A highway vehicle reduction coefficient dynamic calculation algorithm and system based on multi-source sensing data

Abstract

This application provides a dynamic calculation algorithm and system for highway vehicle conversion coefficients based on multi-source perception data. In this scheme, after acquiring target roadside perception data, a dynamic headway is determined for different vehicle types based on a preset vehicle control strategy and the target roadside perception data. Then, based on the dynamic headway for different vehicle types, the vehicle conversion coefficient for each vehicle type is determined. The vehicle types include at least autonomous trucks, and the vehicle control strategy is set based on autonomous trucks. Because this application can correspond to different vehicle control strategies and determine the dynamic headway for different vehicle types based on the determined vehicle control strategy, thereby determining the vehicle conversion coefficient, the method in this application is more targeted and can improve the accuracy of the vehicle conversion coefficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transportation, and particularly relates to a dynamic calculation algorithm and system for a highway vehicle conversion coefficient based on multi-source perception data. BACKGROUND

[0002] The vehicle conversion coefficient, also known as the passenger car unit (PCU), is used for conversion between mixed traffic flow and 100% standard vehicle flow, so as to make the mixed traffic volume under various road and traffic conditions comparable, and is an important part of the study of road traffic capacity evaluation. That is, it is a parameter reflecting the equivalent value of the influence of non-standard vehicles on traffic flow compared with standard vehicles. The concept of vehicle conversion coefficient was first proposed in the Traffic Capacity Manual of the United States in 1965, which also first proposed the concept of passenger car equivalent, defined as the number of trucks equivalent to passenger cars in the traffic flow under normal road conditions.

[0003] At present, the highway vehicle conversion coefficient generally adopts the provisions of Highway Engineering Technical Standards (JTG B01-2014), and different vehicle types use different values for equivalent conversion. The standard vehicle type is a passenger car (a passenger car with ≤19 seats and a truck with ≤2t load), and other vehicle types include a medium-sized vehicle (a passenger car with >19 seats and a truck with 2t < load ≤7t), a large-sized vehicle (a truck with 7t < load ≤20t), and a train of cars (a truck with >20t load).

[0004] However, the inventors have found that at least the following technical problems exist in the related art:

[0005] With the development of autonomous vehicles, especially the large-scale operation of L4 and above autonomous trucks on highways, the composition of vehicle types in the highway traffic flow has changed, and the vehicle type is one of the important factors affecting the size of the vehicle conversion coefficient. Therefore, the existing method for determining the vehicle conversion coefficient cannot adapt to and meet the needs of the large-scale operation of autonomous trucks on highways, resulting in low accuracy of the vehicle conversion coefficient determined by the existing method for determining the vehicle conversion coefficient. SUMMARY

[0006] An object of the present application is to provide a dynamic calculation algorithm and system for a highway vehicle conversion coefficient based on multi-source perception data, at least to solve the problem of low accuracy of the vehicle conversion coefficient determined by the existing method for determining the vehicle conversion coefficient.

[0007] To achieve the above object, some embodiments of the present application provide a method for determining a vehicle conversion coefficient, comprising: obtaining target road side perception data; determining a dynamic headway for different vehicle types according to a preset vehicle management strategy in combination with the target road side perception data, wherein the vehicle types at least include an autonomous driving truck, and the vehicle management strategy is set based on the autonomous driving truck; and determining a vehicle conversion coefficient of the different vehicle types according to the dynamic headway for the different vehicle types.

[0008] Some embodiments of the present application also provide a method for evaluating traffic capacity, comprising: determining a vehicle conversion coefficient of the different vehicle types by the method for determining a vehicle conversion coefficient as described above; and determining a vehicle conversion coefficient for representing a dynamic traffic capacity of an expressway according to the vehicle conversion coefficient of the different vehicle types, to evaluate the traffic capacity of the expressway.

[0009] Some embodiments of the present application also provide a device for determining a vehicle conversion coefficient, comprising: one or more processors; and a memory storing computer program instructions which, when executed, cause the processor to execute the method for determining a vehicle conversion coefficient as described above.

[0010] Some embodiments of the present application also provide a device for evaluating traffic capacity, comprising: one or more processors; and a memory storing computer program instructions which, when executed, cause the processor to execute the method for evaluating traffic capacity as described above.

[0011] Some embodiments of the present application also provide a computer readable medium having stored thereon computer program instructions executable by a processor to implement the method as described above.

