CN111476491A - Vehicle motion simulation analysis method in urban traffic scene - Google Patents

Vehicle motion simulation analysis method in urban traffic scene Download PDF

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CN111476491A
CN111476491A CN202010278241.8A CN202010278241A CN111476491A CN 111476491 A CN111476491 A CN 111476491A CN 202010278241 A CN202010278241 A CN 202010278241A CN 111476491 A CN111476491 A CN 111476491A
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vehicle
lane
lane change
acceleration
vehicles
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杜金莲
周昊
王丹
金雪云
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Beijing University of Technology
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Beijing University of Technology
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Abstract

The invention discloses a vehicle motion simulation analysis method in an urban traffic scene, which improves an intelligent following model based on a safe distance, and can better simulate the characteristics of small vehicle safe distance and faster acceleration of a vehicle before a rear vehicle starts to follow in the urban traffic scene by adding an acceleration adjusting item to a vehicle acceleration formula, thereby improving the applicability of the model. The acceleration analysis-based dual-lane model is improved, the lane changing process of the vehicle is evaluated, whether lane changing behavior can be executed or not is judged, and the rationality of the proposed model is verified through experiments.

Description

Vehicle motion simulation analysis method in urban traffic scene
Technical Field
The invention belongs to the field of computer graphics, and relates to group motion simulation, in particular to a simulation analysis method for vehicle motion in an urban traffic scene.
Background
In recent years, with the rapid development of urban traffic, the traffic flow is increasing, and the problems of traffic jam, vehicle accidents and the like are more and more obvious, so that the problems seriously hinder the development of urban economy and society and reduce the living standard and quality of life of people. At present, modern computer simulation technology is adopted, modeling simulation is carried out on a complex dynamic traffic system of a city, and particularly vehicle motion is simulated to study how to carry out scientific and reasonable planning on urban traffic, so that traffic accidents and congestion are reduced, and the quality of travel is guaranteed.
At present, research on vehicle motion simulation is mostly established on the basis of a vehicle-following model, and the vehicle-following model mainly researches the relation between the acceleration change of a rear vehicle and the speed of a front vehicle when the two vehicles run on the same lane and the rear vehicle does not change the lane. The following model is originally proposed by Gerlough, and the model is used for researching the traffic flow characteristics in the non-free running state and has important significance for simulation and evaluation of the modern traffic flow. The pipeline model provides an assumption that the driver will adjust the speed according to the speed difference between the host vehicle and the preceding vehicle, and subsequent research is basically carried out on the assumption. Kometani originally proposed a safe distance based following model that considers that the driver should maintain a sufficient safe separation to avoid a collision if the driver cannot be certain that the leading vehicle is moving. Treiber et al concluded that an intelligent car-following model based on safe distance is suitable for simulating the movement of vehicles on highways, but in real traffic scenes, due to factors such as traffic jams or traffic lights, the vehicle is kept at a high acceleration when starting to follow the car, and the model cannot achieve the expected effect when being used for simulating the movement of the vehicle. For the problem, the accelerating control item and the decelerating control item are considered by Roc to solve the problem that the rear vehicle is accelerated too slowly when the vehicle starts, but the method is troublesome, and the acceleration of the rear vehicle is changed greatly when the vehicle changes lane at a low speed, so that the method is not suitable for the motion behavior of the vehicle in the urban traffic scene. Therefore, it is necessary to provide a new vehicle motion model to simulate the behavior of the vehicle motion.
Disclosure of Invention
According to the intelligent follow-up model provided by Treiber, the intelligent follow-up model is improved, and the characteristics of small safety distance of vehicles and higher acceleration of vehicles before a rear vehicle starts to follow in an urban traffic scene can be better simulated by adding an acceleration adjusting item to a vehicle acceleration formula, so that the applicability of the model is improved. The method is characterized in that a double-lane change model based on acceleration analysis and proposed by Kesting is improved, safety evaluation is carried out on a vehicle in a lane change process, and whether the lane change behavior can be executed or not is considered according to an evaluation result.
