A lane-changing decision model for divergence under heterogeneous traffic flow conditions

Abstract

The present invention provides a diversion lane-changing decision model under heterogeneous traffic flow conditions, a basic lane-changing condition model, which is used to express the lane-changing acceptable gap of the diversion vehicle DV by the positional relationship between the diversion vehicle DV and the adjacent front vehicle FV and rear vehicle RV on the deceleration lane before and after the lane-changing; a variable gap acceptance model, which takes the gap of direct diversion as the maximum acceptable gap and the gap of squeeze-in diversion as the minimum acceptable gap; a variable acceptance gap fusion model, which is used to set the weights of the maximum acceptable gap and the minimum acceptable gap and fuse them to obtain the diversion lane-changing decision. According to the real-time traffic conditions and vehicle characteristics, the size of the acceptable gap is dynamically adjusted to adapt to the changes in traffic flow and the mutual influence between vehicles, so as to improve the efficiency and safety of diversion lane-changing.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic design, and in particular to a diversion lane-changing decision model under heterogeneous traffic flow conditions. Background Art

[0002] Heterogeneous traffic flows consisting of intelligent connected vehicles and manually driven vehicles will become the mainstream in the future. In a heterogeneous flow environment, manually driven vehicles and autonomous vehicles affect each other, and their diversion behaviors on the road will directly affect the traffic safety and efficiency of the road. Therefore, it is very important to establish a diversion and lane change decision model in a heterogeneous flow traffic environment.

[0003] At present, most of the research on the diverging lane-changing decision model of vehicles focuses on the diverging lane-changing model of manually driven vehicles, ignoring the special needs and behaviors of autonomous vehicles. Existing research mainly focuses on the vehicle path selection, traffic flow prediction and the formulation of lane-changing decision rules. However, these models fail to fully consider the heterogeneity between vehicles and the differences in acceptable gaps during the lane-changing process. In particular, when considering the acceptable gap, existing research is mainly based on a fixed gap value, while ignoring the impact of factors such as vehicle type, speed, and lane traffic density on the gap. This simplified model leads to inaccuracy and decreased effectiveness of lane-changing decisions. Therefore, a new diverging lane-changing model is needed that can dynamically determine the acceptable gap based on vehicle type and current traffic conditions to more accurately guide lane-changing decisions. Summary of the invention

[0004] The present invention proposes a divergence lane-changing decision model under heterogeneous traffic flow conditions, which solves the technical problem that it is difficult to dynamically determine the acceptable gap according to the vehicle type and the current traffic conditions in the prior art.

[0005] In order to solve the above technical problems, the present invention provides a lane-changing decision model under heterogeneous traffic flow conditions, which is special in that:

[0006] The decision model includes a basic lane-changing condition model, a variable acceptance gap acceptance model and a variable acceptance gap fusion model;

[0007] The basic lane-changing condition model is used to represent the lane-changing acceptable gap of the diverting vehicle DV by the positional relationship between the diverting vehicle DV and the adjacent front vehicle FV and the rear vehicle RV on the deceleration lane before and after the lane-changing;

[0008] The variable gap acceptance model uses the gap of direct shunt as the maximum acceptable gap and the gap of squeeze-in shunt as the minimum acceptable gap;

[0009] When the DV is a manually driven vehicle, the minimum acceptable gap and the maximum acceptable gap are determined based on the diverging lane-changing time and the operating speed of the DV;

[0010] When the DV is an autonomous driving vehicle, the maximum acceptable gap and the minimum acceptable gap are obtained by calculating the driving distance of the FV, the driving distance of the RV, and the maximum required distance and the minimum required distance between the DV and the FV and the RV after the lane change, and combining the basic lane change condition model;

[0011] The variable acceptance gap fusion model is used to set the weights of the maximum acceptable gap and the minimum acceptable gap and fuse them to obtain a divergence lane-changing decision.

[0012] Preferably, the expression of the basic lane-changing condition model is:

[0013] ;

[0014] In the formula, for Acceptable gap for lane change, express and The longitudinal distance at the completion of the lane change, express and The longitudinal distance at the completion of the lane change, and Indicates the process of lane change and The longitudinal driving distance, is the vehicle length.