[0012] Compared with the prior art, in the dynamic calculation scheme of an expressway vehicle conversion coefficient based on multi-source perception data provided by the embodiments of the present application, after obtaining target road side perception data, a dynamic headway for different vehicle types is determined according to a preset vehicle management strategy in combination with the target road side perception data, and then a vehicle conversion coefficient of the different vehicle types is determined according to the dynamic headway for the different vehicle types. The vehicle types at least include an autonomous driving truck, and the vehicle management strategy is set based on the autonomous driving truck. In the present application, different vehicle management strategies can be corresponded to different application scenarios, such as different vehicle driving characteristics, and the dynamic headway of different vehicle types is determined based on the determined vehicle management strategy, and then the vehicle conversion coefficient is determined. Thus, the method for determining a vehicle conversion coefficient in the present application is more targeted, and the accuracy of the vehicle conversion coefficient can be improved. Attached Figure Description

[0013] Figure 1 A flowchart illustrating a method for determining a vehicle discount factor provided in this application embodiment;

[0014] Figure 2 The flowchart of step S102 in the method for determining the vehicle discount factor provided in this application embodiment;

[0015] Figure 3 Another processing flowchart of step S102 in a method for determining a vehicle discount factor provided in an embodiment of this application;

[0016] Figure 4 A flowchart illustrating a traffic capacity assessment method provided in this application embodiment;

[0017] Figure 5 A schematic diagram of a device for determining vehicle conversion factors provided in an embodiment of this application;

[0018] Figure 6 This is a schematic diagram of the structure of a traffic capacity assessment device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The following terms are used in this document.

[0021] The vehicle conversion factor, also known as the Passenger Car Unit (PCU), is used to convert mixed traffic flow to 100% standard traffic flow. This is to make the mixed traffic volume comparable under different road and traffic conditions and is an important component of road capacity assessment research.

[0022] Time Headway, or TH for short, refers to the time interval between the front ends of two consecutive vehicles passing a certain cross section in a convoy traveling in the same lane. Time Headway represents the time difference between the front ends of two vehicles passing the same point.

[0023] Onboard vehicle ID: the identity of the onboard vehicle, the full name in English is Identity document.

[0024] Vehicle VIN code: Vehicle Identification Number, the full name in English is Vehicle Identification Number.

[0025] Platoon driving: also known as platoon driving, refers to three or more vehicles driving in the same section. The following vehicles can automatically drive without the need for drivers to control the vehicle, and the vehicles can transmit road status, vehicle information, and driving instructions through sensors, V2X (vehicle wireless communication technology, the full name in English is "vehicle to X") and other technologies. Vehicles that join the platoon can be passenger cars, commercial vehicles, or a mix of various models. According to the current relevant policies of the ministries of the state on the permission of automatic driving vehicles on the road, in this paper, platoon driving refers to automatic driving trucks that join the platoon on the highway.

[0026] In addition, the vehicle type in this paper refers to manually driven vehicles and automatic driving trucks, and manually driven vehicles are divided into small, medium and large vehicles.

[0027] The embodiment of the present application provides a kind of determination method of vehicle conversion coefficient, in this method, after obtaining target road side perception data, according to the preset vehicle control strategy, the dynamic headway for different vehicle types is determined in combination with the target road side perception data, then according to the dynamic headway for different vehicle types, the vehicle conversion coefficient of the different vehicle types is determined.Therein, the vehicle type at least includes automatic driving truck, and the vehicle control strategy is set based on the automatic driving truck.Due to the application, different vehicle control strategies can be corresponded to different application scenarios, such as the difference of vehicle driving characteristics, and the dynamic headway of different vehicle types is determined based on the determined vehicle control strategy, and then the vehicle conversion coefficient is determined.Therefore, the determination method of vehicle conversion coefficient in the application is more targeted, and the accuracy of vehicle conversion coefficient can be improved.

[0028] Figure 1 The processing flow of a kind of determination method of vehicle conversion coefficient provided by the embodiment of the present application is shown, which can be applied to the cloud control basic platform of vehicle-road cooperation system, and the processing flow of the method can at least include the following steps:

[0029] Step S101, obtaining target road side perception data.

[0030] In step S102, according to a preset vehicle management strategy, dynamic headway time intervals for different vehicle types are determined in combination with the target road-side perception data; wherein the vehicle types at least include an automatic driving truck, and the preset vehicle management strategy is set based on the automatic driving truck.

[0031] In step S103, vehicle conversion coefficients of the different vehicle types are determined according to the dynamic headway time intervals for the different vehicle types.

[0032] For step S101, specifically, a cloud control basic platform of a vehicle-road cooperation system can obtain road-side perception data of a target traffic flow in real time through a road-side perception system. Here, the road-side perception system can be a sensor such as a camera, a millimeter wave radar, a laser radar, a radar-vision integrated machine, etc. that uploads perception data, and the present embodiment does not make a specific limitation thereon. For example, an over-the-horizon millimeter wave radar can be installed on a roadside of an expressway, and the over-the-horizon millimeter wave radar uploads road-side perception data to the cloud control basic platform in real time.

[0033] In the application scenario of the present embodiment, the target road-side perception data can be road-side perception data for a target expressway. In some examples, the target road-side perception data can include basic data and real-time traffic flow data of the target expressway.

