WO2021073523A1

WO2021073523A1 - Method for estimating road capacity and connected automatic driving vehicle equivalent coefficient

Info

Publication number: WO2021073523A1
Application number: PCT/CN2020/120821
Authority: WO
Inventors: 马万经; 林启恒; 赫子亮; 王玲; 俞春辉
Original assignee: 同济大学
Priority date: 2019-10-15
Filing date: 2020-10-14
Publication date: 2021-04-22
Also published as: CN110992676B; CN110992676A

Abstract

A method for estimating road capacity and a connected automatic driving vehicle equivalent coefficient, comprising: obtaining a saturated headway average value and a layout type (S1); obtaining the number of connected automatic driving vehicles in a research range on the basis of a penetration rate and the total number of vehicles, N, in the research range (S2); on the basis of the total number of vehicles, N, and the number of connected automatic driving vehicles, obtaining the number of queues formed by the connected automatic driving vehicles (S3); obtaining a mixed saturated traffic flow headway average value in a specific arrangement form on the basis of the number of queues, the layout type, and the saturated headway average value (S4); on the basis of the number of queues, the layout type, the total number of vehicles, N, and the number of connected automatic driving vehicles, calculating the probability of the specific arrangement form by means of a probability mass function (S5); obtaining a final mixed saturated traffic flow headway average value on the basis of the mixed saturated traffic flow headway average value and the probability (S6); and obtaining a road capacity and a connected automatic driving vehicle equivalent coefficient PCE on the basis of the final mixed saturated traffic flow headway average value (S7). The calculation is effectively simplified and the weakness that the formation strategy of connected automatic driving vehicles is generally ignored in the current research is made up for.

Description

A Method for Estimating Road Capacity and Equivalent Coefficient of Connected Autonomous Vehicle

Technical field

The invention relates to the field of road traffic planning and management oriented to networked automatic driving technology, in particular to a method for estimating road capacity and the equivalent coefficient of networked automatic driving vehicles.

Background technique

Connected autonomous vehicles can fundamentally improve road capacity, traffic flow stability and traffic safety. The connected autonomous vehicle obtains real-time speed and location information of several connected autonomous vehicles in front of the vehicle through the vehicle-to-vehicle (V2V) communication or the vehicle-road coordination system. Several networked autonomous vehicles can form a fleet by sharing this information in real time, and maintain a significantly lower headway within the fleet than between human-driven vehicles, thereby improving road traffic. When forming a formation of connected autonomous vehicles, different formation strategies can be adopted to form a fleet of connected autonomous vehicles of different lengths and positions, thereby further improving traffic capacity, fuel economy and traffic safety. On the other hand, the popularization of connected autonomous driving technology is still in its infancy. The penetration rate of connected autonomous vehicles in the road network will gradually increase. A new type of mixed traffic flow composed of connected autonomous vehicles and human-driven vehicles will be long-term exist. Therefore, it is necessary to consider the penetration rate of connected autonomous vehicles and the formation strategy under the new mixed traffic flow conditions to quantify the impact of connected autonomous driving technology on road capacity. However, existing domestic and foreign studies trying to quantify this impact generally ignore the impact of the formation strategy of connected autonomous vehicles. In addition, because the behavior settings of connected autonomous vehicles and human-driven vehicles in various studies are not the same, the calculation results of the improvement degree of the capacity are not consistent, which may not be consistent with the actual operating state of the new mixed traffic flow.

Vehicle equivalent coefficient is an important tool for studying mixed traffic flow. Its function is to convert mixed traffic flow into standard vehicle (usually passenger car) traffic flow, so that mixed traffic flows of different compositions are comparable. At present, whether in theoretical research or engineering practice, the target models of the equivalent coefficient are mainly concentrated in large vehicles such as trucks and buses, and take into account small vehicles such as non-motor vehicles and motorcycles. They serve to deal with traditional mixed traffic flows and cannot be handled. A new type of mixed traffic flow that introduces networked autonomous driving technology. Only Bujanovic and Lochrane in the United States have studied the equivalent coefficient of connected autonomous vehicles, but this study also did not involve the formation strategy of connected autonomous vehicles, and only limited the length of the fleet of connected autonomous vehicles based on the V2V communication range.

Current problems: 1. The formation strategy of connected autonomous vehicles is ignored when quantifying the capacity of mixed traffic flow roads; 2. The formation strategy of connected autonomous vehicles is also not involved when the equivalent coefficient of connected autonomous vehicles is obtained.

Summary of the invention

The purpose of the present invention is to provide a method for estimating the equivalent coefficient of road capacity and networked autonomous vehicles in order to overcome the above-mentioned defects in the prior art.

