels image information and the car status information in front；
Instrumental panel real-time image information acquisition algorithm:
1. read pending instrumental panel image, be translated into bianry image；
2. the region that area is too small, can affirm non-pointer and scale is removed in image；
3. being positioning pointer, expanded by white portion, unrelated small articles (travelling milimeter number, ambient temperature) is removed in corrosion；
4. search connected region border, retain this figure simultaneously, in case labelling below；
5. find out in (the largest connected region) of most probable pointer in all connected regions；
6. extract pointer angle with Radon algorithm, obtain velocity information by with the contrast of instrumental panel template database；
7. button removes the connected region figure in largest connected region, identifies R, N, D, P information, determines gear information；
8. extract the form of data storage after critical data and non-image, reduce the pressure of storage image；
(3) data processing module monitoring vehicle-state, if finding, vehicle-state is abnormal, as abnormal in Vehicle Speed, turn to abnormal, braking is abnormal, instrumental panel is reported to the police: extraction apparatus dial plate image information and instrumental panel template database phase comparison, identify vehicle speed data and instrumental panel indication signal data, instrumental panel can be divided into mechanical type and electronic type two kinds, in mechanical type instrumental panel, the position of various indication signals is fixing, can there is the switching at difference in functionality interface in electronic instrument dish, the content of display would also vary from.Therefore, when template is set up for electronic instrument dish, should first define the feature of each function interface, then describe the implication of instrument display information in each function interface.Such as in mechanical type instrumental panel, speed shows the form mostly being pointer rotation, in electronic instrument dish, speed is then indirectly displayed as digital form by some, OCR technique should be used to be identified the numeral in electronic instrument dish or Word message, due to the installation site of instrumental panel video camera in each vehicle, certain difference can be there is in angle, it is thus desirable to the carrying out of instrumental panel image with instrumental panel template is mated by accuracy registration algorithm, calculate alignment error, view data can be modified by actual moving process, it is beneficial to the automatic identification to instrumental panel information, reduce operand；Travel the image information in front in combination with vehicle, car status information generates the image with digital watermarking and travelling data, image includes instrumental panel image or instrumental panel image and the combination of forward direction multi-view image, digital watermarking comprises instrumental panel analytical data, onboard sensor data, shooting time labelling, and the data after extraction process carry out Macro or mass analysis preservation and by alarm unit to vehicle driver's alert；
(4) if finding, vehicle-state strange happening part is vehicle accident, extract two travelling datas relating to thing vehicle, as shown in Figure 2 and Figure 3, on-the-spot reduction and simulation module is on the basis of the time point that each vehicle has an accident, retrodict out the travelling data of each vehicle before having an accident, show the combination image on corresponding time point, reduction accident generating process simultaneously；Determine crucial 2, vehicle in vehicle accident, extraction apparatus dial plate data and vehicle status data, merge various data analysiss by the statistical method in data mining and go out triggered time point；Extract multi-section vehicle front view picture involved in this time point in vehicle accident to carry out based on SIFTF extracting and matching feature points, recovery accident panoramic view.
On-the-spot reduction and simulation module is realized by the following method:
In conjunction with vehicle speed data and Vehicular turn data, simulate the actual travel track of vehicle, and while display driving trace, show the vehicle-mounted image on corresponding time point, thus reproduce the driving process of vehicle；
Wherein: vehicle-mounted image is instrumental panel image or instrumental panel figure image and the combination of two kinds of images of forward direction multi-view image, by the combination of above two image, can more comprehensively reappear the scene that driver observe by ratio；
Vehicle speed data is by extracting in instrumental panel image or being provided by speed pulse sensor, and Vehicular turn data are provided by angular transducer or gyro sensor, or are analyzed the image of forward direction viewpoint cameras and obtain；
By to the relative analysis of the consecutive image that forward direction viewpoint cameras shoots, the object of reference information extracted in image, it can be determined that vehicle is in the state that craspedodrome state is in turning, it is possible to obtain the angle turned.
