CN109733383B - Self-adaptive automatic parking method and system - Google Patents
Self-adaptive automatic parking method and system Download PDFInfo
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- CN109733383B CN109733383B CN201811527904.4A CN201811527904A CN109733383B CN 109733383 B CN109733383 B CN 109733383B CN 201811527904 A CN201811527904 A CN 201811527904A CN 109733383 B CN109733383 B CN 109733383B
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Abstract
The invention relates to the field of intelligent driving, in particular to a self-adaptive automatic parking method and a self-adaptive automatic parking system; in the prior art, self-adaptive and omnibearing user personalized service cannot be realized. The invention can provide an automatic parking and warehousing mode and/or a mode for automatically recalling vehicles from a garage. Detecting whether a corresponding map of a parking or recalling area exists in an automatic driving system of a vehicle; setting starting and stopping positions of the vehicle in a map used by a user; matching a preset global map with a map used finally to generate an automatic driving route plan; parking or recall is accomplished by automatic driving. The preassembly parking system provides an online map building function for a ground warehouse and a parking lot without map building; therefore, self-adaptive and omnibearing service can be realized.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a method or a system for automatic parking or vehicle recall.
Background
With the development of various technologies of artificial intelligence, the automatic driving technology is continuously matured, and users have further demands for the automatic driving technology, especially for automatic parking and automatic vehicle recall in an indoor parking lot. The number of the parking lots is large, and a user cannot be limited to parking in a certain parking lot, so that the automatic parking function needs to meet the condition that the automatic driving system does not build a map of the parking lot in the preassembly process.
How to adaptively and comprehensively provide user personalized services is a key and unsolved problem for the automatic driving system. In fact, various factors such as various requirements, various modes, adaptive parking of the indoor parking lot under multiple scenes, corresponding map matching technology and the like are mixed, and great challenges are provided for the design, logical design and technology model selection of the whole parking system.
The existing automatic parking system for the indoor parking lot cannot rely on the signals for automatic parking or recalling because the GPS signals are weak; generally, only limited modes can be processed, for example, automatic parking can be performed only when the existing map of the system is pre-installed, or only parking and warehousing services can be performed, and automatic vehicle recall services cannot be performed.
Disclosure of Invention
In view of this, the present application provides an adaptive parking method for an indoor parking lot, which can provide an automatic parking-in-garage mode and a mode of automatically recalling a vehicle from a garage at the same time, and can adaptively respond to changes in a parking environment through dynamic semantic feature matching, thereby improving accuracy and speed of map matching. In addition, an online map building function is provided for a ground warehouse and a parking lot of which the pre-installed parking system does not build a map. On the basis of the key elements, the self-adaptive parking method designed by the invention is also designed in the aspects of a parking mode of an application layer, interaction with a user and the like, and is finally fused into a set of self-adaptive parking system of the indoor parking lot.
In a first aspect of the present invention, a global path planning method for valet parking or automatic recall is provided, which is characterized by comprising the following steps:
activating and setting; a user sets and activates a passenger-assistant parking or automatic recall function, and sets starting and stopping positions of the vehicle; wherein the start and stop positions are fixed points;
a map matching step; the map matching step includes: calculating by a map matching algorithm to obtain whether a map of the current parking or recall environment is stored in the current automatic driving system; judging whether a map of the current parking or recalling environment is obtained by matching;
self-learning graph building; taking a self-learning map as the map; the self-learning map building comprises a parking path learning mode and/or a recall path learning mode;
judging whether the self-learning graph building is successful: when the self-learning map building comprises a driving termination point set by a user and the self-learning map building information is enough for global path planning, completing map building and entering a global path planning step; judging whether the map building fails, if so, the system stores the part which does not finish learning the map building, and finishes the self-learning map building function and the automatic global path planning;
planning a global path; and generating a global path plan based on the self-learning map based on the starting point and the ending point of the automatic driving set by the user.