[0034] The basic data can be basic data of vehicles registered in the cloud control basic platform, for example. The basic data can include vehicle ID, vehicle VIN code, vehicle type, etc. In addition, the vehicle type can include a manually driven vehicle and an automatic driving truck, and the manually driven vehicle is divided into a small car, a medium car and a large car. The small car refers to a passenger car with ≤19 seats and a truck with a load of ≤2t, the medium car refers to a passenger car with >19 seats and a truck with a load of 2t< load ≤7t, and the large car refers to a truck with a load of 7t< load ≤20t, an automobile train with a load of >20t, and the automatic driving truck refers to a truck whose platoon driving state can be remotely controlled by the cloud control basic platform.

[0035] The real-time traffic flow data of the expressway can be traffic flow data at a lane level of the expressway. For example, vehicle flow data of all lanes of a certain expressway section from time 1 to time t can be represented by Q, that is, Q=[q1,q2,…,qT]. If the number of all lanes of the expressway section is r, and the vehicle flow data monitored by all lanes of the expressway section at time t can be represented by qt, that is, qt=[q1,q2,…,qr], t∈[1,T], and qn represents the vehicle flow data of the nth lane at time t, then qn=[q1n,q2n,…,qrn], n∈[1,r], t∈[1,T]. T t t 1,t 2,t r,t i,t ​​​​​​a monitored traffic flow of the lane i of the expressway section at the time t, q i,t,1 a monitored small vehicle traffic flow of the lane i of the expressway section at the time t, q i,t,2 a monitored medium vehicle traffic flow of the lane i of the expressway section at the time t, q i,t,3 a monitored large vehicle traffic flow of the lane i of the expressway section at the time t, q i,t,4 a monitored autonomous driving truck traffic flow of the lane i of the expressway section at the time t, q i,t = q i,t,1 + q i,t,2 + q i,t,3 + q i,t,4 .

[0036] For step S102, specifically, after obtaining the roadside perception data for the target traffic flow, the dynamic headway for different vehicle types can be determined according to the preset vehicle management strategy based on the autonomous driving truck.

[0037] In this embodiment, the headway determined in this embodiment can be the headway in a certain period of time, and can be used to represent the change of the headway. In this embodiment, the average headway is used to represent the dynamic headway.

[0038] For step S103, specifically, the vehicle conversion coefficient is determined according to the dynamic headway for different vehicle types. Therefore, in this embodiment, the dynamic headway for different vehicle types can be determined by different calculation methods to obtain the vehicle conversion coefficient. For example, the dynamic headway of the manually driven vehicle can be determined by a first method to obtain the conversion coefficient of the manually driven vehicle, and the dynamic headway of the autonomous driving truck can be determined by a second method to obtain the conversion coefficient of the autonomous driving truck.

[0039] In summary, in this application, different vehicle management strategies can be corresponded to different application scenarios, such as different vehicle driving characteristics, and the dynamic headway for different vehicle types is determined based on the determined vehicle management strategy, and then the vehicle conversion coefficient is determined. Therefore, the determination method of the vehicle conversion coefficient in this application is more targeted, and the accuracy of the vehicle conversion coefficient can be improved.

[0040] In some embodiments of the present application, the preset vehicle management strategy can include a first management strategy for mixed driving of the autonomous driving truck and the manually driven vehicle, and / or a second management strategy for platoon driving of the autonomous driving truck and following driving of the manually driven vehicle.

[0041] Specifically, in actual application, relevant personnel can determine which vehicle management strategy to be adopted through the cloud control basic platform. That is, if the cloud control basic platform starts the first management strategy of mixed driving of automatic driving trucks and manually driving vehicles, corresponding operations can be performed according to the first management strategy; if the cloud control basic platform starts the second management strategy of automatic driving truck platoon driving and manually driving vehicle following driving, corresponding operations can be performed according to the second management strategy.

[0042] In some examples, only one of the above vehicle management strategies can be set as a main strategy, for example, the second management strategy of automatic driving truck platoon driving and manually driving vehicle following driving is set as the main strategy. In this way, if the second management strategy is in an off (not on) state, a default strategy is executed; otherwise, if the second management strategy is in an on state, corresponding operations are performed according to the second management strategy. Of course, in some examples, the default strategy can also be the first management strategy of mixed driving of automatic driving trucks and manually driving vehicles.

[0043] In some embodiments of the present application, the determining of the dynamic headway time for different vehicle types according to the preset vehicle management strategy and in combination with the roadside perception data can include: if the vehicle management strategy is the first management strategy of mixed driving of automatic driving trucks and manually driving vehicles, vehicle flow data is obtained according to the roadside perception data; then, the average headway time of manually driving vehicles is determined according to the vehicle flow data, to obtain the dynamic headway time of the manually driving vehicles; and the average headway time of the automatic driving trucks is determined according to the average headway time of the manually driving vehicles, to obtain the dynamic headway time of the automatic driving trucks.

[0044] As shown in FIG. 10, step S102 can further include the following steps: Figure 2

[0045] Step S201, if the vehicle management strategy is the first management strategy of mixed driving of automatic driving trucks and manually driving vehicles, vehicle flow data is obtained according to the roadside perception data.

