Signal and vehicle cooperative intelligent control method and system based on traffic conflict

Abstract

The invention provides a signal and vehicle cooperative intelligent control method and system based on traffic conflicts, and the method employs a double-layer cooperative optimization control architecture: an upper-layer traffic signal control model dynamically adjusts the duration of a green light; according to the lower-layer vehicle track control model, the length of a conflict area is calculated in combination with the maximum braking distance of the vehicle according to traffic conflicts caused by bus entering and exiting, conflict recognition conditions and conflict elimination constraints are provided, and on the basis, the acceleration of a vehicle team pilot vehicle is decided with the fuel consumption and comfort of the vehicle as optimization targets. According to the method, a double-layer optimization control model is solved by adopting dynamic planning, green light duration and vehicle tracks are adjusted in real time in combination with vehicle-road cooperative communication, bus priority passing and safe and orderly avoidance of common vehicles can be guaranteed, average passenger delay and travel time of buses in a conflict area are effectively reduced, and the traffic safety of the buses is improved. The overall oil consumption is reduced; and the intersection passing efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of Intelligent Transport Systems (ITS) technology, and more specifically, to a method for bus priority signal control and vehicle trajectory collaborative optimization for CAB, CAV and connected human-driven vehicles (CHV) in intelligent connected transportation environments, particularly a two-layer dynamic programming collaborative control strategy that takes into account both the safety and traffic efficiency of the bus entry and exit conflict zone (CFZ). Background Technology

[0002] With the development of intelligent connected transportation technologies, CAV (Connected and Automated Vehicles) and CAB (Connected and Automated Buses) have gradually become important components of urban road traffic. In traditional traffic control schemes, intersection signal control and dedicated lane allocation typically employ fixed methods, which are difficult to adapt to the dynamic changes caused by buses entering and exiting stations and the intermingling of regular traffic. For example, when buses exit at bay-style bus stops near intersections, they often interfere with through traffic, creating conflict zones in front of the exit, causing localized congestion, increasing bus passenger delays, and reducing the overall traffic efficiency of the intersection.

[0003] Some studies have proposed dynamic lane allocation or signal priority strategies for CAVs, but most of them focus on ordinary traffic flow, ignoring the special conflicts caused by buses leaving the station, and lacking systematic optimization of bus priority and vehicle trajectory coordination control.

[0004] Therefore, considering the traffic conflicts caused by the intermingling of buses exiting stations and regular vehicles, as well as the need to balance lane usage and improve the overall traffic efficiency of intersections, are important issues that the industry urgently needs to address. Summary of the Invention

[0005] In view of the above problems, the purpose of this invention is to provide a signal and vehicle cooperative intelligent control method and system based on traffic conflict. One or more embodiments of this specification also relate to a signal and vehicle cooperative intelligent control method and system based on traffic conflict, an electronic device, and a computer-readable storage medium, so as to solve the technical defects existing in the prior art.

[0006] On one hand, the present invention provides a signal and vehicle cooperative intelligent control method based on traffic conflict, comprising: Traffic data collection and road zone division: Real-time collection of traffic data at the entrance of road intersections and division of roads into zones to obtain road parameters; wherein, the traffic data includes vehicle information and traffic signal information, and the zone division includes: setting the non-solid line lane-changing area of ​​the road as a lane-changing zone, setting the solid line non-lane-changing area of ​​the road as a trajectory control zone, and setting a conflict zone on the road section from the bus stop exit to the intersection. Vehicle platooning: Based on the traffic data and road parameters, and according to the maximum number of vehicles allowed to pass through the intersection during the pre-set green light timing for each cycle, vehicles that can pass through the intersection in the same cycle are initialized into a platoon, sequentially from the stop line towards the lane entrance. The initial convoy is divided into several sub-convoys by determining whether the distance between vehicles in the initial convoy is greater than the critical distance between vehicles. Conflict identification in conflict zones: Based on the traffic data and road parameters, a bus stop conflict analysis module is constructed to generate constraints for use by the two-layer control model; wherein, the two-layer control model includes an upper-layer traffic signal control model and a lower-layer vehicle trajectory control model; Vehicle trajectory and traffic signal control: dynamically adjust the green light duration based on the traffic signal control model, and determine the acceleration of the lead vehicle in the CAV vehicle platoon based on the information output by the traffic signal control model.

[0007] In addition, an optional approach may include one or more of the following steps: Rolling optimization: Select a signal cycle as the optimization cycle, call the dynamic programming algorithm of the traffic signal control model at the beginning of each signal cycle, solve and update the duration and phase sequence of green light, yellow light, and red light, and transmit the results to the intersection signal controller; Closed-loop dynamic update: Based on the green light time, the system repeatedly collects traffic data and divides road areas, platoons vehicles, identifies conflicts in conflict zones, tracks vehicle trajectories and controls traffic signals, and performs rolling optimization to achieve closed-loop optimization control based on real-time feedback.

[0008] Alternatively, the conflict zone conflict identification may include: The length of the conflict zone is determined based on the bus's body length, stationary distance, and maximum braking distance. Based on the positions and speeds of vehicles within the conflict zone and vehicles about to enter the conflict zone, determine whether there is a potential conflict between the time when the connected autonomous vehicles of the i-th platoon in each lane enter / leave the conflict zone and the time when the connected autonomous buses enter / leave the conflict zone; wherein... When a potential conflict exists, an independent passage space is reserved for the connected autonomous driving bus within the conflict zone.

[0009] Alternatively, the length of the conflict zone can be set as the sum of the vehicle length of the connected autonomous driving bus, the stationary vehicle spacing, and the maximum braking distance required by the vehicle in the direction of approach within the conflict zone; wherein, the maximum braking distance required by the vehicle in the direction of approach within the conflict zone is the difference between the vehicle's minimum braking distance and the distance between the vehicle and the entrance of the conflict zone.

[0010] On the other hand, the present invention also provides a signal and vehicle cooperative intelligent control system based on traffic conflict, comprising: The traffic data acquisition and road area division module is used to collect traffic data at the entrance of road intersections in real time and divide the road into areas to obtain road parameters. The traffic data includes vehicle information and traffic signal information. The area division includes setting the non-solid line lane-changing area of ​​the road as a lane-changing area, setting the solid line non-lane-changing area of ​​the road as a trajectory control area, and setting a conflict zone on the road section from the bus stop exit to the intersection. The vehicle platooning module is used to initialize vehicles that can pass through the intersection in the same cycle into a platoon based on the traffic data and road parameters, according to the maximum number of vehicles allowed to pass through the intersection during the green light timing of each preset cycle plan. This initialization is performed sequentially from the stop line towards the lane entrance. The initial convoy is divided into several sub-convoys by determining whether the distance between vehicles in the initial convoy is greater than the critical distance between vehicles. The conflict zone conflict identification module is used to construct a bus stop conflict analysis module based on the traffic data and the road parameters, so as to generate constraints for the two-layer control model to be called; wherein, the two-layer control model includes an upper-layer traffic signal control model and a lower-layer vehicle trajectory control model; The vehicle trajectory and traffic signal control module is used to dynamically adjust the green light duration based on the traffic signal control model, and to determine the acceleration of the lead vehicle in the CAV vehicle platoon based on the information output by the traffic signal control model.

[0011] In addition, an alternative solution is to include one or more of the following modules: The rolling optimization module is used to select a signal cycle as the optimization cycle. At the beginning of each signal cycle, it calls the dynamic programming algorithm of the traffic signal control model to solve and update the duration and phase sequence of green, yellow and red lights, and transmits the results to the intersection signal controller. The closed-loop dynamic update module is used to control the data processing flow of the traffic data collection and road area division module, vehicle platooning module, conflict zone conflict identification module, vehicle trajectory and traffic signal control module, and rolling optimization module repeatedly with the green light time as the cycle, so as to achieve closed-loop optimization control based on real-time feedback.

