A valet parking and vehicle hailing method and terminal device

Abstract

The present application discloses a valet parking and vehicle hailing method and a terminal device, which obtain the starting position and the destination position of a target vehicle; plan a set of accessible paths between the starting position and the destination position based on a parking lot geographic information model, where the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; calculate the comprehensive weight value of each path in the set of accessible paths, and select multiple accessible paths from the set of accessible paths according to the comprehensive weight value, and the comprehensive weight value is used to represent the selectable degree of the path; traverse the multiple accessible paths by means of a mileage counting output method, and determine a target path according to the traversal result. Implementing the technical solution provided by the present application achieves the effect of improving the efficiency of valet parking and vehicle hailing.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation, and particularly to a valet parking and vehicle calling method and terminal device. Background Art

[0002] In the process of the continuous advancement of modern transportation towards intelligence, the valet parking and vehicle recall functions in the field of autonomous driving have become the focus of much attention. However, there are still many defects that need to be urgently solved in the current application situation.

[0003] In the scenarios of valet parking and vehicle calling, the complex conditions of the parking lot environment pose many difficult problems for the path planning and path execution of autonomous vehicles. The internal road directions of the parking lot often present a non-standard layout, with many irregular changes and discontinuities, and there are traffic restrictions in each different area. In this way, it is difficult for the vehicle to quickly and accurately plan an ideal driving path. Moreover, in the scenario of multiple vehicles running simultaneously, it is easy to gather at key sections or entrances and exits, forming a congestion situation, which undoubtedly greatly prolongs the parking and recall time of the vehicle, seriously affecting the efficiency and experience of valet parking and vehicle calling services.

[0004] Therefore, how to improve the efficiency of valet parking and vehicle calling has become an urgent problem to be solved.

[0005] The above content is only used to assist in understanding the technical solution of this application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main purpose of this application is to provide a valet parking and vehicle calling method and terminal device, aiming to solve the technical problem of low efficiency of valet parking and vehicle calling.

[0007] To achieve the above purpose, this application provides a valet parking and vehicle calling method, which obtains the starting position and destination position of the target vehicle; based on the parking lot geographic information model, plans a set of accessible paths between the starting position and the destination position, and the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; calculates the comprehensive weight value of each path in the set of accessible paths, and selects multiple accessible paths from the set of accessible paths according to the comprehensive weight value, and the comprehensive weight value is used to represent the selectable degree of the path; traverses the multiple accessible paths by the mileage counting output method, and determines the target path according to the traversal result.

[0008] Optionally, traverse multiple accessible paths by the mileage counting output method, and determine the target path according to the traversal result, including: segmenting multiple accessible paths according to the areas passed by the multiple accessible paths to obtain a segmented path set; performing feature analysis on the segmented path set to determine the features of each path in the segmented path set, obtaining a segmented path feature set, and the feature analysis includes mileage analysis, curvature analysis, and traffic flow analysis; selecting multiple recommended road segments from the segmented path feature set according to the preset path selection criteria, and generating the target path based on the multiple recommended road segments.

[0009] Optionally, before traversing multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: judging whether the number of multiple accessible paths is lower than the minimum number of accessible paths; if it is lower, obtaining the passable features of the multiple accessible paths through the parking lot geographic information model, and the passable features include path length and passed area; merging the multiple accessible paths according to the passable features to generate a new accessible path; incorporating the new accessible path into the multiple accessible paths and updating the multiple accessible paths.

[0010] Optionally, after traversing multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: obtaining other target paths corresponding to other vehicles; comparing the target path and the other target paths to judge whether there is a situation where the target vehicle and other vehicles pass through the same road segment in the same time period; if there is, adjusting the target path and the other target paths through the distributed cooperation algorithm, and sending the adjusted paths to the target vehicle and other vehicles respectively.

[0011] Optionally, before calculating the comprehensive weight value of each path in the accessible path set and selecting multiple accessible paths from the accessible path set according to the comprehensive weight value, the method further includes: predicting the number of non-autonomous vehicles on each path in the accessible path set through the behavior prediction model; calculating the comprehensive weight value based on the number of non-autonomous vehicles.

[0012] Optionally, before predicting the number of non-autonomous vehicles on each path in the accessible path set through the behavior prediction model, the method further includes: obtaining the historical driving data of non-autonomous vehicles, and the historical driving data includes driving data at different times, driving data on different dates, and driving data in different regions; training the initial neural network model through the historical driving data to obtain the behavior prediction model.

[0013] Optionally, the historical driving data includes driving trajectory, driving speed, and parking location. After obtaining the historical driving data of a non-autonomous vehicle, the method further includes: performing enhancement processing on the historical driving data through a data enhancement method to obtain simulated driving data; updating the historical driving data with the simulated driving data, and training an initial neural network model with the updated historical driving data.

