CN113792424B - Multi-lane changing method and system under heterogeneous traffic flow of automatic driving vehicle - Google Patents
Multi-lane changing method and system under heterogeneous traffic flow of automatic driving vehicle Download PDFInfo
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Abstract
The invention discloses a heterogeneous traffic flow multi-lane changing method and system for an automatic driving vehicle, comprising the following steps: s1, establishing an automatic driving vehicle heterogeneous traffic flow multi-lane traffic flow model based on cellular automata; s2, formulating a multi-lane change rule of the heterogeneous traffic flow of the automatic driving vehicle under the safety principle and the driving time principle; s3, acquiring a microscopic simulation basic diagram of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle through microscopic simulation and analyzing the relation between the dynamic characteristics of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle; and S4, giving out an analytical expression among the dynamic characteristics and performing simulation verification. The invention can meet the requirements of analyzing the vehicle density interval of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle and the vehicle passing efficiency of lane change promotion, and has important significance for the traffic characteristic research of the heterogeneous traffic flow of the automatic driving vehicle and the popularization and application of the automatic driving vehicle.
Description
Technical Field
The invention relates to the field of heterogeneous traffic flow of automatic driving vehicles, in particular to a multi-lane changing method and system under heterogeneous traffic flow of the automatic driving vehicles.
Background
Autopilot plays an important role in modern integrated traffic systems. Firstly, the automatic driving vehicles can communicate and coordinate with each other, so that the traffic efficiency is improved, and the traffic jam condition is reduced. Secondly, the automatic driving vehicle can reduce human interference factors and improve urban traffic safety coefficient. Third, the autopilot generally uses clean energy, which can promote energy conservation and emission reduction.
The current stage of research on heterogeneous traffic flow of an automatic driving vehicle needs the following two aspects of technologies:
on the one hand, it is necessary to build a heterogeneous traffic flow model considering the characteristics of the automated driving vehicle. Even though autopilot development is rapid, its popularity is not one step. According to the statistical predictive report of boston counsel corporation (BCG), the permeability of an automated driving car was 12.9% by 2025, and it was expected that the permeability of an automated driving car was 24.8% by 2035. Therefore, in the long term in the future, the automatic driving vehicle and the manual driving vehicle can operate in the traffic network at the same time, so that it is important to study the traffic characteristics of the heterogeneous traffic flow of the automatic driving vehicle. Meanwhile, the driving data of the current automatic driving vehicle are very little.
On the other hand, a heterogeneous traffic flow multi-lane change model of the automatic driving vehicle conforming to the actual scene needs to be established. As the actual road is a complex scene with lane changing and multiple lanes, the traffic flow characteristics and evolution mechanisms under the complex scene are needed to be studied deeply, and the vehicle density interval of vehicle lane changing and the vehicle passing efficiency of lane changing and lifting are analyzed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a multi-lane change method and a system under heterogeneous traffic of an automatic driving vehicle, so as to meet the requirements of analyzing a vehicle density interval of multi-lane change of the heterogeneous traffic of the automatic driving vehicle and improving the vehicle passing efficiency of lane change.
The technical scheme provided by the invention for achieving the purpose is as follows:
a heterogeneous traffic flow multi-lane changing method of an automatic driving vehicle is characterized by comprising the following steps:
s1, establishing an automatic driving vehicle heterogeneous traffic flow multi-lane traffic flow model based on cellular automata;
s2, formulating a multi-lane change rule of the heterogeneous traffic flow of the automatic driving vehicle under the safety principle and the driving time principle;
s3, acquiring a microscopic simulation basic diagram of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle through microscopic simulation and analyzing the relation between the dynamic characteristics of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle;
and S4, giving out an analytical expression among the dynamic characteristics and performing simulation verification.
Further:
in step S1, the heterogeneous traffic flow multi-lane traffic flow model of the automatic driving vehicle is a heterogeneous traffic flow multi-lane nash traffic flow model of the automatic driving vehicle obtained by combining the following behavior of the automatic driving vehicle and expanding a single vehicle type to the mixed traffic flow of the automatic driving vehicle and the manual driving vehicle on the basis of the nash traffic flow model.
Further:
the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle comprises a speed updating rule:
if the vehicle is a manually driven vehicle, v according to the NaSch traffic flow model i (t) represents the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at the time t, and the speed update formula is as follows:
v i (t+1)＝min{v i (t)+1，v max， d i (t)} (1)
after the random slowing probability q is considered, the probability q of the manual driving vehicle is slowed down after the speed is updated:
if the vehicle is an autonomous vehicle, and is able to collect information of at most k preceding vehicles, v i (t) represents the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at time t,representing the virtual speed formed by the movement state of the k-1 vehicle in front collected by the (i+1) th vehicle, the (i) th vehicle speed update formula is as follows:
if the previous vehicle is also an autonomous vehicle, the i+1th vehicle virtual speed is as follows:
if the previous vehicle is an artificial driving vehicle, the virtual speed of the (i+1) th vehicle is as follows:
further:
the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle further comprises a position updating rule:
x i (t+1)＝x i (t)+v i (t+1) (6)
wherein x is i (t) represents the position of the ith vehicle at time t.
