Automatic parking control method, device and equipment and storage medium

Abstract

The invention discloses an automatic parking control method, device and equipment and a storage medium. When the running speed is smaller than the preset speed, radar data collected by a millimeter wave radar are converted into point cloud data, target detection is conducted based on the point cloud data, whether a parking car stop exists in the current environment or not is detected, and if the parking car stop exists in the current environment, the parking car stop is stopped; if yes, vehicle state data are obtained, the vehicle moving intention of the driver is determined based on the vehicle state data, the state of the automatic parking system is controlled based on the vehicle moving intention of the driver, and the problems that in the vehicle moving process, due to the fact that the automatic parking system is frequently activated, driving smoothness is lowered, and scratching accidents are likely to happen are solved. And the driving smoothness and safety are improved.

Description

Technical Field

[0001] The present invention relates to parking control technology, and in particular to an automatic parking control method, device, equipment and storage medium. Background Art

[0002] The automatic parking function is realized through the coordinated work of the vehicle's braking system, electronic control system and sensors. The sensors detect the vehicle's driving state and stationary state. When the vehicle stops and meets certain conditions, the electronic control system will control the braking system to apply appropriate braking force to the wheels to keep the vehicle stationary. When it is detected that the driver intends to start, the braking force will be quickly released.

[0003] The automatic parking function of existing vehicles is mainly turned on and off by a switch. After the automatic parking system is turned on, the driver activates the automatic parking system by stepping on the automatic pedal during driving, and releases the automatic parking system by stepping on the accelerator.

[0004] However, in the case of parking or unparking, the driver needs to actively turn off the automatic parking system, otherwise the automatic parking system will be frequently activated during the process, reducing driving smoothness. In addition, the automatic parking system needs to be released by stepping on the accelerator, and the space in the car moving scene is usually narrow. If the driver is not proficient in accelerator control, it is easy to cause scratches and accidents, causing unnecessary losses and risks. Summary of the invention

[0005] The present invention provides an automatic parking control method, device, equipment and storage medium to improve driving fluency and safety in a vehicle maneuvering scenario.

[0006] In a first aspect, the present invention provides an automatic parking control method, comprising:

[0007] When the driving speed is less than the preset speed, the radar data collected by the millimeter-wave radar is converted into point cloud data;

[0008] Performing target detection based on the point cloud data to detect whether there is a parking blocker in the current environment;

[0009] If there is a parking barrier in the current environment, obtain vehicle status data;

[0010] determining the driver's intention to move the vehicle based on the vehicle status data;

[0011] The state of the automatic parking system is controlled based on the driver's intention to move the vehicle.

[0012] Optionally, radar data collected by the millimeter wave radar is converted into point cloud data, including:

[0013] Performing a distance-dimensional Fourier transform on the radar data to obtain the distance between the target point and the millimeter-wave radar;

[0014] Performing an angle-dimensional Fourier transform on the radar data to obtain a horizontal angle and a pitch angle between the target point and the millimeter-wave radar;

[0015] The three-dimensional coordinates of the target point in a Cartesian coordinate system are calculated based on the distance, horizontal angle and pitch angle between the target point and the millimeter wave radar to form three-dimensional point cloud data.

[0016] Optionally, the three-dimensional coordinates of the target point in a Cartesian coordinate system are calculated based on the distance, horizontal angle, and pitch angle between the target point and the millimeter wave radar, and the calculation formula is as follows:

[0017] X = R·cos(φ)·sin(θ);

[0018] Y = R·cos(φ)·cos(θ);

[0019] Z = R sin(φ);

[0020] Wherein, R is the distance between the target point and the millimeter-wave radar, φ is the horizontal angle between the target point and the millimeter-wave radar, and θ is the pitch angle between the target point and the millimeter-wave radar.

[0021] Optionally, performing target detection based on the point cloud data to detect whether there is a parking barrier in the current environment includes:

[0022] Performing spatial transformation on the point cloud data, aligning the point cloud data in three-dimensional space, and obtaining original features;

[0023] Performing point-by-point feature extraction on each point in the original feature to obtain a first feature;

[0024] Performing spatial transformation on the first feature, aligning the point cloud data in three-dimensional space, and obtaining a local feature;

[0025] Performing point-by-point feature extraction on each point in the local feature to obtain a second feature;

[0026] Performing global pooling processing on the second feature to obtain a global feature of the point cloud data;

[0027] Fusing the local features and the global features to obtain fused features;

[0028] The point cloud data is semantically segmented based on the fusion features to detect whether there is a parking barrier in the current environment.

