CN112798002A - Intelligent vehicle autonomous path planning method and system and readable storage medium - Google Patents
Intelligent vehicle autonomous path planning method and system and readable storage medium Download PDFInfo
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Abstract
The invention relates to an intelligent vehicle autonomous path planning method, a system and a readable storage medium, wherein the method comprises the following steps: receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information; collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information; planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information; acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate; judging whether the deviation rate is greater than a preset threshold value or not; if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result; and transmitting the correction result to the terminal according to a preset mode.
Description
Technical Field
The present invention relates to an autonomous path planning method, and more particularly, to an autonomous path planning method and system for an intelligent vehicle, and a readable storage medium.
Background
The garbage is solid waste generated in daily life and production of human beings, has large discharge amount, complex and various components, pollution, resource and socialization, needs harmless, resource, reduction and socialization treatment, and can pollute the environment, influence the environmental sanitation, waste resources, destroy the safety of production and life and destroy the social harmony if the garbage cannot be properly treated. The garbage disposal is to rapidly remove the garbage, perform harmless treatment and finally reasonably utilize the garbage. The garbage disposal methods widely used today are sanitary landfills, high temperature composting and incineration. The purpose of garbage treatment is harmlessness, resource utilization and reduction. All in the garbage disposal process all is in the garbage truck is unified to be poured into to rubbish in the garbage bin in each region through the garbage truck, along with the popularization of waste classification, traditional garbage collection mode is in order can't to satisfy the demand of waste classification processing, consequently needs an intelligent vehicle to unify the transportation to appointed place to rubbish and handles.
In order to realize accurate control on the autonomous path planning of the intelligent vehicle, a system matched with the intelligent vehicle needs to be developed for control, the system receives a garbage cleaning instruction, acquires position information of a garbage can and generates destination information; collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information; planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information; when the position of the vehicle deviates from the preset position, the vehicle displacement parameters are dynamically corrected in real time according to the correction information, but in the control process, how to realize accurate control and simultaneously realize the intelligent path planning of the vehicle are all problems which are urgent to solve.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent vehicle autonomous path planning method, an intelligent vehicle autonomous path planning system and a readable storage medium.
In order to achieve the purpose, the invention adopts the technical scheme that: an intelligent vehicle autonomous path planning method, comprising:
receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset threshold value or not;
if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and transmitting the correction result to the terminal according to a preset mode.
In a preferred embodiment of the present invention, the collecting image information, extracting road characteristics, and generating a travel decision by combining destination information further includes:
collecting the state of a traffic signal lamp to generate signal lamp state information;
calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
establishing virtual road information, and inverting the optimal path to obtain inversion information;
and correcting the average running speed of the vehicle according to the inversion information.
In a preferred embodiment of the invention, path planning is carried out according to the advancing decision to obtain path information; the method specifically comprises the following steps:
establishing a world coordinate system, acquiring the position coordinates of the garbage can, and generating destination coordinates;
acquiring initial position information of a vehicle, and generating initial position coordinates of the vehicle;
calculating the linear distance between the initial position coordinate of the vehicle and the destination coordinate;
acquiring barrier information in a target area between an initial position and a destination coordinate of a vehicle to generate a barrier coordinate;
and eliminating the coordinate points of the obstacles, establishing the shortest broken line between the initial position and the destination coordinates of the vehicle, and generating path information.
In a preferred embodiment of the present invention, the path planning is performed according to the travel decision to obtain the path information, and the vehicle moves according to the path information, further comprising:
obtaining vehicle parameter information, and establishing a motion state prediction model;
collecting the motion state of the vehicle and generating state information;
comparing the state information with preset state information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating warning information,
correcting the advancing decision according to the warning information to obtain result information;
and transmitting the result information to the terminal according to a preset mode.
In a preferred embodiment of the present invention, the method further comprises:
acquiring image information, extracting road characteristics, determining a vehicle traveling mode according to the road characteristics, and generating a corresponding control strategy;
acquiring road traffic data, generating an expert prediction database, predicting vehicle dynamic information according to the expert prediction database,
matching the control strategy with the vehicle dynamic information to generate matching degree information, and comparing the matching degree information with preset information to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not,
if the number of the vehicle paths is larger than the preset number, switching to a local path planning mode, and performing local path planning on the vehicle traveling path;
if the total route is smaller than the preset threshold value, switching to a global route planning mode, and carrying out full route planning on the vehicle traveling route.