When using on-the-spot reduction and simulation resume module vehicle accident, it is realized by the following method:
First, obtain the travelling data of the data processing module relating to vehicle in vehicle accident and collect；
Second, determine, according to the travelling data of each vehicle, the corresponding time point that has an accident with each vehicle；
The time point of the correspondence that has an accident in travelling data can be determined by checking forward direction multi-view image, checking the sudden change of speed, comparison shaking sensor, the method such as data of acceleration transducer；
3rd, on the basis of the time point that each vehicle has an accident, the wheelpath of each vehicle before having an accident of retrodicting out, show the vehicle-mounted image on corresponding time point, the process that reduction accident occurs simultaneously:
Merge various data analysiss by statistical method in data mining and go out triggered time point:
Rectangular histogram is to use branch mailbox to carry out approximate data distribution, is a kind of data regularization form.According to time point, timing statistics point eigenvalue obtains quantity.The data that strange happening part triggers are few event, so statistics with histogram is effective.Multi-class data determines strange happening part time point, utilizes Decision Tree Inductive to obtain final material time point；
Assuming that in vehicle accident, vehicle 1 and 2 is crucial vehicle, the image travelling front photographed is I1(x,y)、I2(two width figure SIFT algorithms y), are carried out characteristic matching and complete panoramic view by x；
SIFT algorithm, scale invariant feature conversion (Scale-invariant feature transform, SIFT) it is that the algorithm of a kind of computer vision is for detecting and describe the locality characteristic in image, it finds extreme point in space scale, and extract its position, yardstick, rotational invariants, this algorithm is delivered in 1999 by David Lowe, within 2004, improves and sums up, and this algorithm may be used for image mosaic.SIFT algorithm key step is as follows:
1, metric space is built: in order to the position of key point is stablized in detection effective in metric space, Lowe proposes difference of Gaussian convolution, difference of Gaussian function D (x, y, σ) can be by calculating with two of constant multiplication factor k adjacent scalogram aberrations:
D (x, y, σ)=(G (x, y, k σ)-G (x, y, σ)) * I (x, y)=L (x, y, k σ)-L (x, y, σ) (1)；
Initial pictures, through progressively Gaussian convolution computing, obtains a series of metric space, i.e. Gauss yardstick (DOG) space.
2, spatial extrema point detection:
Key point is made up of the Local Extremum in DOG space, and middle test point compares with 9 × 2 points, 26 points that 8 consecutive points of yardstick are corresponding with neighbouring yardstick with it, to guarantee extreme point all to be detected at metric space and two dimensional image space totally.
3, the direction distribution of extreme point:
For each sampled point L, (x, y), (x, y) (x, formula y) is with direction to calculate its gradient modulus value m
4, the generation of characteristic point description:
Coordinate axes is rotated on the gradient direction of characteristic point, guarantee rotational invariance, generally use 16 son points to describe a characteristic point, then calculate the gradient accumulated value in 8 directions in every height point, obtain characteristic point and describe the characteristic vector of son, for 4*4*8=128 dimensional vector；Obtained characteristic vector has invariable rotary shape so I can be extracted respectively1And I2Characteristic vector in two width images carries out Feature Points Matching, is spliced into final image I.
Finally, the process occurred according to the accident of on-the-spot reduction and simulation modules exhibit determines the responsibility of accident.
Although as it has been described above, represented and described the present invention with reference to specific preferred embodiment, but it shall not be construed as the restriction to the present invention self.Under the spirit and scope of the present invention premise defined without departing from claims, can various changes can be made in the form and details to it.
Claims (6)
1. vehicle operation data processing method, it is characterised in that comprise the steps:
(1) instrumental panel template database is set up according to automobile model；
(2) obtaining the instrumental panel image information in vehicle travel process in real time, vehicle travels image information and the car status information in front；
(3) monitoring vehicle-state, if finding, vehicle strange happening part is abnormal state: extraction apparatus dial plate image information and instrumental panel template database phase comparison, identify vehicle speed data and instrumental panel indication signal data, travel the image information in front in conjunction with vehicle, car status information generates the image with digital watermarking and travelling data, data after extraction process carry out collecting preservation, and to vehicle driver's alert；
(4) monitoring vehicle-state, if finding, vehicle strange happening part is vehicle accident: determine crucial 2, vehicle in vehicle accident, extract instrumental panel image information and the car status information of crucial vehicle, utilize the histogram analysis in data mining, determine strange happening part material time point, on the basis of material time point, extract the image information that in vehicle accident, in material time point, involved vehicle travels front and carry out extracting and matching feature points based on SIFT algorithm, retrodict out the travelling data of each vehicle before having an accident, recovery accident panoramic view, reduction accident generating process.