[0029] Optionally, the vehicle status data includes driving speed, acceleration, steering wheel angle, and brake signal duration, and determining the driver's intention to move the vehicle based on the vehicle status data includes:

[0030] The long short-term memory neural network is used to process the vehicle status data of multiple nearest time steps including the current time step to obtain the time series characteristics;

[0031] The driver's intention to move the vehicle is determined based on the time series characteristics.

[0032] Optionally, the long short-term memory neural network includes N transfer cells, and the long short-term memory neural network is used to process the vehicle state data of multiple nearest time steps including the current time step to obtain time series features, including:

[0033] The first transfer cell receives the vehicle state data of the first time step for calculation, and obtains the hidden state and cell state output by the first transfer cell;

[0034] The i-th transmission cell receives the vehicle state data of the i-th time step, and calculates the hidden state and cell state output by the i-1-th transmission cell to obtain the hidden state and cell state output by the i-th transmission cell, where i is a positive integer greater than 1 and less than N;

[0035] The Nth transfer cell receives the vehicle state data of the Nth time step, and calculates it in combination with the hidden state and cell state output by the N-1th transfer cell to obtain the cell state output by the Nth transfer cell as the time series feature.

[0036] Optionally, controlling the state of the automatic parking system based on the driver's intention to move the vehicle includes:

[0037] When the automatic parking system is in the on state, if the driver intends to move the vehicle, the automatic parking system is controlled to be turned off;

[0038] When the automatic parking system is in the on state, if the driver has no intention to move the vehicle, the automatic parking system is maintained in the on state;

[0039] When the automatic parking system is in the off state, if the driver intends to move the vehicle, the automatic parking system is maintained in the off state;

[0040] When the automatic parking system is in an off state, if the driver has no intention to move the vehicle, the automatic parking system is controlled to be turned on.

[0041] In a second aspect, the present invention further provides an automatic parking control device, comprising:

[0042] A point cloud data acquisition module, used to convert radar data collected by the millimeter wave radar into point cloud data when the driving speed is less than a preset speed;

[0043] A parking block detection module, used to perform target detection based on the point cloud data to detect whether there is a parking block in the current environment;

[0044] A vehicle status data acquisition module is used to acquire vehicle status data if a parking barrier exists in the current environment;

[0045] A vehicle-moving intention determination module, used to determine the driver's vehicle-moving intention based on the vehicle status data;

[0046] The state control module is used to control the state of the automatic parking system based on the driver's intention to move the vehicle.

[0047] In a third aspect, the present invention further provides an electronic device, comprising:

[0048] one or more processors;

[0049] A storage device for storing one or more programs;

[0050] When the one or more programs are executed by the one or more processors, the one or more processors implement the automatic parking control method provided in the first aspect of the present invention.

[0051] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the automatic parking control method provided in the first aspect of the present invention.

[0052] The automatic parking control method provided by the present invention converts radar data collected by the millimeter-wave radar into point cloud data when the driving speed is lower than a preset speed, performs target detection based on the point cloud data, and detects whether a parking block exists in the current environment. If a parking block exists in the current environment, vehicle status data is obtained, and the driver's intention to move the vehicle is determined based on the vehicle status data. The state of the automatic parking system is controlled based on the driver's intention to move the vehicle, thereby avoiding the problem of reduced driving smoothness and easy occurrence of scratch accidents caused by frequent activation of the automatic parking system during the vehicle moving process, thereby improving driving smoothness and safety.

[0053] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1 A flow chart of an automatic parking control method provided by the present invention;

[0056] Figure 2 A schematic diagram of the structure of PointNet provided by the present invention;

[0057] Figure 3 A schematic structural diagram of an automatic parking control device provided by the present invention;

[0058] Figure 4 The present invention provides a schematic structural diagram of an electronic device.

[0059] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0061] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] Figure 1This is a flow chart of an automatic parking control method provided by the present invention. This embodiment can be applied to automatic parking control in a process of moving a car in a narrow space. The method can be executed by the automatic parking control device provided by the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, such as Figure 1 As shown, the automatic parking control method includes the following steps:

[0063] S101. When the driving speed is less than a preset speed, convert radar data collected by the millimeter-wave radar into point cloud data.