1 improved following model
When the intelligent following model is used for simulating the motion of the vehicle, the vehicle starts to accelerate slowly when following the vehicle ahead, and can be pulled away from the vehicle ahead by a larger difference, so that the intelligent following model is not in line with the motion of the vehicle in a real urban traffic scene. The invention improves an acceleration formula of a rear vehicle in an intelligent following model, and the acceleration is divided into a free acceleration part and an acceleration adjusting part.
When two vehicles L and M are traveling on the same lane and no lane change behavior occurs, the acceleration of the rear vehicle M is largely influenced by the behavior of the front vehicle L
a(t)＝afree-aadjust(1)
Wherein:
Figure BDA0002445555550000031
afreethe term of free acceleration represents the acceleration process of free running of the vehicle on a sparse road. In the formula amaxIs the maximum acceleration of the rear vehicle M, v (t) is the speed of the rear vehicle M at time t, vdA target speed of the rear vehicle M, i.e., an ideal driving speed: (>0) Is an acceleration index.
Figure BDA0002445555550000032
Is an acceleration adjustment term.
In the above formula, bcomFor braking deceleration, s*(v (t), Δ v (t)) is a desired inter-vehicle distance M of the following vehicle at time t, s (t) is an inter-vehicle distance between the two preceding and following vehicles at time t,
Figure BDA0002445555550000033
indicating that the degree of adjustment of the vehicle is influenced by the usual deceleration b of the following vehiclecomΔ v (T) is the speed difference between the rear vehicle M and the front vehicle L, T is the reaction time of the vehicle S, v (T) T is the reaction distance of the vehicle, and (x) is a step function using Δ v (T) and Smin+0.2v (T) T-s (T) to control whether the adjustment term is active to adjust the acceleration value of the vehicle.
2 lane changing model
The Kesting double-lane change model is a widely used lane change model, takes the influence of vehicle lane change on the rear vehicle acceleration of a target lane into consideration, evaluates the safety distance of a vehicle after lane change and solves the safety problem after the vehicle lane change. However, the safe distance of the vehicles in the lane changing process is not considered in the model, and the lane changing of the vehicles is considered to be completed instantly, which obviously does not accord with the lane changing behavior of the vehicles in the urban traffic scene. The method is improved on the basis of a double-lane change model based on acceleration analysis proposed by Kesting, safety evaluation is carried out on the vehicle in the lane change process, and whether the lane change behavior can be executed or not is considered according to the evaluation result.
The lane change models of vehicles may be classified into a free lane change model and an assisted lane change model according to lane change intentions of the vehicles. The following describes two lane-changing behavior models respectively
2.1 free lane-changing model
The free lane change often occurs on a sparse road, the distances among vehicles are large, the vehicles change lanes from the current lane to the target lane due to the requirement of higher speed, and the driving of the vehicles behind the target lane is not influenced. Before the vehicle changes lane in the following state, the speed of the vehicle after lane changing and the speed of the vehicle before lane changing need to be compared, meanwhile, the influence on the vehicles in front of and behind the target lane is also considered, if the speed of the vehicle after lane changing is larger than that before lane changing, the vehicle behind the target lane needs to be decelerated properly, and the current vehicle can perform lane changing. In the whole lane changing process, the driving of the rear vehicle is not greatly influenced after the vehicles are changed, and meanwhile, the distance between the vehicles is not smaller than the expected safe distance.
2.2 assistance lane changing model
Assisted lane changes often occur at points where lane changes are forced, with small vehicle-to-vehicle spacing on the target lane, requiring the rear vehicle on the target vehicle to properly decelerate to match the lane change behavior of the current vehicle. When the assistant lane change occurs, the time of the rear vehicle on the target lane matching the current vehicle should not be longer than the preset maximum lane change time, and in addition, the safety distance between the vehicles in the lane change process is also required to be ensured not to be smaller than the expected safety distance.