[0012] On the other hand, the present invention also provides an electronic device, the electronic device including a memory, a processor and a traffic conflict-based signal and vehicle cooperative intelligent control program stored in the memory and executable on the processor, wherein the traffic conflict-based signal and vehicle cooperative intelligent control program, when executed by the processor, implements the traffic conflict-based signal and vehicle cooperative intelligent control method as described above.

[0013] In another aspect, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the signal and vehicle cooperative intelligent control method based on traffic conflict as described above.

[0014] The present invention provides a signal and vehicle cooperative intelligent control method and system based on traffic conflict. By setting up conflict zone modeling at the bus stop exit and adopting a signal-trajectory two-layer cooperative optimization strategy, it achieves the unity of bus priority control, energy consumption reduction and system efficiency improvement, providing a feasible technical solution for urban intersection traffic optimization in an intelligent connected environment.

[0015] Compared with existing technologies, the present invention can effectively alleviate traffic conflicts and congestion, significantly improve the efficiency and priority of public transportation, thereby reducing average passenger delays and overall fuel consumption, and improving energy efficiency.

[0016] To achieve the foregoing and related objectives, one or more aspects of the invention include the features which will be described in detail below and specifically pointed out in the claims. The following description and accompanying drawings illustrate certain exemplary aspects of the invention. However, these aspects indicate only a few of the various ways in which the principles of the invention can be used. Furthermore, the invention is intended to include all such aspects and their equivalents. Attached Figure Description

[0017] Other objects and results of the invention will become more apparent and readily understood with reference to the following description taken in conjunction with the accompanying drawings and the contents of the claims, and with a more complete understanding of the invention. In the drawings: Figure 1 This is a schematic diagram illustrating an application scenario of the intelligent signal and vehicle cooperative control method based on traffic conflict according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the intelligent signal and vehicle cooperative control method based on traffic conflict according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the overall technical route of the intelligent signal and vehicle cooperative control method based on traffic conflict according to an embodiment of the present invention; Figure 4This is a schematic diagram of an intersection traffic structure according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the bus exit conflict zone according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the vehicle trajectory after collision elimination according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the traffic signal phase structure according to an embodiment of the present invention; Figure 8 This is a schematic diagram of vehicle platooning according to an embodiment of the present invention; Figure 9 This is a diagram illustrating the driving process of a connected autonomous driving bus according to an embodiment of the present invention. Figure 10 A schematic diagram of the vehicle trajectories of a connected autonomous vehicle and a connected autonomous bus that conflict according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the iterative process of the rolling optimization algorithm according to an embodiment of the present invention; Figure 12 This illustrates how the overall average passenger delay varies with the penetration rate of connected autonomous vehicles under different methods according to embodiments of the present invention. Figure 13 This illustrates the variation of traffic flow in ordinary lanes and shared dedicated lanes with the penetration rate of connected autonomous vehicles according to an embodiment of the present invention. Figure 14a and Figure 14b This is a schematic diagram of vehicle trajectories in lane 9 under different methods when the CAV penetration rate is 40% according to an embodiment of the present invention; Figure 15 This is a schematic diagram illustrating the variation of overall vehicle average fuel consumption with CAV penetration under different optimization methods according to embodiments of the present invention; Figure 16 This is a schematic diagram of the average fuel consumption of various vehicle types under different optimization methods at a CAV penetration rate of 40% according to an embodiment of the present invention. Figure 17 This is a schematic diagram of the framework structure of a traffic conflict-based signal and vehicle cooperative intelligent control system according to an embodiment of the present invention. Figure 18 This is a schematic diagram of the logical structure of an electronic device for implementing a signal and vehicle cooperative intelligent control method based on traffic conflict, according to an embodiment of the present invention.

[0018] In all the accompanying drawings, the same reference numerals indicate similar or corresponding features or functions. Detailed Implementation

[0019] In the following description, numerous specific details are set forth for illustrative purposes and to provide a thorough understanding of one or more embodiments. However, it will be apparent that these embodiments may also be implemented without these specific details. In other instances, well-known structures and devices are shown in block diagram form for ease of description of one or more embodiments. Various modifications and embodiments can be applied to the invention, and specific embodiments are illustrated in the accompanying drawings. However, the invention is not limited to this particular implementation and all modifications, equivalents, and substitutions falling within the spirit and scope of the invention are to be understood and included.

[0020] In this invention, ordinal terms such as "first," "second," etc., are used to describe various constituent elements, but the constituent elements are not limited to these terms. The terms are used only to distinguish one constituent element from another. For example, without departing from the scope of the claims, a second constituent element may be named a first constituent element, and similarly, a first constituent element may be named a second constituent element. Terms and / or include combinations of multiple associated items or one of multiple associated items.

[0021] The terminology used in this invention is for illustrative purposes only and is not intended to limit the invention. Unless the context clearly specifies otherwise, singular expressions include plural expressions. In this invention, it should be understood that terms such as "comprising" or "having" are used to specify the presence of features, numbers, steps, operations, constituent elements, components, or combinations thereof described in the specification, and do not preclude the presence or additional possibilities of one or more other features, numbers, steps, operations, constituent elements, components, or combinations thereof.

[0022] Furthermore, unless otherwise defined, including technical or scientific terms, all terms used herein have the same meaning as commonly understood by those skilled in the art. Terms as defined in commonly used dictionaries should be interpreted in a meaning consistent with their meaning in the context of the relevant art, and should not be construed as having an ideal or overly formal meaning unless explicitly defined in this invention.

[0023] To more clearly illustrate the technical solution of the present invention, the following is a brief explanation of some of the technical terms involved in the present invention.

[0024] Traffic signal cycle: The signal cycle is the time it takes for the red, yellow, and green traffic lights to complete one full cycle; Traffic signal phase: The time period during which traffic lights allocate non-conflicting right-of-way to traffic flows in different directions at an intersection; Free Flow Speed: The comfortable driving speed that a driver naturally chooses based on road conditions in an ideal state where there is no interference from other traffic flows and no obvious external constraints (such as speed enforcement or environmental restrictions).

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] Example 1 To address the shortcomings of the aforementioned existing methods, this invention provides a signal and vehicle cooperative intelligent control method based on traffic conflict. This method achieves a balance between bus priority control, energy consumption reduction, and system efficiency improvement by setting up CFZ modeling at bus stop exits and employing a signal-trajectory dual-layer cooperative optimization strategy.

[0027] Figure 1 This illustrates an application scenario of a traffic conflict-based signal and vehicle cooperative intelligent control method according to an embodiment of the present invention. For example... Figure 1 As shown, in the application scenario of this embodiment, the computing device 100 can acquire traffic signal information 101 and vehicle information 102. Then, the computing device 100 can construct a platoon based on the traffic signal information 101 and vehicle information 102, obtaining platoon information 103. Afterwards, the computing device 100 inputs the platoon information 103 into the conflict zone conflict identification module and the vehicle platooning model to obtain decision information 104, including at least the next cycle. Finally, the computing device 100 can control the traffic lights and vehicles based on the decision information 104.

[0028] It should be noted that the aforementioned computing device 100 can be either hardware or software. When the computing device 100 is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device 100 is software, it can be installed in the hardware devices listed above. It can be implemented as, for example, multiple software programs or software modules used to provide distributed services, or as a single software program or software module. No specific limitations are made here.

