Intersection vehicle trajectory control method and system in low-proportion automatic driving environment

Abstract

The invention belongs to the technical field of intelligent vehicle-road cooperative traffic control, and discloses an intersection vehicle trajectory control method and system in a low-proportion automatic driving environment, and the method comprises the steps: determining a CAV target control speed; defining a control area, evaluating whether the CAVs entering the control area can pass through the intersection in the latest green light period, and regarding the first CAV which cannot pass through the intersection as a guide CAV; a trajectory control strategy is designed according to the position of the guide CAV in the mixed traffic flow, trajectory control is conducted on the guide CAV, a shock wave spreading forwards is generated in a controlled vehicle queue behind the guide CAV, and the traffic flow state of vehicles behind the guide CAV is changed; and when the shock wave propagation reaches a set condition, the influence of the trajectory control on the state of the controlled vehicle behind disappears, and the traffic flow state is recovered to the state before the trajectory control is not carried out. According to the invention, under the scene of low CAV permeability, track guidance of the following manual driving vehicle is realized, the parking frequency of the vehicle at the intersection is reduced, and the passing efficiency is not sacrificed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent vehicle-road cooperative traffic control, and in particular to a method and system for controlling vehicle trajectory at an intersection in a low-proportion autonomous driving environment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Signalized intersections are generally considered to be traffic flow bottlenecks in urban transportation systems. In addition, vehicles are forced to stop and restart by traffic lights, resulting in high fuel consumption and environmental pollution. Therefore, improving traffic operation at signalized intersections is of great significance to the safety, efficiency and energy conservation of transportation systems. In recent years, due to the increasing severity of energy problems, the improvement of energy efficiency at signalized intersections has received increasing attention. In short, avoiding vehicle stops or frequent acceleration and deceleration and maintaining a constant speed for ecological driving is a key principle. Vehicle trajectory control (VTC) is an effective method to reduce the number of vehicle stops and has received widespread attention in recent years. However, practical VTC strategies should be based on real-time traffic information, such as signal phase and timing information, vehicle position and speed information, etc. With the technological advancement of connected and automated vehicles (CAVs), vehicles can achieve two-way communication between vehicles (V2V) and roadside infrastructure (V2I). This two-way communication enables CAVs to adjust their own trajectories according to the status of signal lights, reduce start-up delays, and improve the utilization efficiency of green light time. Therefore, CAVs provide a new means of regulation for intersection control.

[0004] Traffic lights were originally designed to resolve vehicle conflicts at intersections. Similarly, VTC was originally proposed to ensure traffic safety. From this perspective, VTC strategies can replace traffic signals to a certain extent. It has been found that some VTC methods can reduce delays in some scenarios compared with intersection management by optimizing signals. These strategies, namely reservation-based methods, try to find vehicle trajectory control strategies to avoid vehicle collisions and pursue the trajectory of individual vehicles. In order to better guide drivers through signalized intersections in an environmentally friendly way from a micro perspective, the establishment of vehicle adaptive cruise control systems and driver assistance systems has always been a research focus. In addition, other studies similar to green wave control aim to guide vehicles through signalized intersections without stopping. Such studies usually develop various vehicle speed planning algorithms to ensure that vehicles pass through intersections without stopping by providing dynamic speed recommendations to drivers. In particular, some studies try to reduce the total fuel consumption of vehicle formations, in other words, formation-based trajectory control, by optimizing the trajectory of the entire fleet to reduce overall delays.

[0005] Obviously, vehicle platooning trajectory control is a more effective way to reduce total fuel consumption. However, in most cases, the application of vehicle platooning control requires a high CAV penetration rate. However, the popularization of CAVs is a long-term task and an ongoing development process. Some studies predict that the penetration rate of CAVs may not reach 100% until the 2060s. Some studies have attempted to indirectly control vehicle platoons through limited CAVs. However, most of these studies rely on relatively high CAV penetration rates. Therefore, vehicle platooning trajectory control under low CAV penetration rates remains a challenging research area. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a vehicle trajectory control method and system for intersections in a low-proportion autonomous driving environment. In a scenario with a low CAV penetration rate, the propagation law of shock waves caused by the speed change of mixed traffic at the intersection is analyzed. On this basis, the parameters of CAV trajectory control are accurately calculated to achieve trajectory guidance for vehicles following manual driving, reducing the number of vehicle stops at intersections without sacrificing traffic efficiency.

