CN113327421A - Road network control method and system based on V2X - Google Patents
Road network control method and system based on V2X Download PDFInfo
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- CN113327421A CN113327421A CN202110626426.8A CN202110626426A CN113327421A CN 113327421 A CN113327421 A CN 113327421A CN 202110626426 A CN202110626426 A CN 202110626426A CN 113327421 A CN113327421 A CN 113327421A
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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Abstract
The invention provides a road network control method and system based on V2X, wherein the method comprises the following steps: acquiring real-time traffic state perception data based on a V2X road network control technology, and acquiring traffic flow running state prediction data based on traffic simulation; forming a traffic control model strategy; carrying out timely simulation and short-term prediction on road traffic based on a traffic control model strategy; generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result; forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic sensing data, and acquiring the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior according to the feedback result; and realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent network vehicle behavior. The behavior coordination and optimization of the vehicles during running are realized, and the purposes of improving the traffic flow operation efficiency and traffic safety are achieved.
Description
Technical Field
The invention relates to the technical field of road network control, in particular to a road network control method and system based on V2X.
Background
At present, the cooperative vehicle-road control is a novel control mode based on road management and control, and can realize the optimal traffic running state of each traffic short-circuit surface and an intelligent networking vehicle behavior optimization mode by acquiring the traffic state of the road;
however, at present, an optimal traffic operation state and an intelligent network-connected vehicle behavior optimization mode cannot be generated after vehicle-road cooperative control is lacked, so that vehicle behavior cooperation and optimization cannot be realized, and the purposes of improving traffic flow operation efficiency and traffic safety are achieved, therefore, the invention provides a V2X-based road network control method and system.
Disclosure of Invention
The invention provides a road network control method and system based on V2X, which are used for realizing behavior coordination and optimization of vehicles during driving and achieving the purposes of improving traffic flow operation efficiency and traffic safety.
The invention provides a road network control method based on V2X, which comprises the following steps:
step 1: acquiring real-time traffic state perception data based on a V2X road network control technology, and acquiring traffic flow running state prediction data based on traffic simulation;
step 2: forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
and step 3: performing real-time simulation and short-term prediction on road traffic based on the traffic control model strategy, and acquiring a simulation result and a prediction result;
and 4, step 4: generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
and 5: forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data, and acquiring the optimal traffic running state of each road section and the optimization mode of the behavior of the intelligent networked vehicle according to the feedback result;
step 6: and realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior.
Preferably, in the V2X-based road network control method, in step 1, a working process of obtaining traffic flow running state prediction data based on traffic simulation includes:
acquiring a simulation road of the traffic simulation, and determining the area where a first simulation vehicle is located based on the simulation road;
simultaneously, acquiring the vehicle behavior of the first simulated vehicle;
constructing a two-dimensional geometric simulation structure based on the vehicle behavior of the first simulated vehicle and the area in which the first simulated vehicle is located;
according to the two-dimensional geometric simulation structure, vehicle behaviors of the first simulated vehicle are obtained, and meanwhile, a moving range of the vehicle behaviors of the first simulated vehicle is obtained;
judging whether a second simulated vehicle exists in the moving range, and acquiring first running state prediction data according to the vehicle behavior of the first simulated vehicle;
otherwise, acquiring an information vehicle set according to the first simulation vehicle and the second simulation vehicle based on the two-dimensional set simulation structure;
and meanwhile, acquiring the comprehensive vehicle behavior of the information vehicle set, and acquiring a second running state prediction parameter according to the comprehensive vehicle behavior.
Preferably, the work process of determining the area where the first simulated vehicle is located based on the V2X road network control method includes:
judging whether the position of the first simulation vehicle is within the range of a preset intersection or not;
if the position of the first simulation vehicle is within a preset intersection range, the area where the first vehicle is located is a road coordination area;
otherwise, the area where the first vehicle is located is the road queue area.
Preferably, in step 2, a concrete process of forming a traffic control model policy according to the real-time traffic state perception data and the traffic flow running state prediction data includes:
acquiring real-time traffic density of current traffic based on the real-time traffic state perception data;
meanwhile, the predicted traffic density of the current traffic is obtained according to the traffic flow running state prediction data;
comparing the current real-time traffic density with the predicted traffic density, and judging the traffic control state;
when the real-time traffic density is larger than the predicted traffic density, judging that the traffic control state is poor, and matching a first traffic control protocol in a preset traffic control protocol library;
when the real-time traffic density is equal to the predicted traffic density, judging that the traffic control state is good, and meanwhile, matching a second traffic control protocol in the preset traffic control protocol library;
when the real-time traffic density is smaller than the predicted traffic density, judging that the traffic control state is excellent, and matching a third traffic control protocol in the preset traffic control protocol library;
and generating a traffic control model strategy based on the first traffic control protocol, the second traffic control protocol and the third traffic control protocol.