[0029] This invention is applicable to intelligent connected transportation environments, particularly for traffic control scenarios at urban intersections with bus stops. The system consists of an onboard terminal, a roadside unit (RSU), a V2X module, and a traffic signal controller, enabling real-time acquisition and information exchange of vehicle operating status. This method can operate in mixed traffic environments, suitable for CHV, CAV, and CAB mixed traffic conditions. Through joint optimization of signal control and vehicle trajectory, it achieves bus priority and improves the overall traffic efficiency of intersections.

[0030] Figure 2The flowchart of a signal and vehicle cooperative intelligent control method based on traffic conflict according to an embodiment of the present invention is shown. Figure 3 This paper illustrates the overall technical approach of a traffic conflict-based signal and vehicle cooperative intelligent control method according to an embodiment of the present invention. Figure 2 and Figure 3 As shown in the figure, the intelligent signal and vehicle cooperative control method based on traffic conflict provided by the present invention mainly includes the following steps: S110, Traffic Data Collection and Road Area Division: Real-time collection of traffic data at the entrance of road intersections, and division of roads into areas to obtain road parameters; wherein, the traffic data includes vehicle information and traffic signal information, and the area division includes: setting the non-solid line lane-changing area of ​​the road as a lane-changing zone, setting the solid line non-lane-changing area of ​​the road as a trajectory control zone, and setting a conflict zone on the road section from the bus stop exit to the intersection.

[0031] Specifically, as an example, at the intersection entrance, vehicle-mounted terminals, roadside sensors, and traffic detection equipment can be used to collect real-time data on vehicle location, speed, acceleration, passenger capacity, lane information, and traffic signal cycles and phases. This data can then be preprocessed and converted into a unified data format. Simultaneously, non-solid line lane-changing areas can be designated as Lane Changing Zones (LCZs), while solid line non-lane-changing areas can be designated as Trajectory Control Zones (TCZs). Furthermore, Conflict Zones (CFZs) can be established on the road section from the bus stop exit towards the intersection. Figure 3 As shown.

[0032] S120, Vehicle platooning: Based on the traffic data and road parameters, and according to the maximum number of vehicles allowed to pass through the intersection during the green light timing of each preset cycle plan, vehicles that can pass through the intersection in the same cycle are initialized into a platoon, sequentially from the stop line towards the lane entrance. The initial convoy is divided into several sub-convoys by determining whether the distance between vehicles in the initial convoy is greater than the critical distance between vehicles.

[0033] Figure 6 This is a schematic diagram of vehicle platooning according to an embodiment of the present invention. Figure 6 As shown, the vehicle platooning strategy in this embodiment consists of two stages: platoon initialization and sub-platoon division.

[0034] In the platoon initialization phase, based on the maximum number of vehicles allowed to pass under the current green light timing, vehicles capable of passing through the intersection within the same cycle are initialized into a platoon. That is, each cycle of the signal plan corresponds one-to-one with an initial platoon. During the green light period of the k-th signal plan cycle, all vehicles capable of passing the stop line without violating the minimum safe headway constraint are defined as the initial platoon k, whose maximum number of vehicles can be released. Calculate according to the following formula: (1) in, For the first The first planning cycle The duration of the green light for each phase; The minimum safe headway between adjacent vehicles; For floor function (e.g.) (This ensures that the number of vehicles is an integer.)

[0035] During the sub-team division phase, the sub-team division conditions and critical vehicle spacing are calculated as follows: (2) in, The critical vehicle spacing; For the first The first in the initial team Vehicle and the The distance between vehicles is calculated as shown in equation (3); This refers to the length of the vehicle body; This refers to the distance between stationary vehicles. For the first The vehicle speed. The critical distance between vehicles is calculated by considering the minimum safe headway, vehicle speed, stationary distance, and vehicle length. .

[0036] The calculation method is shown in the following formula: (3) in, for The first in the team The location of the vehicle; for The first in the team The location of the vehicle.

[0037] S130, Conflict identification in conflict zone: Based on the traffic data and the road parameters, a bus stop conflict analysis module is constructed to generate constraints for use by the two-layer control model; wherein, the two-layer control model includes an upper-layer traffic signal control model and a lower-layer vehicle trajectory control model.

[0038] In this invention, a CFZ conflict identification module is used to identify conflicts in conflict zones. Based on the traffic data obtained in step S110, a bus stop conflict analysis module is constructed to generate constraints for use by the two-layer model, specifically including: S131, CFZ length calculation: The length of the conflict zone is determined based on the bus body length, stationary vehicle spacing and maximum braking distance.

[0039] Specifically, as an example, the CFZ length The calculation is as follows: (4) in, For the approach direction index; This refers to the length of the CAB vehicle body; This refers to the distance between stationary vehicles. for The maximum braking distance required for a vehicle within the CFZ in the approach direction. This formula represents... Set as , The sum of the maximum braking distance required by the vehicle within the CFZ. Wherein, The formula for calculating the maximum braking distance required for a vehicle within the CFZ in the approach direction is: (5) in, For lane indexing; For the first The speed of the vehicle closest to the CFZ entrance within each lane; For the first The location of the vehicle closest to the CFZ entrance within each lane; This is the CFZ entry point location; This is the vehicle's maximum acceleration; The theoretical minimum stopping distance (assuming the vehicle immediately decelerates at maximum speed). Minimum braking distance). for The set of lane indices within the approach direction; for the westbound approach, this set contains lanes 7, 8, and 9; for the eastbound approach, this set contains lanes 1, 2, and 3. That is, this formula... The first item The second item is the minimum braking distance of the vehicle. This is the distance between the vehicle and the CFZ entrance; the difference between the two distances is the braking distance of the vehicle within the CFZ.

[0040] In other words, equation (5) calculates: for the th The vehicle closest to the CFZ in each lane, from the CFZ entrance ( Starting from the beginning, calculate how much distance the vehicle needs to "occupy" within the CFZ to complete the entire braking process. A positive result indicates that the vehicle requires this length of space within the CFZ to stop safely; a negative result indicates that the vehicle can stop completely before reaching the CFZ entrance, requiring no space within the CFZ. Therefore, [the calculation continues]. The term "maximum braking distance" required in the direction of vehicle travel refers to maximizing demand, not maximizing physical distance; "maximum braking distance required by the vehicle within the CFZ" can also be understood as "maximum braking space required by the vehicle within the CFZ".

[0041] Accordingly, the calculation results of equation (5) The physical meaning is: the vehicle decelerates at its maximum speed. During braking, the required length within the CFZ (Cross-Cross-Cross-Cross-Cross) is determined to complete the stopping process. The maximum value of this value for each lane represents the most stringent (i.e., maximum) space requirement to ensure safety.

[0042] S132, Conflict Identification: Based on the vehicle positions and speeds of vehicles within the conflict zone and vehicles about to enter the conflict zone, determine whether there is a potential conflict between the time when the connected autonomous vehicles of the i-th platoon in each lane enter / leave the conflict zone and the time when the connected autonomous buses enter / leave the conflict zone.

[0043] Specifically, in the conflict identification process, the positions and speeds of vehicles within the conflict zone and those about to enter the conflict zone are used to calculate the speeds of each lane. Upper The moment when a fleet of connected autonomous vehicles (CAVs) enters / departs from CFZ. , And the times when connected autonomous buses (hereinafter referred to as CAB) enter / leave CFZ. , In this embodiment, conflict identification, i.e., the condition for a potential conflict between the CAV and the CAB turning left out of the station, is determined according to equation (6) or equation (7). Meeting either condition is considered a conflict. (6) (7) The moment when CAV enters CFZ The calculation formula is as follows: (8) in, For the first Lane 1 The position of the lead vehicle in each convoy; For the first Lane 1 The speed of the lead vehicle in a convoy. The formula stipulates that if the current vehicle has not entered the CFZ, the time to enter the CFZ is the distance between it and the CFZ entrance divided by its current speed; if the current vehicle has already entered the CFZ, the time to enter the CFZ is 0.

