Method and system for efficiently planning track of vehicle queue at intelligent network connection mixed intersection

Abstract

The invention provides an efficient planning method and system for a vehicle queue track at an intelligent network connection mixed intersection, and belongs to the technical field of intelligent traffic. The method comprises the following steps: constructing a control area based on signal timing information and vehicle information; constructing an initial segmented trajectory based on passing through the intersection at the highest speed limit, and adjusting the trajectory according to a safety boundary constraint to obtain a candidate segmented trajectory; if the candidate segmented track arrives at the intersection in the red light period, based on a next green light starting moment, reversely constructing a uniform acceleration section for the CAV and connecting the uniform acceleration section with the candidate segmented track through a connecting section, and reversely constructing a parking section for the HV and connecting the parking section with the candidate segmented track through the connecting section to obtain a final segmented track; the connecting section comprises a uniform deceleration section or a combination of the uniform deceleration section and a parking section. The influence of the HV trajectory on CAV trajectory planning is considered, so that the whole mixed traffic flow passes through the intersection in a smooth trajectory, and the traffic capacity of the intersection is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a method and system for efficiently planning vehicle queue trajectories at an intelligent networked mixed-traffic intersection. Background Art

[0002] With the development of vehicle-road collaboration and connected vehicle (CAV) technology, the programmable nature of CAV provides a new way to achieve coordinated optimization of vehicle trajectory and traffic signals. However, in intersection scenarios where CAVs and human-driven vehicles (HVs) co-exist, existing trajectory planning methods still face significant challenges. On the one hand, the penetration rate of CAVs requires a long-term transition (expected to reach 100% by 2060), and the trajectory coordination of mixed traffic needs to be compatible with the uncertainty of HV behavior; on the other hand, the coupling of intersection signal control and vehicle dynamics constraints requires trajectory planning to meet safety, traffic efficiency and real-time requirements at the same time.

[0003] The existing technology has the following defects in vehicle trajectory planning at mixed traffic intersections: (1) Insufficient computational complexity and real-time performance: For example, the invention patent with application number 2021103149821 indirectly optimizes vehicle acceleration by calculating the travel time of the conflict point, which requires frequent solution of multi-variable nonlinear equations, and the computational burden increases dramatically in multi-vehicle mixed traffic scenarios; while the invention patent with application number 2021104898377 relies on a global optimization model of signal timing and lane sharing, which requires high-precision numerical iteration and is difficult to meet online control requirements.

[0004] (2) It is difficult to handle the trajectory planning problem of multiple vehicles at the same time: Since existing methods usually divide vehicles into convoys and only plan the trajectory of the lead vehicle in the convoy, it is assumed that the rear vehicles follow the lead vehicle in the convoy, which fails to handle the trajectory planning problem of multiple vehicles at the same time.

[0005] (3) Trajectory coordination lacks systematicity: The existing solutions have not established a unified analytical solution framework for segmented trajectories, making it difficult to ensure the overall smoothness of the vehicle queue and the matching accuracy of the green light window, which can easily lead to secondary conflicts or loss of traffic efficiency.

[0006] In addition, although the traditional optimal control model can theoretically describe the trajectory planning problem, its reliance on numerical solutions and its inability to generate analytical solutions lead to low algorithm efficiency. It can only be applied to the control of single vehicles or small-scale fleets and cannot support traffic-level collaborative optimization. In the context of long-term mixed traffic of CAVs and HVs, a trajectory planning method with low computational complexity and high real-time performance is urgently needed to take into account the dynamic adaptability of HV behavior and the collaborative efficiency of CAV queues, so as to break through the existing technical bottlenecks and achieve a systematic improvement in the traffic capacity of intersections. Summary of the invention