Preferably, in step 3, the road network control method based on V2X includes performing timely simulation and short-term prediction on road traffic based on the traffic control model policy, and acquiring a simulation result and a prediction result, where the method includes:
constructing a vehicle running track simulation equation, and performing simulation training verification on the vehicle running track simulation equation based on historical training data of a target road section to obtain a vehicle running track of a vehicle on the target road section;
the historical training data are driving data corresponding to different driving track types;
when a target vehicle observes a preset traffic identification position on the target road section, acquiring an observation distance and an observation angle of the target vehicle;
substituting the observation distance and the observation angle of the target vehicle into the calculation based on the vehicle running track simulation equation, and predicting to obtain specific position information of the target vehicle on the target road section;
wherein the target vehicle is a plurality of vehicles on the target road segment;
predicting a driving speed of the traffic flow of the target road section and the traffic volume of the traffic flow of the target road section based on the specific position information of the target vehicle on the target road section;
determining a speed-time relation between the running speed of the traffic flow of the target road section and the running time of a target vehicle passing through the target road section, and determining a relation between the time occupancy rate and the space occupancy rate of the target road section based on the speed-time relation;
obtaining a road network length calculation model of the target road section based on the relation between the time occupancy and the space occupancy of the target road section;
inputting the traffic volume of the traffic flow of the target road section into a road network length calculation model of the target road section, and predicting to obtain the length information of the current vehicle on the target road section;
recording and analyzing the current vehicle length information by a traffic control model strategy control platform based on the traffic control model, obtaining control information corresponding to the current road condition of the target road section, and sending the control information to each vehicle on the target road section in a wireless mode;
the management and control information comprises a vehicle running route, a driving limiting speed limit instruction, a temporary traffic control instruction and interval duration for controlling different indication states of a traffic signal lamp on the target road section;
recording the time length for the target road section to get rid of the current road condition after the road traffic is controlled through the traffic control model strategy;
comparing the time length with a preset time length;
if the time length is less than or equal to the preset time length, completing the timely simulation and short-term prediction of the road traffic;
and if not, controlling the current road condition of the target road section again based on the traffic control model strategy until the time length is less than or equal to the preset time length.
Preferably, the method for controlling a road network based on V2X to obtain the control information corresponding to the current road condition of the target road segment further includes:
acquiring a first influence range of the control information on the current road condition of the target road section to obtain an evaluation index corresponding to the control information;
meanwhile, acquiring the length information of the current vehicle on the target road section, predicting the running state evolution condition of the current road condition information of the target road section under the condition of no control information through a preset rule, determining a second influence range of the running state evolution condition on the target road section, and obtaining a comparison evaluation index corresponding to the control information;
performing difference calculation on the evaluation index and the comparison evaluation index to obtain a target difference value;
if the target difference is larger than or equal to a preset difference, judging that the management and control information reaches target management and control efficiency;
otherwise, judging that the management and control information does not reach the target management and control efficiency.
Preferably, in step 4, a concrete working process of generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result includes:
acquiring a simulation entropy corresponding to the simulation result, and extracting a prediction entropy corresponding to the prediction result;
calculating a road traffic optimization control comprehensive coefficient according to the simulation entropy and the prediction entropy;
wherein K represents the road traffic optimization control comprehensive coefficient; delta represents an optimization coefficient, and the value range of the optimization coefficient is (0.3, 0.6); s1Representing the simulation entropy corresponding to the simulation result; s2Representing the prediction entropy corresponding to the prediction result; mu represents a synergistic factor, and the value range is (0.21, 0.22); w represents a traffic dimension, and generally takes a value of 3; t represents the time length of a preset time period;
performing optimization collaborative training on the road traffic optimization control comprehensive coefficient in a preset neural convolution network, and generating a real-time optimization collaborative control model based on a training result;
acquiring potential optimized cooperative control schemes from the real-time optimized cooperative control model, wherein the number of the potential optimized cooperative control schemes is more than 1;
performing real-time simulated traffic drilling on the road traffic based on the potential optimized cooperative control scheme, and respectively grading the potential optimized cooperative control scheme based on the real-time simulated traffic drilling result;
and taking the highest scoring as a real-time optimization cooperative control scheme.
Preferably, in step 5, a road network control method based on V2X is configured to form feedback on the real-time optimized cooperative control scheme according to the real-time traffic awareness data, and obtain an optimal traffic running state of each road section and an optimization mode of intelligent networked vehicle behavior according to a feedback result, where the method includes:
acquiring a road condition comprehensive value of the real-time traffic perception data, and forming feedback on the real-time optimization cooperative control scheme according to the road condition comprehensive value;
acquiring a traffic operation feedback coefficient and an intelligent networking behavior feedback coefficient corresponding to a feedback result;
calculating a traffic operation comprehensive value of the road section based on the traffic operation feedback coefficient;
wherein Z represents the traffic running comprehensive value of the road section; xi1The traffic operation feedback coefficient is represented, and the value range is (0.45, 0.48); tau represents the terrain state coefficient of the road section, and the value range is (1, 2); d represents the road condition comprehensive value, and the value range is (0.5, 1); lambda represents the sparse degree value between vehicles on the road section, and the value range is (0, 1); a represents the number of vehicles on the road section;
analyzing the road section traffic operation comprehensive value, and performing correlation mapping on the analysis result and a preset road traffic operation state library;
acquiring the optimal traffic running state of each road section based on the mapping result;
meanwhile, calculating a dynamic value of the behavior of the intelligent networked vehicle based on the feedback coefficient of the behavior of the intelligent networked vehicle;
wherein X represents the dynamic value of the behavior of the intelligent networked vehicle；ξ2The feedback coefficient of the intelligent networking behavior is represented, and the value range is (0.46, 0.49); v represents the running speed of the intelligent networked vehicle; a represents the average acceleration of the intelligent networked vehicle; t represents the actual running time of the intelligent networked vehicle on the section of the preset road section; t' represents the average running time of the intelligent networked vehicle on the section of the preset road section; y represents the influence rate of the intelligent networked vehicle behaviors on road traffic, and the value range is (0.5, 0.9);
matching the intelligent networked vehicle behavior dynamic value in a preset intelligent networked vehicle behavior dynamic comparison table;
acquiring a matching position of the behavior of the intelligent networked vehicle in the preset intelligent networked vehicle comparison table based on a matching result;
and reading the optimization mode of the intelligent networked vehicle behavior according to the matching position.
