CN115565390A - Intelligent internet automobile multi-lane queue traffic control method and system and computer readable storage medium - Google Patents
Intelligent internet automobile multi-lane queue traffic control method and system and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses an intelligent networked automobile multi-lane queue traffic control method, a system and a computer readable storage medium, wherein the method comprises the following steps: collecting an urban traffic network map to form basic map data of a control center; collecting traffic data, acquiring traffic route information, and marking the traffic route information on the traffic network map according to the statistical frequency of traffic routes; the control center acquires the traffic information, analyzes the traffic data in the traffic information, calculates the vehicle formation information, the optimal speed of the queue and the vehicle distance according to the traffic data, and transmits the command to the vehicle terminal through a wireless network; the pass message includesRSI、BSM、SPAT、MAPAndBSMa message; and the vehicle terminal receives an instruction of the control center, judges whether the vehicle enters a control area or not, and performs lane initial 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 internet automobile traffic flow prediction, and particularly relates to an intelligent internet automobile multi-lane queue traffic control method.
Background
According to the latest data of Ministry of industry and communications, the market permeability of the new car of the Chinese L2-grade assistant driving passenger car in 2021 year reaches 23.5%, and the permeability of the new car in the first half of 2022 year is increased to 30%. China has opened a road and the mileage tested exceeds 5000 kilometers. Under the support of policies, breakthrough of innovative technologies accelerates the forward progress of intelligent networked automobiles, road management of networked automobiles is an indispensable important component in a road network in the future, and the method has positive significance in the aspects of improving the traffic capacity, the transportation efficiency, the safety and the like of a road traffic system. Therefore, it is very important to select a road management method for the internet connected vehicles, which is simple and convenient to manage and has the calculation amount meeting the global optimal time efficiency.
In recent years, with the development of car networking technology, vehicles can share information with each other and perceive local environments 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 technology. Compared with the planning of single vehicle individuals, the space-time distance of continuous vehicles in the queue is much smaller, the managed information amount is smaller than that of single vehicles, the running requirements of the single vehicles are met simultaneously in the queue management, the global optimal road passing method is finally achieved, and the road passing efficiency and the safety management can be greatly improved for the networked vehicles in the future.
According to the existing patent data, the method for forming the networked vehicles basically only considers the traffic strategy on a single lane or a straight lane, and the networked vehicle speed planning method can only achieve local optimization of road sections and has a promotion space in the aspect of optimizing the overall road traffic capacity.
Disclosure of Invention
The invention aims to: aiming at the problems in the prior art, the invention provides a rule-based intelligent internet-connected automobile multi-lane queue global optimal traffic method. A solution is provided for the situation that the networked vehicle queue aims at various roads and driving requirements on the roads.
The technical scheme is as follows: the intelligent networked automobile multi-lane queue traffic control method comprises the following steps:
s1, collecting an urban traffic network map to form basic map data of a control center; collecting 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 route;
s2, the control center acquires the traffic information, analyzes traffic data in the traffic information, calculates vehicle formation information, queue optimal speed and vehicle distance according to the traffic data, and transmits an instruction to a vehicle terminal through a wireless network; the pass message comprises 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 or not, and performs lane initial selection according to the path planning information when the vehicle enters the control area.
According to an aspect of the present application, the step S1 further includes:
obtaining passing route information, and constructing a passing route directed graph formed by directed line segments of different road sections based on the passing route information; traffic intersection nodes are arranged between directional line segments of adjacent road sections;
aiming at each road section directed graph, respectively dividing a first directed interval and a second directed interval at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed interval and the second directed interval according to the length and the statistical frequency of the road section directed line segments; the first directed interval and/or the second directed interval are/is a control area;
and marking the first directed interval and the second directed interval in the traffic network graph.
According to an aspect of the application, the step S2 further comprises: and extracting the green wave speed from the SPAT, calculating the difference value between the current queue optimal speed and the green wave speed, and updating the queue optimal speed to the green wave speed if the difference value is smaller than the difference value.
