CN115565390B - Intelligent network-connected automobile multi-lane queue traffic control method, system and computer readable storage medium - Google Patents
Intelligent network-connected automobile multi-lane queue traffic control method, system and computer readable storage medium Download PDFInfo
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
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- G—PHYSICS
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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Abstract
The invention discloses a method, a system and a computer readable storage medium for controlling intelligent network-connected automobile multi-lane queue traffic, wherein the method comprises the following steps: collecting an urban traffic network map to form basic map data of a control center; acquiring traffic data, acquiring traffic route information, and marking the traffic route information on the traffic network map according to the statistical frequency of the traffic routes; the control center acquires the traffic message, analyzes the traffic data in the traffic message, calculates the vehicle formation information, the optimal speed of the queue and the vehicle distance from the traffic data, and transmits the instruction to the vehicle terminal through the wireless network; the pass message includes RSI, BSM, SPAT, MAP and BSM messages; the vehicle terminal receives the instruction of the control center, judges whether the vehicle enters the control area, and performs primary lane selection according to the path planning information when the vehicle enters the control area. The invention can greatly improve the passing efficiency.
Description
Technical Field
The invention belongs to the technical field of network-connected automobile traffic flow prediction, and particularly relates to an intelligent network-connected automobile multi-lane queue traffic control method.
Background
According to the latest data of the Ministry of industrial and telecommunication, the market permeability of the new vehicle of the L2-level auxiliary driving passenger vehicle in 2021 China reaches 23.5 percent, and the permeability of the new vehicle in 2022 is increased to 30 percent in the last half year. The test mileage of the open road in China exceeds 5000 km. Under policy support, the breakthroughs of innovative technologies accelerate the forward propulsion of intelligent network-connected automobiles, and the road management of the network-connected automobiles is an indispensable important component in road network in the future, so that the system has positive significance in improving the traffic capacity, the transportation efficiency, the safety and the like of a road traffic system. Therefore, it is important to select an online lane management method which is simple and convenient to manage and has the calculated amount meeting the requirement of global optimal time efficiency.
In recent years, with the development of internet of vehicles technology, vehicles are able to share information with each other and sense a local environment through advanced communication technologies such as V2V and V2I. In this case, all the networked vehicles in the future can perform queue division management and driving control by using communication and automatic control technologies. Compared with the planning of single vehicle, the space-time distance of continuous vehicles in the queue is much smaller, the information quantity is smaller than that of single vehicles, the running requirement of single vehicles is met in the queue management, the global optimal road passing method is finally achieved, and the road passing efficiency and the safety management of future networked vehicles can be greatly improved.
According to the prior patent data, the method for forming the network-connected vehicles basically only considers the traffic strategy on a single lane or a straight lane, and the method for planning the network-connected vehicles can only achieve local optimum road sections, thereby optimizing the overall road traffic capacity and improving the space.
Disclosure of Invention
The invention aims to: aiming at the problems in the prior art, the invention provides a rule-based intelligent network-connected vehicle multi-lane queue global optimal passing method, which is used for realizing traffic control by combining real-time traffic information of roads and network-connected vehicle state information through lane selection, queue division and global speed guiding. A solution is provided for the situation that the network-connected vehicle queues aim at various roads and driving requirements on the roads.
The technical scheme is as follows: the intelligent network-connected automobile multi-lane queue traffic control method comprises the following steps:
Step S1, acquiring an urban traffic network map to form basic map data of a control center; acquiring traffic data, acquiring traffic route information, and marking the traffic route information on the traffic network map according to the statistical frequency of the traffic routes;
S2, the control center acquires a traffic message, analyzes traffic data in the traffic message, calculates vehicle formation information, the optimal speed of a queue and the distance between vehicles according to the traffic data, and transmits an instruction to a vehicle terminal through a wireless network; the pass message includes RSI, BSM, SPAT, MAP and BSM messages;
And S3, the vehicle terminal receives the instruction of the control center, judges whether the vehicle enters the control area, and performs primary lane selection according to the path planning information when the vehicle enters the control area.
