CN117315971A - Intelligent parking track formation control method - Google Patents
Intelligent parking track formation control method Download PDFInfo
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- CN117315971A CN117315971A CN202311173519.5A CN202311173519A CN117315971A CN 117315971 A CN117315971 A CN 117315971A CN 202311173519 A CN202311173519 A CN 202311173519A CN 117315971 A CN117315971 A CN 117315971A
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096833—Systems involving transmission of navigation instructions to the vehicle where different aspects are considered when computing the route
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
Abstract
The application relates to the technical field of intelligent parking and provides a control method for intelligent parking track formation. The method comprises the following steps: determining a target vehicle, and determining a target parking lot according to a driving end point of the target vehicle; carrying out road analysis to obtain lane information from the target vehicle to the target parking lot; the method comprises the steps of performing multi-angle acquisition on a target lane through space network monitoring to obtain multi-angle lane information; monitoring the target lane based on an infrared sensor, and acquiring the real-time state of the target lane; constructing a theoretical vehicle flow prediction network, and acquiring a theoretical track of a target vehicle; acquiring an actual track of a target vehicle based on a path tracking algorithm; and performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle. The technical problem that the time consumption is long when automatic parking is solved, and the technical effects of optimizing the parking route and reducing time consumption when automatic parking is achieved.
Description
Technical Field
The application relates to the technical field of intelligent parking, in particular to a control method for intelligent parking track formation.
Background
The intelligent parking is to comprehensively apply wireless communication technology, mobile terminal technology, GPS positioning technology, GIS technology and the like to the collection, management, inquiry, reservation and navigation services of urban parking spaces, realize the integration of real-time update, inquiry, reservation and navigation services of parking space resources, and realize the maximization of the utilization rate of the parking space resources, the maximization of the profit of a parking lot and the optimization of the parking services of an owner of the vehicle. The off-line intelligent is realized by enabling a person parking in the parking space to be better, and the parking space is mainly in the aspect of rapid passing, and the parking of the person becomes inconvenient due to long time consumption of the road finding process when parking at present.
In summary, the technical problem that the time spent in automatic parking is long is solved.
Disclosure of Invention
Accordingly, it is desirable to provide a control method for intelligent parking trajectory execution that can optimize a parking route and reduce time consumption when parking. The technical problem that the time consumption is long when automatic parking is solved, and the technical effects of optimizing the parking route and reducing time consumption when automatic parking is achieved.
In a first aspect, the present application provides a control method for intelligent parking track formation, where the method includes: determining a target vehicle, and determining a target parking lot according to a driving end point of the target vehicle; road analysis is carried out based on the position of the target vehicle and the position of the target parking lot, so that lane information of the target vehicle to the target parking lot is obtained; the method comprises the steps of performing multi-angle acquisition on a target lane through space network monitoring to obtain multi-angle lane information; monitoring the target lane based on an infrared sensor, and acquiring the real-time state of the target lane; constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane, and acquiring a theoretical track of the target vehicle; acquiring an actual track of a target vehicle based on a path tracking algorithm; and performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
In a second aspect, the present application provides a control system for intelligent parking track formation, wherein the system includes: the method comprises the steps of determining a target vehicle and a target parking lot module, wherein the determining target vehicle and the target parking lot module are used for determining the target vehicle and determining a target parking lot according to the driving terminal point of the target vehicle; the lane information acquisition module is used for carrying out road analysis based on the position of the target vehicle and the position of the target parking lot to acquire lane information of the target vehicle to the target parking lot; the multi-angle lane information acquisition module is used for acquiring the target lane at multiple angles through space network monitoring to acquire multi-angle lane information; the real-time state acquisition module of the target lane is used for monitoring the target lane based on an infrared sensor and acquiring the real-time state of the target lane; the target vehicle theoretical track acquisition module is used for constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane to acquire the theoretical track of the target vehicle; the system comprises a target vehicle actual track acquisition module, a target vehicle actual track acquisition module and a target vehicle control module, wherein the target vehicle actual track acquisition module is used for acquiring an actual track of a target vehicle based on a path tracking algorithm; and the parking track formation control optimization module is used for controlling and optimizing the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