According to an aspect of the application, the step S3 further comprises:
step S31, primarily selecting lanes comprises the steps that straight-going vehicles enter a straight-going lane preferentially, enter a right-turn straight-going lane and enter a left-turn straight-going lane finally, and do not enter the lane if left-turn vehicles wait in the left-turn straight-going lane; the left-turning vehicle firstly enters a left-turning lane and then enters a left-turning and straight lane; the right-turn vehicle firstly enters a right-turn lane and then enters a right-turn and straight lane, and if the lane to which the rule belongs does not exist on the road, the right-turn vehicle can directly skip the lane for adjustment; the first vehicle in the single 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 adjusting area, and a lane changing task is completed in the vehicle buffer area based on a lane adjusting strategy; if the vehicle is set as the head of the queue, the vehicle speed is adjusted according to the following modes:
(1) when the intersection is green, and meet
When, V aim ＝V max Wherein, V t To achieve the desired maximum speed at the intersection, a is the vehicle's safe acceleration, T g Time remaining for green light,/ R The distance from the front end of the head car to the front signal lamp intersection is obtained;
(2) the time of the next green light at the intersection is T r When the vehicle decelerates to the minimum lane limit speed V min Can reach the intersection of the signal lamp, then V t Satisfies the following conditions:
then V aim ＝V t ；
(3) The rest of the conditionsMoreover, the vehicle can not pass through the crossing without stopping V aim =0; when the queue can not pass through the signal lamp crossing without stopping, V aim =0, the distance between the parking position of the head car and the intersection of the signal lamp is d, and the number of times m of the vehicles in the front queue and the distance l of the vehicles are calculated according to the queue sequence d And average length of vehicle body l c The vehicles advance when passing in front of the motorcade and finally accelerate to pass through the intersection in sequence when passing; wherein d = m (l) d +l c )。
According to one aspect of the application, the method further comprises the step S33, if the vehicle is set to be a queue following vehicle, the initial vehicle speed is V';
(1) finishing a lane changing task in the buffer area based on a lane adjusting strategy;
(2) entering a speed adjusting area after the channel change is finished, wherein T =0, and the target is that the speed is accelerated to V in the area max Then is decelerated to V aim And adjusting to the optimal distance from the front vehicle, keeping equal distance with the front vehicle, and running at constant speed, wherein the known distance from the head vehicle to the tail of the front vehicle in the queue is l, the number of the front vehicles in the queue is n, and the optimal distance is l d Average vehicle length lc, target vehicle speed V aim The time of the acceleration stage is T1, the time of reaching the target speed is T2, and the maximum and minimum speed limits of the road speed limit are V max 、V min The acceleration of the vehicle is a', and the acceleration and braking speed of the maximum acceleration of the vehicle are
And
obtained by the vehicle itself;
(3) if the first vehicle is waiting for parking, its target speed V aim ＝0。
In another embodiment of the present application, there is also provided an intelligent internet vehicle multi-lane queue passage control system, including:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the intelligent internet automobile multi-lane queue traffic control method in any embodiment.
In another embodiment of the present application, a computer-readable storage medium is further provided, where the computer-readable storage medium stores computer instructions for executing the method for controlling intelligent internet vehicle multi-lane queuing to pass, according to any one of the above embodiments, by the computer.
Has the advantages that:
different from a common queue planning method, the method provided by the invention considers different path planning requirements of the vehicles, and simultaneously provides a lane adjustment strategy for lane selection and queue division, so that the vehicles can be conveniently formed and managed under the condition of effectively using road resources.
In the management of the internet-of-road automobiles, if the calculated amount of the automobile individuals serving as management objects is large and complex, a vehicle management method based on intelligent internet-of-road automobile queue combination is provided, the internet-of-road automobiles on the section are formed into a team according to the difference between single signal lamp passability and lane selection, and the vehicle planning management is performed by taking a team as a unit, and comprises the calculation of the head speed and the calculation of the optimal distance between the attached automobiles.