In a preferred embodiment of the present invention, the vehicle displacement parameter includes one or more of vehicle speed, braking time, vehicle turning radius, vehicle distance, vehicle starting time, and vehicle lane-changing yaw angle.
The second aspect of the present invention also provides an intelligent vehicle autonomous path planning system, which includes: the intelligent vehicle autonomous path planning method comprises a memory and a processor, wherein the memory comprises an intelligent vehicle autonomous path planning method program, and the intelligent vehicle autonomous path planning method program realizes the following steps when being executed by the processor:
receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset threshold value or not;
if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and transmitting the correction result to the terminal according to a preset mode.
In a preferred embodiment of the present invention, the collecting image information, extracting road characteristics, and generating a travel decision by combining destination information further includes:
collecting the state of a traffic signal lamp to generate signal lamp state information;
calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
establishing virtual road information, and inverting the optimal path to obtain inversion information;
and correcting the average running speed of the vehicle according to the inversion information.
In a preferred embodiment of the present invention, the path planning is performed according to the travel decision to obtain the path information, and the vehicle moves according to the path information, further comprising:
obtaining vehicle parameter information, and establishing a motion state prediction model;
collecting the motion state of the vehicle and generating state information;
comparing the state information with preset state information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating warning information,
correcting the advancing decision according to the warning information to obtain result information;
and transmitting the result information to the terminal according to a preset mode.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an intelligent vehicle autonomous path planning method, and when the program of the intelligent vehicle autonomous path planning method is executed by a processor, the method implements the steps of the intelligent vehicle autonomous path planning method described in any one of the above.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) the destination and the initial position of the vehicle are uniformly positioned by establishing a world coordinate system, the obstacle in a target area between the destination and the initial position of the vehicle is detected, the coordinate points corresponding to the obstacle are removed, and after the removal, the vehicle path planning is carried out on the rest coordinate points, so that the obstacle is prevented from interfering with the path planning, and the path planning is more accurate.
(2) The state information of the traffic signal lamp is combined with the average running speed of the vehicle to simulate, so that the optimal path is searched, various interference factors are integrated to judge and plan, the planning accuracy is improved, the autonomous and efficient driving of the vehicle is realized, and the intellectualization of urban garbage collection and treatment is realized.
(3) And predicting the dynamic information of the vehicle according to the expert prediction database, switching between full path planning and local path planning for the path planning mode, ensuring the accuracy in the path planning process and realizing the real-time autonomous path planning in the vehicle running process.
(4) The optimal path is inverted by establishing the virtual road information, so that the average running speed of the vehicle is corrected according to the inversion result, the vehicle is guaranteed to run according to the preset path in the traffic signal lamp switching interval, and the path planning accuracy and the intelligentization are realized.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 illustrates a flow chart of a method for intelligent vehicle autonomous path planning in accordance with the present invention;
FIG. 2 illustrates a flow chart of an optimal path inversion method;
FIG. 3 shows a flow chart of an obstacle avoidance method;
FIG. 4 illustrates a flow chart of a revised travel decision method;
fig. 5 shows a flow chart of a path planning mode switching method;
FIG. 6 shows a block diagram of an intelligent vehicle autonomous path planning system;
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an intelligent vehicle autonomous path planning method according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides an intelligent vehicle autonomous path planning method, including:
s102, receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
s104, collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
s106, planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
s108, acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
s110, judging whether the deviation rate is greater than a preset threshold value;
s112, if the vehicle displacement parameter is larger than the preset value, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and S114, transmitting the correction result to the terminal according to a preset mode.
It should be noted that, the vehicle-mounted sensor detects information such as road environment and the vehicle itself, and predicts the motion state of the vehicle, and when the vehicle is in a dangerous state and is greater than a set value, the system triggers a warning such as a visual warning, a touch warning or a sound warning, so that the vehicle control terminal can make appropriate response to various dangerous states, thereby reducing the occurrence of accidents. The early warning system comprises a lane departure early warning system and an anti-collision early warning system, the state information of the vehicle and the surrounding environment of the vehicle is sensed by using the sensors, the self-adaptive cruise, lane keeping, autonomous obstacle avoidance, auxiliary parking and the like of the vehicle are realized, automatic driving control is realized, the vehicle can sense the state of the vehicle and the environment outside the vehicle according to the vehicle-mounted sensors, information required by decision control is obtained by using various data processing algorithms, the information is used as a basis for decision control, and meanwhile, a control command is autonomously sent according to the current driving task, so that the vehicle is controlled to be driven completely autonomously, safely and effectively without the supervision of a driver. The sensors comprise an internal sensor and an external sensor, and the internal sensor is used for acquiring various state parameter information of the vehicle; the external sensor is installed outside the intelligent vehicle and used for sensing external environment information. The sensor can effectively acquire the internal and external information of the vehicle, is beneficial to the normal work of the intelligent vehicle, and improves the working efficiency, and comprises a vision sensor, a distance sensor, a laser radar, a GPS and the like.