Further:
in step S2, the heterogeneous traffic flow multi-lane change rule of the autopilot includes:
the two-lane change needs to meet the following three conditions:
d i (t)＜v i (t) (7)
d i，otherfront (t)＞d i (t) (8)
d i，otherback (t)＞d safe (9)
wherein d is i (t) represents a vehicle distance between the ith vehicle and the (i+1) th vehicle at time t; d, d i，otherfront (t) represents a vehicle distance between the ith vehicle and a preceding vehicle of another lane at time t; d, d i，otherback (t) represents a vehicle distance between the ith vehicle and a rear vehicle of another lane at time t; d, d safe Representing a safe distance between vehicles;
further:
the heterogeneous traffic flow multi-lane change rule of the automatic driving vehicle further comprises:
the three-lane left lane change needs to meet the following three conditions:
d i (t)＜v i (t) (10)
d i，otherfront (t)＞d i (t) (11)
d i，otherback (t)＞d safe (12)
wherein d is i (t) represents the distance between the ith vehicle in the left lane and the (i+1) th vehicle in front of the same lane at the time t; d, d i，otherfron t (t) represents a vehicle distance between the ith vehicle and a preceding vehicle of the intermediate lane at the time t; d, d i，otherback (t) represents a vehicle distance between the ith vehicle and a rear vehicle of the intermediate lane at time t;
meanwhile, when the left lane is changed, whether the vehicles on the right lane enter the same cell or not needs to be judged, if the lane changing condition is met, the vehicles on the left lane and the right lane enter the middle lane at 50% probability respectively, and if the lane is changed on the left lane, the lane is not changed on the right lane, otherwise, the vehicles on the left lane and the right lane are the same.
Further:
in step S3, the dynamics features include vehicle density, automatic driving ratio, automatic driving detectability, maximum vehicle speed, random slowing probability and vehicle flow.
Further:
in step S4, when the traffic flow in the lane is a heterogeneous traffic flow, the analysis expression is given by initializing the distribution of the automatic driving vehicles and giving the distribution under the initialization.
Further:
in step S4, the simulation verification is to verify the correctness of the analytical expression by comparing the analytical expression image with the microscopic simulation basic diagram.
The invention also provides an automatic driving vehicle heterogeneous traffic flow multi-lane changing system, and the method is used for carrying out the automatic driving vehicle heterogeneous traffic flow multi-lane changing.
The present invention also proposes a computer-readable storage medium storing one or more programs executable by one or more processors to implement the steps of the above-described method.
The invention has the following beneficial effects:
the invention establishes the heterogeneous traffic flow multi-lane change model of the automatic driving vehicle, can simulate the road traffic condition in the urban traffic network in the future, and takes the communication (vehicle following characteristic) among the automatic driving vehicles into consideration in comparison with the traditional lane change rule, thereby being more in line with the actual condition;
the invention uses microscopic simulation technology to carry out simulation analysis on the dynamics characteristics of the heterogeneous traffic flow of the automatic driving vehicle, and has important significance on the traffic characteristic research of the heterogeneous traffic flow of the automatic driving vehicle and the popularization and application of the automatic driving vehicle;
the heterogeneous traffic flow multi-lane change system for the automatic driving vehicle can realize multi-lane change of the heterogeneous traffic flow of the automatic driving vehicle.
Drawings
FIG. 1 is a step diagram of a heterogeneous traffic flow multi-lane change method for an automatic pilot according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a two-lane change model for heterogeneous traffic flow of an autonomous vehicle according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a left lane change model of an automated driving vehicle heterogeneous traffic flow three-lane model according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a lane change model in the middle of an autopilot heterogeneous traffic flow three-lane model in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a right lane change model of an autopilot heterogeneous traffic flow three-lane model in accordance with an embodiment of the present invention;
FIGS. 6A-6F are basic flow density diagrams of a two-lane master model and a lane change model for different autopilot ratios according to embodiments of the present invention;
FIGS. 7A-7F are basic flow density diagrams of a two-lane original model and a lane change model under different random slowing probabilities according to the embodiment of the invention;
FIGS. 8A-8J are basic flow density diagrams of a two-lane original model and a lane change model under different detectability of an automatic driving vehicle according to the embodiment of the invention;
fig. 9A-9F are basic flow density diagrams of a three-lane master model and a lane change model for different autopilot ratios according to embodiments of the present invention.
