A road bump detection method, a detection system, a storage medium and a vehicle

Abstract

The application provides a road bump detection method, a detection system, a storage medium and a vehicle. The road bump detection method comprises the following steps: acquiring the vertical acceleration of a target vehicle when the target vehicle is driving on a bump road; acquiring pit groove characteristic data of the bump road; acquiring a bump danger index of the bump road according to the vertical acceleration and the pit groove characteristic data; determining the bump grade of the bump road according to the bump danger index; and uploading the determination result of the bump grade to a sharing platform for bump information sharing. The technical problem solved by the application is that the existing road bump detection method usually acquires the road surface condition by a single vehicle-mounted sensor, can only detect the bump condition of the road where the vehicle is currently located, and cannot predict the bump condition of the road outside the line of sight, so that the driver or the vehicle system can only perceive when the vehicle enters the bump road section, cannot slow down or adjust the driving posture in advance, and affects the safety and comfort of the driver during driving.

Description

Technical Field

[0001] This application relates to the technical field of road inspection, and more specifically, to a road bump detection method, detection system, storage medium, and vehicle. Background Technology

[0002] With the continuous advancement of sensing technology and the rapid development of computers, computer vision and image processing technologies have also made significant progress. Driven by this technological wave, the concepts of "smart transportation" and "smart city" have emerged.

[0003] Bumpy roads pose numerous hazards to vehicle operation, such as tire damage and blowouts, deformation and breakage of the suspension system and shock absorbers leading to reduced comfort, mechanical damage and deformation of the chassis and body, and poor contact and malfunction of electronic devices. Therefore, using computer vision and sensors to detect bumpy roads is a core component, with the main goal of improving vehicle safety and comfort during driving.

[0004] However, the relevant technologies have at least one of the following problems: existing road bump detection methods usually rely on vehicle-mounted sensors to obtain road conditions, which can only detect the bump conditions of the road surface where the vehicle is currently located, and cannot predict the bump conditions of the road beyond the driver's line of sight. As a result, the driver or vehicle system can only perceive the bumpy road section when the vehicle enters it, and cannot slow down or adjust the driving posture in advance, which affects the safety and comfort of the driver during the driving process. Summary of the Invention

[0005] The technical problem addressed by this application is that existing road bump detection methods typically rely on onboard sensors to acquire road conditions. These methods can only detect the bump conditions of the road surface where the vehicle is currently located and cannot predict the bump conditions of roads outside the driver's line of sight. As a result, the driver or vehicle system can only perceive the bumps when the vehicle enters a bumpy section, making it impossible to slow down or adjust the driving posture in advance, which affects the driver's safety and comfort during driving.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a road bump detection method, which includes: When the target vehicle is traveling on a bumpy road, obtain the vertical acceleration of the target vehicle; Obtain pothole feature data of bumpy road surface, including one or more of the following: pothole depth, pothole area, and pothole density. Vertical acceleration and pothole feature data are sent as input to a cloud-based bump calculation model to obtain the bump hazard index of bumpy roads. The bumpiness level of a bumpy road surface is determined based on the bumpiness hazard index. The results of the turbulence level assessment are uploaded to a cloud-based sharing platform for turbulence information sharing.

[0007] Compared with existing technologies, the technical effects achieved by this solution are as follows: This solution acquires the vertical acceleration of the target vehicle and the pothole feature data of the bumpy road surface when the target vehicle is driving on a bumpy road. Based on the vertical acceleration and pothole feature data, the bump level of the bumpy road surface is determined. Finally, the bump level determination result is uploaded to a cloud-based sharing platform for bump information sharing. This allows subsequent vehicles to anticipate the bumpy road conditions ahead by using the bump information shared on the platform, enabling them to allow sufficient time and distance to slow down or adjust their driving posture in advance, thereby improving the safety and comfort of the driver during the driving process.