Information fusion is carried out aiming at information collected by different sensors, redundant or complementary information in space or time provided by a plurality of sensors or multi-feature information from the same sensor is fused and integrated according to a criterion, consistency description or explanation of external environment features is formed, the multi-sensor information fusion after integrated processing enlarges the space-time and frequency coverage range of the system, and the working blind area of a single sensor is avoided.
As shown in FIG. 2, the present invention discloses a flow chart of an optimal path inversion method;
according to the embodiment of the invention, the method for generating the travel decision by collecting the image information, extracting the road characteristics and combining the destination information further comprises the following steps:
s202, collecting the state of a traffic signal lamp and generating signal lamp state information;
s204, calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
s206, acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
s208, searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
s210, establishing virtual road information, and inverting the optimal path to obtain inversion information;
and S212, correcting the average running speed of the vehicle according to the inversion information.
It should be noted that the search of the optimal path is realized by combining the state information of the traffic signal lamp and the average running speed of the vehicle and simulating, so that various interference factors are integrated to judge and plan, the planning accuracy is improved, the autonomous and efficient driving of the vehicle is realized, and the intellectualization of the urban garbage collection and treatment is realized; the optimal path is inverted by establishing the virtual road information, so that the average running speed of the vehicle is corrected according to the inversion result, the vehicle is guaranteed to run according to the preset path in the traffic signal lamp switching interval, and the path planning accuracy and the intelligentization are realized.
As shown in FIG. 3, the invention discloses a flow chart of a method for avoiding obstacles;
according to the embodiment of the invention, path planning is carried out according to the advancing decision to obtain path information; the method specifically comprises the following steps:
s302, establishing a world coordinate system, acquiring the position coordinates of the garbage can, and generating destination coordinates;
s304, acquiring vehicle initial position information and generating a vehicle initial position coordinate;
s306, calculating the linear distance between the initial position coordinate of the vehicle and the destination coordinate;
s308, acquiring barrier information in a target area between the initial position of the vehicle and the destination coordinate to generate a barrier coordinate;
s310, eliminating the coordinate points of the obstacles, establishing the shortest broken line between the initial position of the vehicle and the coordinates of the destination, and generating path information.
It should be noted that the destination and the initial position of the vehicle are uniformly positioned by establishing a world coordinate system, the obstacle in the target area between the destination and the initial position of the vehicle is detected, the coordinate point corresponding to the obstacle is removed, and after the removal, the vehicle path planning is performed on the remaining coordinate points, so that the obstacle is prevented from interfering with the path planning, and the path planning is more accurate.
As shown in FIG. 4, the present invention discloses a flow chart of a method for making a revised travel decision;
according to the embodiment of the invention, the path planning is carried out according to the advancing decision to obtain the path information, and the vehicle moves according to the path information, and the method further comprises the following steps:
s402, obtaining vehicle parameter information and establishing a motion state prediction model;
s404, collecting the motion state of the vehicle and generating state information;
s406, comparing the state information with preset state information to obtain a deviation rate;
s408, judging whether the deviation rate is larger than a preset deviation rate threshold value or not;
s410, if the value is larger than the preset value, generating warning information,
s412, correcting the advancing decision according to the warning information to obtain result information;
and S414, transmitting the result information to the terminal according to a preset mode.
As shown in fig. 5, the present invention discloses a flow chart of a path planning mode switching method;
according to the embodiment of the invention, the method further comprises the following steps:
s502, collecting image information, extracting road characteristics, determining a vehicle traveling mode according to the road characteristics, and generating a corresponding control strategy;
s504, acquiring road traffic data, generating an expert prediction database, and predicting vehicle dynamic information according to the expert prediction database;
s506, matching the control strategy with the vehicle dynamic information to generate matching degree information, and comparing the matching degree information with preset information to obtain a deviation rate;
s508, judging whether the deviation ratio is larger than a preset deviation ratio threshold value or not,
s510, if the number of the vehicle paths is larger than the preset number, switching to a local path planning mode, and performing local path planning on the vehicle traveling path;
and S512, if the total route is smaller than the preset threshold value, switching to a global route planning mode, and carrying out full route planning on the vehicle traveling route.