The purpose of the present invention can be achieved through the following technical solutions:

A method for estimating the equivalent coefficient of road capacity and connected autonomous vehicles, the method includes the following steps:

Step S1: Obtain the average value of the saturated headway and the layout type;

Step S2: Based on the penetration rate η and the total number of vehicles in the research area N, obtain the number of connected autonomous driving vehicles N _{A in the} research area;

Step S3: Based on the total number N of the vehicle and the number of network-linked automatic driving car N _A, the number of queues to give automatic driving vehicle with web formation [alpha];

Step S4: Based on the number of queues α, the layout type and the average headway headway of saturated traffic, the average headway headway of the mixed saturated traffic flow of a specific arrangement is obtained

Step S5: Calculate the probability Pr((α,J)|(N,N _A )) of the specific arrangement form based on the number of queues α, the layout type, the total number of vehicles N and the number of connected autonomous vehicles N _{A through the probability mass function} ；

Step S6: Based on the average value and probability of the headway of the mixed saturated traffic flow, obtain the final average headway of the mixed saturated traffic flow

Step S7: Based on the average headway headway of the final mixed saturated traffic flow

Obtain road capacity

Equivalent coefficient PCE for connected autonomous vehicles.

The number of network-linked autonomous vehicles N _A is:

N _A =N·η.

The average value of saturated headway includes the average value of saturated headway of a human-driven vehicle following a human-driven vehicle

Saturated headway average value of a human-driven vehicle following a connected autonomous vehicle

Saturated headway average value of connected autonomous vehicles following human-driving vehicles

The saturated headway average value of the connected autonomous vehicle and the following Chi connected autonomous vehicle

Said

Obtained by statistics of saturated traffic flow of purely human-driven vehicles,

Obtained from the experimental data of closed or open field of connected autonomous vehicles, the

with

It is obtained through simulation experiments on saturated traffic flow of purely connected autonomous vehicles.

The described layout types include the E layout type where the head queue and the tail queue are both human-driven vehicles, the head queue is the F layout type where the connected autonomous vehicles and the tail queue are human-driven vehicles, and the head queue is the human-driven vehicle and the tail queue. It is the G layout type of the connected autonomous vehicle, and the head and tail queues are both the H layout type of the connected autonomous vehicle.

The average headway headway of the mixed saturated traffic flow in the specific arrangement

for:

If the formation strategy of arbitrary formation of human-driven vehicles and networked autonomous vehicles is adopted, the probability Pr((α,J)|(N,N _A )) includes Pr((α,E)|(N,N _A )), Pr((α,F)|(N,N _A )), Pr((α,G)|(N,N _A )) and Pr((α,H)|(N,N _A )) :

Among them, Pr((α,E)|(N,N _A )) is the probability of the arrangement type of E layout and the number of queues is α, Pr((α,F)|(N,N _A )) is F The probability of the arrangement type and the number of queues is α, Pr((α,G)|(N,N _A )) is the probability of the arrangement type of G layout and the number of queues is α, Pr((α, H)|(N,N _A )) is the probability of the arrangement type with H layout type and the number of queues being α;

If the formation strategy of fixing the length of the queue formed by networked autonomous vehicles is adopted, and the length of the queue is limited to λ ₀ , the probability Pr((α,J)|(N,N _A )) is:

Among them, J is the layout type,

include

with

for:

The average headway headway of the final mixed saturated traffic flow

for:

Road capacity

for:

The equivalent coefficient PCE of the connected autonomous vehicle is:

Compared with the prior art, the present invention has the following advantages:

(1) Through the saturated traffic flow statistics of purely human-driven vehicles, the saturated headway average value obtained in closed or open field experiments and simulation experiments is used as model input, which effectively reflects the actual operating state of the new mixed traffic flow and adapts to the actual traffic flow operation Environment, the data acquisition method is operability, the estimation result has high credibility, and the estimation method is convenient for popularization and application.

(2) According to the queue formation strategy adopted by the connected autonomous vehicles (including the formation strategy of using human-driven vehicles and the arbitrary formation of connected autonomous vehicles and the formation strategy of fixing the length of the queue formed by the connected autonomous vehicles), The probability mass function is used to reflect the influence of the formation strategy on the average headway time of the final mixed saturated traffic, and the modeling is carried out directly from the result of the formation strategy. The specific algorithm for forming the formation is not involved, which effectively simplifies the calculation and makes up for it at the same time. Current research generally ignores the weakness of the formation strategy of connected autonomous vehicles.

(3) The obtained road capacity and the equivalent coefficient of connected autonomous vehicles can be used to guide the setting of dedicated lanes for connected autonomous vehicles, the number of newly built or rebuilt highway lanes, and other traffic planning and management practices oriented to connected autonomous driving technology. Meet the actual needs, promote the popularization and application of network-linked autonomous driving technology, and further improve the operational efficiency and safety of my country's road network and urban road network.