firstly, determining a target vehicle, and determining a target parking lot according to a driving end point of the target vehicle; secondly, road analysis is carried out based on the position of the target vehicle and the position of the target parking lot, and lane information from the target vehicle to the target parking lot is obtained; then, multi-angle acquisition is carried out on the target lane through the space network monitoring, and multi-angle lane information is obtained; according to the monitoring of the target lane based on the infrared sensor, acquiring the real-time state of the target lane; then constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane, and acquiring a theoretical track of the target vehicle; the actual track of the target vehicle is acquired based on a path tracking algorithm; and finally, performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle. The technical problem that the time consumption is long when automatic parking is solved, and the technical effects of optimizing the parking route and reducing time consumption when automatic parking is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
FIG. 1 is a flow chart of a method for controlling intelligent parking track formation in one embodiment;
FIG. 2 is a schematic flow chart of a control method for intelligent parking track formation for optimizing control of the parking track formation in one embodiment;
FIG. 3 is a block diagram of a control system for intelligent parking track execution in one embodiment.
Reference numerals illustrate: the system comprises a target vehicle and target parking lot determining module 11, a lane information acquiring module 12, a multi-angle lane information acquiring module 13, a target lane real-time state acquiring module 14, a target vehicle theoretical track acquiring module 15, a target vehicle actual track acquiring module 16 and a parking track running control optimizing module 17.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Having introduced the basic principles of the present application, the technical solutions herein will now be clearly and fully described with reference to the accompanying drawings, it being apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
As shown in fig. 1, the present application provides a control method for intelligent parking track formation, which includes:
s100: determining a target vehicle, and determining a target parking lot according to a driving end point of the target vehicle;
the intelligent parking is to comprehensively apply wireless communication technology, mobile terminal technology, GPS positioning technology, GIS technology and the like to the collection, management, inquiry, reservation and navigation services of urban parking spaces, realize the integration of real-time update, inquiry, reservation and navigation services of parking space resources, and realize the maximization of the utilization rate of the parking space resources, the maximization of profit of a parking lot and the optimization of parking services of an owner.
The target vehicle refers to a vehicle that needs to find a parking lot and optimize a path to the parking lot; the travel end point is a place where the target vehicle wants to reach; the target parking lot is a parking lot closest to the driving destination of the target vehicle, and is obtained by a smart transportation system. And determining a target vehicle, determining a target parking lot according to the driving end point of the target vehicle, and obtaining the distance from the target vehicle to the target parking lot and road information by determining the target vehicle and the target parking lot so as to make a bedding for the follow-up optimized parking track.
S200: road analysis is carried out based on the position of the target vehicle and the position of the target parking lot, so that lane information of the target vehicle to the target parking lot is obtained;
the road analysis refers to analyzing the distance from the position of the target vehicle to the position of the target parking lot, and comprises measuring the length of the distance, measuring the width of the distance and the like; the lane information refers to data information and equipment information from the position of the target vehicle to the position of the target parking lot, wherein the data information comprises the length of a journey, the width of the journey, the number of turns and the like, and the equipment information comprises laid sensors and the like. Road analysis is carried out according to the position of the target vehicle and the position of the target parking lot, lane information of the target vehicle to the target parking lot is obtained, and a mat is made for subsequent parking track analysis by obtaining the target lane information.
S300: the method comprises the steps of performing multi-angle acquisition on a target lane through space network monitoring to obtain multi-angle lane information;
the space network monitoring is a video monitoring system for real-time monitoring and information recording of a fixed area by utilizing GIS map, image acquisition, transmission, control, display and other equipment and control software to meet the requirements of urban security and management; the multi-angle acquisition is to shoot images of target lanes at different angles according to different monitoring systems; the multi-angle lane information refers to image information of a plurality of angles in a path, such as a top view information, a side view information, and the like, where the target vehicle is located and where the target parking lot is located.
