CN105667397A - Automobile rearview system and method - Google Patents

Abstract

The invention relates to an automobile rearview system and method. An acquisition module is installed on the rear of an automobile, and the acquisition module is used for acquiring blind spot data, lane departure data and reverse image data of the automobile. Therefore the blind spot data, lane departure data and reverse image data of the automobile can be acquired only through the acquisition module, and multiple acquisition modules used for acquiring the blind spot data, lane departure data and reverse image data of the automobile respectively are not needed to be arranged. The structure complexity of the system is lowered, the module required by the system is reduced, thus the cost of the automobile rearview system is low, and the system is simple in structure.

Description

Rear-view system for automobile and method

Technical field

The present invention relates to rear-view system for automobile, particularly relate to a kind of cost low, the simple rear-view system for automobile of system architecture and method.

Background technology

Along with the increase at full speed of China's automobile guarantee-quantity, people also improve gradually for the consciousness of driving safety. Comparatively several driving safeties subsidiary functions of focus comprise reverse image, blind spot detection and lane departure warning at present. Wherein, reverse image function is higher in Chinese market popularity. Traditional blind spot detection, to there is rear ablepsia district scope in various degree in deviation detection big, and detection system is complicated, visual angle is not concentrated, the near and affected by environment defect such as seriously of sighting distance, and cannot meet the demand of modern car group, the speed of a motor vehicle and driving safety.

Summary of the invention

Based on this, it is necessary to provide a kind of cost low, the simple rear-view system for automobile of system architecture and method.

A kind of rear-view system for automobile, comprise acquisition module, control module and display module, described acquisition module is installed on the afterbody of described automobile, described acquisition module is for gathering the view data of described automobile tail, and described view data is exported to described control module, described control module is used for going out at least one in the blind spot data of described automobile, deviation data and reverse image data according to described analysis of image data; Described display module is for receiving and shows described blind spot data, deviation data and reverse image data.

Wherein in an embodiment, described acquisition module is ultra wide-angle imaging head, and is installed on the central position of automobile tail.

Wherein in an embodiment, also comprise the blind spot detection module being connected with described control module, described control module is for exporting to described blind spot detection module by described blind spot data, and described blind spot detection module is used for obtaining the blind spot region image of described automobile according to described blind spot data.

Wherein in an embodiment, described blind spot detection module comprises wave filtering layer, pond layer, parts detection layers, deformation layer, barrier bed and processing layer;

Described wave filtering layer is for carrying out filtering process to described blind spot data;

Described pond layer is for representing filtered blind spot data separate average pond compressive features;

Described parts detection layers be used for according to described pond layer process after blind spot data calculate in blind spot data comprise automobile, track detection mark;

Described deformation layer be used for according to described pond layer process after blind spot data calculate in blind spot data comprise automobile, track deformation parameter;

Described barrier bed exports according to described detection mark and described deformation parameter and blocks parameter;

Described processing layer according to described detection mark, described deformation parameter and described in block the blind spot region image of automobile described in parameter acquiring.

Wherein in an embodiment, also comprise the deviation detection module being connected with described control module, described control module is for exporting to described deviation detection module by described deviation data, and described deviation detection module is used for obtaining the deviation parameter of described automobile according to described deviation data.

Wherein in an embodiment, described deviation detection module comprises curve model unit, track line computation unit and the first processing unit;

Described curve model unit characterizes the curve model of track line information for setting up;

Described track line computation unit is used for calculating the parameter of track line according to described curve model;

Described first processing unit is for being optimized the parameter of described track line according to likelihood function, and calculates deviation parameter.

Wherein in an embodiment, described deviation detection module comprises straight line model unit, track straight line extraction unit and the 2nd processing unit;

Described straight line model unit characterizes the straight line model of track line information for setting up;

Described track straight line extraction unit extracts the parameter of track line according to described straight line model;

Described 2nd processing unit is used for the parameter to described track line and carries out hough conversion, and calculates deviation parameter.

Wherein in an embodiment, also comprise the reverse image detection module being connected with described control module; Described control module is for exporting to described reverse image detection module by described reverse image data, and described reverse image detection module is used for obtaining the reverse image of described automobile according to described reverse image data.

