CN114896784A - Intersection unmanned vehicle passing track prediction algorithm based on edge calculation - Google Patents
Intersection unmanned vehicle passing track prediction algorithm based on edge calculation Download PDFInfo
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Abstract
The invention discloses an intersection unmanned vehicle passing track prediction algorithm based on edge calculation; the system can collect the positioning data of the unmanned vehicle, collect the positioning data of the intersection terminal point, and perform positioning collection, analysis and integration processing on the intersection environment; the method has the advantages that the simulation calculation processing can be carried out on the passing track of the unmanned vehicle, the evasion processing can be carried out on the environmental mark of the intersection, the prediction judgment processing can be carried out on the passing track according to two judgment standards, the accuracy of the predicted passing track of the unmanned vehicle is ensured, the environmental data of the intersection can be monitored in real time, the accuracy and the safety of the prediction of the passing track of the unmanned vehicle at the intersection can be adjusted in real time according to the environmental data, and the intersection passing condition under the complex condition can be effectively responded; meanwhile, the predicted passing track is subjected to prediction evaluation, so that the stability of the predicted passing track is further ensured, and the passing track is prevented from deviating from a normal path seriously.
Description
Technical Field
The invention relates to the technical field of unmanned prediction, in particular to an intersection unmanned vehicle passing track prediction algorithm based on edge calculation.
Background
An unmanned vehicle is also called an automatic vehicle, a computer-driven vehicle or a wheeled mobile robot, and is an intelligent vehicle which realizes unmanned driving through a computer system. Unmanned vehicles rely on the cooperative cooperation of artificial intelligence, radar, vision computing, monitoring components and global positioning systems, allowing computers to operate motor vehicles automatically and safely without any human active operation. Edge computing means that an open platform integrating network, computing, storage and application core capabilities is adopted on one side close to an object or a data source to provide nearest-end services nearby. Compared with cloud computing, which intensively deploys software and hardware resources in a large data center far away from users, edge computing is to place computing resources close to the edge of users or equipment, so that delay and bandwidth consumption are reduced, and real-time processing close to data sources is provided.
Most of the existing prediction algorithms for the passing track of the unmanned vehicle at the intersection only predict the passing track of the conventional intersection, but the prediction processing accuracy of the passing track is not good at the complex intersection, such as the intersection which is modifying the road.
Disclosure of Invention
The invention aims to provide an intersection unmanned vehicle passing track prediction algorithm based on edge calculation so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the intersection unmanned vehicle passing track prediction algorithm based on edge calculation comprises the following steps:
s1, collecting positioning data of the unmanned vehicle, collecting positioning data of an intersection terminal point, and collecting positioning data of an intersection environment;
s2, analyzing and integrating the positioning data of the unmanned vehicle, the positioning data of the intersection terminal and the intersection environment positioning data;
s3, performing analog calculation processing on the passing track of the unmanned automobile, performing analog calculation processing on the passing track, and performing avoidance processing on an intersection environment mark;
and S4, when the unmanned vehicle enters the specified lane, analyzing and judging whether the unmanned vehicle and the front vehicle have the same terminal, and further judging whether the unmanned vehicle enters the automatic following mode.
Further, in step S1, positioning data acquisition processing is performed on the obstacles and vehicles in the intersection environment; the unmanned vehicle has a positioning coordinate of (X) i ，Y i ，Z i ) The barrier in the intersection environment is a fixed mark (A) k ，B k ，C k ) Vehicle in the intersection environment as a moving marker (D) i ，E i ，F i )。
Further, in step S3,
the prediction and judgment standard of the passing track is as follows: g i And H i ；
When min (G) i ) Less than or equal to 0 or min (H) i ) When the content is less than or equal to 0; sending out a predicted abnormal passing track warning, and readjusting the passing track;
when min (G) i ) > 0 and min (H) i ) When the pressure is more than 0; the predicted passing track is normal.
Further, in step S3,
estimated moving track value P i ；
When P is more than or equal to 0.8 i When the temperature is less than or equal to 1.2; the predicted passing track is normal;
when 0.8 > P i Or P i When the pressure is more than 1.2; and sending out a predicted abnormal passing track warning, and readjusting the passing track.
Further, in step S4, when the destination of the driverless vehicle is different from the destination of the preceding vehicle at the intersection, the process proceeds to step S3; when the unmanned vehicle and the front vehicle are at the same end point of the intersection, entering an automatic following mode without performing the operation of the step S3; and in the following mode, the path is the same as that of the front vehicle, and the speed is correspondingly adjusted.
