A unmanned AVP vehicle detection system and method

Abstract

The present invention relates to the field of unmanned driving technology, specifically a self-driving AVP vehicle detection system and method, comprising: a user operation module for enabling a user to send a parking request to a cloud platform via a mobile phone; a parking space acquisition module for acquiring unused parking spaces; a cloud processing module for allocating an optimal parking space for the vehicle based on the parking spaces acquired by the parking space acquisition module and generating a parking path plan after the cloud platform receives the user's parking request; and an environmental perception module for acquiring the vehicle's surrounding environment. By providing an image optimization module, the present invention is capable of optimizing images when rain, snow, fog, or insufficient light are present in the image, thereby ensuring the accuracy of the self-driving AVP vehicle detection system's detection of environmental information in complex environments.

Description

Technical Field

[0001] The present invention relates to the field of unmanned driving technology, and in particular to an unmanned AVP vehicle detection system and method. Background Art

[0002] AVP is a low-speed, Level 4 autonomous driving technology that enables vehicles to automatically complete maneuvers such as weaving in and out of traffic, overtaking, reversing, and avoiding pedestrians without driver intervention. It excels particularly in parking scenarios. The driverless AVP vehicle detection system is a complex system integrating multiple sensors, controllers, and algorithms, designed to enable autonomous parking in parking lots and other environments.

[0003] However, existing unmanned AVP vehicle detection systems can be affected by complex operating environments, such as inclement weather (e.g., rain, snow, fog, etc.) or low light conditions. This can affect sensor performance, leading to inaccurate detection results and hindering the operation of the unmanned AVP vehicle detection system. Therefore, an unmanned AVP vehicle detection system and method are invented. Summary of the Invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] An unmanned AVP vehicle detection system, comprising:

[0006] The user operation module is used to enable users to send parking requests to the cloud platform via mobile phones;

[0007] Parking space acquisition module, used to obtain unused parking spaces;

[0008] The cloud processing module is used to allocate the optimal parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module and generate a parking path plan after receiving the user's parking request on the cloud platform;

[0009] Environmental perception module, used to obtain the vehicle's surrounding environment;

[0010] The vehicle-side execution module is used to generate vehicle control instructions based on the environmental information perceived by the environmental perception module after receiving the parking path plan sent by the cloud platform, so as to control the vehicle to drive along the parking path and realize automatic parking;

[0011] The parking completion module is used to send parking completion information to the cloud platform after the vehicle completes automatic parking, so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user;

[0012] An analysis module is used to analyze the images acquired by the parking space acquisition module and the environment perception module to determine whether there is rain, snow, fog, or insufficient light in the images;

[0013] The image optimization module is used to optimize the image to improve the clarity of the image when the analysis module finds that there is rain, snow, fog or insufficient light in the image;

[0014] The image optimization module includes:

[0015] Image rain removal module, used to remove rain from the image when there is rain in the image;

[0016] Image snow removal module, used to remove snow from the image when there is snow in the image;

[0017] Image defogging module, used to remove fog from an image when there is fog in the image;

[0018] The image light module is used to remove light from the image when there is insufficient light in the image.

[0019] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the parking space acquisition module includes:

[0020] The field-side image acquisition module is used to collect images of the parking lot;

[0021] The parking space detection module is used to detect whether there are empty parking spaces in the images collected by the field image acquisition module;

[0022] The temporary parking space planning module is used to form a temporary parking space elsewhere in the parking lot when the parking space detection module does not detect an empty parking space.

[0023] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the parking space detection module includes:

[0024] The first storage module is used to store various images of unparked parking spaces;

[0025] a first comparison module, configured to compare the image captured by the field-side image acquisition module with the image stored in the first storage module;

[0026] The first judgment module is used to judge whether the image collected by the field-side image collection module is similar to the image in the first storage module. If so, it indicates that there is an unused parking space in the parking lot.

[0027] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the temporary parking space planning module includes:

[0028] A vacant space acquisition module is used to acquire images of vacant spaces in a parking lot;

[0029] The second storage module is used to store various environmental information of vehicles that are not parked in parking spaces.

[0030] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the parking space detection module further includes:

[0031] a second comparing module, configured to compare the image acquired by the vacant space acquiring module with the image stored in the second storing module;

[0032] The second judgment module is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space will be acquired.

[0033] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the parking space detection module further includes:

[0034] A size acquisition module is used to obtain the size of the vehicle;

[0035] a marking module, configured to mark the vehicle in the temporary parking space according to the vehicle size acquired by the size acquisition module, so as to mark a space to be parked in the temporary parking space;

[0036] The parking space forming module is used to form a temporary parking space from the parking space marked by the marking module.