[0044] The moment CAV left CFZ The calculation formula is as follows: (9) CAB enters CFZ at the moment The value is calculated based on its location relative to the bus stop, using the following formula: (10) in, For the CAB arrival time, refer to formula (11) for the specific formula. For the specific formula of the estimated stop time for CAB, please refer to formula (12); This is the location of the bus stop exit.

[0045] To ensure passenger comfort, CAB decelerates smoothly as it enters the station, and its entry time is [not specified]. The calculation is as follows: (11) CAB stop time Calculate according to the following formula: (12) in, The number of passengers who have arrived at the station is determined by detectors within the station. The time required for passengers to board the vehicle; This represents the station's passenger arrival rate. The formula stipulates that the CAB stopping time is the sum of the time required for passengers who have already arrived at the station and those about to arrive to board.

[0046] CAB departs CFZ time The calculation formula is as follows: (13) in, The formula for the time taken for CAB to traverse CFZ is as follows: (14) in, For CAB comfort acceleration; The diagonal distance of CFZ, i.e. , The width of the road is defined in this formula. The time it takes for CAB to cross CFZ refers to the time it takes to travel at a comfortable acceleration from its departure from the station. The time required.

[0047] S133, Conflict Elimination: When a potential conflict exists, an independent passage space is reserved for the connected autonomous driving bus within the conflict zone.

[0048] Specifically, if a potential conflict exists—that is, if the time when the CAV enters / leaves the conflict zone conflicts with the time when the CAB enters / leaves the conflict zone—to ensure the CAB has priority, this embodiment stipulates that the CAV can only enter the area when the CAB has just left the conflict zone. Figure 10 As shown. This elimination strategy creates a region for CAB within the CFZ area, such as... Figure 8 The independent space shown allows for safe and efficient lane changes. The following formula is incorporated as a conflict resolution constraint into the lower-level trajectory planning model: (15) in, For the first Lane number The type of the lead vehicle in each fleet is a 0-1 variable. This indicates that the lead vehicle of the convoy is CAB. This indicates that the lead vehicle in the convoy is a CAV; For the first Lane number The time when the CAV, the lead vehicle of the convoy, left CFZ; For the first Lane 1 The position of the lead vehicle in each convoy.

[0049] S140, Vehicle trajectory and traffic signal control: Dynamically adjust the green light duration based on the traffic signal control model, and determine the acceleration of the lead vehicle in the CAV vehicle platoon based on the information output by the traffic signal control model.

[0050] In one embodiment of the present invention, the above-mentioned vehicle trajectory and traffic signal control steps are implemented based on a preset two-layer control model, wherein the upper layer model of the two-layer control model is a traffic signal control model and the lower layer model is a vehicle trajectory control model.

[0051] The traffic signal control model is responsible for dynamically adjusting the green light duration. Its objective function is the total passenger delay within the intersection's TCZ (Traffic Control Zone), which is calculated by weighting and summing the delay times of each vehicle fleet according to the number of passengers. The specific calculation is as follows: (16) in, DelayThis represents the total passenger delay in TCZ during the current optimization period; For lane indexing; Index the fleet; For phase motion index; This represents the total number of lanes at the intersection. For lane Total number of vehicles in the fleet; For the first Lane 1 The delay of the lead vehicle in a convoy is defined as the excess of the lead vehicle's actual arrival time from its current position to the stop line relative to its free-flow arrival time. For lane Inner The number of passengers in each convoy; for Signal timing for phase motion; For lane Inner The time it takes for the lead vehicle of each convoy to arrive at the intersection.

[0052] Among them, the delay of the convoy's lead vehicle The calculation method is as follows: (17) in, For free flow velocity; For lane Inner Position of the lead vehicle in each fleet; This indicates the stop line position.

[0053] Number of passengers in the convoy The calculation method is as follows: (18) in, The number of passengers in a car; For CAB passenger numbers; For lane Inner Number of cars in each fleet; For lane Inner Number of CABs in each team.

[0054] In a specific embodiment of the present invention, the traffic signal control model satisfies the following constraints: 1.1) Minimum arrival time constraint To prevent vehicles from exceeding their maximum speed and acceleration limits upon reaching the intersection, the minimum arrival time at the intersection needs to be calculated to constrain the actual arrival time of the vehicles. Therefore, the location of bus stops affects the travel process of CAB and CAV vehicles. The minimum arrival time constraints for different vehicle types are shown in the following formula: (19) in, The minimum arrival time for CAB is given by formula (18). The minimum arrival time for CAV is calculated as follows: The impact of the bus's arrival time on the three stages of entering, stopping, and leaving the station was considered, such as... Figure 9 As shown, the minimum arrival time of CAB The calculation is as follows: (20) in, The minimum arrival time for CAB is the time it takes to travel from the station to the stopping line. This formula stipulates that when CAB has not entered the station, the minimum arrival time is the sum of the time taken to enter, the time taken to stop, and the time taken to exit; when CAB stops at the station, the minimum arrival time is the sum of the time taken to stop and the time taken to exit; and when CAB has exited the station, the minimum arrival time is the time taken to exit. Calculate according to the following formula: (twenty one) in, The position is the stop line. This formula specifies that the minimum arrival time for CAB after leaving the station is the time it takes to travel from the current position to the stop line with maximum acceleration.

[0055] The minimum arrival time of CAV is calculated as follows: (twenty two) in, Minimum arrival time for CAV; This is the shortest arrival time from the CFZ entrance to the stop line; The shortest time to reach the stop line from the current position. and The calculation method and The same applies, so I won't repeat it further. This formula stipulates that if there is a potential conflict between CAV and CAB, i.e. or The minimum arrival time is and The sum; if there is no conflict, then the minimum arrival time is .

[0056] 1.2) Signal Timing Constraints Excessive signal timing can lead to long waiting times for vehicles in other phases, while insufficient timing can result in insufficient passage time for vehicles in the current phase, causing traffic congestion. To avoid these situations, signal timing needs to be limited, as shown in the following formula.

[0057] (twenty three) in, and These are the minimum and maximum green light timings, respectively.

[0058] 1.3) Period length constraint The sum of the phase timings of each phase in loops 1 and 2 must be equal to the period length, hence the following constraint applies: (twenty four) (25) in, The period length is denoted as .

[0059] 1.4) Vehicle arrival time green light time window constraint To avoid collisions between vehicles and vehicles moving in conflict phases, it is necessary to ensure that vehicles pass through the intersection during the green light period. Therefore, it is necessary to constrain the vehicle arrival time to fall within the green light time window, hence the following constraints: (26) (27) (28) (29) (30) (31) (32) (33) in, To optimize the number of cycles considered; This represents the lane index corresponding to the phase movement. Equation (26-33) specifies that the arrival time of each controlled CAV or CAB is in the [missing information - likely a specific phase or time]. The time between the start and end of the green light cycle.

[0060] 1.5) Safety headway constraint This constraint is designed to prevent the current convoy leader vehicle from colliding with the rear vehicle of the convoy ahead. Therefore, the following constraint is defined for the arrival times of the leader vehicles of two adjacent convoys: (34) in, This is the minimum safe headway when adjacent vehicles pass through an intersection. Equation (34) stipulates that the difference in arrival time between the lead vehicles of two adjacent convoys shall not be less than the product of the total number of vehicles in the preceding convoy and the minimum safe headway.

