Assessment method and unit for stability of mixed traffic queue, and electronic equipment

Abstract

The invention provides a mixed traffic queue stability assessment method, unit and electronic equipment, and relates to the technical field of intelligent traffic. On the basis of considering the permeability of intelligent connected vehicles in mixed traffic, the information flow topological structure and the maximum queue size of a mixed vehicle queue are considered in an emphasized manner; based on the information flow topological structure of the hybrid vehicle queue, the maximum queue size and the permeability of the intelligent connected vehicles in the hybrid traffic, the stability of the hybrid traffic queue is evaluated, the stability of the hybrid traffic flow queue can be evaluated objectively and comprehensively, and the accuracy of stability evaluation of the hybrid traffic is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent transportation technology, and in particular to a method, unit, and electronic device for evaluating the stability of a mixed traffic queue. Background Art

[0002] Intelligent and connected vehicles (ICVs), integrating advanced intelligent and connected technologies, have garnered increasing attention in recent years. Significant progress has been made in perception, decision-making, and control, and they are gradually becoming industrialized. ICVs hold the potential to improve road traffic, which has been facing challenges such as frequent accidents, congestion, and increased energy consumption.

[0003] Currently, because the full commercialization of intelligent connected vehicles (ICVs) will take time, there will be a mixed traffic phase in which ICVs and manually driven vehicles coexist. This coexistence of manually driven vehicles and ICVs increases the complexity of mixed traffic, necessitating the development of performance evaluation methods for mixed traffic. Stability of mixed traffic is a key indicator of its ability to operate smoothly in response to disturbances. By assessing the stability of mixed traffic, the results can guide the setting of signal spacing and other parameters, thereby reducing the probability of rear-end collisions.

[0004] Traffic stability assessment includes platoon stability (also known as string stability), which primarily considers the propagation of disturbances within a platoon system. Existing platoon stability assessment methods are primarily based on fully connected vehicle platoons, meaning fleets composed entirely of connected vehicles. However, existing research shows that at low penetration rates of connected vehicles, fully connected vehicle platoons are rare, with most platoons consisting of mixed vehicles, including both manually driven vehicles and connected vehicles. Therefore, assessment methods based on fully connected vehicle platoons are not suitable for mixed traffic. The current system of stability assessment methods for mixed traffic remains incomplete and inaccurate. Summary of the Invention

[0005] The present application provides a method, unit, and electronic device for evaluating the stability of a mixed traffic queue. By comprehensively considering important parameters such as the information flow topology of the vehicle queue and the maximum queue size, the method evaluates the stability of the mixed traffic queue objectively and comprehensively, thereby improving the accuracy of the mixed traffic stability assessment.

[0006] To achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, a method for evaluating the stability of mixed traffic queues is provided, the method comprising: obtaining a penetration rate of intelligent connected vehicles in mixed traffic, a first maximum queue size, and a first information flow topology; obtaining vehicle queues existing in the mixed traffic and the queue size of each vehicle queue based on the first maximum queue size and the first information flow topology; obtaining an occurrence probability of each vehicle queue in the mixed traffic based on the queue size and penetration rate of each vehicle queue; and obtaining a first evaluation result based on a motion model of each vehicle queue and the occurrence probability of each vehicle queue, the first evaluation result being used to characterize the stability of the queues in the mixed traffic.

[0008] The solution provided in the first aspect above, while considering the penetration rate of intelligent connected vehicles in mixed traffic, focuses on the information flow topology and maximum queue size of the mixed vehicle queue. Thus, based on the information flow topology and maximum queue size, the vehicle queues existing in the mixed traffic and the queue size of each vehicle queue can be determined. Furthermore, based on the queue size and penetration rate of each vehicle queue, the probability of occurrence of each vehicle queue in the mixed traffic can be obtained. In this way, based on the motion model of each vehicle queue and the probability of occurrence of each vehicle queue, a first assessment result of the queue stability of the mixed traffic can be obtained. This can achieve the purpose of objectively and comprehensively assessing the stability of the mixed traffic flow queues, thereby improving the accuracy of the mixed traffic stability assessment.

[0009] As one possible implementation, when each vehicle platoon includes an intelligent connected vehicle, the position of the intelligent connected vehicle within each platoon is determined based on the first information flow topology. Thus, by determining the position of the intelligent connected vehicle within the platoon using the first information flow topology, the vehicle platoon in mixed traffic is determined. Furthermore, when assessing the stability of the platoon in mixed traffic based on the vehicle platoon, this objectively and comprehensively assesses the stability of the platoon in mixed traffic flow, thereby improving the accuracy of the stability assessment for mixed traffic.

[0010] As a possible implementation, vehicle queues in mixed traffic may include one or more of the following: a first vehicle queue with a queue size equal to a first maximum queue size; a second vehicle queue with a queue size smaller than the first maximum queue size but greater than 1; a third vehicle queue with a queue size of 1 and consisting of intelligent connected vehicles; and a fourth vehicle queue with a queue size of 1 and consisting of non-intelligent connected vehicles. Thus, by determining vehicle queues in mixed traffic based on the first maximum queue size and then assessing queue stability in mixed traffic based on the vehicle queues, objectively and comprehensively assessing queue stability in mixed traffic flow can be achieved, thereby improving the accuracy of mixed traffic stability assessments.

[0011] As a possible implementation, when each vehicle platoon includes both intelligent and non-intelligent connected vehicles, the motion model of each platoon is derived based on the deviation between the actual and expected state information of each vehicle in the platoon. This state information includes vehicle speed and the distance to the preceding vehicle. In this way, a motion model of the mixed platoon is constructed based on the deviation between the actual and expected states of each vehicle. This motion model is then used to characterize the motion of the mixed platoon, thereby achieving a more accurate stability assessment of mixed traffic.

[0012] As one possible implementation, obtaining a first evaluation result based on the motion model of each vehicle queue and the probability of occurrence of each vehicle queue includes: obtaining a transfer function for each vehicle queue based on the motion model of each vehicle queue; and obtaining the first evaluation result based on the transfer function of each vehicle queue and the probability of occurrence of each vehicle queue. Thus, evaluating the stability of mixed traffic queues based on the transfer function and the probability of occurrence of each vehicle queue can achieve the goal of objectively and comprehensively evaluating the stability of mixed traffic queues, thereby improving the accuracy of mixed traffic stability assessment.

[0013] As a possible implementation, the method further includes obtaining the vehicle speed of each vehicle in mixed traffic, and obtaining a second evaluation result based on the vehicle speed of each vehicle; the second evaluation result includes the speed standard deviation and the speed mean absolute deviation, and the second evaluation result is used to characterize the queue stability of the mixed traffic. Thus, by indexing the data of vehicles in mixed traffic, a second evaluation result of the queue stability of the mixed traffic is obtained. The queue stability of the mixed traffic is evaluated based on the second evaluation result and the first evaluation result, thereby objectively and comprehensively assessing the queue stability of the mixed traffic.

[0014] As one possible implementation, the first evaluation result is an evaluation result for characterizing queue stability of mixed traffic under a first combination, the first combination being obtained based on a first maximum queue size and a first information flow topology. The method further includes obtaining the first evaluation result under a second combination, the second combination being obtained based on a second maximum queue size and a second information flow topology, the second maximum queue size being different from the first maximum queue size and / or the second information flow topology being different from the first information flow topology; and obtaining a target first evaluation result for characterizing queue stability of mixed traffic based on the first evaluation result under the first combination and the first evaluation result under the second combination. In this manner, by analyzing queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the quality of queue stability of mixed traffic can be truly and objectively reflected, resulting in a more accurate first evaluation result.

[0015] As one possible implementation, the second evaluation result is an evaluation result for the first combination used to characterize the queue stability of mixed traffic. The method further includes: obtaining a second evaluation result for the second combination; and obtaining a target second evaluation result for characterizing the queue stability of mixed traffic based on the second evaluation result for the first combination and the second evaluation result for the second combination. In this way, by analyzing the queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the quality of the queue stability of mixed traffic can be truly and objectively reflected, resulting in a more accurate second evaluation result.

[0016] As one possible implementation, the first maximum queue size and the second maximum queue size are candidate maximum queue sizes from a set of preset maximum queue sizes; and / or the first information flow topology and the second information flow topology are candidate information flow topologies from a set of preset information flow topologies; the set of preset maximum queue sizes includes multiple candidate maximum queue sizes, and the set of preset information flow topologies includes multiple candidate information flow topologies. In this manner, by analyzing the queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the queue stability quality of mixed traffic can be truly and objectively reflected, resulting in more accurate first and second evaluation results.

[0017] In a second aspect, a mixed traffic queue stability evaluation unit is provided, the evaluation unit comprising: a parameter acquisition module for obtaining a penetration rate of intelligent connected vehicles in mixed traffic, a first maximum queue size, and a first information flow topology; a queue analysis module for obtaining vehicle queues existing in the mixed traffic and the queue size of each vehicle queue based on the first maximum queue size and the first information flow topology; a probability calculation module for obtaining an occurrence probability of each vehicle queue in the mixed traffic based on the queue size and penetration rate of each vehicle queue; and a stability determination module for obtaining a first evaluation result based on a motion model of each vehicle queue and the occurrence probability of each vehicle queue.

[0018] The solution provided in the second aspect above, while considering the penetration rate of intelligent connected vehicles in mixed traffic, focuses on the information flow topology and maximum queue size of the mixed vehicle queues. Thus, based on the information flow topology and maximum queue size, the vehicle queues existing in the mixed traffic and the queue size of each vehicle queue can be determined. Furthermore, based on the queue size and penetration rate of each vehicle queue, the probability of occurrence of each vehicle queue in the mixed traffic can be obtained. In this way, based on the motion model of each vehicle queue and the probability of occurrence of each vehicle queue, a first assessment result of the queue stability of the mixed traffic can be obtained. This can achieve the purpose of objectively and comprehensively assessing the stability of the mixed traffic flow queues, thereby improving the accuracy of the mixed traffic stability assessment.