Wherein in an embodiment, described reverse image detection module comprises turning predicting unit and vehicle route predicting unit;

Described turning predicting unit predicts vehicle turning path according to described reverse image;

Described vehicle route predicting unit is used for the direction according to current vehicle, and utilizes world's coordinate to the geometric transformation relation of described acquisition module imaging plane, obtains the path locus image of vehicle.

A kind of automotive rear-view method, comprises the following steps:

The afterbody that acquisition module is installed on automobile, and gather the view data of automobile tail;

At least one in the blind spot data of described automobile, deviation data and reverse image data is gone out according to described analysis of image data;

Receive and show described blind spot data, deviation data and reverse image data.

Above-mentioned rear-view system for automobile and method are passed through to install acquisition module at automobile tail, by the blind spot data of acquisition module collection automobile, deviation data and reverse image data. Therefore, only utilize acquisition module just can obtain the blind spot data of automobile, deviation data and reverse image data, it is not necessary to multiple acquisition module to be set and obtains the blind spot data of automobile, deviation data and reverse image data respectively. Thus, reducing the complicacy of system architecture, decrease the module needed for system, so that the cost of above-mentioned rear-view system for automobile is low, and system architecture is simple.

Accompanying drawing explanation

Fig. 1 is the module map of rear-view system for automobile;

Fig. 2 is blind spot detection schematic diagram;

Fig. 3 is lane departure warning schematic diagram;

Fig. 4 is reverse image schematic diagram;

Fig. 5 is the schema of automotive rear-view method.

Embodiment

For the ease of understanding the present invention, below with reference to relevant drawings, the present invention is described more fully.Accompanying drawing gives the preferred embodiment of the present invention. But, the present invention can realize in many different forms, is not limited to embodiment described herein. On the contrary, it is provided that the object of these embodiments makes the understanding of the disclosure to the present invention more comprehensively thorough.

It should be noted that, when element is called as " being fixed on " another element, it can directly on another element or can also there is element placed in the middle. When an element is considered as " connection " another element, it can be directly connected to another element or may there is element placed in the middle simultaneously. Term as used herein " vertical ", " level ", "left", "right" and similar statement are just for illustrative purposes.

Unless otherwise defined, all technology used herein are identical with the implication that the those skilled in the art belonging to the present invention understand usually with scientific terminology. The term used in the description of the invention herein is the object in order to describe specific embodiment, is not intended to be restriction the present invention. Term as used herein " and/or " comprise arbitrary and all combinations of one or more relevant Listed Items.

As shown in Figure 1, it is the module map of rear-view system for automobile.

A kind of rear-view system for automobile, comprise acquisition module 101, control module 112 and display module 105, described acquisition module 101 is installed on the afterbody of described automobile, described acquisition module 101 is for gathering the view data of described automobile tail, and described view data is exported to described control module 112, described control module 112 is for going out at least one in the blind spot data of described automobile, deviation data and reverse image data according to described analysis of image data; Described display module 105 is for receiving and shows described blind spot data, deviation data and reverse image data.

In the present embodiment, acquisition module 101, for gathering the view data of automobile tail, comprises the carriageway image information of automobile tail, neighbouring vehicle image information. The view data real-time Transmission that acquisition module 101 gathers is to control module 112.

Acquisition module 101 is ultra wide-angle imaging head, and is installed on the central position of automobile tail.

In the present embodiment, the rear vision mirror blind spot usually said refers to the visual field blocked by B pillar, at the oblique rear of officer. Vehicle in blind area cannot be seen from rear vision mirror. When vehicle lane change, driver easily because of proceeds posterolateral that can't see in blind spot in the same way driving vehicle and produce side and hit accident.

With blind spot data instance.

Owing to acquisition module 101 is installed on automobile tail, adopt ultra wide-angle imaging head again, therefore, it is set to initial point with the installation position of acquisition module 101, radiate out the fan-shaped image pickup scope of 160 ° to automobile both sides, existing image pickup scope then can not reach the fan-shaped image pickup scope of 160 °, therefore, relative to existing image pickup scope, the image pickup scope exceeded in the present invention is blind spot data. The blind spot image of the blind spot region that the blind spot data that control module 112 analyzes based on realtime image data are corresponding is sent to display module 105, and display module 105 shows the blind spot image of blind spot region in real time. Thus, it is possible to help officer to detect the situation of blind spot region, avoid colliding with the pedestrian, vehicle and the obstacle that occur in blind spot region.