In a second aspect of the present invention, a method for parking a car as a passenger is provided, which is characterized in that: the method comprises the following steps:
step S101: a user sets and activates a passenger-riding parking function, and sets starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
step S102: matching a map; the map matching comprises the sub-steps of:
s1021: the vehicle determines a target area and target semantic features in the target area from a preset three-dimensional map according to the vehicle initial pose information;
s1022: matching semantic features extracted by using the all-round-looking image of the vehicle with semantic features of the map;
s1023: through the map matching algorithm of step S1021 and step S1022, the vehicle gives a map of whether a map of the current parking environment has already been stored in the current automatic driving system; if the map of the current parking environment is obtained through matching, the system directly enters the step S105; if no corresponding map is matched, the system proceeds to step S103;
step S103: the self-learning map is built as a preset map; the self-learning map building comprises a parking path learning mode;
step S104: judging whether the self-built image is successful or not; when the self-learning established map already contains the driving termination point set by the user and the established map information is enough for global path planning, the map establishment is completed, and the system automatically returns to the step 101; if the map building fails, the system stores the part which does not finish the learning map building and ends the self-learning map building function;
step S105: planning a global path; generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
step S106: confirming the use of the parking function;
step S107: and (4) autonomous parking driving, wherein the system enters a parking driving state.
Preferably, the method further comprises the step of:
step S108: the real-time fault positioning module detects faults in the parking process;
step S109: detecting obstacles in the parking process by a real-time obstacle detection module;
step S110: an interruption of autonomous driving functions; according to the fault locating module in the step S108 and the fault and obstacle detection of the real-time obstacle detecting module in the step S109, when a running area in front of the vehicle encounters a fault or an obstacle in the parking process, the system can automatically perform stable braking to ensure that the vehicle is in a safe state;
step S111: finishing the passenger-replacing parking; the vehicle automatically drives to a target place to complete a task, and after the passenger car is parked, the system informs a driver of the completion of functions through a mobile phone terminal and attaches vehicle position information.
Preferably, the step S103 includes the following sub-steps:
step S1031, obtaining current pose information of the target vehicle;
step S1032, predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
step S1033, acquiring target map data in a preset range from the automatic driving electronic navigation map according to the estimated pose information;
and S1034, combining the target map data and the estimated pose information to generate a target driving strategy for guiding the user to automatically drive.
Preferably, after the path planning in step 103 is successful, the user is prompted whether to perform automatic parking; in the parking mode, the user autonomously parks the vehicle or terminates the automatic parking function by selecting to enter step 107.
A third aspect of the present invention provides an automatic vehicle recall method, including: the method comprises the following steps:
s101, a user sets and activates an automatic recall function, and sets starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
s102, map matching; the map matching comprises the sub-steps of:
s1021, the vehicle determines a target area and target semantic features in the target area from a preset three-dimensional map according to the vehicle initial pose information;
s1022, semantic features extracted by utilizing the vehicle around view image are matched with the semantic features of the map;
s1023, through the map matching algorithm of the step S1021 and the step S1022, the vehicle gives out whether a map of the current recall environment is stored in the current automatic driving system; if the map of the current recall environment is matched, the system directly enters the step S105; if no corresponding map is matched, the system proceeds to step S103;
step S103, self-learning to build a map as a preset map; the self-learning map comprises a recall path learning mode;
step S104, judging whether the self-created graph is successful; when the self-learning established map already contains the driving termination point set by the user and the established map information is enough for global path planning, the map establishment is completed, and the system automatically returns to the step 101; if the map building fails, the system stores the part which does not finish the learning map building and ends the self-learning map building function;
s105, planning a global path; generating a global path plan by adopting a dynamic planning method based on a preset map based on a starting point and an end point of automatic driving set by a user;
step S106: confirming recall function usage;
step S107: and (5) autonomously recalling driving, and enabling the system to enter a recalling driving state.
Preferably, the step S103 includes the following sub-steps:
step S1031, obtaining current pose information of the target vehicle;
step S1032, predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
step S1033, acquiring target map data in a preset range from the automatic driving electronic navigation map according to the estimated pose information;
and S1034, combining the target map data and the estimated pose information to generate a target driving strategy for guiding the user to automatically drive.
Preferably, after the path planning in step 105 is successful, the user is prompted whether to perform automatic recall; in the park mode, the user autonomously recalls driving by selectable entry to step 107 or terminates the auto park function.
In a fourth aspect of the present invention, there is provided an automatic parking system for a vehicle, comprising:
a user setting module: activating a parking mode, and setting starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
a map module: adopting an existing map or a self-built map as a preset map;
a map matching module: matching semantic features extracted by the panoramic image of the vehicle with semantic features of the start-stop point map;
a global path planning module: generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
an automatic driving module: and according to the global path plan, the vehicle automatically parks.
In a fifth aspect of the present invention, there is provided an automatic vehicle recall system, comprising:
a user setting module: activating a recall mode, and setting starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
a map module: adopting an existing map or a self-built map as a preset map;
a map matching module: matching semantic features extracted by the panoramic image of the vehicle with semantic features of the start-stop point map;
a global path planning module: generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
an automatic driving module: and automatically recalling the vehicle according to the global path plan.
