Road driving auxiliary system capable of sensing severe environment intensity

Abstract

The invention relates to the technical field of automobile automatic driving, in particular to a road driving auxiliary system capable of sensing severe environment intensity. The system comprises a mobile terminal and a fixed terminal background. An environment video image near a vehicle body is transmitted to the point division processing unit through the vehicle, the point division processing unit processes the environment video image transmitted by the vehicle, information of vehicles and roads near the vehicle body is extracted, and the information is combined and then transmitted to the summarizing unit. The summarizing unit integrates information transmitted by a plurality of vehicles to form the conditions of roads and automobiles of the whole lane, and transmits corresponding information according to the information transmitted by the vehicles, so that each vehicle can obtain the information of roads and automobiles near the corresponding vehicle body, and the vehicles can be prevented from being damaged when encountering severe weather. By integrating other automobiles and nearby environments, each automobile can acquire road information and the position of the automobile.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile automatic driving, and in particular to a road driving assistance system capable of sensing the intensity of adverse environments. Background Art

[0002] With the continuous innovation of artificial intelligence, self-driving cars are gradually replacing conventional car driving methods to free people's hands. At present, the main method for self-driving cars on the road is still to use radar and cameras to obtain road conditions, and then move the car according to the information obtained by the car. However, when encountering bad weather, such as rain, snow, haze and other bad weather conditions, the car's perception system will be disturbed, resulting in a decrease in perception accuracy, especially on highways with many cars and fast running speeds. When the car's perception system is disturbed, it will affect the normal driving of the car and even affect the safety of the car. For this reason, we provide a road driving assistance system to improve the safety of car driving in harsh environments. Summary of the invention

[0003] The purpose of the present invention is to provide a road driving assistance system that can sense the intensity of harsh environments, so as to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, a road driving assistance system capable of sensing the intensity of a harsh environment is provided, comprising a mobile terminal and a fixed terminal background, wherein the mobile terminal comprises a plurality of vehicles, and the fixed terminal background comprises a plurality of point processing units and a summarizing unit. When the vehicle determines that the video is not clear, the vehicle transmits the collected road information to the point processing unit, and the point processing unit processes the video information transmitted by the vehicle, obtains the road environment around each vehicle, and combines the road environment around each vehicle with each other, and transmits the data to the summarizing unit after the combination is completed, and the summarizing unit simulates and transmits the lane environment according to the position of the vehicle corresponding to the vehicle body, so that the road conditions in front and behind the vehicle body are displayed in the vehicle.

[0005] As a further improvement of the technical solution, the vehicle includes a video clarity judgment module and a video image acquisition module;

[0006] The video clarity judgment module judges the image clarity based on the video content collected by the cameras around the vehicle body, and defines the environment outside the vehicle body as a harsh environment when it is judged that the image is not clear, and transmits an instruction that the environment outside the vehicle body is a harsh environment to the video image acquisition module;

[0007] The video image acquisition module uses a camera near the vehicle body to acquire video images of the road near the vehicle body, obtains video images of the road environment near the vehicle body, and transmits the acquired video images to the point processing unit.

[0008] As a further improvement of the technical solution, the point processing unit includes a video data analysis module, a road information extraction module and an image combination module;

[0009] The video data analysis module receives the video image data of the road environment near the vehicle body transmitted by the video image acquisition module, analyzes the video image, and identifies the information of other vehicles and the road environment in the image;

[0010] The road information extraction module extracts the image data of other cars and road environment identified by the video data analysis module and numbers the image data of different frames during the extraction process. After the numbering is completed, the numbering and image data are transmitted to the image combination module;

[0011] The image combination module receives the data transmitted from the road information extraction module, sorts the image data according to the corresponding numbers, and combines the sorted data so that images with different frame numbers are combined to form video content.