[0019] As one possible implementation, when each vehicle queue includes an intelligent connected vehicle, the position of the intelligent connected vehicle within each vehicle queue is determined based on the first information flow topology. Thus, by determining the position of the intelligent connected vehicle within the vehicle queue using the first information flow topology, the vehicle queue in mixed traffic is determined. Furthermore, when assessing the stability of the mixed traffic queue based on the vehicle queue, this objectively and comprehensively assesses the stability of the mixed traffic queue, thereby improving the accuracy of the mixed traffic stability assessment.

[0020] As a possible implementation, vehicle queues in mixed traffic may include one or more of the following: a first vehicle queue with a queue size equal to a first maximum queue size; a second vehicle queue with a queue size smaller than the first maximum queue size but greater than 1; a third vehicle queue with a queue size of 1 and consisting of intelligent connected vehicles; and a fourth vehicle queue with a queue size of 1 and consisting of non-intelligent connected vehicles. Thus, by determining vehicle queues in mixed traffic based on the first maximum queue size and then assessing queue stability in mixed traffic based on the vehicle queues, objectively and comprehensively assessing queue stability in mixed traffic flow can be achieved, thereby improving the accuracy of mixed traffic stability assessments.

[0021] As a possible implementation, when each vehicle platoon includes both intelligent and non-intelligent connected vehicles, the motion model of each platoon is derived based on the deviation between the actual and expected state information of each vehicle in the platoon. This state information includes vehicle speed and the distance to the preceding vehicle. In this way, a motion model of the mixed platoon is constructed based on the deviation between the actual and expected states of each vehicle. This motion model is then used to characterize the motion of the mixed platoon, thereby achieving a more accurate stability assessment of mixed traffic.

[0022] As one possible implementation, the probability calculation module is configured to: derive a transfer function for each vehicle queue based on its motion model; and to obtain a first evaluation result based on the transfer function and the probability of occurrence of each vehicle queue. Thus, by evaluating the stability of mixed traffic queues based on the transfer function and the probability of occurrence of each vehicle queue, the stability of mixed traffic queues can be objectively and comprehensively assessed, thereby improving the accuracy of mixed traffic stability assessments.

[0023] As a possible implementation, the stability determination module is further configured to obtain the speed of each vehicle in mixed traffic and, based on the speed of each vehicle, generate a second evaluation result. The second evaluation result includes the speed standard deviation and the speed mean absolute deviation, and is used to characterize the stability of the mixed traffic queue. Thus, by indexing the data of vehicles in mixed traffic to obtain a second evaluation result of the mixed traffic queue stability, the mixed traffic queue stability is evaluated based on the second evaluation result and the first evaluation result, thereby enabling an objective and comprehensive assessment of the mixed traffic queue stability.

[0024] As one possible implementation, the first evaluation result is an evaluation result for characterizing queue stability of mixed traffic under a first combination, the first combination being obtained based on a first maximum queue size and a first information flow topology. The method further includes obtaining the first evaluation result under a second combination, the second combination being obtained based on a second maximum queue size and a second information flow topology, the second maximum queue size being different from the first maximum queue size and / or the second information flow topology being different from the first information flow topology; and obtaining a target first evaluation result for characterizing queue stability of mixed traffic based on the first evaluation result under the first combination and the first evaluation result under the second combination. In this manner, by analyzing queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the quality of queue stability of mixed traffic can be truly and objectively reflected, resulting in a more accurate first evaluation result.

[0025] As one possible implementation, the second evaluation result is an evaluation result for the first combination used to characterize the queue stability of mixed traffic. The method further includes: obtaining a second evaluation result for the second combination; and obtaining a target second evaluation result for characterizing the queue stability of mixed traffic based on the second evaluation result for the first combination and the second evaluation result for the second combination. In this way, by analyzing the queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the quality of the queue stability of mixed traffic can be truly and objectively reflected, resulting in a more accurate second evaluation result.

[0026] As one possible implementation, the first maximum queue size and the second maximum queue size are candidate maximum queue sizes from a set of preset maximum queue sizes; and / or the first information flow topology and the second information flow topology are candidate information flow topologies from a set of preset information flow topologies; the set of preset maximum queue sizes includes multiple candidate maximum queue sizes, and the set of preset information flow topologies includes multiple candidate information flow topologies. In this manner, by analyzing the queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the queue stability quality of mixed traffic can be truly and objectively reflected, resulting in more accurate first and second evaluation results.

[0027] In a third aspect, an electronic device is provided, comprising: a memory for storing computer program instructions; and a processor for executing the computer program instructions to support the electronic device in implementing a method as any possible implementation method in the first aspect.

[0028] In a fourth aspect, an electronic device is provided, comprising an evaluation unit for mixed traffic queue stability according to any possible implementation of the second aspect.

[0029] In a fifth aspect, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processing circuit, a method of any possible implementation method in the first aspect is implemented.

[0030] In a sixth aspect, a computer program product comprising instructions is provided, which, when the computer program product is run on a computer, enables the computer to execute a method as any possible implementation method of the first aspect.

[0031] In the seventh aspect, a chip system is provided, which includes a processing circuit and a storage medium, wherein the storage medium stores computer program instructions; when the computer program instructions are executed by the processing circuit, a method of any possible implementation method in the first aspect is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0033] Figure 2 This is a flow chart of a method for evaluating the stability of a mixed traffic queue provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of a first information flow topology structure provided in an embodiment of the present application;

[0035] Figure 4 A second flow chart of the method for evaluating the stability of a mixed traffic queue provided in an embodiment of the present application;

[0036] Figure 5 A schematic diagram of a simulation example provided in an embodiment of the present application;

[0037] Figure 6 A schematic diagram of a method for evaluating the stability performance of a mixed traffic queue in a simulation example provided in an embodiment of the present application;

[0038] Figure 7 A schematic diagram of the experimental process for evaluating the stability performance of a mixed traffic queue in a simulation example provided in an embodiment of the present application;

[0039] Figure 8 A schematic diagram of the structure of a mixed traffic queue stability performance evaluation device in a simulation example provided in an embodiment of the present application;

[0040] Figure 9 A simulation effect diagram provided for an embodiment of the present application;

[0041] Figure 10 A schematic diagram of the structure of a mixed traffic queue stability evaluation unit provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0043] The terms "including," "having," and any variations thereof mentioned in the description of the embodiments of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0044] In the following, the terms "first," "second," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the quantity of the technical features indicated. Therefore, a feature specified as "first," "second," etc. may explicitly or implicitly include one or more of the features.

[0045] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0046] In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more. "And / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone.

[0047] Currently, longitudinal control technology for intelligent connected vehicles (ICVs) is fundamental to their practical deployment. Existing longitudinal control technologies primarily include adaptive cruise control (ACC), cooperative adaptive cruise control (CACC), full ICV platoon control, and connected cruise control (CCC). The deployment of ICVs, and their gradual marketization, is driving the development of intelligent transportation systems (ITSs). However, since the full marketization of ICVs will take time, ITSs will undergo a mixed traffic phase, where ICVs and manually driven vehicles coexist. The coexistence of manually driven vehicles and ICVs increases the complexity of mixed traffic, necessitating the development of performance evaluation methods for mixed traffic. Among these, platoon stability assessment and testing methods for mixed traffic are a crucial component of the ITS evaluation and testing framework. By evaluating platoon stability in mixed traffic, the results can guide the setting of signal spacing and other parameters to reduce the occurrence of traffic accidents.

[0048] However, current methods for evaluating platoon stability primarily focus on the single-vehicle or vehicle layer, rather than the traffic layer. The vehicle layer encompasses multiple single vehicles, while the traffic layer encompasses multiple vehicle layers. Regarding traffic-layer performance evaluation, some methods primarily evaluate ACC, CACC, and fully intelligent connected vehicle platoons. These evaluation methods are effective for traffic composed of fully intelligent connected vehicles, but for mixed traffic, the penetration rate of intelligent connected vehicles is low, and fully intelligent connected vehicle platoons rarely occur, while mixed vehicle platoons (where the platoon includes both intelligent connected vehicles and manually driven vehicles) have a high probability of occurring. Therefore, evaluation methods based on fully intelligent connected vehicle platoons are not suitable for mixed traffic, and the current system of platoon stability evaluation methods for mixed traffic remains incomplete and inaccurate.

[0049] This application considers that information flow topology has a significant impact on queue stability. Furthermore, due to limitations in communication bandwidth and perception range, the maximum queue size of mixed vehicle queues in a mixed traffic system is limited. A limited maximum queue size is required to ensure the effectiveness and real-time control of intelligent connected vehicles. Therefore, this application provides a method for evaluating the stability of mixed traffic queues. This method comprehensively considers important parameters such as the information flow topology and maximum queue size of the vehicle queue to evaluate the stability of mixed traffic queues. This method can objectively and comprehensively evaluate the stability of mixed traffic flow queues, thereby improving the accuracy of mixed traffic stability assessments.

[0050] The mixed traffic queue stability assessment method provided in the embodiments of this application can be applied to electronic devices. For example, the electronic device can be a computer with wireless transceiver functionality, a server, or other data processing device. The embodiments of this application do not impose any restrictions on the specific type of electronic device.

[0051] In an embodiment of the present application, the electronic device is in communication with roadside equipment on the road, capable of acquiring and processing vehicle information measured by the roadside equipment. For example, the stability of a mixed traffic queue may be assessed based on the vehicle information measured by the roadside equipment.

[0052] As an example, electronic devices can be deployed on the road as roadside equipment to measure vehicles on the road, obtain vehicle information, process this information, and send the results to the cloud. For example, the stability of a mixed traffic queue can be assessed based on vehicle information, and the assessment results can be sent to the cloud.

[0053] As an example, the electronic device is communicatively connected to the intelligent connected vehicle, and the intelligent connected vehicle is communicatively connected to the roadside device. The intelligent connected vehicle can obtain vehicle information from the roadside device and send the vehicle information to the electronic device so that the electronic device can process based on the vehicle information, such as stability assessment of a mixed traffic queue.