The invention is characterized by the following points, but not limited to the following points:
(1) the automatic parking and warehousing mode and the automatic vehicle recalling mode from the garage can be provided simultaneously; for garages, particularly underground garages, because signals such as GPS (global positioning system) and the like are weak, the conventional positioning mode cannot realize accurate control on automatic parking and recall, and the self-established map adopted by the invention does not depend on signals such as an external GPS and the like, and still has higher automatic parking and recall accuracy. This is one of the points of the present invention.
(2) The parking or vehicle recall system also has an online map building function so as to adapt to the condition that no map is pre-installed; the online map building function is adopted, and a map data acquisition vehicle is not required to be used for acquiring data in each garage. Each vehicle that normally travels can be considered to be a collection vehicle of the map of the ground reservoir. Because the possible driving paths in the ground library are different every time, after a plurality of limited driving, relatively complete image data are acquired for drawing.
(3) According to the method and the device, the semantic features stored when the autonomous parking start and stop points build the map in a self-learning mode are stored as initial semantic features, and the weight of the semantic features is the highest in map matching. In order to adaptively update the semantic features of the start-stop point environment image, the application also provides a dynamic semantic feature, and the weight of the dynamic semantic feature is inferior to the weight of the initial semantic feature in map matching. In the prior art, map matching is not found in links of automatic parking and recall which need high-precision automatic driving strategies, and map matching with dynamic semantic features does not occur.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of an adaptive automatic passenger-assistant parking method in the embodiment of the present application;
FIG. 2 is a flow chart of an adaptive automatic vehicle recall method in an embodiment of the present application;
fig. 3 is an explanatory diagram of the parking mode and the recall mode referred to in the embodiment of the present application.
FIG. 4 is a flowchart of a map-based driving strategy generation in an embodiment of the present application.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following embodiments and accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The application example provides an adaptive parking method. The parking mode and the online map building function can be applied to parking applications in other scenes besides the indoor parking lot.
The following describes a specific implementation of the embodiments of the present application with reference to the drawings.
First, a method for adaptive parking in an indoor parking lot provided in an embodiment of the present application is described.
FIG. 1 is a flow chart of an exemplary valet parking system of the present application; fig. 2 is a flowchart of a system for retrieving a vehicle by a valet in an example of the present application, where both the valet parking and the valet retrieving are applied to the field of automatic driving, and the specific method includes:
step 101: and the user sets and activates the passenger-riding parking function.
After the passenger car parking is activated, a user needs to set a car driving mode, and the parking mode refers to that a car automatically drives from a specified position to park and enter a garage. As shown in fig. 3, from point B, i.e., the point of traffic, to point a, i.e., the parking space or garage. The recall mode refers to the vehicle automatically traveling from the garage to the location where the driver needs to pick up the vehicle. From point a, i.e. the parking space or garage, to point B, i.e. the driver's place of pick-up. It should be noted that, whether the point a or the point B is a fixed point, the fixed point is already built in the parking system before the valet parking function is activated, and cannot be changed, which is to reduce the large amount of calculation caused by the optional selection of the starting point or the ending point. In an application level, the fixed points may be a parking start point and a parking end point of the guest, which are set inside a parking facility in some embodiments.
Step 102: map matching
In the embodiment of the invention, the preset three-dimensional map can be a high-precision map. Among them, the High-precision Map may also be referred to as a High Definition Map (HD Map), which is a Map dedicated to automatic driving of a vehicle. The HD Map stores various traffic elements in a traffic scene, for example, data such as road network data, lane line data, and traffic sign data, to assist a vehicle in automatic driving. In addition, the high-precision map can also comprise prior information, such as the curvature, the heading, the gradient and the slope angle of a road, so that the electronic equipment can automatically drive and control the vehicle through the prior information, and the safety and the comfort of the vehicle are further improved.
The map in the embodiment of the invention is mainly used for generating an automatic driving strategy and confirming the starting point and the ending point set by a user.