Drawings
Fig. 1 is a schematic intersection segment diagram 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
The first embodiment is as follows: as shown in fig. 1, an embodiment is provided as follows: under the networking environment, the cloud platform acquires basic road information through the SPAT, wherein the basic road information comprises information of positions, speeds, numbers and paths of vehicles in a road section, lane passing and speed limiting information, real-time state information of signal lamps at the tail of an intersection and the like. 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 an instruction to the vehicle terminal through the wireless network for automatic control, wherein the vehicle terminal needs to carry a vehicle-mounted communication system.
The single road section is taken as a basic unit, and lanes in the road section are divided into 5 types, namely a left-turn lane, a left-turn straight lane, a right-turn straight lane and a right-turn lane. The entrance part of the road section is set as a queue buffer area, the exit part is set as a speed adjusting area, and the area is not divided clearly, so that the goal of completing lane change by a single vehicle is taken as the standard.
When a vehicle enters a control road section area, firstly, preliminarily selecting lanes in an array buffer area according to path planning information, and dividing an internet connection vehicle array by an array stacking method aiming at real-time states of different lanes and passing road section tail signal lamps, vehicle position and number information and speed limit information on the road sections in the adjusting process. When the vehicle meets the condition of passing the next green light under the current green light or red light state, planning the vehicle to an initial queue, and adjusting the lane according to a lane adjustment strategy; when the passing condition is not met or the condition is met but the initial queue is full, it is adjusted to the waiting queue and vice versa. After the queue division is completed, optimizing the optimal target speed of the pilot vehicle and the optimal distance between the auxiliary vehicles in the agreement queue to form a global optimal queue, and improving the safety and the comfort of the vehicles in the queue on the premise of ensuring the vehicles to pass through the queue.
And (3) lane adjustment strategy: the straight-going vehicles firstly enter a straight-going lane, then enter a right-turn and straight-going lane and finally enter a left-turn and straight-going lane, and if the left-turn and straight-going lane has left-turn vehicles to wait at the moment, the vehicles are selected not to enter. The left-turn vehicle preferentially enters a left-turn lane and then enters a left-turn plus straight lane. The right-turn vehicle enters the right-turn lane first and then the right-turn plus straight lane second. If the road has no traffic lane in the rule, the traffic lane can be directly skipped to be adjusted.
The limiting conditions are as follows: 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 passing condition.
Lane rule based queue partitioning: setting 3 straightable lanes, dividing the entering vehicles into initial straight queues 1, 2 and 3 (if left-turn vehicles waiting on a left-turn and straight lane do not consider the queue 3) when the entering vehicles meet the limiting conditions, and dividing the initial straight queues 1, 2 and 3 into waiting straight queues 4 and 5 if the initial straight queues 1, 2 and 3 are full; if the limit condition is not met, the initial queue is left according to the number of the straight roads, and the waiting straight (or left/right) queues 4 and 5 are sequentially entered. And finally selecting the lane according to a lane selection strategy. Turning vehicles and straight-driving vehicles have the same principle.
Global queue speed/spacing adjustment strategy: vehicle number in queue: the first vehicle in the single queue on each lane is taken as the head vehicle, and the rest vehicles are taken as auxiliary vehicles.
For a vehicle entering the zone, the initial speed is V 0 If the vehicle is set as the head vehicle of the queue:
firstly, completing a lane change task in a buffer area based on a lane adjustment strategy (assuming that the vehicle speed does not change at the moment); entering a speed adjusting area after the lane changing is finished, and adjusting to a target expected vehicle speed V at the moment aim . (target vehicle speed is the upper limit of the solution vehicle speed range)
1. When the intersection is green and meets
When, V aim ＝V max . Wherein V t To achieve the desired maximum speed at the intersection, a is the vehicle's safe acceleration, T g Time remaining for green light,/ R The distance from the front end of the head car to the front signal lamp intersection is shown.