[0054] As an example, the electronic device may also be an onboard module, onboard module, onboard component, onboard chip, or onboard unit built into an intelligent connected vehicle as one or more components or units. The intelligent connected vehicle receives vehicle information measured by roadside equipment through the built-in onboard module, onboard module, onboard component, onboard chip, or onboard unit, processes the vehicle information, and transmits the processing results to the corresponding device. For example, the intelligent connected vehicle can perform a stability assessment of a mixed traffic queue based on the vehicle information, obtain an assessment result, and transmit the assessment result to the corresponding device.

[0055] Among them, vehicle information can be information such as the penetration rate of intelligent connected vehicles, the speed and location of each vehicle on the road, etc.

[0056] For ease of understanding, the specific structure of the electronic device is introduced below. Figure 1 As shown, the electronic device 100 may include a processor 110, a memory 120, and a communication interface 130. The communication interface 130 is used to communicate with other devices. For example, information exchange with roadside equipment and intelligent connected vehicles can be performed through the communication interface 130.

[0057] The processor 110 may be a central processing unit (CPU) or other specific integrated circuit. The processor 110 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. In actual applications, the receiving device 120 may also include multiple processors 110, each of which may include one or more processor cores.

[0058] The processor 110 is connected to the memory 120 via a double data rate (DDR) bus or other types of buses. The memory 120 is generally used to store computer program executable program code. The executable program code includes instructions, and the processor 110 executes various functional applications and data processing of the electronic device 100 by running the instructions stored in the memory 120. The memory 120 may include a program storage area and a data storage area. The program storage area may store an operating system, an application required for at least one function, etc., and the data storage area may store data created during the use of the electronic device 100, etc.

[0059] In addition, the memory 120 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the memory 120.

[0060] In the embodiment of the present application, the memory 120 also includes a cache memory for storing instructions or data that have just been used or are being recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can directly call it from the cache memory, avoiding repeated access, reducing the waiting time of the processor 110, and thus improving efficiency.

[0061] It is understandable that this application Figure 1The structure shown does not constitute a specific limitation on the electronic device 100. In other examples of the present application, the electronic device 100 may include more or fewer components than shown, or combine certain components, or split certain components, or arrange the components differently, and the components may be implemented in hardware, software, or a combination of software and hardware. For example, when the electronic device 100 is a roadside device, the electronic device 100 may also include radar, cameras and other equipment to achieve real-time perception of the road traffic environment and status, such as the speed of vehicles on the road, the position of vehicles and other information. The specific settings are based on actual needs.

[0062] For ease of understanding, the following will specifically describe the method for evaluating the stability of a mixed traffic queue provided by the embodiment of the present application in conjunction with the accompanying drawings. Figure 2 , Figure 2 This is a flow chart of a method for evaluating the stability of a mixed traffic queue provided in an embodiment of the present application. Figure 2 The method shown is applied to Figure 1 The electronic device shown is executed by the electronic device. Figure 2 As shown, the mixed traffic queue stability evaluation method provided by the embodiment of the present application includes steps S201 to S204.

[0063] S201: Obtain a penetration rate of intelligent connected vehicles in mixed traffic, a first maximum queue size, and a first information flow topology.

[0064] Mixed traffic refers to mixed traffic flows occurring on a single lane. When there are multiple lanes on a road, each lane corresponds to a mixed traffic flow. For ease of description, in the embodiments of this application, mixed traffic flows on a single lane are used as an example.

[0065] In the embodiment of the present application, mixed traffic includes different types of motor vehicles. For example, in the embodiment of the present application, mixed traffic includes intelligent network-connected cars and manually driven cars.

[0066] In this embodiment of the present application, the penetration rate of intelligent connected vehicles in mixed traffic refers to the proportion of intelligent connected vehicles in mixed traffic. For example, if there are 100 vehicles in mixed traffic and the number of intelligent connected vehicles is 20, the penetration rate of intelligent connected vehicles is 20%.

[0067] As an example, when the electronic device is a roadside device, it can monitor intelligent connected vehicles in mixed traffic and calculate the penetration rate of intelligent connected vehicles in mixed traffic. The electronic device can monitor intelligent connected vehicles in mixed traffic and calculate the penetration rate of intelligent connected vehicles in mixed traffic in real time or periodically, without specific limitations and can be configured according to actual needs.

[0068] As an example, when the electronic device is not a roadside device, the roadside device can monitor the smart connected vehicles appearing in mixed traffic, calculate the penetration rate of the smart connected vehicles in the mixed traffic, and send the penetration rate to the electronic device.

[0069] Optionally, the roadside equipment can monitor the smart connected vehicles in mixed traffic in real time, calculate the penetration rate of the smart connected vehicles in mixed traffic, and send the penetration rate to the electronic device in real time, or, after obtaining a change in the penetration rate, send the changed penetration rate to the electronic device.

[0070] Optionally, the roadside equipment can also periodically transmit the penetration rate of intelligent connected vehicles in mixed traffic to the electronic device. For example, the roadside equipment monitors the presence of intelligent connected vehicles in mixed traffic in real time, calculates the penetration rate of intelligent connected vehicles in mixed traffic at a preset period, and transmits the calculated rate to the electronic device.

[0071] In this embodiment of the present application, the first maximum queue size refers to the maximum queue size of a set vehicle queue. For example, if the first maximum queue size is 6, the vehicle queue will contain a maximum of 6 vehicles. A vehicle queue refers to a queue of vehicles traveling in the same lane and can be a mixed vehicle queue, i.e., a queue containing both intelligent connected vehicles and non-intelligent connected vehicles (e.g., manually driven vehicles).

[0072] In the embodiments of the present application, the first information flow topology refers to the topological relationship for information transmission between vehicles in a vehicle platoon. As an example, the first information flow topology may include, but is not limited to, a leading cruise control mode, a following cruise control mode, or a leading and following cruise control mode.

[0073] Among them, such as Figure 3 As shown in (a) in Figure 2, for the navigator cruise control mode, the first vehicle in the vehicle queue is a smart connected vehicle, and the remaining vehicles are non-smart connected vehicles, such as manually driven vehicles. Figure 3 As shown in (b) of Figure 2, for the follower cruise control mode, the last vehicle in the vehicle queue is a smart connected vehicle, and the remaining vehicles are non-smart connected vehicles. Figure 3 As shown in (c) in the figure, for the cruise control mode combining the leader and the follower, the first vehicle and the last vehicle in the vehicle queue are both intelligent connected vehicles, and the remaining vehicles are non-intelligent connected vehicles.

[0074] The intelligent connected car in the vehicle queue can obtain the status information of the non-intelligent connected car in the vehicle queue, such as speed, displacement, etc. For example, the intelligent connected car can obtain the status information of the non-intelligent connected car through the roadside equipment.

[0075] As an example, the first maximum queue size and the first information flow topology are pre-set in the electronic device. When the electronic device performs a queue stability assessment for mixed traffic, the electronic device can obtain the first maximum queue size and the information flow topology locally. As an example, the first maximum queue size and the first information flow topology can also be obtained from other devices. For example, when the electronic device performs a queue stability assessment for mixed traffic, the electronic device can initiate a data request to the other device, and the other device responds to the data request initiated by the electronic device and sends the first maximum queue size and the first information flow topology to the electronic device.

[0076] As an example, the first maximum queue size is obtained based on extensive data processing and is preferably suitable for mixed vehicle queues in mixed traffic scenarios. Accordingly, the information flow topology is obtained based on extensive data processing and is preferably suitable for mixed vehicle queues in mixed traffic scenarios.

[0077] S202 : Obtain vehicle queues existing in mixed traffic and a queue size of each vehicle queue according to the first maximum queue size and the first information flow topology.

[0078] In the embodiment of the present application, the queue size of the vehicle queue in mixed traffic is less than or equal to the first maximum queue size. Therefore, based on the first maximum queue size, it can be obtained that the vehicle queues existing in the mixed traffic include a first vehicle queue with a queue size of the first maximum queue size, a second vehicle queue with a queue size less than the first maximum queue size but greater than 1, and a vehicle queue with a queue size of 1.

[0079] A vehicle queue with a queue size of 1 is composed of individual vehicles. A single vehicle can be either an intelligent connected vehicle or a non-intelligent connected vehicle. Therefore, a vehicle queue with a queue size of 1 includes a third queue with a queue size of 1 and a vehicle type of intelligent connected vehicle, and a fourth queue with a queue size of 1 and a vehicle type of non-intelligent connected vehicle.

[0080] It can be understood that when the queue size of the vehicle queue is greater than 1, the vehicle queue includes multiple vehicles.

[0081] In an embodiment of the present application, when the queue size of a vehicle queue is greater than 1, the vehicle queue is a mixed vehicle queue. At this time, the vehicle queue includes intelligent connected vehicles and non-intelligent connected vehicles.

[0082] In the implementation of this application, the information flow topology structure can be used to determine the position of an intelligent connected vehicle in a mixed vehicle queue. Exemplarily, when the vehicle queue includes an intelligent connected vehicle, the position of the intelligent connected vehicle in the vehicle queue is obtained based on the first information flow topology structure.

[0083] For example, if the first information flow topology is the navigator cruise control mode, the intelligent connected car is located at the head of the vehicle queue, that is, the first car in the vehicle queue is the intelligent connected car, and the rest are non-intelligent connected cars.

[0084] Taking the first information flow topology structure as the navigator cruise control mode and the first maximum queue size as 4 as an example, based on the first maximum queue size and the first information flow topology structure, it can be obtained that the vehicle queues existing in mixed traffic include a vehicle queue with a queue size of 4 and the first vehicle being an intelligent connected vehicle, a vehicle queue with a queue size of 3 and the first vehicle being an intelligent connected vehicle, a vehicle queue with a queue size of 2 and the first vehicle being an intelligent connected vehicle, a vehicle queue including only intelligent connected vehicles, and a vehicle queue including only non-intelligent connected vehicles.