[0007] In order to solve the problem that the existing intersection vehicle trajectory control method only focuses on the vehicle dimension and it is difficult to simultaneously plan the trajectories of all CAVs from the overall dimension of traffic flow so that they can pass smoothly through the intersection, the present invention proposes an efficient planning method and system for the trajectory of vehicle queues at intelligent networked mixed intersections, which decomposes the CAV trajectory into several smooth curves and constructs the safety boundary of the CAV trajectory based on the Newell following model; on this basis, an analytical solution method for each curve is given to achieve rapid planning of the CAV trajectory. For HVs in mixed traffic, a rapid estimation method for the HV trajectory curve is proposed, and the influence of the HV trajectory on the CAV trajectory planning is considered, so that the entire mixed traffic flow passes through the intersection with a smooth trajectory, thereby efficiently utilizing the green light window to ensure that the vehicle queue passes through the intersection safely and efficiently, and improve the traffic capacity of the intersection.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for efficiently planning vehicle queue trajectories at an intelligent networked mixed traffic intersection, comprising: Construct the control area based on the signal timing information, the initial speed and initial time of the intelligent connected vehicles and manually driven vehicles entering the control area; In the control area, an initial segmented trajectory is constructed based on passing through the intersection at the maximum speed limit, and the trajectory is adjusted according to the safety boundary constraint to obtain a candidate segmented trajectory; If the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, a uniform acceleration segment is constructed in reverse and connected with the candidate segmented trajectory through a connecting segment to obtain the final segmented trajectory; for manually driven vehicles, a parking segment is constructed in reverse and connected with the candidate segmented trajectory through a connecting segment to obtain the final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; The final segmented trajectory of the current vehicle is stored in the queue. When the subsequent vehicles enter the control area, the trajectory information is iteratively updated to achieve trajectory planning.

[0009] In a second aspect, the present invention provides an efficient vehicle queue trajectory planning system for an intelligent networked mixed traffic intersection, comprising: A scenario construction module is configured to construct a control area based on signal timing information, initial speeds and initial times of intelligent connected vehicles and manually driven vehicles entering the control area; A candidate trajectory construction module is configured to construct an initial segmented trajectory based on passing through the intersection at a maximum speed limit, and adjust the trajectory according to a safety boundary constraint to obtain a candidate segmented trajectory; The final trajectory construction module is configured to, if the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, reversely construct a uniform acceleration segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; for manually driven vehicles, reversely construct a parking segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; The trajectory update module is configured to store the final segmented trajectory of the current vehicle into a queue, and iteratively update the trajectory information when a subsequent vehicle enters the control area to implement trajectory planning.

[0010] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for efficiently planning the trajectory of a vehicle queue at an intelligent connected mixed-traffic intersection described in the first aspect.

[0011] In a fourth aspect, the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method for efficiently planning the trajectory of a vehicle queue at an intelligent connected mixed-traffic intersection described in the first aspect are implemented.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention constructs the safety boundary of the CAV trajectory based on the Newell following model, decomposes the CAV trajectory into several segments of curves that can be analytically solved, and establishes a solution method for each segment of the trajectory curve. At the same time, the trajectory estimation of the human-driven vehicle (HV) is realized, and together with the signal timing scheme, it is used as the input of the CAV trajectory planning. Different from the existing intersection vehicle trajectory control method, the vehicle trajectory planning method proposed in the present invention has high computational efficiency and can handle the trajectory planning problems of a series of vehicles at the same time, and can be fully applied to the online control of the vehicle trajectory at the intersection.

[0013] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention but do not constitute a limitation of the present invention.