[0007] 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 controlling vehicle trajectory at an intersection in a low-proportion autonomous driving environment, comprising the following steps: Based on the historical observation data of the intersection entrance lane, the free flow speed, blockage density and maximum capacity of the entrance lane are determined, and the CAV target control speed is determined; Define an intersection control area. When a CAV enters the intersection control area, it is automatically evaluated whether it can pass through the intersection during the most recent green light period. The first CAV that cannot pass through the intersection during the most recent green light period is considered as the guiding CAV. A trajectory control strategy is designed according to the position of the guiding CAV in the mixed traffic flow, and the trajectory of the guiding CAV is controlled based on the trajectory control strategy, and a forward-propagating shock wave is generated in the controlled vehicle queue behind the guiding CAV to change the traffic flow state of the controlled vehicle queue behind. When the shock wave propagation reaches the set conditions, the impact of trajectory control on the state of the controlled vehicle behind disappears, and the traffic flow state returns to the state before trajectory control.

[0008] As an optional implementation, the CAV is automatically evaluated as to whether it can pass through the intersection during the most recent green light period, specifically: The time when the CAV enters the intersection control area is taken as the reference time. At this time, the distance from the CAV to the stop line is the initial distance. When the initial distance is less than or equal to the set value, it is determined that the CAV can pass through the intersection during the most recent green light period.

[0009] As an optional implementation, a trajectory control strategy is designed according to the position of the CAV in the mixed traffic flow, specifically: When the leading CAV is the first vehicle that cannot pass through the intersection during the current green light phase, the trajectory control parameters of the leading CAV are calculated, and the speed of the leading CAV is controlled from a certain moment when the leading CAV enters the intersection control area according to the trajectory control parameters, so that the leading CAV can just reach the intersection stop line at the next green light on moment; When the leading CAV is not the first vehicle that cannot pass through the intersection during the current green light phase, the trajectory control parameters of the leading CAV are calculated, and the speed of the leading CAV is controlled from a certain moment when the leading CAV enters the intersection control area according to the trajectory control parameters, so that it can reach the end of the uncontrolled vehicle queue when the last vehicle in the uncontrolled vehicle queue ahead starts to move.

[0010] As an optional embodiment, when the leading CAV is the first vehicle that cannot pass through the intersection during the current green light phase, the influence of the trajectory control on the state of the rear controlled vehicle disappears when the shock wave propagates to the stop line position of the intersection; When the leading CAV is not the first vehicle that cannot pass through the intersection during the current green light phase, the effect of trajectory control on the state of the rear controlled vehicle disappears when the shock wave meets the evacuation wave of the uncontrolled vehicle queue ahead.

[0011] As an optional implementation, the parameters guiding the trajectory control of the CAV include a target control speed of the CAV, a time to start control, and a position to start control.

[0012] As an optional implementation method, the target control speed needs to meet the following conditions: (1) the number of vehicle stops is reduced as much as possible; (2) the target control speed must be less than the free flow speed; (3) vehicles can pass through the intersection at the maximum evacuation flow rate during the green light; and (4) the number of changes in traffic flow status should be as small as possible.

[0013] In a second aspect, the present invention provides a vehicle trajectory control system for an intersection in a low-proportion autonomous driving environment, comprising: The target control speed determination module is configured to: determine the free flow speed, blocking density and maximum capacity of the import lane based on the historical observation data of the import lane of the intersection, and determine the CAV target control speed; The leading CAV determination module is configured to: delineate an intersection control area, and when a CAV enters the intersection control area, automatically evaluate whether it can pass through the intersection during a recent green light period, and regard the first CAV that cannot pass through the intersection during the recent green light period as the leading CAV; The trajectory control module is configured to: design a trajectory control strategy according to the position of the leading CAV in the mixed traffic flow, perform trajectory control on the leading CAV based on the trajectory control strategy, and generate a forward-propagating shock wave in the rear controlled vehicle queue of the leading CAV to change the traffic flow state of the rear controlled vehicle queue; The trajectory control release module is configured such that when the propagation of the shock wave reaches a set condition, the influence of the trajectory control on the state of the rear controlled vehicle disappears, and the traffic flow state is restored to the state before the trajectory control is performed.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0016] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This disclosure proposes a method for controlling vehicle trajectory at intersections in a low-proportion autonomous driving environment. First, the shock wave and its propagation law caused by CAV trajectory control are analyzed. Based on this, the impact of a single CAV on the overall traffic flow is analyzed. Subsequently, a mixed traffic flow vehicle trajectory control method based on CAV trajectory planning is established based on traffic wave theory, and an accurate control parameter calculation method is given, including the starting position and starting time of CAV trajectory control. The purpose of reducing the number of stops of autonomous driving vehicles and traditional manually driven vehicles at intersections is achieved without sacrificing traffic efficiency. Finally, the impact of CAV penetration rate on the mixed traffic flow trajectory control effect is analyzed. The results show that the proposed control method is feasible and practical when the autonomous driving penetration rate is low.