1) The electronic equipment determines a target area and target semantic features in the target area from a preset three-dimensional map according to the vehicle initial pose information; the initial pose information at least comprises longitude, latitude and altitude of the position of the vehicle, and a heading angle, a pitch angle and a roll angle of the vehicle. The target area is an area with the position of the vehicle as the center of a circle and the preset length as the radius. In the embodiment of the invention, the vehicle-mounted terminal is provided with a plurality of cameras, and a plurality of target images shot at the same time are spliced into a ring view. The target semantic features may be: traffic elements such as lane lines, pool lines, lane arrows, etc. The terminal firstly extracts semantic features from the ring view, and the method for extracting the semantic features can be an Encoder-Decoder model-based deep learning method or an image segmentation method.
2) And matching the semantic features extracted by the panoramic image with the semantic features of the start-stop point map. The semantic features of the start-stop point map extracted by the autonomous parking in the application can be extracted and stored in the system. During matching, the semantic features extracted from the panoramic image are directly matched with the stored start-stop point semantic features without matching in the semantic special of the global map, so that the matching accuracy is improved, and the matching speed can be greatly improved.
The local map comprises three image semantic features of a library bit line, a library site and a lane arrow. If the library bit lines and the library bit points are used for matching, a plurality of areas possibly matched from the global map are the same as the local map, and the matching accuracy is low at the moment. In contrast, since the lane arrows at different positions are different in shape and size and in positional relationship with the surrounding library sites and library sites, the probability of successful matching can be improved by matching the local map and the global map using the lane arrows.
With an autonomous parking system, over time, some minor changes in semantic features in the parking environment may occur due to ambient light, depreciation, and the like, such as wear of lane arrows, and the like. These changes are somewhat cumulative and may increase the probability of a failed match if the map cannot be adaptively updated. According to the method and the device, the semantic features stored when the autonomous parking start and stop points build the map in a self-learning mode are stored as initial semantic features, and the weight of the semantic features is the highest in map matching. In order to adaptively update the semantic features of the start-stop point environment image, the application also provides a dynamic semantic feature, and the weight of the dynamic semantic feature is inferior to the weight of the initial semantic feature in map matching. The dynamic semantic features may be a plurality of groups, and the number of the groups mainly depends on the difference between the extracted corresponding semantic features and the stored semantic features in the autonomous parking process. Taking the semantic features of the starting point as an example:
step1, when the matching confidence of the semantic features of the starting point environment image and the initial semantic features in the map is larger than a certain threshold, the matching is considered to be successful, and the dynamic semantic features do not need to be updated.
Step2, when the confidence coefficient of the semantic features of the starting point environment image and the initial semantic features in the map is smaller than a certain threshold, comparing the semantic features of the starting point environment image with the dynamic semantic features, and if the confidence coefficient is larger than the certain threshold, the matching is successful.
Step3, calculating the distance between the semantic features of the starting point environment image and the initial semantic features and the dynamic semantic features of the map, and adopting Euclidean distance or cosine distance. Suppose that
d＝min(d0,d1,…,dn)
Wherein d0 is the distance between the semantic features of the starting point environment image and the initial semantic features of the map, and d1, …, dn is the distance between the semantic features of the starting point environment image and the n groups of dynamic semantic features of the map. When d is larger than a certain threshold value, the semantic features of the initial point environment image are added to the dynamic semantic features, and the number of the dynamic semantic features becomes n + 1.
When the map is matched, the semantic features of all the stored start-stop point environment images need to be traversed, so that the matching success rate and the use experience of the system can be greatly improved. The dynamic features are used for map matching, and the threshold is set for judgment, and the judgment is carried out on the basis of real-time self-constructed maps. The above-mentioned use of dynamic semantic features is one of the innovative points of the present invention.
3) Through the map matching algorithm in the steps 1) and 2), the system can give out whether a map of the current parking environment is stored in the current automatic driving system; if the map of the current parking environment has been matched, the system proceeds to step 103; if no corresponding map is matched, the system proceeds to step 104.
Step 103: and (3) global path planning:
and generating a global path plan by adopting a dynamic planning method based on the learned map based on the starting point and the ending point of the automatic driving set by the user. Referring to fig. 4, the specific path planning method includes the following steps:
In the embodiment of the invention, the current pose information of the target vehicle is the position information and the pose information of the target vehicle at the current moment, for example, the current pose information of the target vehicle can be forward running at a certain angle; the IMU data of the target vehicle at the current moment can be obtained by an Inertial Measurement Unit (IMU), the IMG data of the target vehicle at the current moment can be obtained by an Image (IMG) sensor, other data of the target vehicle at the current moment can be obtained by other sensors, the current pose information of the target vehicle is calculated by the obtained IMU data, IMG data and the like, and the current pose information of the target vehicle can be calculated by integrating the sensor data obtained by various sensors in the process, so that the more reliable current pose information of the target vehicle can be obtained.