[0061] The vehicle trajectory control model, as the lower-level model, determines the acceleration of the lead vehicle in the CAV platoon based on the information output from the upper-level model (traffic signal control model). Its objective function is a weighted sum of vehicle fuel consumption and comfort index (jerk), as shown below: (35) in, Index vehicles within the fleet; For the first The speed of the vehicle; For the first The acceleration of a vehicle; For vehicle fuel consumption model; For the first The car's acceleration, and These are the weights for fuel consumption and acceleration, respectively. For the terminal time, if the convoy's lead vehicle is a CAV or a CAB during the exit phase, then If the lead vehicle of the convoy is a CAB during the pit stop phase, then ; This is the initial time. This represents the total number of vehicles in the fleet.

[0062] In a specific embodiment of the present invention, the vehicle trajectory control model satisfies the following constraints: 2.1) Initial Condition Constraints This constraint is to ensure that the vehicle's position and velocity at the start are equal to the initial conditions, as shown below: (36) (37) in, This is the initial time. For the first The location of the vehicle; and The first The vehicle's initial position and speed.

[0063] 2.2) CAB Terminal Condition Constraints To ensure the lead vehicle of the convoy reaches the specific location at the end time, the CAB's driving process is divided into three stages: entering the station, stopping, and leaving the station. During entering and leaving the station, trajectory control of the CAB is required, and the trajectory control terminal conditions for these two stages must be provided.

[0064] 1) Terminal conditions When CAB enters the station, it uses the bus stop location as its terminal location, and its terminal speed is 0. The specific formula is as follows: (38) (39) in, Lead vehicle for the convoy Time and location; Lead vehicle for the convoy Speed ​​in a given moment; This is the initial time. This is the terminal moment of trajectory control. ; The type of vehicle leading the fleet is a 0-1 variable. This indicates that the lead vehicle of the convoy is CAB. This indicates that the lead vehicle in the convoy is a CAV.

[0065] To ensure the lead vehicle of the convoy reaches the specific location at the end time, the CAB's driving process is divided into three stages: entering the station, stopping, and leaving the station. During entering and leaving the station, trajectory control of the CAB is required, and the trajectory control terminal conditions for these two stages must be provided.

[0066] When CAB enters the station, it uses the bus stop location as its terminal location, and its terminal speed is 0. The specific formula for the CAB's terminal entry condition is as follows: (40) (41) in, Lead vehicle for the convoy Time and location; Lead vehicle for the convoy Speed ​​in a given moment; This is the initial time. This is the terminal moment of trajectory control. ; The type of vehicle leading the fleet is a 0-1 variable. This indicates that the lead vehicle of the convoy is CAB. This indicates that the lead vehicle in the convoy is a CAV.

[0067] 2) Outbound terminal conditions Accordingly, when CAB departs from the station, it uses the stop line position as its terminal position. The formula for the CAB departure terminal condition, derived from the traffic signal control model, is as follows: (42) 2.3) CAV terminal condition constraints The CAV terminal condition constraints are calculated as follows: (43) in, Minimum arrival time for CAV; This is the shortest arrival time from the CFZ entrance to the stop line; The shortest time to reach the stop line from the current position. and The calculation method and The same applies, so I won't repeat it further. This formula stipulates that if there is a potential conflict between CAV and CAB, i.e. or The minimum arrival time is and The sum; if there is no conflict, then the minimum arrival time is .

[0068] 2.4) Conflict Elimination Constraints This constraint, by requiring the CAV to enter the area when the CAB exits the CFZ, creates independent space for the lane-changing CAB within the CFZ, thus improving the lane-changing efficiency of the CAB. The formula is: (44) 2.5) State Equation Constraints State equation constraints are used to ensure that the vehicle's position and velocity update rules satisfy basic physical laws, as shown below: (45) (46) 2.6) Acceleration constraints for following vehicles This constraint applies to car-following vehicles in the platoon, whose acceleration is modeled using the car-following method, as shown below: (47) 2.7) Speed ​​Constraints To ensure that vehicles do not exceed road speed limits and cause traffic accidents, vehicle speeds must meet the following constraints: (48) 2.8) Acceleration Constraints Considering the physical limitations of a vehicle's maximum and minimum acceleration, constraints need to be placed on acceleration to ensure that the vehicle does not exceed these limits. (49) in, This is the minimum acceleration of the vehicle.

[0069] 2.9) Safety distance constraints Safety distance constraints are designed to prevent collisions between vehicles during operation. Therefore, the following constraints apply to the distance between adjacent vehicles: (50) in, This refers to the safe distance between vehicles.

[0070] The signal and vehicle cooperative intelligent control method based on traffic conflict provided by this invention can further protect one or more of the following steps: S150, Rolling Optimization: Select a signal cycle as the optimization cycle, and at the beginning of each signal cycle, call the dynamic programming algorithm of the traffic signal control model to solve and update the duration and phase sequence of green, yellow, and red lights (e.g., ...). Figure 7 As shown in the figure, the results are transmitted to the intersection signal controller. Within the same optimization cycle, the dynamic programming algorithm of the vehicle trajectory control model is called at each time step. Based on vehicle dynamics and safety constraints, the acceleration of the lead vehicle in the platoon is solved in real time and sent to the vehicle controller to adjust the driving trajectory of the CAB and other vehicles. The upper and lower layers interact with signals and vehicle status in real time through V2X communication to realize integrated rolling cooperative control of signals and vehicles.

[0071] S160, Closed-loop dynamic update: During the execution of traffic control, the system uses the green light time as a cycle to repeatedly collect traffic data and divide road areas, vehicle platooning, conflict zone conflict identification, vehicle trajectory and traffic signal control, and rolling optimization to achieve closed-loop optimization control based on real-time feedback, thereby continuously ensuring the optimal effect of bus priority passage and dynamic allocation of right-of-way.

[0072] Figure 11 A rolling optimization framework according to an embodiment of the present invention is shown. Figure 11 As shown, with time step To proceed, first identify conflicts and update the status; when % The upper signal control layer is triggered, and the phase and green light are optimized with the total passenger delay in equations (6)-(8) as the target. The dynamic programming method is used to solve the problem, and the green light signal timing is sent to the traffic signal control system. Then, immediately within the time window, the optimal trajectory and arrival time are solved by dynamic programming according to equations (54)-(60) and sent back. % In this case, only the lower-level solution is executed within the current time window. After completion, the signals and vehicle states are updated, and then... Repeat the above process until... .

[0073] Furthermore, in a specific embodiment of this application, the dynamic programming solution method for the upper-level traffic signal control model in the two-level control model includes the following steps: (1) Input: Period length unit time length Total number of stages in dynamic programming Total number of stages It equals the total number of phases.

[0074] (2) Discrete period length and state variables Period length In terms of unit time length Discretized One time step: (51) by Define the interval Stage state variable set : (52) in, Let be the state variable in dynamic programming, representing the th . The number of time steps corresponding to the end of each stage. Let represent The Middle Each element.

[0075] Given the state variables of adjacent stages, the following formula can be used to obtain... Stage control variables, i.e., signal timing : (53) Among them, when hour, ;when hour, .

[0076] (3) Dynamic Programming Backward Search The basic idea of ​​dynamic programming backward search is to traverse the set of state variables of adjacent phases, calculate the signal timing, and if it satisfies the signal layer constraints, calculate the arrival time of the vehicle in the current phase and the accumulated objective function value of the signal control layer, and record the optimal decision. The specific process of the dynamic programming algorithm backward search is as follows: 1) Given the set of state variables for each stage Total number of phases .

[0077] 2) Initialize the phase index Phase state , Total passenger delays during the period .

[0078] 3) Perform the final stage of backward search.

[0079] Traversal elements in , will the element Assign to Based on this, the signal timing is calculated. .like If the signal timing constraints are met, then the minimum arrival time is determined based on the vehicle arrival time constraints. and calculate Total cumulative passenger delays during the period Then, store. corresponding , as well as .