Drawings
Fig. 1 is a vehicle motion behavior state diagram.
Fig. 2 is a schematic diagram of a free lane change.
FIG. 3 is a schematic diagram of a forced lane change.
FIG. 4 is a schematic diagram of a free lane change for a vehicle.
FIG. 5 is a schematic view of a vehicle assisted lane change
FIG. 6 is vehicle parameter information used by the formulas in the model.
Fig. 7 is a graph comparing the speed change of a vehicle at a signal intersection.
Fig. 8 is a graph comparing the change in acceleration of a vehicle at a signal intersection.
Fig. 9 is a comparison graph of a simulation of the car-following behavior.
Fig. 10 is a simulation diagram of the lane change behavior of the vehicle.
Detailed Description
In order to make the objects, technical schemes and advantages of the invention clearer, the motion behavior of the vehicle is specifically analyzed, the motion behavior of the vehicle is modeled, and the modeling is explained by combining a figure and a formula, and finally the feasibility of the model is tested.
Firstly, analyzing the characteristics of the vehicle motion to obtain the formation form of a vehicle motion model. In a real urban traffic scene, a driver usually keeps a certain speed to follow a preceding vehicle, and sometimes changes lanes for reasons such as steering or partial overtaking. According to these characteristics, the behavior of the vehicle on the road is classified into two categories, namely, a following behavior and a lane-changing behavior, so that the vehicle motion model is classified into a following model and a lane-changing model. When the vehicle has a lane change intention in a following state, the vehicle performs the lane change behavior according to the lane change model, and continues to maintain the following state according to the following model after changing to a target lane, as shown in fig. 1. The two models jointly form a vehicle motion behavior model in the urban traffic scene.
The basic principle of the model is that when two vehicles L and M run on the same lane and no lane change occurs, the acceleration of the rear vehicle M is influenced by the behavior of the front vehicle L to a great extent, when the distance between the front vehicle and the rear vehicle is increased, the rear vehicle M is accelerated, when the distance is decreased, the rear vehicle M is decelerated.
Figure BDA0002445555550000061
Figure BDA0002445555550000062
Δv(t)＝v(t)-vL(t) (6)
In the formula, a (t) is the acceleration of the rear vehicle M at the time t, amaxIs the maximum acceleration of the rear vehicle M, v (t) is the speed of the rear vehicle M at time t, vdA target speed of the rear vehicle M, i.e., an ideal driving speed: (>0) Is an acceleration index, s (t) is the distance between the front and rear vehicles at time t, s*(v (t), Δ v (t)) is the expected distance of the following vehicle M at the time t; sminThe minimum safety distance in the static state of the vehicle; t is the reaction time of the rear car M, v (T) T is the reaction distance of the rear car M, amaxMaximum acceleration of the rear vehicle M, bcomV (t) L is the velocity of the preceding vehicle L at time t, which is the usual braking deceleration.
When the vehicle is driven on a sparse highway lane, the distance s (t) between the two vehicles is very large when the vehicle is used for simulating the motion of the vehicle,
Figure BDA0002445555550000063
toward 0, the rear vehicle M will continue to accelerate until the speed equals the target speed, and then the vehicle M will enter a constant speed running state. Therefore, the model can better simulate the motion behavior of the vehicle on the highway lane. In addition, when Δ v (t)>When 0, it indicates that the rear vehicle is approaching the front vehicle, the desired distance of the rear vehicle M will be increased, and when Δ v (t)<The model is provided for the vehicle motion behavior in the expressway, does not consider complex traffic conditions, and actually uses the model to simulate the vehicle motion in a real traffic scene, if two vehicles run on the same lane and do not have lane change behavior, when the front vehicle L runs to a signal light area or a congestion area and is forced to stop, the rear vehicle M is decelerated to stop in advance, and the distance between the two vehicles is the static minimum safety distance s generally at the momentminWhen the signal lamp is changed into the light or the front road begins to recover to be smooth, the front vehicle L starts to accelerate from the standstill, and the distance between the two vehicles is the minimum safety distance s of the standstillminHowever, in a real traffic scene, even though the distance between the two vehicles is small, the rear vehicle M accelerates at an acceleration close to the front vehicle L without slowing down in order not to fall behind the front vehicle L too much, so the rear vehicle M should accelerate more first to keep the distance from being pulled too much when restarting, rather than pulling the distance between the two vehicles to the desired safe distance.
And thirdly, improving the intelligent following model and providing a new following model. The acceleration of a rear vehicle in the intelligent following model is improved, the acceleration is divided into a free acceleration part and an acceleration adjusting part, and a formula is defined as follows
a(t)＝afree-aadjust
Wherein:
Figure BDA0002445555550000071
the acceleration process of the vehicle running freely on a sparse road is represented as a free acceleration term;
Figure BDA0002445555550000081
is an acceleration adjustment term.
In the above-mentioned formula,
Figure BDA0002445555550000082
indicating that the degree of adjustment of the vehicle is influenced by the usual deceleration b of the following vehiclecomBy using the limiting terms Δ v (t) and smin+0.2v (T) T-s (T) to solve the deficiency of the basic intelligent following model, (x) is a step function with Δ v (T) and smin+0.2v (T) T-s (T) to control whether the adjustment term is active. The adjustment term only works in the following two cases:
(1) when the relative speed delta v (t) of the front vehicle and the rear vehicle is greater than 0, the rear vehicle M is close to the front vehicle L, the fact that the rear vehicle M needs to pay attention to the distance between the two vehicles is shown, and the speed is the same when the two vehicles reach the safe distance by adjusting the acceleration value;
(2) when the distance s (t) between the two vehicles is less than a defined value(s)min+0.2v (T), the rear vehicle M needs to adjust the acceleration to make it more closely fit the front vehicle L.
And fourthly, analyzing the intention of the lane change of the vehicle and the lane change mode of the vehicle. In a real urban traffic scene, vehicles run following a current lane most of the time, and the vehicles need to change lanes due to the fact that road conditions of adjacent lanes are better or the vehicles need to turn and the like. The invention analyzes the lane changing behavior of the vehicle in the urban traffic scene, selects the lane according to the lane changing intention of the vehicle and the actual traffic environment, detects the safety and determines the specific lane changing mode. Two common vehicle lane-change scenarios are shown in fig. 2 and 3.
The vehicle in fig. 2 is running in a following state, and the vehicle makes an intention of lane change because the adjacent lane is better, and after the target lane is determined, if the vehicle considers that the behavior of changing from the current lane to the lane of the target lane is safe, the vehicle can perform the lane change behavior. FIG. 3 is a forced lane change situation for a vehicle that must perform a lane change action because the vehicle is to turn left on the road ahead, at which time the lane change action may be performed if the required safe distance for the target lane is large enough; otherwise, the rear vehicle on the target lane is required to be properly decelerated to match the lane change behavior of the current vehicle, then the safety of the lane change behavior of the current vehicle is detected, if the detection is met, the current vehicle can change lanes under the matching of the rear vehicle on the target lane, if the detection is not met, the current vehicle decelerates or stops on the current lane, and the lane change is performed when the safety detection condition is met. Based on this, lane change intentions of the vehicle are classified into a free lane change intention and a forced lane change intention, and correspond to the scenarios described in fig. 2 and 3, respectively.
And fifthly, establishing a free lane change model of the vehicle on the basis of the double lane change model. The Kesting double-lane change model is a widely used lane change model, takes the influence of vehicle lane change on the rear vehicle acceleration of a target lane into consideration, evaluates the safety distance of a vehicle after lane change and solves the safety problem after the vehicle lane change. However, the safe distance of the vehicles in the lane changing process is not considered in the model, and the lane changing of the vehicles is considered to be completed instantly, which obviously does not accord with the lane changing behavior of the vehicles in the urban traffic scene. The method is improved on the basis of a double-lane change model based on acceleration analysis proposed by Kesting, safety evaluation is carried out on the vehicle in the lane change process, and whether the lane change behavior can be executed or not is considered according to the evaluation result. The following describes two lane change behavior models respectively.
Before the vehicle changes lane in the following state, the speed of the vehicle after lane changing and the speed of the vehicle before lane changing need to be compared, meanwhile, the influence on the vehicles in front of and behind the target lane is also considered, if the speed of the vehicle after lane changing is larger than that before lane changing, the vehicle behind the target lane needs to be decelerated properly, and the current vehicle can perform lane changing. The schematic diagram of the lane change of the vehicle is shown in fig. 4, and the limiting conditions of the model lane change proposed by Kesting are as follows:
(a)
Figure BDA0002445555550000101
(b)
Figure BDA0002445555550000102
(c)
Figure BDA0002445555550000103
wherein the upper scribed lines of the band are values after the lane change is assumed;
in the condition (a)
Figure BDA0002445555550000104
The acceleration value of the rear vehicle NS after the vehicle S enters the target lane. The acceleration value is estimated by a formula in a following model by assuming that the vehicle S is a front vehicle and the vehicle NS takes the vehicle S as a front vehicle after the vehicle S changes lanes. If it is not
Figure BDA0002445555550000105
It means that the vehicle NS does not decelerate after the lane change behavior of the vehicle S is performed, so that it is safe for the following vehicle NS to change the lane of the vehicle S; if it is not
Figure BDA0002445555550000106
This means that the vehicle NS decelerates after the lane change is performed by the vehicle S, and the acceleration of the brake of the following vehicle NS should not exceed the usual deceleration b of the vehicle in order to ensure that the normal running of the following vehicle NS is not affectedcom。
Conditions (b) and (c) are to evaluate whether the distance between the vehicles NS, NE and the post-lane-change vehicle S is larger than the safety between any two vehiclesThe total distance is equal to the total distance,
Figure BDA0002445555550000107
and
Figure BDA0002445555550000108
representing the safe distances of the vehicle S from the following vehicle NS and the preceding vehicle NE, respectively.
The safety problem after lane changing is only considered in the model, and the safety distance between vehicles in the lane changing process is not considered. The improved lane-changing model conditions are as follows:
(a)
Figure BDA0002445555550000109
(b)
Figure BDA00024455555500001010
(c)
Figure BDA00024455555500001011
Figure BDA00024455555500001012
considering the safety problem during lane change when calculating the desired distance between vehicles in conditions (b) and (c), a parameter time point T is addedc. Equation (7) is an evaluation equation for calculating the safe distance, in which sminIs the minimum safe distance, w, at rest of the vehicle1(s*(a)-(v(a)-v(b))Tc) Is the desired spacing during the lane change of the vehicle S, which will vary with time, TcIs a time point in the lane change process, and detects T in consideration of small speed change of the vehicle in the free lane change processc＝0.0,0.5Th,Th(ThTime required for lane change) whether the distance between the three times meets the requirement, the vehicle S may perform the lane change action when the three conditions are met at the same time.
And sixthly, establishing an assistant lane change model of the vehicle. Vehicles sometimes need to be forced to change lanes due to traffic accidents and the like, and in such cases, the vehicles often need to assist the vehicles on the target lane to change lanes. One scenario in which the vehicle S is forced to change lanes is shown in fig. 5. Since the vehicle S has an intention to turn left on the road ahead, at this time, the vehicle S must change lanes to the lane on the inner side to turn, and it is first judged whether the vehicle S can safely change lanes using the free lane change model, if so, the lane change behavior is executed, otherwise, the rear vehicle NS on the target lane needs to be properly decelerated to match the lane change behavior of the current vehicle S. If the safety of lane change of the vehicle S is not yet satisfied in the case of the cooperation of the vehicle NS, the vehicle S needs to perform a lane change behavior while the original lane waits for the safety check to be satisfied. The schematic diagram of the assisted lane change and the lane change conditions are as follows:
(a)
Figure BDA0002445555550000111
(b)
Figure BDA0002445555550000112
(c)
Figure BDA0002445555550000113
FIG. 5 shows the assisted lane change process of the vehicle, t in condition (a)nsIs the time, Δ V, taken for the lane change of the vehicle SnsIs the speed value that the vehicle NS can reduce in cooperation with the vehicle S, bcomFor a usual braking deceleration, max _ time is a set maximum lane change time of the vehicle, and the condition (a) is satisfied only when the vehicle S can complete lane change within the maximum lane change time of the vehicle when the vehicle NS is willing to cooperate with deceleration. Under conditions (b) and (c)
Figure BDA0002445555550000121
And
Figure BDA0002445555550000122
respectively representSafe distances of the vehicle S from the following vehicle NS and the preceding vehicle NE. The calculations involved are substantially similar to those in the free-lane-change model. However, there are two differences: the following vehicle NS on the target lane in the assisted lane change model is matched with the usual braking deceleration, so the acceleration is used by b when calculating the distance between the two vehiclescom(ii) a Since the desired spacing of the vehicles during assisted lane change may be small, the value of the weight w1 is reduced. It is also the case that the vehicle S can perform the lane change behavior if all three conditions are satisfied.
The method adopts an OpenG L graphic library and a Qt5.4.1 platform to develop a vehicle motion simulation system and carry out simulation test on the behavior of vehicle motion, and the test system is configured by an inter (R) core (TM) i5-45903.30GHz, an NVIDIA GeforceGTS250 as a display card and a memory of 16 GB.
When the intelligent distance-based follow-up model is used for simulating the behavior of the vehicle, the vehicle starts slowly when the traffic flow restarts circulation for vehicles which are forced to stop due to traffic jam or traffic lights. The present invention proposes an improved follow-up model to solve this problem, which is tested below in order to verify the vehicle motion model constructed by the present invention. (FIG. 6 shows parameter information of a vehicle under test)
And selecting an intersection with a traffic light, and recording the speed change of the vehicle meeting the traffic light. During the period, the traffic light is firstly red and is changed into green light at the moment of 20s, the corresponding vehicle is gradually decelerated to stop before the red light, and the traffic light is changed into green light and then is accelerated to pass through the intersection. The acceleration of the vehicle is updated by respectively using an acceleration calculation formula (4) of an intelligent follow-up model based on a safe distance and a formula (1) of an improved follow-up model, the speed and the change contrast of the acceleration of the vehicle are shown in figures 7 and 8, the simulation of the follow-up behavior of the vehicle passing through an intersection by using the intelligent follow-up model and the improved follow-up model is shown in figure 9, and as can be seen from figures 7 and 8, when the motion of the vehicle is simulated by using the improved follow-up model, the acceleration process of the vehicle is faster when a signal lamp of the vehicle is changed into a green lamp, and a larger speed can be reached in a short time; it can be seen from fig. 9 that the improved following model has smaller distance between vehicles passing through the signal lamp, and is more consistent with the motion situation of vehicles in real traffic scene.
In urban traffic scenes, because the road condition of adjacent lanes is better or the vehicle needs to turn and the like, the invention carries out safety detection on the vehicle in the lane changing process, and the time point T of the detectionc＝0.0，0.5Th，Th(ThTime required for lane change), the test of the lane change model of the vehicle is shown in fig. 10
The double-lane changing model provided by Kesting simplifies the lane changing behavior of the vehicle into the instant movement of the vehicle between two lanes, and the improved lane changing model considers the safety problem of the vehicle in the lane changing process, detects the safety distance of the vehicle in the lane changing process and better ensures the safety of the vehicle lane changing.

Claims (2)

1. A vehicle motion simulation analysis method in an urban traffic scene is characterized by comprising the following steps;
step 1, constructing a car following behavior model
When two vehicles L and M are traveling on the same lane and no lane change occurs, the acceleration of the rear vehicle M is greatly affected by the behavior of the front vehicle L, the rear vehicle M accelerates when the distance between the front and rear vehicles becomes large, and the rear vehicle M decelerates when the distance becomes small, wherein the acceleration formula of M is as follows:
a(t)＝afree-aadjust(1.1)
in the formula, a (t) is the acceleration of the rear vehicle M at the time t, wherein:
Figure FDA0002445555540000011
afreeis a free acceleration term representing the acceleration process of the vehicle in free running on a sparse road, amaxIs the maximum acceleration of the rear vehicle M, v (t) is the speed of the rear vehicle M at time t, vdFor rear vehicleThe target speed of M, i.e. the ideal driving speed, is an acceleration index;
Figure FDA0002445555540000012
aadjustin order to adjust the term for the acceleration,
Figure FDA0002445555540000013
indicating that the degree of adjustment of the vehicle is influenced by the usual deceleration b of the following vehiclecomThe deficiency of the basic intelligent follow-up model is solved by using a restriction term; (x) Is a step function, using Δ v (t) and smin+0.2v (T) T-s (T) to control whether the adjustment term is active; the adjustment term only works in the following two cases: relative speed Δ v (t) of the front and rear vehicles>When 0, it indicates that the rear vehicle M is approaching the front vehicle L, indicating that the rear vehicle M needs to pay attention to the distance between the two vehicles, adjusting the acceleration value to make the speed the same when the safety distance between the two vehicles is reached, and when the current distance s (t) is less than a defined value(s)min+0.2v (T), the rear vehicle M needs to adjust the acceleration so as to better fit the front vehicle L;
step 2, constructing a vehicle lane change behavior model
In a real urban traffic scene, vehicles run following a current lane most of the time, and the vehicles need to change lanes due to the fact that the road condition of adjacent lanes is better or the vehicles need to turn and the like; the lane change intention of the vehicle can be classified into a free lane change model and an assisted lane change model.
2. The method for simulating and analyzing the vehicle motion in the urban traffic scene as claimed in claim 1, wherein the step 2 is specifically as follows:
2.1) establishing a free lane-changing model
When the vehicle S runs, the rear vehicle on the target lane after lane change is NS, and the front vehicle is NE; the conditions under which the vehicle S can perform lane change behavior are as follows:
(a)
Figure FDA0002445555540000021
(b)
Figure FDA0002445555540000022
(c)
Figure FDA0002445555540000023
Figure FDA0002445555540000024
the underlined values in the formula are the values after the assumed lane change, in condition (a)
Figure FDA0002445555540000025
The acceleration value of a vehicle NS behind a target lane of the vehicle S is assumed, and the vehicle NS takes the vehicle S as a front vehicle after the vehicle S changes lanes; if it is not
Figure FDA0002445555540000026
It means that the vehicle NS does not decelerate after the lane change behavior of the vehicle S is performed, so that it is safe for the following vehicle NS to change the lane of the vehicle S; if it is not
Figure FDA0002445555540000027
This means that the NS decelerates after the lane change is performed by the vehicle S, and the acceleration of the brake does not exceed the usual deceleration b of the vehicle in order to ensure that the normal driving of the following vehicle NS is not affectedcomThe vehicle S performs lane change behavior under the condition of not exceeding;
the conditions (b) and (c) are to evaluate whether the distance between the vehicles NS, NE and the post-lane-change vehicle S is larger than the safe distance between any two vehicles,
Figure FDA0002445555540000031
and
Figure FDA0002445555540000032
respectively representing the safe distances of the vehicle S from the rear vehicle NS and the front vehicle NE;
equation (2.1) is an evaluation equation for calculating the safe distance, where sminIs the minimum safe distance, w, at rest of the vehicle1(s*(a)-(v(a)-v(b))Tc) Is the desired spacing during the lane change of the vehicle S, which will vary with time, TcIs a time point in the lane change process, and detects T in consideration of small speed change of the vehicle in the free lane change processc＝0.0,0.5Th,Th(ThTime required for lane change) whether the distance between the three moments meets the requirement, and the vehicle S can execute lane change action when the three conditions are met simultaneously;
2.2) establishing an assisted lane change model
Assisting lane changing often occurs at a forced lane changing place, the distance between a vehicle and a vehicle on a target lane is small, and a rear vehicle on the target vehicle needs to be properly decelerated to match the lane changing behavior of the current vehicle; when the assisted lane change occurs, the time of the rear vehicle on the target lane matching the current vehicle should not be longer than the preset maximum lane change time, and in addition, the safety distance between the vehicles in the lane change process is also required to be ensured not to be smaller than the expected safety distance;
the method comprises the following steps that a vehicle S has an intention of turning left on a road in front, the vehicle S can turn only when needing to change lanes to a target lane on the inner side, firstly, a free lane changing model is used for judging whether the vehicle S can safely change lanes, if so, lane changing behavior is executed, otherwise, a rear vehicle NS on the target lane is required to be properly decelerated to match with the lane changing behavior of the current vehicle S; if the lane change safety of the vehicle S cannot be met under the condition that the vehicle NS is matched, the vehicle S needs to execute lane change behavior under the condition that the original lane waits for meeting the safety detection; the conditions under which the vehicle S can perform lane change behavior are as follows:
(a)
Figure FDA0002445555540000041
(b)
Figure FDA0002445555540000042
(c)
Figure FDA0002445555540000043
t in the condition (a)nsIs the time, Δ V, taken for the lane change of the vehicle SnsIs the speed value that the vehicle NS can reduce in cooperation with the vehicle S, bcomFor the commonly used braking deceleration, the max _ time is the set maximum lane changing time of the vehicle, and under the condition that the vehicle NS is willing to cooperate with deceleration, the condition (a) is met only when the vehicle S can complete lane changing within the maximum lane changing time of the vehicle;
under conditions (b) and (c)
Figure FDA0002445555540000044
And
Figure FDA0002445555540000045
respectively representing the safe distances of the vehicle S from the rear vehicle NS and the front vehicle NE; the following vehicle NS on the target lane in the assisted lane change model is matched with the usual braking deceleration, so the acceleration value b is used when calculating the expected distance between the two vehiclescom(ii) a Since the expected spacing of the vehicles during assisted lane change may be small, the value of weight w1 is reduced; it is also the case that the vehicle S can perform the lane change behavior if all three conditions are satisfied.
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CN114708730A (en) * 2022-04-01 2022-07-05 广州大学 Bridge floor traffic space-time distribution reconstruction random traffic flow virtual-real mixed simulation method and device
CN115906265A (en) * 2022-12-27 2023-04-04 中交第二公路勘察设计研究院有限公司 Near main line outlet marking optimization method based on lane changing behavior characteristics
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112417642A (en) * 2020-09-30 2021-02-26 天津大学 Motorcade traffic flow simulation method considering randomness of wave travel time
CN114708730A (en) * 2022-04-01 2022-07-05 广州大学 Bridge floor traffic space-time distribution reconstruction random traffic flow virtual-real mixed simulation method and device
CN115906265A (en) * 2022-12-27 2023-04-04 中交第二公路勘察设计研究院有限公司 Near main line outlet marking optimization method based on lane changing behavior characteristics
CN115906265B (en) * 2022-12-27 2024-05-10 中交第二公路勘察设计研究院有限公司 Near main line outlet marking optimization method based on channel change behavior characteristics

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