Vehicle operation data processing method the most according to claim 1, it is characterised in that: the information of described instrumental panel template data library storage includes instrumental panel picture, the figure of each indication signal, position, indicating mode and instruction implication in instrumental panel.
Vehicle operation data processing method the most according to claim 1, it is characterised in that: described instrumental panel image information supports at least one instrumental panel video camera shooting acquisition on seat by being fixed on steering wheel；Described vehicle is travelled the image information in front and is obtained by the forward direction visual angle photographic head shooting on vehicle；Described car status information is by the one or more acquisitions in the angular-rate sensor being arranged on vehicle, shaking sensor, acceleration transducer, gyro sensor, speed pulse sensor.
Vehicle operation data processing method the most according to claim 1, it is characterised in that: the instrumental panel image information obtained in vehicle travel process in real time in described step (2) comprises the steps:
(21) read pending instrumental panel image, be translated into bianry image；
(22) area in bianry image is removed too small, it is possible to certainly for non-pointer and the region of scale；
(23) position finder hand of dial, expands white portion, removes unrelated parameter；
(24) search connected region border, retain image simultaneously, in case labelling below；
(25) the largest connected region of pointer in all connected regions is found out；
(26) extract pointer angle with Radon algorithm, obtain velocity information by the scale template of storage in instrumental panel template database；
(27) deduct the connected region figure in largest connected region, identify R, neutral gear N, automatic catch D, the parking P information of falling back, determine gear information；
(28) store image with data form after extracting critical data, reduce the pressure of storage image.
Vehicle operation data processing method the most according to claim 1, it is characterised in that: described digital watermarking comprises instrumental panel analytical data, onboard sensor data, shooting time labelling.
Vehicle operation data processing method the most according to claim 1, it is characterised in that: in described step (4), the method for reduction accident generating process is as follows:
First, extract and vehicle accident relates to the travelling data of thing vehicle and collects；
Second, by the instrumental panel image information in the travelling data of each vehicle, vehicle travels the image information in front and car status information determines that each vehicle has an accident the time point of correspondence；
3rd, on the basis of the time point that each vehicle has an accident, the wheelpath of each vehicle before having an accident of retrodicting out, show the vehicle-mounted image on corresponding time point, the process that reduction accident occurs simultaneously；
(31) be distributed by the rectangular histogram approximate data in data mining, Decision Tree Inductive analyzes triggered time point；
(32) to relating to image information I in two crucial vehicle traveling fronts in thing vehicle1(x,y)、I2(x, y), carries out extracting and matching feature points based on SIFT algorithm, recovery accident panoramic view: use Gaussian convolution computing to build metric space, difference of Gaussian function D (x, y, σ) is by calculating with two of constant multiplication factor k adjacent scalogram aberrations:
D (x, y, σ)=(G (x, y, k σ)-G (x, y, σ)) * I (x, y)=L (x, y, k σ)-L (x, y, σ) (1)
Initial pictures is through progressively Gaussian convolution computing, obtain a series of metric space, i.e. Gauss yardstick DOG space, spatial extrema point detection key point is made up of the Local Extremum in DOG space, middle test point compares with 9 × 2 points, 26 points that 8 consecutive points of yardstick are corresponding with neighbouring yardstick with it, to guarantee extreme point all to be detected at metric space and two dimensional image space totally；
Extreme point direction distribution: for each sampled point L (x, y), calculate its gradient modulus value m (x, y) and direction (x, formula y) is:
Characteristic point describes the generation of son: rotated to by coordinate axes on the gradient direction of characteristic point, guarantee rotational invariance, 16 son points are generally used to describe a characteristic point, then the gradient accumulated value in 8 directions in every height point is calculated, obtain characteristic point and describe the characteristic vector of son, for 4*4*8=128 dimensional vector；Obtained characteristic vector has invariable rotary shape so I can be extracted respectively1And I2Characteristic vector in two width images carries out Feature Points Matching, is spliced into a width panoramic view；
4th, the process occurred according to the accident of on-the-spot reduction and simulation modules exhibit determines the responsibility of accident.
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| CN107529045A (en) * | 2017-10-19 | 2017-12-29 | 南京中高知识产权股份有限公司 | A kind of 360 degree of full-view image systems of automobile and its method of work |
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| CN113361413A (en) * | 2021-06-08 | 2021-09-07 | 南京三百云信息科技有限公司 | Mileage display area detection method, device, equipment and storage medium |
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