Preferably, the V2X-based road network control system includes:
the data acquisition module is used for acquiring real-time traffic state perception data based on a V2X road network control technology and acquiring traffic flow running state prediction data based on traffic simulation;
the strategy making module is used for forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
the strategy implementation module is used for implementing timely simulation and short-term prediction on road traffic based on the traffic control model strategy and acquiring a simulation result and a prediction result;
the optimization cooperative module is used for generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
the feedback module is used for forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data and acquiring the optimal traffic running state of each road section and the optimization mode of intelligent networked vehicle behaviors according to the feedback result;
and the control module is used for realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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 flowchart of a V2X-based road network control method according to an embodiment of the present invention;
fig. 2 is a structural diagram of a V2X-based road network control system according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1:
the present embodiment provides a road network control method based on V2X, as shown in fig. 1, including:
step 1: acquiring real-time traffic state perception data based on a V2X road network control technology, and acquiring traffic flow running state prediction data based on traffic simulation;
step 2: forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
and step 3: performing real-time simulation and short-term prediction on road traffic based on the traffic control model strategy, and acquiring a simulation result and a prediction result;
and 4, step 4: generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
and 5: forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data, and acquiring the optimal traffic running state of each road section and the optimization mode of the behavior of the intelligent networked vehicle according to the feedback result;
step 6: and realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior.
In this embodiment, the real-time traffic status sensing data refers to the number of vehicles on the road and the road condition information of the current road.
In this embodiment, the traffic flow operation state prediction data is predicted, and is the operation speed, the traffic volume, and the like of the traffic flow on the road obtained by the traffic simulation.
In this embodiment, the traffic control model policy refers to a policy for controlling vehicles on a road to avoid or alleviate a congestion situation of the vehicles on the road.
In this embodiment, the cooperative control scheme refers to a specific control manner that can be actually executed according to the policy generation.
The beneficial effects of the above technical scheme are: by acquiring the traffic state perception data and the traffic flow running state prediction data, a corresponding road management and control scheme is formulated, the behavior coordination and optimization of vehicles during running are realized, and the purposes of improving the traffic flow running efficiency and traffic safety are achieved.
Example 2:
on the basis of the foregoing embodiment 1, the present embodiment provides a road network control method based on V2X, and in step 1, a working process of obtaining traffic flow operation state prediction data based on traffic simulation includes:
acquiring a simulation road of the traffic simulation, and determining the area where a first simulation vehicle is located based on the simulation road;
simultaneously, acquiring the vehicle behavior of the first simulated vehicle;
constructing a two-dimensional geometric simulation structure based on the vehicle behavior of the first simulated vehicle and the area in which the first simulated vehicle is located;
according to the two-dimensional set simulation structure, acquiring the vehicle behavior of the first simulated vehicle, and acquiring the moving range of the vehicle behavior of the first simulated vehicle;
judging whether a second simulated vehicle exists in the moving range, and acquiring first running state prediction data according to the vehicle behavior of the first simulated vehicle;
otherwise, acquiring an information vehicle set according to the first simulation vehicle and the second simulation vehicle based on the two-dimensional geometric simulation structure;
and meanwhile, acquiring the comprehensive vehicle behavior of the information vehicle set, and acquiring a second running state prediction parameter according to the comprehensive vehicle behavior.
In this embodiment, the simulated road is simulated and is not an actual road, and is used to obtain the traffic flow operation state prediction data.
In this embodiment, the vehicle behavior refers to the vehicle running or stopping, or the pipe bending, or the like.
In this embodiment, the first running state prediction data refers to the corresponding running information of the first simulated vehicle on the road.
In this embodiment, the information vehicle set includes vehicle travel information corresponding to a plurality of vehicles.
In this embodiment, the integrated vehicle behavior refers to corresponding behaviors of a plurality of vehicles traveling straight on a road, or traveling in a pipe bend, or the like.
The beneficial effects of the above technical scheme are: the simulation demonstration of running vehicles on the road is performed by establishing a simulation road, so that the method is favorable for accurately acquiring the traffic flow running state prediction data, improves the accuracy and the scientificity of data acquisition, and prepares for realizing behavior coordination and optimization of vehicles during running, thereby achieving the purposes of improving the traffic flow running efficiency and traffic safety.
Example 3:
on the basis of embodiment 2, this embodiment provides a working process for determining a region where the first simulated vehicle is located based on a V2X road network control method, including:
judging whether the position of the first simulation vehicle is within the range of a preset intersection or not;
if the position of the first simulation vehicle is within a preset intersection range, the area where the first vehicle is located is a road coordination area;
otherwise, the area where the first vehicle is located is the road queue area.
In this embodiment, the range of the preset intersection is determined in advance, and is used to determine the position information of the first simulated vehicle.