When the next green light time at the intersection is still T r When the vehicle decelerates to the minimum lane limit speed V min Can reach the intersection of the signal lamp, then V t Satisfies the following conditions:
then V aim ＝V t 。
3. The other conditions are that the vehicle can not pass through the intersection without stopping, V aim ＝0。
When the queue can not pass through the signal lamp crossing without stopping, V aim =0, the distance between the parking position of the head car and the intersection of the signal lamp is d, and the number of times m of the vehicles in the front queue and the distance l of the vehicles are calculated according to the queue sequence d Length l of the vehicle body c In relation to and advancing when vehicles in front of the fleet of vehicles pass and finally accelerating sequentially through the intersection when passing is possible.
Wherein d = m (l) d +l c )
For a vehicle entering the zone, the initial speed is V 0 If the vehicle is set to be following the train, then:
firstly, completing a lane change task in a buffer area based on a lane adjustment strategy (assuming that the vehicle speed does not change at the moment);
entering a speed adjusting region after the lane change is finished, wherein T =0, and the target is that the speed is accelerated to V in the region max Then is decelerated to V aim And adjusting to the optimal distance from the front vehicle, keeping equal distance with the front vehicle, and running at constant speed, wherein the known distance from the head vehicle to the tail of the front vehicle in the queue is l, the number of the front vehicles in the queue is n, and the optimal distance is l d Average length of vehicle body l c Target vehicle speed is V aim Acceleration phase time of T 1 Time to reach target vehicle speed is T 2 . The maximum and minimum speed limit of the road speed limit is V max 、V min The acceleration of the vehicle is a', and the acceleration and braking speed of the maximum acceleration of the vehicle are
And
obtained by the vehicle itself.
If the first vehicle is waiting for parking, the target vehicleSpeed V aim ＝0。
Example two
The intelligent networked automobile multi-lane queue traffic control method comprises the following steps:
s1, collecting an urban traffic network map to form basic map data of a control center; collecting 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 route;
s2, the control center acquires the traffic message, analyzes the traffic data in the traffic message, calculates vehicle formation information, the optimal speed of the queue and the vehicle distance from the traffic data, and transmits an instruction to the vehicle terminal through a wireless network; the pass messages include RSI, BSM, SPAT, MAP, and BSM messages.
BSM, basic Safety Message including speed, steering, braking, double flashing, position and the like, and can be used for lane change early warning, blind area early warning, intersection collision early warning and the like.
The system comprises RSI, road Side Information and roadside Information, wherein the RSI, the Road Side Information and the roadside RSU are used below an event, integrated on a roadside and issued by a platform, and the system can be used for Road construction, speed limit signs, overspeed early warning, bus lane early warning and the like.
RSM, road Safety Message, road side Safety Message, mainly interfacing with roadside edge devices for event identification, such as vehicle accident, vehicle abnormality, intrusion of foreign matter, etc.;
SPAT, signal phase timing message, traffic light phase and time sequence message, V2I, roadside RSU integrated Signal machine, or Signal machine is transmitted to the platform through UU mode, and is used for speed guide, green wave pushing scene and the like.
The MAP, MAP message and SPAT message are used together, the MAP message can describe an intersection, and the corresponding relation exists between the MAP message and the traffic light of the intersection;
and S3, the vehicle terminal receives the instruction of the control center, judges whether to enter a control area, and performs lane initial selection according to the path planning information when entering the control area.
In the second embodiment, the urban traffic network map is collected, the basic map data is extracted from the urban traffic network map, the map data of the physical layer is obtained, 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 thermodynamic diagram is formed by marking the traffic network map according to the communication information, so that the main traffic route and the traffic area are counted at the data layer. And data support is provided for a subsequent lane queuing passing method.
Wherein, in step S1, the method further includes:
acquiring passing route information, and constructing a passing route directed graph composed of directed line segments of different road sections based on the passing route information; traffic intersection nodes are arranged between directional line segments of adjacent road sections;
aiming at each road section directed graph, respectively dividing a first directed interval and a second directed interval at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed interval and the second directed interval according to the length and the statistical frequency of the road section directed line segments; the first directed interval and/or the second directed interval are/is a control area;
and marking the first directed interval and the second directed interval in the traffic network graph.