[0046] Step S202, the average headway time of manually driving vehicles is determined according to the vehicle flow data, to obtain the dynamic headway time of the manually driving vehicles.

[0047] Step S203, the average headway time of the automatic driving trucks is determined according to the average headway time of the manually driving vehicles, to obtain the dynamic headway time of the automatic driving trucks.

[0048] ​Specifically, the determination manner of the dynamic headway of the small, medium and large vehicles in the manually driven vehicle can respectively include: obtaining target roadside perception data through a roadside perception system, the target roadside perception data can include real-time traffic data of the small, medium and large vehicles, and then determining the average headway of the manually driven vehicle according to the real-time traffic data of the vehicle to obtain the dynamic headway of the manually driven vehicle. Wherein, the average headway of the manually driven vehicle may be equal to the inverse of the real-time traffic data corresponding to the vehicle. That is, the average headway of the manually driven vehicle

[0049]

[0050] Wherein, q i,t,j represents the average headway of the i-lane, t-time, j-vehicle type. In the embodiment, j = 1, 2, 3, respectively representing the small, medium and large vehicles in the manually driven vehicle. That is, the average headway of the small vehicle is equal to the inverse of the vehicle traffic data of the small vehicle, the average headway of the medium vehicle is equal to the inverse of the vehicle traffic data of the medium vehicle, and the average headway of the large vehicle is equal to the inverse of the vehicle traffic data of the large vehicle. It should be noted that since the unit of the average headway is generally expressed in seconds, and the unit of the vehicle traffic is generally vehicle / hour, therefore in the formula in the embodiment, the numerator is multiplied by 3600 to convert the unit into seconds.

[0051] Further, the average headway of the automatic driving truck can be determined according to the average headway of the manually driven vehicle to obtain the dynamic headway of the automatic driving truck. In the embodiment, since the vehicle control strategy is the first control strategy of the mixed driving of the automatic driving truck and the manually driven vehicle, it is self-evident that the automatic driving truck and the manually driven vehicle are mixed driving, which can be understood as the automatic driving truck also belongs to the large vehicle, therefore, the average headway of the automatic driving truck can be equal to the average headway of the large vehicle in the manually driven vehicle, that is, the average headway of the automatic driving truck

[0052]

[0053] Wherein, is the average headway of the large vehicle of the i-lane of the expressway at t-time, is the average headway of the automatic driving truck of the i-lane of the expressway at t-time.

[0054] In some embodiments of the present application, the determining, according to the preset vehicle management strategy and in combination with the roadside perception data, of the dynamic headway for different vehicle types can further include: if the vehicle management strategy is a second management strategy in which the automatic driving truck travels in a platoon and the manually driven vehicle travels following, obtaining vehicle flow data according to the roadside perception data; determining an average headway of the manually driven vehicle according to the vehicle flow data to obtain a dynamic headway of the manually driven vehicle, and then determining the dynamic headway of the automatic driving truck according to an average headway issued by a cloud control platform, wherein the average headway issued by the cloud control platform is less than the dynamic headway of the manually driven vehicle.

[0055] As shown in FIG. 10, step S102 can further include the following steps: Figure 3

[0056] Step S301, if the vehicle management strategy is a second management strategy in which the automatic driving truck travels in a platoon and the manually driven vehicle travels following, obtaining vehicle flow data according to the roadside perception data.

[0057] Step S302, determining an average headway of the manually driven vehicle according to the vehicle flow data to obtain a dynamic headway of the manually driven vehicle.

[0058] Step S303, determining the dynamic headway of the automatic driving truck according to an average headway issued by a cloud control platform, wherein the average headway issued by the cloud control platform is less than the dynamic headway of the manually driven vehicle.

[0059] Specifically, in this embodiment, the determining manner of the dynamic headway of the small, medium and large vehicles in the manually driven vehicle is the same as that in the previous embodiment, i.e., can include: obtaining roadside perception data by a roadside perception system, the roadside perception data can include real-time flow data of the small, medium and large vehicles, and then determining an average headway of the manually driven vehicle according to the real-time flow data of the vehicle to obtain a dynamic headway of the manually driven vehicle. Wherein the average headway of the manually driven vehicle is equal to the inverse of the real-time flow data corresponding to the vehicle, to avoid repetition, this will not be described again.

[0060] ​Different from the previous embodiment, in the present embodiment, since the vehicle management and control strategy is the second management and control strategy for the automatic driving truck platooning driving and the manually driving vehicle following driving, it means that the intelligent network connection vehicle-road cooperation product such as V2X can be started in the actual application to execute the second management and control strategy. In the present embodiment, since the automatic driving truck platooning driving, the average headway of the automatic driving truck is no longer equal to the average headway of the large vehicle in the manually driving vehicle as in the previous embodiment, but the average headway of the automatic driving truck is equal to the average headway issued by the cloud control basic platform, that is, the average headway of the automatic driving truck:

[0061]

[0062] wherein, is the average headway of the automatic driving truck in the lane i of the highway section at time t, is the average headway issued by the cloud control basic platform.