Further, the steps of the present application further include:
s310: connecting an intelligent traffic system, and collecting multi-angle information of the target lane according to the space network monitoring;
s320: extracting overlooking image information of the target lane through multi-angle information of the target lane;
s330: and comparing the overlooking image information of the target lane with a plurality of lane overlooking images with lane data in big data to acquire lane data information of the target lane.
The intelligent traffic is realized based on real-time acquisition and analysis of mass data, and traffic data such as position information, traffic flow, speed, occupancy, queuing length, travel time, interval speed and the like are obtained; the intelligent traffic system is a system established based on intelligent traffic and Internet big data; the multi-angle information of the target lane refers to image information of a plurality of angles in the path of the position of the target vehicle and the position of the target parking lot, such as a top view, a side view and the like; the overlooking image information of the target lane refers to video images shot by a monitoring system which is arranged perpendicular to the road; the plurality of lane top view images with lane data in the big data refer to lane top view images with the length, width and other data of lanes marked in the intelligent traffic system; the comparison means that the data such as the length, the width and the like of the target lane are obtained through proportion calculation. According to the method and the device, the multi-angle lane information is obtained, the lane information with the data mark in the big data is compared, the data information of the target lane is obtained through calculation, and convenience is provided for subsequent calculation.
S400: monitoring the target lane based on an infrared sensor, and acquiring the real-time state of the target lane;
the infrared sensor is a sensor that performs data processing using infrared rays as a medium; the real-time state of the target lane refers to a state of a lane with a time mark on the target lane, for example, whether a traffic accident occurs in the target lane, whether an obstacle exists in the target lane, and the like. And monitoring the target lane according to the infrared sensor, and acquiring the real-time state of the target lane.
Further, the steps of the present application further include:
s410: connecting an infrared sensor to monitor the target lane and vehicles in the target lane to obtain road information of the target lane and vehicle distance in the target lane;
s420: monitoring the lengths of adjacent vehicles according to the vehicle distance, and extracting barrier information in the target lane according to a monitoring result;
s430: and analyzing the road information of the target lane according to the obstacle information, and determining the real-time state of the target lane.
The road information of the target lane refers to road condition information in the target lane; the distance between vehicles in the target lane is used for researching whether barriers exist between adjacent vehicles or not, namely whether traffic accidents occur or not; the vehicle distance refers to the distance between adjacent vehicles from the tail of a first vehicle to the head of a second vehicle; the obstacle information includes vehicle part information at the time of a car accident, junk information between vehicles, and the like; the real-time state of the target lane refers to a condition that the target lane has a time identifier, such as a normal lane, a traffic accident lane, and the like. And by acquiring the real-time state of the target lane, paving is performed for the subsequent analysis of the running track of the target vehicle.
S500: constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane, and acquiring a theoretical track of the target vehicle;
the theoretical traffic flow prediction network is used for analyzing traffic conditions of the target lane, wherein the traffic flow calculated according to the data is not a real number; the theoretical track of the target vehicle refers to a theoretical parking driving route of the target vehicle; and constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane, and acquiring a theoretical track of the target vehicle. By acquiring the theoretical track of the target vehicle, a contribution is made to the subsequent analysis of the parking route of the vehicle.
Further, the steps of the present application further include:
s510: constructing a theoretical vehicle flow prediction network according to the multi-angle lane information and the lane information;
s520: outputting theoretical traffic flow with a time mark based on the theoretical traffic flow prediction network;
s530: and predicting the theoretical running track of the target vehicle according to the theoretical vehicle flow with the time mark to acquire the theoretical track of the target vehicle.
And constructing a theoretical traffic flow prediction network according to the multi-angle lane information and the lane information, wherein the theoretical traffic flow prediction network is used for acquiring theoretical traffic flow information of the lane and acquiring theoretical traffic flow with time marks of the target vehicle, the theoretical traffic flow with time marks is obtained according to the information on the real-time lane, and the theoretical traffic flow of the target vehicle is a parking driving route of the target vehicle in a real-time state of the lane information and the target lane. Constructing a theoretical vehicle flow prediction network according to the multi-angle lane information and the lane information; outputting theoretical traffic flow with a time mark based on the theoretical traffic flow prediction network; and predicting the theoretical running track of the target vehicle according to the theoretical vehicle flow with the time mark to acquire the theoretical track of the target vehicle. And according to the theoretical track of the target vehicle, contribution is made to the follow-up optimization of the parking route.
S600: acquiring an actual track of a target vehicle based on a path tracking algorithm;
the path tracking algorithm is a path tracking control method for realizing vehicle transverse control based on the kinematic relation between the vehicle position and the reference track, and has the advantages of high operation speed, strong instantaneity and good adaptability. The actual trajectory of the target vehicle refers to the actual parking travel route of the target vehicle.
Further, the method comprises the following steps:
s610: acquiring a driving process image of the position of the target vehicle and the position of the target parking lot through space network monitoring;
s620: analyzing the geometric relationship between the target vehicle and the road according to the driving process image, and establishing a path tracking algorithm among a fixed parameter pretightening distance, a pretightening point and a vehicle position;
s630: calculating the steering radius and the running curvature according to the algorithm to obtain the steering radius and the running curvature of the target vehicle;
s640: and tracking the track of the target vehicle by a method of approaching an expected path to obtain the actual track of the target vehicle.
Representing the target vehicle by a monorail vehicle model, wherein the pretightening distance is a connecting line between a pretightening point and the center of a rear axle of the vehicle, taking the center point of the rear axle of the vehicle as the center of a circle, taking the pretightening distance as the radius, making a circle, intersecting the circle with a theoretical track in the front running direction of the vehicle, and taking the intersection point as the pretightening point; r is a steering radius, an arc corresponding to the steering circle center and the steering radius is a running track of the vehicle, and the vehicle reaches a pre-aiming point according to the arc track. θ represents the angle between the actual heading of the vehicle and the pretightening point, also known as the heading deviation angle. The geometrical relationship is as follows:
wherein L is d For the pretightening distance, when the PP algorithm is used for controlling, the pose and the wheelbase of the controlled vehicle are all fixed information, so that the tracking effect mainly depends on the pretightening distance. If the pre-aiming distance is too small, the pre-aiming point of the tracking target can be continuously rewound during vehicle tracking, and then the phenomenon of driving oscillation is generated, so that the stability of vehicle control is poor, the driving track is long, and the vehicle speed is difficult to effectively improve. If the pre-aiming distance is too large, the straight line tracking of the controlled vehicle is slow, the over-bending steering is insufficient, the vehicle is difficult to accurately run along the track, and the tracking precision is reduced.
Further, the steps of the present application further include:
s621: collecting the real-time position of the target vehicle;
s622: acquiring road information from the real-time position of the target vehicle to the target parking lot;
s623: obtaining nonlinear parameters of the road information according to the real-time position of the target vehicle and the road information;
s624: and calculating a fixed parameter pretightening distance based on the nonlinear parameter of the road information.
Because the influence of the pretightening distance is larger, the step provides a method for calculating the pretightening distance of the fixed parameter, and the real-time position of the target vehicle is acquired; acquiring road information from the real-time position of the target vehicle to the target parking lot; based on the real-time position of the target vehicle and the road information, nonlinearity (non-li near), i.e., a mathematical relationship between variables, not a straight line, but a curve, a curved surface, or an indeterminate property, called nonlinearity. Nonlinearity is one of the typical properties of natural complexity; non-linearities more closely approximate the objective thing properties themselves than linearities; the calculation of the pretightening distance L has a certain functional relation with the current vehicle speed V and the curvature K of the tracking track, and the formula is used for expressing the following formula: l=f (V, K); and calculating a fixed parameter pretightening distance according to the different curvatures K of different vehicles at the real-time positions through the nonlinear parameters of the road information. According to the nonlinear parameter, the pretightening distance can be more accurate.
S700: and performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
And performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle. The control optimization refers to optimizing the parking driving route of the target vehicle, and finding the driving route with less time consumption.
As shown in fig. 2, further, the steps of the present application further include:
s710: extracting a deviation track from a theoretical track based on the target vehicle and an actual track of the target vehicle;
s720: analyzing the road according to the time point of the deviation track and the place of the road where the deviation track is located;
s730: if the road has a running problem, feeding back to an intelligent traffic center;
s740: and re-planning a parking route through the intelligent traffic center and transmitting the re-planning parking route to the target vehicle.
The deviation track refers to a deviation road of a theoretical track of the target vehicle and an actual track of the target vehicle; the deviation track time point refers to a time point when a theoretical track and an actual track start to deviate in the road; the driving problem refers to a road problem that a traffic accident or the like exists in the road and affects the driving of the target vehicle; the re-planning of the parking route means re-analyzing and calculating the road with the driving problem according to the above technology. Extracting a deviation track from a theoretical track based on the target vehicle and an actual track of the target vehicle; analyzing the road according to the time point of the deviation track and the place of the road where the deviation track is located; if the road has a running problem, feeding back to an intelligent traffic center; and re-planning a parking route through the intelligent traffic center and transmitting the re-planning parking route to the target vehicle. By re-planning the parking route, the running route with problems is optimized, and the technical effect of optimizing the parking route is achieved. The technical problem that the time consumption is long when automatic parking is solved, and the technical effects of optimizing the parking route and reducing time consumption when automatic parking is achieved.
As shown in fig. 3, the present application provides a control system for intelligent parking track formation, the system comprising:
the system comprises a target vehicle and target parking lot module 11, wherein the target vehicle and target parking lot module 11 is used for determining a target vehicle and determining a target parking lot according to the driving terminal point of the target vehicle;
a lane information obtaining module 12, where the lane information obtaining module 12 is configured to perform road analysis based on a location of the target vehicle and a location of the target parking lot, and obtain lane information of the target vehicle to the target parking lot;
the multi-angle lane information acquisition module 13 is used for acquiring the target lane at multiple angles through the space network monitoring to acquire multi-angle lane information;
the real-time state acquisition module 14 of the target lane, the real-time state acquisition module 14 of the target lane is used for monitoring the target lane based on an infrared sensor and acquiring the real-time state of the target lane;
the target vehicle theoretical track acquisition module 15 is used for constructing a theoretical vehicle flow prediction network according to the lane information and the real-time state of the target lane to acquire the theoretical track of the target vehicle;
the target vehicle actual track acquisition module 16, wherein the target vehicle actual track acquisition module 16 is used for acquiring an actual track of the target vehicle based on a path tracking algorithm;
the parking track formation control optimization module 17, wherein the parking track formation control optimization module 17 is used for performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
Further, the steps of the present application further include:
the intelligent traffic system comprises a target lane multi-angle information acquisition module, a target lane multi-angle information acquisition module and a target traffic system, wherein the target lane multi-angle information acquisition module is used for connecting an intelligent traffic system and acquiring multi-angle information of the target lane according to the monitoring of a space network;
the system comprises a target lane overlooking image information module, a target lane overlooking image information module and a target lane overlooking image information processing module, wherein the target lane overlooking image information module is used for extracting overlooking image information of a target lane through multi-angle information of the target lane;
the lane data information acquisition module of the target lane is used for acquiring the lane data information of the target lane through comparing the overlooking image information of the target lane with a plurality of lane overlooking images with lane data in big data.
Further, the steps of the present application further include:
the vehicle distance acquisition module is used for connecting an infrared sensor to monitor the target lane and vehicles in the target lane to acquire road information of the target lane and vehicle distance in the target lane;
the object lane internal obstacle information extraction module is used for monitoring the length of adjacent vehicles according to the vehicle distance and extracting the obstacle information in the object lane according to the monitoring result;
the real-time state determining module of the target lane is used for analyzing the road information of the target lane according to the obstacle information and determining the real-time state of the target lane.
Further, the steps of the present application further include:
the system comprises a theoretical traffic flow prediction network construction module, a traffic flow prediction network analysis module and a traffic flow prediction network analysis module, wherein the theoretical traffic flow prediction network construction module is used for constructing a theoretical traffic flow prediction network according to multi-angle lane information and lane information;
the theoretical traffic flow output module is used for outputting theoretical traffic flow with a time mark based on a theoretical traffic flow prediction network;
and the theoretical track acquisition module of the target vehicle is used for predicting the theoretical running track of the target vehicle according to the theoretical vehicle flow with the time mark to acquire the theoretical track of the target vehicle.
Further, the steps of the present application further include:
the driving process image acquisition module is used for acquiring driving process images of the position of the target vehicle and the position of the target parking lot through space network monitoring;
the path tracking algorithm establishment module is used for analyzing the geometric relationship between the target vehicle and the road according to the driving process image and establishing a path tracking algorithm among a fixed parameter pretightening distance, a pretightening point and a vehicle position;
the steering radius and running curvature acquisition module of the target vehicle is used for calculating the steering radius and the running curvature according to the algorithm to obtain the steering radius and the running curvature of the target vehicle;
the actual track acquisition module of the target vehicle is used for realizing track tracking of the target vehicle by a method of approaching an expected path to obtain the actual track of the target vehicle.
Further, the steps of the present application further include:
the system comprises a target vehicle real-time position acquisition module, a target vehicle real-time position acquisition module and a target vehicle real-time position acquisition module, wherein the target vehicle real-time position acquisition module is used for acquiring the real-time position of the target vehicle;
the road information acquisition module is used for acquiring road information from the real-time position of the target vehicle to the target parking lot;
the road information nonlinear parameter acquisition module is used for acquiring nonlinear parameters of the road information according to the real-time position of the target vehicle and the road information;
and the fixed parameter pretightening distance calculation module is used for calculating the fixed parameter pretightening distance based on the nonlinear parameter of the road information.
Further, the steps of the present application further include:
the deviation track extraction module is used for extracting a deviation track from a theoretical track based on the target vehicle and an actual track of the target vehicle;
the road analysis module is used for analyzing the road according to the time point of the deviation track and the place of the road where the deviation track is located;
the running problem feedback module is used for feeding back to the intelligent traffic center if the running problem exists on the road;
and the parking route re-planning module is used for re-planning a parking route through the intelligent traffic center and transmitting the parking route to the target vehicle.
For a specific embodiment of a control method for intelligent parking track formation, reference may be made to the above embodiment of a control method for intelligent parking track formation, which is not described herein. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (8)
1. A method for controlling intelligent parking track formation, the method comprising:
determining a target vehicle, and determining a target parking lot according to a driving end point of the target vehicle;
road analysis is carried out based on the position of the target vehicle and the position of the target parking lot, so that lane information of the target vehicle to the target parking lot is obtained;
the method comprises the steps of performing multi-angle acquisition on a target lane through space network monitoring to obtain multi-angle lane information;
monitoring the target lane based on an infrared sensor, and acquiring the real-time state of the target lane;
constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane, and acquiring a theoretical track of the target vehicle;
acquiring an actual track of a target vehicle based on a path tracking algorithm;
and performing control optimization on the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
2. The method of claim 1, wherein the target lane is acquired at multiple angles by means of space-network monitoring to obtain multi-angle lane information, the method further comprising:
connecting an intelligent traffic system, and collecting multi-angle information of the target lane according to the space network monitoring;
extracting overlooking image information of the target lane through multi-angle information of the target lane;
and comparing the overlooking image information of the target lane with a plurality of lane overlooking images with lane data in big data to acquire lane data information of the target lane.