[0015] Preferably, in the variable clearance acceptance model, the minimum acceptable clearance of a manually driven vehicle is The expression is:

[0016] = ;

[0017] Maximum acceptable clearance for manually driven vehicles The expression is:

[0018] ;

[0019] In the formula, To divert the running speed of vehicles in main lane 1, is the running speed of the diverting vehicle at the diverting nose, is the critical acceptance gap time for lane changing in the diverging area.

[0020] Preferably, in the variable gap acceptance model, the maximum acceptable gap of the autonomous driving vehicle is The expression is:

[0021] ;

[0022] Minimum acceptable clearance for autonomous vehicles The expression is:

[0023] ;

[0024] In the formula, and They represent the diverted vehicles DV and The maximum required following distance and the minimum required following distance, and They represent the diverted vehicles DV and The maximum required following distance and the minimum required following distance.

[0025] Preferably, The calculation expression is:

[0026] ;

[0027] The calculation expression is:

[0028] ;

[0029] The calculation expression is:

[0030] ;

[0031] The calculation expression is:

[0032] ;

[0033] In the formula, , and Respectively represent the running speeds of DV, FV and RV, is the vehicle reaction time, Indicates the running speed of FV, For comfortable braking deceleration of the vehicle, Indicates the speed after deceleration using the maximum braking deceleration when completing a lane change.

[0034] Preferably, the expression of the variable acceptance gap fusion model is:

[0035] ;

[0036] In the formula, Indicates the set weight, and Indicates the maximum and minimum acceptable gaps for shunting vehicles.

[0037] Preferably, the weight is characterized by the lane change urgency. The expression is:

[0038] ;

[0039] In the formula, is the distance between the vehicle and the starting point of the deceleration lane transition section, is a random number from a standard normal distribution, and is the control coefficient, is the length of the diversion decision area.

[0040] Preferably, when the diverted vehicle is an autonomous vehicle, the above weights are used for calculation; when the diverted vehicle is a manually driven vehicle, the weights are The expression is:

[0041] ;

[0042] In the formula, i represents the number of the four segments into which the diversion decision area is divided according to the length from the diversion starting point; represents the weight of the i-th segment.

[0043] The beneficial effects of the present invention include at least: the present invention conducts research based on the distribution and behavior characteristics of vehicle gaps under heterogeneous traffic flow conditions. By fully considering the differences between manually driven vehicles and autonomous driving vehicles, the model can accurately estimate and adjust the acceptable gap, thereby improving the accuracy and practicality of diversion lane changing decisions. At the same time, the model can dynamically adjust the size of the acceptable gap according to real-time traffic conditions and vehicle characteristics to adapt to changes in traffic flow and mutual influences between vehicles, thereby improving the efficiency and safety of diversion lane changing. In addition, the model focuses on the optimization of the overall traffic flow, comprehensively considers the lane changing behavior and traffic conditions of vehicles, and provides a more reasonable and feasible diversion lane changing strategy to improve traffic fluidity and reduce congestion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a vehicle diverging lane-changing scenario according to an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of basic lane-changing conditions for diverted vehicles according to an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a simulation of a diversion decision area according to an embodiment of the present invention;

[0048] Figure 5 A schematic diagram of the change in relative traffic efficiency of a diversion lane according to an embodiment of the present invention;

[0049] Figure 6 Schematic diagram of comparative experiments under different traffic volume conditions according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, an embodiment of the present invention provides a diversion lane-changing decision model under heterogeneous traffic flow conditions, which is implemented based on a computer system.