[0064] Exemplarily, in an embodiment of the present invention, the vehicle controller obtains the vehicle's driving speed in real time through a speed sensor, and when the driving speed is less than a preset speed (e.g., 3 km / h) and the duration exceeds a preset duration (e.g., 3 seconds), the radar data collected by the millimeter-wave radar is converted into point cloud data. Exemplarily, the radar data collected by the millimeter-wave radar can be continuously obtained during the vehicle's driving process, and when the driving speed is less than a preset speed (e.g., 3 km / h) and the duration exceeds a preset duration (e.g., 3 seconds), the millimeter-wave radar data obtained thereafter is converted into point cloud data.

[0065] The radar signal emitted by the millimeter-wave radar is a frequency-modulated continuous-wave radar signal. The interference radar periodically emits a radar wave signal with a continuous frequency. This period is called a sweep period. In a sweep period, the frequency of the frequency-modulated continuous-wave radar signal increases linearly with time.

[0066] In the embodiment of the present invention, for the echo signal received by the millimeter wave, the echo signal is mixed with the transmission signal to obtain an intermediate frequency signal. Mixing is to mix the transmission signal and the echo signal in frequency and phase to obtain an intermediate frequency signal reflecting the motion information of the target.

[0067] In order to reduce the amount of processed data and improve processing efficiency, the intermediate frequency signal can be sampled, and then the sampled data can be mapped into a two-dimensional matrix diagram, in which each row of data in the two-dimensional matrix diagram represents the intermediate frequency signal in each frequency sweeping cycle of the transmitted signal, and multiple rows of data are arranged in the column direction according to the sequence of the frequency sweeping cycles. Exemplarily, the sampled data of the intermediate frequency signal in the first frequency sweeping cycle is mapped to the first row, the sampled data of the intermediate frequency signal in the second frequency sweeping cycle is mapped to the second row, and so on, to obtain a two-dimensional matrix diagram.

[0068] The two-dimensional matrix image is subjected to a distance-dimensional Fourier transform (i.e., a Fourier transform is performed in the row direction) to obtain the distance between the target point and the millimeter-wave radar, and the distances of multiple target points constitute a distance distribution diagram of the target point in the distance. Exemplarily, the Fourier transform may be a fast Fourier transform.

[0069] By performing Fourier transform on the range distribution diagram in the column direction, a range-Doppler diagram of the target's distribution in distance and velocity (Doppler frequency) can be obtained.

[0070] The time at which the same echo signal reaches different receiving antennas is different, which is called the path difference, resulting in phase differences in the echo signals of different receiving antennas. By extracting the phase differences of multiple receiving antennas, the horizontal angle and elevation angle between the target point and the millimeter-wave radar can be calculated. For example, the range Doppler map is Fourier transformed again in the receiving antenna dimension (angle dimension) to obtain the horizontal angle and elevation angle between the target point and the millimeter-wave radar.

[0071] The three-dimensional coordinates of the target point in the Cartesian coordinate system are calculated based on the distance, horizontal angle and pitch angle between the target point and the millimeter-wave radar to form three-dimensional point cloud data. Specifically, the millimeter-wave radar is a spherical coordinate system data, which needs to be converted to the Cartesian coordinate system. For example, the conversion process is as follows:

[0072] X = R·cos(φ)·sin(θ);

[0073] Y = R·cos(φ)·cos(θ);

[0074] Z = R sin(φ);

[0075] Among them, R is the distance between the target point and the millimeter-wave radar, φ is the horizontal angle between the target point and the millimeter-wave radar, and θ is the pitch angle between the target point and the millimeter-wave radar.

[0076] S102: Perform target detection based on the point cloud data to detect whether there is a parking barrier in the current environment.

[0077] In the embodiment of the present invention, target detection is performed on the point cloud data to detect whether there is a parking blocker in the current environment. The present invention does not limit the target detection algorithm, as long as the parking blocker can be detected.

[0078] Exemplarily, PointNet is used to perform target detection on point cloud data. PointNet is a neural network specifically used to process point cloud data. It can directly process irregular point cloud data without converting the point cloud data into regular networks or voxels. Figure 2 A schematic diagram of the structure of PointNet provided by the present invention, referring to Figure 2 ,The process of target detection on point cloud data is as follows:

[0079] 1. Perform spatial transformation on the point cloud data, align the point cloud data in three-dimensional space, and obtain the original features.