Further, in step S4,
the front vehicle coordinate is (R) t ，S t ，W t )；
The following standard of the unmanned vehicle is Q t ；
When Q is t When the acceleration is more than or equal to 1, the acceleration of the front vehicle is in the acceleration state: compared with the front vehicle, the speed raising efficiency of the unmanned vehicle is the same, and the speed raising starting time is delayed by 1.5 seconds;
when Q is t When the acceleration is less than 1, the acceleration of the front vehicle is in a deceleration state: compared with the front vehicle, the speed reduction efficiency of the unmanned vehicle is 1.2 times, and the speed reduction starting time is synchronous.
The system comprises a road junction unmanned vehicle passing track prediction system based on edge calculation, a road junction unmanned vehicle passing track prediction system and a road junction unmanned vehicle passing track prediction system, wherein the road junction unmanned vehicle passing track prediction system comprises a data acquisition module, a data processing module, a route track prediction module, a following management module and a data uploading module; the system comprises a data acquisition module, a data processing module, a route track prediction module, a following management module and a system cloud end, wherein the data acquisition module is used for acquiring and processing intersection data and unmanned vehicle data, the data processing module is used for analyzing and integrating the data, the route track prediction module is used for integrating and simulating predicted route tracks of the data and judging and evaluating the simulated predicted route tracks, the following management module is used for judging a following mode and managing the following mode, and the data uploading module is used for uploading the data in the system to the system cloud end; the data acquisition module is respectively in communication data connection with the data processing module, the route track prediction module, the car following management module and the data uploading module, and the route track prediction module and the car following management module are respectively in communication data connection with the data uploading module.
Further, the data acquisition module comprises: the unmanned vehicle positioning and collecting unit and the intersection data collecting unit are arranged; the unmanned vehicle positioning and collecting unit is used for carrying out positioning data collection processing on the unmanned vehicle, and the intersection data collection unit is used for carrying out positioning data collection processing on fixed obstacles and moving vehicles at the intersection.
Further, the route trajectory prediction module includes: the system comprises a route track simulation unit, a route track judgment unit and a route track evaluation unit; the route trajectory simulation unit is used for performing simulation prediction processing on the route trajectory, the route trajectory judgment unit is used for analyzing, judging and processing the simulation predicted route trajectory, and the route trajectory evaluation unit is used for evaluating and processing the simulation predicted route trajectory.
Further, the car following management module comprises: the following vehicle management system comprises a following vehicle judgment unit, a following vehicle path management unit and a following vehicle speed management unit; with car judgement unit and be used for judging the processing with the car whether, with car route management unit and be used for managing the operation with the car route, with car speed management unit and be used for managing the operation with the car speed.
Compared with the prior art, the invention has the following beneficial effects:
1. the system can collect the positioning data of the unmanned vehicle, collect the positioning data of the intersection terminal, perform positioning collection processing on the intersection environment (barriers and vehicles), and perform analysis integration processing on the positioning data of the unmanned vehicle, the positioning data of the intersection terminal and the positioning data of the intersection environment; the method has the advantages that the traffic track of the unmanned vehicle can be subjected to analog calculation processing, the traffic track is subjected to analog calculation processing, the environmental mark of the intersection is subjected to evasion processing, the traffic track can be subjected to prediction and judgment processing according to two judgment standards, the accuracy of the predicted traffic track of the unmanned vehicle is ensured, the environmental data of the intersection can be monitored in real time, the accuracy and the safety of the predicted traffic track of the unmanned vehicle at the intersection are adjusted in real time according to the environmental data, and the intersection traffic condition under complex conditions can be effectively coped with; meanwhile, the predicted passing track of the unmanned vehicle is subjected to prediction evaluation, so that the stability of the predicted passing track is further ensured, and the passing track is prevented from deviating from a normal path seriously;