[0015] Figure 1 A main flow chart of an efficient planning method for vehicle queue trajectories at an intelligent connected mixed-traffic intersection provided by an embodiment of the present invention; Figure 2 A trajectory planning flow chart provided by an embodiment of the present invention; Figure 3 A Newell car-following model provided in an embodiment of the present invention; Figure 4 A schematic diagram of an initial segmented trajectory provided by an embodiment of the present invention; Figure 5 A schematic diagram of initial segmented trajectory correction provided by an embodiment of the present invention; Figure 6 A schematic diagram of the final segmented trajectory estimation of a CAV provided in an embodiment of the present invention; wherein (a) is a case where the distance between the reversely constructed uniform acceleration segment and the candidate segmented trajectory is relatively close; (b) is a case where the distance between the reversely constructed uniform acceleration segment and the candidate segmented trajectory is relatively far; Figure 7 A schematic diagram of HV trajectory estimation provided by an embodiment of the present invention; Figure 8 A schematic diagram of CAV arrival time calculation provided by an embodiment of the present invention; Fig. 9 A schematic diagram of trajectory node state calculation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] At signalized intersections, traffic flow on signalized arteries is often forced to slow down and speed up suddenly as traffic signals alternate between green and red. When traffic volume is high, a stop-and-go traffic pattern is formed. This stop-and-go traffic has many adverse effects on traffic flow. Obviously, frequent stops and starts will result in a poor experience for drivers and passengers, significantly increase the risk of traffic conflicts, and increase vehicle fuel consumption and emissions. In addition, when vehicles slow down or stop, it will cause a decrease in traffic capacity, further leading to increased travel time and delays.

[0018] Although the stop-and-go traffic phenomenon has been widely studied in the context of highway traffic through theoretical models and empirical observations, few studies have explored how to effectively alleviate traffic congestion and its adverse effects on signal-controlled highways before the emergence of vehicle-road collaboration and autonomous driving technologies. It can be said that before the emergence of intelligent connected vehicles (CAVs), the dynamic behavior of vehicles was mainly determined by the micro-behavior of human drivers. However, due to the unpredictability of human behavior and the lack of empirical data that fully describes such behavior, there is no widely accepted model of human driving behavior. Therefore, traditional infrastructure-based control measures (such as traffic signals) are difficult to perfectly smooth vehicle trajectories. These control measures are designed to adapt to human behavior, but their effectiveness is limited by the uncertainty of human behavior.

[0019] In contrast, CAVs are able to (at least partially) replace human drivers with programmable robots whose driving algorithms can be flexibly customized and precisely executed. This opens up the possibility of coordinating individual vehicle trajectories with overall infrastructure-based control to optimize the driving experience and overall traffic performance. These potentials have inspired a series of groundbreaking research, for example, exploring how CAVs can be used to improve the efficiency and safety of intersections and reduce the environmental impact of highway sections.

[0020] However, most existing studies establish the vehicle trajectory planning problem as an optimal control model. However, the optimal control model usually cannot give an analytical solution, and the existing control algorithm relies on complex numerical calculations. Even so, the solution efficiency of the optimal control model is not high, and it is difficult to meet the online control of a large number of control objectives. For this reason, the control object of existing control is limited to controlling one or a few vehicles in a specific highway facility (such as an intersection or road section) to achieve specific goals (such as stability, safety or fuel efficiency), rather than improving overall traffic performance by smoothing vehicle flow. Therefore, how to achieve efficient vehicle trajectory control with low computational complexity remains an important challenge for current research.

[0021] Smooth driving trajectories mean safer intersections, less fuel consumption and emissions, and a better riding experience. For each vehicle, its trajectory has infinite-dimensional control points, and there are complex interactions between vehicles. This makes it an extremely complex task to simultaneously plan the trajectories of all CAVs in the control area. On the other hand, the popularization of CAVs is a long-term task and a continuous development process. Some studies predict that the penetration rate of CAVs may not reach 100% until around 2060. In this environment, accurately estimating the driving trajectory of human-driven vehicles (HVs) is a necessary task to achieve CAV trajectory planning, which makes this problem more challenging.

[0022] Therefore, the following embodiments provide an efficient planning method, system, medium and device for the trajectory of vehicle queues at intelligent connected mixed-traffic intersections. Different trajectory planning strategies are adopted for different types of vehicles (HV and CAV), which can give full play to the characteristics of various types of vehicles and enable vehicles to pass through intersections smoothly, thereby effectively improving intersection traffic efficiency, reducing vehicle delays and energy consumption, and ensuring smooth traffic.