And step 402, predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map.
In the embodiment of the invention, the automatic driving electronic navigation map is a map with high precision. After determining the location information and the road type, the following steps may also be performed:
determining a road inclination angle of the position information in the road, wherein the road inclination angle is an included angle between a road section of the position information in the road and a horizontal line;
when the road type is a straight road, predicting that the estimated pose information of the target vehicle at the next moment is forward driving may include:
and when the road type is a straight road, predicting to obtain the estimated pose information of the target vehicle at the next moment so as to drive forwards at the road inclination angle.
For example, when the angle between the road segment of the position information in the road and the horizontal line is a certain angle, if the road type of the position information in the road is a straight road, the estimated pose information of the target vehicle at the next moment is predicted to be driven forward at the certain angle.
By implementing the mode, the predicted estimated pose information can also comprise the driving angle information of the target vehicle, and the accuracy of the estimated pose information is further improved.
And step 403, acquiring target map data within a preset range from the automatic driving electronic navigation map according to the estimated pose information.
In the embodiment of the present invention, the estimated pose information may include position information of the target vehicle and pose information of the target vehicle.
As an optional implementation manner, the obtaining of the target map data within the preset range in the automatic driving electronic navigation map based on the estimation of the pose information may include:
determining position information of a target vehicle in an automatic driving electronic navigation map;
and selecting target map data in a preset range of the direction indicated by the attitude information of the target vehicle according to the position information.
For example, when the position information of the target vehicle indicates that the target vehicle is located in a road a (the road a is a north-south road, one side of the road a is connected to the south, and the other side of the road a is connected to the north), and the posture information of the target vehicle indicates that the target vehicle faces the south of the road a, target map data within a predetermined range of the position information in the road a is selected. The preset range may be a preset range, for example, the preset range may be 10m, may also be 80m, and may also be other ranges, which is not limited in the embodiment of the present invention. When the preset range is 10m, the target map data within 10m south of the position information in the road a may be selected.
By implementing the optional implementation mode, all map information in the automatic driving electronic navigation map does not need to be selected for analysis, and only target map data in a more effective preset range is selected according to the estimated pose information, so that the efficiency of analyzing the map data is improved, and the real-time property of generating a target driving strategy is improved.
And step 404, generating a target driving strategy for guiding the user to automatically drive by combining the target map data and the estimated pose information.
Step 104: self-learning to build a graph:
the self-learning map building part is also divided into two modes according to a parking mode and a recall mode set by a user.
1) Parking path learning mode
In some embodiments, "parking" refers to the vehicle automatically traveling to a future point of intersection. The driver drives the vehicle or the vehicle is automatically driven to the point of the future traffic point. The future vehicle transfer point can be a fixed parking place arranged in the garage.
The driver drives the vehicle into the indoor parking lot and stops the vehicle slightly at the expected future traffic point. Then, the vehicle-mounted terminal (through a vehicle-mounted display screen) firstly selects a self-learning valet parking mode, then can click a parking path learning start icon, and then the display screen can display a parking path learning in-process prompt.
The driver drives the vehicle to the target parking space at low speed (below 10 km/h). After the parking behavior is finished, the parking path learning 'end' icon is clicked, and then the vehicle-mounted end displays the system learning completion progress percentage under the path.
In the case that the path learning is completed (within 3 times of product functions) when the path learning is not completed 100% at a time, the driver can click the 'storage unfinished parking path' to save the path.
For the parking lot with partial parking path learning completed, when the driver enters the environment of the parking lot again in the future, the vehicle-mounted terminal displays a historical parking path continuous learning icon in time. In the case where the driver confirms "continue path learning", the self-driving vehicle completes the parking behavior of the same parking path before completion. After the vehicle is stopped stably, the vehicle-mounted end generates corresponding completion percentage and subsequent operation prompt equivalent to the primary parking path learning.
If the parking path learning reaches 100%, the system prompts that the parking path learning is finished and the passenger-replacing parking function can be used at the vehicle-mounted end after the learning is finished, and the driver clicks the parking path storage to finish the successful storage of the path.
2) Recall path learning mode
In some embodiments, a "recall" refers to an automatic travel of a vehicle from a parking space to a pre-designated location. The driver waits for the arrival of the vehicle at the pre-designated place.
A driver starts a vehicle in a parking space, selects a self-learning visitor recall mode, then can click a recall path learning start icon, and then a display screen can generate a recall path learning in-process prompt.