[0053] The decision-making models include basic lane-changing condition model, variable acceptance gap acceptance model and variable acceptance gap fusion model;

[0054] The basic lane-changing condition model is used to express the lane-changing acceptable gap of the diverting vehicle DV by the positional relationship between the adjacent front vehicle FV and the rear vehicle RV on the deceleration lane before and after the lane-changing;

[0055] The variable gap acceptance model takes the gap with direct shunt as the maximum acceptable gap and the gap with squeeze-in shunt as the minimum acceptable gap;

[0056] When the DV is a manually driven vehicle, the minimum acceptable gap and the maximum acceptable gap are determined based on the diverging lane-changing time and the operating speed of the DV;

[0057] When the DV is an autonomous driving vehicle, the maximum acceptable gap and the minimum acceptable gap are obtained by calculating the driving distance of the FV, the driving distance of the RV, and the maximum required distance and the minimum required distance between the DV and the FV and the RV after the lane change, and combining the basic lane change condition model;

[0058] The variable acceptance gap fusion model is used to set the weights of the maximum acceptable gap and the minimum acceptable gap and fuse them to obtain the divergence lane-changing decision.

[0059] Specifically, in this embodiment, the vehicle diversion and lane changing scenario under heterogeneous traffic flow is established by the following method.

[0060] The main lanes of the road are marked as lane 1, lane 2, ... from the outside of the road; the road diversion ramp connection transition section is divided into three parts: the diversion front end is section A, the diversion decision area is section B, and the diversion nose is section C. This invention only studies the process of diverting vehicles selecting an acceptable gap to enter section B, and sets this area as the diversion decision area, and the length is recorded as L C , the scene is established as Figure 2 As shown. Set the vehicles in the diversion environment. (Diversion Vehicle) is a diversion vehicle, where (Manual Diverting Vehicle) is a manually driven diverting vehicle. (AutomatedDiverting Vehicle) is an automated diverting vehicle; (Front Vehicle) is the front vehicle in the deceleration lane; (Rear Vehicle) is the vehicle behind in the deceleration lane. Maintain a certain lane change angle Perform lane change and keep a constant speed during the lane change. The lane change speed of the vehicle is .

[0061] Unless otherwise specified, the present invention All refer to vehicle speed, in units of ; The unit of distance is m; The unit of time is s; The unit of acceleration is

[0062] The basic lane-changing condition model is used to represent the acceptable gap for lane-changing of the diverting vehicle DV by comparing its positional relationship with the front vehicle FV and the rear vehicle RV adjacent to the diverting vehicle DV on the deceleration lane before and after the lane-changing.

[0063] Specifically, When making a lane change decision, first determine , Does the distance between the two lanes meet the lane changing condition? If so, Send a lane change signal to change lanes. The basic lane change conditions are as follows: Figure 3 shown.

[0064] Lane change requires two adjacent vehicles on the deceleration lane ( , ) is executed when the gap meets the requirements, and the following state is maintained after the lane change is completed. Analysis , , The position before and after lane change can be obtained and , The calculation model of the longitudinal distance when the lane change is completed is:

[0065] (1)

[0066] Where: , They are and , Longitudinal distance when lane change is completed, m; , They are and , Initial longitudinal distance, m; , , During the lane change process , , Longitudinal travel distance, m.

[0067] Acceptable gap The calculation formula is formula (2).

[0068] (2)

[0069] Where: for Acceptable gap for lane change, m; is the vehicle length, m.

[0070] Substituting equation (1) into equation (2), we can get The basic lane-changing condition model is expressed as formula (3).

[0071] (3)

[0072] After that, the maximum and minimum variable acceptance gaps of the manually driven vehicle and the autonomous driving vehicle are calculated through the variable acceptance gap model, and then the variable acceptance gap fusion model is used to determine the final variable acceptance gap for lane changing decision.

[0073] This patent is to quantitatively express the variable acceptance gap, combining the maximum acceptable gap and the minimum acceptable gap. Among them, the maximum acceptable gap is the gap of direct shunt, and the minimum acceptable gap is the gap of squeeze-in shunt. The calculation model of the variable acceptance gap is:

[0074] (4)

[0075] In the formula, is the variable acceptance gap, m; is the maximum acceptable gap, m; is the minimum acceptable gap, m; Indicates the set weight.

[0076] Example 2

[0077] In this embodiment, the maximum acceptable gap and the minimum acceptable gap of a manually driven vehicle are determined by the following method. Specifically, existing research has clarified the acceptable gap time distance for manually driven vehicles to change lanes in the diversion area: is 2.21s. Therefore, the maximum acceptable gap of a manually driven vehicle is calculated as:

[0078] (5)

[0079] Where: for The maximum acceptable gap, m; for Running speed in main lane 1, m / s; is the critical acceptance gap time for lane changing in the diverging area, s.