In this embodiment, the road coordination area refers to a road area where a road needs to be managed, that is, the area cannot be congested with vehicles.
The beneficial effects of the above technical scheme are: whether the first simulation vehicle is in the range of the preset intersection or not is determined, so that whether the vehicle needs to be controlled or not is accurately judged, and the accuracy of obtaining traffic flow running state prediction data is improved.
Example 4:
on the basis of embodiment 1, this embodiment provides a V2X-based road network control method, and in step 2, a specific process of forming a traffic control model policy according to the real-time traffic state perception data and the traffic flow running state prediction data includes:
acquiring real-time traffic density of current traffic based on the real-time traffic state perception data;
meanwhile, the predicted traffic density of the current traffic is obtained according to the traffic flow running state prediction data;
comparing the current real-time traffic density with the predicted traffic density, and judging the traffic control state;
when the real-time traffic density is larger than the predicted traffic density, judging that the traffic control state is poor, and matching a first traffic control protocol in a preset traffic control protocol library;
when the real-time traffic density is equal to the predicted traffic density, judging that the traffic control state is good, and meanwhile, matching a second traffic control protocol in the preset traffic control protocol library;
when the real-time traffic density is smaller than the predicted traffic density, judging that the traffic control state is excellent, and matching a third traffic control protocol in the preset traffic control protocol library;
and generating a traffic control model strategy based on the first traffic control protocol, the second traffic control protocol and the third traffic control protocol.
In this embodiment, the real-time traffic density refers to how many vehicles are passing on the road.
In this embodiment, the predicted traffic density is obtained according to the traffic flow operation state prediction data, is predicted data, is not actual data, and is used to provide a basis for formulating a traffic control model strategy.
The beneficial effects of the above technical scheme are: the real-time traffic density is compared with the preset traffic density, so that a corresponding traffic control protocol is selected, and a traffic control model strategy is accurately formulated, so that behavior coordination and optimization among vehicles are realized, and the purposes of improving traffic flow operation efficiency and traffic safety are achieved.
Example 5:
on the basis of embodiment 1, this embodiment provides a road network control method based on V2X, and in step 3, performing timely simulation and short-term prediction on road traffic based on the traffic control model policy, and acquiring a simulation result and a prediction result, where the method includes:
constructing a vehicle running track simulation equation, and performing simulation training verification on the vehicle running track simulation equation based on historical training data of a target road section to obtain a vehicle running track of a vehicle on the target road section;
the historical training data are driving data corresponding to different driving track types;
when a target vehicle observes a preset traffic identification position on the target road section, acquiring an observation distance and an observation angle of the target vehicle;
substituting the observation distance and the observation angle of the target vehicle into the calculation based on the vehicle running track simulation equation, and predicting to obtain specific position information of the target vehicle on the target road section;
wherein the target vehicle is a plurality of vehicles on the target road segment;
predicting a driving speed of the traffic flow of the target road section and the traffic volume of the traffic flow of the target road section based on the specific position information of the target vehicle on the target road section;
determining a speed-time relation between the running speed of the traffic flow of the target road section and the running time of a target vehicle passing through the target road section, and determining a relation between the time occupancy rate and the space occupancy rate of the target road section based on the speed-time relation;
obtaining a road network length calculation model of the target road section based on the relation between the time occupancy and the space occupancy of the target road section;
inputting the traffic volume of the traffic flow of the target road section into a road network length calculation model of the target road section, and predicting to obtain the length information of the current vehicle on the target road section;
recording and analyzing the current vehicle length information by a traffic control model strategy control platform based on the traffic control model, obtaining control information corresponding to the current road condition of the target road section, and sending the control information to each vehicle on the target road section in a wireless mode;
the management and control information comprises a vehicle running route, a driving limiting speed limit instruction, a temporary traffic control instruction and interval duration for controlling different indication states of a traffic signal lamp on the target road section;
recording the time length for the target road section to get rid of the current road condition after the road traffic is controlled through the traffic control model strategy;
comparing the time length with a preset time length;
if the time length is less than or equal to the preset time length, completing the timely simulation and short-term prediction of the road traffic;
and if not, controlling the current road condition of the target road section again based on the traffic control model strategy until the time length is less than or equal to the preset time length.
In this embodiment, the target road segment refers to a road segment for implementing a traffic control model policy, and may be a single road segment or multiple road segments.
In this embodiment, the vehicle travel track refers to a travel route of the vehicle on the target road segment.
In this embodiment, the preset traffic sign is set on the target road in advance, and is used for determining the position information of the vehicle on the target road, and the traffic sign may be a building, a signboard, or the like.
In this embodiment, the observation distance refers to a distance between the target vehicle and the preset identification position, and the position information of the target vehicle can be determined by the distance.
In this embodiment, the observation angle refers to the degree of an included angle between the target vehicle and the preset identification position.
In this embodiment, the travel speed of the traffic flow of the target link refers to an average travel speed of the traffic flow formed by a plurality of vehicles on the target road.
In this embodiment, the traffic volume of the traffic flow of the target link means the number of vehicles on the target link.
In this embodiment, the target link time occupancy refers to the total time that the vehicle travels on the target link, i.e., the ratio of the theoretical travel time to the actual travel time.
In this embodiment, the space occupancy refers to a ratio of the theoretical number of accommodated vehicles of the target link to the actual accommodated vehicle data.
In this embodiment, the preset time length is set in advance, and is used for measuring whether the time taken by the vehicle to pass through the target road section meets the target requirement.