[0012] As a further improvement of the technical solution, the method by which the video data analysis module analyzes and identifies the video data of the road environment near the vehicle body transmitted by the video image acquisition module is:

[0013] ① Segment the received video image frame by frame, so that the video image is segmented into multiple pictures;

[0014] ② Divide the segmented image into rectangles to obtain n sub-images of equal size. 1 ,o 2 ,...,o n ;

[0015] ③. Set the color threshold of the road and the car, perform histogram analysis on each sub-image, and calculate the grayscale mean θ of the sub-image. 1 , maximum gray value g 1 and the minimum gray value g 2 , when θ 1 -g 1 | or θ 1 -g 2| is less than the set threshold, the average grayscale of the image is close to the highest grayscale or the lowest grayscale, which means that the ratio of the target and the background in the image is too disparate, that is, there is no road or car content in the image. At this time, the segmentation condition is judged to be unsatisfied, while other cases are considered to meet the segmentation condition, that is, θ 1 -g 1 | or θ 1 -g 2 |Greater than the set threshold;

[0016] ④. When the sub-image meets the segmentation condition, stop dividing the image and record these sub-images as p 1 ,p 2 ,...,p r , 1≤r≤n;

[0017] ⑤. If the sub-image does not meet the segmentation conditions, it will be divided into four sub-images of equal size, and the sub-images obtained by the division are recorded as q 1 ,q 2 ,...,q k ,1≤k≤4n;

[0018] ⑥. The sub-images that meet the segmentation conditions are sorted and arranged according to the positions before segmentation, and after the arrangement is completed, the information of the position is marked. The marked information is the image information content of the car and the road recognized by the video data analysis module.

[0019] As a further improvement of the present technical solution, the point processing unit also includes a road positioning sorting module. When the video image acquisition module transmits the video image data to the video data analysis module, the position information of the car itself is transmitted to the road positioning sorting module. The road positioning sorting module receives the positioning information transmitted by the video image acquisition module, and numbers the time and position of the positioning information, and then transmits the time and position numbers to the image combination module. The image combination module sets the time and position numbers in the combined video content.

[0020] As a further improvement of the technical solution, the summary unit includes a multi-segment video image integration module and a lane environment simulation module;

[0021] The multi-segment video image integration module receives the serial numbers with time and location transmitted from the multiple image combination modules and combines them to form video content. The information transmitted by an image combination module represents the environment around a car. The multi-segment video image integration module sorts the received data according to the order of the cars being positioned on the road, and after the sorting is completed, combines the video contents transmitted by the multiple image combination modules to integrate the road information obtained by multiple cars.

[0022] The lane environment simulation module receives the lane information integrated by the multi-segment video image integration module, and simulates the lane conditions according to the received information.

[0023] As a further improvement of the technical solution, the aggregation unit further includes a parking space simulation module and a data transmission module;

[0024] The parking space simulation module receives the position information of multiple cars in the multi-segment video image integration module, and simulates the position of the car according to the positioning information to obtain the position information of the car. At the same time, the parking space simulation module receives the updated information of the car position transmitted by the road positioning sorting module in real time;

[0025] The data transmission module receives information from the parking space simulation module and the lane environment simulation module, and transmits the road environment simulation information to the vehicle.

[0026] As a further improvement of the present technical solution, the vehicle also includes an image receiving and displaying module, which receives the video information of the lanes and nearby cars transmitted by the data transmission module, displays and identifies the received information, and simultaneously identifies the information in the video, and determines the path and speed of the car according to the information in the video.

[0027] As a further improvement of the present technical solution, when the data transmission module receives information transmitted from the parking space simulation module and the lane environment simulation module, it determines the parking space of the corresponding car based on the mobile terminal transmitted by the video, and transmits the video data according to the position information of the car itself.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] In the road driving assistance system that perceives the intensity of adverse environments, the vehicle transmits the environmental video image near the vehicle body to the point processing unit, and the point processing unit processes the environmental video image transmitted by the vehicle, extracts the information of the car and the road near the vehicle body, and transmits the information to the summary unit after combining, and the summary unit integrates the information transmitted by multiple vehicles to form the road and car conditions of the entire lane, and transmits corresponding information according to the information transmitted by the vehicle, so that each vehicle can obtain the information of the road and car near the corresponding vehicle body, so that when the car encounters adverse weather, by integrating other cars and their surrounding environments, each vehicle can obtain the road information and the position of the car, so that the car can control the corresponding path and speed according to the road information and the position of the car, so as to ensure that the car can drive normally in adverse environments and improve the safety of autonomous driving cars driving in adverse environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the overall framework diagram of the present invention;

[0031] Figure 2 A block diagram of a vehicle of the present invention;

[0032] Figure 3 It is a block diagram of a point-by-point processing unit of the present invention;

[0033] Figure 4 It is a summary unit block diagram of the present invention;

[0034] Figure 5 It is the overall detailed framework diagram of the present invention.

[0035] The meaning of each number in the figure is:

[0036] 1. Vehicle; 11. Video clarity judgment module; 12. Video image acquisition module; 13. Image receiving and display module;

[0037] 2. Point processing unit; 21. Video data analysis module; 22. Road information extraction module; 23. Road positioning and sorting module; 24. Image combination module;

[0038] 3. Summarization unit; 31. Multi-segment video image integration module; 32. Parking space simulation module; 33. Lane environment simulation module; 34. Data transmission module. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0041] Example 1

[0042] To improve the safety of autonomous vehicles in inclement weather, see Figure 1-Figure 5 As shown, the purpose of this embodiment is to provide a road driving assistance system that can perceive the intensity of a harsh environment, including a mobile end and a fixed end background, the mobile end includes a plurality of vehicles 1, the fixed end background includes a plurality of point processing units 2 and a summarizing unit 3, when the vehicle 1 determines that the video is not clear, the vehicle 1 transmits the collected road information to the point processing unit 2, the point processing unit 2 processes the video information transmitted by the vehicle 1, obtains the road environment around each vehicle 1, and combines the road environment around each vehicle 1 to form an image of the environment around each vehicle 1, and transmits the data to the summarizing unit 3 after the combination is completed, the summarizing unit 3 simulates and transmits the lane environment according to the position of the vehicle 1 corresponding to the vehicle body, so that the vehicle 1 displays the road conditions in front and behind the vehicle body, and transmits the environmental video image near the vehicle body through the vehicle 1 The information is transmitted to the point processing unit 2, so that the point processing unit 2 processes the environmental video image transmitted by the vehicle 1, extracts the information of the cars and roads near the vehicle body, and transmits the information to the summary unit 3 after combining. The summary unit 3 integrates the information transmitted by multiple vehicles 1 to form the road and car conditions of the entire lane, and transmits corresponding information according to the information transmitted by the vehicle 1, so that each vehicle 1 can obtain the road and car information near the corresponding vehicle body, so that when the car encounters bad weather, by integrating other cars and their surrounding environment, each vehicle 1 can obtain the road information and the position of the car, so that the car can control the corresponding path and speed according to the road information and the position of the car, so as to ensure that the car can drive normally in bad environment, and improve the safety of the autonomous driving car in bad environment.

[0043] Each vehicle 1 includes a video clarity determination module 11 and a video image acquisition module 12;

[0044] The video clarity judgment module 11 judges the image clarity based on the video content collected by the cameras around the vehicle body, and defines the environment outside the vehicle body as a bad environment when it is judged that the image is not clear. At this time, an instruction that the environment outside the vehicle body is a bad environment is transmitted to the video image acquisition module 12. The judgment method is to receive the image information collected by the camera. When the image is blurred or the light brightness is low, and this situation has not changed for a certain period of time, such as 5 minutes, the video clarity judgment module 11 judges that the outside is bad weather. If the image is blurred or the light brightness is low due to darkness, it is not judged as bad weather.

[0045] The video image acquisition module 12 uses a camera near the vehicle body to acquire video images of the road near the vehicle body, obtains a video image of the road environment near the vehicle body, and transmits the acquired video image to the point processing unit 2. In the process of the video image acquisition module 12 transmitting the video image to the point processing unit 2, it means that the data of the mobile end is transmitted to the fixed end background, so that the fixed end background can process the video image. At the same time, the fixed end background receives multiple data. According to the number of vehicles 1 in the road section under bad weather, vehicles 1 in the same area of ​​different sections are received by different point processing units 2;

[0046] A plurality of point processing units 2 receive data transmitted from a plurality of vehicles 1, wherein each point processing unit 2 receives data transmitted from one of the vehicles 1 separately, and when a vehicle 1 determines that the external environment is not a harsh environment, the point processing unit 2 terminates the data transmission with the vehicle 1;

[0047] The point processing unit 2 includes a video data analysis module 21, a road information extraction module 22 and an image combination module 24;

[0048] The video data analysis module 21 receives the video image data of the road environment near the vehicle body transmitted by the video image acquisition module 12, and analyzes the video image to identify the information of other vehicles and the road environment in the image;

[0049] The method by which the video data analysis module 21 analyzes and identifies the video data of the road environment near the vehicle body transmitted by the video image acquisition module 12 is as follows:

[0050] ① Segment the received video image frame by frame, so that the video image is segmented into multiple pictures;

[0051] ② Divide the segmented image into rectangles to obtain n sub-images of equal size. 1 ,o 2 ,...,o n ;

[0052] ③. Set the color threshold of the road and the car, perform histogram analysis on each sub-image, and calculate the grayscale mean θ of the sub-image. 1 , maximum gray value g 1 and the minimum gray value g 2 , when θ 1 -g 1 | or θ 1 -g 2| is less than the set threshold, the average grayscale of the image is close to the highest grayscale or the lowest grayscale, which means that the ratio of the target and the background in the image is too disparate, that is, there is no road or car content in the image. At this time, the segmentation condition is judged to be unsatisfied, while other cases are considered to meet the segmentation condition, that is, θ 1 -g 1 | or θ 1 -g 2 |Greater than the set threshold;

[0053] ④. When the sub-image meets the segmentation condition, stop dividing the image and record these sub-images as p 1 ,p 2 ,...,p r , 1≤r≤n;

[0054] ⑤. If the sub-image does not meet the segmentation conditions, it will be divided into four sub-images of equal size, and the sub-images obtained by the division are recorded as q 1 ,q 2 ,...,q k ,1≤k≤4n.

[0055] ⑥. The sub-images satisfying the segmentation conditions are sorted and arranged according to the positions before segmentation, and after the arrangement is completed, the information of the position is marked. The marked information is the image information content of the car and the road recognized by the video data analysis module 21.

[0056] The road information extraction module 22 extracts the image data of other cars and road environment identified by the video data analysis module 21 and numbers the image data of different frames during the extraction process. After the numbering is completed, the numbering and the image data are transmitted to the image combination module 24. By numbering the image data of different frames, it is convenient to sort them according to the order of numbers when forming a video later, ensuring that the image screen will not skip frames and ensuring the fluency of the subsequent video;

[0057] The image combination module 24 receives the data transmitted from the road information extraction module 22, sorts the image data according to the corresponding numbers, and combines the sorted data so that images with different frame numbers are combined to form video content.

[0058] At the same time, the point processing unit 2 also includes a road positioning sorting module 23. When the video image acquisition module 12 transmits the video image data to the video data analysis module 21, the position information of the car itself is transmitted to the road positioning sorting module 23. The road positioning sorting module 23 receives the positioning information transmitted by the video image acquisition module 12, and numbers the time and position of the positioning information, and then transmits the time and position numbers to the image combination module 24. The image combination module 24 sets the time and position numbers in the combined video content.

[0059] The summarizing unit 3 includes a multi-segment video image integration module 31 and a lane environment simulation module 33;

[0060] The multi-segment video image integration module 31 receives the serial numbers with time and position transmitted from the multiple image combination modules 24 and combines them to form video content. At this time, the multiple image combination modules 24 transmit video data on the same road. When the multiple image combination modules 24 transmit video data of different sections, they are distinguished according to different sections to avoid combining the video data of multiple sections together. The information transmitted by an image combination module 24 represents the environment around a car. The multi-segment video image integration module 31 sorts the received data according to the order of the cars' positioning on the road. For example, if there are 10 cars, they are sorted according to the order of the cars on the road, so that the surrounding environment captured by each car is sequentially combined, so that when the car is captured, the surrounding environment is captured by the car. The situation of the video can be fed back to other cars, so that other cars can obtain the information, so that subsequent cars can avoid it. After the sorting is completed, the video contents transmitted by the multiple image combination modules 24 are combined, so that the road information obtained by multiple cars is integrated to form a road situation. Especially when encountering rainy and snowy weather, in this way, each car can obtain the situation on the road, so that the subsequent autonomous driving car can respond in advance. At the same time, with the help of this method, the problem of interference of autonomous driving cars when encountering rainy and snowy weather is also solved, so that the car can foresee and respond when passing on a highway with many cars and a fast running speed, reducing the interference of rain and snow on the car, so that the car can drive normally;

[0061] The lane environment simulation module 33 receives the lane information integrated by the multi-segment video image integration module 31, and simulates the lane conditions based on the received information. The simulated content is the road conditions of the section, such as cars driving slowly in a certain place of the section, heavy rain and snow ahead, unclear radar video, etc.