[0080] Determine the minimum acceptable gap for manually driven vehicles: Based on the above analysis of the maximum acceptable gap for manually driven vehicles, the calculation of the minimum acceptable gap is shown in formula (6).

[0081] = (6)

[0082] Where: for Minimum acceptable gap, m; for Running speed at the splitter nose, m / s; is the critical acceptance gap time for lane changing in the diverging area, s.

[0083] Example 3

[0084] This embodiment specifically discloses a method for calculating the maximum acceptable gap and the minimum acceptable gap of an autonomous driving vehicle ADV.

[0085] The velocity is the highest near the gradient section of section B, so this patent adopts direct flow splitting, i.e. In the deceleration lane , The distance between vehicles is large, so it will not affect , It does not affect the system and does not need to change its own running speed.

[0086] According to formula (3), During the diversion process, it is necessary to obtain , The maximum acceptable gap of the autonomous driving vehicle can be calculated based on the driving distance, the maximum required following distance after the vehicle diverges and changes lanes, and the vehicle length. Therefore, this step includes the following:

[0087] 1) Calculation , Driving distance.

[0088] The calculation of the lane-changing time during the direct diversion process is shown in equation (7).

[0089] (7)

[0090] Where: It is the time taken for the autonomous driving vehicle to start and end the lane changing behavior; D is the width of a lane; for The lateral velocity, .

[0091] During direct diversion, the lane-changing process is shorter. , During this process, the vehicle maintains a constant speed and the travel distance is calculated as shown in formula (8).

[0092] (8)

[0093] Where: , for , Distance travelled during lane change; , During the lane change process , Running speed, m / s; is the lane-changing time of the vehicle, s.

[0094] 2) Calculation The maximum required following distance after changing lanes.

[0095] After the divergence, Maintain the maximum required following distance to ensure There will be no collision during emergency deceleration. The maximum required following distance after lane changing is calculated as shown in formula (9).

[0096] (9)

[0097] Where: for and The maximum required following distance, m; is the vehicle reaction time, s; for Running speed, m / s; is the vehicle's comfortable braking deceleration (0 to 1.3), m / s 2 .

[0098] After the divergence, Keep a maximum required distance, and then and The following process and and A similar analysis of the following process is conducted, and it is concluded that and The maximum required following distance is calculated as formula (10).

[0099] (10)

[0100] Where: for and The maximum required following distance, m.

[0101] 3) Calculate the maximum acceptable gap for the autonomous vehicle.

[0102] In equations (9) and (10), the vehicle’s reaction time The value varies depending on the type of vehicle. The value for an autonomous vehicle is 0 to 0.5 s, and the value for a manually driven vehicle is 0.3 to 1.0 s. Combining equations (3), (7) to (10), the maximum acceptable gap calculation formula is (11).

[0103] (13)

[0104] Where: for Maximum acceptable gap, m; other parameters have the same meaning as above.

[0105] A squeeze-in diversion is used near the end of section B. and on segment B , The headroom is small, that is Upon receipt After sending the lane change signal, the vehicle decelerates at the maximum braking deceleration to create a lane change gap and ensure According to the analysis of formula (3), During the diversion process, it is necessary to obtain , The minimum acceptable gap of the autonomous driving vehicle can be calculated based on the driving distance, the minimum required following distance after the vehicle diverges and changes lanes, and the vehicle length. Therefore, this step includes the following:

[0106] 1) Calculation , Driving distance.

[0107] During the squeeze-in diversion process, Keep driving at a constant speed. Slow down at maximum braking deceleration. , The calculation formula for the driving distance is (12).

[0108] (12)

[0109] Where: , During the lane change process , Running speed, m / s; for Maximum braking deceleration, m / s 2 .

[0110] 2) Calculation The minimum required following distance after changing lanes.