It should be noted that the method for performing global path planning by using the trellis method is a process of trellis traversal, that is, all possible paths are traversed until a feasible path is found, and the planning space description is standard, simple in form, good in consistency and easy to implement. The value of each cell represents the probability of the presence of an obstacle at that location. When the intelligent vehicle moves, the grid map moves along with the vehicle, and the value of each grid is updated according to the current observation result. Each grid exerts a virtual repulsive force on the vehicle, the magnitude of the virtual repulsive force is in direct proportion to the magnitude of the cell value and in inverse proportion to the distance between the cell and the grid where the intelligent vehicle is located, meanwhile, the target position generates attractive force on the intelligent vehicle, and the vector sum of the two determines the motion direction of the intelligent vehicle.
According to the embodiment of the invention, the vehicle displacement parameters comprise one or more than two of vehicle speed, braking time, vehicle turning radius, vehicle distance, vehicle starting time and vehicle lane changing yaw angle.
As shown in FIG. 6, the present invention discloses a block diagram of an intelligent vehicle autonomous path planning system;
the second aspect of the present invention also provides an intelligent vehicle autonomous path planning system, which includes: the intelligent vehicle autonomous path planning method comprises a memory and a processor, wherein the memory comprises an intelligent vehicle autonomous path planning method program, and the intelligent vehicle autonomous path planning method program realizes the following steps when being executed by the processor:
receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset threshold value or not;
if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and transmitting the correction result to the terminal according to a preset mode.
It should be noted that, the vehicle-mounted sensor detects information such as road environment and the vehicle itself, and predicts the motion state of the vehicle, and when the vehicle is in a dangerous state and is greater than a set value, the system triggers a warning such as a visual warning, a touch warning or a sound warning, so that the vehicle control terminal can make appropriate response to various dangerous states, thereby reducing the occurrence of accidents. The early warning system comprises a lane departure early warning system and an anti-collision early warning system, the state information of the vehicle and the surrounding environment of the vehicle is sensed by using the sensors, the self-adaptive cruise, lane keeping, autonomous obstacle avoidance, auxiliary parking and the like of the vehicle are realized, automatic driving control is realized, the vehicle can sense the state of the vehicle and the environment outside the vehicle according to the vehicle-mounted sensors, information required by decision control is obtained by using various data processing algorithms, the information is used as a basis for decision control, and meanwhile, a control command is autonomously sent according to the current driving task, so that the vehicle is controlled to be driven completely autonomously, safely and effectively without the supervision of a driver. The sensors comprise an internal sensor and an external sensor, and the internal sensor is used for acquiring various state parameter information of the vehicle; the external sensor is installed outside the intelligent vehicle and used for sensing external environment information. The sensor can effectively acquire the internal and external information of the vehicle, is beneficial to the normal work of the intelligent vehicle, and improves the working efficiency, and comprises a vision sensor, a distance sensor, a laser radar, a GPS and the like.
Information fusion is carried out aiming at information collected by different sensors, redundant or complementary information in space or time provided by a plurality of sensors or multi-feature information from the same sensor is fused and integrated according to a criterion, consistency description or explanation of external environment features is formed, the multi-sensor information fusion after integrated processing enlarges the space-time and frequency coverage range of the system, and the working blind area of a single sensor is avoided.
According to the embodiment of the invention, the method for generating the travel decision by collecting the image information, extracting the road characteristics and combining the destination information further comprises the following steps:
collecting the state of a traffic signal lamp to generate signal lamp state information;
calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
establishing virtual road information, and inverting the optimal path to obtain inversion information;
and correcting the average running speed of the vehicle according to the inversion information.
It should be noted that the search of the optimal path is realized by combining the state information of the traffic signal lamp and the average running speed of the vehicle and simulating, so that various interference factors are integrated to judge and plan, the planning accuracy is improved, the autonomous and efficient driving of the vehicle is realized, and the intellectualization of the urban garbage collection and treatment is realized; the optimal path is inverted by establishing the virtual road information, so that the average running speed of the vehicle is corrected according to the inversion result, the vehicle is guaranteed to run according to the preset path in the traffic signal lamp switching interval, and the path planning accuracy and the intelligentization are realized.
According to the embodiment of the invention, the path planning is carried out according to the advancing decision to obtain the path information, and the vehicle moves according to the path information, and the method further comprises the following steps:
obtaining vehicle parameter information, and establishing a motion state prediction model;
collecting the motion state of the vehicle and generating state information;
comparing the state information with preset state information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating warning information,
correcting the advancing decision according to the warning information to obtain result information;
and transmitting the result information to the terminal according to a preset mode.
