CN113658432A - No-signal-lamp intersection traffic optimization method based on vehicle traffic priority game - Google Patents
No-signal-lamp intersection traffic optimization method based on vehicle traffic priority game Download PDFInfo
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- CN113658432A CN113658432A CN202110944171.XA CN202110944171A CN113658432A CN 113658432 A CN113658432 A CN 113658432A CN 202110944171 A CN202110944171 A CN 202110944171A CN 113658432 A CN113658432 A CN 113658432A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/161—Decentralised systems, e.g. inter-vehicle communication
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
A no signal lamp crossing traffic optimization method based on vehicle traffic priority game, when there is collision conflict, each vehicle calculates the traffic time of the vehicle, then broadcast the traffic time to other vehicles; in the priority game stage, the vehicle carries out Nash equilibrium through the passing time, and updates the passing time of all vehicles and broadcasts the passing time to other vehicles after searching the pareto optimal solution which meets the conditions in the obtained Nash equilibrium solution set; when the passing time of all vehicles is the minimum passing time, priority distribution and broadcasting are carried out, and the state quantity of the vehicle and the control instruction are sent to other vehicles so as to execute the control instruction; according to the invention, under the intersection scene without signal lamps and a centralized controller, the communication between vehicles is utilized, so that the vehicles can directly play games, the interaction characteristics among the vehicles can be effectively highlighted, the targeted games among the vehicles are enhanced, and the traffic efficiency of the intersection is improved while the safety is ensured.
Description
Technical Field
The invention relates to a technology in the field of intelligent traffic management, in particular to a no-signal-lamp intersection traffic optimization method based on a vehicle traffic priority game.
Background
Safety and traffic efficiency are the core concerns of intersection research. Under the crossing environment of no signal lamp, because of the disappearance of signal lamp guide, easily lead to the vehicle to travel the confusion, the current inefficiency. In addition, advanced assistant driving equipped for the intelligent vehicle only carries out safety assistance within the visual range of the vehicle, namely, within the sensing range of the vehicle sensor, so that the advanced assistant driving is not enough to ensure the safety of the vehicle at an intersection with a sensing blind area. The intelligent vehicles run in coordination by information interaction, and the running safety can be effectively ensured by acquiring information of other vehicles in advance. However, given traffic rules, such as first-come-first-serve traffic rules, i.e., fixed traffic priorities, do not guarantee optimization of traffic efficiency.
The existing traffic flow control technology of the intersection only aims at the intersection scene with the deployed controller, and if the intersection is not provided with the controller, the existing technology cannot be implemented; meanwhile, if the controller is deployed at each intersection, the installation and maintenance cost is greatly increased.
Disclosure of Invention
The invention provides a no-signal-lamp intersection traffic optimization method based on a vehicle traffic priority game, aiming at the problems that in the prior art, the traffic efficiency of vehicles is not high due to unreasonable bids and the fact that the vehicles are not fit all the time due to the fact that the bids are unreasonable in a mode of determining the priority through a bid cooperation mode, and in the prior art, only vehicles located in a conflict area are cooperatively designed, and the influence of the priority on the traffic efficiency before the vehicles enter the conflict area is ignored.
The invention is realized by the following technical scheme:
the invention relates to a no-signal-lamp intersection traffic optimization method based on a vehicle traffic priority game, which comprises the following steps:
and step A, receiving the state information and the driving intention of the surrounding vehicle by the vehicle based on the broadcast of other vehicles.
The state information includes: the position information of the vehicle includes longitude, latitude and altitude information of the vehicle, speed information of the vehicle, and acceleration information of the vehicle.
The driving intentions include: straight, left turn, right turn.
B, predicting the tracks of other vehicles, and judging that the vehicle safely drives away under the constraint of a specified speed when no collision conflict exists; when there is a collision conflict, each vehicle calculates the transit time of the vehicle and then broadcasts the transit time to other vehicles.
The track prediction means that: and calculating the position of the vehicle in the future time period according to the state information of other vehicles.
The collision conflict is as follows: and in the future time period, if the intersection points exist among the predicted tracks of the vehicles, judging that collision conflict exists.
The safe driving-off means that: and because the collision conflict does not exist, the vehicle safely passes through and leaves the intersection on the premise of meeting the traffic rules.
And step C, in the priority game stage, the vehicle carries out Nash equilibrium through the broadcast transit time of other vehicles obtained in the step B, and updates the transit time of all vehicles and broadcasts the transit time to other vehicles after searching for the pareto optimal solution meeting the conditions in the obtained Nash equilibrium solution set.
And D, when the passing time of all the vehicles is the minimum passing time, distributing and broadcasting the priority, and sending the state quantity of the vehicle and the control command to other vehicles so as to execute the control command, otherwise, returning to the step C to play the priority game again.
The priority allocation comprises the following steps: the vehicles pass through the sequence of the crossroads.
The vehicle state quantity includes: position, speed, acceleration information of the vehicle.
The control instruction comprises: control inputs from vehicle floor actuators such as accelerator pedal, brake pedal, steering wheel.
Technical effects
The invention integrally solves the defect that the prior art only aims at the vehicles in the conflict area (intersection) and ignores the influence of the priority on the passing efficiency before the vehicles enter the conflict area.
Compared with the prior art, the continuity of the vehicles entering and exiting the intersection is increased by adding the distribution layer of the vehicle passing priority in a game mode, and the passing efficiency of the intersection is further improved. In addition, the method does not need to additionally arrange a centralized controller (coordinator/signal lamp) at the intersection, so that the hardware and maintenance cost is greatly reduced.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, the embodiment relates to a no-signal intersection traffic optimization method based on a vehicle traffic priority gaming algorithm, which specifically includes:
and step A, receiving the state information and the driving intention of the surrounding vehicle by the vehicle based on the broadcast of other vehicles.
B, predicting tracks of other vehicles, judging whether the vehicles conflict or not, and safely driving away the vehicles under the constraint of a specified speed when the vehicles do not conflict; when there is a conflict, all vehicle transit times, i.e., the times at which the vehicles pass through the intersection, are calculated.
The vehicle with the maximum passing time of all vehiclesThe time of passage of a vehicle, i.e. the time of passage of the last vehicle, is not the sum of all vehicle times, in particular
Wherein: g () is a function definition with the function of solving the transit time, the time for a single vehicle to pass through the intersection
Wherein: v is velocity, a is acceleration, m is vehicle ID, i is number of iterations, and p is position.
In this embodiment, three vehicles are taken as an example, the lower corner signs 1, 2 and 3 are respectively vehicle IDs, u is an optimized control quantity, and usIn order to control the amount of the process in a stepwise manner,
indicating selection of a control quantity for each vehicle to participate in nash-balance calculations, e.g. combining
By finding time in different combinations, TtotalThe solution sets up 1 × 6 matrices, i.e., there are six different combinations, but each combination requires enumeration calculations.
And step C, in the priority game stage, the vehicle calculates Nash equilibrium by acquiring the passing time broadcasted by other vehicles, and simultaneously updates the passing time of all vehicles after acquiring the pareto optimal solution set and broadcasts the passing time to other vehicles.
The step of updating the passing time of all vehicles is as follows:
wherein: g () is defined as above-mentioned,
the upper-middle index p is the control quantity that distinguishes pareto optima.
And D, when the passing time of all the vehicles is the minimum passing time, distributing and broadcasting the priority, and sending the state quantity of the vehicle and the control command to other vehicles so as to execute the control command, otherwise, returning to the step C to play the priority game again.
In this embodiment, under the environment of hardware in a ring, 3 vehicles are deployed for an experiment, specifically:
vehicle 1: 90m away from the intersection, the vehicle speed is 15m/s, the acceleration is 0, and the priority is 1;
the vehicle 2: 100m away from the intersection, the vehicle speed is 15m/s, the acceleration is 0, and the priority is 3;
the vehicle 3: 90m away from the intersection, the vehicle speed is 13m/s, the acceleration is 0, and the priority is 2;
the experimental data obtained are shown in table 1.
TABLE 1 intersection passage time
Note: s is time unit seconds.
Compared with the prior art, the method can obviously improve the crossing passing efficiency and shorten the time for the vehicle to pass through, namely the time for the last vehicle to pass through the crossing.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (5)
1. A no-signal-lamp intersection traffic optimization method based on a vehicle traffic priority game is characterized by comprising the following steps:
step A, on the basis of the broadcast of other vehicles, the vehicle receives the state information and the driving intention of surrounding vehicles;
b, predicting the tracks of other vehicles, and judging that the vehicle safely drives away under the constraint of a specified speed when no collision conflict exists; when collision conflict exists, each vehicle respectively calculates the passing time of the vehicle, and then broadcasts the passing time to other vehicles;
step C, in the priority game stage, the vehicle carries out Nash equilibrium through the broadcast transit time of other vehicles obtained in the step B, and updates the transit time of all vehicles and broadcasts to other vehicles after searching for pareto optimal solutions meeting the conditions in a concentrated manner through the obtained Nash equilibrium solutions;
the passing time of all vehicles is the maximum passing time of the vehicle, namely the passing time of the last vehicle, and is not the sum of all vehicle times, specifically the sum of all vehicle times
Wherein: g () is a function definition with the function of solving the transit time, the time for a single vehicle to pass through the intersection
Wherein: v is the velocity, a is the acceleration, m is the vehicle ID, i is the number of iterations, and p is the position;
the step of updating the passing time of all vehicles is as follows:
wherein: g () is defined as above-mentioned,
the upper middle corner mark p is the control quantity for distinguishing the pareto optimal;
d, when the passing time of all vehicles is the minimum passing time, priority distribution and broadcasting are carried out, the state quantity of the vehicle and the control instruction are sent to other vehicles so as to execute the control instruction, and if not, the step C is returned to for carrying out the priority game again;
the state information includes: position information of the vehicle including longitude, latitude and altitude information of the vehicle, speed information of the vehicle, acceleration information of the vehicle;
the driving intentions include: straight, left turn, right turn.
2. The no-signal-lamp intersection traffic optimization method based on the vehicle traffic priority game is characterized in that the track prediction is as follows: and calculating the position of the vehicle in the future time period according to the state information of other vehicles.
3. The no-signal-lamp intersection traffic optimization method based on the vehicle traffic priority game as claimed in claim 1, wherein the collision conflict is: and in the future time period, if the intersection points exist among the predicted tracks of the vehicles, judging that collision conflict exists.
4. The no-signal-lamp intersection traffic optimization method based on the vehicle traffic priority game is characterized in that the safe driving-off refers to the following steps: and because the collision conflict does not exist, the vehicle safely passes through and leaves the intersection on the premise of meeting the traffic rules.
5. The no-signal-lamp intersection traffic optimization method based on the vehicle traffic priority game, as claimed in claim 1, wherein the control command includes: control inputs to vehicle floor actuators.
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Cited By (4)
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| CN114495499A (en) * | 2022-01-21 | 2022-05-13 | 东南大学 | Multi-target intelligent networked vehicle collaborative optimization control method |
| CN115331461A (en) * | 2022-07-28 | 2022-11-11 | 武汉理工大学 | Mixed traffic passing control method and device for signalless intersection and vehicle |
| CN116168550A (en) * | 2022-12-30 | 2023-05-26 | 福州大学 | Traffic coordination method for intelligent network-connected vehicles at signalless intersections |
| CN116519004A (en) * | 2023-06-30 | 2023-08-01 | 福思(杭州)智能科技有限公司 | Vehicle track planning method and device, storage medium and electronic equipment |
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| CN114495499B (en) * | 2022-01-21 | 2023-02-14 | 东南大学 | Multi-target intelligent internet vehicle cooperative optimization control method |
| CN115331461A (en) * | 2022-07-28 | 2022-11-11 | 武汉理工大学 | Mixed traffic passing control method and device for signalless intersection and vehicle |
| CN115331461B (en) * | 2022-07-28 | 2023-12-29 | 武汉理工大学 | Mixed traffic passing control method and device for signalless intersection and vehicle |
| CN116168550A (en) * | 2022-12-30 | 2023-05-26 | 福州大学 | Traffic coordination method for intelligent network-connected vehicles at signalless intersections |
| CN116519004A (en) * | 2023-06-30 | 2023-08-01 | 福思(杭州)智能科技有限公司 | Vehicle track planning method and device, storage medium and electronic equipment |
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