[0111] and The minimum required following distance refers to the distance between the vehicle and the vehicle after the lane change and the comfortable braking deceleration. At the same time, the minimum safe distance is maintained. The calculation formula for the minimum required following distance after lane changing is formula (13).

[0112] (13)

[0113] Where: for and Minimum required following distance, m; is the vehicle’s reaction time, s; for Running speed, m / s; is the vehicle's comfortable braking deceleration (0 to 1.3, non-zero value), m / s 2 .

[0114] exist When completing the lane change, The speed is reduced to , the calculation formula is formula (14).

[0115] (14)

[0116] In the formula, Indicates the maximum braking deceleration.

[0117] and The following process and and The process is similar to and The minimum required following distance calculation formula is as follows:

[0118] (15)

[0119] Where: for and Minimum required following distance, m.

[0120] 3) Determine the minimum acceptable gap for autonomous vehicles.

[0121] Combining equations (3), (7), (12) to (15), the minimum acceptable gap calculation formula for the autonomous driving vehicle is obtained as equation (16).

[0122] (16)

[0123] Where: for Minimum acceptable gap, m.

[0124] Example 4

[0125] In this embodiment, in order to further combine the lane changing urgency with the acceptable gap theory, the lane changing urgency is used as a weight for the variable acceptable gap fusion model to make it more adaptable to actual operation scenarios.

[0126] Specifically, the vehicle lane-changing urgency is calculated as formula (17).

[0127] (17)

[0128] Where: The urgency of lane change for diversion; is the distance between the vehicle and the starting point of the deceleration lane gradient, m; is a random number from standard normal distribution; , is the control coefficient; Used to describe the vehicle's awareness of its location; Used to describe the observation and judgment errors of the vehicle.

[0129] Example 5

[0130] In order to further distinguish the urgency of diverging lane changes of manually driven vehicles from that of autonomous driving vehicles, this embodiment determines the calculation methods of the two types of lane change urgency, and then combines the diverging lane change urgency with the acceptable gap theory to obtain a calculation model for the variable acceptable gap.

[0131] Among them, the urgency of the autonomous driving vehicle is a continuous function that changes with the position, so the vehicle lane changing urgency in Example 4 is adopted.

[0132] The urgency of manually driven vehicles is a discontinuous function. The fitting degree is the highest when the acceptable gap changes three times during the diversion process of manually driven vehicles. Therefore, this patent divides the diversion decision area into 4 sections ( =1, 2, 3, 4), the lane change urgency calculation method of the automatic vehicle in formula (17) is used to obtain the lane change urgency of the manually driven vehicle Afterwards, according to The calculation formula for lane change urgency in each of the four segments is obtained as formula (5).

[0133] (18)

[0134] Where: Divert lane change urgency for manually driven vehicles; is the length of the diversion decision area, m; It is the urgency of lane change.

[0135] Finally, after calculating the urgency through this embodiment, the variable acceptance gap diversion decision model of the vehicle is obtained as formula (19).

[0136] (19)

[0137] Example 6

[0138] This embodiment establishes an evaluation system for the diversion decision model proposed in Embodiment 1 from the two aspects of traffic safety and efficiency.

[0139] 1) Determine traffic safety evaluation indicators.

[0140] When vehicles perform divergence and lane change, the speed of traffic flow is unstable. In order to accurately describe the impact of vehicle dispersion on traffic safety, the speed variation coefficient is used as a traffic safety evaluation index, and its calculation formula is formula (20).

[0141] (20)

[0142] Where: is the speed variation coefficient, %; is the standard deviation of vehicle speed in a certain section, m / s; is the average vehicle speed in a certain section, m / s; is the speed of each vehicle in a certain section, m / s; is the number of vehicles passing through a certain section.

[0143] 2) Determine traffic efficiency evaluation indicators.

[0144] In order to better characterize the utilization rate of road resources under actual traffic conditions relative to ideal conditions, relative traffic efficiency is selected as the traffic efficiency evaluation index, which is the ratio of traffic flow power under actual traffic conditions to traffic flow power under ideal conditions. The traffic efficiency calculation formula is formula (21).