[0023] Embodiment 1 like Figure 1 As shown, this embodiment discloses an efficient planning method for vehicle queue trajectories at an intelligent networked mixed traffic intersection, comprising the following steps: S1. Construct a control area based on signal timing information, initial speeds and initial times of intelligent connected vehicles and manually driven vehicles entering the control area; S2. In the control area, construct an initial segmented trajectory based on passing through the intersection at the maximum speed limit, and adjust the trajectory according to the safety boundary constraint to obtain a candidate segmented trajectory; S3. If the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, reversely construct the uniform acceleration segment and connect it with the candidate segmented trajectory through the connecting segment to obtain the final segmented trajectory; for manually driven vehicles, reversely construct the parking segment and connect it with the candidate segmented trajectory through the connecting segment to obtain the final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; S4. The final segmented trajectory of the current vehicle is stored in the queue. When the subsequent vehicle enters the control area, the trajectory information is iteratively updated to realize trajectory planning.

[0024] Next, combine Figure 1 , an efficient planning method for vehicle queue trajectories at an intelligent connected mixed-traffic intersection disclosed in this embodiment is described in detail.

[0025] (1) Intersection vehicle trajectory planning system architecture Consider a typical signalized intersection with a control area of ​​length L. At each entrance, the start of the control area is at position 0 and the downstream stop line is at position L. The speed limit at the intersection is The maximum acceleration and deceleration of the vehicle are and The start time of each control cycle is zero, the start time of the green light phase is S, and the green light duration is G. The green light phase can be expressed as .

[0026] In each signal cycle, the central controller will plan the driving trajectory for each CAV based on the initial state of the CAV entering the control area and the signal timing information.

[0027] Specifically: within the control period c, when a vehicle enters the control area, the intersection central controller can know its initial speed and time of entry into the controlled area The central controller plans the trajectory of the CAV or estimates the trajectory of the HV according to the initial state of the vehicle and the signal timing information, and stores it in the vector P. , where N is the set of vehicles in the convoy. At the same time, the central controller sends the planned trajectory curve to the corresponding CAV, enabling it to move at a speed of Safely pass through intersections and effectively improve the utilization efficiency of green light time.

[0028] It should be noted that the trajectory planning method proposed in the present invention is also applicable to the distributed control mode. Specifically, when the CAV enters the control area, it will receive the trajectory information of the vehicle in front and the traffic signal timing information. It can rely on its own computer to calculate the trajectory curve and send it to the central controller.

[0029] The trajectory planning method for left-turning vehicles and straight-moving vehicles is the same. The specific process is as follows: Figure 2 shown.

[0030] (2) CAV trajectory planning method The trajectory of vehicle n is defined as a piecewise quadratic function , specifically, the acceleration is The uniform acceleration section and deceleration are The vehicle is connected by one or more trajectory segments of a uniform deceleration segment, a uniform speed segment and a parking segment, and the two connected trajectory curves are tangent at the connection point, that is, they have the same speed.

[0031] The goal of the CAV trajectory planning algorithm proposed in the present invention is to construct a smooth vehicle trajectory while taking into account the safety of the vehicle during the following process. Figure 3The Newell following model is shown in Figure 1. The trajectory of the following vehicle n can be determined by the vehicle in front. The trajectory of is translated in time and space, as shown in the following formula:

[0032] In the formula, is the vehicle perception reaction time, It is the minimum distance after emergency braking of the vehicle; Figure 3 middle Indicates the headway between vehicles.

[0033] Therefore, in order to ensure the safety of the vehicle during the following process, the vehicle At any time t, the safety margin cannot be exceeded. According to the Newell following model, the safety margin curve of vehicle n Given by:

[0034] Generally speaking, vehicles always tend to pass through the intersection in the fastest way to achieve the greatest traffic efficiency. After entering the intersection control area, in the absence of traffic lights and vehicles ahead, it will always accelerate Accelerate to the speed limit , and maintain speed Passing the intersection. Therefore, CAV trajectory planning needs to solve two main problems: 1) whether it is affected by the vehicle in front; 2) the state of the traffic light when arriving at the intersection. The specific process is as follows: Step 1) Generate the ideal trajectory for the vehicle to reach the intersection as quickly as possible First, from the initial state point Start building a section to maintain acceleration The uniform acceleration trajectory segment, the acceleration segment trajectory end point coordinates It is given by formula (3). Then, from Click to start the vehicle to maintain speed Drive at a constant speed until you reach the stop line at the intersection. Figure 4 As shown in the figure, the ideal fastest arrival trajectory consists of trajectory segment ① and trajectory segment ②, which are used as the initial segmented trajectory. .