[0085] S203 : Obtaining the appearance probability of each vehicle queue in mixed traffic according to the queue size and penetration rate of each vehicle queue.

[0086] After obtaining possible vehicle queues in mixed traffic based on the first maximum queue size and the information flow topology, the probability of each vehicle queue appearing in mixed traffic is determined based on the queue size and the penetration rate of intelligent connected vehicles in the mixed traffic.

[0087] For example, for the first vehicle queue in mixed traffic, that is, the vehicle queue with a queue size equal to the first maximum queue size, the probability of occurrence in mixed traffic can be obtained by formula (1):

[0088] p(size=M)=P(1-P) M-1 (1)

[0089] Where p is the probability, M is the first maximum queue size, P is the penetration rate of intelligent connected vehicles in mixed traffic, and p(size=M) represents the probability of a vehicle queue with a queue size of the first maximum queue size appearing in mixed traffic.

[0090] For the second vehicle queue in mixed traffic, that is, the vehicle queue whose queue size is smaller than the first maximum queue size but greater than 1, the probability of occurrence in mixed traffic can be obtained by formula (2):

[0091] p(size=S)=P 2(1 - P) S-1 (2)

[0092] Among them, S is the number of vehicles in the second vehicle queue, 1 < S < M, and p(size = S) represents the occurrence probability of a vehicle queue with S vehicles in mixed traffic.

[0093] For the third vehicle queue in mixed traffic, that is, the vehicle queue consisting only of connected and automated vehicles, the occurrence probability in mixed traffic can be obtained through formula (3):

[0094] p(CACC) = P 2 (3)

[0095] Among them, p(CACC) represents the occurrence probability of connected and automated vehicles in mixed traffic.

[0096] For the fourth vehicle queue in mixed traffic, that is, the vehicle queue consisting only of non - connected and automated vehicles, the occurrence probability in mixed traffic can be obtained through formula (4):

[0097] p(HDV) = (1 - P) M (4)

[0098] Among them, p(HDV) represents the occurrence probability of non - connected and automated vehicles, such as manually - driven vehicles, in mixed traffic.

[0099] S204, obtain a first evaluation result according to the motion models of each vehicle queue and the occurrence probabilities of each vehicle queue, and the first evaluation result is used to characterize the queue stability of mixed traffic.

[0100] Among them, the motion model of each vehicle queue refers to the dynamic model of each vehicle queue, and the motion model of each vehicle queue indicates the change rule of the motion state of each vehicle queue.

[0101] Next, taking the first information - flow topology structure as the leader - following cruise control mode as an example, the motion models of each vehicle queue will be specifically described.

[0102] As an example, for the fourth vehicle queue, that is, the vehicle queue consisting only of non - connected and automated vehicles, such as the motion model of a manually - driven vehicle can be obtained through data fitting or calibration of the model. Exemplarily, by obtaining vehicle data such as the speed, distance from the preceding vehicle, and acceleration of a non - connected and automated vehicle through data fitting, the motion model of the non - connected and automated vehicle can be obtained. Among them, the method of fitting the vehicle data can be the least - squares method, step - by - step regression, polynomial fitting, etc., which are specifically set according to actual needs and are not limited in the embodiments of the present application.

[0103] For example, the motion model of a non-intelligent connected vehicle can also be obtained by calibrating the optimal velocity model (OVM). As shown in formula (5), the motion model of a non-intelligent connected vehicle can be expressed as:

[0104]

[0105] in, is the target acceleration of the i-th vehicle, s i (t) represents the distance between the i-th vehicle and the preceding vehicle, V(s i (t)) represents the expected vehicle speed at the current following distance, v i-1 (t) represents the speed of the i-1th vehicle, i.e., the speed of the preceding vehicle, v i (t) represents the speed of the i-th vehicle, α>0 represents the driver's sensitivity to the difference between the desired speed and the current actual speed, and β>0 represents the driver's sensitivity to the speed difference between the i-1-th vehicle and the i-th vehicle. i The (t)) function is usually expressed as shown in formula (6).

[0106]

[0107] Among them, s min Indicates the minimum following distance, s max Indicates the maximum following distance that can produce following behavior, f v (s i (t)) is usually in the form shown in formula (7).

[0108]

[0109] In the embodiment of the present application, α, β, s min 、v max The value of can be set according to actual needs, and there is no specific limit. As an example, α, β, s min 、v max The values ​​can be shown in Table 1.

[0110] Table 1

[0111] parameter Value unit α 0.6 1 / second (s) β 0.9 1 / second (s) <![CDATA[s min ]]> 2 meter (m) <![CDATA[s max ]]> 32 meter (m) <![CDATA[v max ]]> 30 Meter / second (m / s)

[0112] As an example, for the third vehicle column, i.e., the vehicle column consisting only of intelligent connected vehicles, a longitudinal control algorithm can be used to obtain the motion model of the intelligent connected vehicle. For example, an adaptive cruise control algorithm can be used to obtain the motion model of the intelligent connected vehicle. For example, the motion model of the intelligent connected vehicle can be expressed as follows:

[0113]

[0114] Among them, e represents the distance error from the preceding vehicle, represents the differential of the spacing error, s represents the actual spacing from the preceding vehicle, s0 is the minimum safe spacing when the vehicle is stationary, and t h is the desired following distance, v is the vehicle speed, and v prev represents the speed of the vehicle at the previous moment, k p Indicates the control coefficient of spacing error, k d Represents the control coefficient of the differential of the spacing error.

[0115] In the embodiment of the present application, t h ,s0,k p 、k d The value of can be set according to actual needs and is not limited. As an example, t h ,s0,k p 、k d The values ​​can be shown in Table 2.

[0116] Table 2

[0117] parameter Value unit <![CDATA[t h ]]> 1 Seconds (s) <![CDATA[s0]]> 2 meter (m) <![CDATA[k p ]]> 0.45 1 / second (1 / s) <![CDATA[k d ]]> 0.25

[0118] Because the first and second vehicle platoons include multiple vehicles, motion models can be derived for each of the first and second vehicle platoons based on the deviation between the actual and desired state information of each vehicle in the platoon. Specifically, in this embodiment of the present application, for mixed vehicle platoons, the deviation between the actual and desired state information of each vehicle in the platoon can be used as the controlled variable for platoon control. This state information includes vehicle speed and the distance to the preceding vehicle.

[0119] The following takes the first vehicle platoon as an example to illustrate the motion model of the first vehicle platoon.

[0120] For example, the deviation between the actual state information and the expected state information of each vehicle in the first vehicle queue may be as shown in formula (9).

[0121]

[0122] in, Indicates the actual distance s between the i-th vehicle and the preceding vehicle i and the expected spacing s * difference, represents the actual speed v of the i-th vehicle i With the expected speed v * difference.

[0123] In the embodiment of the present application, the system state quantity of the mixed vehicle queue is set to the spacing deviation of each vehicle in the mixed vehicle queue. and speed deviation Therefore, the state vector of the first vehicle queue can be shown as formula (10).

[0124]

[0125] Where x represents the system state of the first vehicle queue, x n is the deviation value of the state information of the nth vehicle in the first vehicle queue, is the distance s between the nth vehicle and the preceding vehicle in the first vehicle queue n and the expected spacing s * difference, is the actual speed v of the nth vehicle in the first vehicle queue n With the expected speed v * The difference between , T represents the matrix transpose.

[0126] After the state vector of the first vehicle queue is obtained, a state space expression of the first vehicle queue, that is, a motion model of the first vehicle queue, can be obtained based on the state vector of the first vehicle queue.

[0127] In this embodiment of the present application, a first vehicle platoon includes both non-intelligent connected vehicles and intelligent connected vehicles. The state vector of the first vehicle platoon includes deviations in the state information of the non-intelligent connected vehicles and the state information of the intelligent connected vehicles. Therefore, the motion model (state space expression) of the first vehicle platoon can be given by the state expressions of the non-intelligent connected vehicles and the state expressions of the intelligent connected vehicles.

[0128] In the embodiment of the present application, the state expression of the non-intelligent connected vehicle can be expressed according to the deviation of the non-intelligent connected vehicle. and deviation get.

[0129] Among them, for non-intelligent connected vehicles, the deviation of non-intelligent connected vehicles can be obtained based on formula (9): As shown in formula (11).

[0130]

[0131] in, for By taking the differential of For example, based on formula (9), we can get

[0132] Bias for non-intelligent connected cars It can be obtained by formula (12).

[0133]

[0134] in, represents the distance s between the i-th vehicle and the preceding vehicle i The partial derivative of express The partial derivative of represents the partial derivative of the velocity of the i-th vehicle.

[0135] Simplify formula (12) and let

[0136]

[0137] Get the deviation of non-intelligent connected cars for:

[0138]

[0139] Among them, α1, α2, and α3 are coefficients.

[0140] In summary, the linearized state expression of the general form of a non-intelligent connected vehicle is shown in formula (15).

[0141]

[0142] Accordingly, in the embodiment of the present application, the state expression of the intelligent network-connected vehicle can also be expressed according to the deviation of the intelligent network-connected vehicle. and deviation The state expression of the intelligent connected vehicle can be shown as formula (16).

[0143]

[0144] Among them, u is the control input of the intelligent connected vehicle.

[0145] After obtaining the state expressions of the non-intelligent connected vehicles and the intelligent connected vehicles, the motion model (state space expression) of the first vehicle platoon can be obtained by combining Formula (10), Formula (15), and Formula (16), as shown in Formula (17). Formula (17) is obtained by differentiating x in Formula (10) and combining Formula (15) and Formula (16).

[0146]

[0147] Among them, the matrix is the system matrix, matrix is the control input matrix, the matrix is the perturbation input matrix, the matrix is the output matrix, u is the control input of the intelligent connected car, is the speed deviation of the disturbance vehicle (the vehicle immediately in front of the first vehicle queue), and y is the output of the speed deviation of the last vehicle in the first vehicle queue.