2. when the unmanned vehicle enters a specified lane, whether the unmanned vehicle and a front vehicle have the same end point is analyzed and judged, whether an automatic following mode is further judged, and when the unmanned vehicle and the front vehicle have different end points at an intersection, the processing process of step S3 is performed; when the unmanned vehicle and the front vehicle are at the same end point of the intersection, entering an automatic following mode without performing the operation of the step S3; in the following mode, the path is the same as that of the front vehicle, and the speed is correspondingly adjusted; when the current vehicle acceleration is in the acceleration state: compared with the front vehicle, the speed raising efficiency of the vehicle is the same, and the speed raising starting time is delayed by 1.5 seconds; when the current vehicle acceleration is in a deceleration state: the speed reduction efficiency of the vehicle speed is 1.2 times that of the front vehicle, and the speed reduction starting time is synchronous.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic view of the overall operation of the present invention;
FIG. 2 is a schematic view of the overall module connection of the present invention;
FIG. 3 is a schematic diagram of a data acquisition module of the present invention;
FIG. 4 is a schematic diagram of a route trajectory prediction module of the present invention;
FIG. 5 is a schematic diagram of a car tracking management module according to the present invention;
in the figure: 1. a data acquisition module; 2. a data processing module; 3. a route trajectory prediction module; 4. a car following management module; 5. and a data uploading module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The algorithm for predicting the driverless vehicle passing track at the intersection based on the edge calculation as shown in fig. 1-5 comprises the following steps:
s1, collecting positioning data of the unmanned vehicle, collecting positioning data of an intersection terminal point, and collecting positioning data of an intersection environment; in step S1, positioning data acquisition processing is performed on obstacles and vehicles in the intersection environment; the unmanned vehicle has a positioning coordinate of (X) i ，Y i ，Z i ) Several obstacles in the intersection environment as fixed markers (A) k ，B k ，C k ) Several vehicles in the intersection environment as mobile signs (D) i ，E i ，F i )；
S2, analyzing and integrating the positioning data of the unmanned vehicle, the positioning data of the intersection terminal and the intersection environment positioning data, and facilitating subsequent calculation;
s3, performing analog calculation processing on the passing track of the unmanned automobile, performing analog calculation processing on the passing track, and performing avoidance processing on an intersection environment mark;
the prediction and judgment standard of the passing track is as follows: g i And H i ；
when min (G) i ) Less than or equal to 0 or min (H) i ) When the content is less than or equal to 0; at the moment, the path of the predicted passing track is intersected with the fixed mark or the movable mark in the intersection, and the unmanned vehicle can scratch or collide with the fixed mark or the movable mark in the intersection when walking according to the predicted passing track; sending out a predicted abnormal passing track warning, and readjusting the passing track;
when min (G) i ) > 0 and min (H) i ) When the pressure is more than 0; at the moment, the path of the predicted passing track is not intersected with the fixed mark and the movable mark in the intersection, and the unmanned vehicle cannot scratch or collide with the fixed mark or the movable mark in the intersection when walking according to the predicted passing track; the predicted passing track is normal;
estimated moving track value P i ；
When P is more than or equal to 0.8 i When the temperature is less than or equal to 1.2; at the moment, the speed uniformity in the passing track of the unmanned vehicle is good, and the smoothness of the motion track is better; the predicted passing track is normal;
when 0.8 > P i Or P i When the pressure is more than 1.2; at the moment, the speed change in the passing track of the unmanned vehicle is large, and the stability of the motion track is poor; sending out a predicted abnormal passing track warning, and readjusting the passing track;
s4, in addition, after the unmanned vehicle enters a specified lane, analyzing and judging whether the unmanned vehicle and a front vehicle have the same terminal point, and further judging whether the unmanned vehicle enters an automatic following mode;
when the destination of the unmanned vehicle is different from the destination of the preceding vehicle at the intersection, the process goes to step S3; when the unmanned vehicle and the front vehicle are at the same end point of the intersection, entering an automatic following mode without performing the operation of the step S3; in the following mode, the path is the same as that of the front vehicle, and the speed is correspondingly adjusted;
the front vehicle coordinate is (R) t ，S t ，W t )；
The following standard of the unmanned vehicle is Q t ；
When Q is t When the acceleration is more than or equal to 1, the acceleration of the front vehicle is in the acceleration state: compared with the front vehicle, the speed raising efficiency of the unmanned vehicle is the same, and the speed raising starting time is delayed by 1.5 seconds;
when Q is t When the acceleration is less than 1, the acceleration of the front vehicle is in a deceleration state: compared with the front vehicle, the speed reduction efficiency of the unmanned vehicle is 1.2 times, and the speed reduction starting time is synchronous.