FIGS. 10A-10F are basic flow density diagrams of a three-lane original model and a lane change model under different random slowing probabilities according to the embodiment of the invention;
FIGS. 11A-11J are basic flow density diagrams of a primary model and a lane change model of a three-lane vehicle according to the embodiment of the invention under different detectability of the vehicle;
FIG. 12 is a theoretical graph of manual driving traffic flow operation without congestion without considering random slowing probability in an embodiment of the invention;
FIG. 13 is a theoretical graph of manual driving traffic flow operation during congestion without considering random slowing probability in an embodiment of the invention;
FIGS. 14A-14D are flow density base graphs comparing a theoretical solution to a simulated solution for a manual driving flow without considering random slowing probability in an embodiment of the invention;
FIG. 15 is a theoretical plot of autonomous driving traffic flow operation without congestion without considering the random slowing probability in accordance with an embodiment of the present invention;
FIG. 16 is a theoretical plot of automated driving traffic flow during congestion without considering random slowing probability in accordance with an embodiment of the present invention;
FIGS. 17A-17D are basic flow density diagrams of an automatic driving traffic flow theoretical solution and a simulation solution without considering random slowing probability according to the embodiment of the invention;
FIG. 18 is a theoretical diagram of the operation of a manual driving traffic flow without congestion in consideration of the random slowing probability in accordance with an embodiment of the present invention;
FIG. 19 is a theoretical diagram of the operation of the flow of a manual drive during congestion, taking into account the probability of random slowdown, according to an embodiment of the invention;
FIGS. 20A-20D are flow density base graphs comparing a theoretical solution and an analog solution of a manual driving traffic flow under the consideration of random slowing probability in the embodiment of the invention;
FIG. 21 is a theoretical graph of heterogeneous driving traffic flow operation without congestion, without considering random slowing probability, according to an embodiment of the present invention;
FIG. 22 is a theoretical diagram of heterogeneous driving traffic flow operation during congestion, without considering random slowing probability, and with an automatic driving traffic ratio of 0.2, according to an embodiment of the present invention;
FIGS. 23A-23D are basic flow density diagrams of a theoretical solution and an simulated solution of heterogeneous driving traffic flow, without considering random slowing probability, and with an automatic driving traffic ratio of 0.2, according to an embodiment of the present invention;
FIG. 24 is a theoretical diagram of heterogeneous driving traffic flow operation in congestion without considering random slowing probability and with an automatic driving traffic ratio of 0.8 in the embodiment of the invention;
FIGS. 25A-25D are basic flow density diagrams of a theoretical solution and an analogous solution of heterogeneous driving traffic flow with an automatic driving traffic ratio of 0.8 without considering random slowing probability in the embodiment of the invention;
FIG. 26 is a theoretical diagram of heterogeneous driving traffic flow operation without congestion under consideration of random slowing probability in an embodiment of the present invention;
FIG. 27 is a theoretical diagram of heterogeneous driving traffic flow operation in congestion, with a random slowing probability of 0.2 being considered, and with an automatic driving traffic ratio in accordance with an embodiment of the present invention;
FIGS. 28A-28D are basic flow density diagrams of a theoretical solution and an emulation solution of heterogeneous driving traffic flow under the consideration of random slowing probability and with an automatic driving traffic ratio of 0.2 in the embodiment of the invention;
FIG. 29 is a theoretical diagram of heterogeneous driving traffic flow operation in congestion, with random slowing probability taken into account, and with an automatic driving traffic occupancy of 0.8 in an embodiment of the present invention;
FIGS. 30A-30D are basic flow density diagrams of a theoretical solution and an analogous solution of heterogeneous driving traffic flow with a random slowing probability of 0.8 and an automatic driving traffic ratio.
Detailed Description
The following describes embodiments of the present invention in detail. It should be emphasized that the following description is merely exemplary in nature and is in no way intended to limit the scope of the invention or its applications.
It should be noted that the terms "first," "second," "left," and "right" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "first", "second", "left" and "right" may explicitly or implicitly include one or more such feature.
The invention provides a multi-lane change method for heterogeneous traffic flow of an automatic driving vehicle, which comprises the steps of firstly establishing a multi-lane traffic flow simulation model of heterogeneous traffic flow of the automatic driving vehicle based on cellular automaton microscopic simulation; and then, under the consideration of a safety principle and a driving time principle, making a multi-lane change rule about heterogeneous traffic flow of the automatic driving vehicle. The method considers the following characteristics of the automatic driving vehicles, can study the influence of different automatic driving vehicle proportions, automatic driving vehicle detectability, vehicle density and random slowing probability on the traffic flow of the lane, and can measure the traffic efficiency of the current lane through the average vehicle flow.