The beneficial effects of the above technical scheme are: the method comprises the steps of obtaining a driving track equation of a vehicle on a target road section, accurately determining specific position information of the vehicle on the target road section, accurately judging traffic volume, vehicle driving speed and the like on the target road section according to the position information, managing and controlling the vehicle on the target road section through a traffic management and control model strategy, realizing behavior coordination and optimization of vehicle driving, and achieving the purposes of improving traffic flow operation efficiency and traffic safety.
Example 6:
on the basis of the foregoing embodiment 5, this embodiment provides a V2X-based road network control method for obtaining management and control information corresponding to the current road condition of the target road segment, and further includes:
acquiring a first influence range of the control information on the current road condition of the target road section to obtain an evaluation index corresponding to the control information;
meanwhile, acquiring the length information of the current vehicle on the target road section, predicting the running state evolution condition of the current road condition information of the target road section under the condition of no control information through a preset rule, determining a second influence range of the running state evolution condition on the target road section, and obtaining a comparison evaluation index corresponding to the control information;
performing difference calculation on the evaluation index and the comparison evaluation index to obtain a target difference value;
if the target difference is larger than or equal to a preset difference, judging that the management and control information reaches target management and control efficiency;
otherwise, judging that the management and control information does not reach the target management and control efficiency.
In this embodiment, the first influence range refers to the degree of influence caused by congestion of vehicles on the road under control.
In this embodiment, the evaluation index refers to an influence degree value for weighing the first influence range.
In this embodiment, the second influence range refers to the degree of influence caused by congestion of vehicles on the road without management and control.
In this embodiment, the comparative evaluation index is an influence degree value for measuring the second influence range.
In this embodiment, the preset difference is set in advance, and is used to measure whether the difference between the evaluation index and the comparison evaluation can meet the vehicle management and control requirement of the management and control information.
The beneficial effects of the above technical scheme are: by acquiring the influence range under the control condition and the influence range which is not controlled, whether the efficiency of the control condition meets the preset requirement or not is favorably and accurately judged, behavior cooperation and optimization between vehicle driving are realized, and the purposes of improving traffic flow operation efficiency and traffic safety are achieved.
Example 7:
on the basis of the foregoing embodiment 1, this embodiment provides a V2X-based road network control method, and in step 4, a specific working process of generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result includes:
acquiring a simulation entropy corresponding to the simulation result, and extracting a prediction entropy corresponding to the prediction result;
calculating a road traffic optimization control comprehensive coefficient according to the simulation entropy and the prediction entropy;
wherein K represents the road traffic optimization control comprehensive coefficient; delta represents an optimization coefficient, and the value range of the optimization coefficient is (0.3, 0.6); s1Representing the simulation entropy corresponding to the simulation result; s2Representing the prediction entropy corresponding to the prediction result; mu represents a synergistic factor, and the value range is (0.21, 0.22); w represents a traffic dimension, and generally takes a value of 3; t represents the time length of a preset time period;
performing optimization collaborative training on the road traffic optimization control comprehensive coefficient in a preset neural convolution network, and generating a real-time optimization collaborative control model based on a training result;
acquiring potential optimized cooperative control schemes from the real-time optimized cooperative control model, wherein the number of the potential optimized cooperative control schemes is more than 1;
performing real-time simulated traffic drilling on the road traffic based on the potential optimized cooperative control scheme, and respectively grading the potential optimized cooperative control scheme based on the real-time simulated traffic drilling result;
and taking the highest scoring as a real-time optimization cooperative control scheme.
In this embodiment, the simulation entropy represents the degree of confusion of the traffic flow in the timely simulation process.
In this embodiment, the prediction entropy represents the degree of confusion of the traffic flow in the short-term prediction process.
In this embodiment, the road traffic optimization control comprehensive coefficient may be a coefficient for obtaining road traffic optimization control through a difference between the simulation entropy and the prediction entropy, and a value range of the coefficient is (0, 1).
In this embodiment, the synergy factor represents a factor that synergizes the road traffic flow in the acquisition.
In this embodiment, the traffic dimension represents a dimension affecting road traffic, and includes: pedestrians, vehicles, crossroads.
The beneficial effects of the above technical scheme are: the road traffic optimization control comprehensive coefficient can be accurately calculated through the simulation entropy and the prediction entropy, so that a potential optimization cooperative control scheme is favorably determined, a final real-time optimization cooperative control scheme is determined through real-time simulation traffic drilling on road traffic, the accuracy of obtaining the real-time optimization cooperative control scheme is greatly improved, and the traffic safety is improved.