The system also comprises an intersection unmanned vehicle passing track prediction system based on edge calculation, and the system comprises a data acquisition module 1, a data processing module 2, a route track prediction module 3, a following management module 4 and a data uploading module 5; the system comprises a data acquisition module 1, a data processing module 2, a route track prediction module 3, a following management module 4 and a data uploading module 5, wherein the data acquisition module 1 is used for acquiring and processing intersection data and unmanned vehicle data, the data processing module 2 is used for analyzing and integrating the data, the route track prediction module 3 is used for integrating and simulating a predicted route track for the data and judging and evaluating the simulated predicted route track, the following management module 4 is used for judging a following mode and managing the following mode, and the data uploading module 5 is used for uploading the data in the system to a system cloud; the data acquisition module 1 is respectively in communication data connection with the data processing module 2, the route track prediction module 3, the car following management module 4 and the data uploading module 5, and the route track prediction module 3 and the car following management module 4 are respectively in communication data connection with the data uploading module 5.
The data acquisition module 1 comprises: the unmanned vehicle positioning and collecting unit and the intersection data collecting unit are arranged; the intersection data acquisition unit is used for acquiring positioning data of fixed obstacles and moving vehicles at an intersection; the route trajectory prediction module 3 includes: the system comprises a route track simulation unit, a route track judgment unit and a route track evaluation unit; the route trajectory simulation unit is used for performing simulation prediction processing on the route trajectory, the route trajectory judgment unit is used for analyzing, judging and processing the simulation predicted route trajectory, and the route trajectory evaluation unit is used for evaluating and processing the simulation predicted route trajectory; the car following management module 4 includes: the following vehicle management system comprises a following vehicle judgment unit, a following vehicle path management unit and a following vehicle speed management unit; with car judgement unit and be used for judging the processing with the car whether, with car route management unit and be used for managing the operation with the car route, with car speed management unit and be used for managing the operation with the car speed.
The invention has the beneficial effects that:
the system can collect the positioning data of the unmanned vehicle, collect the positioning data of the intersection terminal, perform positioning collection processing on the intersection environment (barriers and vehicles), and perform analysis integration processing on the positioning data of the unmanned vehicle, the positioning data of the intersection terminal and the positioning data of the intersection environment; the method has the advantages that the traffic track of the unmanned vehicle can be subjected to analog calculation processing, the traffic track is subjected to analog calculation processing, the environmental mark of the intersection is subjected to evasion processing, the traffic track can be subjected to prediction and judgment processing according to two judgment standards, the accuracy of the predicted traffic track of the unmanned vehicle is ensured, the environmental data of the intersection can be monitored in real time, the accuracy and the safety of the predicted traffic track of the unmanned vehicle at the intersection are adjusted in real time according to the environmental data, and the intersection traffic condition under complex conditions can be effectively coped with; meanwhile, the predicted passing track of the unmanned vehicle is subjected to prediction evaluation, so that the stability of the predicted passing track is further ensured, and the passing track is prevented from deviating from a normal path seriously; when the unmanned vehicle enters a specified lane, analyzing and judging whether the unmanned vehicle and a front vehicle have the same end point, and further judging whether to enter an automatic following mode, and when the unmanned vehicle and the front vehicle have different end points at an intersection, entering the processing process of step S3; when the unmanned vehicle and the front vehicle are at the same end point of the intersection, entering an automatic following mode without performing the operation of the step S3; in the following mode, the path is the same as that of the front vehicle, and the speed is correspondingly adjusted; when the current vehicle acceleration is in the acceleration state: compared with the front vehicle, the speed raising efficiency of the vehicle is the same, and the speed raising starting time is delayed by 1.5 seconds; when the current vehicle acceleration is in a deceleration state: the speed reduction efficiency of the vehicle speed is 1.2 times that of the front vehicle, and the speed reduction starting time is synchronous.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intersection unmanned vehicle passing track prediction algorithm based on edge calculation is characterized in that: the method comprises the following steps:
s1, collecting positioning data of the unmanned vehicle, collecting positioning data of an intersection terminal point, and collecting positioning data of an intersection environment;
s2, analyzing and integrating the positioning data of the unmanned vehicle, the positioning data of the intersection terminal and the intersection environment positioning data;
s3, performing analog calculation processing on the passing track of the unmanned automobile, performing analog calculation processing on the passing track, and performing avoidance processing on an intersection environment mark;
and S4, when the unmanned vehicle enters the specified lane, analyzing and judging whether the unmanned vehicle and the front vehicle have the same terminal, and further judging whether the unmanned vehicle enters the automatic following mode.
2. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 1, wherein: in step S1, positioning data acquisition processing is performed on obstacles and vehicles in the intersection environment; the unmanned vehicle has a location coordinate of (X) i ，Y i ，Z i ) The barrier in the intersection environment is a fixed mark (A) k ，B k ，C k ) Vehicle in the intersection environment as a moving marker (D) i ，E i ，F i )。
3. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 2, wherein: in the step S3, in step S3,
the prediction and judgment standard of the passing track is as follows: g i And H i ；
When min (G) i ) Less than or equal to 0 or min (H) i ) When the content is less than or equal to 0; sending out a predicted abnormal passing track warning, and readjusting the passing track;
when min (G) i ) > 0 and min (H) i ) When the pressure is more than 0; the predicted passing track is normal.
4. The edge-computation-based intersection unmanned vehicle passing trajectory prediction algorithm of claim 1, wherein: in the step S3, in step S3,
estimated moving track value P i ；
When P is more than or equal to 0.8 i When the temperature is less than or equal to 1.2 percent; the predicted passing track is normal;
when 0.8 > P i Or P i When the pressure is more than 1.2; and sending out a predicted abnormal passing track warning, and readjusting the passing track.
5. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 1, wherein: in step S4, when the destination of the driverless vehicle is different from the destination of the preceding vehicle at the intersection, the process proceeds to step S3; when the unmanned vehicle and the front vehicle are at the same end point of the intersection, entering an automatic following mode without performing the operation of the step S3; and in the following mode, the path is the same as that of the front vehicle, and the speed is correspondingly adjusted.
6. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 5, wherein: in the step S4, in step S4,
the front vehicle coordinate is (R) t ，S t ，W t )；
The following standard of the unmanned vehicle is Q t ；
When Q is t When the acceleration is more than or equal to 1, the acceleration of the front vehicle is in the acceleration state: compared with the front vehicle, the speed raising efficiency of the unmanned vehicle is the same, and the speed raising starting time is delayed by 1.5 seconds;
when Q is t When the acceleration is less than 1, the acceleration of the front vehicle is in a deceleration state: compared with the front vehicle, the speed reduction efficiency of the unmanned vehicle is 1.2 times, and the speed reduction starting time is synchronous.
7. The edge-computation-based intersection unmanned vehicle passing trajectory prediction algorithm of claim 1, wherein: the system also comprises an intersection unmanned vehicle passing track prediction system based on edge calculation, which comprises a data acquisition module, a data processing module, a route track prediction module, a following management module and a data uploading module; the system comprises a data acquisition module, a data processing module, a route track prediction module, a following management module and a system cloud end, wherein the data acquisition module is used for acquiring and processing intersection data and unmanned vehicle data, the data processing module is used for analyzing and integrating the data, the route track prediction module is used for integrating and simulating predicted route tracks of the data and judging and evaluating the simulated predicted route tracks, the following management module is used for judging a following mode and managing the following mode, and the data uploading module is used for uploading the data in the system to the system cloud end; the data acquisition module is respectively in communication data connection with the data processing module, the route track prediction module, the car following management module and the data uploading module, and the route track prediction module and the car following management module are respectively in communication data connection with the data uploading module.
8. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 7, wherein: the data acquisition module comprises: the unmanned vehicle positioning and collecting unit and the intersection data collecting unit are arranged; the unmanned vehicle positioning and collecting unit is used for carrying out positioning data collection processing on the unmanned vehicle, and the intersection data collection unit is used for carrying out positioning data collection processing on fixed obstacles and moving vehicles at the intersection.
9. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 8, wherein: the route trajectory prediction module includes: the system comprises a route track simulation unit, a route track judgment unit and a route track evaluation unit; the route trajectory simulation unit is used for performing simulation prediction processing on the route trajectory, the route trajectory judgment unit is used for analyzing, judging and processing the simulation predicted route trajectory, and the route trajectory evaluation unit is used for evaluating and processing the simulation predicted route trajectory.
10. The intersection driverless vehicle transit trajectory prediction algorithm based on edge calculation of claim 8, wherein: the car following management module comprises: the following vehicle management system comprises a following vehicle judgment unit, a following vehicle path management unit and a following vehicle speed management unit; with car judgement unit and be used for judging the processing with the car whether, with car route management unit and be used for managing the operation with the car route, with car speed management unit and be used for managing the operation with the car speed.
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