The embodiment of the invention provides an automatic driving vehicle heterogeneous traffic flow multi-lane changing method based on cellular automaton microscopic simulation, which specifically comprises the following steps 1 to 4:
step 1, establishing an automatic driving vehicle heterogeneous traffic flow multi-lane NaSch (Nagel-Schreckenberg) traffic flow model based on cellular automata. The rules of the heterogeneous traffic flow and multi-lane NaSch traffic flow model of the automatic driving vehicle are as follows:
first speed update
If the vehicle is a manually driven vehicle, v according to the autonomous vehicle heterogeneous traffic flow multi-lane NaSch traffic flow model i (t) represents the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at the time t, and the speed update formula is as follows:
v i (t+1)＝min{v i (t)+1，v max ，d i (t)} (1)
after the random slowing probability q is considered, the probability q of the manual driving vehicle is slowed down after the speed is updated:
if the vehicle is an autonomous vehicle, andcan collect information of k vehicles in front at most, v i (t) represents the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at time t,representing the virtual speed formed by the movement state of the front k-1 vehicle collected by the (i+1) th vehicle. The ith vehicle speed update formula is as follows:
if the previous vehicle is also an autonomous vehicle, the i+1th vehicle virtual speed is as follows:
if the previous vehicle is an artificial driving vehicle, the virtual speed of the (i+1) th vehicle is as follows:
(two) location update
The location update formula is as follows:
x i (t+1)＝x i (t)+v i (t+1) (6)
wherein x is i (t) represents the position of the ith vehicle at time t.
In the rule of the heterogeneous traffic flow NaSch traffic flow model of the automatic driving vehicle, the detectability of the automatic driving vehicle represents that the automatic driving vehicle can detect the running state of k vehicles in front at most, and if the front is an artificial driving vehicle, the front vehicle information can not be continuously detected. Under this rule of autonomous vehicles, an autonomous vehicle driving platoon is formed, at most in the form of a platoon of k+1 vehicles driving in the lane.
And 2, under the condition of considering a safety principle and a running time principle, formulating a multi-lane change rule about heterogeneous traffic flow of the automatic driving vehicle.
Vehicles in the heterogeneous traffic flow of the automatic driving vehicle can change the road only by meeting the safety principle and the driving time principle of the road change of the vehicles. When the current vehicle is congested, whether the distance between the current vehicle and the front vehicle of the other lane meets the driving time principle or not needs to be judged, and meanwhile, whether the distance between the current vehicle and the rear vehicle of the other lane meets the safety principle or not needs to be judged.
Two-lane change
The lane diagram of the two-lane change rule is shown in FIG. 2, wherein d i (t) represents a vehicle distance between the ith vehicle and the (i+1) th vehicle at time t; d, d i，otherfront (t) represents a vehicle distance between the ith vehicle and a preceding vehicle of another lane at time t; d, d i，otherback (t) represents a vehicle distance between the ith vehicle and a rear vehicle of another lane at time t; d, d safe Indicating a safe distance between vehicles.
The following three conditions are satisfied for vehicle lane change:
d i (t)＜v i (t) (7)
d i，otherfront (t)＞d i (t) (8)
d i，otherback (t)＞d safe (9)
d i (t)＜v i (t) represents that the ith vehicle is jammed at the time t; d, d i，otherfront (t)＞d i (t) indicating that at time t the distance between the ith vehicle and the preceding vehicle in the other lane is greater than the distance between the ith vehicle and the preceding vehicle in the current lane; d, d i，otherback (t)＞d safe Indicating that at time t the distance between the ith vehicle and the following vehicle of the other lane is greater than the safe distance. When the above conditions are met, the vehicle may change lanes.
(II) three-lane change
A three-lane left lane change rule lane diagram is shown in FIG. 3, wherein d i (t) represents a vehicle distance between the i-th vehicle of the left lane and the i+1-th vehicle at the time t; d, d i，otherfront (t) represents the ith vehicle and the middle lane at time tThe distance between the front vehicles; d, d i，otherback And (t) represents the distance between the ith vehicle and the rear vehicle of the middle lane at the time t.
Likewise, a three lane left lane change also requires that the following three conditions be met:
d i (t)＜v i (t) (10)
d i，otherfront (t)＞d i (t) (11)
d i，otherback (t)＞d safe (12)
meanwhile, when the left lane is changed, whether the vehicles on the right lane enter the same cell or not needs to be judged, if the lane changing condition is met, the vehicles on the left lane and the right lane enter the middle lane at 50% probability respectively, and if the lane is changed on the left lane, the lane is not changed on the right lane, otherwise, the vehicles on the left lane and the right lane are the same.
The lane diagram of the three-lane middle lane change rule is shown in fig. 4, the vehicle in the middle lane is compared with the left lane first, whether the lane change condition is met is judged, and then the vehicle in the middle lane is compared with the vehicle in the right lane, and whether the lane change condition is met is judged. If only the left lane meets the lane change condition, the vehicle in the middle lane is changed to the left lane. If only the right lane meets the lane change condition, the vehicle in the middle lane is changed to the right lane. If both sides meet the lane change rule, d is required to be judged i，leftfront (t) and d i，rightfront The size of (t), if d i，leftfront (t)＞d i，rightfront (t) the vehicle in the middle lane changes to the left lane; if d i，leftfront (t)＜d i，rightfront (t) the vehicle in the middle lane changes to the right lane; if d i，leftfront (t)＝d i，rightfront (t) each has a 50% probability of changing lanes, if changing to the left lane, not changing to the right lane, if changing to the right lane, not changing to the left lane.