Example 8:
on the basis of embodiment 1, this embodiment provides a V2X-based road network control method, and in step 5, feedback is formed on the real-time optimized cooperative control scheme according to the real-time traffic awareness data, and an optimization mode for obtaining an optimal traffic running state of each road section and an intelligent networked vehicle behavior according to a feedback result includes:
acquiring a road condition comprehensive value of the real-time traffic perception data, and forming feedback on the real-time optimization cooperative control scheme according to the road condition comprehensive value;
acquiring a traffic operation feedback coefficient and an intelligent networking behavior feedback coefficient corresponding to a feedback result;
calculating a traffic operation comprehensive value of the road section based on the traffic operation feedback coefficient;
wherein Z represents the traffic running comprehensive value of the road section; xi1The traffic operation feedback coefficient is represented, and the value range is (0.45, 0.48); tau represents the terrain state coefficient of the road section, and the value range is (1, 2); d represents the road condition comprehensive value, and the value range is (0.5, 1); lambda represents the sparse degree value between vehicles on the road section, and the value range is (0, 1); a represents the number of vehicles on the road section;
analyzing the road section traffic operation comprehensive value, and performing correlation mapping on the analysis result and a preset road traffic operation state library;
acquiring the optimal traffic running state of each road section based on the mapping result;
meanwhile, calculating a dynamic value of the behavior of the intelligent networked vehicle based on the feedback coefficient of the behavior of the intelligent networked vehicle;
wherein X represents the intelligent networked vehicle behavior dynamic value; xi2The feedback coefficient of the intelligent networking behavior is represented, and the value range is (0.46, 0.49); v represents the running speed of the intelligent networked vehicle; a represents the average acceleration of the intelligent networked vehicle; t represents the actual running time of the intelligent networked vehicle on the section of the preset road section; t' represents the average running time of the intelligent networked vehicle on the section of the preset road section; y represents intelligent networked vehicle behavior pairThe influence rate of road traffic is (0.5, 0.9);
matching the intelligent networked vehicle behavior dynamic value in a preset intelligent networked vehicle behavior dynamic comparison table;
acquiring a matching position of the behavior of the intelligent networked vehicle in the preset intelligent networked vehicle comparison table based on a matching result;
and reading the optimization mode of the intelligent networked vehicle behavior according to the matching position.
In this embodiment, the road condition comprehensive value may be a value used for measuring the overall road condition, including a road congestion state, the number of pedestrians and vehicles at the road, the number of crossroads, the duration of traffic lights at the crossroads, and the like.
In this embodiment, the traffic operation comprehensive value of the road section refers to the fluency of traffic operation on the road section.
In this embodiment, the sparsity value of the vehicles on the road section may be determined by obtaining the distance between the vehicles on the road section, so as to determine the sparsity of the vehicles, for example, the smaller the distance between the vehicles on the road section is, the denser the vehicles are, and the larger the sparsity value of the vehicles on the road section is.
In this embodiment, the optimal traffic operation state of each road section is obtained based on the mapping result, which may be that there are various traffic operation states of each road section in a preset road traffic operation state library, and the optimal traffic operation state is obtained by mapping the optimal traffic operation state with the road section traffic operation comprehensive value.
In this embodiment, the dynamic behavior value of the intelligent networked vehicle is a value obtained by integrating parameters such as speed and acceleration of the intelligent networked vehicle, and is used for measuring the behavior state of the intelligent networked vehicle.
In this embodiment, the influence rate of the behavior of the intelligent networked vehicle on the road traffic may be the influence of the intelligent networked vehicle on the road traffic jam and the fluency, for example, the speed of the intelligent networked vehicle with fluent road traffic may be faster than the speed of the intelligent networked vehicle with the road traffic jam.
In this embodiment, the preset intelligent networked vehicle comparison table includes an intelligent networked vehicle behavior dynamic value and an optimization mode of an intelligent networked vehicle behavior corresponding to the intelligent networked vehicle behavior dynamic value.
In this embodiment, the more severe the road profile, the smaller the corresponding terrain state coefficient.
The beneficial effects of the above technical scheme are: by acquiring the road live comprehensive value of the real-time traffic perception data, feedback can be effectively formed according to the road live comprehensive value to a real-time optimization cooperative control scheme, and by acquiring the traffic operation feedback coefficient and the intelligent network connection behavior feedback coefficient, the intelligent network connection vehicle behavior dynamic value and the road section traffic operation comprehensive value can be accurately calculated, the optimal traffic operation state of each road section and the optimization mode of the intelligent network connection vehicle behavior can be efficiently acquired, so that the traffic flow operation efficiency is favorably improved.
Example 9:
the present embodiment provides a road network control system based on V2X, as shown in fig. 2, including:
the data acquisition module is used for acquiring real-time traffic state perception data based on a V2X road network control technology and acquiring traffic flow running state prediction data based on traffic simulation;
the strategy making module is used for forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
the strategy implementation module is used for implementing timely simulation and short-term prediction on road traffic based on the traffic control model strategy and acquiring a simulation result and a prediction result;
the optimization cooperative module is used for generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
the feedback module is used for forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data and acquiring the optimal traffic running state of each road section and the optimization mode of intelligent networked vehicle behaviors according to the feedback result;
and the control module is used for realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior.
The beneficial effects of the above technical scheme are: by acquiring the traffic state perception data and the traffic flow running state prediction data, a corresponding road management and control scheme is formulated, the behavior coordination and optimization of vehicles during running are realized, and the purposes of improving the traffic flow running efficiency and traffic safety are achieved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A road network control method based on V2X is characterized by comprising the following steps:
step 1: acquiring real-time traffic state perception data based on a V2X road network control technology, and acquiring traffic flow running state prediction data based on traffic simulation;
step 2: forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
and step 3: performing real-time simulation and short-term prediction on road traffic based on the traffic control model strategy, and acquiring a simulation result and a prediction result;
and 4, step 4: generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
and 5: forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data, and acquiring the optimal traffic running state of each road section and the optimization mode of the behavior of the intelligent networked vehicle according to the feedback result;
step 6: and realizing V2X road network control according to the optimal traffic running state of each road section and the optimization mode of the intelligent network vehicle behavior.