According to the embodiment of the invention, the method further comprises the following steps:
collecting image information, extracting road characteristics,
determining a vehicle traveling mode according to the road characteristics and generating a corresponding control strategy;
acquiring road traffic data and generating an expert prediction database;
predicting vehicle dynamics information from an expert prediction database,
matching the control strategy with the vehicle dynamic information to generate matching degree information;
comparing the matching degree information with preset information to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not,
if the number of the vehicle paths is larger than the preset number, switching to a local path planning mode, and performing local path planning on the vehicle traveling path;
if the total route is smaller than the preset threshold value, switching to a global route planning mode, and carrying out full route planning on the vehicle traveling route.
It should be noted that the method for performing global path planning by using the trellis method is a process of trellis traversal, that is, all possible paths are traversed until a feasible path is found, and the planning space description is standard, simple in form, good in consistency and easy to implement. The value of each cell represents the probability of the presence of an obstacle at that location. When the intelligent vehicle moves, the grid map moves along with the vehicle, and the value of each grid is updated according to the current observation result. Each grid exerts a virtual repulsive force on the vehicle, the magnitude of the virtual repulsive force is in direct proportion to the magnitude of the cell value and in inverse proportion to the distance between the cell and the grid where the intelligent vehicle is located, meanwhile, the target position generates attractive force on the intelligent vehicle, and the vector sum of the two determines the motion direction of the intelligent vehicle.
According to the embodiment of the invention, the vehicle displacement parameters comprise one or more than two of vehicle speed, braking time, vehicle turning radius, vehicle distance, vehicle starting time and vehicle lane changing yaw angle.
According to the embodiment of the invention, path planning is carried out according to the advancing decision to obtain path information; the method specifically comprises the following steps:
establishing a world coordinate system, acquiring the position coordinates of the garbage can, and generating destination coordinates;
acquiring initial position information of a vehicle, and generating initial position coordinates of the vehicle;
calculating the linear distance between the initial position coordinate of the vehicle and the destination coordinate;
acquiring barrier information in a target area between an initial position and a destination coordinate of a vehicle to generate a barrier coordinate;
and eliminating the coordinate points of the obstacles, establishing the shortest broken line between the initial position and the destination coordinates of the vehicle, and generating path information.
It should be noted that the destination and the initial position of the vehicle are uniformly positioned by establishing a world coordinate system, the obstacle in the target area between the destination and the initial position of the vehicle is detected, the coordinate point corresponding to the obstacle is removed, and after the removal, the vehicle path planning is performed on the remaining coordinate points, so that the obstacle is prevented from interfering with the path planning, and the path planning is more accurate.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an intelligent vehicle autonomous path planning method, and when the program of the intelligent vehicle autonomous path planning method is executed by a processor, the method implements the steps of the intelligent vehicle autonomous path planning method described in any one of the above.
In summary, the destination and the initial position of the vehicle are uniformly positioned by establishing a world coordinate system, the obstacle in the target area between the destination and the initial position of the vehicle is detected, the coordinate points corresponding to the obstacle are removed, and after the removal, the vehicle path planning is performed on the remaining coordinate points, so that the obstacle is prevented from interfering with the path planning, and the path planning is more accurate.
The state information of the traffic signal lamp is combined with the average running speed of the vehicle to simulate, so that the optimal path is searched, various interference factors are integrated to judge and plan, the planning accuracy is improved, the autonomous and efficient driving of the vehicle is realized, and the intellectualization of urban garbage collection and treatment is realized.
And predicting the dynamic information of the vehicle according to the expert prediction database, switching between full path planning and local path planning for the path planning mode, ensuring the accuracy in the path planning process and realizing the real-time autonomous path planning in the vehicle running process.
The optimal path is inverted by establishing the virtual road information, so that the average running speed of the vehicle is corrected according to the inversion result, the vehicle is guaranteed to run according to the preset path in the traffic signal lamp switching interval, and the path planning accuracy and the intelligentization are realized.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent vehicle autonomous path planning method is characterized by comprising the following steps:
receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset threshold value or not;
if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and transmitting the correction result to the terminal according to a preset mode.
2. The intelligent vehicle autonomous path planning method of claim 1, wherein collecting image information, extracting road features, and generating a travel decision in combination with destination information, further comprises:
collecting the state of a traffic signal lamp to generate signal lamp state information;
calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
establishing virtual road information, and inverting the optimal path to obtain inversion information;
and correcting the average running speed of the vehicle according to the inversion information.