[0148] By expanding formula (17), the motion model (state space expression) of the first vehicle queue in the navigator cruise control mode can be obtained as shown in formula (18).

[0149]

[0150] The detailed representation of each sub-block in formula (19) is shown in formula (19).

[0151]

[0152] In an embodiment of the present application, the control input for the intelligent connected vehicles in the first vehicle queue can be obtained based on the cost function of the first vehicle queue and the feedback control gain of the intelligent connected vehicles in the first vehicle queue.

[0153] As an example, a linear quadratic regulator (LQR) control method may be used to control a mixed vehicle platoon. Based on this, the cost function of the first vehicle platoon may be as shown in formula (20).

[0154]

[0155] Where Q = diag[1,…,1,…,1] is a real symmetric positive definite or semi-positive definite matrix, R = 1 is a real symmetric positive definite matrix, and J is the cost function.

[0156] After obtaining the cost function of the first vehicle platoon, the optimal feedback gain K of the intelligent connected vehicles in the first vehicle platoon can be calculated using the algebraic Riccati differential equation. The Riccati differential equation can be expressed as Equation (21), and the feedback control gain K of the intelligent connected vehicles in the first vehicle platoon can be expressed as Equation (22).

[0157]

[0158] Among them, F is the unknown matrix, A is the system matrix, and B is the control input matrix.

[0159] Based on formula (21), the value of the undetermined matrix F can be obtained. After calculating the value of the undetermined matrix F, the value of the undetermined matrix F is substituted into formula (22) to obtain the feedback control gain K of the intelligent connected vehicles in the first vehicle queue. After obtaining the feedback control gain K of the intelligent connected vehicles in the first vehicle queue, the feedback control gain K can be used to determine the control input u of the intelligent connected vehicles in the first vehicle queue.

[0160] u= Kx (23)

[0161] After obtaining the control input u of the intelligent connected vehicles in the first vehicle queue, substituting it into formula (18) can obtain the motion model of the first vehicle queue.

[0162] Correspondingly, the motion model of the second vehicle platoon can also be obtained using formulas (9) to (23). The specific process can refer to the construction process of the motion model of the first vehicle platoon, which will not be described in detail here.

[0163] In the embodiments of the present application, a motion model of a mixed vehicle platoon is constructed based on the deviation between the actual state and the desired state of each vehicle in the platoon. Based on the motion model, a corresponding control method (such as a linear quadratic regulator (LQR) control method) can be applied to control the speed of the intelligent connected vehicles in the mixed vehicle platoon, thereby achieving overall control of the mixed vehicle platoon.

[0164] In addition, the embodiment of the present application constructs a motion model of a mixed vehicle queue by comparing the deviation between the actual state and the expected state of each vehicle. The motion model reflects the motion of the mixed vehicle queue, thereby achieving a more accurate stability assessment of mixed traffic based on the motion model of the mixed vehicle queue.

[0165] After obtaining the motion model of each vehicle queue (including the first vehicle queue, the second vehicle queue, the third vehicle queue, and the fourth vehicle queue), a first evaluation result for characterizing the queue stability of mixed traffic can be obtained based on the motion model of each vehicle queue and the occurrence probability of each vehicle queue.

[0166] As an example, obtaining the first evaluation result according to the motion model of each vehicle queue and the occurrence probability of each vehicle queue may include step (A) and step (B).

[0167] (A) Based on the motion model of each vehicle platoon, the transfer function of each vehicle platoon is obtained.

[0168] For each vehicle queue, the transfer function theory can be used to transform the motion model of the vehicle queue to obtain the transfer function of the vehicle queue.

[0169] For example, for the fourth vehicle platoon, i.e., a platoon consisting only of non-intelligent connected vehicles, a Laplace transform can be performed on the motion model of the platoon to obtain the transfer function of the platoon. The motion model of the fourth vehicle platoon is shown in equations (5), (6), and (7). After the Laplace transform, the transfer function of the fourth vehicle platoon can be obtained as shown in equation (24).

[0170]

[0171] Where d is the complex parameter in Laplace transform, s* represents the expected vehicle distance, Indicates the differential of the desired vehicle speed at the desired vehicle distance.

[0172] For example, for a third vehicle platoon, i.e., a platoon consisting only of intelligent connected vehicles, a Laplace transform can be performed on the motion model of the platoon to obtain the transfer function of the platoon. The motion model of the third vehicle platoon is shown in Formula (8). After performing the Laplace transform, the transfer function of the third vehicle platoon can be obtained as shown in Formula (25).

[0173]

[0174] Wherein, d is a complex parameter in the Laplace transform, Δt is a time step, and the vehicle status information is obtained every Δt. As an example, the value of Δt is 0.1s.

[0175] For the first and second vehicle platoons, i.e., the mixed vehicle platoons, the motion models of the vehicle platoons can also be Laplace transformed to obtain the transfer functions of the vehicle platoons. The motion models of the first and second vehicle platoons are shown in Equations (18) and (19). After Laplace transforms, the transfer functions of the first and second vehicle platoons are shown in Equation (26) and (27), respectively.

[0176] G M (d) = C (dI - (A + BK)) -1 Φ (26)

[0177] G S (d) = C (dI - (A + BK)) -1 Φ (27)

[0178] Where d is the complex parameter in the Laplace transform, I is the identity matrix, C is the output matrix, A is the system matrix, B is the control input matrix, K is the optimal feedback gain of the intelligent connected vehicle, and Φ is the disturbance input matrix.

[0179] (B) A first evaluation result is obtained based on the transfer function of each vehicle train and the occurrence probability of each vehicle train.

[0180] After obtaining the transfer function of each vehicle queue, the Bode diagram can be used to evaluate the stability of the mixed traffic queue based on the transfer function of each vehicle queue and the occurrence probability of each vehicle queue.

[0181] For example, the stability assessment of mixed traffic queues using Bode diagrams can be implemented using formula (28).

[0182]

[0183] Wherein, p(size=M) is the probability of a vehicle queue with a queue size equal to the first maximum queue size appearing in mixed traffic, i.e., the probability of the first vehicle queue appearing in mixed traffic, G M (d) is the transfer function of the first vehicle queue, p(size = S) is the probability of a vehicle queue with S vehicles appearing in mixed traffic, that is, the probability of a vehicle queue with S vehicles appearing in mixed traffic in the second vehicle queue, G S (d) is the transfer function of the second vehicle queue, p(CACC) is the probability of appearance of intelligent connected vehicles in mixed traffic, that is, the probability of appearance of the third vehicle queue in mixed traffic, G CACC (d) is the transfer function of the third vehicle platoon, p(HDV) is the probability of appearance of non-intelligent connected vehicles in mixed traffic, that is, the probability of appearance of the fourth vehicle platoon in mixed traffic, G HDV (d) is the transfer function of the fourth vehicle platoon, where N is the total number of vehicles in mixed traffic.

[0184] When the result of formula (28) is less than or equal to 1, that is, formula (28) holds true, a first evaluation result indicating that mixed traffic satisfies queue stability can be obtained. When the result of formula (28) is greater than 1, that is, formula (28) does not hold true, a first evaluation result indicating that mixed traffic does not satisfy queue stability can be obtained.

[0185] In an embodiment of the present application, after obtaining the first evaluation result, the electronic device may output the first evaluation result to a user terminal (such as a traffic management terminal).

[0186] As an example, when the electronic device is located in a smart connected car, after obtaining the first evaluation result, the electronic device can send the first evaluation result to the cloud, so that the cloud outputs the first evaluation result to the user end.

[0187] As an example, when the electronic device is a roadside device, after obtaining the first evaluation result of the queue stability of mixed traffic, the first evaluation result can be sent to the cloud, so that the cloud outputs the first evaluation result to the user end.

[0188] In order to more comprehensively evaluate the stability of the queue in mixed traffic, in an embodiment of the present application, the data results of all vehicles in the mixed traffic can also be indexed to obtain a second evaluation result of the stability of the queue in mixed traffic. The second evaluation result is used as the evaluation basis for the first evaluation result, thereby achieving a comprehensive, objective and true reflection of the quality of the stability of the queue in mixed traffic.

[0189] As an example, the vehicle speed of each vehicle in mixed traffic may be obtained, and a second evaluation result of the stability of the queue in the mixed traffic may be obtained based on the vehicle speed of each vehicle.

[0190] As an example, each vehicle in mixed traffic can be monitored in real time to obtain the speed of each vehicle. As an example, the vehicle speed can also be obtained based on the motion model of each vehicle queue. For example, after obtaining the motion model of each vehicle queue, the speed of each vehicle can be output based on the motion model.

[0191] After obtaining the vehicle speed of each vehicle in the mixed traffic, a performance analysis may be performed on the vehicle speed of each vehicle to obtain a second evaluation result.

[0192] In the embodiment of the present application, the second evaluation result may include but is not limited to the speed standard deviation and the speed mean absolute deviation.

[0193] As an example, the speed standard deviation can be obtained by formula (29), and the speed mean absolute deviation can be obtained by formula (30).

[0194]

[0195]

[0196] Among them, v i (t) represents the speed of the i-th vehicle at time t, represents the average speed of each vehicle at time t, N represents the total number of vehicles in the mixed traffic flow, T represents the total operation time of the system, SD is the standard deviation of speed, and MAD is the mean absolute deviation of speed.

[0197] As an example, after obtaining the speed standard deviation and the speed average absolute deviation, the speed standard deviation can be compared with a set standard deviation threshold, and the speed average absolute deviation can be compared with a set deviation threshold. When the speed standard deviation is greater than the standard deviation threshold and / or the speed average absolute deviation is greater than the deviation threshold, it indicates that the volatility of the mixed traffic has increased, the stability of the mixed traffic has decreased, and the queue stability requirement is not met. When the speed standard deviation is not greater than the standard deviation threshold and the speed average absolute deviation is not greater than the deviation difference, it indicates that the volatility of the mixed traffic has decreased, the stability of the mixed traffic has increased, and the queue stability requirement is met.