The lane diagram of the three-lane right lane change rule is shown in fig. 5, and the lane change rule is the same as that of the left lane.
And 3, analyzing the relationship among the vehicle density, the ratio of the automatic driving vehicles, the detectability of the automatic driving vehicles, the maximum vehicle speed, the random slowing probability and the vehicle flow under the condition of multi-lane change by utilizing a traffic flow basic diagram through microscopic simulation.
Two-lane change microscopic simulation basic diagram
And under the condition of two-lane exchange, microscopic simulation is carried out on closed loop models with different vehicle densities. The microscopic simulation base graphs ("flow-density" graphs) of lane change for two lanes at different autopilot ratios r are shown in fig. 6A-6F, and lane change has no effect on improving traffic flow when fully autopilot. When the vehicle is not fully driven automatically, the smaller the ratio of the vehicle to be driven automatically, the more obvious the effect of lane changing on improving the vehicle flow is, and the lane changing can only improve the vehicle flow when the density of the vehicle is close to the critical density.
The two-lane change microscopic simulation basic diagrams (flow-density diagrams) under different random slowing probabilities q of the manual driving vehicle are shown in fig. 7A-7F, and the effect of lane change on improving the vehicle flow is more obvious except for the case that the random slowing probability is 1.
8A-8J show that the values of visibility k represent the size of the detectability measure, and when the visibility k of the automatic driving vehicle is smaller than 10, the effect of lane changing on improving the vehicle flow is more obvious along with the increase of k; when the visibility k of the automatic driving vehicle is larger than 10, the effect of changing lanes on improving the vehicle flow is not obviously changed along with the increase of k.
Microcosmic simulation basic diagram for lane change of two and three lanes
And under the condition of three-lane change, microscopic simulation is carried out on closed loop models with different vehicle densities. The three-lane change microscopic simulation basic diagrams (flow-density diagrams) under different automatic driving vehicle ratios r are shown in fig. 9A-9F, and the lane change has no effect on improving the vehicle flow when the vehicle is fully automatically driven; when the vehicle is not an automatic driving vehicle, the smaller the ratio of the automatic driving vehicle is, the more obvious the effect of lane changing on improving the vehicle flow is, and the lane changing can only improve the vehicle flow when the density of the vehicle is close to the critical density.
10A-10F show three-lane change microscopic simulation basic diagrams (flow-density diagrams) under the random slowing probability q of different manual driving vehicles, and under the condition that the random slowing probability is 1, lane change has no effect on improving the traffic flow; under the condition that the random slowing probability is smaller than 1, the larger the random slowing probability of the manual driving vehicle is, the better the effect of lane changing on improving the vehicle flow is. Meanwhile, as the random slowing probability becomes larger, the effect of lane changing of three lanes on improving the vehicle flow is more obvious than that of two lanes.
As shown in FIGS. 11A-11J, the numerical value of the visibility k represents the size of the detectability measure, and when the visibility k is smaller than 10, the effect of changing lanes on improving the traffic flow is more obvious along with the increase of the visibility k of the automatic driving vehicle, and meanwhile, the effect of changing lanes on improving the traffic flow is more obvious than that of changing lanes on two lanes. When the visibility k is larger than 10, the effect of changing lanes on improving the vehicle flow is not changed obviously with the increase of k.
Simulation experiments show that in the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the lane changing method can improve average traffic flow in a section of interval close to critical vehicle density. Lane changing does not affect the traffic flow when the density of the vehicle is too small, and lane changing behavior basically does not exist when the density of the vehicle is too large. Meanwhile, the three-lane change efficiency is higher than that of the two-lane change.
And 4, providing a general analysis expression among the vehicle density, the automatic driving vehicle proportion, the automatic driving vehicle visibility, the maximum vehicle speed, the random slowing probability and the vehicle flow, and comparing the analysis expression image with the microscopic simulation basic diagram (flow-density diagram) obtained in the step 3 to verify the correctness of the analysis expression.
When the traffic flow in the lane is respectively an artificial driving lane and an automatic driving lane, a general analysis expression is given; initializing the distribution of the automatic driving vehicles when the traffic flow in the lane is the heterogeneous traffic flow, and giving out an analytical expression of the heterogeneous traffic flow basic diagram under the initialized distribution. And comparing the analysis expression image with the simulation result basic diagram to verify the correctness of the analysis expression.
Homogeneous traffic flow
Homogeneous traffic flows are classified into artificial driving traffic flows and automatic driving traffic flows. For the manual driving vehicle, the random slowing probability needs to be considered, and the following behavior does not exist; for the automatic driving traffic, there is no random slowing probability and there is a following behavior.
Disregarding random slowing probability
Manual driving traffic flow
When the vehicle density is small, each vehicle has a maximum speed v max And (5) running. A simulation of the manual driving traffic operation when the lane is free of congestion is shown in fig. 12.
According to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
F＝v max ρ (13)
where F is the vehicle flow and ρ is the vehicle density.
When the density of vehicles is high, congestion occurs, and the speed of each vehicle is v. When the traffic lane is congested, a simulation diagram of the manual driving traffic running is shown in fig. 13.
As can be seen from fig. 13, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
F＝vρ＝1-ρ (15)
as shown in fig. 14A to 14D, it was verified by cellular automaton microscopic simulation that different maximum vehicle speeds (v) max =1, 5, 10, 20) accuracy of the artificial traffic "flow-density" analytical expression. Wherein "·" is an analogous solution and "-" is a theoretical solution, it can be found that the analogous solution falls on the theoretical solution, which proves thatAnd (5) determining.
Automatic driving traffic flow
When the vehicle density is small, each vehicle has a maximum speed v max And (5) running. When the lane is not congested, a simulation of the running of the automated driving traffic is shown in fig. 15, where k represents the visibility of the automated driving traffic and k+1 is the maximum following length.
According to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
F＝v max ρ (16)
where F is the vehicle flow and ρ is the vehicle density.
When the density of vehicles is high, congestion occurs, and the speed of each vehicle is v. A simulation of the operation of the autopilot flow when traffic congestion occurs is shown in fig. 16.
From the above graph, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
F＝vρ＝k+1-(k+1)ρ (18)
as shown in fig. 17A to 17D, by cellular automaton microscopic simulation, it was verified that different maximum vehicle speeds (v max =1, 5, 10, 20) accuracy of the autopilot flow "flow-density" analytical expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls on the theoretical solution, indicating that the theory justifies.
Consider the probability of random slowing down
Manual driving traffic flow
Assume thatThe random slowing probability q of the manual driving vehicle is 0.25. When the vehicle density is small, each vehicle is at maximum speed v max While the vehicle is running, but the vehicle with the ratio q is decelerating, the average value of the vehicle flow after a plurality of experiments is represented by E (F), and when the traffic lane is not congested, a simulation diagram of the manual driving vehicle flow operation is shown in FIG. 18.
According to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
E(F)＝(v max -q)ρ (19)
where ρ is the vehicle density.
When the density of vehicles is high, congestion occurs, and the speed of each vehicle is v. When the traffic lane is congested, a simulation diagram of the manual driving traffic is shown in fig. 19.
From the above graph, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
E(F)＝vρ-q＝1-ρ-q+ρq (21)
as shown in fig. 20A to 20D, it was verified that different maximum vehicle speeds (v) under consideration of random slowing down were confirmed by microscopic simulation of cellular automata max =1, 5, 10, 20) accuracy of the artificial traffic "flow-density" analytical expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls near the mean value of the theoretical solution, indicating that the theory proves correct.
(II) heterogeneous traffic flow
Heterogeneous traffic is a mixed traffic of manual and automatic driving vehicles. For the manual driving vehicle, the random slowing probability needs to be considered, and the following behavior does not exist; for an autonomous traffic flow, there is no random slowing probability and there is a following behavior. When deducing the "flow-density" analytical expression of heterogeneous traffic flow, it is necessary to determine the distribution of vehicles first. In this study, assuming that the ratio of autopilot vehicles is r, ten vehicles are used as a cycle, wherein 10 (1-r) manual vehicles are in front of the fleet, and 10 autopilot vehicles are in back of the fleet.
Disregarding random slowing probability
When the vehicle density is small, each vehicle has a maximum speed v max And (5) running. When the lane is free of congestion, a simulation of heterogeneous traffic flow operation is shown in fig. 21, where l represents the length of the autonomous fleet for the rear vehicles in the fleet.
According to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
F＝v max ρ (22)
where F is the vehicle flow and ρ is the vehicle density.
When k is greater than or equal to l (r=0.2)
Taking an autopilot ratio of 0.2 as an example, congestion occurs when the density of vehicles is high, and the speed of each vehicle is v. When the traffic lane is congested and k is larger than or equal to l, a simulation diagram of heterogeneous traffic flow operation is shown in FIG. 22.
From the above graph, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
as shown in FIGS. 23A-23D, through microscopic simulation of cellular automata, it was verified that different maximum vehicle speeds (v max Heterogeneous traffic flow of =1, 5, 10, 20)The correctness of the "stream-secret" parsing expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls on the theoretical solution, indicating that the theory justifies.
When k < l (r=0.8)
Taking an autopilot ratio of 0.8 as an example, congestion occurs when the density of vehicles is high, and the speed of each vehicle is v. When the traffic lane is congested and k < l, a simulation diagram of heterogeneous traffic flow operation is shown in fig. 24.