With deviation data instance.

Comprise lane information in the view data that acquisition module 101 gathers, according to the traveling trend of the trend of lane information and automobile, control module 112 judges whether automobile there will be the situation of deviation.Concrete, can judge that track is currently trends of straight line or curvilinear trend according to lane information. Traveling according to automobile can judge that automobile is currently craspedodrome trend or turns round trend.

If current track is trends of straight line, and automobile is craspedodrome, so can judge that automobile can not exceed track, namely there will not be the situation of deviation.

If current track is trends of straight line, but the trend of turning round occurs in automobile, then the situation of deviation will inevitably occur, now, then to be judged whether officer turns round at control automobile. If officer's control is turned round, then without the need to reporting to the police. If not officer's control is turned round, then send the early warning signal of deviation to officer.

Therefore, according to lane information and running car trend, it is possible to judge initiatively to deviate current track or the current track of passive deviation, and when the current track of passive deviation, send the early warning signal of deviation to officer. Concrete judgement process and above-mentioned judgement process are similar, do not repeat them here.

With reverse image data instance.

Owing to acquisition module 101 is installed on automobile tail, then adopt ultra wide-angle imaging head, therefore, it is set to initial point with the installation position of acquisition module 101, radiate out the fan-shaped image pickup scope of 160 ° to automobile both sides. The reversing image in the reversing region that the reverse image data that control module 112 analyzes based on realtime image data are corresponding is sent to display module 105, and display module 105 shows the reversing image in reversing region in real time. , it is possible to help officer to detect the situation in reversing region, thus avoid colliding with the pedestrian, vehicle and the obstacle that occur in reversing region.

Based on above-mentioned all embodiments, after control module 112 receives realtime image data, real-time analysis going out blind spot data, deviation data and reverse image data, can't there is conflict in the analysis process between three, also can not affect mutually.

In the present embodiment, the blind spot detection module 102 that rear-view system for automobile also comprises with described control module 112 is connected, described control module 112 is for exporting to described blind spot detection module 102 by described blind spot data, and described blind spot detection module 102 for obtaining the blind spot region image of described automobile according to described blind spot data.

In the present embodiment, adopting wide-angle imaging head, therefore, sensing range can cover major part blind area. Detected by the computer vision technique learnt based on the degree of depth and identify that the side of vehicle judges whether have vehicle in blind area. Even if only part vehicle body also can identify in sensing range.

Blind spot detection module 102 comprises wave filtering layer 121, pond layer 122, parts detection layers 123, deformation layer 124, barrier bed 125 and processing layer 126;

Described wave filtering layer 121 is for carrying out filtering process to described blind spot data;

Described pond layer 122 is for representing filtered blind spot data separate average pond compressive features;

Described parts detection layers 123 for according to described pond layer 122 process after blind spot data calculate in blind spot data comprise automobile, track detection mark;

Described deformation layer 124 for according to described pond layer 122 process after blind spot data calculate in blind spot data comprise automobile, track deformation parameter;

Described barrier bed 125 exports according to described detection mark and described deformation parameter and blocks parameter;

Described processing layer 126 according to described detection mark, described deformation parameter and described in block the blind spot region image of automobile described in parameter acquiring.

Incorporated by reference to Fig. 2.

Concrete, when detecting vehicular sideview, the feature of detection mainly comprises the parts such as vehicle window, car door. Owing to part vehicle body can only be detected, therefore these parts are understood some " being blocked ". For addressing this problem, we utilize degree of depth study (deeplearning) method by feature extractions, parts detector, deformation, the module such as to block unified arrives same framework, carries out combined optimization. Combined optimization can strengthen working in coordination with between each module, and then obtains result more better than respective independent optimization. By the feature that obtains of study can better positioning element, better positioning element is also conducive to study and optimization local feature.

Concrete model comprises following components, and 1) wave filtering layer, utilize convolutional neural networks that image is carried out filtering; 2) pond layer, utilizes average pond (averagepooling) compressive features to represent; 3) parts detection layers, calculates the detection mark of each parts; 4) deformation layer, the deformation between processing element; 5) barrier bed, blocks in conjunction with component layer and deformation layer output estimation, and then calculates final blind spot detected result.