[0080] Exemplarily, the point cloud data is input into a T-Net network for processing, the point cloud is transformed by rotation, translation, etc., an approximate affine transformation matrix is ​​learned, the input points are aligned in three-dimensional space, and the original features are obtained.

[0081] 2. Perform feature extraction on each point in the original feature point by point to obtain the first feature.

[0082] Exemplarily, the original features are input into a multi-layer perceptron (MLP) for point-by-point feature extraction. The multi-layer perceptron maps the features of each point from the original 3-dimensional to a higher dimension to obtain the first feature.

[0083] 3. Perform spatial transformation on the first feature, align the point cloud data in three-dimensional space, and obtain local features.

[0084] Exemplarily, the first feature is input into a T-Net network for processing, the first feature is transformed by rotation, translation and the like, an approximate affine transformation matrix is ​​learned, the input features are aligned in three-dimensional space, and local features are obtained.

[0085] 4. Perform point-by-point feature extraction on each point in the local feature to obtain the second feature.

[0086] Exemplarily, the local features are input into a multi-layer perceptron (MLP) for point-by-point feature extraction, and the multi-layer perceptron maps the local features to a higher dimension to obtain a second feature.

[0087] 5. Perform global pooling on the second feature to obtain the global features of the point cloud data.

[0088] Exemplarily, the second feature is input into a global maximum pooling layer (GMP) for global maximum pooling processing to obtain the global feature of the point cloud data. The maximum pooling operation can ensure that the network does not depend on the order of the points and ensure the disorder of the point cloud.

[0089] 6. Fuse local features and global features to obtain fused features.

[0090] For example, the global features are copied multiple times until they have the same dimension as the local features, and then fused with the local features to obtain fused features. By fusing global features and local features, the network can understand the overall structure and local details of the point cloud at the same time, thereby improving the accuracy of target detection.

[0091] 7. Perform semantic segmentation on point cloud data based on fusion features to detect whether there is a parking barrier in the current environment.

[0092] For example, the fused features are input into a fully connected layer (FCL) for processing, a label is predicted for each point, and the probability of the predicted category label of each point is outputted to achieve semantic segmentation. If there is a point cloud set with a category label of "parking stopper", it is considered that there is a parking stopper in the current environment.

[0093] S103: If a parking barrier exists in the current environment, obtain vehicle status data.

[0094] In an embodiment of the present invention, if there is a parking brake in the current environment, it means that there is a possibility of moving the car, and the vehicle status data is further obtained. The vehicle status data may include driving speed, acceleration, steering wheel angle, brake signal duration, etc., which is not limited in this embodiment of the present invention.

[0095] S104: Determine the driver's intention to move the vehicle based on the vehicle status data.

[0096] In the embodiment of the present invention, the driver's intention to move the vehicle is determined based on the vehicle state data. Specifically, a long short-term memory neural network, a support vector machine, a decision tree, etc. can be used to determine the driver's intention to move the vehicle, which is not limited in the embodiment of the present invention.

[0097] Exemplarily, in a specific embodiment of the present invention, a long short-term memory neural network is used to determine the driver's intention to move the vehicle, which is not limited in this embodiment of the present invention.

[0098] Specifically, a long short-term memory neural network is used to process the vehicle state data of multiple time steps closest to the current time step to obtain time series features. Exemplarily, the long short-term memory neural network includes N transfer cells, and the processing process of the long short-term memory neural network is:

[0099] 1. The first transfer cell receives the vehicle state data of the first time step for calculation and obtains the hidden state and cell state output by the first transfer cell.

[0100] Exemplarily, the first transfer cell receives the vehicle state data of the first time step, and sequentially performs calculations through the forget gate, memory gate, and output gate inside the transfer cell to output the hidden state and cell state of the first transfer cell output.

[0101] 2. The i-th transmission cell receives the vehicle state data of the i-th time step, and calculates the hidden state and cell state output by the i-1-th transmission cell to obtain the hidden state and cell state output by the i-th transmission cell, where i is a positive integer greater than 1 and less than N.