[0014] In addition, to achieve the above object, the present application further provides a terminal device, which includes: an acquisition module for acquiring the starting position and the destination position of a target vehicle; a planning module for planning a set of accessible paths between the starting position and the destination position based on a parking lot geographic information model, where the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; a selection module for calculating the comprehensive weight value of each path in the set of accessible paths, and selecting multiple accessible paths from the set of accessible paths according to the comprehensive weight value, where the comprehensive weight value is used to represent the selectable degree of the path; a determination module for traversing the multiple accessible paths by a mileage counting output method, and determining a target path according to the traversal result.

[0015] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any item in the first aspect.

[0016] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method steps according to any item in the first aspect are executed.

[0017] The valet parking and vehicle hailing method provided by the embodiments of the present application, based on a geographic information model, plans a set of accessible paths between the starting position and the destination position, calculates the comprehensive weight value of each path, so as to screen out multiple accessible paths with high accessibility according to the comprehensive weight value, avoiding re-analysis of paths with low accessibility, and then through the mileage counting output method, analyzes the road conditions of each section of the multiple accessible paths to quickly determine a target path with high accessibility, thereby improving the efficiency of valet parking and vehicle hailing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is an application flow chart provided by the embodiments of the present application.

[0019] Figure 2 is a flow chart of the valet parking and vehicle hailing method disclosed by the embodiments of the present application.

[0020] Figure 3It is a schematic structural diagram of a terminal device disclosed in an embodiment of the present application.

[0021] Figure 4 It is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0022] The realization of the purpose of the present application, functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0024] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0025] In the present invention, unless otherwise clearly defined and limited, terms such as "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0026] In addition, if there is a description involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or a solution that satisfies both A and B at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0027] In the process of the continuous acceleration of modern transportation, the valet parking and vehicle recall functions in the field of autonomous driving have become the core focus attracting extensive attention both inside and outside the industry. With the rapid development of technology, people's expectations for transportation convenience and intelligence are rising day by day. The application of autonomous driving technology in many scenarios is highly anticipated, and the valet parking and vehicle recall functions are regarded as the key links to enhance the travel experience. However, in terms of the current actual application situation, its current application status is rather disappointing, and there are many problems that urgently need to be solved.

[0028] In valet parking and vehicle calling scenarios, the internal road directions in parking lots often show non-standard layouts, with many irregular changes and discontinuities, causing many difficult problems for the path planning and path execution of autonomous vehicles. For example, in order to make full use of space, some parking lots have set up parking spaces with various shapes in different areas, resulting in the vehicle driving passages having to wind and twist, with frequent corners and forks; and there are also traffic restrictions in each different area, and vehicles must take these restrictive factors into account when planning paths, which undoubtedly greatly increases the complexity and computational amount of path planning, making it difficult for vehicles to quickly and accurately plan an ideal driving path.

[0029] Moreover, in the scenario of multiple vehicles running simultaneously, it is easy to gather at key sections or entrances and exits, forming a congested situation. Especially in large and medium-sized parking lots, due to their large area and numerous parking spaces, there are often a large number of vehicles entering and leaving at the same time. During peak hours, when vehicles are driving towards parking spaces or exits, they may meet at narrow passages, and autonomous vehicles may stagnate due to waiting for the other party to go first or giving way. The entrance and exit, as the necessary passage for vehicles to enter and leave, is an even more serious congestion area. When vehicles are queuing up to enter or leave the parking lot, if one vehicle breaks down or makes an operation error, it may trigger a chain reaction, causing the traffic at the entire entrance and exit to be paralyzed. This congestion situation greatly prolongs the parking and recall time of vehicles, seriously affecting the efficiency and experience of valet parking and vehicle calling services.

[0030] Therefore, this application proposes a valet parking and vehicle calling method, which can be applied to valet parking and vehicle calling scenarios. Please refer to Figure 1 , Figure 1 which is a flowchart provided by an embodiment of this application. As Figure 1In the process shown, when the vehicle owner needs the autonomous vehicle to park independently or when the vehicle is hailed, the starting position and destination position of the vehicle are sent to the target parking lot. After the parking lot receives the starting position and destination position sent by the autonomous vehicle, based on the entrance and exit information, lane connection relationships, lane directions, and parking space coordinates of the parking lot, all accessible paths from the starting position to the destination position are planned. According to the actual requirements, a path selection criterion is preset, multiple accessible paths are selected from all accessible paths, and then the multiple accessible paths are traversed by the mileage counting method to determine the target path, and the target path is sent to the autonomous vehicle, so that the vehicle can quickly drive to the destination position according to the target path.