[0063] Wherein, the cloud control basic platform can require the automatic driving truck to drive at the speed and acceleration allowed for its platooning driving by issuing instructions to the automatic driving truck, so as to maintain platooning driving with the front vehicle, that is, to maintain driving according to the average headway issued by the cloud control basic platform.

[0064] Further, the average headway issued by the cloud control basic platform can be calculated according to at least the following two factors:

[0065] Factor 1: The total flow of each vehicle type in the highway, including the vehicle flow of large vehicles, medium vehicles, small vehicles, and automatic driving trucks,

[0066] Factor 2: The performance of the automatic driving truck registered in the cloud control basic platform, such as the maximum acceleration value, the maximum deceleration value, the minimum acceleration value, and the minimum deceleration value.

[0067] As understood by those skilled in the art, since the performance of the automatic driving truck in the platooning driving process is better than that of the large vehicle in the manually driving vehicle, such as the reaction time required by the automatic driving truck in the case of emergency braking is less than that of the large vehicle in the manually driving vehicle, therefore, the average headway of the automatic driving truck issued by the cloud control basic platform is less than the average headway of the large vehicle in the manually driving vehicle, thereby helping to improve the vehicle flow of the highway and improve the traffic capacity of the highway.

[0068] In some embodiments of the present application, the determining the vehicle conversion coefficient of the different vehicle types according to the dynamic headway time of the different vehicle types can comprise: for the manually driven vehicles, comparing each vehicle type with a standard vehicle type to obtain a comparison result; and determining the vehicle conversion coefficient of the manually driven vehicles according to the comparison result and in combination with the dynamic headway time of the different vehicle types.

[0069] In actual application, the vehicle conversion coefficient of the manually driven vehicles can be determined in the following two cases. First, a small vehicle can be determined as a standard vehicle type.

[0070] 1) For a vehicle of the small vehicle type, since the vehicle type is the standard vehicle type, the vehicle conversion coefficient of the small vehicle in this case is 1.0, i.e., PCU1 = 1.0.

[0071] 2) For a vehicle of the medium vehicle type and a vehicle of the large vehicle type, since the two vehicle types are larger than the standard vehicle type, the vehicle conversion coefficient of the medium vehicle and the large vehicle can be determined by using the headway time estimation method, specifically as follows:

[0072]

[0073]

[0074] wherein PCU2 is the vehicle conversion coefficient of the medium vehicle, is the average headway time of the medium vehicle in the lane i of the expressway at the time t, is the average headway time of the small vehicle in the lane i of the expressway at the time t; PCU3 is the vehicle conversion coefficient of the large vehicle, is the average headway time of the large vehicle in the lane i of the expressway at the time t.

[0075] In some embodiments of the present application, if the vehicle control strategy is a first control strategy in which the automatic driving truck and the manually driven vehicle are mixed to travel, the vehicle conversion coefficient of the automatic driving truck is determined according to the vehicle conversion coefficient of the manually driven vehicle.

[0076] In some embodiments of the present application, if the preset vehicle control strategy is a second control strategy in which the automatic driving truck is in a platoon and the manually driven vehicle follows to travel, the vehicle conversion coefficient of the automatic driving truck is determined according to the dynamic headway time of the automatic driving truck.

[0077] Specifically, in some examples, there can be two vehicle control strategies corresponding to the cloud control basic platform, and the determination manner of the vehicle conversion coefficient of the automatic driving truck is different under different vehicle control strategies.

[0078] 1) the vehicle management strategy is a first management strategy in which the automatic driving truck and the manually driving vehicle are mixed driving, the vehicle conversion coefficient of the automatic driving truck can be determined according to the vehicle conversion coefficient of the manually driving vehicle. Because the automatic driving truck can belong to a large vehicle in terms of vehicle type, the average headway of the automatic driving truck can be equal to the average headway of the large vehicle, and the vehicle conversion coefficient of the automatic driving truck can be equal to the vehicle conversion coefficient of the large vehicle.

[0079]

[0080] PCU4 is the vehicle conversion coefficient of the automatic driving truck, is the average headway of the automatic driving truck on the highway section i lane at t time, is the average headway of the small vehicle on the highway section i lane at t time. is the average headway of the large vehicle on the highway section i lane at t time, and PCU3 is the vehicle conversion coefficient of the large vehicle.

[0081] 2) the vehicle management strategy is a second management strategy in which the automatic driving truck is platoon driving and the manually driving vehicle is following driving, in some examples, the highway intelligent network connection vehicle-road cooperation product such as V2X can be started in actual application to execute the second management strategy. The vehicle conversion coefficient of the automatic driving truck can be determined according to the dynamic headway of the automatic driving truck.