As can be seen from fig. 24, the relationship between the vehicle density and the speed is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
as shown in FIGS. 25A-25D, through microscopic simulation of cellular automata, it was verified that different maximum vehicle speeds (v max =1, 5, 10, 20) the correctness of the heterogeneous traffic flow "flow-density" analytical expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls on the theoretical solution, indicating that the theory justifies.
Consider the probability of random slowing down
Let the random slowing probability q of the manual driving car be 0.25. When the vehicle density is small, each vehicle is at maximum speed v max While a manually driven vehicle is traveling, there is a proportional q vehicle deceleration, while an automatically driven vehicle is not. The average speed can thus be expressed as:
v average ＝rv max +(1-r)[q(v max -1)+(1-q)v]＝v max -q+qr (27)
and E (F) is used for representing the average value of the traffic flow after a plurality of experiments, and when the traffic lane is free from congestion, a simulation diagram of heterogeneous traffic flow operation is shown in fig. 26.
According to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
E(F)＝v average ρ＝(v max -q+qr)ρ (28)
when k is greater than or equal to l (r=0.2)
When the density of vehicles is high, congestion occurs, and the speed of each vehicle is v. When the traffic lane is congested and k is larger than or equal to l, a simulation diagram of heterogeneous traffic flow operation is shown in FIG. 27.
As can be seen from fig. 27, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
through microscopic simulation of cellular automaton, FIGS. 28A-28D verify that when random slowdown is considered and k is greater than or equal to l, different maximum vehicle speeds (v max =1, 5, 10, 20) the correctness of the heterogeneous traffic flow "flow-density" analytical expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls on the theoretical solution, indicating that the theory justifies.
When k < l (r=0.8)
When the density of vehicles is high, congestion occurs, and the speed of each vehicle is v. When congestion occurs in the lane, and k < l, a simulation diagram of heterogeneous traffic flow operation is shown in fig. 29.
From the above graph, the relationship between the vehicle density ρ and the speed v is as follows:
according to the theory of the heterogeneous traffic flow multi-lane NaSch traffic flow model of the automatic driving vehicle, the theoretical relationship is as follows:
as shown in FIGS. 30A-30D, through microscopic simulation of cellular automata, it was verified that different maximum vehicle speeds (v max =1, 5, 10, 20) the correctness of the heterogeneous traffic flow "flow-density" analytical expression. Where "·" is the simulated solution and "-" is the theoretical solution, it can be found that the simulated solution falls on the theoretical solution, indicating that the theory justifies.
The embodiment of the invention provides an automatic driving vehicle heterogeneous traffic flow multi-lane changing system, which uses an automatic driving vehicle heterogeneous traffic flow multi-lane changing method to change the lane of the automatic driving vehicle heterogeneous traffic flow.
The background section of the present invention may contain background information about the problems or environments of the present invention and is not necessarily descriptive of the prior art. Accordingly, inclusion in the background section is not an admission of prior art by the applicant.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several equivalent substitutions and obvious modifications can be made without departing from the spirit of the invention, and the same should be considered to be within the scope of the invention.
Claims (10)
1. A heterogeneous traffic flow multi-lane changing method of an automatic driving vehicle is characterized by comprising the following steps:
s1, establishing an automatic driving vehicle heterogeneous traffic flow multi-lane traffic flow model based on cellular automata;
s2, formulating a multi-lane change rule of the heterogeneous traffic flow of the automatic driving vehicle under the safety principle and the driving time principle; in a three-lane scene, when a left lane needs to be changed, judging whether vehicles in a right lane enter the same cell, if the right lane also meets the lane changing condition and needs to enter the same cell, the vehicles in the left lane and the right lane enter a middle lane with 50% probability respectively, if the left lane is changed, the right lane is not changed, otherwise, if the right lane is changed, the left lane is not changed;
s3, acquiring a microscopic simulation basic diagram of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle through microscopic simulation and analyzing the relation between the dynamic characteristics of the heterogeneous traffic flow multi-lane change of the automatic driving vehicle;
s4, giving out analytic expressions among the dynamic characteristics and performing simulation verification;
the heterogeneous traffic flow is a mixed traffic flow of a manual driving vehicle and an automatic driving vehicle; the step of giving the analytical expression among the dynamic characteristics comprises the following steps:
considering the probability of random slowdown, when the vehicle density is small, each vehicle is at maximum speed v max When the vehicle runs, the manual driving vehicle decelerates in proportion to q, and the automatic driving vehicle does not decelerate; the average speed is:
v average ＝rv max +(1-r)[q(v max -1)+(1-q)v]＝v max -q+qr
the average flow of heterogeneous traffic is expressed as:
E(F)＝v average ρ＝(v max -q+qr)ρ
r is the ratio of the automatic driving vehicle;
when k is larger than or equal to l, wherein k represents the visibility of the automatic driving vehicle, l represents the length of the automatic driving vehicle fleet of the rear vehicles in the vehicle fleet, the speed of each vehicle is v, and when the traffic lane is congested, the relationship between the vehicle density rho and the speed v is as follows:
the average flow of heterogeneous traffic at this time is expressed as:
when k < l, when congestion occurs in the lane, the relationship between the vehicle density ρ and the speed v is as follows:
the average flow of heterogeneous traffic at this time is expressed as:
2. the method for changing lanes of heterogeneous traffic flow of automatic driving vehicles according to claim 1, wherein in step S1, the heterogeneous traffic flow multi-lane traffic flow model of automatic driving vehicles is an automatic driving vehicle heterogeneous traffic flow multi-lane nash traffic flow model obtained by combining the following behavior of automatic driving vehicles and expanding a single vehicle type to the mixed traffic flow of automatic driving vehicles and manual driving vehicles on the basis of the nash traffic flow model.
3. The method for changing lanes of an automated guided vehicle heterogeneous traffic flow according to claim 2, wherein the automated guided vehicle heterogeneous traffic flow multi-lane nash traffic flow model comprises a speed update rule:
if the vehicle is a manually driven vehicle, v according to the NaSch traffic flow model i (t) TableShows the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at the time t, and the speed update formula is as follows:
v i (t+1)＝min{v i (t)+1，v max ，d i (t)} (1)
after the random slowing probability q is considered, the probability q of the manual driving vehicle is slowed down after the speed is updated:
if the vehicle is an autonomous vehicle, and is able to collect information of at most k preceding vehicles, v i (t) represents the speed of the ith vehicle at time t, v max Represents the maximum vehicle speed d i (t) represents the vehicle distance between the ith vehicle and the (i+1) th vehicle at time t,representing the virtual speed formed by the motion state of the k-1 vehicle in front collected by the (i+1) th vehicle, the speed update formula of the (i) th vehicle is as follows:
if the previous vehicle is also an autonomous vehicle, the i+1th vehicle virtual speed is as follows:
if the previous vehicle is an artificial driving vehicle, the virtual speed of the (i+1) th vehicle is as follows:
。
4. the method for changing lanes of an automated guided vehicle heterogeneous traffic flow according to claim 3, wherein the automated guided vehicle heterogeneous traffic flow multi-lane nash traffic flow model further comprises a location update rule:
x i (t+1)＝x i (t)+v i (t+1) (6)
wherein x is i (t) represents the position of the ith vehicle at time t.
5. The method for changing lanes of a heterogeneous traffic stream of an automatic driving vehicle according to claim 1, wherein in step S2, the rule for changing lanes of the heterogeneous traffic stream of the automatic driving vehicle comprises:
the two-lane change needs to meet the following three conditions:
d i (t)＜v i (t) (7)
d i,otherfront (t)＞d i (t) (8)
d i,otherback (t)＞d safe (9)
wherein d is i (t) represents a vehicle distance between the ith vehicle and the (i+1) th vehicle at time t; d, d i,otherfront (t) represents a vehicle distance between the ith vehicle and a preceding vehicle of another lane at time t; d, d i,otherback (t) represents a vehicle distance between the ith vehicle and a rear vehicle of another lane at time t; d, d safe Indicating a safe distance between vehicles.
6. The method for multi-lane changing of a heterogeneous traffic stream of an automatic guided vehicle according to claim 5, wherein the rule for multi-lane changing of a heterogeneous traffic stream of an automatic guided vehicle further comprises:
the three-lane left lane change needs to meet the following three conditions:
d i (t)＜v i (t) (10)
d i,otherfront (t)＞d i (t) (11)
d i,otherback (t)＞d safe (12)
wherein d is i (t) represents the (i+1) th vehicle in front of the left lane and the same lane at the time tThe vehicle distance between vehicles; d, d i,otherfront (t) represents a vehicle distance between the ith vehicle and a preceding vehicle of the intermediate lane at time t; d, d i,otherback (t) represents a vehicle distance between the ith vehicle and a rear vehicle of the intermediate lane at time t;
meanwhile, when the left lane is changed, whether the vehicles on the right lane enter the same cell or not needs to be judged, if the lane changing condition is met and the vehicles on the left lane and the right lane enter the same cell, the probability of entering the middle lane is 50% respectively, and if the lane is changed on the left lane, the lane is not changed on the right lane, otherwise, the vehicles on the left lane and the right lane are the same.
7. The method according to claim 1, wherein in step S3, the dynamics features include vehicle density, ratio of the autopilot, detectability of the autopilot, maximum vehicle speed, probability of random slowing down, and vehicle flow.
8. The method according to claim 1, wherein in step S4, the analytical expression is given by initializing the distribution of the automated driving vehicles when the traffic flow in the lane is the heterogeneous traffic flow.
9. The method according to claim 1, wherein in step S4, the simulation verification verifies the correctness of the analytical expression by comparing the analytical expression image with the microscopic simulation basic diagram.
10. An automated guided heterogeneous traffic flow multi-lane change system, characterized in that automated guided heterogeneous traffic flow multi-lane change is performed using the method of any of claims 1-9.
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