Therefore, whether blind spot detection module 102 has vehicle for detecting in blind spot region. If detecting there is vehicle in blind spot, and this car is just to this direction lane change, can give the alarm. On image in blind spot also can be displayed in display module 105, for officer's reference simultaneously.

Rear-view system for automobile also comprises the deviation detection module 103 being connected with control module 112, control module 112 for exporting to described deviation detection module 103 by deviation data, and described deviation detection module 103 for obtaining the deviation parameter of described automobile according to described deviation data.

Incorporated by reference to Fig. 3.

In the present embodiment, acquisition module 101 (wide-angle imaging head) takes rear view of vehicle image, deviation detection module 103 uses computer vision algorithms make to identify track line from image, measure it width, radian, the information such as spacing, and the track line according to these information forward prediction about vehicle body length, when vehicle touches the track line of prediction, judge vehicle run-off-road. Whether open steering indicating light is selected whether to give the alarm according to vehicle again. Namely when officer does not open steering indicating light, signal that officer is given the alarm,

In the present embodiment, straight line model and curve model is used during predict lane line respectively according to practical situation. Straight line model is used for comparatively straight motorway, and curve model is used for the situations such as bend.

In an embodiment, deviation detection module 103 comprises curve model unit 131, track line computation unit 132 and the first processing unit 133;

Described curve model unit 131 characterizes the curve model of track line information for setting up;

Described track line computation unit 132 for calculating the parameter of track line according to described curve model;

Described first processing unit 133 is for being optimized the parameter of described track line according to likelihood function, and calculates deviation parameter.

Curve model is as follows:

u-n₀=B (v-m₀)+K/(v-m₀)

According to curve model, K, B_L, B_R, n₀Four parameters can characterize the information of track line completely. During use curve model, the method for maximum a-posteriori estimation is adopted to ask for the parameter K of track line, B_L, B_R, n₀. Make x=[K, B_L, B_R, n₀]. , then being estimated as of track line parameter x:

$\hat{x}=\arg \underset{x\∈R^4}{\max }p\left(x\right)p\left(P_{set}\right|x)$

Can drawing from the key element of upper formula, the committed step of maximum a-posteriori estimation becomes structure prior probability distribution p (x) and likelihood function p (P_set| x).The likelihood function herein adopted is:

$\begin{array}{c}p\left(P_{set}\right|x)\∝\underset{(m,n)\∈P_{set}}{\Σ}(1-\exp (\frac{grad{(m,n)}^2}{{\mathrm{\σ}}_1^2}\left)\right)\left(\exp \right(\frac{-{(n-{\mathrm{mB}}_L-K/m-n_0)}^2}{{\left({\mathrm{m\σ}}_2\right)}^2})\\ +\exp \left(\frac{-{(n-{\mathrm{mB}}_R-K/m-n_0)}^2}{{\left({\mathrm{m\σ}}_2\right)}^2}\right))\end{array}$

Owing to maximum likelihood function is Non-monotonic function, cannot try to achieve it by traditional method has solution most. If searching for optimum solution by the method for traversal in the scope limited, even if the span of x being reduced according to experience, the calculated amount of traversal is also very big, therefore just needs to look for the algorithm of a kind of intelligence to come with the fastest speed, obtains optimum solution. The method of the particle swarm optimization improved is adopted to obtain the optimum solution of track parameter. This kind of algorithm have concept simple, easily realize, parameter is less, speed of convergence is very fast, robustness is good advantage.

In another embodiment, deviation detection module 103 comprises straight line model unit 134, track straight line extraction unit 135 and the 2nd processing unit 136;

Described straight line model unit 134 characterizes the straight line model of track line information for setting up;

Described track straight line extraction unit 135 extracts the parameter of track line according to described straight line model;

Described 2nd processing unit 136 is for carrying out hough conversion to the parameter of described track line, and calculates deviation parameter.

When track line is set as straight line, track line model can represent and is:

U=B_LRv+D_LR

In formula, u and v is the coordinate in the picture of the point on the line of track, B_LR, D_LRFor track line model parameter. Such situation only needs the track line data after by extraction to do Hough transform respectively just can obtain good effect.

Rear-view system for automobile also comprises the reverse image detection module 104 being connected with control module 112; Control module 112 for exporting to described reverse image detection module 104 by reverse image data, and described reverse image detection module 104 for obtaining the reverse image of described automobile according to described reverse image data.