2. The V2X-based road network control method according to claim 1, wherein the work process of obtaining traffic flow operation state prediction data based on traffic simulation in step 1 comprises:
acquiring a simulation road of the traffic simulation, and determining the area where a first simulation vehicle is located based on the simulation road;
simultaneously, acquiring the vehicle behavior of the first simulated vehicle;
constructing a two-dimensional geometric simulation structure based on the vehicle behavior of the first simulated vehicle and the area in which the first simulated vehicle is located;
according to the two-dimensional geometric simulation structure, vehicle behaviors of the first simulated vehicle are obtained, and meanwhile, a moving range of the vehicle behaviors of the first simulated vehicle is obtained;
judging whether a second simulated vehicle exists in the moving range, and acquiring first running state prediction data according to the vehicle behavior of the first simulated vehicle;
otherwise, acquiring an information vehicle set according to the first simulation vehicle and the second simulation vehicle based on the two-dimensional set simulation structure;
and meanwhile, acquiring the comprehensive vehicle behavior of the information vehicle set, and acquiring a second running state prediction parameter according to the comprehensive vehicle behavior.
3. The V2X-based network control method of claim 2, wherein determining the operation of the area in which the first simulated vehicle is located comprises:
judging whether the position of the first simulation vehicle is within the range of a preset intersection or not;
if the position of the first simulation vehicle is within a preset intersection range, the area where the first vehicle is located is a road coordination area;
otherwise, the area where the first vehicle is located is the road queue area.
4. The V2X-based network control method according to claim 1, wherein in step 2, the concrete process of forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow operation state prediction data includes:
acquiring real-time traffic density of current traffic based on the real-time traffic state perception data;
meanwhile, the predicted traffic density of the current traffic is obtained according to the traffic flow running state prediction data;
comparing the current real-time traffic density with the predicted traffic density, and judging the traffic control state;
when the real-time traffic density is larger than the predicted traffic density, judging that the traffic control state is poor, and matching a first traffic control protocol in a preset traffic control protocol library;
when the real-time traffic density is equal to the predicted traffic density, judging that the traffic control state is good, and meanwhile, matching a second traffic control protocol in the preset traffic control protocol library;
when the real-time traffic density is smaller than the predicted traffic density, judging that the traffic control state is excellent, and matching a third traffic control protocol in the preset traffic control protocol library;
and generating a traffic control model strategy based on the first traffic control protocol, the second traffic control protocol and the third traffic control protocol.
5. The V2X-based network control method according to claim 1, wherein in step 3, performing timely simulation and short-term prediction on road traffic based on the traffic control model strategy, and obtaining a simulation result and a prediction result comprises:
constructing a vehicle running track simulation equation, and performing simulation training verification on the vehicle running track simulation equation based on historical training data of a target road section to obtain a vehicle running track of a vehicle on the target road section;
the historical training data are driving data corresponding to different driving track types;
when a target vehicle observes a preset traffic identification position on the target road section, acquiring an observation distance and an observation angle of the target vehicle;
substituting the observation distance and the observation angle of the target vehicle into the calculation based on the vehicle running track simulation equation, and predicting to obtain specific position information of the target vehicle on the target road section;
wherein the target vehicle is a plurality of vehicles on the target road segment;
predicting a driving speed of the traffic flow of the target road section and the traffic volume of the traffic flow of the target road section based on the specific position information of the target vehicle on the target road section;
determining a speed-time relation between the running speed of the traffic flow of the target road section and the running time of a target vehicle passing through the target road section, and determining a relation between the time occupancy rate and the space occupancy rate of the target road section based on the speed-time relation;
obtaining a road network length calculation model of the target road section based on the relation between the time occupancy and the space occupancy of the target road section;
inputting the traffic volume of the traffic flow of the target road section into a road network length calculation model of the target road section, and predicting to obtain the length information of the current vehicle on the target road section;
recording and analyzing the current vehicle length information by a traffic control model strategy control platform based on the traffic control model, obtaining control information corresponding to the current road condition of the target road section, and sending the control information to each vehicle on the target road section in a wireless mode;
the management and control information comprises a vehicle running route, a driving limiting speed limit instruction, a temporary traffic control instruction and interval duration for controlling different indication states of a traffic signal lamp on the target road section;
recording the time length for the target road section to get rid of the current road condition after the road traffic is controlled through the traffic control model strategy;
comparing the time length with a preset time length;
if the time length is less than or equal to the preset time length, completing the timely simulation and short-term prediction of the road traffic;
and if not, controlling the current road condition of the target road section again based on the traffic control model strategy until the time length is less than or equal to the preset time length.
6. The V2X-based network control method according to claim 5, wherein obtaining control information corresponding to the current road condition of the target road segment further comprises:
acquiring a first influence range of the control information on the current road condition of the target road section to obtain an evaluation index corresponding to the control information;
meanwhile, acquiring the length information of the current vehicle on the target road section, predicting the running state evolution condition of the current road condition information of the target road section under the condition of no control information through a preset rule, determining a second influence range of the running state evolution condition on the target road section, and obtaining a comparison evaluation index corresponding to the control information;
performing difference calculation on the evaluation index and the comparison evaluation index to obtain a target difference value;
if the target difference is larger than or equal to a preset difference, judging that the management and control information reaches target management and control efficiency;
otherwise, judging that the management and control information does not reach the target management and control efficiency.