[0198] As an example, after obtaining the speed standard deviation and the speed mean absolute deviation, the speed standard deviation can be compared with the historical standard deviation, and the speed mean absolute deviation can be compared with the historical deviation. When the speed standard deviation is greater than the historical standard deviation and / or the speed mean absolute deviation is greater than the historical deviation, it indicates that the volatility of mixed traffic has increased and the stability of mixed traffic has decreased. When the speed standard deviation is not greater than the historical standard deviation and the speed mean absolute deviation is not greater than the historical deviation, it indicates that the volatility of mixed traffic has decreased and the stability of mixed traffic has increased.

[0199] Optionally, the historical standard deviation is the speed standard deviation obtained during the last stability assessment, and the historical deviation is the speed mean absolute deviation obtained during the last stability assessment.

[0200] Optionally, the historical standard deviation is obtained by using the speed standard deviations obtained during multiple stability assessments within a historical time period, and the historical deviation is obtained by using the speed mean absolute deviations obtained during multiple stability assessments within a historical time period. For example, the historical standard deviation can be the mode, median, average, or minimum of the multiple speed standard deviations. Correspondingly, the historical deviation can be the mode, median, average, or minimum of the multiple speed mean absolute deviations.

[0201] As an example, after obtaining the speed standard deviation and the mean absolute deviation, a weighted sum of the speed standard deviation and the mean absolute deviation can be performed, and stability assessment can be performed based on the weighted sum. If the weighted sum exceeds a set threshold, it indicates that the volatility of the mixed traffic has increased, the stability of the mixed traffic has decreased, and the queue stability requirement is not met. If the weighted sum is within the set threshold, it indicates that the volatility of the mixed traffic has decreased, the stability of the mixed traffic has increased, and the queue stability requirement is met.

[0202] The above is only an example of performing stability assessment based on the speed standard deviation and the speed mean absolute deviation after obtaining the speed standard deviation and the speed mean absolute deviation in the embodiment of the present application, and is not intended to be limiting. The specific configuration is based on actual needs.

[0203] In the embodiment of the present application, determining the speed standard deviation and the speed mean absolute deviation based on the vehicle speed is merely an example of determining the second evaluation result and is not intended to be a specific limitation. In some examples, other data may also be used to determine the second evaluation result.

[0204] For example, in some examples, the second assessment result may be determined using vehicle spacing. By obtaining the position of each vehicle in mixed traffic, the spacing between each vehicle is determined based on the position of each vehicle. Based on the spacing between each vehicle, the standard deviation of the spacing and the mean absolute deviation of the spacing in mixed traffic are obtained. These standard deviation and mean absolute deviation of the spacing are used as the second assessment result for queue stability.

[0205] For another example, in some examples, vehicle acceleration can be used to determine the second assessment result. By obtaining the acceleration of each vehicle in mixed traffic, the standard deviation and mean absolute deviation of acceleration in mixed traffic are calculated based on the acceleration of each vehicle. The standard deviation and mean absolute deviation of acceleration are then used as the second assessment result of queue stability.

[0206] To obtain a more comprehensive second assessment result, in some examples, multiple types of second assessment results may be used. For example, vehicle data for each vehicle in mixed traffic may be obtained, including vehicle speed, vehicle position, and vehicle acceleration. Based on the vehicle data for each vehicle, a second assessment result for queue stability in mixed traffic may be obtained. The second assessment result may include one or more of speed standard deviation, speed mean absolute deviation, spacing standard deviation, spacing mean absolute deviation, acceleration standard deviation, and acceleration mean absolute deviation.

[0207] To improve data accuracy, as an example, after obtaining the vehicle speed, the vehicle speed may be preprocessed to eliminate erroneous and invalid data, thereby improving data accuracy. Optionally, the vehicle speed preprocessing includes, but is not limited to, filtering, classification, and other operations, which are configured based on actual needs and are not specifically limited in this embodiment of the present application.

[0208] After preprocessing the vehicle data, a second evaluation result of the mixed traffic queue stability can be obtained based on the preprocessed vehicle data.

[0209] The embodiment of the present application does not impose any specific restrictions on the type of the second evaluation result, and it can be set according to actual needs.

[0210] As an example, after obtaining the second evaluation result of the queue stability in mixed traffic, the second evaluation result may also be output to the user terminal. In this way, the user terminal can obtain the second evaluation result and the first evaluation result at the same time.

[0211] The mixed traffic queue stability assessment method proposed in the embodiments of the present application considers the penetration rate of intelligent connected vehicles in mixed traffic and focuses on the information flow topology structure and maximum queue size of the mixed vehicle queue. This addresses the problem that existing methods do not consider important parameters of mixed vehicle queues in mixed traffic, and provides an objective metric for evaluating the stability of mixed traffic queues.

[0212] The mixed traffic queue stability evaluation method provided in the embodiments of the present application theoretically analyzes queue stability using Bode plots and experimentally presents the results using data indicators. Thus, through both theoretical and experimental evaluation methods, the mixed traffic queue stability can be objectively and comprehensively evaluated.

[0213] In order to obtain more accurate evaluation results, in the embodiment of the present application, the information flow topology and maximum queue size of the mixed vehicle queue can be changed, and the stability of the mixed traffic queue can be evaluated under different information flow topology and maximum queue size. Figure 4 , Figure 4 The second flow chart of the method for evaluating the stability of a mixed traffic queue provided in the embodiment of the present application is as follows: Figure 4 As shown, the method for evaluating the stability of a mixed traffic queue provided by the embodiment of the present application further includes steps S301 to S303.

[0214] S301: Construct multiple combinations based on a preset maximum queue size set and a preset information flow topology structure set.

[0215] The preset maximum queue size set includes a plurality of candidate maximum queue sizes, and the preset information flow topology structure set includes a plurality of candidate information flow topology structures.

[0216] As an example, the multiple candidate maximum queue sizes include the first maximum queue size in step S201, and the multiple candidate information flow topologies include the first information flow topology in step S201. That is, the first maximum queue size in step S201 is a candidate maximum queue size in a set of preset maximum queue sizes, and the first information flow topology in step S201 is a candidate information flow topology in a set of preset information flow topologies.

[0217] In an embodiment of the present application, when constructing combinations based on a set of preset maximum queue sizes and a set of preset information flow topologies, for each combination, a candidate maximum queue size may be selected from the set of preset maximum queue sizes, and a candidate information flow topology may be selected from the set of preset information flow topologies. The combination is constructed based on the selected candidate maximum queue size and candidate information flow topology.

[0218] In an embodiment of the present application, multiple combinations are constructed based on a preset maximum queue size set and a preset information flow topology structure set, and each combination includes a candidate maximum queue size and a candidate information flow topology structure.

[0219] In order to evaluate the queue stability of mixed traffic under different information flow topologies and maximum queue sizes, in this embodiment of the present application, each combination includes different candidate maximum queue sizes and / or candidate information flow topologies. That is, for each combination, at least one of the candidate maximum queue sizes and candidate information flow topologies included in that combination is different from the candidate maximum queue sizes and candidate information flow topologies included in other combinations.

[0220] S302 : Determine a first evaluation result and a second evaluation result representing queue stability of mixed traffic under each combination.

[0221] In this embodiment of the present application, after constructing the combinations, a first evaluation result and a second evaluation result of the mixed traffic queue stability under each combination can be determined. Specifically, the first evaluation result and the second evaluation result of the mixed traffic queue stability under the candidate maximum queue size and candidate information flow topology included in the combination are determined. The specific process is described in steps S201 to S204, as well as the process of determining the second evaluation result based on the first maximum queue size and the first information flow topology, and is not further described here.

[0222] In the embodiments of the present application, different information flow topologies lead to different vehicle queues in mixed traffic. When the maximum queue size varies, the probability of each vehicle queue occurring also varies. When the vehicle queues and / or the probability of their occurrence vary, the resulting first assessment results will also vary. Therefore, in the embodiments of the present application, different combinations yield different first assessment results for queue stability in mixed traffic.

[0223] In the embodiments of the present application, when the vehicle queue sizes vary, the control inputs obtained for the intelligent connected vehicles in the vehicle queue also vary. The control inputs of the intelligent connected vehicles affect the speed of the intelligent connected vehicles in the vehicle queue, which in turn affects the speed of each vehicle in the vehicle queue. The degree of influence varies under different information flow topologies. Therefore, the speeds of each vehicle in mixed traffic vary under different combinations, and consequently, the second assessment results for mixed traffic queue stability also vary under different combinations.

[0224] S303: Obtain a target first evaluation result based on the first evaluation result of each combination, and obtain a target second evaluation result based on the second evaluation result of each combination.

[0225] In order to obtain the optimal maximum queue size and information flow topology, in an embodiment of the present application, when determining the target first evaluation result of the queue stability of mixed traffic, the first evaluation results of each combination can be compared, and the smallest first evaluation result can be used as the target first evaluation result characterizing the queue stability of mixed traffic.

[0226] When determining the target second evaluation result of the stability of the queue of mixed traffic, the second evaluation result of the combination corresponding to the target first evaluation result is used as the target second evaluation result representing the stability of the queue of mixed traffic.

[0227] Among them, the candidate maximum queue size included in the combination corresponding to the target first evaluation result is the optimal maximum queue size among multiple candidate first maximum queue sizes, and accordingly, the candidate information flow topology structure included in the combination corresponding to the target first evaluation result is the optimal information flow topology structure among multiple candidate information flow topology structures.

[0228] For illustration, multiple combinations including a first combination and a second combination are used as an example. The first combination is derived based on a first maximum queue size and a first information flow topology. That is, the candidate maximum queue size included in the first combination is the first maximum queue size, and the candidate information flow topology is the first information flow topology. The second combination is any combination other than the first combination in the multiple combinations. The candidate maximum queue size included in the second combination is the second maximum queue size, and the candidate information flow topology is the second information flow topology. The second maximum queue size is different from the first maximum queue size and / or the second information flow topology is different from the first information flow topology.