[0082]

[0083] PCU4 is the vehicle conversion coefficient of the automatic driving truck, is the dynamic headway of the automatic driving truck, that is, the average headway issued by the cloud control basic platform, is the average headway of the small vehicle on the highway section i lane at t time.

[0084] The embodiment of the application further provides a traffic capacity evaluation method, which determines the vehicle conversion coefficients of different vehicle types by the above-mentioned vehicle conversion coefficient determination method, and then determines the vehicle conversion coefficient for representing the dynamic traffic capacity of the highway according to the vehicle conversion coefficients of different vehicle types, and further can evaluate the traffic capacity of the highway based on the vehicle conversion coefficient for representing the dynamic traffic capacity of the highway.

[0085] As shown in Figure 4 , a traffic capacity evaluation method can include the following steps:

[0086] Step S401: Determine the vehicle conversion factor for the different vehicle types using the vehicle conversion factor determination method.

[0087] Step S402: Based on the vehicle conversion factors for the different vehicle types, determine the vehicle conversion factors used to characterize the dynamic traffic capacity of the expressway, so as to assess the traffic capacity of the expressway.

[0088] In some cases, the vehicle conversion factor used to characterize the dynamic capacity of a highway can be calculated using the following formula:

[0089]

[0090] The following is an explanation of the formula:

[0091] First, lane-level vehicle traffic flow data can be obtained from the cloud control platform. For example, the vehicle flow data of all lanes on a certain highway section from time 1 to time t can be represented by Q, then Q = [q1, q2, ..., q T The total number of lanes on the highway section is r; the vehicle flow data monitored for all lanes on the highway section at time t can be represented by q. t If q represents... t =[q 1，t q 2，t , ..., q r，t ], t∈[1, T]; if q i，t Let q be the traffic flow monitored in lane i of the highway segment at time t. i，t，1 The small vehicle flow rate q is the rate monitored in lane i of the highway section at time t. i，t，2 For time t, the medium-sized vehicle flow rate and q are monitored in lane i of the highway section. i，t，3 For time t, the large traffic flow q is monitored in lane i of the highway section. i，t，4 Let be the traffic flow of autonomous trucks monitored in lane i of the highway segment at time t, then the traffic flow monitored in lane i of the highway segment at time t is q. i，t =q i，t，1 +q i，t，2 +q i，t，3 +q i，t，4 PCU1, PCU2, PCU3, and PCU4 are the vehicle conversion factors for small cars, medium-sized cars, large cars, and autonomous trucks, respectively.

[0092] In some embodiments of the present application, the vehicle conversion factor for representing the dynamic traffic capacity of the expressway can be determined according to the vehicle conversion factors of different vehicle types, which can include: if the preset vehicle management strategy is the first management strategy in which the automatic driving truck and the manually driving vehicle are mixed to drive, a first formula is used to evaluate the traffic capacity of the expressway; if the preset vehicle management strategy is the second management strategy in which the automatic driving truck is in a platoon and the manually driving vehicle follows to drive, a second formula is used to evaluate the traffic capacity of the expressway.

[0093] Specifically, in some examples, there can be two vehicle management strategies on the cloud control basic platform, and under different vehicle management strategies, different determination methods of vehicle conversion factors are correspondingly used, and then different methods are used to evaluate the traffic capacity of the expressway according to the different vehicle conversion factors.

[0094] 1) If the preset vehicle management strategy is the first management strategy in which the automatic driving truck and the manually driving vehicle are mixed to drive, a first formula is used to determine the vehicle conversion factor for representing the dynamic traffic capacity of the expressway according to the vehicle conversion factors of different vehicle types, so as to evaluate the traffic capacity of the expressway. The first formula is as follows:

[0095] PCU i，t = q i，t，1 × PCU1 + q i，t，2 × PCU2 + q i，t，3 × PCU3 + q i，t，4 × PCU3

[0096] = q i，t，1 × PCU1 + q i，t，2 × PCU2 + (q i，t，3 + q i，t，4 ) × PCU3

[0097] Wherein, PCU i，t is the vehicle conversion factor of the lane i of the expressway at time t, q i，t，1 is the small vehicle flow monitored by the lane i of the expressway at time t, q i，t，2 is the medium vehicle flow monitored by the lane i of the expressway at time t, q i，t，3 is the large vehicle flow monitored by the lane i of the expressway at time t, q i，t，4 is the automatic driving truck flow monitored by the lane i of the expressway at time t, PCU1, PCU2, PCU3 and PCU4 are respectively the vehicle conversion factors of the small vehicle, the medium vehicle, the large vehicle and the automatic driving truck.