[0102] Exemplarily, the i-th transmission cell receives the vehicle state data of the i-th time step, the hidden state and cell state output by the i-1-th transmission cell, and calculates in sequence through the forget gate, memory gate and output gate inside the transmission cell to output the hidden state and cell state output by the i-th transmission cell.

[0103] 3. The Nth transfer cell receives the vehicle state data of the Nth time step, and calculates it in combination with the hidden state and cell state output by the N-1th transfer cell to obtain the cell state output by the Nth transfer cell as the time series feature.

[0104] Exemplarily, the Nth transfer cell receives the vehicle state data of the Nth time step, the hidden state and cell state output by the N-1th transfer cell, and sequentially performs calculations through the forget gate, memory gate, and output gate inside the transfer cell to output the hidden state and cell state output by the Nth transfer cell, and uses the cell state output by the Nth transfer cell as a time series feature.

[0105] After the time series features are obtained, the driver's intention to move the car is determined based on the time series features. Exemplarily, the time series features are input into a fully connected layer for processing, and the processed results are input into a classifier to obtain the driver's intention to move the car (there is an intention to move the car or there is no intention to move the car).

[0106] S105: Control the state of the automatic parking system based on the driver's intention to move the vehicle.

[0107] For example, when the automatic parking system is on, if the driver intends to move the car, the automatic parking system is controlled to be turned off, so as to avoid the problem of reduced driving smoothness and easy scratch accidents caused by frequent activation of the automatic parking system during the moving process, thereby improving driving smoothness and safety.

[0108] When the automatic parking system is turned on, if the driver has no intention to move the car, indicating that the car is currently driving normally, the automatic parking system will remain turned on.

[0109] When the automatic parking system is in the off state, if the driver intends to move the car, the automatic parking system will be maintained in the off state to avoid the automatic parking system being frequently activated during the moving process, which will lead to reduced driving smoothness and easy occurrence of scratches and accidents, thereby improving driving smoothness and safety.

[0110] When the automatic parking system is in the off state, if the driver has no intention to move the car, indicating that the car is currently driving normally, the automatic parking system will be controlled to turn on so that the automatic parking system can be activated normally.

[0111] The automatic parking control method provided by the present invention converts radar data collected by the millimeter-wave radar into point cloud data when the driving speed is lower than a preset speed, performs target detection based on the point cloud data, and detects whether a parking block exists in the current environment. If a parking block exists in the current environment, vehicle status data is obtained, and the driver's intention to move the vehicle is determined based on the vehicle status data. The state of the automatic parking system is controlled based on the driver's intention to move the vehicle, thereby avoiding the problem of reduced driving smoothness and easy occurrence of scratch accidents caused by frequent activation of the automatic parking system during the vehicle moving process, thereby improving driving smoothness and safety.

[0112] Figure 3 A schematic diagram of the structure of an automatic parking control device provided by the present invention is shown in FIG. Figure 3 As shown, the automatic parking control device includes:

[0113] The point cloud data acquisition module 201 is used to convert the radar data collected by the millimeter wave radar into point cloud data when the driving speed is less than a preset speed;

[0114] A parking block detection module 202, configured to perform target detection based on the point cloud data to detect whether there is a parking block in the current environment;

[0115] The vehicle status data acquisition module 203 is used to acquire the vehicle status data if a parking barrier exists in the current environment;

[0116] A vehicle moving intention determination module 204, configured to determine the driver's vehicle moving intention based on the vehicle status data;

[0117] The state control module 205 is used to control the state of the automatic parking system based on the driver's intention to move the vehicle.

[0118] In some embodiments of the present invention, the point cloud data acquisition module 201 includes:

[0119] A distance calculation submodule is used to perform a distance-dimensional Fourier transform on the radar data to obtain the distance between the target point and the millimeter-wave radar;

[0120] An angle calculation submodule, used for performing angle-dimensional Fourier transform on the radar data to obtain the horizontal angle and the pitch angle between the target point and the millimeter-wave radar;

[0121] The coordinate conversion submodule is used to calculate the three-dimensional coordinates of the target point in the Cartesian coordinate system based on the distance, horizontal angle and pitch angle between the target point and the millimeter wave radar to form three-dimensional point cloud data.