[0008] In one example of this application, the bump hazard index of a bumpy road surface is obtained based on vertical acceleration and pothole characteristic data, including: The geometric feature weights of the pit are calculated based on the settlement depth and pit area. The dynamic weights of the craters are calculated based on vertical acceleration and crater density. The turbulence risk index is calculated based on geometric feature weights and dynamic weights.

[0009] Compared with existing technologies, the technical advantages of this solution are as follows: This solution calculates the bump hazard index by considering the depth, area, and density of potholes, as well as the vertical acceleration of the target vehicle. This provides a more comprehensive and accurate reflection of the actual bump conditions and potential risks of the road. It avoids the misjudgment that can occur in existing technologies that rely solely on vertical acceleration to determine the bump level.

[0010] In one example of this application, the formula for calculating the geometric feature weight of the pit based on the settlement depth and pit area is as follows: ; The formula for calculating the dynamic weight of a crater based on vertical acceleration and crater density is as follows: ; The formula for calculating the turbulence risk index based on geometric feature weights and dynamic weights is as follows: ; Where G is the geometric feature weight, D is the dynamic weight, P is the turbulence hazard index, d is the subsidence depth, s is the crater density, and a is the crater density. z Let ω be the vertical acceleration, ω be the pit density, b1, b2, b3, b4, b5, and b6 be constants, and b1+b2=1, b3+b4=1, b5+b6=1, tanh be the hyperbolic tangent function, and σ be the sigmoid logic function. This represents the product of b5 and G. This represents the product of b6 and D.

[0011] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: This solution makes the calculation results of the bump risk index more scientific, so as to more accurately reflect the bump condition of bumpy roads.

[0012] In one example of this application, the road bump detection system includes a camera device installed on the target vehicle, the bump hazard index includes a first bump hazard index at bridge joints, and the road bump detection method further includes: Based on the road surface images obtained by the camera device, determine whether the bumpy road surface is at the bridge joint; If the bumpy road surface is at a bridge joint, then the first bumpy danger index is recorded as zero.

[0013] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: When the bumpy road surface is at the bridge joint, the first bump hazard index is set to zero, so as to eliminate the interference of the fixed structure at the bridge joint on the road bump condition detection results, and further improve the accuracy of the actual road bump condition detection results.

[0014] In one example of this application, the road bump detection method further includes: Determine whether the weather conditions in the area where the target vehicle is located are rainy based on the road surface image; If the weather is rainy, then the turbulence risk index for rainy days is: Pr = b7·P; Where Pr is the turbulence risk index in rainy weather, and b7 is a constant.

[0015] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: This solution, by recording the bump risk index of rainy days as Pr=b7·P, overcomes the interference of the reduced road surface friction coefficient effect on the road bump condition detection results, and further improves the accuracy of the actual road bump condition detection results.

[0016] In one instance of this application, a road segment with a bumpy surface is defined as an abnormal road segment, and a vehicle about to travel to an abnormal road segment is defined as the first vehicle. The turbulence levels are divided into Level 1, Level 2, and Level 3 turbulence. The turbulence hazard index is defined as P1 for Level 1 turbulence, P2 for Level 2 turbulence, and P3 for Level 3 turbulence. Wherein, P1... <P2<P3; When the bump level is Level 1, the first vehicle continues to move forward in its current driving posture; When the bump level is level 2, the first vehicle triggers the vehicle suspension stiffness adjustment mode. When the bump level is level two, the first vehicle enters the forced deceleration mode.

[0017] Compared with existing technologies, the technical effects achieved by this solution are as follows: By differentiating between first-level, second-level, and third-level bumps, this solution enables precise control of the first vehicle approaching an abnormal road section, further improving driver safety and comfort. Specifically, for first-level bumps, the first vehicle maintains normal driving to ensure efficiency; for second-level bumps, suspension stiffness is adjusted to improve comfort or protect the suspension system; and for third-level bumps, forced deceleration is implemented to prioritize driver safety.

[0018] Secondly, this application also provides a road bump detection system for performing the road bump detection method as described in any of the above examples. The road bump detection system includes: The first acquisition module is used to acquire the vertical acceleration of the target vehicle when the target vehicle is driving on a bumpy road. The second acquisition module is used to acquire pothole feature data of bumpy road surfaces; The data input module is used to send vertical acceleration and pothole feature data as input to the cloud-based bump calculation model to obtain the bump risk index of bumpy roads. The level determination module is used to determine the bump level of a bumpy road surface based on the bump hazard index. The sharing module is used to upload the turbulence level determination results to the cloud-based sharing platform for turbulence information sharing.

[0019] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: The road bump detection system provided by this solution can achieve the technical effects corresponding to any of the above examples, which will not be elaborated here.

[0020] In one example of this application, the road bump detection system includes: The roadside data fusion module is used to fuse the bump information of abnormal road sections and send the fused bump information and the location information of the abnormal road sections to the first vehicle. The communication module is used to establish communication connections between the target vehicle, the cloud, the first vehicle, and the roadside detection module to share bump information.

[0021] Compared with existing technologies, the technical effects achieved by this solution are as follows: This solution connects the target vehicle, the cloud, the first vehicle, and the roadside detection module through a roadside data fusion module and a communication module, enabling the target vehicle, the cloud, the first vehicle, and the roadside detection module to share bump information.

[0022] Thirdly, this application also provides a computer-readable storage medium, which, when the instructions in the computer-readable storage medium are executed by a processor corresponding to the road bump detection system in any of the above examples, enables the road bump detection system to implement the road bump detection method in any of the above examples.

[0023] Compared with the prior art, the technical effects achieved by adopting this technical solution are as follows: The computer-readable storage medium provided by this solution can achieve the technical effects corresponding to any of the above examples, which will not be repeated here.

[0024] Fourthly, this application also provides a vehicle, which includes a road bump detection system as described in any of the above examples.

[0025] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: The vehicle provided by this solution can achieve the technical effects corresponding to any of the above examples, which will not be elaborated here.

[0026] By adopting the technical solution of this application, the following technical effects can be achieved: This application acquires the vertical acceleration of the target vehicle and the pothole feature data of the bumpy road surface when the target vehicle is driving on a bumpy road. Based on the vertical acceleration and pothole feature data, the bump level of the bumpy road surface is determined. Finally, the bump level determination result is uploaded to a cloud-based sharing platform for bump information sharing. This allows subsequent vehicles to anticipate the bumpy road conditions ahead by using the bump information shared on the platform, enabling them to allow sufficient time and distance to slow down or adjust their driving posture in advance, thereby improving the safety and comfort of the driver during the driving process. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 One of the flowcharts for a road bump detection method provided in this application embodiment; Figure 2 A second flowchart illustrating a road bump detection method provided in this application embodiment; Figure 3 This is one of the block diagrams of a road bump detection system provided in an embodiment of this application; Figure 4 This is a second block diagram of a road bump detection system provided in an embodiment of this application.

[0028] Explanation of reference numerals in the attached figures: 100. First acquisition module; 200. Second acquisition module; 300. Data input module; 400. Grade determination module; 500. Sharing module; 600. Roadside data fusion module; 700. Communication module. Detailed Implementation

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0030] like Figure 1 As shown, in a first aspect, this application provides a road bump detection method, which includes: S100: Obtain the vertical acceleration of the target vehicle when it is traveling on a bumpy road. S200: Acquire pothole feature data of bumpy road surfaces; S300: A bump calculation model that sends vertical acceleration and pothole feature data as input to the cloud to obtain the bump hazard index of bumpy roads. S400: The bumpiness level of a bumpy road surface is determined based on the bumpiness hazard index; S500: Uploads the turbulence level determination results to a cloud-based sharing platform for turbulence information sharing; The pothole feature data includes one or more of the following: pothole depth, pothole area, and pothole density.