[0031] Based on Figure 1 the application scenario shown, please continue to refer to Figure 2 , Figure 2 which is a process of the valet parking and vehicle hailing method provided by an embodiment of the present application. The valet parking and vehicle hailing method can be applied to any terminal device that can run a program.

[0032] Next, in combination with Figure 2 a detailed description of a valet parking and vehicle hailing method according to an embodiment of the present application will be given.

[0033] Step S201: Obtain the starting position and destination position of the target vehicle.

[0034] The starting position is used to represent the initial position of the autonomous vehicle during valet parking and vehicle hailing. In the valet parking scenario, the starting position can be the real-time position of the autonomous vehicle or any position independently input by the vehicle owner.

[0035] The destination position is used to represent the position that the autonomous vehicle should reach. This position can be specific coordinates or an area. For example, in the valet parking scenario, the destination position can be Area 1, and Area 1 includes multiple parking spaces, then the autonomous vehicle can park in any one of the parking spaces in Area 1.

[0036] Optionally, in the valet parking scenario, before obtaining the starting position and destination position of the target vehicle, the terminal device can send the parkable areas near the parking lot and the available parking spaces to the autonomous vehicle, instructing the vehicle owner to select the initial position from the parkable areas and the destination position from the available parking spaces.

[0037] Step S202: Based on the parking lot geographic information model, plan an accessible path set between the starting position and the destination position. The parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information.

[0038] The parking lot geographic information model is used to store all-round information within the parking lot. In the embodiments of the present application, the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information. Optionally, the parking lot geographic information model may further include obstacle distribution information, which is not specifically limited in the present application.

[0039] The set of accessible paths refers to all accessible paths from the starting position to the destination position.

[0040] Exemplarily, obtain the entrance and exit information, lane connection relationship information, lane direction information, and parking space information from the parking lot geographic information model. Define the starting position as the starting node of the path search algorithm (such as the A* algorithm), and set the destination position as the target node. The A* algorithm will gradually expand the search scope according to the connection relationship between nodes, the lane direction, and the set heuristic function, and all possible paths from the starting node to the target node are used as the set of accessible paths. Optionally, each path is composed of a series of consecutive nodes, and these nodes correspond to key positions within the parking lot, such as lanes, turning points, entrances, and exits.

[0041] Step S203: Calculate the comprehensive weight value of each path in the set of accessible paths, and select multiple accessible paths from the set of accessible paths according to the comprehensive weight value. The comprehensive weight value is used to represent the selectability degree of the path.

[0042] It is easy to understand that the higher the comprehensive weight value, the higher the cost of reaching the destination position from the initial position. Therefore, when selecting multiple accessible paths from the set of accessible paths, all paths included in the set of accessible paths are sorted in ascending order according to the comprehensive weight value, and the highest or lowest ranked multiple passable paths are selected according to the sorting result.

[0043] Exemplarily, the calculation factors for setting the comprehensive weight value include path length factor, curvature factor, and parking space availability factor, with weights of 40%, 30%, and 30% respectively. For example, for a path with a length of L, the number of bends is C, and the average curvature is K, the number of available parking spaces S within a certain range of the path (such as within 50 meters) is counted. Suppose the scores of a certain path in path length, curvature, and parking space availability factors are S1, S2, and S3 respectively, then S1 = (1-L / L_max) * 0.4, L_max is the length of the longest path in the accessible path set; S2 = (1-(C*K) / (C_max*K_max)) * 0.3, C_max and K_max are the maximum values of the number of bends and the average curvature in the accessible path set respectively; S3 = S / S_max * 0.3, S_max is the maximum number of available parking spaces in the accessible path set; then its comprehensive weight value W = S1 + S2 + S3. The accessible path set is sorted from high to low according to the comprehensive weight value, and then multiple accessible paths with the highest ranking are selected according to the preset number of screening (for example, 5). These multiple accessible paths selected are relatively high selectivity paths selected after comprehensively considering factors such as path length, curvature, and parking space availability, and can therefore be used as candidate paths for the subsequent determination of the final target path.

[0044] It should be understood that, since the curvature values of each path are different in different sections, the above average curvature is the average value of the sum of the curvature values of each path in different sections.

[0045] Optionally, before calculating the comprehensive weight value of each path in the accessible path set and selecting multiple accessible paths from the accessible path set based on the comprehensive weight values, the method also includes: predicting the number of non-autonomous driving vehicles on each path in the accessible path set through a behavior prediction model; and calculating the comprehensive weight value based on the number of non-autonomous driving vehicles.