[0080] 4) Perform a backward search in the intermediate stage.

[0081] Depend on The process proceeds in reverse from stage 1 to stage 2. First, for any two adjacent stages, the process is repeated... elements in , will the element Assign to Then, for each traversal , of which element Assign to Based on this, the signal timing is calculated. .like If the signal timing constraints are met, then the minimum arrival time is determined based on the vehicle arrival time constraints. and calculate Phase to Total cumulative passenger delays during the period Finally, select the ones that are suitable for... optimal , as well as And store.

[0082] 5) Perform the first stage of backward search. make , traversal medium elements , of which element Assign to Based on this, the signal timing is calculated. .like If the signal timing constraints are met, then the minimum arrival time is determined based on the vehicle arrival time constraints. And calculate the total cumulative passenger delays. Finally, select the one that is optimal for the first stage. , as well as And store.

[0083] 6) Based on the optimal decision information stored at each stage's state point, output the optimal signal timing. With vehicle arrival time .

[0084] Accordingly, as an example, the dynamic programming solution method for the lower layer (vehicle trajectory control model) in the two-layer control model follows these steps: (1) Discrete time and distance Travel time of the lead vehicle Discretized Each time step will determine the distance traveled. Discretized part: (54) (55) in, The unit time step; The distance is expressed in units.

[0085] CAB drove out of CFZ time step for: (56) Discrete segment corresponding to the CFZ inlet position for: (57) (2) Define the vehicle position state set To facilitate searching, a set of lead vehicle position states is defined for each time step. : (58) in, For time step index; The initial speed of the lead vehicle; For the first The location status of the navigation vehicle at each time step is abbreviated below for ease of expression. .when hour, The elements in the set are at their initial positions. ;when hour, The elements in the set represent the positions reached by the initial velocity; when hour, The elements in the set are Discretize the CFZ entrance position between 0 and 0 to ensure that vehicles do not enter the CFZ before exiting the CAB; when hour, The elements in the set are Ensure that the vehicle arrives at the CFZ entrance when it exits the CAB; when hour, The elements in the set are Discretize the CFZ inlet and stop line positions using units; when hour, The elements in the set are the discrete positions corresponding to the stop lines.

[0086] Given the position and state variables of adjacent time steps, the velocity can be obtained according to the following formula: (59) in, For the first The speed of the lead vehicle at each time step is abbreviated below. Based on this, the first... Acceleration at each time step: (60) in, For the first The acceleration of the lead vehicle at each time step, abbreviated below. .

[0087] (3) Dynamic Programming Forward Search To satisfy the initial velocity conditions, dynamic programming forward search is used when solving the vehicle control layer, traversing each time step. The velocity and acceleration are obtained from the position and state points in adjacent discrete sets. If they satisfy the relevant constraints of the vehicle trajectory control model, the optimal decision is stored. The following describes the steps of the forward search: 1) Given the set of position states of the lead vehicle at each time step Total number of steps .

[0088] 2) From the first time step to the second Perform a forward traversal at each time step.

[0089] 3) Let the first Each time step The total number of elements is , No. Each time step The total number of elements is For two adjacent time steps, first iterate through... elements in , will the element Assign to Then, for each traversal , will be the first element Assign to Based on this, the calculation is obtained. .

[0090] 4) After obtaining Based on, according to optimal storage Calculate acceleration .

[0091] 5) Judgment Does the speed constraint meet? Does the speed constraint satisfy the following vehicle's status? Does the following vehicle's status satisfy the following constraint? If satisfied, then calculate the... The objective function value of the trajectory control layer accumulated over time steps .

[0092] 6) For each After traversing After selecting the middle element, choose the optimal one. and store its corresponding and .

[0093] 7) Repeat steps 3) to 6) until... .

[0094] 8) According to The optimal information stored in the unique state is used for reverse search.

[0095] 9) Output the optimal acceleration sequence .

[0096] Example 2 Corresponding to the traffic conflict-based signal and vehicle cooperative intelligent control method in Example 1, this Example 2 provides a traffic conflict-based signal and vehicle cooperative intelligent control system to implement the traffic conflict-based signal and vehicle cooperative intelligent control method in Example 1. By setting up CFZ modeling at the bus station exit and adopting a signal-trajectory two-layer cooperative optimization strategy, it achieves the unity of bus priority control, energy consumption reduction and system efficiency improvement.

[0097] The system architecture of this invention provides a modular structure, combined with Figure 3 The technology roadmap shown Figure 17 The logical framework structure of the signal and vehicle cooperative intelligent control system based on traffic conflict according to this embodiment is shown. Figure 17 As shown, the intelligent signal and vehicle cooperative control system 700 based on traffic conflict provided by this invention mainly includes a traffic data acquisition and road area division module 710, a vehicle platooning module 720, a conflict zone conflict identification module 730, and a vehicle trajectory and traffic signal control module (implemented based on the upper-layer traffic signal control model 741 and the lower-layer vehicle trajectory control model 742 of a two-layer control model, respectively). The module of this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0098] The traffic data acquisition and road area division module 710 is used to collect traffic data at the entrance of road intersections in real time and divide the road into areas to obtain road parameters. The traffic data includes vehicle information and traffic signal information. The area division includes setting the non-solid line lane-changing area of ​​the road as a lane-changing area, setting the solid line non-lane-changing area of ​​the road as a trajectory control area, and setting a conflict area on the road section from the bus stop exit to the intersection. The vehicle platooning module 720 is used to initialize vehicles that can pass through the intersection in the same cycle into a platoon based on the traffic data and road parameters, according to the maximum number of vehicles allowed to pass through the intersection during the green light timing of each preset cycle plan, starting from the stop line and proceeding sequentially towards the lane entrance. The initial convoy is divided into several sub-convoys by determining whether the distance between vehicles in the initial convoy is greater than the critical distance between vehicles. The conflict zone conflict identification module 730 is used to construct a bus stop conflict analysis module based on the traffic data and the road parameters, so as to generate constraints for the two-layer control model to call; wherein, the two-layer control model includes an upper-layer traffic signal control model 741 and a lower-layer vehicle trajectory control model 742. The vehicle trajectory and traffic signal control module is used to dynamically adjust the green light duration based on the traffic signal control model 741, and to determine the acceleration of the lead vehicle in the CAV vehicle platoon in the vehicle trajectory control model 742 based on the information output by the traffic signal control model 741.

[0099] In one possible implementation, the traffic conflict-based signal and vehicle cooperative intelligent control system further includes one or more of the following modules: The rolling optimization module 750 is used to select a signal cycle as the optimization cycle, call the dynamic programming algorithm of the traffic signal control model at the beginning of each signal cycle, solve and update the duration and phase sequence of green light, yellow light, and red light, and transmit the results to the intersection signal controller. The closed-loop dynamic update module 760 is used to control the data processing flow of the traffic data acquisition and road area division module, vehicle platooning module, conflict zone conflict identification module, vehicle trajectory and traffic signal control module, and rolling optimization module to repeat the process with the green light time as the cycle, so as to realize closed-loop optimization control based on real-time feedback.

[0100] More specific implementations of the above-mentioned intelligent control system for signal and vehicle cooperation based on traffic conflict can be found in the foregoing description of the embodiments of the intelligent control method for signal and vehicle cooperation based on traffic conflict, and will not be detailed here.

[0101] Example 3 In this embodiment, intelligent signal and vehicle cooperative control based on traffic conflicts is achieved through the following steps: Step 1: Traffic Data Collection and Communication Interaction The vehicle-mounted terminal and the RSU are connected via V2X communication to obtain real-time information on the lane position, speed, arrival time of each vehicle, and bus entry / exit status. The data is then transmitted to the traffic signal controller and vehicle control unit to provide input for subsequent two-layer optimization.