7. The V2X-based network control method according to claim 1, wherein in step 4, the specific working process of generating the real-time optimized cooperative control scheme according to the simulation result and the prediction result includes:
acquiring a simulation entropy corresponding to the simulation result, and extracting a prediction entropy corresponding to the prediction result;
calculating a road traffic optimization control comprehensive coefficient according to the simulation entropy and the prediction entropy;
wherein K represents the road traffic optimization control comprehensive coefficient; delta represents an optimization coefficient, and the value range of the optimization coefficient is (0.3, 0.6); s1Representing the simulation entropy corresponding to the simulation result; s2Representing the prediction entropy corresponding to the prediction result; mu represents a synergistic factor, and the value range is (0.21, 0.22); w represents a traffic dimension, and generally takes a value of 3; t represents the time length of a preset time period;
performing optimization collaborative training on the road traffic optimization control comprehensive coefficient in a preset neural convolution network, and generating a real-time optimization collaborative control model based on a training result;
acquiring potential optimized cooperative control schemes from the real-time optimized cooperative control model, wherein the number of the potential optimized cooperative control schemes is more than 1;
performing real-time simulated traffic drilling on the road traffic based on the potential optimized cooperative control scheme, and respectively grading the potential optimized cooperative control scheme based on the real-time simulated traffic drilling result;
and taking the highest scoring as a real-time optimization cooperative control scheme.
8. The V2X-based network control method according to claim 1, wherein in step 5, forming feedback to the real-time optimized cooperative control scheme according to the real-time traffic perception data, and obtaining optimal traffic running states of road sections and an optimized mode of intelligent networked vehicle behaviors according to a feedback result comprises:
acquiring a road condition comprehensive value of the real-time traffic perception data, and forming feedback on the real-time optimization cooperative control scheme according to the road condition comprehensive value;
acquiring a traffic operation feedback coefficient and an intelligent networking behavior feedback coefficient corresponding to a feedback result;
calculating a traffic operation comprehensive value of the road section based on the traffic operation feedback coefficient;
wherein Z represents the traffic running comprehensive value of the road section; xi1The traffic operation feedback coefficient is represented, and the value range is (0.45, 0.48); tau represents the terrain state coefficient of the road section, and the value range is (1, 2); d represents the road condition comprehensive value, and the value range is (0.5, 1); lambda represents the sparse degree value between vehicles on the road section, and the value range is (0, 1); a represents the number of vehicles on the road section;
analyzing the road section traffic operation comprehensive value, and performing correlation mapping on the analysis result and a preset road traffic operation state library;
acquiring the optimal traffic running state of each road section based on the mapping result;
meanwhile, calculating a dynamic value of the behavior of the intelligent networked vehicle based on the feedback coefficient of the behavior of the intelligent networked vehicle;
wherein X represents the intelligent networked vehicle behavior dynamic value; xi2The feedback coefficient of the intelligent networking behavior is represented, and the value range is (0.46, 0.49); v represents the running speed of the intelligent networked vehicle; a represents the average acceleration of the intelligent networked vehicle; t represents the actual running time of the intelligent networked vehicle on the section of the preset road section; t' represents the average running time of the intelligent networked vehicle on the section of the preset road section; y represents the influence rate of the intelligent networked vehicle behaviors on road traffic, and the value range is (0.5, 0.9);
matching the intelligent networked vehicle behavior dynamic value in a preset intelligent networked vehicle behavior dynamic comparison table;
acquiring a matching position of the behavior of the intelligent networked vehicle in the preset intelligent networked vehicle comparison table based on a matching result;
and reading the optimization mode of the intelligent networked vehicle behavior according to the matching position.
9. A road network control system based on V2X is characterized by comprising:
the data acquisition module is used for acquiring real-time traffic state perception data based on a V2X road network control technology and acquiring traffic flow running state prediction data based on traffic simulation;
the strategy making module is used for forming a traffic control model strategy according to the real-time traffic state perception data and the traffic flow running state prediction data;
the strategy implementation module is used for implementing timely simulation and short-term prediction on road traffic based on the traffic control model strategy and acquiring a simulation result and a prediction result;
the optimization cooperative module is used for generating a real-time optimization cooperative control scheme according to the simulation result and the prediction result;
the feedback module is used for forming feedback on the real-time optimization cooperative control scheme according to the real-time traffic perception data and acquiring the optimal traffic running state of each road section and the optimization mode of intelligent networked vehicle behaviors according to the feedback result;
and the control module is used for realizing the control of the V2X road network according to the optimal traffic running state of each road section and the optimization mode of the intelligent networked vehicle behavior.
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| CN116923442B (en) * | 2023-09-13 | 2023-11-28 | 华东交通大学 | Control strategy generation method and system for intelligent network-connected automobile |
| CN117275232A (en) * | 2023-09-28 | 2023-12-22 | 广东省电信规划设计院有限公司 | Dynamic sensing method and device based on vehicle-road cooperation |
| CN117275232B (en) * | 2023-09-28 | 2024-05-31 | 广东省电信规划设计院有限公司 | Dynamic sensing method and device based on vehicle-road cooperation |
| CN117351714A (en) * | 2023-10-11 | 2024-01-05 | 深圳达普信科技有限公司 | Intelligent traffic flow optimization method and system based on Internet of vehicles |
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