[0145] (twenty one)

[0146] Where: is the relative traffic efficiency; is the number of vehicles passing under actual traffic conditions, vehicles; is the average operating speed under actual traffic conditions, ; is the statistical time, take 1 ; is the standard number of vehicles passing under ideal conditions, vehicles; is the average running speed under ideal conditions, .

[0147] Based on this model, different heterogeneous flow conditions are used to calculate the heterogeneous flow conditions that are most unfavorable to traffic safety and efficiency. Finally, under this condition, the model of Example 1 is used to compare and evaluate with other heterogeneous flow lane change decision models. This part includes the following steps:

[0148] 1) Determine the conditions that have the greatest impact on heterogeneous flow.

[0149] In order to analyze the impact of the penetration rate of autonomous driving vehicles and the proportion of diverted vehicles on the traffic system in the diversion decision area, the penetration rate of autonomous driving vehicles (Pacc) and the proportion of diverted vehicles (Poff) were selected as the heterogeneous flow condition group, and multiple groups of simulation control experiments were carried out to calculate the evaluation indicators of different autonomous driving vehicle penetration rates and vehicle proportions. Based on comparative experiments under different traffic volumes, the heterogeneous flow conditions with the greatest impact on the diversion lane change decision model were obtained.

[0150] 2) Evaluation model.

[0151] Based on the heterogeneous flow conditions obtained in step 1) of this section, the decision model of this embodiment is compared and evaluated with other heterogeneous flow lane change decision models under the same traffic environment.

[0152] Specifically, the SUMO simulation software was used to set the road scene to a 6-lane bidirectional highway main line. The specific research area was the off-ramp area of ​​the highway. The design speed of the main road was 120 km / h, the diversion gate was a single lane with a design speed of 60 km / h, and the length of the diversion decision area Lc was 280 m. The specific simulation diagram of the diversion decision area is shown in the figure below. Figure 4 shown.

[0153] In this simulation scenario, when vehicles perform lane-changing operations, they strictly follow the basic conditions for vehicle lane-changing, that is, the acceptable gap between vehicles can be calculated using formula (3).

[0154] SUMO has some built-in models for following and lane changing, and has an internal TraCI (Traffic Control Interface) interface, which can be used to perform secondary development on the vehicle's lane changing model, control the simulated vehicle and obtain its related data information. In the simulation scenario of the present invention, SUMO simulates the vehicle's lane change and strictly follows the variable acceptance gap lane change decision model. The present invention selects the speed variation coefficient and relative traffic efficiency as the traffic safety and efficiency evaluation system of the model. The specific calculation method is shown in formulas (20) and (21), and it is input into the SUMO simulation.

[0155] In order to analyze the impact of the penetration rate of autonomous driving vehicles and the proportion of diverted vehicles on the traffic system in the diversion decision area, in the SUMO simulation, the penetration rate of autonomous driving vehicles (Pacc) was selected as 0-1, the step size was set to 0.2, and the proportion of diverted vehicles (Poff) was selected as 0.05-0.25, and the step size was set to 0.05. A total of 30 groups of control experiments were simulated to calculate the evaluation indicators of different Pacc and Poff.

[0156] Get the speed variation coefficient of the diverging lane The calculation and change analysis are shown in Table 1.

[0157] Table 1

[0158]

[0159] Under the above conditions, simulation is carried out to analyze the changes in the relative traffic efficiency of the diversion lanes, such as Figure 5 According to the above results, it is concluded that when Pacc = 0.2, 0.5, 0.8; Poff = 0.15, 0.25, the decision model of the present invention has a more obvious impact on traffic safety and efficiency. Set up an orthogonal experiment, set different traffic volumes, and conduct comparative experiments under different traffic volumes, such as Figure 6 As shown. It is concluded that when P acc =0.2, P off=0.25 is the most unfavorable condition for traffic safety and efficiency under this model, and it tends to be stable when Q=1650pcu / h (single lane), so this condition is selected for comparative evaluation of related models.