The driver drives the vehicle to a future expected vehicle receiving point at a low speed (below 10 km/h), stops slightly, clicks a recall path learning 'end' icon, and the vehicle-mounted end displays the system learning completion progress percentage under the path.
In the case where the path learning is completed (within 3 times of the product function) in a single time without reaching 100%, the driver may click on "store incomplete recall path" to save the path.
For a parking lot similar to the parking lot which finishes partial recall path learning, the vehicle-mounted terminal displays a 'history recall path continuous learning' icon in time before the driver starts to start from the parking space again in the future. In the event that the driver confirms "continue with route learning", the self-driving vehicle recalls driving behavior of the route before completion. After the preset expected street car point is reached, the corresponding completion percentage and subsequent operation prompt equivalent to the primary recall path learning appear at the vehicle-mounted end.
If the recall path learning reaches 100%, the system prompts that the recall path learning is finished and the customer recall function can be used at the vehicle-mounted end after the learning is finished, and a driver clicks the recall path storage to finish the successful storage of the path.
After the self-learning graph is successfully built, the method proceeds to step 105.
The parking and recall model described above uses a self-created map. Those skilled in the art will appreciate that because it is not certain whether the self-created map is complete, there are situations in which the self-created map may not be available for parking or recall. When this occurs, the automatic parking or recall is selected to be ended, and the driver is prompted to notice the termination of the automatic mode. In some embodiments, the system may switch to the above mode if the parking lot is pre-loaded with a complete map, which may be downloaded to the system wirelessly by real-time request.
Step 105: judging whether the self-built image is successful:
when the self-learning established map already contains the driving termination point set by the user and the established map information is enough for global path planning, the map establishment is completed, and the system automatically returns to step 101. If the map building fails, the system saves the part which does not complete the learning map building, and the self-learning map building function is finished.
Step 106: confirmation recall function usage:
and step 103, prompting the user whether to automatically park after the path planning is successful. In the parking mode, if the user selects "Yes", the automatic parking state is performed in step 107; the user selects "No" and terminates the auto park function. In the recall mode, if the user selects "Yes", the automatic recall state in step 107 is entered; the user selects "No" and terminates the automatic recall function.
The above steps 105, 106 include the use of self-created maps. The invention is an innovative point, the problem that the parking factory in the prior art generally has the shortage of map data for parking, particularly, the underground garage is inaccurate in GPS positioning, and the technical obstacle is brought to automatic parking. The automatic parking function can be completed only by daily driving of the vehicle after a plurality of times of driving of the garage, which is an innovation point of the automatic parking system.
Step 107: autonomous driving:
when the system carries out autonomous parking or recalling, the global planning path generated in the step 103 is called, and a real-time fault positioning module in the step 108 and a real-time obstacle detection module in the step 109 are started at the same time. The real-time monitoring in the steps 108 and 109 can make self-adaptive response to some sudden situations and temporary environmental changes in the parking environment, and the safety of autonomous driving is guaranteed. The real-time positioning faults are frequently generated in parking or recalling environments and have temporary large changes, so that semantic features in the currently acquired images cannot be matched with a pre-stored map.
Step 108: positioning a fault module in real time;
step 109: a real-time obstacle detection module;
step 110: an interruption of autonomous driving functions;
the vehicle for the valet parking is provided with a relatively high-level obstacle detection function and a real-time positioning function, and when a driving area in front of the vehicle meets an obstacle in the parking and recalling process, the system can automatically perform stable braking, so that the vehicle is ensured to be in a safe state. And if the front obstacle still does not leave or meets special traffic conditions within a specific time, the system can remind the driver of interrupting the parking function or the recall function of the passenger car through the mobile phone terminal. The system prompts a driver to return to the vehicle for taking over, and the vehicle can automatically enter a double-flash state.
Step 111: and finishing the parking recall of the valet.
Under normal conditions, the vehicle automatically drives to the target location to complete the task. If the parking mode is adopted, after the passenger car is parked by the passenger car, the system informs the driver of completing the function through the mobile phone terminal and attaches the vehicle position information.