[0098] 2) If the preset vehicle management strategy is the second management strategy for the automatic driving truck platooning driving and the manually driving vehicle following driving, a second formula is adopted to determine the vehicle conversion coefficient for representing the dynamic traffic capacity of the expressway according to the vehicle conversion coefficient of the vehicle of the different vehicle type, and the traffic capacity of the expressway is evaluated. The second formula is as follows:

[0099] PCU i，t = q i，t，1 x PCU1 + q i，t，2 x PCU2 + q i，t，3 x PCU3 + q i，t，4 x PCU4

[0100] wherein PCU i，t is the vehicle conversion coefficient of the lane i of the expressway section at time t, q i，t，1 is the small vehicle flow monitored by the lane i of the expressway section at time t, q i，t2 is the medium vehicle flow monitored by the lane i of the expressway section at time t, q i，t，3 is the large vehicle flow monitored by the lane i of the expressway section at time t, q i，t，4 is the automatic driving truck flow monitored by the lane i of the expressway section at time t, and PCU1, PCU2, PCU3 and PCU4 are respectively the vehicle conversion coefficients of the small vehicle, the medium vehicle, the large vehicle and the automatic driving truck.

[0101] The embodiment of the present application further provides a vehicle conversion coefficient determination device, the structure of which is shown in Figure 5 Fig. 2, which comprises a memory 11 for storing computer readable instructions and a processor 12 for executing the computer readable instructions, wherein when the computer readable instructions are executed by the processor, the processor is triggered to execute the vehicle conversion coefficient determination method.

[0102] In addition, the embodiment of the present application further provides a traffic capacity evaluation device, the structure of which is shown in Figure 6 Fig. 3, which comprises a memory 21 for storing computer readable instructions and a processor 22 for executing the computer readable instructions, wherein when the computer readable instructions are executed by the processor, the processor is triggered to execute the vehicle conversion coefficient determination method.

[0103] The method and / or embodiment in the embodiment of the present application can be implemented as a computer software program. For example, the embodiment of the present disclosure comprises a computer program product comprising a computer program loaded on a computer readable medium, and the computer program comprises program codes for executing the method shown in the flow chart. When the computer program is executed by a processing unit, the above-mentioned functions defined in the method of the present application are executed.

[0104] Note that the computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable medium can be, for example but not limited to, a computer-readable storage medium such as a floppy disk, a flexible disk, a hard disk, a magnetic tape, a magnetic disk, an optical fiber, a compact disk (CD), a digital versatile disk (DVD), a Blu-ray disk, a memory stick, a floppy disk, a random access memory (RAM), a read-only memory (ROM), a programmable ROM (EPROM), an erasable and programmable ROM (EPROM), an electrically erasable and programmable ROM (EEPROM), a flash ROM, a flash memory, a solid state disk, a hard disk, a computer-readable storage medium, or any appropriate combination thereof. In the present application, the computer-readable medium can be any tangible medium that contains or stores a program that can be used by an instruction execution system, apparatus, or device to execute the program.

[0105] In the present application, the computer-readable signal medium can include a data signal that propagates in a baseband or as part of a carrier wave by any medium, including but not limited to wire, wireline, optical fiber, cable, RF, etc., or any appropriate combination thereof. The computer-readable signal medium can also be any computer-readable medium that can send, propagate, or transport programming code to be used by an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wireline, optical fiber, cable, RF, etc., or any appropriate combination thereof.

[0106] The computer program code for carrying out operations of the present application can be written in one or more programming languages or combinations of languages including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages such as C or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0107] The computer readable medium can include a non-transitory computer readable medium (e.g., based on nature of the medium) such as a tangible arrangement (e.g., tangible devices) of one or more computer readable instructions such as instructions 140 executable by a processor 120 (e.g., a hardware processor such as a central processing unit (CPU), a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc.). The computer readable medium can include a computer readable storage medium (e.g., storage device) and / or a computer readable signal medium (e.g., a carrier wave). The computer readable storage medium can include one or more of a tangible arrangement (e.g., tangible devices) of RAM, ROM, EEPROM, solid state drives (SSDs) that are based on RAM, SSDs that are based on flash memory, phase-change memory (PRAM), other types of RAM, other types of memory, other types of storage mediums, or any combination thereof. The computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. The computer readable program code can be transmitted on a single carrier wave, or multiple different waves. The computer readable program code can be transmitted in both directions of a communication link (e.g., from a base station to a mobile device or from a mobile device to a base station) or in only one direction (e.g., from base station to mobile device). The propagation medium carrying the computer readable program code can be any tangible or intangible medium that software can be transmitted over including any combination of wired or wireless data signals.

[0108] As another aspect, the embodiments of the present application also provide a computer readable medium, which can be included in the devices described in the above embodiments, or can exist separately without being assembled into the devices. The above computer readable medium carries one or more computer readable instructions, which can be executed by a processor to implement the steps of the methods and / or technical solutions of the above embodiments of the present application.

[0109] In a typical configuration of the present application, the devices of the terminal and the service network each include one or more processors (CPU), input / output interfaces, network interfaces, and memories.

[0110] The memory can include non-persistent memory, random access memory (RAM), and / or non-volatile memory such as read only memory (ROM) or flash memory, etc. in a computer readable medium. The memory is an example of a computer readable medium.

[0111] The computer readable medium includes a permanent and non-permanent, removable and non-removable medium, which can be implemented by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage device, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0112] In addition, the embodiment of the present application further provides a computer program, which is stored in a computer device, so that the computer device executes the method performed by the control code.

[0113] It should be noted that the present application can be implemented in software and / or a combination of software and hardware, for example, can be implemented by using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In some embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) can be stored in a computer readable recording medium, for example, a RAM memory, a magnetic or optical drive or a soft disk and similar devices. In addition, some steps or functions of the present application can be implemented by using hardware, for example, as a circuit cooperating with the processor to execute the respective steps or functions.

[0114] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all aspects as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and range of the equivalent elements of the claims are intended to be embraced in the present application. Any reference signs in the claims should not be considered as limiting the involved claims. In addition, it is obvious that the word "comprise" does not exclude other units or steps, and the singular does not exclude the plural. The plurality of units or devices stated in the device claims can also be implemented by one unit or device by software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method of determining a vehicle reduction factor, the method comprising: determining a vehicle reduction factor based on a vehicle type and a vehicle weight. The method comprises: acquiring target road side perception data; the target road side perception data comprises basic data and traffic flow real-time data of the expressway, the basic data comprises a network vehicle ID, a vehicle VIN code, and a vehicle type, the vehicle type comprises a manually driven vehicle and an automatic driving truck; the automatic driving truck refers to a truck whose platoon driving state can be remotely controlled by a cloud control basic platform; the traffic flow real-time data is lane-level traffic flow data in the expressway; determining a dynamic headway for different vehicle types according to a preset vehicle control strategy and in combination with the target road side perception data; wherein the vehicle control strategy is set based on the automatic driving truck; the preset vehicle control strategy comprises a first control strategy for mixed driving of the automatic driving truck and the manually driven vehicle, and / or a second control strategy for platoon driving of the automatic driving truck and following driving of the manually driven vehicle; determining a vehicle conversion coefficient of the different vehicle types according to the dynamic headway for the different vehicle types.

2. The method of claim 1, wherein, The determining of the dynamic headway for the different vehicle types according to the preset vehicle control strategy and in combination with the road side perception data comprises: if the vehicle control strategy is the first control strategy for mixed driving of the automatic driving truck and the manually driven vehicle, acquiring vehicle flow data according to the road side perception data; determining an average headway of the manually driven vehicle according to the vehicle flow data to obtain a dynamic headway of the manually driven vehicle; determining an average headway of the automatic driving truck according to the average headway of the manually driven vehicle to obtain a dynamic headway of the automatic driving truck.

3. The method of claim 1, wherein, The determining of the dynamic headway for the different vehicle types according to the preset vehicle control strategy and in combination with the road side perception data comprises: if the vehicle control strategy is the second control strategy for platoon driving of the automatic driving truck and following driving of the manually driven vehicle, acquiring vehicle flow data according to the road side perception data; determining an average headway of the manually driven vehicle according to the vehicle flow data to obtain a dynamic headway of the manually driven vehicle; determining a dynamic headway of the automatic driving truck according to an average headway issued by a cloud control platform; wherein the average headway issued by the cloud control platform is less than the dynamic headway of the manually driven vehicle.

4. The method of claim 1, wherein, The determining of the vehicle conversion coefficient of the different vehicle types according to the dynamic headway for the different vehicle types comprises: for the manually driven vehicle, comparing each vehicle type with a standard vehicle type respectively to obtain a comparison result; determining a vehicle conversion coefficient of the manually driven vehicle according to the comparison result and in combination with the dynamic headway for the different vehicle types.

5. The method according to claim 4, wherein, if the vehicle control strategy is the first control strategy for mixed driving of the automatic driving truck and the manually driven vehicle, determining a vehicle conversion coefficient of the automatic driving truck according to the vehicle conversion coefficient of the manually driven vehicle. ​ 6. The method of claim 3, wherein if the preset vehicle management strategy is a second management strategy for the automatic driving truck platoon to travel and the manually driving vehicle to follow, a vehicle conversion coefficient of the automatic driving truck is determined according to a dynamic headway of the automatic driving truck. The method comprises:

7. A method of capacity assessment, characterized by determining the vehicle conversion coefficient of the different vehicle types by the method for determining the vehicle conversion coefficient according to any one of claims 1 to 6; determining the vehicle conversion coefficient for representing the dynamic traffic capacity of the expressway according to the vehicle conversion coefficient of the different vehicle types, so as to evaluate the traffic capacity of the expressway. comprise:

8. A vehicle reduction factor determination device characterized by comprising: characterized in that the device comprises: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the method according to any one of claims 1 to 6. The device comprises:

9. A traffic capacity assessment device, characterized by, one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the method according to claim 7.

10. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method according to any one of claims 1 to 7. ​

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