Edge computing node-based vehicle-road perception data sharing system

Abstract

The application provides a vehicle-road perception data sharing system based on an edge computing node, comprising: an information acquisition system, configured to acquire road condition data on a target road section corresponding to a sharing system setting area where the information acquisition system is located, wherein the target road section corresponding to each sharing system setting area comprises a road section where the sharing system setting area is located; and an edge computing system, configured to perform preset processing on the road condition data on the target road section acquired by the information acquisition system arranged in the sharing system setting area where the edge computing system is located, wherein the preset processing comprises sharing the road condition data on the target road section acquired by the information acquisition system arranged in the sharing system setting area where the edge computing system is located to a target device, and the target device comprises a background general control server on a network side and / or a vehicle to be driven on a road section ahead and including the target road section.

Description

Technical Field

[0001] This invention relates to the field of information sharing technology, and in particular to a vehicle-road perception data sharing system based on edge computing nodes. Background Technology

[0002] Currently, vehicles on the road can share information. This typically involves drivers uploading road condition data they observe during their journey to a data sharing platform. This platform then shares the data with other vehicles; for example, a data sharing platform could function as a radio station, transmitting the shared data to other vehicles in real-time via voice broadcasts. However, this method is highly dependent on human intervention and suffers from high latency and poor real-time performance. Summary of the Invention

[0003] This invention provides a vehicle-road perception data sharing system based on edge computing nodes.

[0004] This invention provides a vehicle-road perception data sharing system based on edge computing nodes. Multiple sharing system setting areas are set at preset distances along the roadside. Each sharing system setting area is equipped with one vehicle-road perception data sharing system. Each vehicle-road perception data sharing system includes:

[0005] The information collection system is used to collect traffic condition data on the target road segment corresponding to the area set by the shared system. The target road segment corresponding to each shared system setting area includes the road segment where the shared system setting area is located.

[0006] An edge computing system is used to perform preset processing on traffic condition data of a target road segment collected by an information collection system set up within the shared system setting area where the edge computing system is located. The preset processing includes sharing the traffic condition data of the target road segment collected by the information collection system set up within the shared system setting area where the edge computing system is located with a target device. The target device includes a back-end central control server on the network side and / or a vehicle that will be traveling on the target road segment ahead.

[0007] In one embodiment, the road condition data includes any one or more of the following: vehicle driving video on the target road segment, driving speed of each vehicle on the target road segment, pedestrian walking video on the target road segment, walking speed of each pedestrian on the target road segment, and road obstacle data on the target road segment.

[0008] In one embodiment, the preset processing further includes: analyzing the road condition data on the target road segment collected by the information collection system set within the shared system setting area where the edge computing system is located, and sending the analysis results to the vehicle and / or the back-end central control server.

[0009] In one embodiment, the analysis results include any one or more of the following: average vehicle speed on the target road segment, vehicle congestion level on the target road segment, average pedestrian walking speed on the target road segment, pedestrian congestion level on the target road segment, and the degree of danger posed by road obstacles on the target road segment to the vehicle.

[0010] In one embodiment, the analysis method on the target road segment includes:

[0011] Acquire an image of the road surface on the target road segment;

[0012] Determine the size of the depression on the target road segment based on the image;

[0013] Obtain the vehicle's size and the driver's facial image;

[0014] The degree of danger posed to the vehicle by road obstacles on the target road section is determined based on the vehicle size, facial image, and the size of the depression on the target road section.

[0015] In one embodiment, determining the degree of danger posed to the vehicle by road obstacles on the target road segment based on the vehicle size, facial image, and the size of the depression on the target road segment includes:

[0016] The age of the driver of the vehicle is determined based on the facial image;

[0017] Determine the ratio between the size of the depression on the target road section and the size of the vehicle.

[0018] The degree of danger posed to the vehicle by road obstacles on the target road segment is determined based on the driver's age and the ratio.

[0019] In one embodiment, determining the degree of danger posed to the vehicle by road obstacles on the target road segment based on the driver's age and the ratio includes:

[0020] The degree of danger posed by road obstacles on the target road segment to the vehicle is calculated using the following formula:

[0021]

[0022] Wherein, Q represents the danger level index value of the road obstacles on the target road section to the vehicle, and the larger the Q value, the higher the danger level of the road obstacles on the target road section to the vehicle; θ represents the age weight value, which is a number greater than 0 and less than 1; T represents the age of the driver of the vehicle, in years; and B represents the ratio between the size of the depression on the target road section and the size of the vehicle.

[0023] In one embodiment, the edge computing system is also used to send obstacle avoidance alerts to the vehicle based on the degree of danger posed to the vehicle by road obstacles on the target road segment.

[0024] In one embodiment, the obstacle avoidance alert includes one or more of the vehicle's recommended driving speed and the location of the indentation.

[0025] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0026] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0027] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 This is a schematic diagram of a vehicle-road perception data sharing system based on edge computing nodes in an embodiment of the present invention. Detailed Implementation

[0029] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0030] This invention provides a vehicle-road perception data sharing system based on edge computing nodes, such as... Figure 1 As shown, multiple shared system setting areas 11 are set at preset distances along the roadside of road 10. Each shared system setting area 11 is equipped with one vehicle-road perception data sharing system. Each vehicle-road perception data sharing system includes:

[0031] The information collection system 12 is used to collect traffic condition data on the target road segment corresponding to the shared system setting area where it is located. The target road segment corresponding to each shared system setting area includes the road segment where the shared system setting area is located.

[0032] The edge computing system 13 is used to perform preset processing on the road condition data of the target road segment collected by the information collection system set in the shared system setting area where the edge computing system is located. The preset processing includes sharing the road condition data of the target road segment collected by the information collection system set in the shared system setting area where the edge computing system is located with the target device. The target device includes the background control server on the network side and / or the vehicle that will be traveling on the road segment ahead, which includes the target road segment.

[0033] In one embodiment, the road condition data includes any one or more of the following: vehicle driving video on the target road segment, driving speed of each vehicle on the target road segment, pedestrian walking video on the target road segment, walking speed of each pedestrian on the target road segment, and road obstacle data on the target road segment.

[0034] In one embodiment, the preset processing further includes: analyzing the road condition data on the target road segment collected by the information collection system set within the shared system setting area where the edge computing system is located, and sending the analysis results to the vehicle and / or the back-end central control server;

[0035] The analysis results include any one or more of the following: average vehicle speed on the target road segment, vehicle congestion level on the target road segment, average pedestrian walking speed on the target road segment, pedestrian congestion level on the target road segment, and the degree of danger posed by road obstacles on the target road segment to the vehicle.

[0036] In one embodiment, a method for analyzing the degree of danger posed by road obstacles on the target road segment to the vehicle includes:

[0037] The method for analyzing the degree of danger posed by road obstacles on the target road section to the vehicle includes:

[0038] Acquire an image of the road surface on the target road segment;

[0039] Determine the size of the depression on the target road segment based on the image;

[0040] Obtain the vehicle's size and the driver's facial image;

[0041] The degree of danger posed to the vehicle by road obstacles on the target road section is determined based on the vehicle size, facial image, and the size of the depression on the target road section.

[0042] In one embodiment, determining the degree of danger posed to the vehicle by road obstacles on the target road segment based on the vehicle size, facial image, and the size of the depression on the target road segment includes:

[0043] The age of the driver of the vehicle is determined based on the facial image;

[0044] Determine the ratio between the size of the depression on the target road section and the size of the vehicle.

[0045] The degree of danger posed to the vehicle by road obstacles on the target road segment is determined based on the driver's age and the ratio.

[0046] In one embodiment, determining the degree of danger posed to the vehicle by road obstacles on the target road segment based on the driver's age and the ratio includes:

[0047] The degree of danger posed by road obstacles on the target road segment to the vehicle is calculated using the following formula:

[0048]

[0049] Wherein, Q represents the danger level index value of the road obstacles on the target road section to the vehicle, and the larger the Q value, the higher the danger level of the road obstacles on the target road section to the vehicle; θ represents the age weight value, which is a number greater than 0 and less than 1; T represents the age of the driver of the vehicle, in years; and B represents the ratio between the size of the depression on the target road section and the size of the vehicle.

[0050] In one embodiment, the edge computing system is further configured to send an obstacle avoidance warning to the vehicle based on the degree of danger posed to the vehicle by road obstacles on the target road segment; wherein the obstacle avoidance warning includes one or more of the vehicle's suggested driving speed and the location of the depression.

[0051] For example, multiple risk level index value ranges can be preset, and a corresponding recommended driving speed can be set for each risk level index value range. After calculating the current risk level index value according to the above formula, the risk level index value range in which the current risk level index value is located is determined, and then the recommended driving speed corresponding to the risk level index value range in which the current risk level index value is located is determined. Then, an obstacle avoidance reminder is sent to the vehicle. The reminder includes a reminder message such as "Please note that there is a dent ahead" and the recommended driving speed value is the previously determined recommended driving speed.

[0052] The above-mentioned technical solution uses intelligent analysis to determine the degree of danger posed to drivers by indentations on target road sections and provides warnings, thereby improving vehicle driving safety.

[0053] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A vehicle-road perception data sharing system based on edge computing nodes, characterized in that, Multiple shared system setting areas are set up at preset distances along the roadside. Each shared system setting area is equipped with one vehicle-road perception data sharing system. Each vehicle-road perception data sharing system includes: The information collection system is used to collect traffic condition data on the target road segment corresponding to the area set by the shared system. The target road segment corresponding to each shared system setting area includes the road segment where the shared system setting area is located. An edge computing system is used to perform preset processing on road condition data of a target road segment collected by an information collection system set up within the shared system setting area where the edge computing system is located. The preset processing includes sharing the road condition data of the target road segment collected by the information collection system set up within the shared system setting area where the edge computing system is located with a target device. The target device includes a back-end central control server on the network side and / or a vehicle that will be traveling on the target road segment ahead. The preset processing further includes: analyzing the road condition data on the target road segment collected by the information collection system set in the shared system setting area where the edge computing system is located, and sending the analysis results to the vehicle and / or the back-end central control server; The analysis results include the average vehicle speed on the target road segment, the degree of vehicle congestion on the target road segment, the average pedestrian walking speed on the target road segment, the degree of pedestrian congestion on the target road segment, and the degree of danger posed by road obstacles on the target road segment to the vehicles. The method for analyzing the degree of danger posed to the vehicle by road obstacles on the target road section includes: Acquire an image of the road surface on the target road segment; Determine the size of the depression on the target road segment based on the image; Obtain the vehicle's size and the driver's facial image; The degree of danger posed to the vehicle by road obstacles on the target road section is determined based on the vehicle size, facial image, and the size of the depression on the target road section. The determination of the degree of danger posed to the vehicle by road obstacles on the target road section based on the vehicle size, facial image, and the size of the depression on the target road section includes: The age of the driver of the vehicle is determined based on the facial image; Determine the ratio between the size of the depression on the target road section and the size of the vehicle. The degree of danger posed to the vehicle by road obstacles on the target road segment is determined based on the driver's age and the ratio.

2. The system as described in claim 1, characterized in that, The road condition data includes vehicle driving videos on the target road segment, the driving speed of each vehicle on the target road segment, pedestrian walking videos on the target road segment, the walking speed of each pedestrian on the target road segment, and road obstacle data on the target road segment.

3. The system as described in claim 1, characterized in that, Determining the degree of danger posed to the vehicle by road obstacles on the target road segment based on the driver's age and the ratio includes: The degree of danger posed by road obstacles on the target road segment to the vehicle is calculated using the following formula: Wherein, Q represents a dangerous degree index value of the road surface obstacle on the target road section to the vehicle, and the greater Q is, the higher the dangerous degree of the road surface obstacle on the target road section to the vehicle is; represents an age weight value, which is a number greater than 0 and less than 1; T represents the age of the driver of the vehicle in years; and B represents a ratio between the size of the depression on the target road section and the size of the vehicle model.

4. The system as described in claim 1 or 3, characterized in that, The edge computing system is also used to send obstacle avoidance alerts to the vehicle based on the degree of danger posed by road obstacles on the target road segment.

5. The system as described in claim 4, characterized in that, The obstacle avoidance warning includes one or more of the vehicle's recommended driving speed and the location of the dent.

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