In another embodiment of the present application, the step S2 further includes: and extracting the green wave speed from the SPAT, calculating the difference between the current queue optimal speed and the green wave speed, and updating the queue optimal speed to the green wave speed if the difference is smaller than the difference.
In another embodiment of the present application, an intelligent internet vehicle 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 content of the first and second substances,
the memory stores instructions executable by the processor, and the instructions are used for being executed by the processor to realize the intelligent internet automobile multi-lane queue traffic control method in any embodiment.
The system comprises computer equipment, an operating system and application software, wherein the operating system and the application software are installed on the computer equipment. The computer device comprises a memory, a processor and a network interface which are mutually connected through a system bus in a communication way. As will be understood by those skilled in the art, the computer device herein is a device capable of automatically performing numerical calculation and information processing according to preset or stored instructions, and the hardware 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 device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can enter man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory includes at least one type of readable storage medium including a memory, a hard disk, a random access memory, a read-only memory, a magnetic disk, an 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, or the like is provided on the computer device. Of course, the memory may also include both internal and external memory units of the computer. In this embodiment, the memory is generally used for storing 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 processor, 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, such as the computer readable instructions for executing the above method.
The network interface includes a wireless network interface or a wired network interface, which is typically used to establish a communication link between the computer device and other electronic devices.
The present application further provides another embodiment, which is to provide 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.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent modifications can be made within the technical spirit of the present invention, and the scope of the present invention is also within the scope of the present invention.
Claims (7)
1. The intelligent networked automobile multi-lane queue traffic control method is characterized by comprising the following steps of:
s1, collecting an urban traffic network map to form basic map data of a control center; collecting 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 route;
s2, the control center acquires the traffic message, analyzes the traffic data in the traffic message, calculates vehicle formation information, the optimal speed of the queue and the vehicle distance from the traffic data, and transmits an instruction to the vehicle terminal through a wireless network; the pass message comprises RSI, BSM, SPAT, MAP and BSM messages;
and S3, the vehicle terminal receives the instruction of the control center, judges whether to enter a control area, and performs lane initial selection according to the path planning information when entering the control area.
2. The intelligent internet automobile multi-lane queue traffic control method according to claim 1, wherein in the step S1, the method further comprises:
acquiring passing route information, and constructing a passing route directed graph composed of directed line segments of different road sections based on the passing route information; traffic intersection nodes are arranged between directional line segments of adjacent road sections;
aiming at each road section directed graph, respectively dividing a first directed interval and a second directed interval at the front end and the rear end of the road section directed graph, and dividing the lengths of the first directed interval and the second directed interval according to the length and the statistical frequency of the road section directed line segments; the first directed interval and/or the second directed interval are/is a control area;
and marking the first directed interval and the second directed interval in the traffic network graph.
3. The intelligent internet automobile 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 queue optimal speed and the green wave speed, and updating the queue optimal speed to the green wave speed if the difference value is smaller than the difference value.