The above embodiment has been described with parking as the main line, but it should be understood that the process is also applicable to an automatic recall mode. This can be borrowed by those skilled in the art. If the vehicle is in the recall mode, the vehicle can automatically enter a double-flashing state after reaching a vehicle taking point, and meanwhile, a driver can receive the prompt of taking over the vehicle when the vehicle is in place at the mobile phone end. It will be appreciated that the recall mode is similar to the park mode, which is performed in a manner similar to parking. It should be noted that, for the recall mode, both the point a and the point B are fixed points, and the fixed points are already built in the recall system before the customer-representative recall function is activated and cannot be changed in some embodiments, so as to reduce the large amount of calculation caused by the random selection of the starting point or the ending point. At an application level, these fixed points may be referred to as a customer recall starting point and a customer recall ending point established inside the parking facility in some embodiments.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
Claims (8)
1. A global path planning method for valet parking or automated recall, the method comprising the steps of:
activating and setting; a user sets and activates a passenger-assistant parking or automatic recall function and sets starting and stopping positions of the vehicle;
a map matching step; the map matching step includes: calculating by a map matching algorithm to obtain whether a map of the current parking or recall environment is stored in the current automatic driving system; judging whether a map of the current parking or recalling environment is obtained by matching;
self-learning graph building; taking a self-learning map as the map; the self-learning map building comprises a parking path learning mode and/or a recall path learning mode;
judging whether the self-learning graph building is successful: when the self-learning map building comprises a driving termination point set by a user and the self-learning map building information is enough for global path planning, completing map building and entering a global path planning step; judging whether the map building fails, if so, the system stores the part which does not finish learning the map building, and finishes the self-learning map building function and the global path planning;
planning a global path; generating a global path plan based on the self-learning map based on the starting point and the ending point of the automatic driving set by the user; wherein the global path planning step comprises the following substeps:
acquiring current pose information of a target vehicle;
predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
acquiring target map data within a preset range from the automatic driving electronic navigation map based on the estimated pose information;
and generating a target driving strategy for guiding the user to automatically drive by combining the target map data and the estimated pose information.
2. A method of valet parking, comprising the steps of:
step S101: a user sets and activates a passenger-riding parking function, and sets starting and stopping positions of a vehicle on a preset map; wherein the start and stop positions are fixed points;
step S102: matching a map; the map matching comprises the sub-steps of:
s1021: the vehicle determines a target area and target semantic features in the target area from a preset three-dimensional map according to the vehicle initial pose information;
s1022: matching semantic features extracted by using the all-round-looking image of the vehicle with target semantic features of the map;
s1023: through the map matching algorithm of step S1021 and step S1022, the vehicle gives a map of whether a map of the current parking environment has already been stored in the current automatic driving system; if the map of the current parking environment is obtained through matching, the system directly enters the step S105; if no corresponding map is matched, the system proceeds to step S103;
step S103: the self-learning map is built as a preset map; the self-learning map building comprises a parking path learning mode;
step S104: judging whether the self-built image is successful or not; when the self-learning established map already contains the driving termination point set by the user and the established map information is enough for global path planning, the map establishment is completed, and the system automatically returns to the step S101; if the map building fails, the system stores the part which does not finish the learning map building and ends the self-learning map building function;
step S105: planning a global path; generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
step S106: confirming the use of the parking function;
step S107: the autonomous parking driving is carried out, and the system enters a parking driving state;
wherein the step S105 includes the following substeps:
step S1031: acquiring current pose information of a target vehicle;
step S1032: predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
step S1033: acquiring target map data within a preset range from the automatic driving electronic navigation map based on the estimated pose information;
step S1034: and generating a target driving strategy for guiding the user to automatically drive by combining the target map data and the estimated pose information.
3. The vehicle parking method according to claim 2, wherein: after the path planning in the step S105 is successful, prompting a user whether to automatically park; in the parking mode, the user autonomously parks the driver or terminates the automatic parking function by selecting to proceed to step S107.
4. An automatic vehicle recall method characterized by: the method comprises the following steps:
step S101: setting and activating an automatic recall function by a user, and setting starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
step S102: matching a map; the map matching comprises the sub-steps of:
s1021: the vehicle determines a target area and target semantic features in the target area from a preset three-dimensional map according to the vehicle initial pose information;
s1022: matching semantic features extracted by using the all-round-looking image of the vehicle with target semantic features of the map;
s1023: through the map matching algorithm of step S1021 and step S1022, the vehicle gives a map of whether or not a current recall environment has been stored in the current automatic driving system; if the map of the current recall environment is matched, the system directly enters the step S105; if no corresponding map is matched, the system proceeds to step S103;
step S103: the self-learning map is built as a preset map; the self-learning map comprises a recall path learning mode;
step S104: judging whether the self-built image is successful or not; when the self-learning established map already contains the driving termination point set by the user and the established map information is enough for global path planning, the map establishment is completed, and the system automatically returns to the step S101; if the map building fails, the system stores the part which does not finish the learning map building and ends the self-learning map building function;
step S105: planning a global path; generating a global path plan by adopting a dynamic planning method based on a preset map based on a starting point and an end point of automatic driving set by a user;
step S106: confirming recall function usage;
step S107: the system enters a driving recalling state;
wherein the step S105 includes the following substeps:
step S1031, obtaining current pose information of the target vehicle;
step S1032, predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
step S1033, acquiring target map data in a preset range from the automatic driving electronic navigation map according to the estimated pose information;
and S1034, combining the target map data and the estimated pose information to generate a target driving strategy for guiding the user to automatically drive.