Reverse image detection module 104 comprises turning predicting unit 141 and vehicle route predicting unit 142;

Described turning predicting unit 141 predicts vehicle turning path according to described reverse image;

Described vehicle route predicting unit 142 is for the direction according to current vehicle, and utilizes world's coordinate to the geometric transformation relation of described acquisition module imaging plane, obtains the path locus image of vehicle.

Incorporated by reference to Fig. 4.

In the present embodiment, show rear view of vehicle image in real time when moveing backward, help officer to enter parking stall smoothly. Owing to employing ultra wide-angle imaging head, affect visual angle than common reversing wider. Prediction vehicle turning path, better auxiliary officer is stopped. During prediction vehicle route, system according to the direction of current wheel, can utilize world's coordinate to the geometric transformation relation of camera imaging plane, is drawn in the picture (assuming that wheel direction remains unchanged) by the track in vehicle future.

Based on above-mentioned all embodiments, incorporated by reference to Fig. 5, a kind of automotive rear-view method, comprises the following steps:

Step S510, is installed on the afterbody of automobile by acquisition module, and gathers the view data of automobile tail.

Step S520, goes out at least one in the blind spot data of described automobile, deviation data and reverse image data according to described analysis of image data.

Step S530, receives and shows described blind spot data, deviation data and reverse image data.

In the present embodiment, step S520 comprises:

A, the blind spot data going out automobile according to analysis of image data;

B, the deviation data going out automobile according to analysis of image data;

C, the reverse image data going out automobile according to analysis of image data.

In the present embodiment, steps A comprises:

A1, described blind spot data are carried out filtering process;

A2, filtered blind spot data separate average pond compressive features is represented;

A3, according to described pondization process after blind spot data calculate in blind spot data comprise automobile, track detection mark;

A4, according to described pondization process after blind spot data calculate in blind spot data comprise automobile, track deformation parameter;

A5, export according to described detection mark and described deformation parameter and block parameter;

A6, according to described detection mark, described deformation parameter and described in block the blind spot region image of automobile described in parameter acquiring.

In the present embodiment, step B comprises:

Step B1, the curve model setting up sign track line information;

Step B2, the parameter calculating track line according to described curve model;

Step B3, according to likelihood function, the parameter of described track line is optimized, and calculates deviation parameter.

In yet another embodiment, step B also comprises:

Step B4, the straight line model setting up sign track line information;

Step B5, the parameter extracting track line according to described straight line model;

Step B6, parameter to described track line carry out hough conversion, and calculate deviation parameter.

In the present embodiment, step C comprises:

Step C1, according to described reverse image predict vehicle turning path;

Step C2, in the direction according to current vehicle, and utilize world's coordinate to the geometric transformation relation of described acquisition module imaging plane, obtain the path locus image of vehicle.

Above-mentioned rear-view system for automobile and method use the wide-angle imaging head of the tailstock to realize blind spot detection, deviation detection and reverse image three functions.

Above-mentioned rear-view system for automobile and method can combined with intelligent rear vision mirrors. Traditional centre rear-view mirror is optical rearview mirror. Sight line is comparatively narrow, is easily blocked by back-seat passengers and foreign material. When rear car opens high beam, officer cannot observe rear situation by rear vision mirror. Rear-view system for automobile comprises a display module 105 (intelligent back vision mirror), is displayed on display module by the image that wide-angle imaging head photographs, replaces traditional centre rear-view mirror. Doing so avoids the problem blocked and dazzle the eyes with high light. Meanwhile, the result of blind spot detection, deviation detection and reverse image can also be simultaneously displayed in intelligent back vision mirror.

Above-mentioned rear-view system for automobile and method realize the three zones originally needing the driving of multiple camera auxiliary altogether with single wide-angle imaging head, incorporate system, reduce cost.

Above-mentioned rear-view system for automobile and method, by installing acquisition module 101 at automobile tail, gather the blind spot data of automobile, deviation data and reverse image data by acquisition module 101. Therefore, only utilize acquisition module 101 just can obtain the blind spot data of automobile, deviation data and reverse image data, it is not necessary to multiple acquisition module 101 to be set and obtains the blind spot data of automobile, deviation data and reverse image data respectively. Thus, reducing the complicacy of system architecture, decrease the module needed for system, so that the cost of above-mentioned rear-view system for automobile is low, and system architecture is simple.