4. The intelligent networked automobile multi-lane queue traffic control method according to claim 1, wherein the step S3 further comprises:
step S31, primarily selecting lanes comprises the steps that straight-going vehicles enter a straight-going lane preferentially, enter a right-turn straight-going lane and enter a left-turn straight-going lane finally, and do not enter the lane if left-turn vehicles wait in the left-turn straight-going lane; the left-turning vehicle firstly enters a left-turning lane and then enters a left-turning and straight lane; the right-turn vehicle firstly enters a right-turn lane and then enters a right-turn and straight lane, and if the lane to which the rule belongs does not exist on the road, the right-turn vehicle can directly skip the lane for adjustment; the first vehicle in the single 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 adjusting area, and a lane changing task is completed in the vehicle buffer area based on a lane adjusting strategy; if the vehicle is set as the head of the queue, the vehicle speed is adjusted according to the following modes:
(1) when the intersection is green, and meets the requirements
When, V aim ＝V max Wherein V is t To achieve the desired maximum speed through the intersection, a is the vehicle's safe acceleration, T g Time remaining for green light,/ R The distance from the front end of the head car to the front signal lamp intersection is obtained;
(2) the time of the next green light at the intersection is T r When the vehicle decelerates to the minimum limit speed V of the lane min Can reach the intersection of the signal lamp, then V t Satisfies the following conditions:
then V aim ＝V t ；
(3) And the rest conditions are that the vehicle can not pass through the intersection without stopping, V aim =0; when the queue can not pass through the signal lamp crossing without stopping, V aim =0, the distance between the parking position of the head car and the intersection of the signal lamp is d, and the number of times m of the vehicles in the front queue and the distance l of the vehicles are calculated according to the queue sequence d And average length of vehicle body l c The vehicles advance when passing in front of the motorcade and finally accelerate to pass through the intersection in sequence when passing; wherein d = m (l) d +l c )。
5. The intelligent internet automobile multi-lane queue passing control method according to claim 4, characterized by further comprising the step S33, if the vehicle is set to be a queue following vehicle, the initial speed is V';
(1) finishing a lane changing task in the buffer area based on a lane adjusting strategy;
(2) entering a speed adjusting area after the lane change is finished, wherein T =0 and the target is that the speed is accelerated to V in the area max Then is decelerated to V aim And adjusting to the optimal distance from the front vehicle, keeping the same distance with the front vehicle, and running at constant speed, wherein the known distance from the tail of the front vehicle to the tail of the queue is l, the number of the front vehicles in the queue is n, and the optimal distance is l d Average vehicle length lc, target vehicle speed V aim The time of the acceleration stage is T1, the time of reaching the target speed is T2, and the maximum and minimum speed limits of the road speed limit are V max 、V min Acceleration of vehicleDegree a' and the acceleration and braking speed of the maximum acceleration of the vehicle
And
obtained by the vehicle itself;
(3) if the first vehicle is waiting for parking, its target speed V aim ＝0。
6. The utility model provides an intelligence networking car multilane queue control system that passes which characterized in that includes:
at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein the content of the first and second substances,
the memory stores instructions executable by the processor for implementing the intelligent internet automobile multi-lane queuing traffic control method of any one of claims 1-5.
7. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for being executed by the computer to implement the intelligent internet-connected vehicle multi-lane queuing traffic control method according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116013062A (en) * | 2023-03-27 | 2023-04-25 | 江苏天一航空工业股份有限公司 | Intelligent formation method and system for unmanned heavy-load mobile platform in harbor district |
| CN117079451A (en) * | 2023-07-11 | 2023-11-17 | 清华大学 | Control method and device for mixed traffic system in urban continuous intersection scene |
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2022
- 2022-09-24 CN CN202211168390.4A patent/CN115565390A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116013062A (en) * | 2023-03-27 | 2023-04-25 | 江苏天一航空工业股份有限公司 | Intelligent formation method and system for unmanned heavy-load mobile platform in harbor district |
| CN117079451A (en) * | 2023-07-11 | 2023-11-17 | 清华大学 | Control method and device for mixed traffic system in urban continuous intersection scene |
| CN117079451B (en) * | 2023-07-11 | 2024-04-19 | 清华大学 | Control method and device for mixed traffic system in urban continuous intersection scene |
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Country or region after: China Address after: 406-413A, Building 1, Qilin Artificial Intelligence Industrial Park, Qilin Hi tech Zone, Nanjing, Jiangsu Province, 211000 Applicant after: Nanjing Changjiang Automation Research Institute Co.,Ltd. Address before: 406-413A, Building 1, Qilin Artificial Intelligence Industrial Park, Qilin Hi tech Zone, Nanjing, Jiangsu Province, 211000 Applicant before: NANJING LIGONG AUTOMATION Co.,Ltd. Country or region before: China |