According to an aspect of the present application, in the step S1, further includes:
Acquiring traffic route information, and constructing a traffic route directed graph consisting of directed line segments of different road sections based on the traffic route information; the directed line segments of adjacent road segments are traffic intersection nodes;
for each road section directed graph, dividing a first directed section and a second directed section at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed section and the second directed section according to the lengths of the road section directed line segments and the statistical frequency; the first directed interval and/or the second directed interval are/is a control area;
and labeling the first directed interval and the second directed interval in the traffic network map.
According to one aspect of the present application, the step S2 further includes: and extracting the green wave speed from the SPAT, calculating the difference value between the current optimal speed of the queue and the green wave speed, and if the current optimal speed of the queue is smaller than the difference value, updating the optimal speed of the queue into the green wave speed.
According to one aspect of the present application, the step S3 further includes:
Step S31, the preliminary selection of the lane comprises that the straight-going vehicle enters the straight-going lane preferentially, then enters the right-turning and straight-going lane, finally enters the left-turning and straight-going lane, and if the left-turning and straight-going lane has left-turning vehicles waiting at the moment, the vehicle is selected not to enter; the left-turn vehicle enters a left-turn lane preferentially and then enters a left-turn straight lane; the right-turn vehicle enters a right-turn vehicle lane preferentially, then enters a right-turn straight-going lane, and if the lane belonging to the right-turn vehicle lane does not exist in a rule on the road, the right-turn vehicle can directly jump over the lane to adjust; the first vehicle of the independent queue on each lane is a head vehicle, and the other vehicles are auxiliary vehicles;
Step S32, the control area is a vehicle buffer area and a vehicle speed adjustment area, and the lane change task is completed in the vehicle buffer area based on a lane adjustment strategy; if the vehicle is set as a train head, the vehicle speed is adjusted as follows:
① . When the intersection is green light and meets V aim＝Vmax, wherein V t is an ideal maximum vehicle speed for completing passing through an intersection, a is a vehicle safety acceleration, T g is a green light remaining time, and l R is a distance from the front end of a head vehicle to a front signal lamp intersection;
② . When the next green time of the intersection is still T r, if the vehicle is decelerated to the minimum limit speed V min of the lane can reach the signal lamp intersection, V t satisfies: v aim＝Vt;
③ . The other conditions are that the vehicle cannot pass through the intersection without stopping, and V aim =0; when the train cannot pass through the signal lamp intersection without stopping, V aim = 0, the distance between the parking position of the head vehicle and the signal lamp intersection is d, the calculation is carried out according to the train sequence, the number m of vehicles in the front train, the vehicle distance l d and the average length l c of the vehicle body are related, the head vehicle advances when the vehicles in front of the train pass through, and finally the head vehicle is sequentially accelerated when the vehicles can pass through the intersection; where d=m (l d+lc).
According to one aspect of the present application, the method further comprises step S33, if the vehicle is set as a train following vehicle, the initial vehicle speed is V';
① . Finishing a lane changing task based on a lane adjustment strategy in the buffer area;
② . After the lane change is completed, the vehicle enters a speed adjustment area, T=0, the target is to accelerate to V max at first, then decelerate to V aim and adjust to the position of the optimal distance from the front vehicle, the vehicle is equidistant and uniformly driven with the front vehicle, the distance between the front vehicle and the tail of the front vehicle is known as l, the number of vehicles in front of the queue is n, the optimal distance between the front vehicle and the vehicle is l d, the average length lc of the vehicle body, the target vehicle speed is V aim, the acceleration stage time is T1, the time for reaching the target vehicle speed is T2, the maximum and minimum road speed limit is V max、Vmin, the vehicle acceleration is a', and the acceleration and the braking speed of the maximum acceleration of the vehicle are And/>Obtained by the vehicle itself;
③ . If the head vehicle is waiting for parking, the target vehicle speed V aim =0.
In another embodiment of the present application, there is also provided an intelligent networked car multi-lane queue traffic control system, including:
At least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the intelligent networked car multi-lane queue traffic control method of any of the embodiments described above.
In another embodiment of the present application, there is further provided a computer readable storage medium storing computer instructions for execution by the computer of the intelligent network-connected car multi-lane queue traffic control method according to any one of the above embodiments.