[0062] The aggregation unit 3 also includes a parking space simulation module 32 and a data transmission module 34;

[0063] The parking space simulation module 32 receives the position information of multiple cars in the multi-segment video image integration module 31, and simulates the position of the car according to the positioning information to obtain the position information of the car. At the same time, the parking space simulation module 32 receives the updated information of the car position transmitted by the road positioning sorting module 23 in real time;

[0064] The data transmission module 34 receives information from the parking space simulation module 32 and the lane environment simulation module 33, and transmits the road environment simulation information to the vehicle 1. When the data transmission module 34 receives the information transmitted from the parking space simulation module 32 and the lane environment simulation module 33, it determines the parking space of the corresponding car according to the mobile terminal transmitted by the video, and transmits the video data according to the position information of the car itself, so as to ensure that each car can obtain the environment near each car, avoid the situation where the situation of its own car is sent to other cars, and improve the accuracy of data transmission.

[0065] At the same time, the vehicle 1 also includes an image receiving and displaying module 13, which receives the video information of the lanes and nearby cars transmitted by the data transmission module 34, and displays and identifies the received information, and identifies the information in the video, and formulates the path and speed of the car according to the information in the video, so that the car can respond in time according to the conditions of the road ahead, so that the car can accurately predict in advance whether it needs to accelerate or decelerate, or adjust the path of travel, etc., so that the autonomous driving car can respond in advance, and improve the safety of autonomous driving cars in rainy and snowy weather.

[0066] When a car encounters bad weather, by integrating other cars and their surrounding environment, each vehicle 1 can obtain road information and the car's position, so that the car can control the corresponding path and speed according to the road information and the car's position, to ensure that the car can drive normally in bad conditions, reduce the impact of rain and snow on autonomous cars, and improve the safety of autonomous driving cars in bad conditions.

[0067] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A road driving assistance system that senses the intensity of harsh environments, including a mobile terminal and a fixed terminal background, characterized in that: The mobile terminal includes a plurality of vehicles (1), and the fixed terminal backend includes a plurality of point processing units (2) and a summarizing unit (3). When the vehicle (1) determines that the video is not clear, it transmits the collected road information to the point processing unit (2). The point processing unit (2) processes the video information transmitted by the vehicle (1), obtains the road environment around each vehicle (1), and combines the road environment around each vehicle (1). After the combination is completed, the data is transmitted to the summarizing unit (3). The summarizing unit (3) simulates and transmits the lane environment according to the position of the vehicle (1) corresponding to the vehicle body, so that the road conditions in front and behind the vehicle body are displayed in the vehicle (1).

2. The road driving assistance system capable of sensing the intensity of harsh environments according to claim 1, characterized in that: The vehicle (1) comprises a video clarity judgment module (11) and a video image acquisition module (12); The video clarity judgment module (11) judges the clarity of the image based on the video content collected by the cameras around the vehicle body, and defines the environment outside the vehicle body as a harsh environment when it is judged that the image is not clear, and transmits an instruction that the environment outside the vehicle body is a harsh environment to the video image acquisition module (12); The video image acquisition module (12) uses a camera near the vehicle body to acquire video images of the road near the vehicle body, obtains video images of the road environment near the vehicle body, and transmits the acquired video images to the point processing unit (2).

3. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 2, characterized in that: The point-by-point processing unit (2) comprises a video data analysis module (21), a road information extraction module (22) and an image combination module (24); The video data analysis module (21) receives the video image data of the road environment near the vehicle body transmitted by the video image acquisition module (12), analyzes the video image, and identifies information about other vehicles and the road environment in the image; The road information extraction module (22) extracts the image data of other cars and road environment identified by the video data analysis module (21), numbers the image data of different frames during the extraction process, and after the numbering is completed, transmits the number and the image data to the image combination module (24); The image combination module (24) receives the data transmitted from the road information extraction module (22), sorts the image data according to the corresponding numbers, and combines the sorted data so that images with different frame numbers are combined to form video content.

4. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 3, characterized in that: The method by which the video data analysis module (21) analyzes and identifies the video data of the road environment near the vehicle body transmitted by the video image acquisition module (12) is: ① Segment the received video image frame by frame, so that the video image is segmented into multiple pictures; ② Divide the segmented image into rectangles to obtain n sub-images of equal size o1, o2, ..., o n ; ③. Set the color threshold of roads and cars, perform histogram analysis on each sub-image, calculate the grayscale mean θ1, maximum grayscale value g1 and minimum grayscale value g2 of the sub-image. When |θ1-g1| or |θ1-g2| is less than the set threshold, the average grayscale of the image is close to the highest grayscale or the lowest grayscale. This means that the ratio of the target and the background in the image is too disparate, that is, there is no road or car content in the image. At this time, the segmentation condition is judged to be unsatisfied, while other cases are considered to meet the segmentation condition, that is, |θ1-g1| or |θ1-g2| is greater than the set threshold; ④. When the sub-image meets the segmentation condition, stop dividing the image and record these sub-images as p1, p2, ..., p r , 1≤r≤n; ⑤. If the sub-image does not meet the segmentation condition, it will be divided into four sub-images of equal size, and the sub-images obtained by the division are recorded as q1, q2, ..., q k ,1≤k≤4n; ⑥. The sub-images satisfying the segmentation conditions are sorted and arranged according to their positions before segmentation, and after the arrangement is completed, the information of the position is marked. The marked information is the image information content of the car and the road identified by the video data analysis module (21).

5. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 3, characterized in that: The point processing unit (2) further comprises a road positioning and sorting module (23). When the video image acquisition module (12) transmits the video image data to the video data analysis module (21), the position information of the vehicle itself is transmitted to the road positioning and sorting module (23). The road positioning and sorting module (23) receives the positioning information transmitted by the video image acquisition module (12), numbers the time and position of the positioning information, and then transmits the time and position numbers to the image combination module (24). The image combination module (24) sets the time and position numbers in the combined video content.

6. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 5, characterized in that: The aggregation unit (3) includes a multi-segment video image integration module (31) and a lane environment simulation module (33); The multi-segment video image integration module (31) receives the serial numbers with time and position transmitted from the multiple image combination modules (24) and combines them to form video content. The information transmitted from one image combination module (24) represents the environment around a car. The multi-segment video image integration module (31) sorts the received data according to the order in which the cars are positioned on the road. After the sorting is completed, the video contents transmitted from the multiple image combination modules (24) are combined to integrate the road information obtained by the multiple cars. The lane environment simulation module (33) receives the lane information integrated by the multi-segment video image integration module (31), and simulates the lane conditions based on the received information.

7. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 6, characterized in that: The aggregation unit (3) further includes a parking space simulation module (32) and a data transmission module (34); The parking space simulation module (32) receives the position information of multiple cars in the multi-segment video image integration module (31), and performs position simulation of the cars according to the positioning information to obtain the position information of the cars. At the same time, the parking space simulation module (32) receives the updated information of the car positions transmitted by the road positioning sorting module (23) in real time. The data transmission module (34) receives information from the parking space simulation module (32) and the lane environment simulation module (33), and transmits the road environment simulation information to the vehicle (1).

8. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 7, characterized in that: The vehicle (1) further comprises an image receiving and displaying module (13), which receives video information of lanes and nearby cars transmitted by the data transmission module (34), displays and identifies the received information, identifies information in the video, and determines the path and speed of the car according to the information in the video.

9. The road driving assistance system capable of sensing the intensity of adverse environments according to claim 7, characterized in that: When the data transmission module (34) receives information transmitted from the parking space simulation module (32) and the lane environment simulation module (33), it determines the parking space of the corresponding car according to the mobile terminal transmitted by the video, and transmits the video data according to the position information of the car itself.

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