[0102] Step 2: CFZ Identification and Constraint Generation Based on the location of the bus bay and the vehicle speed, the CFZ range in front of the bus exit is determined in conjunction with the maximum braking distance. Conflict identification and elimination constraints are established to constrain the deceleration and yielding of ordinary vehicles and the priority of buses exiting the station.

[0103] Step 3: Optimization of Upper-Level Signal Control With the goal of minimizing the total passenger delay at the upstream intersection, a dynamic programming algorithm is invoked at the beginning of each signal cycle to optimize the green light duration and phase sequence of each phase by taking into account vehicle arrival time and traffic conditions, and the optimization results are sent to the traffic signal controller.

[0104] Step 4: Lower-level vehicle trajectory planning and execution With the optimization goals of fuel consumption and comfort of vehicles in the downstream CFZ, at each time step, based on the upper-level signal timing results and conflict constraints, the trajectory control algorithm is invoked to optimize the acceleration, deceleration, and lane-changing trajectories of buses leaving the station and ordinary vehicles, so as to coordinate safe passage within the CFZ.

[0105] Step 5: V2X Linkage and Scrolling Optimization Execution During system operation, the upper and lower layer models are linked in real time via V2X, dynamically updating signal timing and vehicle trajectories through rolling optimization to achieve integrated signal-vehicle collaborative control. After initializing parameters, outbound conflicts are identified. At the beginning of each signal cycle, the traffic signal control layer algorithm is called to optimize signal timing and the arrival time of the lead vehicle. At each time step, the vehicle trajectory control layer algorithm is called to optimize the acceleration of the lead vehicle.

[0106] Based on the above embodiments, it can be seen that the signal and vehicle cooperative intelligent control method and system based on traffic conflict provided by the present invention, taking a medium level of traffic demand as an example, has been simulated and verified under different CAV penetration rates. The performance differences in average passenger delay and average vehicle fuel consumption between the proposed Transit Priority Signal and Vehicle Trajectory Cooperative Control Model (TVM-BSC) considering bus station conflict and the traditional Optimization Method without considering bus station conflict (OMWC) are compared. The results show that the present invention has significant technical advantages and application effects, as detailed below: 1) Significantly reduce average passenger delay Under the same traffic demand, as the CAV penetration rate increases from 20% to 100%, the overall average passenger delay under the method of this invention is reduced by 2.55%–7.98% compared to the OMWC method, and the average delay for public transport passengers is reduced by 2.59%–5.74%. Figure 12 , Figure 13 As shown. The reason is that the present invention sets up a CFZ in front of the bus stop exit and eliminates the interference between CAB and ordinary vehicles through trajectory collaborative optimization, so that buses can smoothly merge into the traffic flow, significantly reducing the waiting time of passengers in the CFZ and improving travel efficiency.

[0107] 2) Effectively alleviate traffic conflicts and congestion Simulation results show that in the OMWC method, CABs and cars frequently weave near the station, leading to significant vehicle deceleration and queuing within the CFZ. However, this invention optimizes the acceleration trajectory of CAVs behind the CFZ, creating independent lane-changing space for CABs exiting the station, resulting in smoother CFZ vehicle operation and a significant improvement in local congestion. Figure 14a and Figure 14b As shown.

[0108] 3) Reduce overall fuel consumption and improve energy efficiency. At different CAV penetration rates, the overall average fuel consumption of the vehicle using the method of this invention is lower than that of the OMWC method, demonstrating significant energy-saving effects. When the CAV penetration rate is 20%, 80%, and 100%, the overall fuel consumption is reduced by 4.5%, 3.6%, and 3.7%, respectively. This is because the present invention avoids frequent acceleration, deceleration, and idling within the CFZ (Cross-Function Zone) through coordinated control, thereby reducing fuel consumption. Taking a 40% CAV penetration rate as an example, the average fuel consumption of CAB (Carbon Able-Up) is reduced by 16.01% compared to the traditional method, showing good energy-saving potential. Figure 15 , Figure 16 As shown.

[0109] 4) Significantly improve public transportation efficiency and priority service levels The following table shows the time (s) taken by the westbound access road CAB to cross the CFZ under different methods when the CAV penetration rate is 40% according to an embodiment of the present invention. As shown in the table above, under a 40% CAV penetration rate, this invention reduces the average time for CAB to cross CFZ from 16.29 s to 5.71 s, a reduction of 59.73%, verifying its effectiveness in bus priority control. This invention can ensure bus priority passage while reducing interference with ordinary vehicles and improving the overall operational efficiency of intersections.

[0110] like Figure 18 As shown, the present invention also provides an electronic device 1 for implementing a traffic conflict-based signal and vehicle cooperative intelligent control method. The electronic device 1 may include a processor 10, a memory 11, and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a traffic conflict-based signal and vehicle cooperative intelligent control program 12. The memory 11 may include both internal storage units and external storage devices for the traffic conflict-based signal and vehicle cooperative intelligent control system. The memory 11 can be used not only to store application software and various types of data, such as the code of the traffic conflict-based signal and vehicle cooperative intelligent control program, but also to temporarily store data that has been output or will be output.

[0111] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart memory card, secure digital card, flash memory card, etc., equipped on the electronic device 1. Furthermore, the memory 11 can include both internal and external storage units of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as code for intelligent control programs for signal and vehicle coordination based on traffic conflicts, but also to temporarily store data that has been output or will be output.

[0112] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units, microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., intelligent control programs for signal and vehicle coordination based on traffic conflicts) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0113] The bus can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0114] Figure 18 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 18 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0115] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. This power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power sources, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, or any other components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be elaborated further here.

[0116] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0117] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit. Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (organic light-emitting diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0118] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in terms of the scope of patent protection.

[0119] The signal and vehicle cooperative intelligent control program 12 based on traffic conflict stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When it runs in the processor 10, it can implement the steps in the signal and vehicle cooperative intelligent control method based on traffic conflict as described above.

[0120] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figure 2 The descriptions of the relevant steps in the corresponding embodiments are not repeated here. Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0121] This invention also provides a computer-readable storage medium, which may be non-volatile or volatile, and stores a computer program that, when executed by a processor, implements the aforementioned intelligent control method for signal and vehicle coordination based on traffic conflicts.

[0122] Specifically, the specific implementation method of the computer program when executed by the processor can be referred to the description of the relevant steps in the embodiment of the intelligent control method for signal and vehicle cooperation based on traffic conflict, and will not be repeated here.

[0123] In the several embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0124] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional modules in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional modules. Clearly, the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in the system claims can also be implemented by a single unit or system through software or hardware.

[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the present invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0127] The signal and vehicle cooperative intelligent control method and system based on traffic conflict according to the present invention have been described above by way of example with reference to the accompanying drawings. However, those skilled in the art should understand that various modifications can be made to the signal and vehicle cooperative intelligent control method and system based on traffic conflict according to the present invention without departing from the scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the contents of the appended claims.