[0037] As a preferred solution of the unmanned AVP vehicle detection system described in the present invention, the environment perception module includes:

[0038] The vehicle-side image acquisition module is used to collect the vehicle's surrounding environment;

[0039] The image analysis module is used to analyze the images collected by the vehicle-side image acquisition module to analyze the obstacles in the image and their locations.

[0040] A method for detecting an unmanned AVP vehicle includes the following specific steps:

[0041] Step 1: The user operation module enables the user to send a parking request to the cloud platform via mobile phone;

[0042] Step 2: The image of the parking lot is collected by the field-side image acquisition module. After the collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the clarity of the image. After that, the image collected by the field-side image acquisition module will be compared with the image stored in the first storage module through the first comparison module. After the comparison, the first judgment module will be used to judge whether the image collected by the field-side image acquisition module is similar in the first storage module. If so, it means that there is an undocked parking space in the parking lot. If not, the vacant space acquisition module will be used to compare the parking space. An image of a vacant space in the parking lot is acquired. After acquisition, the image acquired by the vacant space acquisition module is compared with the image stored in the second storage module through the second comparison module. After comparison, the second judgment module is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space is acquired. After acquisition, the size of the vehicle is acquired through the size acquisition module. Then, the marking module is used to mark the temporary parking space according to the vehicle size acquired by the size acquisition module to mark the space to be parked in the temporary parking space. After marking, the space to be parked marked by the marking module is formed into a temporary parking space through the parking space forming module.

[0043] Step 3: After receiving the user's parking request on the cloud platform through the cloud processing module, it can allocate the optimal parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module and generate a parking path plan;

[0044] Step 4: The vehicle's surrounding environment is collected through the vehicle-side image acquisition module. After collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the image clarity. After that, the image collected by the vehicle-side image acquisition module will be analyzed by the image analysis module to analyze the obstacles in the image and their locations;

[0045] Step 5: After receiving the parking path plan from the cloud platform, the vehicle-side execution module generates vehicle control instructions based on the environmental information sensed by the environmental perception module to control the vehicle to drive along the parking path and achieve automatic parking;

[0046] Step 6: After the vehicle completes automatic parking, the parking completion module can send parking completion information to the cloud platform so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user.

[0047] Compared with existing technologies:

[0048] By setting up an image optimization module, it is possible to optimize the image when there is rain, snow, fog or insufficient light in the image, thereby ensuring the accuracy of the unmanned AVP vehicle detection system's detection of environmental information when encountering complex environments; in addition, by setting up a parking space acquisition module, it is possible to generate temporary parking spaces elsewhere in the parking lot when there are no empty parking spaces in the parking lot, thereby ensuring that there is a place for the vehicle to park. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0051] The present invention provides an unmanned AVP vehicle detection system, please refer to Figure 1 ;

[0052] The system includes: a user operation module, which is used to enable users to send parking requests to the cloud platform through their mobile phones; a parking space acquisition module, which is used to obtain unused parking spaces; a cloud processing module, which is used to allocate the best parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module after the cloud platform receives the user's parking request, and generate a parking path plan; an environmental perception module, which is used to obtain the surrounding environment of the vehicle; and a vehicle-side execution module, which is used to generate vehicle control instructions based on the environmental information perceived by the environmental perception module after receiving the parking path plan sent by the cloud platform to control the vehicle. The vehicle drives along the parking path to achieve automatic parking; the parking completion module is used to send parking completion information to the cloud platform after the vehicle completes automatic parking, so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user; the analysis module is used to analyze the images obtained by the parking space acquisition module and the environment perception module to determine whether there is rain, snow, fog or insufficient light in the image; the image optimization module is used to optimize the image to improve the clarity of the image when the analysis module determines that there is rain, snow, fog or insufficient light in the image;

[0053] The image optimization module includes: an image rain removal module for removing rain from an image when rain is present; an image snow removal module for removing snow from an image when snow is present; an image fog removal module for removing fog from an image when fog is present; and an image light module for removing light from an image when insufficient light is present.

[0054] The steps of the image deraining module are as follows: a large number of rainy images and corresponding rain-free images are collected as training data sets, and the generator and discriminator network structures are constructed. The generator usually uses a convolutional neural network (CNN) to extract image features and generate rain-free images, and the discriminator also uses CNN to judge the authenticity of input images. The generator and discriminator are alternately trained using the training data sets. During the training process, the parameters of the generator and discriminator are continuously adjusted to make the rain-free images generated by the generator more and more realistic, and the discriminator can more accurately judge the authenticity of images. After training is completed, the rainy images to be processed are input into the trained generator, and the generator outputs the rain-free images.

[0055] The steps for the image snow removal module are as follows: Build a CNN model, such as a U-Net architecture, and train it on a large number of paired data sets of images with and without snow. This allows the model to learn the mapping relationship between images with and without snow. During training, the model automatically extracts features from the image, identifies snowflakes, and removes them, generating clean, snow-free images.