3. The method of claim 1, wherein the real-time status of the target lane is obtained based on monitoring the target lane by an infrared sensor, the method further comprising:
connecting an infrared sensor to monitor the target lane and vehicles in the target lane to obtain road information of the target lane and vehicle distance in the target lane;
monitoring the lengths of adjacent vehicles according to the vehicle distance, and extracting barrier information in the target lane according to a monitoring result;
and analyzing the road information of the target lane according to the obstacle information, and determining the real-time state of the target lane.
4. The method of claim 1, wherein the lane information builds a theoretical traffic prediction network with the real-time status of the target lane to obtain a theoretical trajectory of the target vehicle, the method comprising:
constructing a theoretical vehicle flow prediction network according to the multi-angle lane information and the lane information;
outputting theoretical traffic flow with a time mark based on the theoretical traffic flow prediction network;
and predicting the theoretical running track of the target vehicle according to the theoretical vehicle flow with the time mark to acquire the theoretical track of the target vehicle.
5. The method of claim 1, wherein the actual trajectory of the target vehicle is obtained based on a path tracking algorithm, the method comprising:
acquiring a driving process image of the position of the target vehicle and the position of the target parking lot through space network monitoring;
analyzing the geometric relationship between the target vehicle and the road according to the driving process image, and establishing a path tracking algorithm among a fixed parameter pretightening distance, a pretightening point and a vehicle position;
calculating the steering radius and the running curvature according to the algorithm to obtain the steering radius and the running curvature of the target vehicle;
and tracking the track of the target vehicle by a method of approaching an expected path to obtain the actual track of the target vehicle.
6. The method of claim 5, wherein the method comprises:
collecting the real-time position of the target vehicle;
acquiring road information from the real-time position of the target vehicle to the target parking lot;
obtaining nonlinear parameters of the road information according to the real-time position of the target vehicle and the road information;
and calculating a fixed parameter pretightening distance based on the nonlinear parameter of the road information.
7. The method of claim 1, wherein the parking trajectory formation is control optimized based on a theoretical trajectory of the target vehicle and an actual trajectory of the target vehicle, the method comprising:
extracting a deviation track from a theoretical track based on the target vehicle and an actual track of the target vehicle;
analyzing the road according to the time point of the deviation track and the place of the road where the deviation track is located;
if the road has a running problem, feeding back to an intelligent traffic center;
and re-planning a parking route through the intelligent traffic center and transmitting the re-planning parking route to the target vehicle.
8. A control system for intelligent parking track formation, the system comprising:
the method comprises the steps of determining a target vehicle and a target parking lot module, wherein the determining target vehicle and the target parking lot module are used for determining the target vehicle and determining a target parking lot according to the driving terminal point of the target vehicle;
the lane information acquisition module is used for carrying out road analysis based on the position of the target vehicle and the position of the target parking lot to acquire lane information of the target vehicle to the target parking lot;
the multi-angle lane information acquisition module is used for acquiring the target lane at multiple angles through space network monitoring to acquire multi-angle lane information;
the real-time state acquisition module of the target lane is used for monitoring the target lane based on an infrared sensor and acquiring the real-time state of the target lane;
the target vehicle theoretical track acquisition module is used for constructing a theoretical vehicle flow prediction network through the lane information and the real-time state of the target lane to acquire the theoretical track of the target vehicle;
the system comprises a target vehicle actual track acquisition module, a target vehicle actual track acquisition module and a target vehicle control module, wherein the target vehicle actual track acquisition module is used for acquiring an actual track of a target vehicle based on a path tracking algorithm;
and the parking track formation control optimization module is used for controlling and optimizing the parking track formation based on the theoretical track of the target vehicle and the actual track of the target vehicle.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117516584A (en) * | 2024-01-05 | 2024-02-06 | 每日互动股份有限公司 | Method, device, medium and equipment for acquiring predicted driving path information |
| CN117516584B (en) * | 2024-01-05 | 2024-04-05 | 每日互动股份有限公司 | Method, device, medium and equipment for acquiring predicted driving path information |
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