[0046] Exemplarily, after determining multiple passable paths, the information of the multiple passable paths is input into a behavior prediction model. The behavior prediction model predicts the number of non-autonomous vehicles on each path based on the path information of the multiple passable paths; and performs comprehensive calculation with other weight factors according to the preset weight proportion of the number of non-autonomous vehicles in the comprehensive weight value calculation to obtain the comprehensive weight value. For example, the weight proportion of the number of non-autonomous vehicles factor is 25%. For other traditional weight factors, such as path length, curvature, and parking space availability, the weight proportions are 30%, 25%, and 20% respectively. A certain path scores 0.4 for the path length factor, 0.35 for the curvature factor, 0.28 for the parking space availability factor, and 0.53 * 0.25 = 0.1325 for the number of non-autonomous vehicles factor. Then the comprehensive weight value of this path is 0.4 * 0.3 + 0.35 * 0.25 + 0.28 * 0.2 + 0.1325 = 0.3055.

[0047] In this embodiment, by predicting the number of potential non-autonomous vehicles in the accessible paths through the behavior prediction model and incorporating the number of non-autonomous vehicles into the comprehensive weight calculation, it is possible to effectively avoid the paths with concentrated non-autonomous vehicles and reduce the delays and risks caused by autonomous vehicles in dealing with unpredictable human driving behaviors.

[0048] Optionally, before predicting the number of non-autonomous vehicles on each path in the accessible path set through the behavior prediction model, the method further includes: obtaining the historical driving data of non-autonomous vehicles, where the historical driving data includes driving data at different times, driving data on different dates, and driving data in different regions; and training the initial neural network model with the historical driving data to obtain the behavior prediction model.

[0049] The initial neural network refers to a neural network model that has not been trained with data. In this application, the CNN-LSTM model is taken as an example for illustration.

[0050] Exemplarily, extract the historical driving records of non-autonomous vehicles in the past year from the database of the parking lot management system. The historical driving records include the timestamps of the vehicles entering and leaving the parking lot, the driving trajectories of the vehicles in the parking lot, the parking areas of the vehicles, and the corresponding date information; divide and store them according to different times (such as morning rush hour, lunchtime, evening rush hour, night, etc.), different dates (weekdays, weekends, holidays, etc.), and different regions (areas near the parking lot entrance, internal passage areas, each parking partition, etc.). Preprocess the historical driving data, convert the time data into digital codes, perform area coding on the area data, assign unique digital identifiers to different parking areas and passage areas, and perform normalization processing on the vehicle trajectory data to improve the efficiency and stability of model training.

[0051] The architecture of the initial CNN-LSTM model includes two convolutional layers. The first layer (CNN layer) has a convolutional kernel size of 3x3, 64 kernels, a stride of 1, and uses the ReLU activation function to extract spatial feature maps with different angles and certain non-linear characteristics from historical driving data. The second layer has a convolutional kernel size of 3x3, 128 kernels, a stride of 1, and also uses the ReLU activation function to further combine and transform more types of features from the 64 feature maps output by the first layer to adapt to more complex changes in the vehicle trajectory spatial pattern and capture the combination relationships between different trajectory segments, the overall layout characteristics of the trajectory in a larger area, etc. After each convolutional layer, a 2x2 max-pooling layer is connected to reduce the data dimension. Two LSTM layers are set. The first LSTM layer receives the trajectory feature sequence processed by the CNN, as well as time and region features as inputs, and preliminarily learns and extracts the time series features in these data. For example, it captures time-related features such as the driving speed change and the length of stay of the vehicle at different time periods. The second LSTM layer further deepens the learning of the time series features on the basis of the first layer, such as the long-term change rules of the vehicle driving behavior between different dates and different time periods. Through the fully connected layer, various features processed by the CNN and LSTM are integrated to form a new feature vector, and the ReLU activation function is still used to introduce non-linear factors into the model and further enhance the model's learning ability for complex relationships. The output layer uses a linear activation function to process the feature vector integrated by the fully connected layer and outputs a numerical value representing the prediction result of the number of non-autonomous vehicles on each accessible path.

[0052] The preprocessed historical driving data is divided into a training set, a validation set, and a test set, and the ratio can be set to 70%, 20%, 10%. Through the training set, the validation set, and the test set, the initial CNN-LSTM model is trained, validated, and tested to obtain a behavior prediction model.

[0053] In this embodiment, the initial neural network is trained according to the characteristics of different time periods, different dates, different regions, etc. of the historical driving data to obtain a behavior prediction model, so that the behavior prediction model can predict the number of non-autonomous vehicles when querying the target path according to the time change rule of the number of non-autonomous vehicles, making the prediction result more accurate.