[0031] Specifically, this solution acquires the vertical acceleration of the target vehicle and the pothole feature data of the bumpy road surface when the target vehicle is driving on a bumpy road. Based on the vertical acceleration and pothole feature data, it determines the bump level of the bumpy road surface and finally uploads the bump level determination result to a cloud-based sharing platform for bump information sharing. This allows subsequent vehicles to anticipate the bumpy road conditions ahead by using the bump information shared on the platform, enabling them to allow sufficient time and distance to slow down or adjust their driving posture in advance, thereby improving the safety and comfort of the driver during the driving process.

[0032] like Figure 2 As shown, in one embodiment provided in this application, the bumpiness hazard index of a bumpy road surface is obtained based on vertical acceleration and pothole characteristic data, including: S310: Calculate the geometric feature weights of the pit based on the settlement depth and pit area; S320: Calculate the dynamic weight of the crater based on vertical acceleration and crater density; S330: Calculate the bump risk index based on geometric feature weights and dynamic weights.

[0033] Specifically, this method calculates the bump hazard index by considering the pothole's depth, area, density, and the target vehicle's vertical acceleration. This provides a more comprehensive and accurate reflection of the actual road bump conditions and their potential risks. It avoids the misjudgments that occur in existing technologies that rely solely on vertical acceleration to determine bump levels.

[0034] In one embodiment provided in this application, S310: The formula for calculating the geometric feature weight of the pit based on the settlement depth and pit area is as follows: ; S320: The formula for calculating the dynamic weight of a crater based on vertical acceleration and crater density is as follows: ; S330: The formula for calculating the turbulence hazard index based on geometric feature weights and dynamic weights is as follows: ; Where G is the geometric feature weight, D is the dynamic weight, P is the turbulence hazard index, d is the subsidence depth, s is the crater density, and a is the crater density. z Let ω be the vertical acceleration, ω be the pit density, b1, b2, b3, b4, b5, and b6 be constants, and b1+b2=1, b3+b4=1, b5+b6=1, tanh be the hyperbolic tangent function, and σ be the sigmoid logic function. This represents the product of b5 and G. This represents the product of b6 and D.

[0035] Specifically, this scheme makes the calculation results of the bump risk index more scientific, so as to more accurately reflect the bump condition of bumpy roads.

[0036] In one specific embodiment provided in this application, the value of b1 is 0.6, the value of b2 is 0.4, the value of b3 is 0.7, the value of b4 is 0.3, the value of b5 is 0.4, and the value of b6 is 0.6; The formula for calculating the geometric feature weights of the pit based on the settlement depth and pit area is as follows: ; The formula for calculating the dynamic weight of a crater based on vertical acceleration and crater density is as follows: ; The formula for calculating the turbulence risk index based on geometric feature weights and dynamic weights is as follows: .

[0037] In one embodiment provided in this application, the road bump detection system includes a camera device installed on the target vehicle, the bump hazard index includes a first bump hazard index at bridge joints, and the road bump detection method further includes: Based on the road surface images obtained by the camera device, determine whether the bumpy road surface is at the bridge joint; If the bumpy road surface is at a bridge joint, then the first bumpy danger index is recorded as zero.

[0038] Specifically, this scheme sets the first bump risk index to zero when the bumpy road surface is at a bridge joint, thereby eliminating the interference of the fixed structure at the bridge joint on the road surface bump condition detection results and further improving the accuracy of the actual road bump condition detection results.

[0039] In one embodiment provided in this application, the road bump detection method further includes: Determine whether the weather conditions in the area where the target vehicle is located are rainy based on the road surface image; If the weather is rainy, then the turbulence risk index for rainy days is: Pr = b7·P; Where Pr is the turbulence risk index in rainy weather, and b7 is a constant.

[0040] Specifically, this scheme uses the rainy day bump risk index, Pr=b7·P, to overcome the interference of the reduced road surface friction coefficient effect on the road bump condition detection results, and further improves the accuracy of the actual road bump condition detection results.

[0041] In one specific embodiment provided in this application, the value of b7 is 1.2, that is, the turbulence hazard index when the weather condition is rainy is: Pr=1.2P.