Each technology feature of the above embodiment can combine arbitrarily, for making description succinct, each all possible combination of technology feature in above-described embodiment is not all described, but, as long as the combination of these technology features does not exist contradiction, all it is considered to be the scope that this specification sheets is recorded.

The above embodiment only have expressed several enforcement modes of the present invention, and it describes comparatively concrete and detailed, but can not therefore be construed as limiting the scope of the patent., it is also possible to make some distortion and improvement, it should be appreciated that for the person of ordinary skill of the art, without departing from the inventive concept of the premise these all belong to protection scope of the present invention. Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. a rear-view system for automobile, it is characterized in that, comprise acquisition module, control module and display module, described acquisition module is installed on the afterbody of described automobile, described acquisition module is for gathering the view data of described automobile tail, and described view data is exported to described control module, described control module is used for going out at least one in the blind spot data of described automobile, deviation data and reverse image data according to described analysis of image data; Described display module is for receiving and shows described blind spot data, deviation data and reverse image data.

2. rear-view system for automobile according to claim 1, it is characterised in that, described acquisition module is ultra wide-angle imaging head, and is installed on the central position of automobile tail.

3. rear-view system for automobile according to claim 1 and 2, it is characterized in that, also comprise the blind spot detection module being connected with described control module, described control module is for exporting to described blind spot detection module by described blind spot data, and described blind spot detection module is used for obtaining the blind spot region image of described automobile according to described blind spot data.

4. rear-view system for automobile according to claim 3, it is characterised in that, described blind spot detection module comprises wave filtering layer, pond layer, parts detection layers, deformation layer, barrier bed and processing layer;

Described wave filtering layer is for carrying out filtering process to described blind spot data;

Described pond layer is for representing filtered blind spot data separate average pond compressive features;

Described parts detection layers be used for according to described pond layer process after blind spot data calculate in blind spot data comprise automobile, track detection mark;

Described deformation layer be used for according to described pond layer process after blind spot data calculate in blind spot data comprise automobile, track deformation parameter;

Described barrier bed exports according to described detection mark and described deformation parameter and blocks parameter;

Described processing layer according to described detection mark, described deformation parameter and described in block the blind spot region image of automobile described in parameter acquiring.

5. rear-view system for automobile according to claim 1 and 2, it is characterized in that, also comprise the deviation detection module being connected with described control module, described control module is for exporting to described deviation detection module by described deviation data, and described deviation detection module is used for obtaining the deviation parameter of described automobile according to described deviation data.

6. rear-view system for automobile according to claim 5, it is characterised in that, described deviation detection module comprises curve model unit, track line computation unit and the first processing unit;

Described curve model unit characterizes the curve model of track line information for setting up;

Described track line computation unit is used for calculating the parameter of track line according to described curve model;

Described first processing unit is for being optimized the parameter of described track line according to likelihood function, and calculates deviation parameter.

7. rear-view system for automobile according to claim 5, it is characterised in that, described deviation detection module comprises straight line model unit, track straight line extraction unit and the 2nd processing unit;

Described straight line model unit characterizes the straight line model of track line information for setting up;

Described track straight line extraction unit extracts the parameter of track line according to described straight line model;

Described 2nd processing unit is used for the parameter to described track line and carries out hough conversion, and calculates deviation parameter.

8. rear-view system for automobile according to claim 1 and 2, it is characterised in that, also comprise the reverse image detection module being connected with described control module; Described control module is for exporting to described reverse image detection module by described reverse image data, and described reverse image detection module is used for obtaining the reverse image of described automobile according to described reverse image data.

9. rear-view system for automobile according to claim 8, it is characterised in that, described reverse image detection module comprises turning predicting unit and vehicle route predicting unit;

Described turning predicting unit predicts vehicle turning path according to described reverse image;

Described vehicle route predicting unit is used for the direction according to current vehicle, and utilizes world's coordinate to the geometric transformation relation of described acquisition module imaging plane, obtains the path locus image of vehicle.

10. an automotive rear-view method, it is characterised in that, comprise the following steps:

The afterbody that acquisition module is installed on automobile, and gather the view data of automobile tail;

At least one in the blind spot data of described automobile, deviation data and reverse image data is gone out according to described analysis of image data;

Receive and show described blind spot data, deviation data and reverse image data.