[0229] After obtaining the first evaluation result and the second evaluation result under the second combination, when obtaining the target first evaluation result for characterizing the queue stability of mixed traffic based on the first evaluation result under the first combination and the first evaluation result under the second combination, the first evaluation result under the first combination can be compared with the first evaluation result under the second combination. If the first evaluation result under the first combination is greater than the first evaluation result under the second combination, the first evaluation result under the second combination is the target first evaluation result.

[0230] Accordingly, when a target second evaluation result for characterizing queue stability of mixed traffic is obtained based on the second evaluation result under the first combination and the second evaluation result under the second combination, the second evaluation result under the second combination can be used as the target second evaluation result.

[0231] The above is merely an example of obtaining the target first evaluation result and the target second evaluation result for queue stability of mixed traffic in the embodiment of the present application, and is not intended to be a specific limitation and can be set according to actual needs.

[0232] For example, to obtain a more comprehensive first and second evaluation results, when determining a target first evaluation result for mixed traffic queue stability, a first average of the first evaluation results for each combination can be determined, and the first average can be used as the target first evaluation result representing mixed traffic queue stability. Accordingly, when determining a target second evaluation result for mixed traffic queue stability, a second average of the second evaluation results for each combination can be determined, and the second average can be used as the target second evaluation result representing mixed traffic queue stability.

[0233] For another example, when determining a target first assessment result for mixed traffic queue stability, different weights can be assigned to the first assessment results for each combination and the results can be summed to obtain a weighted first assessment result. This weighted first assessment result is then used as the target first assessment result representing mixed traffic queue stability. Similarly, when determining a target second assessment result for mixed traffic queue stability, different weights can be assigned to the second assessment results for each combination and the results can be summed to obtain a weighted second assessment result. This weighted second assessment result is then used as the target second assessment result representing mixed traffic queue stability.

[0234] In some examples, after obtaining the target first evaluation result and the target second evaluation result, the target first evaluation result and the target second evaluation result can be sent to the user end to facilitate the user to perform queue stability analysis of mixed traffic.

[0235] In some examples, after obtaining the target first evaluation result and the target second evaluation result, the target first evaluation result, the target second evaluation result, the first evaluation result under the first combination, and the second evaluation result can also be sent to the user end to facilitate the user to conduct a comparative analysis of the queue stability of mixed traffic.

[0236] By analyzing the queue stability of mixed traffic under different maximum queue sizes and different information flow topologies, the embodiment of the present application can truly and objectively reflect the queue stability quality of mixed traffic and obtain more accurate first evaluation results and second evaluation results.

[0237] The embodiment of the present application provides a method for evaluating the stability of mixed traffic queues. It proposes a new framework for evaluating the stability of mixed traffic based on mixed vehicle queues. The framework integrates the vehicle longitudinal control algorithm and its probabilistic characteristics. The framework theoretically uses a frequency domain method to intuitively describe the stability of mixed traffic queues. Experimentally, it describes the stability of mixed traffic queues through indicator results, and can objectively and comprehensively evaluate the stability of mixed traffic queues.

[0238] The following is a specific simulation example to illustrate: Figure 5 As shown, in this simulation example, the evaluation of mixed traffic queue stability can include three parts: a mixed traffic queue stability performance evaluation method, a mixed traffic queue stability performance evaluation experimental process, and a mixed traffic queue stability performance evaluation device.

[0239] The mixed traffic queue stability performance evaluation method proposes an evaluation method for queue stability that considers the maximum queue size and information flow topology of mixed vehicle queues. The mixed traffic queue stability performance evaluation experimental process provides detailed implementation details for this evaluation method. The mixed traffic queue stability performance evaluation device analyzes the data generated during this process to determine a second evaluation result for mixed traffic queue stability.

[0240] In the simulation example, the evaluation method for the mixed traffic queue stability performance can be as follows: Figure 6 As shown, it includes parameter selection, vehicle modeling, vehicle model transfer function determination, calculation of the probability of various vehicle queues appearing in mixed traffic, and queue stability evaluation in mixed traffic.

[0241] Parameter selection refers to setting the information flow topology, maximum queue size, ICV penetration rate, and road scenario in mixed traffic. Vehicle modeling refers to modeling various vehicle queues (e.g., manually driven vehicles, ICVs, and mixed vehicle queues) to obtain motion models of various vehicle queues. Vehicle model transfer function determination refers to linearizing the motion models of various vehicle queues and generating transfer functions using transfer function theory. Mixed traffic queue stability assessment refers to evaluating mixed traffic queue stability using the mixed traffic queue stability discriminant, such as Equation (28).

[0242] Furthermore, parameters such as information flow topology and maximum queue size can be changed to analyze the queue stability of mixed traffic under different parameters.

[0243] In the simulation example, the experimental process for evaluating the queuing stability performance of mixed traffic can be as follows: Figure 7 As shown, it includes four parts: traffic scene setting, important parameter setting, vehicle selection, experimental setting and data acquisition.

[0244] Traffic scene setting includes a road scene selection module and a traffic rule design module, wherein the road scene selection module is used to select road scenes, and the traffic rule design module is used to set traffic rules.

[0245] Important parameter settings include the maximum queue size selection module and the information flow topology selection module. The maximum queue size selection module is used to select the maximum queue size, and the information flow topology selection module is used to select the information flow topology.

[0246] The vehicle selection process includes a manual vehicle selection module, an intelligent connected vehicle selection module, and a mixed vehicle platoon selection module. The manual vehicle selection module is used to model and select manually driven vehicles, the intelligent connected vehicle selection module is used to model and select intelligent connected vehicles, and the mixed vehicle platoon selection module is used to model and select mixed vehicle platoons. The detailed modeling process is described in step S204 and will not be detailed here.

[0247] The experimental setup consists of four parts: an intelligent connected vehicle penetration rate setting module, a location generation module, an event trigger selection module, and a simulation experiment module. The intelligent connected vehicle penetration rate setting module is used to set the penetration rate of intelligent connected vehicles. The location generation module is used to randomly generate the locations of intelligent connected vehicles according to the set penetration rate. The event trigger selection module is used to design the corresponding experimental trigger mechanism to give vehicles in mixed traffic a bounded disturbance (such as changing the speed of vehicles in mixed traffic) so that the vehicles respond in sequence. The simulation experiment module is used to conduct simulation experiments based on the set parameters and vehicle model under the set penetration rate, the generated locations of intelligent connected vehicles, and the given trigger mechanism. In the simulation scenario, to improve the accuracy and authenticity of the data, the location of the intelligent connected vehicle in mixed traffic can be changed multiple times, and simulation experiments can be conducted at different locations to obtain the results of multiple simulation experiments.

[0248] Data acquisition is used to obtain simulation experiment data, that is, the data generated by each simulation experiment, including vehicle data generated by each simulation experiment, such as speed, acceleration, position, etc., as well as the first evaluation result obtained based on the mixed traffic queue stability discriminant after each simulation.

[0249] In the simulation example, the mixed traffic queue stability performance evaluation device can be as follows Figure 8 As shown, the system includes a data receiving module, a data preprocessing module, a performance analysis module, and a result output module. The data receiving module is used to obtain vehicle data from the simulation experiment data. The data preprocessing module is used to preprocess the vehicle data, such as filtering and classification. The performance analysis module is used to perform performance analysis based on the preprocessed vehicle data. Specifically, it integrates the vehicle data using specific indicators to obtain a second evaluation result, such as the speed standard deviation and mean absolute deviation. The result output module is used to output the second evaluation result of the mixed traffic queue stability to the user end.

[0250] The data receiving module can obtain vehicle data generated by each simulation experiment. The performance analysis module calculates the second evaluation result of each simulation experiment based on the vehicle data generated by each simulation experiment, and averages the second evaluation results of each simulation experiment to obtain an average second evaluation result. The result output module can output the second evaluation result of each simulation experiment and the average second evaluation result to the user terminal.

[0251] The data receiving module is further configured to obtain a first evaluation result for each simulation experiment. The performance analysis module can calculate an average value of the first evaluation result for each simulation experiment to obtain an average first evaluation result. The result output module can output the first evaluation result and the average first evaluation result for each simulation experiment to a user terminal.

[0252] The performance analysis module can also weight the second evaluation results after indexing the vehicle data to obtain a performance analysis result, for example, by weighting the speed standard deviation and the speed mean absolute deviation to obtain a performance analysis result. The result output module is also used to output the performance analysis result.

[0253] based on Figures 5 to 8 The following is a description of a specific simulation scenario. The traffic scenario is set to a single-lane straight road with no special conditions such as uphill or downhill. The traffic rule is a speed limit of 120 km / h, the maximum queue size M is set to 6, and the information flow topology is the navigator cruise control mode. For vehicles, a configuration of 1 leading vehicle and 100 simulated vehicles (including intelligent connected vehicles and manually driven vehicles) is used. The leading vehicle is numbered 0, and the simulated vehicles are numbered 1, 2, ..., 100 from downstream to upstream. The vehicle length is 5m, and the maximum acceleration and deceleration of the vehicle is set to 2m / s. 2 For the penetration rate, the penetration rate of intelligent connected vehicles is set to P, then the number of intelligent connected vehicles is 100P, and the number of manually driven vehicles is 100(1-P). In this simulation experiment, P is set to 20%, and the positions of intelligent connected vehicles and manually driven vehicles are randomly generated based on the value of P.

[0254] Then, based on the set maximum queue size, information flow topology and penetration rate, the occurrence probability of different vehicle queues is determined, and a movement model of the vehicle queue is established.

[0255] After establishing the vehicle motion model, the leading vehicle is made to maintain a constant speed of 15 m / s. When all vehicles enter a stable following state, the timing starts at t = 0. Then, at t = 10s, the leading vehicle moves at a speed of -1 m / s. 2 The acceleration moves for 3s, and then moves at 1m / s 2The vehicle is accelerated for 3 seconds, and finally the guide vehicle continues to move at a constant speed of 15m / s until t=150s. During this process, the occurrence probability of different vehicle queues and the motion model determined above are simulated to obtain the first evaluation result of the queue stability. The simulation diagram is shown in the figure below. Figure 9 Then, the speed standard deviation and the speed mean absolute deviation are calculated according to the vehicle speed of each vehicle during the simulation process.

[0256] Randomly change the positions of the intelligent connected vehicles and repeat the simulation at different positions to obtain the first evaluation results, speed standard deviation, and speed mean absolute deviation for each position. Determine the average value of the first evaluation results, the average value of the speed standard deviation, and the average value of the speed mean absolute deviation for each position. Stability can then be evaluated based on the average value of the first evaluation results, the average value of the speed standard deviation, and the average value of the speed mean absolute deviation for each position. A larger average value of the speed standard deviation and the average value of the speed mean absolute deviation indicates poor mixed traffic queue stability. A smaller average value of the speed standard deviation and the average value of the speed mean absolute deviation indicates good mixed traffic queue stability. Accordingly, if the average value of the first evaluation results is greater than 1, the queue is not stable. If the average value of the first evaluation results is less than 1, the queue is stable.

[0257] Among them, the average value of the first evaluation result is used to reflect the theoretical queue stability, and the average value of the speed standard deviation and the average value of the speed mean absolute deviation are used to reflect the experimental queue stability, which can be used as a theoretical evaluation basis.

[0258] The method for evaluating the stability performance of mixed traffic queues proposed in the embodiments of the present application focuses on the information flow topology structure of the mixed vehicle queue and the maximum mixed vehicle queue size. This solves the problem that existing methods do not consider important parameters of mixed vehicle queues in mixed traffic, and provides an objective measure for evaluating the stability of mixed traffic queues.

[0259] The mixed traffic queue stability performance evaluation experimental process proposed in the embodiment of the present application can fill the gap in the mixed traffic performance evaluation experimental method under mixed vehicle queue conditions. The experimental process is clear and includes comprehensive key parameters of mixed vehicle queues.

[0260] The mixed traffic queue stability performance evaluation device proposed in the embodiment of the present application has strong applicability. By calculating indicators through a single experiment or calculating the average indicator through multiple experiments, it can truly and objectively reflect the mixed traffic queue stability quality and can be output as an evaluation basis.

[0261] On the basis of the above, the embodiment of the present application further provides an evaluation unit for the stability of a mixed traffic queue, which is applied to Figure 1 The electronic equipment shown. Figure 10 As shown, the evaluation unit 140 includes a parameter acquisition module 141 , a queue analysis module 142 , a probability calculation module 143 and a stability determination module 144 .

[0262] Among them, the parameter acquisition module 141 is used to obtain the penetration rate of intelligent connected vehicles in mixed traffic, the first maximum queue size, and the first information flow topology structure; the queue analysis module 142 is used to obtain the vehicle queues existing in the mixed traffic and the queue size of each of the vehicle queues based on the first maximum queue size and the first information flow topology structure; the probability calculation module 143 is used to obtain the probability of occurrence of each vehicle queue in mixed traffic based on the queue size and penetration rate of each vehicle queue; and the stability judgment module 144 is used to obtain a first evaluation result based on the motion model of each vehicle queue and the probability of occurrence of each vehicle queue. The first evaluation result is used to characterize the queue stability in mixed traffic.

[0263] For the convenience and brevity of description, the specific working process of the evaluation unit described above can refer to the corresponding process in the aforementioned method embodiment.

[0264] In addition, an embodiment of the present application further provides a vehicle, comprising the above-mentioned mixed traffic queue stability evaluation unit.

[0265] In addition, an embodiment of the present application further provides an electronic device, comprising: a memory for storing computer program instructions; and a processor for executing the computer program instructions to support the electronic device in implementing the functions or steps in the above-mentioned evaluation method.

[0266] In addition, an embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processing circuit, the functions or steps in the above-mentioned evaluation method are implemented.

[0267] In addition, an embodiment of the present application may also provide a computer program product containing instructions, which, when executed on a computer, enables the computer to execute the functions or steps in the above-mentioned evaluation method.

[0268] In addition, an embodiment of the present application can also provide a chip system, which includes a processing circuit and a storage medium, in which computer program instructions are stored; when the computer program instructions are executed by the processing circuit, the functions or steps in the above-mentioned evaluation method are implemented.

[0269] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the above-described electronic device, chip system, computer-readable storage medium, computer program product containing instructions, and specific working process of the vehicle can refer to the corresponding process in the aforementioned method embodiment.

[0270] The steps of the method or algorithm described in conjunction with the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in a random access memory, a flash memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk, a mobile hard disk, a read-only optical disc, or any other form of storage medium. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor.

[0271] In an optional manner, when software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is implemented in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disk (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)).

[0272] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for evaluating the stability of mixed traffic queues, characterized in that: The method comprises: Obtaining a penetration rate of intelligent connected vehicles in mixed traffic, a first maximum queue size, and a first information flow topology; Obtaining, according to the first maximum queue size and the first information flow topology, vehicle queues existing in the mixed traffic and a queue size of each of the vehicle queues; Obtaining, according to the queue size of each of the vehicle queues and the penetration rate, an appearance probability of each of the vehicle queues in the mixed traffic; A first evaluation result is obtained according to the motion model of each vehicle queue and the occurrence probability of each vehicle queue. The first evaluation result is used to characterize the queue stability of the mixed traffic.

2. The method according to claim 1, characterized in that When each of the vehicle queues includes the intelligent connected vehicle, the position of the intelligent connected vehicle in each of the vehicle queues is obtained based on the first information flow topology structure.

3. The method according to claim 1 or 2, characterized in that The vehicle queues in the mixed traffic include one or more of the following: a first vehicle queue whose queue size is the first maximum queue size, a second vehicle queue whose queue size is smaller than the first maximum queue size but greater than 1, a third vehicle queue whose queue size is 1 and whose vehicle type is intelligent connected vehicles, and a fourth vehicle queue whose queue size is 1 and whose vehicle type is non-intelligent connected vehicles.

4. The method according to any one of claims 1 to 3, characterized in that When each of the vehicle queues includes the intelligent connected vehicles and non-intelligent connected vehicles, the motion model of each of the vehicle queues is obtained based on a deviation between actual state information of each vehicle in the vehicle queue and expected state information, wherein the state information includes vehicle speed and a distance to a preceding vehicle.

5. The method according to any one of claims 1 to 4, characterized in that Obtaining a first evaluation result based on the motion model of each vehicle queue and the occurrence probability of each vehicle queue includes: Obtaining a transfer function of each of the vehicle trains according to a motion model of each of the vehicle trains; The first evaluation result is obtained based on the transfer function of each of the vehicle queues and the occurrence probability of each of the vehicle queues.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: obtaining a vehicle speed of each vehicle in the mixed traffic, and obtaining a second evaluation result based on the vehicle speed of each vehicle; The second evaluation result includes a speed standard deviation and a speed mean absolute deviation, and the second evaluation result is used to characterize the queue stability of the mixed traffic.

7. The method according to any one of claims 1 to 6, characterized in that The first evaluation result is an evaluation result for characterizing queue stability of the mixed traffic under a first combination, where the first combination is obtained based on the first maximum queue size and the first information flow topology structure; The method further comprises: Obtaining a first evaluation result for a second combination, where the second combination is obtained based on a second maximum queue size and a second information flow topology, where the second maximum queue size is different from the first maximum queue size and / or the second information flow topology is different from the first information flow topology; According to the first evaluation result under the first combination and the first evaluation result under the second combination, a target first evaluation result for characterizing the stability of the queue of the mixed traffic is obtained.

8. The method according to claim 7, characterized in that The second evaluation result is an evaluation result for characterizing the stability of the mixed traffic queue under the first combination. The method further includes: Obtaining a second evaluation result under the second combination; According to the second evaluation result under the first combination and the second evaluation result under the second combination, a target second evaluation result for characterizing the stability of the queue of the mixed traffic is obtained.

9. The method according to claim 7 or 8, characterized in that The first maximum queue size and the second maximum queue size are candidate maximum queue sizes in a preset maximum queue size set; And / or, the first information flow topology structure and the second information flow topology structure are candidate information flow topologies in a preset information flow topology structure set; The preset maximum queue size set includes a plurality of candidate maximum queue sizes, and the preset information flow topology structure set includes a plurality of candidate information flow topology structures.

10. A mixed traffic queue stability evaluation unit, characterized in that: The evaluation unit comprises: a parameter acquisition module, configured to acquire a penetration rate of intelligent connected vehicles in mixed traffic, a first maximum queue size, and a first information flow topology; a queue analysis module, configured to obtain vehicle queues existing in the mixed traffic and a queue size of each of the vehicle queues according to the first maximum queue size and the first information flow topology; a probability calculation module, configured to obtain an appearance probability of each of the vehicle queues in the mixed traffic according to the queue size of each of the vehicle queues and the penetration rate; The stability determination module is configured to obtain a first evaluation result based on the motion model of each vehicle queue and the occurrence probability of each vehicle queue, wherein the first evaluation result is used to characterize the stability of the mixed traffic queue.

11. An electronic device, characterized in that: The electronic device comprises: a memory for storing computer program instructions; A processor, configured to execute the computer program instructions to support the electronic device in implementing the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which implement the method according to any one of claims 1 to 9 when executed by a processing circuit.

13. A chip system, characterized in that: The chip system includes a processing circuit and a storage medium, wherein the storage medium stores computer program instructions; when the computer program instructions are executed by the processing circuit, the method according to any one of claims 1 to 9 is implemented.

14. A computer program product comprising instructions, characterized in that When the computer program product is run on a computer, the computer is caused to perform the method according to any one of claims 1 to 9.

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