[0122] In some embodiments of the present invention, the three-dimensional coordinates of the target point in the Cartesian coordinate system are calculated based on the distance, horizontal angle and pitch angle between the target point and the millimeter wave radar, and the calculation formula is as follows:

[0123] X = R·cos(φ)·sin(θ);

[0124] Y = R·cos(φ)·cos(θ);

[0125] Z = R sin(φ);

[0126] Wherein, R is the distance between the target point and the millimeter-wave radar, φ is the horizontal angle between the target point and the millimeter-wave radar, and θ is the pitch angle between the target point and the millimeter-wave radar.

[0127] In some embodiments of the present invention, the vehicle blocker detection module 202 includes:

[0128] A first transformation submodule is used to perform spatial transformation on the point cloud data, align the point cloud data in three-dimensional space, and obtain original features;

[0129] A first feature extraction submodule is used to extract features of each point in the original feature point by point to obtain a first feature;

[0130] A second transformation submodule is used to perform spatial transformation on the first feature, align the point cloud data in three-dimensional space, and obtain a local feature;

[0131] A second feature extraction submodule is used to extract features of each point in the local feature point by point to obtain a second feature;

[0132] A pooling submodule, used for performing global pooling processing on the second feature to obtain a global feature of the point cloud data;

[0133] A feature fusion submodule, used to fuse the local features and the global features to obtain fused features;

[0134] The semantic segmentation submodule is used to perform semantic segmentation on the point cloud data based on the fusion features to detect whether there is a parking barrier in the current environment.

[0135] In some embodiments of the present invention, the vehicle state data includes driving speed, acceleration, steering wheel angle, and brake signal duration, and the vehicle moving intention determination module 204 includes:

[0136] The time series feature calculation submodule is used to process the vehicle state data of multiple nearest time steps including the current time step using a long short-term memory neural network to obtain the time series features;

[0137] The vehicle moving intention determination submodule is used to determine the driver's vehicle moving intention based on the time series characteristics.

[0138] In some embodiments of the present invention, the long short-term memory neural network includes N transfer cells, and the time series feature calculation submodule includes:

[0139] A first calculation unit, used for the first transfer cell to receive the vehicle state data of the first time step for calculation, and obtain the hidden state and cell state output by the first transfer cell;

[0140] The second calculation unit is used for the i-th transmission cell to receive the vehicle state data of the i-th time step, and to calculate in combination with the hidden state and cell state output by the i-1-th transmission cell to obtain the hidden state and cell state output by the i-th transmission cell, where i is a positive integer greater than 1 and less than N;

[0141] The third calculation unit is used for the Nth transfer cell to receive the vehicle state data of the Nth time step, and to calculate in combination with the hidden state and the cell state output by the N-1th transfer cell to obtain the cell state output by the Nth transfer cell as the time series feature.

[0142] In some embodiments of the present invention, the state control module 205 includes:

[0143] A first control submodule, configured to control the automatic parking system to be turned off if the driver intends to move the vehicle when the automatic parking system is turned on;

[0144] A second control submodule, configured to maintain the automatic parking system in the on state if the driver has no intention to move the vehicle when the automatic parking system is in the on state;

[0145] A third control submodule, configured to maintain the automatic parking system in the off state if the driver intends to move the vehicle when the automatic parking system is in the off state;

[0146] The fourth control submodule is used to control the automatic parking system to be turned on when the automatic parking system is in the off state and if the driver has no intention to move the car.

[0147] The automatic parking control device can execute the automatic parking control method provided by the aforementioned embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the automatic parking control method.

[0148] Figure 4A schematic diagram of the structure of an electronic device provided by the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0149] like Figure 4 As shown, the electronic device includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0150] A number of components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0151] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an automatic parking control method.

[0152] In some embodiments, the automatic parking control method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the automatic parking control method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the automatic parking control method in any other appropriate manner (e.g., by means of firmware).

[0153] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0154] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0155] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0156] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0157] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0158] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0159] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the automatic parking control method provided in any embodiment of the present application.

[0160] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0161] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0162] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An automatic parking control method, characterized in that: include: When the driving speed is less than the preset speed, the radar data collected by the millimeter-wave radar is converted into point cloud data; Performing target detection based on the point cloud data to detect whether there is a parking blocker in the current environment; If there is a parking barrier in the current environment, obtain vehicle status data; determining the driver's intention to move the vehicle based on the vehicle status data; The state of the automatic parking system is controlled based on the driver's intention to move the vehicle.