Claims

1. A traffic conflict-based signal and vehicle cooperative intelligent control method, characterized in that, The method comprises the following steps: Traffic data collection and road region division: real-time collection of traffic data at the entrance of a road intersection, and division of the road into regions to obtain road parameters; wherein the traffic data comprises vehicle information and traffic signal information, and the region division comprises: setting the non-solid line lane-changing region of the road as a lane-changing zone, setting the solid line non-lane-changing region of the road as a trajectory control zone, and setting the road section from the bus station exit to the intersection as a conflict zone; Vehicle platoon: based on the traffic data and the road parameters, the maximum number of vehicles allowed to pass according to the preset green light timing of each cycle, the vehicles that can pass the intersection in the same cycle are initialized as a vehicle platoon, and the vehicle platoon is initialized in sequence from the stop line to the entrance of the lane K Then, by judging whether the distance between the vehicles in the initial vehicle platoon is greater than the critical distance between vehicles, the initial vehicle platoon is divided into multiple sub-vehicle platoons Conflict identification in the conflict zone: based on the traffic data and the road parameters, a bus station conflict analysis module is constructed to generate constraints for calling a double-layer control model; wherein the double-layer control model comprises an upper-layer traffic signal control model and a lower-layer vehicle trajectory control model; Vehicle trajectory and traffic signal control: dynamic adjustment of the green light duration based on the traffic signal control model, and decision of the acceleration of the lead vehicle of the CAV vehicle formation based on the information output by the traffic signal control model.

2. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 1, wherein, The method further comprises one or more of the following steps: Rolling optimization: selecting a signal cycle as an optimization cycle, calling a dynamic programming algorithm of the traffic signal control model at the beginning of each signal cycle, solving and updating the green light, yellow light and red light durations and phase sequence, and transmitting the results to the intersection signal controller; Closed-loop dynamic updating: using the green light time as a cycle, repeating the traffic data collection and road region division, vehicle formation, conflict identification in the conflict zone, vehicle trajectory and traffic signal control, and rolling optimization to realize closed-loop optimization control based on real-time feedback. 3.The traffic conflict based signal and vehicle cooperative intelligent control method according to claim 1 or 2, wherein, The conflict identification in the conflict zone comprises: Determining the length of the conflict zone according to the length of the bus body, the static vehicle spacing and the maximum braking distance; Based on the vehicle positions and speeds of the vehicles in the conflict zone and the vehicles about to enter the conflict zone, determining whether there is a potential conflict between the time when the ith vehicle formation of the connected and automatic driving vehicle enters / leaves the conflict zone and the time when the connected and automatic driving bus enters / leaves the conflict zone; wherein, When there is a potential conflict, an independent passing space is reserved for the connected and automatic driving bus in the conflict zone.

4. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 3, wherein, The length of the conflict zone is set as the sum of the length of the bus body, the static vehicle spacing and the maximum braking distance required by the vehicle in the conflict zone in the approach direction; wherein the maximum braking distance required by the vehicle in the conflict zone in the approach direction is the difference between the minimum braking distance of the vehicle and the distance between the vehicle and the entrance of the conflict zone.

5. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 4, wherein, The objective function of the traffic signal control model is: wherein, Delay represents the total passenger delay in the trajectory control zone in the current optimization period; is the lane index; is the platoon index; is the phase movement index; is the total number of lanes at the intersection; is the total number of platoons in the lane ; is the delay of the th platoon leader vehicle in the th lane, is the number of passengers in the th platoon in the lane ; is the signal timing of the phase movement ; is the time of arrival of the th platoon leader vehicle at the intersection in the lane .

6. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 5, wherein, The objective function of the vehicle trajectory control model is: in, Index vehicles within the fleet; For the first The speed of the vehicle; For the first The acceleration of a vehicle; For vehicle fuel consumption model; For the first The acceleration of the vehicle, and The weights for fuel consumption and acceleration are respectively. For the terminal time, if the lead vehicle in the fleet is a connected autonomous vehicle or a connected autonomous bus in the departure phase, then If the lead vehicle in the fleet is a connected autonomous driving bus entering the station, then ; This is the initial time. This represents the total number of vehicles in the fleet.

7. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 6, wherein, The driving process of the connected and automatic driving bus comprises three stages of entering the station, stopping at the station and leaving the station, and the terminal condition of trajectory control of the connected and automatic driving bus when entering and leaving the station in the vehicle trajectory control model comprises: The terminal condition of entering the station: when the connected and automatic driving bus enters the station, the bus station position is taken as the terminal position, the terminal speed is 0, and the terminal condition constraint of entering the station is as follows: wherein, is a platoon lead vehicle is a time position; is a platoon lead vehicle is a time speed; is an initial time; is a terminal time of trajectory control; is a platoon lead vehicle type, which is a 0-1 variable, indicates that the platoon lead vehicle is a connected and automated bus, indicates that the platoon lead vehicle is a connected and automated car, is a stop position coordinate of a harbor bus stop; Outbound terminal condition: when the net-connected automatic driving bus leaves the station, the stop line position is taken as the terminal position, The outbound terminal condition is constrained as follows according to the decision of the traffic signal control model: 。 8. The traffic conflict based signal and vehicle cooperative intelligent control method of claim 7, wherein, In the vehicle trajectory control model, the terminal condition of trajectory control of the connected automatic driving vehicle is: If there is a potential conflict between the connected automatic driving vehicle and the connected automatic driving bus, the minimum arrival time of the connected automatic driving vehicle is the sum of the time when it enters the conflict area and the shortest arrival time from the entrance of the conflict area to the stop line; if there is no conflict, the minimum arrival time is the shortest time for the connected automatic driving vehicle to reach the stop line from its current position.

9. A traffic conflict based signal and vehicle cooperative intelligent control system, characterized in that, Comprise: A traffic data collection and road region division module for collecting traffic data at the entrance of a road intersection in real time and dividing the road into regions to obtain road parameters; wherein the traffic data includes vehicle information and traffic signal information, and the region division includes: setting the non-solid line lane-changing region of the road as a lane-changing zone, setting the solid line lane-changing region of the road as a trajectory control zone, and setting the conflict zone on the road segment from the bus station exit to the intersection; The vehicle platoon module is used for initializing vehicles that can pass the intersection in the same period as a platoon from a stop line to a lane entrance direction according to a preset maximum number of vehicles allowed to pass by a green light timing of each period based on the traffic data and the road parameters The initial platoon is divided into multiple sub-platoons by judging whether the distance between the vehicles in the initial platoon is greater than a critical distance between vehicles. A conflict zone conflict identification module for constructing a bus station conflict analysis module based on the traffic data and the road parameters to generate constraints for calling a double-layer control model; wherein the double-layer control model includes a traffic signal control model in the upper layer and a vehicle trajectory control model in the lower layer; A vehicle trajectory and traffic signal control module for dynamically adjusting the green light duration based on the traffic signal control model, and deciding the acceleration of the lead vehicle of the CAV vehicle formation based on the information output by the traffic signal control model.

10. The traffic conflict based signal and vehicle cooperative intelligent control system of claim 9, wherein, Further comprising one or more of the following modules: A rolling optimization module for selecting a signal period as an optimization period, calling a traffic signal control model dynamic programming algorithm at the beginning of each signal period to solve and update the green, yellow and red light durations and phase sequence, and transmitting the results to the intersection signal controller; A closed-loop dynamic update module for controlling the data processing flow of the traffic data collection and road region division module, vehicle formation module, conflict zone conflict identification module, vehicle trajectory and traffic signal control module, and rolling optimization module repeatedly in the green light time period to realize closed-loop optimization control based on real-time feedback.

11. An electronic device, comprising: The electronic device comprises a memory, a processor, and a traffic conflict-based signal and vehicle cooperative intelligent control program stored on the memory and executable on the processor, and the traffic conflict-based signal and vehicle cooperative intelligent control program, when executed by the processor, implements the traffic conflict-based signal and vehicle cooperative intelligent control method of any one of claims 1 to 9.

12. A computer readable storage medium storing a computer program, wherein the computer program comprises program instructions configured to cause a processor to perform the method according to any one of claims 1 to 11. The computer program, when executed by the processor, implements the traffic conflict-based signal and vehicle cooperative intelligent control method of any one of claims 1 to 9.

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