[0054] Optionally, the historical driving data includes driving trajectories, driving speeds, and parking positions. After obtaining the historical driving data of non-autonomous vehicles, the method further includes: performing enhancement processing on the historical driving data through a data enhancement method to obtain simulated driving data; updating the historical driving data with the simulated driving data, and training the initial neural network model with the updated historical driving data.

[0055] Take data augmentation of driving trajectory data and parking location data as an example. Among them, for driving trajectory data, a method of adding random noise is adopted; for parking location data, a parking location offset method is adopted.

[0056] For example, on the coordinate points of the original driving trajectory, a random offset of 10% is added to each coordinate point. The offset is randomly selected from a preset small range (such as [-0.5 meters, 0.5 meters]) in the horizontal and vertical directions respectively. For example, if the original trajectory point coordinate is (10.0, 20.0), it becomes (10.2, 19.9) after data augmentation; for each parking location coordinate, a new coordinate point is randomly selected within a small range around it (such as a circular area with a radius of 1 meter) as the simulated parking location. Through this data augmentation method, each historical driving data is processed to generate corresponding simulated driving data; then these simulated driving data are integrated with the original historical driving data to form a historical driving data containing original data and simulated data, and the initial neural network model is trained based on this historical driving data.

[0057] In this embodiment, by training the initial neural network model with the updated historical driving data, the initial neural network model can learn more comprehensive and complex vehicle driving behavior feature patterns, enhance the accuracy and generalization ability of predicting the number of non-autonomous vehicles on the accessible paths, and thus improve the reliability of the prediction results.

[0058] In an alternative embodiment, before traversing multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: judging whether the number of multiple accessible paths is lower than the minimum number of accessible paths; if so, obtaining the passable features of the multiple accessible paths through the parking lot geographic information model, and the passable features include path length and passing areas; merging the multiple accessible paths according to the passable features to generate new accessible paths; incorporating the new accessible paths into the multiple accessible paths to update the multiple accessible paths.

[0059] Exemplarily, assume that the minimum number of accessible paths is set to 5. If the currently counted number of accessible paths is only 3, then it is determined that it is lower than the minimum number of accessible paths. Obtain the path length of each path and the area information passed through, such as the entrance area, A parking area, passage connection area, construction area, etc., and record the area order and relevant attribute information. Analyze the overlapping parts and different parts of the three paths, and connect and merge the three paths at reasonable nodes (such as a certain area exit or entrance passed through in common) to form a new accessible path, and incorporate the newly generated accessible path into the original set of multiple accessible paths to complete the update operation of the accessible path set.

[0060] In this embodiment, the number of paths in the accessible path set increases, and the new paths integrate the advantages of multiple previous paths, improving the quality and diversity of the overall accessible paths, and providing a richer and better selection basis for traversing and determining a better target path by the mileage counting output method subsequently.

[0061] Step S204: Traverse multiple accessible paths by the mileage counting output method, and determine the target path according to the traversal result.

[0062] Exemplarily, create an independent mileage counting variable for each of multiple accessible paths (e.g., 5 paths), and obtain the detailed information of the 5 accessible paths, including the lane information, turning point information, entrance and exit information, etc. of each section of the path. Take the path with the highest comprehensive weight value among the 5 accessible paths (the first path) as the path to be traversed currently. Starting from the starting position of the first path, determine all the segmented paths included in the path, and obtain the path length, turning point, special road conditions (such as slope, speed limit change, etc.) and other information of each segmented path, and accumulate the preset variable values corresponding to these information respectively. Take the accumulated result as the segmented path value of this segmented path, and then sum up the segmented path values of all segmented paths as the traversal value of the first path; Based on the traversal method of the first path, continue to traverse the other 4 accessible paths, calculate the traversal values of the other 4 accessible paths, compare the traversal values of the 5 accessible paths, and take the path with the lowest traversal value as the target path.

[0063] In the above example, the segmented paths are obtained by dividing multiple accessible paths according to the regional layout of the parking lot (such as different parking areas, passage areas, entrance and exit areas, etc.). For example, divide the path according to the main regional nodes passed through. If a path passes from the parking lot entrance through the central passage to a certain parking area, then the section from the entrance to the central passage entrance, the central passage section, and the section from the central passage exit to the parking area can be used as different segmented paths respectively. Optionally, the path can also be divided into multiple lane segments according to a preset length, and the present application does not make specific limitations on this.

[0064] The valet parking and vehicle calling method provided by the embodiments of the present application plans an accessible path set between the starting position and the destination position based on a geographic information model, calculates the comprehensive weight value of each path, so as to screen out multiple accessible paths with high accessibility, avoiding re - analyzing paths with low accessibility, and then analyzes the road conditions of each section of multiple accessible paths by the mileage counting output method to quickly determine a target path with high accessibility, thereby improving the efficiency of valet parking and vehicle calling.