3. The intelligent vehicle autonomous path planning method according to claim 1, characterized in that path planning is performed according to a travel decision to obtain path information; the method specifically comprises the following steps:
establishing a world coordinate system, acquiring the position coordinates of the garbage can, and generating destination coordinates;
acquiring initial position information of a vehicle, and generating initial position coordinates of the vehicle;
calculating the linear distance between the initial position coordinate of the vehicle and the destination coordinate;
acquiring barrier information in a target area between an initial position and a destination coordinate of a vehicle to generate a barrier coordinate;
and eliminating the coordinate points of the obstacles, establishing the shortest broken line between the initial position and the destination coordinates of the vehicle, and generating path information.
4. The intelligent vehicle autonomous path planning method of claim 1, wherein path planning is performed according to a travel decision to obtain path information, and the vehicle moves according to the path information, further comprising:
obtaining vehicle parameter information, and establishing a motion state prediction model;
collecting the motion state of the vehicle and generating state information;
comparing the state information with preset state information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating warning information,
correcting the advancing decision according to the warning information to obtain result information;
and transmitting the result information to the terminal according to a preset mode.
5. The intelligent vehicle autonomous path planning method of claim 1, further comprising:
collecting image information, extracting road characteristics,
determining a vehicle traveling mode according to the road characteristics and generating a corresponding control strategy;
acquiring road traffic data and generating an expert prediction database;
predicting vehicle dynamics information from an expert prediction database,
matching the control strategy with the vehicle dynamic information to generate matching degree information;
comparing the matching degree information with preset information to obtain a deviation rate;
judging whether the deviation rate is larger than a preset deviation rate threshold value or not,
if the number of the vehicle paths is larger than the preset number, switching to a local path planning mode, and performing local path planning on the vehicle traveling path;
if the total route is smaller than the preset threshold value, switching to a global route planning mode, and carrying out full route planning on the vehicle traveling route.
6. The intelligent vehicle autonomous path planning method of claim 1, wherein the vehicle displacement parameters comprise one or more of vehicle speed, braking time, vehicle turning radius, vehicle distance, vehicle starting time, and vehicle lane-changing yaw angle.
7. An intelligent vehicle autonomous path planning system, the system comprising: the intelligent vehicle autonomous path planning method comprises a memory and a processor, wherein the memory comprises an intelligent vehicle autonomous path planning method program, and the intelligent vehicle autonomous path planning method program realizes the following steps when being executed by the processor:
receiving a garbage cleaning instruction, acquiring garbage can position information, and generating destination information;
collecting image information, extracting road characteristics, and generating a traveling decision by combining destination information;
planning a path according to the advancing decision to obtain path information, and moving the vehicle according to the path information;
acquiring real-time vehicle position information, and comparing the real-time vehicle position information with preset position information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset threshold value or not;
if so, generating correction information, and dynamically correcting the vehicle displacement parameter according to the correction information to obtain a correction result;
and transmitting the correction result to the terminal according to a preset mode.
8. The intelligent vehicle autonomous path planning system of claim 7, wherein collecting image information, extracting road features, and generating travel decisions in combination with destination information, further comprises:
collecting the state of a traffic signal lamp to generate signal lamp state information;
calculating the signal lamp state switching time and the adjacent intersection signal lamp switching interval time according to the signal lamp state information to generate signal lamp switching information;
acquiring vehicle parameter information, predicting the average running speed of the vehicle through a model, and generating the average running speed information of the vehicle;
searching an optimal path according to the vehicle running speed information and the signal lamp switching information,
establishing virtual road information, and inverting the optimal path to obtain inversion information;
and correcting the average running speed of the vehicle according to the inversion information.
9. The system of claim 7, wherein the path planning is performed according to the travel decision to obtain path information, and the vehicle moves according to the path information, further comprising:
obtaining vehicle parameter information, and establishing a motion state prediction model;
collecting the motion state of the vehicle and generating state information;
comparing the state information with preset state information to obtain a deviation rate;
judging whether the deviation rate is greater than a preset deviation rate threshold value or not;
if so, generating warning information,
correcting the advancing decision according to the warning information to obtain result information;
and transmitting the result information to the terminal according to a preset mode.
10. A computer-readable storage medium, characterized in that a smart vehicle autonomous path planning method program is included in the computer-readable storage medium, which when executed by a processor, implements the steps of the smart vehicle autonomous path planning method according to any one of claims 1 to 6.
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