[0056] The steps of the image dehazing module are as follows: first, the dark channel image of the input image is calculated; then, the atmospheric light value is estimated based on the dark channel image; then, the transmittance is estimated; finally, the image is dehazed using the inversion formula of the atmospheric scattering model;

[0057] Regarding the steps of the image light module: By adjusting the grayscale histogram of the image and evenly distributing it, the grayscale value range of the image is expanded to the entire grayscale interval [0, 255]. This can increase the contrast of the image, improve the overall brightness, and make the dark details caused by insufficient light appear more clearly.

[0058] The parking space acquisition module includes: a field-side image acquisition module for collecting images of the parking lot; a parking space detection module for detecting whether there are empty parking spaces in the images collected by the field-side image acquisition module; and a temporary parking space planning module for creating temporary parking spaces elsewhere in the parking lot when the parking space detection module does not detect an empty parking space.

[0059] The parking space detection module includes: a first storage module for storing various images of vacant parking spaces; a first comparison module for comparing the images captured by the field-side image acquisition module with the images stored in the first storage module; and a first judgment module for judging whether the images captured by the field-side image acquisition module are similar to those in the first storage module. If so, it indicates that there are vacant parking spaces in the parking lot.

[0060] The temporary parking space planning module includes: a vacant space acquisition module, which is used to acquire images of vacant spaces in the parking lot; a second storage module, which is used to store environmental information of various vehicles that are not parked in parking spaces; a second comparison module, which is used to compare the image acquired by the vacant space acquisition module with the image stored in the second storage module; a second judgment module, which is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space will be acquired; a size acquisition module, which is used to acquire the size of the vehicle; a marking module, which is used to mark the temporary parking space according to the vehicle size acquired by the size acquisition module, so as to mark the space to be parked in the temporary parking space; and a parking space formation module, which is used to form a temporary parking space from the space to be parked marked by the marking module.

[0061] The environmental perception module includes: a vehicle-side image acquisition module, which is used to collect the vehicle's surrounding environment; and an image analysis module, which is used to analyze the images collected by the vehicle-side image acquisition module to analyze the obstacles in the image and their locations.

[0062] A method for detecting an unmanned AVP vehicle includes the following specific steps:

[0063] Step 1: The user operation module enables the user to send a parking request to the cloud platform via mobile phone;

[0064] Step 2: The image of the parking lot is collected by the field-side image acquisition module. After the collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the clarity of the image. After that, the image collected by the field-side image acquisition module will be compared with the image stored in the first storage module through the first comparison module. After the comparison, the first judgment module will be used to judge whether the image collected by the field-side image acquisition module is similar in the first storage module. If so, it means that there is an undocked parking space in the parking lot. If not, the vacant space acquisition module will be used to compare the parking space. An image of a vacant space in the parking lot is acquired. After acquisition, the image acquired by the vacant space acquisition module is compared with the image stored in the second storage module through the second comparison module. After comparison, the second judgment module is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space is acquired. After acquisition, the size of the vehicle is acquired through the size acquisition module. Then, the marking module is used to mark the temporary parking space according to the vehicle size acquired by the size acquisition module to mark the space to be parked in the temporary parking space. After marking, the space to be parked marked by the marking module is formed into a temporary parking space through the parking space forming module.

[0065] Step 3: After receiving the user's parking request on the cloud platform through the cloud processing module, it can allocate the optimal parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module and generate a parking path plan;

[0066] Step 4: The vehicle's surrounding environment is collected through the vehicle-side image acquisition module. After collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the image clarity. After that, the image collected by the vehicle-side image acquisition module will be analyzed by the image analysis module to analyze the obstacles in the image and their locations;

[0067] Step 5: After receiving the parking path plan from the cloud platform, the vehicle-side execution module generates vehicle control instructions based on the environmental information sensed by the environmental perception module to control the vehicle to drive along the parking path and achieve automatic parking;

[0068] Step 6: After the vehicle completes automatic parking, the parking completion module can send parking completion information to the cloud platform so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user.

[0069] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An unmanned AVP vehicle detection system, characterized in that: include: The user operation module is used to enable users to send parking requests to the cloud platform via mobile phones; Parking space acquisition module, used to obtain unused parking spaces; The cloud processing module is used to allocate the optimal parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module and generate a parking path plan after receiving the user's parking request on the cloud platform; Environmental perception module, used to obtain the vehicle's surrounding environment; The vehicle-side execution module is used to generate vehicle control instructions based on the environmental information perceived by the environmental perception module after receiving the parking path plan sent by the cloud platform, so as to control the vehicle to drive along the parking path and realize automatic parking; The parking completion module is used to send parking completion information to the cloud platform after the vehicle completes automatic parking, so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user; An analysis module is used to analyze the images acquired by the parking space acquisition module and the environment perception module to determine whether there is rain, snow, fog, or insufficient light in the images; The image optimization module is used to optimize the image to improve the clarity of the image when the analysis module finds that there is rain, snow, fog or insufficient light in the image; The image optimization module includes: Image rain removal module, used to remove rain from the image when there is rain in the image; Image snow removal module, used to remove snow from the image when there is snow in the image; Image defogging module, used to remove fog from an image when there is fog in the image; The image light module is used to remove light from the image when there is insufficient light in the image.