2. The automatic parking control method according to claim 1, characterized in that: Convert radar data collected by millimeter-wave radar into point cloud data, including: Performing a distance-dimensional Fourier transform on the radar data to obtain the distance between the target point and the millimeter-wave radar; Performing an angle-dimensional Fourier transform on the radar data to obtain a horizontal angle and a pitch angle between the target point and the millimeter-wave radar; The three-dimensional coordinates of the target point in a Cartesian coordinate system are calculated based on the distance, horizontal angle and pitch angle between the target point and the millimeter wave radar to form three-dimensional point cloud data.

3. The automatic parking control method according to claim 2, characterized in that: The three-dimensional coordinates of the target point in the Cartesian coordinate system are calculated based on the distance, horizontal angle and pitch angle between the target point and the millimeter wave radar. The calculation formula is as follows: X = R·cos(φ)·sin(θ); Y = R·cos(φ)·cos(θ); Z = R sin(φ); Wherein, R is the distance between the target point and the millimeter-wave radar, φ is the horizontal angle between the target point and the millimeter-wave radar, and θ is the pitch angle between the target point and the millimeter-wave radar.

4. The automatic parking control method according to claim 1, characterized in that: Performing target detection based on the point cloud data to detect whether there is a parking barrier in the current environment includes: Performing spatial transformation on the point cloud data, aligning the point cloud data in three-dimensional space, and obtaining original features; Performing point-by-point feature extraction on each point in the original feature to obtain a first feature; Performing spatial transformation on the first feature, aligning the point cloud data in three-dimensional space, and obtaining a local feature; Performing point-by-point feature extraction on each point in the local feature to obtain a second feature; Performing global pooling processing on the second feature to obtain a global feature of the point cloud data; Fusing the local features and the global features to obtain fused features; The point cloud data is semantically segmented based on the fusion features to detect whether there is a parking barrier in the current environment.

5. The automatic parking control method according to claim 1, characterized in that: The vehicle status data includes driving speed, acceleration, steering wheel angle, and brake signal duration. Determining the driver's intention to move the vehicle based on the vehicle status data includes: The long short-term memory neural network is used to process the vehicle status data of multiple nearest time steps including the current time step to obtain the time series characteristics; The driver's intention to move the vehicle is determined based on the time series characteristics.

6. The automatic parking control method according to claim 5, characterized in that: The long short-term memory neural network includes N transfer cells. The long short-term memory neural network is used to process the vehicle state data of multiple nearest time steps including the current time step to obtain time series features, including: The first transfer cell receives the vehicle state data of the first time step for calculation, and obtains the hidden state and cell state output by the first transfer cell; The i-th transmission cell receives the vehicle state data of the i-th time step, and calculates the hidden state and cell state output by the i-1-th transmission cell to obtain the hidden state and cell state output by the i-th transmission cell, where i is a positive integer greater than 1 and less than N; The Nth transfer cell receives the vehicle state data of the Nth time step, and calculates it in combination with the hidden state and cell state output by the N-1th transfer cell to obtain the cell state output by the Nth transfer cell as the time series feature.

7. The automatic parking control method according to claim 1, characterized in that: Controlling the state of the automatic parking system based on the driver's intention to move the vehicle includes: When the automatic parking system is in the on state, if the driver intends to move the vehicle, the automatic parking system is controlled to be turned off; When the automatic parking system is in the on state, if the driver has no intention to move the vehicle, the automatic parking system is maintained in the on state; When the automatic parking system is in the off state, if the driver intends to move the vehicle, the automatic parking system is maintained in the off state; When the automatic parking system is in an off state, if the driver has no intention to move the vehicle, the automatic parking system is controlled to be turned on.

8. An automatic parking control device, characterized in that: include: A point cloud data acquisition module, used to convert radar data collected by the millimeter wave radar into point cloud data when the driving speed is less than a preset speed; A parking block detection module, used to perform target detection based on the point cloud data to detect whether there is a parking block in the current environment; A vehicle status data acquisition module is used to acquire vehicle status data if a parking barrier exists in the current environment; A vehicle-moving intention determination module, used to determine the driver's vehicle-moving intention based on the vehicle status data; The state control module is used to control the state of the automatic parking system based on the driver's intention to move the vehicle.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the automatic parking control method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the automatic parking control method as described in any one of claims 1 to 7 is implemented.

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