[0065] The above example traverses multiple accessible paths based on the mileage count output method and selects one path from the multiple accessible paths as the target path.

[0066] In another alternative example, multiple accessible paths are traversed by the mileage count output method, and the target path is determined according to the traversal result, including: segmenting the multiple accessible paths according to the areas passed by the multiple accessible paths to obtain a set of segmented paths; performing feature analysis on the set of segmented paths to determine the features of each path in the set of segmented paths to obtain a set of segmented path features, and the feature analysis includes mileage analysis, curvature analysis, and traffic flow analysis; selecting multiple recommended road segments from the set of segmented path features according to a preset path selection criterion, and generating a target path based on the multiple recommended road segments.

[0067] The preset path selection criterion is a criterion set before selecting the recommended road segments for selecting the recommended road segments. For example, the preset path selection criterion can be a criterion that comprehensively considers multiple factors such as path length, curvature, and traffic flow, sets corresponding weight ratios for each factor, calculates the comprehensive score of the path through weighted calculation, and screens the recommended road segments and generates the target path according to the score.

[0068] As described above, the multiple accessible paths are segmented according to the regional layout of the parking lot, a unique identifier is established for each segmented path, and the order information of each segmented path in the original accessible path is stored to form a set of segmented paths.

[0069] Exemplarily, by querying the lane length data in the parking lot geographic information model, the mileage of the segmented path is calculated. For example, if a segmented path includes three consecutive lanes with lengths of 50 meters, 30 meters, and 40 meters respectively, the mileage of this segmented path is 120 meters; using the lane direction information and geometric calculation methods, the curvature of each path is determined. For example, for a curve with a radius of 20 meters, its curvature is calculated as 1 / 20 = 0.05; interacting with the real-time traffic monitoring system of the parking lot or the traffic flow prediction model based on historical data to obtain the traffic flow information of the area where each path is located. For example, a segmented path is located on the main passage of the parking lot and is during the peak period, and according to historical data, the predicted hourly traffic volume in this area is 100 vehicles, then the traffic flow value of this segmented path is recorded as 100. The weight ratio of the set mileage is set to 40%; the weight ratio of the curvature is 30%; the weight ratio of the traffic flow is 30%. According to these weights, the comprehensive score of each path is calculated. For example, the mileage score S1, curvature score S2, and traffic flow score S3 of a segmented path, and the comprehensive score W = S1 * 0.4 + S2 * 0.3 + S3 * 0.3. Then, the set of segmented path features is sorted according to the comprehensive score, and multiple segmented paths with higher scores are selected as the recommended road segments. According to the order information of the recommended road segments in the original accessible path and their connection relationships, these recommended road segments are combined to generate the target path.

[0070] In this example, by segmenting multiple accessible paths and combining and generating a target path based on feature analysis, the advantageous sections in each accessible path can be integrated, thereby reducing the driving time of the vehicle in the parking lot and improving the efficiency of valet parking and vehicle hailing.

[0071] In an alternative embodiment, after traversing multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: obtaining other target paths corresponding to other vehicles; comparing the target path and the other target paths to determine whether there is a situation where the target vehicle and other vehicles pass through the same section of the road in the same time period; if so, adjusting the target path and the other target paths through a distributed collaborative algorithm, and sending the adjusted paths to the target vehicle and the other vehicles respectively.

[0072] The other target path is used to represent the path of other autonomous vehicles from the starting position to the destination position.

[0073] Exemplarily, after determining the destination path corresponding to the target vehicle, obtaining other target path information corresponding to other vehicles, and comparing and analyzing the target path of the target vehicle with the other target paths, for each lane segment in the target path of the target vehicle, check whether there is a target path of other vehicles passing through this section within the same 5-minute time window. For example, if the target vehicle is scheduled to pass through a certain section of the central passage of the parking lot from 10:00 to 10:05, and the target path of other vehicles shows that it also passes through this section during this time period, it is determined that there is a conflict situation. After determining the conflicting paths, the distributed collaborative algorithm selects one of the vehicles (assuming the target vehicle is selected) according to the set priority order (such as giving priority to adjusting the path of the vehicle with the longest path), finds other passages or areas around, plans a new path segment, and inserts it into the original target path to form an adjusted target path.

[0074] In this embodiment, by using the distributed collaborative algorithm to adjust the conflicting target path and other target paths, it is possible to effectively avoid the interference caused by vehicles independently planning paths, optimize and integrate the traffic flow in the parking lot, reduce the situation where vehicles are forced to stop or frequently adjust their routes due to path conflicts, and improve the overall traffic efficiency and stability.

[0075] It can be understood that in order for the terminal device to achieve Figure 2The functions described above include the corresponding hardware and / or software modules for performing each function. In combination with the steps of the examples described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the form of hardware or computer software driving the hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application in combination with the embodiments, but such implementation should not be considered to exceed the scope of the present application.

[0076] In this embodiment, the terminal device can be divided into functional modules according to the above method examples. For example, each different functional module can be divided corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is illustrative, only a logical function division, and there may be other division methods in actual implementation.

[0077] In the case of dividing each functional module corresponding to each function, Figure 3 FIG. shows a possible schematic diagram of the terminal device 300 involved in the above embodiment. The terminal device 300 includes: an acquisition module 301 for acquiring the starting position and the destination position of the target vehicle; a planning module 302 for planning a set of accessible paths between the starting position and the destination position based on the parking lot geographic information model, where the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; a selection module 303 for calculating the comprehensive weight value of each path in the set of accessible paths, and selecting multiple accessible paths from the set of accessible paths according to the comprehensive weight value, where the comprehensive weight value is used to represent the selectability of the path; a determination module 304 for traversing the multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result.

[0078] In an alternative implementation manner of the embodiment of the present application, the determination module 304 is further configured to segment the multiple accessible paths according to the areas passed by the multiple accessible paths to obtain a set of segmented paths; perform feature analysis on the set of segmented paths to determine the features of each segment of the path in the set of segmented paths to obtain a set of segmented path features, where the feature analysis includes mileage analysis, curvature analysis, and traffic flow analysis; select multiple recommended road segments from the set of segmented path features according to a preset path selection criterion, and generate a target path based on the multiple recommended road segments.

[0079] In an optional implementation manner of the embodiment of the present application, the determining module 304 is further configured to determine whether the number of multiple accessible paths is lower than the minimum number of accessible paths; if it is lower, obtain the passable features of the multiple accessible paths through the parking lot geographic information model, where the passable features include path length and passing area; merge the multiple accessible paths according to the passable features to generate a new accessible path; incorporate the new accessible path into the multiple accessible paths to update the multiple accessible paths.

[0080] In an optional implementation manner of the embodiment of the present application, the determining module 304 is further configured to obtain other target paths corresponding to other vehicles; compare the target path and the other target paths, and determine whether there is a situation where the target vehicle and other vehicles pass through the same road section in the same time period; if there is, adjust the target path and the other target paths through a distributed collaborative algorithm, and send the adjusted paths to the target vehicle and other vehicles respectively.

[0081] In an optional implementation manner of the embodiment of the present application, the selecting module 303 is further configured to predict the number of non-autonomous vehicles on each path in the accessible path set through a behavior prediction model; calculate a comprehensive weight value based on the number of non-autonomous vehicles.

[0082] In an optional implementation manner of the embodiment of the present application, the selecting module 303 is further configured to obtain historical driving data of non-autonomous vehicles, where the historical driving data includes driving data at different time periods, driving data on different dates, and driving data in different regions; train an initial neural network model through the historical driving data to obtain a behavior prediction model.

[0083] In an optional implementation manner of the embodiment of the present application, the selecting module 303 is further configured to perform enhancement processing on the historical driving data through a data enhancement method to obtain simulated driving data; update the historical driving data through the simulated driving data, and train the initial neural network model through the updated historical driving data.

[0084] The present application also discloses an electronic device. Refer to Figure 4 , Figure 4 FIG. is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application. The electronic device 400 may include: at least one processor 401, at least one network interface 404, a user interface 403, a memory 405, and at least one communication bus 402.

[0085] Among them, the communication bus 402 is used to implement connection communication between these components.

[0086] Among them, the user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may further include a standard wired interface and a wireless interface.

[0087] Among them, the network interface 404 may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0088] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and circuits to connect various parts within the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling the data stored in the memory 405, it executes various functions of the server and processes data. Optionally, the processor 401 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 401 may integrate one or a combination of several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401 and may be implemented separately by a single chip.

[0089] Among them, the memory 405 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store the data involved in the above-mentioned various method embodiments. The memory 405 is optionally also at least one storage device located far from the aforementioned processor 401. Refer to Figure 4 , the memory 405, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a valet parking and vehicle hailing method.

[0090] In Figure 4 In the electronic device 400 shown, the user interface 403 is mainly used to provide an interface for the user to input and obtain the data input by the user. The processor 401 can be used to call the application program stored in the memory 405 for valet parking and vehicle hailing methods. When executed by one or more processors 401, the electronic device 400 is caused to execute one or more of the methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0091] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] In several implementation manners provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0093] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, the functional units in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0095] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0096] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A valet parking and vehicle hailing method, characterized in that, Including: Obtain the starting position and destination position of the target vehicle; Based on the parking lot geographic information model, plan a set of accessible paths between the starting position and the destination position. The parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; Calculate the comprehensive weight value of each path in the set of accessible paths, and select multiple accessible paths from the set of accessible paths according to the comprehensive weight value. The comprehensive weight value is used to represent the selectability of the path; Traverse the multiple accessible paths by the mileage counting output method, and determine the target path according to the traversal result; The step of traversing the multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result includes: Segment the multiple accessible paths according to the areas passed by the multiple accessible paths to obtain a set of segmented paths; Conduct feature analysis on the set of segmented paths to determine the features of each segment of the paths in the set of segmented paths, and obtain a set of segmented path features. The feature analysis includes mileage analysis, curvature analysis, and traffic flow analysis; Select multiple recommended road segments from the set of segmented path features according to the preset path selection criteria, and generate the target path based on the multiple recommended road segments. Among them, the preset path selection criteria is a criterion that takes multiple factors into consideration, sets corresponding weight ratios according to each factor, calculates the comprehensive score of the path through weighted calculation, and screens and generates the target path according to the score. The factors include path length, curvature, and traffic flow; 2. The method according to claim 1, wherein Before the step of traversing the multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: Judge whether the number of the multiple accessible paths is lower than the minimum number of accessible paths; If it is lower, obtain the passable features of the multiple accessible paths through the parking lot geographic information model. The passable features include path length and passing area; Merge the multiple accessible paths according to the passable features to generate new accessible paths; Incorporate the new accessible paths into the multiple accessible paths and update the multiple accessible paths.

3. The method according to claim 1, characterized in that, After the step of traversing the multiple accessible paths by the mileage counting output method and determining the target path according to the traversal result, the method further includes: Obtain other target paths corresponding to other vehicles; Compare the target path and the other target paths, and judge whether there is a situation where the target vehicle and the other vehicle pass through the same road segment in the same time period; If there is, adjust the target path and the other target paths through a distributed collaborative algorithm, and send the adjusted paths to the target vehicle and the other vehicle respectively.

4. The method according to claim 1, characterized in that, Before the step of calculating the comprehensive weight value of each path in the set of accessible paths and selecting multiple accessible paths from the set of accessible paths according to the comprehensive weight value, the method further includes: Predict the number of non-autonomous vehicles on each path in the set of accessible paths through a behavior prediction model; Calculate the comprehensive weight value based on the number of non-autonomous vehicles.

5. The method according to claim 4, wherein Before predicting the number of non-autonomous vehicles on each path in the accessible path set through the behavior prediction model, the method further includes: Obtaining historical driving data of non-autonomous vehicles, where the historical driving data includes driving data at different time periods, driving data on different dates, and driving data in different regions; Training an initial neural network model with the historical driving data to obtain the behavior prediction model.

6. The method according to claim 5, characterized in that The historical driving data includes driving trajectories, driving speeds, and parking positions. After obtaining the historical driving data of non-autonomous vehicles, the method further includes: Performing enhancement processing on the historical driving data through a data enhancement method to obtain simulated driving data; Updating the historical driving data with the simulated driving data, and training the initial neural network model with the updated historical driving data.

7. A terminal device, characterized in that, The terminal device includes: An acquisition module for acquiring the starting position and the destination position of the target vehicle; A planning module for planning an accessible path set between the starting position and the destination position based on a parking lot geographic information model, where the parking lot geographic information model includes entrance and exit information, lane connection relationship information, lane direction information, and parking space information; A selection module for calculating the comprehensive weight value of each path in the accessible path set, and selecting multiple accessible paths from the accessible path set according to the comprehensive weight value, where the comprehensive weight value is used to represent the selectability of the path; A determination module for traversing the multiple accessible paths through a mileage counting output method, and determining a target path according to the traversal result; traversing the multiple accessible paths through the mileage counting output method and determining a target path according to the traversal result includes: Segmenting the multiple accessible paths according to the regions passed by the multiple accessible paths to obtain a segmented path set; Performing feature analysis on the segmented path set to determine the features of each path in the segmented path set, and obtaining a segmented path feature set, where the feature analysis includes mileage analysis, curvature analysis, and traffic flow analysis; Selecting multiple recommended road segments from the segmented path feature set according to a preset path selection criterion, and generating the target path based on the multiple recommended road segments; where the preset path selection criterion is a criterion for considering multiple factors, setting corresponding weight ratios according to each factor, calculating the comprehensive score of the path through weighted calculation, and screening and generating the target path according to the score, and the factors include path length, curvature, and traffic flow.

8. An electronic device, characterized in that, Including a processor, a memory, a user interface, and a network interface, where the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to execute the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1-6 is executed.

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