The beneficial effects are that:
Different from the common queue planning method, the method provided by the invention has the advantages that the lane adjustment strategy is provided for lane selection and queue division while the different path planning requirements of the vehicles are considered, so that the convenience is brought to the formation management of the vehicles under the condition of effectively using road resources.
In the management of internet-connected vehicles on roads, if the calculation amount taking the individual vehicles as management objects is large and complex, a vehicle management method based on intelligent internet-connected vehicle queue combination is provided, the internet-connected vehicles on the road sections are formed into teams according to the difference of single signal lamp trafficability and lane selection, and vehicle planning management is carried out by taking small teams as units, wherein the calculation of the head speed and the calculation of the optimal distance between the auxiliary vehicles are included.
Drawings
Fig. 1 is a schematic illustration of an intersection segment according to a first embodiment of the present invention.
FIG. 2 is a flow chart of one embodiment of the present invention.
Fig. 3 is a flow chart of another embodiment of the present invention.
Detailed Description
Embodiment one: as shown in fig. 1, an embodiment is provided, specifically as follows: under the networking environment, the cloud platform acquires road basic information including vehicle position, speed, number and path information in a road section, lane traffic and speed limit information, real-time state information of a signal lamp at the end of an intersection and the like through SPAT. The cloud platform calculates vehicle formation information, the optimal speed of the queue and the vehicle distance according to the real-time data, and transmits the instruction to the vehicle terminal through the wireless network for automatic control, and the vehicle terminal is required to be provided with a vehicle-mounted communication system.
The method is characterized in that a single road section is taken as a basic unit, and lanes in the road section are divided into 5 types, namely a left-turning lane, a left-turning straight lane, a right-turning straight lane and a right-turning lane. The road section inlet part is set as a queue buffer area, and the outlet part is set as a speed adjustment area, which is not explicitly divided, based on the fact that a single vehicle completes a lane change target.
The method comprises the steps that vehicles enter a control road section area, firstly, lane preliminary selection is conducted in a queue buffer area according to path planning information, and in the adjustment process, network vehicle connection queues are divided according to different lanes, real-time states of traffic lights passing through road sections, vehicle position and number information and speed limit information on the road sections through a queue stacking method. When the vehicle meets the next green light in the current green light or red light state, planning the vehicle to an initial queue, and carrying out lane adjustment according to a lane adjustment strategy; when the pass condition is not met or the condition is met but the initial queue is already full, it is adjusted to the wait queue and vice versa. After the queue is divided, optimizing the optimal target speed of the pilot vehicle and the optimal distance between the auxiliary vehicles in the agreed queue to form a global optimal queue, and improving the safety and the comfort of the queue vehicles on the premise of ensuring the passing of the queue.
Lane adjustment strategy: the straight-going vehicle enters the straight-going lane preferentially, then enters the right-turning and straight-going lane, finally enters the left-turning and straight-going lane, and if the left-turning and straight-going lane has left-turning vehicles waiting at the moment, the vehicle is selected not to enter. The left-turn vehicle enters the left-turn lane preferentially and then enters the left-turn straight lane. The right-turn vehicle enters the right-turn lane preferentially and then enters the right-turn and straight-going lane. If the road has no lane to which the rule belongs, the lane can be directly skipped to carry out adjustment.
Limiting conditions: when the vehicle enters the road section of the control area, other vehicles are not considered, and only the speed, the position and the traffic light time of the vehicle are considered to meet the traffic conditions.
Queue partitioning based on lane rules: setting 3 straight-running lanes, dividing the vehicles into initial straight-running queues 1,2 and 3 (the left-turning vehicle waiting on the left-turning straight-running lane is not considered in the queue 3) when the vehicles enter the vehicle to meet the limiting condition, and dividing the vehicles into waiting straight-running queues 4,5 if the initial straight-running queues 1,2 and 3 are full; the limit condition is not satisfied, and the initial queues are emptied according to the number of straight roads, and the waiting straight (or left/right) queues 4,5 are sequentially entered. And finally, selecting a lane according to a lane selection strategy. The turning vehicle is the same as the straight vehicle.
Global queue speed/spacing adjustment policy: vehicle number in queue: the first vehicle in each independent queue on each lane is taken as a head vehicle, and the other vehicles are auxiliary vehicles.
For a vehicle entering the area, the initial vehicle speed is V 0, if the vehicle is set as a ride-on, then:
Firstly, completing a lane change task (assuming that the vehicle speed is not changed at the moment) in a buffer area based on a lane adjustment strategy; after the lane change is completed, the vehicle enters a speed adjustment area, and is adjusted to a target expected vehicle speed V aim. (the target vehicle speed is the upper limit of the solution vehicle speed range)
1. When the intersection is green light and meetsV aim＝Vmax. Wherein V t is the ideal maximum speed of the vehicle passing through the intersection, a is the safe acceleration of the vehicle, T g is the green light remaining time, and l R is the distance from the front end of the head vehicle to the intersection of the front signal lamp.
When the next green time of the intersection is still T r, if the vehicle is decelerated to the minimum limit speed V min of the lane can reach the signal lamp intersection, V t satisfies: V aim＝Vt.
3. The rest conditions are that the vehicle cannot pass through the intersection without stopping, and V aim =0.
When the train cannot pass through the signal lamp intersection without stopping, V aim = 0, the distance between the stop position of the head vehicle and the signal lamp intersection is d, the calculation is carried out according to the train sequence, the number m of vehicles in the front train, the vehicle distance l d and the average length l c of the vehicle body are related, and the head vehicle advances when the vehicles in front of the train pass through and finally sequentially accelerates when the vehicles can pass through the intersection.
Wherein d=m (l d+lc)
For a vehicle entering an area, the initial vehicle speed is V 0', if the vehicle is set as a train follower, then:
firstly, completing a lane change task (assuming that the speed of the vehicle does not change at the moment) in a buffer area based on a lane adjustment strategy;
After the lane change is completed, the vehicle enters a speed adjustment area, T=0, the target is to accelerate to V max at the area, then decelerate to V aim, and adjust to the optimal distance from the front vehicle, the vehicle is equidistant and uniformly driven with the front vehicle, the distance between the vehicle and the tail of the front vehicle is known as l, the number of vehicles in front of the queue is n, the optimal distance between the vehicle and the front vehicle is l d, the average length of the vehicle body is l c, the target vehicle speed is V aim, the acceleration period time is T 1, and the time for reaching the target vehicle speed is T 2. The maximum and minimum road speed limit is V max、Vmin, the vehicle acceleration is a', and the acceleration and braking speed of the maximum acceleration of the vehicle are And/>Obtained by the vehicle itself.
If the head vehicle is waiting for parking, the target vehicle speed V aim =0.
Example two
The intelligent network-connected automobile multi-lane queue traffic control method comprises the following steps:
Step S1, acquiring an urban traffic network map to form basic map data of a control center; acquiring traffic data, acquiring traffic route information, and marking the traffic route information on the traffic network map according to the statistical frequency of the traffic routes;
S2, the control center acquires a traffic message, analyzes traffic data in the traffic message, calculates vehicle formation information, the optimal speed of a queue and the distance between vehicles according to the traffic data, and transmits an instruction to a vehicle terminal through a wireless network; the pass message includes RSI, BSM, SPAT, MAP and BSM messages.
BSM, basic SAFETY MESSAGE, basic safety information including speed, steering, braking, double flashing, position, etc. can be used for lane change early warning, blind zone early warning, intersection collision early warning, etc.
RSI, road Side Information, road side information, be used for the below of incident, road side RSU is integrated, and the platform is issued, can be used to road construction, speed limit sign, overspeed early warning, bus lane early warning etc..
RSM, road SAFETY MESSAGE, roadside safety message, mainly for recognition of events such as vehicle accidents, vehicle anomalies, foreign body intrusion, etc., to the edge devices of the Road side;
SPAT, SIGNAL PHASE TIMING MESSAGE, traffic light phase and timing messages, also V2I, road side RSU integrated annunciators, or annunciators are transmitted to a platform in a UU mode for speed guidance, green wave pushing scenes and the like.
MAP, MAP message and SPAT message are used together, the MAP message can describe an intersection, and a corresponding relationship exists between the MAP message and traffic lights of the intersection;
And S3, the vehicle terminal receives the instruction of the control center, judges whether the vehicle enters the control area, and performs primary lane selection according to the path planning information when the vehicle enters the control area.
In the second embodiment, the urban traffic network map is collected, then the basic map data is extracted, the map data of the physical layer is obtained, then the traffic route information is obtained through the big data by collecting the traffic data, the statistical frequency of the traffic route, namely the traffic information of the big data layer is counted, and the traffic network map is marked according to the communication information to form the traffic thermodynamic diagram, so that the main traffic route and the traffic area are counted on the data layer. And providing data support for the subsequent lane queuing traffic method.
Wherein, in the step S1, the method further includes:
Acquiring traffic route information, and constructing a traffic route directed graph consisting of directed line segments of different road sections based on the traffic route information; the directed line segments of adjacent road segments are traffic intersection nodes;
for each road section directed graph, dividing a first directed section and a second directed section at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed section and the second directed section according to the lengths of the road section directed line segments and the statistical frequency; the first directed interval and/or the second directed interval are/is a control area;
and labeling the first directed interval and the second directed interval in the traffic network map.
In another embodiment of the present application, the step S2 further includes: and extracting the green wave speed from the SPAT, calculating the difference value between the current optimal speed of the queue and the green wave speed, and if the current optimal speed of the queue is smaller than the difference value, updating the optimal speed of the queue into the green wave speed.
In another embodiment of the present application, an intelligent network-connected car multi-lane queue traffic control system is characterized by comprising:
At least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the intelligent networked car multi-lane queue traffic control method of any one of the embodiments described above.
The system consists of computer equipment, an operating system installed on the computer equipment and application software. The computer device includes a memory, a processor, and a network interface communicatively coupled to each other via a system bus. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculation and information processing according to a preset or stored instruction, and the hardware thereof includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can enter man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory includes at least one type of readable storage medium including memory, hard disk, random access memory, read only memory, magnetic disk, optical disk, and the like. In some embodiments, the memory may also be an external storage device of the computer device, for example, a smart memory card, an SD card, etc. may be provided on the computer device. Of course, the memory may also include both the internal memory unit of the computer and its external memory device. In this embodiment, the memory is typically used to store an operating system and various application software installed on the computer device, such as computer readable instructions for executing the above method.
The processor may be a central processing unit, controller, microcontroller, or other data processing chip in some embodiments. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is configured to execute the computer readable instructions stored in the memory or process data, for example, the computer readable instructions for executing the above method.
The network interface includes a wireless network interface or a wired network interface that is commonly used to establish communication connections between the computer device and other electronic devices.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the method as described above.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (5)
1. The intelligent network-connected automobile multi-lane queue traffic control method is characterized by comprising the following steps of:
Step S1, acquiring an urban traffic network map to form basic map data of a control center; acquiring traffic data, acquiring traffic route information, and marking the traffic route information on the traffic network map according to the statistical frequency of the traffic routes;
S2, the control center acquires a traffic message, analyzes traffic data in the traffic message, calculates vehicle formation information, the optimal speed of a queue and the distance between vehicles according to the traffic data, and transmits an instruction to a vehicle terminal through a wireless network; the pass message includes RSI, BSM, SPAT, MAP and BSM messages;
S3, the vehicle terminal receives an instruction of a control center, judges whether the vehicle enters a control area, and performs primary lane selection according to path planning information when the vehicle enters the control area;
The step S3 further includes:
Step S31, the preliminary selection of the lane comprises that the straight-going vehicle enters the straight-going lane preferentially, then enters the right-turning and straight-going lane, finally enters the left-turning and straight-going lane, and if the left-turning and straight-going lane has left-turning vehicles waiting at the moment, the vehicle is selected not to enter; the left-turn vehicle enters a left-turn lane preferentially and then enters a left-turn straight lane; the right-turn vehicle enters a right-turn vehicle lane preferentially, then enters a right-turn straight-going lane, and if the lane belonging to the right-turn vehicle lane does not exist in a rule on the road, the right-turn vehicle can directly jump over the lane to adjust; the first vehicle of the independent queue on each lane is a head vehicle, and the other vehicles are auxiliary vehicles;
Step S32, the control area is a vehicle buffer area and a vehicle speed adjustment area, and the lane change task is completed in the vehicle buffer area based on a lane adjustment strategy; if the vehicle is set as a train head, the vehicle speed is adjusted as follows:
① . When the intersection is green light and meets
Time,/>，
Wherein V t is the ideal maximum speed for completing the passing through the intersection, a is the automobile safety acceleration, T g is the green light remaining time, and l R is the distance from the front end of the head car to the front signal lamp intersection;
② . When the next green time of the intersection is still T r, if the vehicle is decelerated to the minimum limit speed V min of the lane can reach the signal lamp intersection, V t satisfies:
then/> ；
③ . The other conditions are that the vehicle cannot pass through the intersection without stopping, and V aim =0; when the train cannot pass through the signal lamp intersection without stopping, V aim = 0, the distance between the parking position of the head vehicle and the signal lamp intersection is d, the calculation is carried out according to the train sequence, the number m of vehicles in the front train, the vehicle distance l d and the average length l c of the vehicle body are related, the head vehicle advances when the vehicles in front of the train pass through, and finally the head vehicle is sequentially accelerated when the vehicles can pass through the intersection; wherein the method comprises the steps of；
Step S33, if the vehicle is set as a queue following vehicle, the initial vehicle speed is V0';
① . Finishing a lane changing task based on a lane adjustment strategy in the buffer area;
② . After the lane change is completed, the vehicle enters a speed adjustment area, T=0, the target is to accelerate to V max at first, then decelerate to V aim and adjust to the position of the optimal distance from the front vehicle, the vehicle is equidistant and uniformly driven with the front vehicle, the distance between the front vehicle and the tail of the front vehicle is known as l, the number of vehicles in front of the queue is n, the optimal distance between the front vehicle and the vehicle is l d, the average length lc of the vehicle body, the target vehicle speed is V aim, the acceleration stage time is T1, the time for reaching the target vehicle speed is T2, the maximum and minimum road speed limit is V max、Vmin, the vehicle acceleration is a', and the acceleration and the braking speed of the maximum acceleration of the vehicle are And/>Obtained by the vehicle itself;
；
③ . If the head vehicle is waiting for parking, the target vehicle speed V aim =0.
2. The intelligent network-connected car multi-lane queue traffic control method according to claim 1, wherein in the step S1, further comprising:
Acquiring traffic route information, and constructing a traffic route directed graph consisting of directed line segments of different road sections based on the traffic route information; the directed line segments of adjacent road segments are traffic intersection nodes;
for each road section directed graph, dividing a first directed section and a second directed section at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed section and the second directed section according to the lengths of the road section directed line segments and the statistical frequency; the first directed interval and/or the second directed interval are/is a control area;
and labeling the first directed interval and the second directed interval in the traffic network map.
3. The intelligent network-connected car multi-lane queue traffic control method according to claim 1, wherein the step S2 further comprises: and extracting the green wave speed from the SPAT, calculating the difference value between the current optimal speed of the queue and the green wave speed, and if the current optimal speed of the queue is smaller than the difference value, updating the optimal speed of the queue into the green wave speed.
4. An intelligent network-connected car multi-lane queue traffic control system, which is characterized by comprising:
At least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by the processor for execution by the processor to implement the intelligent networked car multi-lane queue traffic control method of any one of claims 1-3.
5. A computer readable storage medium storing computer instructions for execution by the computer to implement the intelligent networked car multi-lane queue traffic control method of any one of claims 1 to 3.
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| CN202211168390.4A CN115565390B (en) | 2022-09-24 | 2022-09-24 | Intelligent network-connected automobile multi-lane queue traffic control method, system and computer readable storage medium |
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| CN202211168390.4A CN115565390B (en) | 2022-09-24 | 2022-09-24 | Intelligent network-connected automobile multi-lane queue traffic control method, system and computer readable storage medium |
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| CN115565390A CN115565390A (en) | 2023-01-03 |
| CN115565390B true CN115565390B (en) | 2024-06-07 |
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