[0035]

[0036] Step 2) Adjust the trajectory based on whether the fastest driving trajectory is affected by the vehicle in front According to the trajectory curve of the vehicle ahead , use formula (2) to generate the safety boundary curve . Establish the following quadratic equation:

[0037] If the equation has no real solution, it does not need to be modified as a candidate trajectory; on the contrary, if there is a real solution, the initial segmented trajectory can be determined. With security boundaries There is an intersection. At this time, the initial segmented trajectory needs to be corrected so that it is at the appropriate position with maximum deceleration. Slow down and finally smoothly cut into the safety boundary, such as Figure 5 In this case, the candidate trajectory after the start of deceleration is composed of two parts, namely the merging trajectory segment ③ and the safety boundary after the merging point ④.

[0038] It should be understood that the determination of the “appropriate position” for inserting the uniform deceleration segment and the determination of all nodes in this embodiment are all achieved through the method described in “(5) Method for calculating trajectory segment endpoints” below.

[0039] Step 3) Based on the candidate trajectory The state of reaching the intersection further adjusts the trajectory to form the final trajectory

[0040] use Represents the trajectory function The generalized inverse function of is defined by formula (5).

[0041]

[0042] When the candidate trajectory reaches the intersection within the green light phase, it satisfies When , there is no need to adjust the candidate trajectory, and it can be directly used as a feasible trajectory for vehicle n .

[0043] On the contrary, if the candidate trajectory reaches the intersection within the red light time (i.e. ), the candidate trajectories need to be adjusted.

[0044] like Figure 6 As shown in (a), firstly, the candidate trajectory The part of the vehicle with a position greater than L moves to the right until the next green light starts, and then builds a reverse line to maintain the maximum acceleration. The acceleration segment trajectory ⑥, until it meets the candidate trajectory The distance is close enough. Finally, from the candidate trajectory Construct a deceleration line at a suitable position on The deceleration trajectory segment ⑤ is moved until it becomes tangent to the trajectory segment ⑥.

[0045] In addition, if Figure 6As shown in (b), when trajectory segment ⑤ is consistent with the candidate trajectory When the distance is far, it is necessary to add a parking trajectory segment ⑦ between trajectory segment ⑤ and trajectory segment ⑥. In this way, by smoothly connecting the trajectory after adjusting the arrival time with the candidate trajectory, the final segmented trajectory of vehicle n is obtained. .

[0046] (3) HV trajectory estimation method In the present invention, the preceding vehicle trajectory is an important basis for CAV trajectory planning. Therefore, the intersection central controller needs to estimate the HV trajectory, and its method is similar to the CAV trajectory planning method.

[0047] Specifically, HV trajectory estimation also includes three steps: 1) generating the fastest trajectory for the vehicle to reach the intersection under ideal conditions; 2) adjusting the trajectory according to whether the fastest driving trajectory is affected by the vehicle in front; 3) The state of reaching the intersection further adjusts the trajectory.

[0048] Among them, step 1) and step 2) are completely consistent with step 1) and step 2) in CAV trajectory planning, and step 3) is mainly explained below.

[0049] Step 3): Similarly, if the candidate trajectory Arriving at an intersection during a green light satisfies When , the candidate trajectory does not need to be corrected and can be directly used as the estimated trajectory of HV .