2. The unmanned AVP vehicle detection system according to claim 1, characterized in that: The parking space acquisition module includes: The field-side image acquisition module is used to collect images of the parking lot; The parking space detection module is used to detect whether there are empty parking spaces in the images collected by the field image acquisition module; The temporary parking space planning module is used to form a temporary parking space elsewhere in the parking lot when the parking space detection module does not detect an empty parking space.

3. The unmanned AVP vehicle detection system according to claim 2, characterized in that: The parking space detection module includes: The first storage module is used to store various images of unparked parking spaces; a first comparison module, configured to compare the image captured by the field-side image acquisition module with the image stored in the first storage module; The first judgment module is used to judge whether the image collected by the field-side image collection module is similar to the image in the first storage module. If so, it indicates that there is an unused parking space in the parking lot.

4. The unmanned AVP vehicle detection system according to claim 3, characterized in that: The temporary parking space planning module includes: A vacant space acquisition module is used to acquire images of vacant spaces in a parking lot; The second storage module is used to store various environmental information of vehicles that are not parked in parking spaces.

5. The unmanned AVP vehicle detection system according to claim 4, characterized in that: The parking space detection module also includes: a second comparing module, configured to compare the image acquired by the vacant space acquiring module with the image stored in the second storing module; The second judgment module is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space will be acquired.

6. The unmanned AVP vehicle detection system according to claim 5, characterized in that: The parking space detection module also includes: A size acquisition module is used to obtain the size of the vehicle; a marking module, configured to mark the vehicle in the temporary parking space according to the vehicle size acquired by the size acquisition module, so as to mark a space to be parked in the temporary parking space; The parking space forming module is used to form a temporary parking space from the parking space marked by the marking module.

7. The unmanned AVP vehicle detection system according to claim 1, characterized in that: The environment perception module includes: The vehicle-side image acquisition module is used to collect the vehicle's surrounding environment; The image analysis module is used to analyze the images collected by the vehicle-side image acquisition module to analyze the obstacles in the image and their locations.

8. A method for detecting unmanned AVP vehicles, characterized in that: The specific steps are as follows: Step 1: The user operation module enables the user to send a parking request to the cloud platform via mobile phone; Step 2: The image of the parking lot is collected by the field-side image acquisition module. After the collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the clarity of the image. After that, the image collected by the field-side image acquisition module will be compared with the image stored in the first storage module through the first comparison module. After the comparison, the first judgment module will be used to judge whether the image collected by the field-side image acquisition module is similar in the first storage module. If so, it means that there is an undocked parking space in the parking lot. If not, the vacant space acquisition module will be used to compare the parking space. An image of a vacant space in the parking lot is acquired. After acquisition, the image acquired by the vacant space acquisition module is compared with the image stored in the second storage module through the second comparison module. After comparison, the second judgment module is used to judge whether the image acquired by the vacant space acquisition module is similar to the image in the second storage module. If so, a temporary parking space is acquired. After acquisition, the size of the vehicle is acquired through the size acquisition module. Then, the marking module is used to mark the temporary parking space according to the vehicle size acquired by the size acquisition module to mark the space to be parked in the temporary parking space. After marking, the space to be parked marked by the marking module is formed into a temporary parking space through the parking space forming module. Step 3: After receiving the user's parking request on the cloud platform through the cloud processing module, it can allocate the optimal parking space for the vehicle based on the parking spaces obtained by the parking space acquisition module and generate a parking path plan; Step 4: The vehicle's surrounding environment is collected through the vehicle-side image acquisition module. After collection, the acquired image will be analyzed by the analysis module to analyze whether there is rain, snow, fog or insufficient light in the image. If so, the image will be optimized by the image optimization module to improve the image clarity. After that, the image collected by the vehicle-side image acquisition module will be analyzed by the image analysis module to analyze the obstacles in the image and their locations; Step 5: After receiving the parking path plan from the cloud platform, the vehicle-side execution module generates vehicle control instructions based on the environmental information sensed by the environmental perception module to control the vehicle to drive along the parking path and achieve automatic parking; Step 6: After the vehicle completes automatic parking, the parking completion module can send parking completion information to the cloud platform so that the cloud platform can update the parking space status information of the parking lot and send a parking completion notification to the user.

Images