[0018] 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

[0019] The accompanying drawings in the specification, which constitute 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 descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0020] Figure 1A flowchart of a method for controlling vehicle trajectory at an intersection in a low-proportion autonomous driving environment provided in Example 1 of the present invention; Figure 2 A schematic diagram of a vehicle queue in a low-ratio autonomous driving environment provided by Embodiment 1 of the present invention; Figure 3 A basic triangle diagram provided by Embodiment 1 of the present invention; Figure 4 A schematic diagram of the evolution process of the traffic flow state at an intersection under special circumstances provided by Embodiment 1 of the present invention; Figure 5 A schematic diagram of the evolution process of traffic flow state at an intersection under general conditions provided in Example 1 of the present invention; Figure 6 The alternative control strategy for the same guided CAV provided in Example 1 of the present invention; Figure 7 A schematic diagram of the control effect of the guiding CAV at different positions provided by Example 1 of the present invention; Figure 8 This is a distribution diagram of the number of uncontrolled vehicles under different CAV penetration rates provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0023] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0025] Example 1 like Figure 1 As shown, this embodiment provides a method for controlling vehicle trajectory at an intersection in a low-proportion autonomous driving environment, comprising the following steps: S1. Based on the historical observation data of the intersection entrance lane, determine the free flow speed, blockage density and maximum capacity of the entrance lane, and determine the CAV target control speed.

[0026] S2. Define an intersection control area. When a CAV enters the intersection control area, it is automatically evaluated whether it can pass through the intersection during the most recent green light period. The first CAV that cannot pass through the intersection during the most recent green light period is regarded as a guiding CAV.

[0027] S3. Design a trajectory control strategy according to the position of the guiding CAV in the mixed traffic flow, perform trajectory control on the guiding CAV based on the trajectory control strategy, and generate a forward-propagating shock wave in the controlled vehicle queue behind the guiding CAV to change the traffic flow state of the controlled vehicle queue behind.

[0028] S4. When the shock wave propagation reaches the set conditions, the influence of trajectory control on the state of the controlled vehicle behind disappears, and the traffic flow state returns to the state before trajectory control.

[0029] An important feature of CAVs is that they can achieve two-way communication with other vehicles and intelligent roadside systems. Based on the real-time traffic status information collected from CAVs, traffic signals can be dynamically controlled, and in turn, based on the traffic signal status information, efficient vehicle trajectory control can be achieved. Therefore, the popularity and application of CAVs have made intersection vehicle trajectory control possible. Therefore, the key issue here is how to achieve trajectory control of vehicle queues with a small number of CAVs when the CAV penetration rate is low.

[0030] We consider a typical four-entry signalized intersection, where each entrance allows vehicles to turn left, go straight, and turn right. Since right-turning vehicles are usually not controlled by traffic lights, intersection vehicle trajectory control only needs to focus on left-turning and straight-going vehicle queues. Figure 2 As shown in Figure 1, in a mixed platoon, conventional vehicles can be divided into two categories: controlled vehicles and uncontrolled vehicles. Obviously, the trajectories of conventional vehicles following the CAV will be affected by the CAV, so their trajectories are controlled, that is, controlled vehicles; on the contrary, conventional vehicles in front of the CAV are uncontrolled vehicles. Here, we regard the leading CAV and the controlled vehicles behind it as a vehicle platoon.

[0031] Here we use Figure 3 The basic diagram of the triangle shown is composed of the free stream speed u , blocking density k j and the maximum traffic capacity. The traffic flow upstream of the intersection entrance is q d , the corresponding traffic status is Figure 3 In a Since the traffic environment inside the functional area of ​​the intersection is relatively complex, the traffic capacity of the entrance lane of the intersection during the green light period is q g , which is usually less than the theoretical capacity.

[0032] During the green light phase, the traffic demand is less than the capacity of the approaching lane, so vehicles can pass through the intersection at a free flow speed and no vehicle queue is generated. When the red light comes on, straight and left-turning vehicles need to stop and wait in front of the stop line, the entrance lane evacuation flow rate is 0, and the entrance lane traffic flow state is b point. At this point, the vehicles begin to queue up in front of the stop line and generate a gathering wave that propagates backwards. When the green light turns on, the queued vehicles begin to pass through the intersection, and at the same time generate an evacuation wave that also propagates backwards. When the evacuation wave catches up with the gathering wave, the traffic flow returns to the normal arrival state. In this case, the CAV's driving speed can be reduced to avoid the vehicle joining the vehicle queue and pass the intersection smoothly in a close following state. The key to the proposed vehicle trajectory control method lies in analyzing the propagation law of the shock wave (including gathering wave and evacuation wave) at the intersection, and accurately calculating the parameters that guide the CAV trajectory control, mainly including the target control speed of the CAV and the time and position to start control. Among them, the target control speed needs to meet the following conditions: (i) minimize the number of vehicle stops; (ii) the target control speed must be less than the free flow speed u ; (iii) vehicles can pass through the intersection at the maximum evacuation flow rate during the green light period; (iv) the number of changes in traffic flow state should be as small as possible. Based on the above principles, the optimal choice of the target control speed of the CAV should correspond to the state d .

[0033] When the CAV enters the intersection control area, it will automatically evaluate whether it can pass through the intersection during the last green light according to its own motion state and signal timing state. We choose the time when the CAV enters the control area as the reference time. At this time, the distance from the CAV to the stop line is the initial distance. .when When , it can be determined that the CAV can pass through the intersection during the most recent green light period, where Indicates the start time of the red light phase. The VTC method proposed in the present invention does not harm the interests of the leading CAV, that is, the leading CAV will not delay the signal cycle of passing through the intersection due to trajectory control. In addition, the trajectory control of the mixed vehicle queue can only rely on the first CAV that cannot pass through the intersection during the previous green light, that is, the leading CAV.

[0034] First, consider a special scenario where the guiding CAV is the first vehicle that cannot pass through the intersection during the current green light phase. Figure 4 The evolution of the traffic flow state at the intersection entrance caused by the guided CAV trajectory control is given, where the trajectory of the guided CAV is represented by a thick solid line. u Entering the intersection control area, in order to avoid the vehicle stopping before the intersection stop line, the CAV trajectory control is based on the time (Point F) and the speed is reduced to v d This creates a forward-propagating shock wave behind the guide CAV. , the traffic flow state behind it will be a becomes d In addition, the trajectory control of the guided CAV will also generate a forward-propagating shock wave at the rear of the uncontrolled vehicle in front. , and a blank area is generated in front of the guiding CAV, that is, the state o Ultimately, the CAV is guided to Just reaching the stop line of the intersection, the CAV and the controlled vehicles behind it can pass through the intersection as soon as the green light turns on. When propagating to the intersection stop line position ( G Point), the influence of trajectory control on vehicle status disappears, and the traffic flow status is changed to d Restore to a In this case, the proposed VTC method allows all vehicles to pass through the intersection without stopping.

[0035] Under the condition of low CAV penetration, the position of CAV in mixed traffic has greater uncertainty. In most cases, there will be some conventional uncontrolled vehicles in front of the guiding CAV. Figure 5 The figure shows the traffic state evolution process of the entrance lane of the intersection in a more general case. In this case, from the moment the red light turns on, the uncontrolled vehicles in front of the CAV continue to drive at the initial speed and form a shock wave that propagates forward at the rear of the uncontrolled vehicles. , and eventually join the vehicle queue in front, generating a backward propagating gathering wave Eventually, the two shock waves will B The vehicles will meet at the green light, and the queue will not increase. , guiding the uncontrolled vehicle queue in front of the CAV to start evacuating, and the traffic flow state changes from b becomes d , and forms a backward propagating evacuation wave For the leading CAV and the controlled vehicle behind, FAfter point 2, under the trajectory control of the guiding CAV, the convoy speed changes from the initial speed u Reduce to , forming a blank area in front of the guiding CAV and a shock wave that propagates forward , when the shock wave With evacuation wave exist D When the two shock waves meet, the traffic flow remains the same. d Through the intersection.

[0036] In order to maximize the effect of trajectory control, the CAV trajectory should be carefully planned so that its trajectory passes through D That is to say, it is necessary to ensure that the guided CAV reaches the end of the queue when the last vehicle in the uncontrolled vehicle queue starts to move. This can ensure that the guided CAV and the controlled vehicles pass through the intersection without stopping, and avoid wasting the green light time. Although the stopping of uncontrolled vehicles cannot be avoided through refined trajectory control, the parking situation of controlled vehicles can be effectively improved. Generally speaking, the entire queue dissipation process can be achieved through shock waves. The propagation of is represented by the time when it propagates to the intersection. , which can reflect the impact time of trajectory control.

[0037] In addition, during the trajectory control process, the principle of restoring the traffic state as quickly as possible should be followed. To achieve this goal, two conditions need to be met: first, the shock wave must propagate downstream; secondly, the shock wave It should be spread as quickly as possible. Through analysis and comparison, it can be found that these two principles are completely consistent with the above four conditions.

[0038] The control logic and control flow of the proposed VTC method are described above. Next, the precise calculation method of the control parameters will be specifically described, mainly including the start time and start position of guiding the CAV trajectory control.

[0039] In the intelligent network mixed traffic environment, intelligent roadside equipment can collect real-time operation status information of intersections, including signal timing, CAV real-time status (such as location, speed, etc.) and traffic flow. A point( , 0) and C point( , 0) is known. In addition, the propagation speed of the shock wave can be calculated according to the LWR (Lighthill-Whitham-Richards) theory as follows:

[0040] in, It is the speed of the shock wave.

[0041] The above information is the input for VTC control. B point( , )and D point( , ) can be calculated from this information as follows:

[0042]

[0043]

[0044] in, Represents the propagation speed of the shock wave, and the others are similar.

[0045] Based on this technology, the starting time and starting position of the guided CAV trajectory control can be further calculated, that is, F The coordinates of the point ( , ).

[0046] Figure 4 The scenario shown can be considered as B Dot and D Points A Dot and C Based on the control input information, the triangle Calculated F The coordinates of the point.

[0047]

[0048]

[0049] for Figure 5 The general scenario shown, F The calculation of point coordinates is relatively complex and needs to be based on B Dot and D The coordinate information of the point is used for calculation, as follows:

[0050]

[0051] As mentioned above, the proposed trajectory control method cannot harm the interests of guiding CAVs. During the green light, if the vehicle's start-up acceleration process is ignored, then the vehicles will pass through the intersection at the same speed regardless of whether the vehicle is trajectory controlled. Therefore, the vehicle's travel time depends only on its arrival time at the intersection, and the proposed trajectory control method only changes the way the vehicle arrives at the intersection, and does not affect the time when the vehicle arrives at the intersection.

[0052] Although the proposed VTC method will not reduce the traffic efficiency of the intersection, it will cause changes in the traffic flow state of the import lane, which is also an issue that needs to be paid attention to in intersection control. In addition to the trajectory control parameters of the guided CAV, the recovery time of the traffic flow state of the import lane is also an issue that needs to be paid attention to in implementing trajectory control.

[0053] When the penetration rate of CAV is low, there is a great deal of uncertainty in guiding the position of CAV in mixed traffic. The trajectory control strategies of CAVs at different positions are different. F The point coordinates can also be different. In order to improve the flexibility of trajectory control, the present invention proposes alternative control strategies, such as Figure 6 The alternative strategies numbered ① and ②.

[0054] For the same guided CAV, its control strategy is not unique, that is, the start time and start position of its trajectory control are not unique. Figure 6 As shown, FE is the baseline strategy for guiding CAV, and ① and ② are two alternative strategies. It can be seen that in some cases, the trajectory control start time of the guiding CAV can be earlier than the baseline strategy, thus generating additional spatiotemporal buffer (i.e. ). In this case, guiding the CAV needs to be done at a specific time ( H point) by the target speed Accelerate to free flow speed again u , to ensure that the vehicle's travel time does not increase. At the same time, this strategy can also allow the vehicle to pass the intersection at a higher speed. In addition, the control parameters of the alternative control strategy are also more complicated. In addition to calculating the start time and start position of the guided CAV trajectory control, it is also necessary to calculate the time and position of re-acceleration, that is, H The coordinates of the point ( , ). H The coordinates of the point can be obtained by E Dot and F The points are calculated as follows:

[0055]

[0056] The present invention proposes a method for controlling vehicle trajectory at intersections in a low autonomous driving penetration scenario. Since CAVs are randomly distributed in mixed traffic, the effect of trajectory control is uncertain. Figure 7 As shown in the figure, ①, ②, and ③ respectively represent the control trajectory and control effect of the guiding CAV at different positions. Since the proposed VTC method will not affect the traffic efficiency of vehicles, it is only necessary to analyze the difference in traffic flow state recovery time under different control strategies. It can be found that the closer the position of the guiding CAV is, the faster the traffic flow state recovers. In addition, it can also be proved by the following differential relationship. and , so it can be confirmed and , This means that the closer the guiding CAV is to the front, the earlier the control is initiated and the earlier the traffic flow state is restored.

[0057]

[0058]

[0059] Obviously, from the perspective of traffic flow state recovery time, the CAV at the front should be selected as the leading CAV as much as possible. In addition, the leading CAV at the front also helps to reduce the number of vehicles that need to stop. However, the position of the first CAV is determined by the CAV penetration rate. Next, we analyze the trajectory control effect under different CAV penetration rates. Assuming that the CAV penetration rate is p , which means that the probability that any vehicle in the mixed traffic flow is a CAV is p Since CAVs are randomly distributed in mixed traffic, vehicles n and vehicles n The vehicle types of +1 are independent. Therefore, the vehicle queues before the stop line at the intersection can be regarded as independent Bernoulli trials.

[0060] By inferring the probability density function (PDF is denoted as f ), cumulative probability distribution function (CDF, ​​denoted as F), and the expected value of the number of uncontrolled vehicles to evaluate the impact of CAV penetration on trajectory control. The PDF function can be expressed as n The probability that the car is the first CAV, and then according to probability theory, the CDF function can be obtained as follows:

[0061]

[0062] First, assume p = 0.2, then the index of the first CAV in the vehicle queue n follows a geometric distribution. Therefore, the index of the first CAV is n The expected value is equal to p The expected value of the number of uncontrolled vehicles can be obtained by taking the inverse of N :

[0063] when p =0.2, the expected number of uncontrolled vehicles is equal to 4, that is, on average there are 4 uncontrolled vehicles. Figure 8 Given p = 0.2. It can be seen that even at a low CAV penetration rate, the probability of no more than 4 uncontrolled vehicles is about 67.2%, and when there are no more than 6 uncontrolled vehicles, this probability is as high as about 80%. This result shows that the proposed VTC method can be applied to scenarios with low CAV penetration rates and can achieve good control effects.

[0064] Example 2 This embodiment provides a vehicle trajectory control system at an intersection in a low-proportion autonomous driving environment, including: The target control speed determination module is configured to: determine the free flow speed, blocking density and maximum capacity of the import lane based on the historical observation data of the import lane of the intersection, and determine the CAV target control speed; The leading CAV determination module is configured to: delineate an intersection control area, and when a CAV enters the intersection control area, automatically evaluate whether it can pass through the intersection during a recent green light period, and regard the first CAV that cannot pass through the intersection during the recent green light period as the leading CAV; The trajectory control module is configured to: design a trajectory control strategy according to the position of the leading CAV in the mixed traffic flow, perform trajectory control on the leading CAV based on the trajectory control strategy, and generate a forward-propagating shock wave in the rear controlled vehicle queue of the leading CAV to change the traffic flow state of the rear controlled vehicle queue; The trajectory control release module is configured such that when the propagation of the shock wave reaches a set condition, the influence of the trajectory control on the state of the rear controlled vehicle disappears, and the traffic flow state is restored to the state before the trajectory control is performed.

[0065] It should be noted that the above modules correspond to the steps described in Example 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above Example 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer executable instructions.

[0066] In further embodiments, there is also provided: An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, it will not be described in detail here.

[0067] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0068] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0069] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the method described in Example 1 is completed.

[0070] The method in Example 1 can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it is not described in detail here.

[0071] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in embodiment 1 is implemented.

[0072] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0073] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0074] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, and the like. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, and the like.

[0075] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0076] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A vehicle trajectory control method at an intersection in a low-proportion autonomous driving environment, characterized in that: The following steps are involved: Based on the historical observation data of the intersection entrance lane, the free flow speed, blockage density and maximum capacity of the entrance lane are determined, and the CAV target control speed is determined; Define an intersection control area. When a CAV enters the intersection control area, it is automatically evaluated whether it can pass through the intersection during the most recent green light period. The first CAV that cannot pass through the intersection during the most recent green light period is considered as the guiding CAV. A trajectory control strategy is designed according to the position of the guiding CAV in the mixed traffic flow, and the trajectory of the guiding CAV is controlled based on the trajectory control strategy, and a forward-propagating shock wave is generated in the controlled vehicle queue behind the guiding CAV to change the traffic flow state of the controlled vehicle queue behind. When the shock wave propagation reaches the set conditions, the impact of trajectory control on the state of the controlled vehicle behind disappears, and the traffic flow state returns to the state before trajectory control.

2. The method for controlling vehicle trajectory at an intersection in a low-proportion automatic driving environment according to claim 1, characterized in that: Automatically evaluate whether the CAV can pass through the intersection during the most recent green light period, specifically: The time when the CAV enters the intersection control area is taken as the reference time. At this time, the distance from the CAV to the stop line is the initial distance. When the initial distance is less than or equal to the set value, it is determined that the CAV can pass through the intersection during the most recent green light period.

3. The method for controlling vehicle trajectory at an intersection in a low-proportion autonomous driving environment according to claim 1, characterized in that: The trajectory control strategy is designed according to the position of the CAV in the mixed traffic flow, specifically: When the leading CAV is the first vehicle that cannot pass through the intersection during the current green light phase, the trajectory control parameters of the leading CAV are calculated, and the speed of the leading CAV is controlled from a certain moment when the leading CAV enters the intersection control area according to the trajectory control parameters, so that the leading CAV can just reach the intersection stop line at the next green light on moment; When the leading CAV is not the first vehicle that cannot pass through the intersection during the current green light phase, the trajectory control parameters of the leading CAV are calculated, and the speed of the leading CAV is controlled from a certain moment when the leading CAV enters the intersection control area according to the trajectory control parameters, so that it can reach the end of the uncontrolled vehicle queue when the last vehicle in the uncontrolled vehicle queue ahead starts to move.

4. The method for controlling vehicle trajectory at an intersection in a low-proportion automatic driving environment as claimed in claim 3, characterized in that: When the leading CAV is the first vehicle that cannot pass through the intersection during the current green light phase, the impact of trajectory control on the state of the rear controlled vehicle disappears when the shock wave propagates to the stop line position of the intersection; When the leading CAV is not the first vehicle that cannot pass through the intersection during the current green light phase, the effect of trajectory control on the state of the rear controlled vehicle disappears when the shock wave meets the evacuation wave of the uncontrolled vehicle queue ahead.

5. The method for controlling vehicle trajectory at an intersection in a low-proportion automatic driving environment as claimed in claim 3, characterized in that: The parameters guiding the trajectory control of the CAV include the target control speed of the CAV, the time to start control, and the position to start control.

6. The method for controlling vehicle trajectory at an intersection in a low-proportion automatic driving environment as claimed in claim 5, characterized in that: The target control speed needs to meet the following conditions: (1) the number of vehicle stops should be minimized; (2) the target control speed must be lower than the free flow speed; (3) vehicles can pass through the intersection at the maximum evacuation rate during the green light; and (4) the number of changes in traffic flow status should be as small as possible.

7. The intersection vehicle trajectory control system in a low-ratio autonomous driving environment is characterized by: include: The target control speed determination module is configured to: determine the free flow speed, blocking density and maximum capacity of the import lane based on the historical observation data of the import lane of the intersection, and determine the CAV target control speed; The leading CAV determination module is configured to: delineate an intersection control area, and when a CAV enters the intersection control area, automatically evaluate whether it can pass through the intersection during a recent green light period, and regard the first CAV that cannot pass through the intersection during the recent green light period as the leading CAV; The trajectory control module is configured to: design a trajectory control strategy according to the position of the leading CAV in the mixed traffic flow, perform trajectory control on the leading CAV based on the trajectory control strategy, and generate a forward-propagating shock wave in the rear controlled vehicle queue of the leading CAV to change the traffic flow state of the rear controlled vehicle queue; The trajectory control release module is configured such that when the propagation of the shock wave reaches a set condition, the influence of the trajectory control on the state of the rear controlled vehicle disappears, and the traffic flow state is restored to the state before the trajectory control is performed.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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