[0160] Based on the above conditions, under the same traffic environment, a comparative simulation experiment was carried out on the SUMO built-in SL2015 lane changing model (model 1) and the model of the present invention (model 2). The information data of heterogeneous flow vehicles was obtained through the TraCI interface, and the traffic safety and efficiency evaluation index values ​​were calculated, as shown in Table 2.

[0161] Table 2

[0162]

[0163] The hourly traffic volume of Model 1 and Model 2 is similar, but the overall operating speed of Model 2 is higher than that of Model 1; the speed fluctuation of Model 2 is smaller than that of Model 1, and the speed variation coefficient is reduced by 23.13% under this patent model, and traffic safety is improved; the relative traffic efficiency of Model 2 is improved by 12.09% compared with Model 1.

[0164] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0165] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A lane-changing decision model for diversion under heterogeneous traffic flow conditions, characterized by: The decision model includes a basic lane-changing condition model, a variable gap acceptance model, and a variable acceptance gap fusion model; The basic lane-changing condition model is used to represent the lane-changing acceptable gap of the diverting vehicle DV by the positional relationship between the diverting vehicle DV and the adjacent front vehicle FV and the rear vehicle RV on the deceleration lane before and after the lane-changing; The variable gap acceptance model uses the gap of direct shunt as the maximum acceptable gap and the gap of squeeze-in shunt as the minimum acceptable gap; When the DV is a manually driven vehicle, the minimum acceptable gap and the maximum acceptable gap are determined based on the diverging lane-changing time and the operating speed of the DV; When the DV is an autonomous driving vehicle, the maximum acceptable gap and the minimum acceptable gap are obtained by calculating the driving distance of the FV, the driving distance of the RV, and the maximum required distance and the minimum required distance between the DV and the FV and the RV after the lane change, and combining the basic lane change condition model; The variable acceptance gap fusion model is used to set the weights of the maximum acceptable gap and the minimum acceptable gap and fuse them to obtain a divergence lane-changing decision; The weight is characterized by the lane change urgency. The expression is: ； In the formula, is the distance between the vehicle and the starting point of the deceleration lane transition section, is a random number from a standard normal distribution, and is the control coefficient, is the length of the diversion decision area.

2. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 1, characterized in that: The expression of the basic lane-changing condition model is: ； In the formula, for Acceptable gap for lane change, express and The longitudinal distance at the completion of the lane change, express and The longitudinal distance at the completion of the lane change, and Indicates the process of lane change and The longitudinal driving distance, is the vehicle length.

3. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 1, characterized in that: In the variable gap acceptance model, the minimum acceptable gap of a manually driven vehicle is The expression is: = ； Maximum acceptable clearance for manually driven vehicles The expression is: ； In the formula, To divert the running speed of vehicles in main lane 1, is the running speed of the diverting vehicle at the diverting nose, is the critical acceptance gap time for lane changing in the diverging area.

4. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 2, characterized in that: The maximum acceptable gap of the autonomous vehicle in the variable gap acceptance model The expression is: ； Minimum acceptable clearance for autonomous vehicles The expression is: ； In the formula, and They represent the diverted vehicles DV and The maximum required following distance and the minimum required following distance, and They represent the diverted vehicles DV and The maximum required following distance and the minimum required following distance.

5. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 4, characterized in that: The calculation expression is: ； The calculation expression is: ； The calculation expression is: ； The calculation expression is: ； In the formula, , and Respectively represent the running speeds of DV, FV and RV, is the vehicle reaction time, Indicates the running speed of FV, For comfortable braking deceleration of the vehicle, Indicates the speed after deceleration using the maximum braking deceleration when completing a lane change.

6. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 1, characterized in that: The expression of the variable receptive gap fusion model is: ； In the formula, Indicates the set weight, and Indicates the maximum and minimum acceptable gaps for shunting vehicles.

7. The lane-changing decision model for diverging traffic under heterogeneous traffic flow conditions according to claim 1, characterized in that: When the diverted vehicle is an autonomous vehicle, the weights of claim 1 are used for calculation; when the diverted vehicle is a manually driven vehicle, the weights The expression is: ； In the formula, i represents the number of the four segments into which the diversion decision area is divided according to the length from the diversion starting point; represents the weight of the i-th segment.

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