[0050] On the contrary, if the candidate trajectory reaches the intersection within the red light time, the candidate trajectory needs to be adjusted. ), because the HV needs to stop and wait at the intersection. Figure 7 As shown, first, we need to construct a line with an initial speed of 0 and a maximum acceleration from the next green light start time (position L). Then, a horizontal trajectory segment ⑦ (parking segment) is constructed in the reverse direction from the next green light start time (position L) until a specific parking point; finally, a merging trajectory segment ⑥ is constructed to make the candidate trajectory (①-⑤, ⑤ is a uniform speed segment) is smoothly connected with trajectory segment ⑦. In this way, the estimated trajectory of HV is obtained. .

[0051] This embodiment uses a trajectory segment with a fixed acceleration to estimate the trajectory of the HV. However, since the trajectory of the HV is uncontrolled, in the actual car-following process, the HV usually does not move at a fixed acceleration or deceleration (such as , ) to accelerate or decelerate. According to the Newell following model, the acceleration of the HV during the following process can be given by formula (6), where Obviously, its acceleration is not fixed, but is determined by multiple factors. This means that the proposed HV trajectory estimation method has a certain error.

[0052]

[0053] According to formula (6), the acceleration (deceleration) speed of the HV during the following process is Always at maximum deceleration and maximum acceleration In order to reduce this estimation bias, relatively small acceleration and deceleration are used when estimating the HV trajectory, that is, ,in .

[0054]

[0055] (4) Calculation of CAV expected arrival time In a mixed traffic environment of CAVs and HVs, the trajectory of a CAV may be affected by the HV in front. Figure 8 As shown in the figure, the HV that arrives at the intersection during the red light needs to stop and wait, and accelerate from a standstill to pass through the intersection after the green light turns on; when the rear CAV is at the maximum speed When the HV reaches the intersection, it may not have accelerated to its maximum speed. At this time, if the distance between the two vehicles is already close, the CAV must slow down to maintain a safe distance from the HV in front, which will destroy the stability of the traffic flow behind the CAV, causing certain safety hazards and increased vehicle energy consumption.

[0056] In order to avoid the above situation, it is necessary to delay the arrival of the CAV at the intersection for a certain period of time. , so that the trajectory of the CAV can smoothly merge into its safety boundary In this embodiment, the delayed arrival time is called the expected arrival time, which is recorded as .

[0057] Trajectory on Point represents the state of the HV ahead passing through the intersection, and the speed of this point is After this point, the HV continues to accelerate at maximum Accelerate until Accelerate to maximum speed In this embodiment, Point to the right , pan down The trajectory and safety margin of the CAV can be obtained The tangent point .therefore, and can be given by formula (8) respectively. Accordingly, the judgment condition of whether the arrival time needs to be adjusted in step 3) of CAV trajectory planning should be updated to formula (9).

[0058]

[0059] (5) Calculation method of trajectory segment endpoints The vehicle trajectory planning method proposed in the present invention uses fixed control parameters , vehicle trajectory curve function It can be uniquely determined by the tangent points of each trajectory segment. Therefore, the main work of CAV trajectory planning and HV trajectory estimation is to calculate the state of the endpoints of each trajectory segment, and the calculation method of all endpoints is the same. Here, the endpoint calculation in the candidate trajectory correction process of step 2) in the CAV trajectory planning method is used as an example to illustrate.

[0060] To facilitate the explanation of the calculation method, some basic concepts and definitions are given below.

[0061] Definition 1: Any point p in the trajectory can be represented as a tuple consisting of three elements ( ),in and represent the time and position of node p respectively, It represents the velocity of node p, that is, the slope of the tangent line at node p.

[0062] Definition 2: Vehicle Trajectory Is The segment trajectories are connected smoothly in sequence, so It can be expressed as .

[0063] Adjacent trajectory segments in a vehicle trajectory curve have the same position and velocity at the connection point. Fig. 9 As shown, the curve represents the safety boundary of vehicle n A certain section of the curve represents the forward trajectory of vehicle n The starting and ending points of the two trajectories are known. The start time of the incoming trajectory segment is , the two trajectories are at point A ( ) are tangent, then the two trajectories have the same position at the tangent point m. and speed Through the arrangement, this embodiment can obtain a and (Node A position) quadratic equation system with two variables is given by formula (10). By solving the equation system, we can get and The value of , and then the position and speed of the tangent point can be obtained.

[0064]

[0065] Among them, the subscript n represents the current vehicle information, and the subscript n-1 represents the preceding vehicle information, for example Indicates the current vehicle acceleration, Indicates the acceleration of the vehicle ahead.

[0066] It should be noted that the equation group (10) may have no solution, and even if there is a solution, it may be invalid. In this case, it means that the two trajectories cannot be tangent, and it is necessary to find a feasible solution by traversing the remaining trajectory segments. The specific calculation process is shown as follows:

[0067] in, and Represents a track segment The start and end time, For track segment End time.

[0068] This specific embodiment simplifies the complex trajectory planning problem into solving each node by providing a unified calculation method for nodes between segmented curves, which greatly reduces the computational complexity, so that the solution can quickly handle the trajectory planning tasks of a large number of vehicles and meet the needs of online control. At the same time, the trajectory planning process is meticulous and scientific, and the trajectory is gradually optimized in three steps. It not only considers the initial situation of the vehicle passing through the intersection at the maximum speed limit, but also adjusts the trajectory according to the safety boundary constraints, and finally further adjusts it according to the status of the traffic light. For HV, the accuracy of planning is improved by dynamically adjusting its acceleration to adapt to the trajectory estimation error. In addition, the CAV expected arrival time delay mechanism involved effectively avoids conflicts with the HV in front, ensures the stability of traffic flow, and reduces safety hazards and energy consumption. On the whole, this embodiment improves the traffic capacity of the intersection, optimizes traffic flow, reduces vehicle delays, and provides strong support for the development of intelligent transportation.

[0069] Embodiment 2 This embodiment provides an efficient vehicle queue trajectory planning system for an intelligent connected mixed traffic intersection, including: A scenario construction module is configured to construct a control area based on signal timing information, initial speeds and initial times of intelligent connected vehicles and manually driven vehicles entering the control area; A candidate trajectory construction module is configured to construct an initial segmented trajectory based on passing through the intersection at a maximum speed limit, and adjust the trajectory according to a safety boundary constraint to obtain a candidate segmented trajectory; The final trajectory construction module is configured to, if the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, reversely construct a uniform acceleration segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; for manually driven vehicles, reversely construct a parking segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; The trajectory update module is configured to store the final segmented trajectory of the current vehicle into a queue, and iteratively update the trajectory information when a subsequent vehicle enters the control area to implement trajectory planning.

[0070] Embodiment 3 This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps in the method for efficiently planning the trajectory of a vehicle queue at an intelligent connected mixed-traffic intersection as described in the first embodiment above are implemented.

[0071] Embodiment 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for efficiently planning the trajectory of a vehicle queue at an intelligent connected mixed-traffic intersection as described in the first embodiment above are implemented.

[0072] The steps or modules involved in the above embodiments 2 to 4 correspond to those in embodiment 1. For the specific implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An efficient vehicle queue trajectory planning method for an intelligent networked mixed traffic intersection, characterized in that: include: Construct the control area based on the signal timing information, the initial speed and initial time of the intelligent connected vehicles and manually driven vehicles entering the control area; In the control area, an initial segmented trajectory is constructed based on passing through the intersection at the maximum speed limit, and the trajectory is adjusted according to the safety boundary constraint to obtain a candidate segmented trajectory; If the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, a uniform acceleration segment is constructed in reverse and connected with the candidate segmented trajectory through a connecting segment to obtain the final segmented trajectory; for manually driven vehicles, a parking segment is constructed in reverse and connected with the candidate segmented trajectory through a connecting segment to obtain the final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; The final segmented trajectory of the current vehicle is stored in the queue. When the subsequent vehicles enter the control area, the trajectory information is iteratively updated to achieve trajectory planning.

2. The method for efficiently planning the trajectory of a vehicle queue at an intelligent networked mixed traffic intersection as claimed in claim 1, characterized in that: The uniform acceleration section is when the vehicle accelerates at the maximum acceleration, and the uniform deceleration section is when the vehicle decelerates at the maximum deceleration.

3. The efficient trajectory planning method for vehicle queues at intelligent networked mixed traffic intersections as claimed in claim 1, characterized in that: The segmented trajectory is a segmented quadratic function, and adjacent trajectory segments are tangent at the connection point and have the same position and speed; wherein the position and speed of the connection point are solved by establishing a set of two-variable quadratic equations.

4. The method for efficiently planning the trajectory of a vehicle queue at an intelligent networked mixed traffic intersection as claimed in claim 1, characterized in that: The initial segmented trajectory is constructed based on passing the intersection at the maximum speed limit, and the trajectory is adjusted according to the safety boundary constraint to obtain the candidate segmented trajectory, specifically including: Construct the initial segmented trajectory based on the uniform acceleration segment and the maximum speed limit uniform speed segment; A safety boundary curve is generated based on the trajectory of the vehicle ahead. If the initial segmented trajectory and the safety boundary curve have an intersection, a uniform deceleration segment is inserted into the initial segmented trajectory to obtain a candidate segmented trajectory; otherwise, it is directly used as a candidate segmented trajectory.

5. The method for efficiently planning the trajectory of a vehicle queue at an intelligent networked mixed traffic intersection as claimed in claim 1, characterized in that: Based on the generalized inverse function of the candidate segmented trajectory, it is determined whether the candidate segmented trajectory can pass through the intersection at the current green light phase. If so, it is output as the final segmented trajectory; otherwise, the trajectory passing time is set at the start time of the next green light based on the generalized inverse function.

6. The efficient vehicle queue trajectory planning method for an intelligent networked mixed traffic intersection as claimed in claim 1, characterized in that: For intelligent connected vehicles, if the reversely constructed uniform acceleration segment cannot be connected to the candidate segmented trajectory through the uniform deceleration segment, a parking segment is added after the uniform deceleration segment to connect to the candidate segmented trajectory; For manually driven vehicles, the connecting section is a uniform deceleration section.

7. The method for efficiently planning the trajectory of a vehicle queue at an intelligent networked mixed traffic intersection as claimed in claim 1, characterized in that: For manually driven vehicles, it also includes dynamically adjusting the acceleration of the uniform acceleration and uniform deceleration segments to adapt to the trajectory estimation error of manually driven vehicles in mixed traffic, which is specifically expressed as: ； in, and Indicates the maximum acceleration and deceleration, is the coefficient.

8. An efficient vehicle queue trajectory planning system for intelligent networked mixed traffic intersections, characterized in that: include: A scenario construction module is configured to construct a control area based on signal timing information, initial speeds and initial times of intelligent connected vehicles and manually driven vehicles entering the control area; A candidate trajectory construction module is configured to construct an initial segmented trajectory based on passing through the intersection at a maximum speed limit, and adjust the trajectory according to a safety boundary constraint to obtain a candidate segmented trajectory; The final trajectory construction module is configured to, if the candidate segmented trajectory reaches the intersection during the red light period, based on the next green light start time: for intelligent connected vehicles, reversely construct a uniform acceleration segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; for manually driven vehicles, reversely construct a parking segment and connect it with the candidate segmented trajectory through a connecting segment to obtain a final segmented trajectory; the connecting segment includes a uniform deceleration segment, or a combination of a uniform deceleration segment and a parking segment; The trajectory update module is configured to store the final segmented trajectory of the current vehicle into a queue, and iteratively update the trajectory information when a subsequent vehicle enters the control area to implement trajectory planning.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the steps in the efficient planning method of vehicle queue trajectories at an intelligent connected mixed-traffic intersection as described in any one of claims 1-7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the efficient planning method of vehicle queue trajectories at an intelligent connected mixed-traffic intersection as described in any one of claims 1-7 are implemented.

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