Description of the drawings

Figure 1 is a flow chart of the present invention;

Figure 2 is a schematic diagram of the types of headway of the present invention;

Fig. 3 is a schematic diagram of the layout type of the present invention.

Detailed ways

The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation mode and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

Example

This embodiment provides a method for estimating the equivalent coefficient of road capacity and networked autonomous vehicles, which includes the following steps:

Step S1: Obtain the average saturated headway and layout type, and the average saturated headway of the human-driven vehicle will follow the human-driven vehicle

Step S4: Based on the number of queues α, layout type,

with

Obtain the average headway time of mixed saturated traffic flow in a specific arrangement

Obtain road capacity

Equivalent coefficient PCE for connected autonomous vehicles.

in particular:

Step S1

Obtained by the statistics of saturated traffic flow of purely human-driven vehicles,

Obtained from experimental data of closed or open field of connected autonomous vehicles, or hypothetical

versus

equal,

with

There are four types of layouts, namely, the head queue and the tail queue are E layout types of human-driven vehicles, the head queue is the F layout type of networked autonomous vehicles, the tail queue is the F layout type of human-driven vehicles, and the head queue is human-driven vehicles. The tail queue is the G layout type of the connected autonomous vehicle, and the head and tail queues are both the H layout type of the connected autonomous vehicle. If the traffic flow in the study area has only one queue of a specific vehicle type, the queue is considered to be both the head queue and the tail queue.

The number of connected autonomous vehicles N _{A in} step S2 is:

N _A ＝N·η

Among them, the selection of N should be such that when the research scope is continued to increase, the change in the estimated value of the road capacity and the equivalent coefficient of the connected autonomous vehicle is smaller than the preset threshold, that is, the estimated road capacity and network under the research scope. The equivalent coefficient of the joint autonomous vehicle can reflect the true operating state of the mixed traffic flow.

In step S3, the number of queues α multiplied by the length of each queue is not greater than N.

Average headway headway of mixed saturated traffic flow in a specific arrangement in step S4

for:

In step S5, if the formation strategy of arbitrary formation of human-driven vehicles and networked autonomous vehicles is adopted, the probability Pr((α,J)|(N,N _A )) includes Pr((α,E)|(N,N _A )), Pr((α,F)|(N,N _A )), Pr((α,G)|(N,N _A )) and Pr((α,H)|(N,N _A )) :

If the formation strategy of fixing the length of the queue formed by networked autonomous vehicles is adopted, and the length of the queue is limited to λ ₀ , _{the value of λ 0} should be predetermined, and the probability Pr((α,J)|(N,N _A ) )for:

Among them, J is the layout type,

Indicates that when the total number of vehicles is N and the number of network-connected autonomous vehicles is N _A , the number of arrangement forms forming layout type J;

Represents a total number of vehicles is N, the number of network-linked vehicles autopilot N _A, J layout type is formed, and comprises a number of networks arranged in the form of α-linked columns autopilot team, comprising

with

for:

Average headway headway of the final mixed saturated traffic flow in step S6

for:

Road capacity in step S7

for:

The equivalent coefficient PCE for connected autonomous vehicles is dimensionless and expressed as:

The following is an explanation with a specific example:

1) As shown in Figure 2, the saturated traffic flow of purely human-driven vehicles at the entrance of the intersection is obtained by statistics

And the value of

equal;

with

The value of is obtained by simulating the saturated traffic flow of the networked autonomous vehicle through the computer simulation platform. The specific value is shown in Table 1, and the unit is second: the layout type can be divided into E and F according to the vehicle type of the head queue and the tail queue. There are 4 categories of, G and H, as shown in Figure 3.

Table 1

with

The specific value of

2) In this example, the penetration rate is η=0.5, and the road capacity and the network-linked autonomous vehicle equivalent coefficient under this penetration rate are calculated.

3) Intercept a continuous segment in the mixed traffic flow as the research scope. As shown in Figure 2, the scale is N=100, and the number of networked autonomous vehicles in the research scope is N _A =N·η=50.

4) Within the scope of the study in step 3), the array of networked autonomous vehicle queues has a total of

Kind.

5) Calculate the average headway of mixed saturated traffic flow in different arrangements. For example, to calculate the average headway headway of a mixed saturated traffic flow with two networked autonomous vehicle queues F layout type arrangement form, the calculation process is as follows:

The remaining layout types and the arrangement of the number of queues of different connected autonomous vehicles use the same algorithm, and the calculation results are not listed one by one.

6) In this example, using the formation strategy of arbitrary formation of human-driven vehicles and networked autonomous vehicles, the probability of having two networked autonomous vehicle queue F layout types is calculated as:

The remaining layout types and the arrangement of the number of queues of different networked autonomous vehicles use the same method to calculate the probability, and the calculation results are not listed one by one.