[0042] In one embodiment provided in this application, a road segment with a bumpy surface is defined as an abnormal road segment, and a vehicle about to travel to an abnormal road segment is defined as a first vehicle; The turbulence levels are divided into Level 1, Level 2, and Level 3 turbulence. The turbulence hazard index is defined as P1 for Level 1 turbulence, P2 for Level 2 turbulence, and P3 for Level 3 turbulence. Wherein, P1... <P2<P3; When the bump level is Level 1, the first vehicle continues to move forward in its current driving posture; When the bump level is level 2, the first vehicle triggers the vehicle suspension stiffness adjustment mode. When the bump level is level two, the first vehicle enters the forced deceleration mode.

[0043] In one specific embodiment provided in this application, when P=30~60 is set as Level 1 bump, and when P=60~85 is set as Level 2 bump, the first vehicle triggers the vehicle suspension stiffness adjustment mode. When P>85 is set as Level 3 bump, the first vehicle enters the forced deceleration mode and will be forced to decelerate to below 30km / h.

[0044] Specifically, this solution differentiates between Level 1, Level 2, and Level 3 bumps, enabling precise control of the first vehicle approaching an abnormal road section, further improving driver safety and comfort. Specifically, for Level 1 bumps, the first vehicle maintains normal driving to ensure efficiency; for Level 2 bumps, suspension stiffness is adjusted to improve comfort or protect the suspension system; and for Level 3 bumps, forced speed reduction is implemented to prioritize driver safety.

[0045] Secondly, such as Figure 3 As shown, this application also provides a road bump detection system. The road bump detection system is used to execute the road bump detection method as described in any of the above embodiments. The road bump detection system includes a first acquisition module 100, a second acquisition module 200, a data input module 300, a level determination module 400, and a sharing module 500. Specifically, the first acquisition module 100 is used to acquire the vertical acceleration of the target vehicle when it travels on a bumpy road surface. The second acquisition module 200 is used to acquire pothole feature data of the bumpy road surface. The data input module 300 is used to send the vertical acceleration and pothole feature data as input to a bump calculation model in the cloud to obtain a bump hazard index of the bumpy road surface. The level determination module 400 is used to determine the bump level of the bumpy road surface based on the bump hazard index. The sharing module 500 is used to upload the bump level determination result to a cloud-based sharing platform for bump information sharing.

[0046] In one embodiment provided in this application, such as Figure 4 As shown, the road bump detection system includes a roadside data fusion module 600 and a communication module 700. The roadside data fusion module 600 is used to fuse the bump information of abnormal road sections and send the fused bump information and the location information of the abnormal road sections to the first vehicle. The communication module 700 is used to establish a communication connection between the target vehicle, the cloud, the first vehicle, and the roadside detection module to share the bump information.

[0047] Specifically, this solution uses the roadside data fusion module 600 and the communication module 700 to connect the target vehicle, the cloud, the first vehicle, and the roadside detection module, enabling the sharing of bump information among the target vehicle, the cloud, the first vehicle, and the roadside detection module.

[0048] Thirdly, this application also provides a computer-readable storage medium, which, when the instructions in the computer-readable storage medium are executed by a processor corresponding to the road bump detection system as described in any of the above embodiments, enables the road bump detection system to implement the road bump detection method as described in any of the above embodiments.

[0049] Fourthly, this application also provides a vehicle, which includes a road bump detection system as described in any of the above embodiments.

[0050] While this application discloses the above information, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application shall be determined by the scope defined in the claims.

Claims

1. A method for detecting road bumps, characterized in that, The road bump detection method includes: When the target vehicle is traveling on a bumpy road, the vertical acceleration of the target vehicle is obtained; Obtain pothole feature data of the bumpy road surface, the pothole feature data including the pothole's sinking depth, the pothole's area, and the pothole's density. The vertical acceleration and the pothole feature data are sent as input to the cloud-based bump calculation model to obtain the bump hazard index of the bumpy road surface. The bumpiness level of the bumpy road surface is determined based on the bumpiness hazard index. The results of the turbulence level determination are uploaded to the cloud-based sharing platform for turbulence information sharing; The step of obtaining the bumpiness hazard index of the bumpy road surface based on the vertical acceleration and the pothole feature data includes: The geometric feature weights of the pit are calculated based on the subsidence depth and the pit area. The dynamic weight of the pit is calculated based on the vertical acceleration and the pit density; The turbulence risk index is calculated based on the geometric feature weights and the dynamic weights. The formula for calculating the geometric feature weight of the pit based on the subsidence depth and the pit area is as follows: ； The formula for calculating the dynamic weight of the pit based on the vertical acceleration and the pit density is as follows: ； The formula for calculating the bump risk index based on the geometric feature weights and the dynamic weights is as follows: ； Wherein, G is the geometric feature weight, D is the dynamic weight, P is the turbulence hazard index, d is the subsidence depth, s is the pit area, and a z Let ω be the vertical acceleration, ω be the pit density, b1, b2, b3, b4, b5, and b6 be constants, and b1+b2=1, b3+b4=1, b5+b6=1, tanh be the hyperbolic tangent function, and σ be the sigmoid logic function. This represents the product of b5 and G. This represents the product of b6 and D.

2. The road bump detection method according to claim 1, characterized in that, The road bump detection method includes a camera device installed on the target vehicle, the bump hazard index includes a first bump hazard index at bridge joints, and the road bump detection method further includes: Based on the road surface image acquired by the camera device, determine whether the bumpy road surface is the bridge joint; If the bumpy road surface is at the bridge joint, then the first bumpy danger index is recorded as zero.

3. The road bump detection method according to claim 2, characterized in that, The road bump detection method also includes: Based on the road surface image, determine whether the weather conditions in the area where the target vehicle is located are rainy; If the weather condition is rainy, then the turbulence hazard index for rainy days is: Pr = b7·P; Where Pr is the turbulence risk index for rainy days, and b7 is a constant.

4. The road bump detection method according to any one of claims 1-3, characterized in that, The road segment with the bumpy road surface is defined as an abnormal road segment, and the vehicle about to travel to the abnormal road segment is defined as the first vehicle; The bump levels include Level 1, Level 2, and Level 3 bumps. The bump hazard index for Level 1 bumps is defined as P1, for Level 2 bumps as P2, and for Level 3 bumps as P3. Wherein, P1... <P2<P3; When the bump level is Level 1, the first vehicle continues to move forward in its current driving posture. When the bump level is level two, the first vehicle triggers the vehicle suspension stiffness adjustment mode. When the bump level is level two, the first vehicle enters a forced deceleration mode.

5. A road bump detection system, characterized in that, The road bump detection system is used to perform the road bump detection method as described in claim 4, and the road bump detection system includes: The first acquisition module (100) is used to acquire the vertical acceleration of the target vehicle when the target vehicle is traveling on a bumpy road. The second acquisition module (200) is used to acquire the pothole feature data of the bumpy road surface; The data input module (300) is used to send the vertical acceleration and the pothole feature data as input to the cloud-based bump calculation model to obtain the bump risk index of the bumpy road surface. Level determination module (400), the level determination module (400) is used to determine the bump level of the bumpy road surface according to the bump hazard index; The sharing module (500) is used to upload the turbulence level determination result to the cloud-based sharing platform for turbulence information sharing.

6. The road bump detection system according to claim 5, characterized in that, The road bump detection system includes: Roadside data fusion module (600), the roadside data fusion module (600) is used to fuse the bump information of the abnormal road section, and send the fused bump information and the location information of the abnormal road section to the first vehicle; A communication module (700) is used to establish a communication connection between the target vehicle, the cloud, the first vehicle, and the roadside detection module to share the bump information.

7. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by a processor corresponding to the road bump detection system as described in claim 5 or 6, the road bump detection system is enabled to implement the road bump detection method as described in any one of claims 1-5.

8. A vehicle, characterized in that, The vehicle includes: the road bump detection system as described in claim 5 or 6.

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