5. The vehicle automatic recall method according to claim 4, characterized in that: the method further comprises the steps of:
step S108: the real-time positioning fault module detects faults in the recall process;
step S109: detecting obstacles in the recalling process by a real-time obstacle detection module;
step S110: an interruption of autonomous driving functions; according to the fault locating module in the step S108 and the fault and obstacle detection of the real-time obstacle detection module in the step S109, when a driving area in front of the vehicle encounters a fault or an obstacle in the recalling process, the system can automatically perform stable braking to ensure that the vehicle is in a safe state;
step S111: the customer is recalled and finished; the vehicle automatically runs to a target place to complete a task, the vehicle automatically enters a double-flashing state after reaching a vehicle taking point, and meanwhile, a driver can receive the prompt of taking over the mobile phone terminal when the vehicle is in place.
6. The vehicle automatic recall method according to claim 4, characterized in that: after the path planning in step S105 is successful, prompting the user whether to perform automatic recall; in the recall mode, the user autonomously recalls driving by selectable entry to step S107, or terminates the automatic recall function.
7. An automatic parking system for a vehicle, comprising:
a user setting module: activating a parking mode, and setting starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
a map module: adopting an existing map or a self-built map as a preset map;
a map matching module: matching semantic features extracted from the all-round-looking images of the vehicles with semantic features of the start and stop point maps, and judging whether a map of the current parking environment is stored in the current automatic driving system or not; if the map of the current parking environment is obtained through matching, executing a global path planning step; if the map is not matched with the corresponding map, the self-learning map is built as a preset map; the self-learning map building comprises a parking path learning mode; judging whether the self-built image is successful or not; when the self-learning established map contains a driving termination point set by a user and the established map information is enough for global path planning, completing map establishment and returning to activate the passenger-riding parking function; if the map building fails, storing the part which does not finish the learning map building, and finishing the self-learning map building function;
a global path planning module: generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
an automatic driving module: according to the global path plan, the vehicle automatically parks;
wherein the global path planning comprises the following sub-steps:
acquiring current pose information of a target vehicle;
predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
acquiring target map data within a preset range from the automatic driving electronic navigation map based on the estimated pose information;
and generating a target driving strategy for guiding the user to automatically drive by combining the target map data and the estimated pose information.
8. An automatic vehicle recall system, the system comprising:
a user setting module: activating a recall mode, and setting starting and stopping positions of the vehicle on a preset map; wherein the start and stop positions are fixed points;
a map module: adopting an existing map or a self-built map as a preset map;
a map matching module: matching semantic features extracted by the all-round-looking image of the vehicle with semantic features of the start-stop point map, judging whether a map of the current recall environment is stored in the current automatic driving system or not, and executing a global path planning step if the map of the current recall environment is obtained by matching; if the map is not matched with the corresponding map, the self-learning map is built as a preset map; the self-learning map comprises a recall path learning mode; judging whether the self-built image is successful or not; when the self-learning established map already contains a driving termination point set by a user and the established map information is enough for global path planning, completing map establishment and activating an automatic recall function; if the map building fails, storing the part which does not finish the learning map building, and finishing the self-learning map building function;
a global path planning module: generating a global path plan by adopting a dynamic planning method based on the preset map based on the starting point and the ending point of automatic driving set by the user;
an automatic driving module: automatically recalling the vehicle according to the global path plan;
wherein the global path planning comprises the following sub-steps:
acquiring current pose information of a target vehicle;
predicting estimated pose information of the target vehicle at the next moment according to the current pose information and the automatic driving electronic navigation map;
acquiring target map data within a preset range from the automatic driving electronic navigation map based on the estimated pose information;
and generating a target driving strategy for guiding the user to automatically drive by combining the target map data and the estimated pose information.
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