7) According to the average value and probability of headway of mixed saturated traffic calculated in steps 5) and 6), calculate the final average headway of mixed saturated traffic according to the following formula

8) Calculate road capacity according to the following formula:

Calculate the equivalent coefficient of connected autonomous vehicles according to the following formula:

The results show that under the conditions of the formation strategy of a networked autonomous vehicle penetration rate of 0.5 and an arbitrary formation of human-driven vehicles and connected autonomous vehicles, the road capacity is 1964veh/(h·ln), equivalent to the equivalent of connected autonomous vehicles. The coefficient is 0.53, which can be used to convert the mixed traffic flow into a standard vehicle (usually a small passenger car) traffic flow, so that mixed traffic flows of different compositions are comparable.

Claims

A method for estimating the equivalent coefficient of road capacity and networked autonomous vehicles, characterized in that the method includes the following steps:

Step S1: Obtain the average value of the saturated headway and the layout type;

Step S2: Based on the penetration rate η and the total number of vehicles in the research area N, obtain the number of connected autonomous driving vehicles N A in the research area;

Step S3: Based on the total number N of the vehicle and the number of network-linked automatic driving car N A, the number of queues to give automatic driving vehicle with web formation [alpha];

Step S4: Based on the number of queues α, the layout type and the average headway headway of saturated traffic, the average headway headway of the mixed saturated traffic flow of a specific arrangement is obtained

Step S5: Calculate the probability Pr((α,J)|(N,N A )) of the specific arrangement form based on the number of queues α, the layout type, the total number of vehicles N and the number of connected autonomous vehicles N A through the probability mass function ；

Step S6: Based on the average value and probability of the headway of the mixed saturated traffic flow, obtain the final average headway of the mixed saturated traffic flow

Step S7: Based on the average headway headway of the final mixed saturated traffic flow
Obtain road capacity
Equivalent coefficient PCE for connected autonomous vehicles.
According to a network of road capacity associated with said automatic driving a car equivalent coefficient estimating method as claimed in claim, characterized in that said automatic driving mesh with N A is the number of vehicles:

N A =N·η.
The method for estimating the equivalent coefficient of road capacity and networked autonomous vehicles according to claim 1, wherein the saturated headway average value includes the saturated headway time of a human-driven vehicle following a human-driven vehicle average value
Saturated headway average value of a human-driven vehicle following a connected autonomous vehicle
Saturated headway average value of connected autonomous vehicles following human-driving vehicles
The saturated headway average value of the connected autonomous vehicle and the following Chi connected autonomous vehicle
Said
Obtained through the statistics of saturated traffic flow of purely human-driven vehicles, the
Obtained from the experimental data of closed or open field of connected autonomous vehicles, the
with
It is obtained through simulation experiments on saturated traffic flow of purely connected autonomous vehicles.
The method for estimating the equivalent coefficient of road capacity and network-linked autonomous vehicles according to claim 3, wherein the layout type includes the E layout type of the head queue and the tail queue, both of which are human-driven vehicles, and the head queue It is the F layout type of connected autonomous vehicles, the tail queue is a human-driven vehicle, the head queue is a human-driven vehicle, and the tail queue is a G layout type of a connected autonomous vehicle. The head and tail queues are both connected autonomous vehicles. H layout type.
The method for estimating the equivalent coefficient of road capacity and network-linked autonomous vehicles according to claim 4, wherein the average headway headway of the mixed saturated traffic flow in the specific arrangement form
for:
The method for estimating the equivalent coefficient of road capacity and connected autonomous driving vehicles according to claim 5, characterized in that, if a formation strategy of human-driven vehicles and connected autonomous vehicles in any formation is adopted, the probability Pr( (α,J)|(N,N A )) includes Pr((α,E)|(N,N A )), Pr((α,F)|(N,N A )), Pr((α ,G)|(N,N A )) and Pr((α,H)|(N,N A )):

Among them, Pr((α,E)|(N,N A )) is the probability of the arrangement type of E layout and the number of queues is α, Pr((α,F)|(N,N A )) is F The probability of the arrangement type and the number of queues is α, Pr((α,G)|(N,N A )) is the probability of the arrangement type of G layout and the number of queues is α, Pr((α, H)|(N,N A )) is the probability of the arrangement type with H layout type and the number of queues being α;

If the formation strategy of fixing the length of the queue formed by networked autonomous vehicles is adopted, and the length of the queue is limited to λ 0 , the probability Pr((α,J)|(N,N A )) is:

Among them, J is the layout type,
include
with

for:
The method for estimating the equivalent coefficient of road capacity and network-linked autonomous vehicles according to claim 6, characterized in that the average headway headway of said final mixed saturated traffic flow
for:
The method for estimating the equivalent coefficient of road capacity and network-linked autonomous vehicles according to claim 7, wherein the road capacity is
for:

The equivalent coefficient PCE of the connected autonomous vehicle is: