CN115482663A - Intersection traffic control method considering special automatic driving phase - Google Patents

Intersection traffic control method considering special automatic driving phase Download PDF

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CN115482663A
CN115482663A CN202211106668.5A CN202211106668A CN115482663A CN 115482663 A CN115482663 A CN 115482663A CN 202211106668 A CN202211106668 A CN 202211106668A CN 115482663 A CN115482663 A CN 115482663A
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traffic
intersection
lane
automatic driving
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CN115482663B (en
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吴伟
秦少敏
胡林
杜荣华
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Changsha University of Science and Technology
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/015Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/065Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/07Controlling traffic signals
    • G08G1/08Controlling traffic signals according to detected number or speed of vehicles
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/07Controlling traffic signals
    • G08G1/085Controlling traffic signals using a free-running cyclic timer
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/09Arrangements for giving variable traffic instructions
    • G08G1/0962Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02TCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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Abstract

The invention discloses an intersection traffic control method considering a special phase for automatic driving, which aims at an intersection where an automatic driving vehicle and a manual driving vehicle run in a mixed mode. Firstly, acquiring the number of lanes in each entrance direction of an intersection and the initial traffic volume of an automatic driving vehicle and a manual driving vehicle; secondly, considering the cross-over type traffic of the automatic driving vehicles, establishing a prediction model of the phase traffic capacity special for automatic driving, and establishing a lane layout model; then, designing a phase structure considering the phase special for automatic driving, establishing a signal timing model, and distributing the optimal traffic volume of each entrance lane; and finally, optimizing to obtain an intersection signal timing scheme by taking the maximum traffic capacity of the intersection as a control target. The invention reasonably distributes the right of passage of the automatic driving vehicle and the manual driving vehicle based on the special phase for automatic driving, realizes the separation control of the automatic driving vehicle and the manual driving vehicle, and improves the passage capacity of the intersection.

Description

Intersection traffic control method considering special automatic driving phase
Technical Field
The invention belongs to the field of intelligent traffic control, and particularly relates to an intersection traffic control method considering a special phase for automatic driving.
Background
Under the premise that the automatic driving technology is gradually mature, the automatic driving vehicle is gradually applied in a commercial demonstration mode, the automatic driving becomes one of future traffic development trends, and the 'development of travel services such as automatic driving and vehicle-road cooperation and the like, encouragement of testing applications in limited areas such as ports, logistics parks and the like' is listed as a key research and development target. According to the classification standard of automatic driving of vehicles formulated by the American engineering society, under the automatic driving environment above the L4 level, intelligent information sharing can be realized among the vehicles, infrastructure road facilities and the vehicles, the traffic control strategy of 'stop at red light-go at green light' based on lane groups can be broken through, and the mutual cooperation and penetration type traffic of the automatic driving vehicles can be realized.
According to the plan of the Chinese society of automotive engineering, the market share of highly automatic driving vehicles (more than L4 grade) reaches about 15% by 2025 years. The canadian traffic policy institute predicts that autonomous vehicles will grow step by step and predicts that autonomous vehicles will account for 50% of car sales, 30% of car inventories, and 40% of car trips by 2040. The prevalence of autonomous vehicles is difficult to reach 100% in the foreseeable time, and it is expected that traffic flow will still dominate mixed traffic flow (man-driven and autonomous vehicle mixing) in the next 20-30 years.
However, the existing traffic control method for the mixed crossing mainly has the following defects: a unified control mode is adopted for mixed traffic flow, namely, automatic driving vehicles and manual driving vehicles run on the same lane in a mixed mode, the same phase control is observed together, and the right of way and the green light time are shared, so that mutual interference between the automatic driving vehicles and the manual driving vehicles is caused, and the passing efficiency advantage of the automatic driving vehicles is difficult to embody. Therefore, the special automatic driving phase is provided, the special automatic driving lane is arranged, and during the running period of the special automatic driving phase, the automatic driving vehicles pass through the intersection in a penetrating manner, so that the separation control is realized on the automatic driving vehicles and the manual driving vehicles, and the transportation efficiency of the intersection is effectively improved.
Disclosure of Invention
The invention aims to overcome the defects and establish an intersection traffic control method considering the special automatic driving phase. The method avoids mutual conflict between automatic driving and manual driving vehicles, provides a special phase for automatic driving, lays a special lane for automatic driving, separates the automatic driving and manual driving vehicles from two dimensions of time and space, and realizes separation control of the mixed intersection.
The technical scheme is as follows: in order to solve the technical problem, the intersection traffic control method considering the special automatic driving phase comprises the following specific steps of:
step 1: collecting the number of lanes at the entrance and the exit of each direction of an intersection and the traffic volume of automatic driving and manual driving vehicles with different steering directions at each entrance;
and 2, step: considering the cross-over type passing of the automatic driving vehicle, establishing a prediction model of the phase passing capacity special for automatic driving;
and 3, step 3: dividing an intersection entrance lane into a common lane and a dynamic lane, and establishing an intersection lane layout and traffic capacity model;
and 4, step 4: designing a phase structure considering a special automatic driving phase, and constructing an intersection signal timing model;
and 5: the optimal traffic volume of each import lane is distributed by taking the traffic volume of the lane not exceeding the traffic capacity as a constraint;
step 6: and optimizing and obtaining an intersection signal timing scheme considering the automatic driving special phase by taking the intersection traffic capacity maximization as an objective.
Preferably, the step 1 of acquiring the number of the lanes of the inlet and the outlet in each direction of the intersection to acquire the traffic volume of the automatic driving vehicle and the manual driving vehicle with different steering in each direction of the inlet of the intersection comprises the following steps:
step 11: acquiring the number of lanes at each direction of an intersection, using a parameter L to represent an entrance direction, using a parameter L 'to represent an exit direction, using a set L to represent an entrance direction set, using a set L' to represent an exit direction set, and using L = L '= { E, S, W, N }, wherein E, S, W, N respectively represent east, west, and y, and L = L' = { E, S, W, N }, and,South, west and north directions, parameter e l Number of lanes representing the entry direction l;
step 12: the method comprises the steps of obtaining traffic volumes of automatic driving and manual driving vehicles with different steering directions at each entrance direction of an intersection, representing vehicle steering by using a parameter K, representing a vehicle steering set by using a set K, wherein K = { S, L }, K belongs to K, S and L respectively represent vehicle straight running and left turning, representing a vehicle type by using a parameter Z, representing a vehicle type set by using a set Z, Z = { A, H }, Z belongs to Z, wherein A and H respectively represent an automatic driving vehicle and a manual driving vehicle, and parameters A and H respectively represent an automatic driving vehicle and a manual driving vehicle
Figure BDA0003840201060000021
Indicating an initial amount of traffic of type z turning k vehicles in the intersection approach direction i.
Preferably, the step 2 of establishing a prediction model of the passing capacity of the automatic driving-dedicated phase in consideration of the cross-passing of the automatic driving vehicle comprises the following steps:
step 21: determining the safe time interval of the automatic driving vehicle, considering the cross-over type traffic of the automatic driving vehicle, and in order to prevent the collision between the vehicles, a safe time interval delta h must be kept between two vehicles n and n' which drive into the intersection in succession n Wherein, the vehicle n comes from a traffic flow i, n belongs to i, the vehicle n 'comes from a traffic flow j, n' belongs to j, and according to the conflict relationship between two vehicle tracks, the safety time distance between vehicles specifically comprises the following three conditions:
(1) vehicles n and n' from the same set of traffic flows, Δ h n =h 0 ,j=i;
(2) Vehicles n and n' are from two sets of traffic flows, Δ h, without conflict n =h 1 ,j∈A i
(3) Vehicles n and n' are from two sets of traffic flows with conflict, Δ h n =h 2 ,j∈B i
Therefore, the specific calculation of the vehicle safety time interval is shown as the formula (1):
Figure BDA0003840201060000022
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Set of traffic flows, h, representing conflicts with traffic flow i 0 、h 1 、h 2 Respectively representing the safe time distance between two vehicles which are driven into the intersection successively from the same traffic flow, two groups of traffic flows without conflict and two groups of traffic flows with conflict;
step 22: determining the service time of the automatic driving vehicle, and taking the time gap between two vehicles n and n' which successively enter the intersection as the service time S based on the first-come first-serve and queuing theory n On the premise of ensuring the safety of vehicle passing, the service time of the vehicle is reduced to improve the passing efficiency of the intersection to the maximum extent, so that the service time of the automatic driving vehicle is equal to the safety time interval S n =Δh n The specific relationship is as follows:
Figure BDA0003840201060000023
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Set of traffic flows, s, representing conflicts with traffic flow i 0 、s 1 、s 2 Respectively representing the service time between vehicles which are driven into the intersection successively from the same traffic flow, two groups of traffic flows without conflict and two groups of traffic flows with conflict;
step 23: calculating the service probability of the automatic driving vehicles, wherein the arrival of the vehicles is independent, and the probability P that the m-th vehicle entering the intersection comes from the traffic flow i i m Equal to the average arrival rate mu of vehicles on the traffic flow i i Dividing by the sum of the average arrival rates of vehicles on each group of traffic flows at the intersection, and calculating as follows:
Figure BDA0003840201060000031
wherein the set I represents an intersection traffic flow set, I, j belongs to I, P i Representing the probability that the vehicle is from the traffic flow i;
two vehicles drive in one after the otherThe probability of collision states of vehicles at an intersection includes three conditions: (1) probability that two vehicles are from the same traffic flow, i.e. following probability P 0 As calculated in equation (4); (2) probability that two vehicles come from two groups of non-conflicted traffic flows, i.e. probability of being in the same row P 1 As calculated in equation (5); (3) probability of two vehicles coming from two groups of traffic flows with conflict, i.e. conflict probability P 2 Calculated by equation (6), as follows:
Figure BDA0003840201060000032
Figure BDA0003840201060000033
Figure BDA0003840201060000034
step 24: calculating average service time of automatic driving vehicle, and running down-inserting type traffic flow I by automatic driving special phase p p Is equal to the sum of the service times multiplied by their probabilities, calculated as:
Figure BDA0003840201060000035
step 25: predicting the traffic capacity of the automatic driving special phase, two vehicles from two groups of non-conflict traffic flows can drive into the intersection at the same time, and the safe time interval s of the vehicles 1 =0, so its service time s 1 =0, average service time of autonomous vehicle, traffic capacity C of autonomous driving-dedicated phase p p The calculation is as follows:
Figure BDA0003840201060000036
wherein the parameter p represents the autopilot-specific phase, set
Figure BDA0003840201060000037
Indicating a set of traffic flows that conflict with the autopilot-specific phase p traffic flow i.
Preferably, the step 3 of dividing the intersection entrance lane into a common lane and a dynamic lane, and establishing an intersection lane layout and traffic capacity model comprises the following steps:
step 31: the intersection entrance lane is divided into a common lane and a dynamic lane; when the dynamic lane is set as a common lane, the automatic driving vehicle and the manual driving vehicle are driven in the common lane in a mixed mode; when the dynamic lane is set as an automatic driving special lane, the automatic driving vehicle runs on the automatic driving special lane, and the manual driving vehicle runs on the common lane; when the green time of the automatic driving special phase at the intersection is more than 0, the corresponding dynamic lane is set as an automatic driving special lane; when the special phase for automatic driving at the intersection is equal to 0, the corresponding dynamic lane is set as a common lane; therefore, the lane layout constraint is as follows:
t p ≥M×(β l,k -1) (9)
t p ≤M×β l,k (10)
wherein, a set P intersection automatic driving special phase set is used, P = { EWA, SNA }, P belongs to P, EWA, SNA respectively represent east-west automatic driving special phase, north-south automatic driving special phase; m represents a large positive integer, t p Green time, beta, indicating autopilot-specific phase p l,k Is a binary variable, beta l,k =1 denotes that the dynamic lane turned to k in the entry direction l is set as the driveway for automatic driving, β l,k =0 indicates that the dynamic lane turned to k in the entry direction i is set as the ordinary lane;
step 32: the traffic capacity of a common lane is equal to the unit time divided by the saturated headway time of a manually driven vehicle multiplied by the split green ratio, the split green ratio is equal to the split green time divided by the signal cycle duration:
Figure BDA0003840201060000038
wherein the content of the first and second substances,
Figure BDA0003840201060000039
indicating the capacity of the ordinary lane to pass with a turn k in the direction of entry i, τ H Representing the saturated headway of the manually driven vehicle;
step 33: the traffic capacity of the automatic driving special lane consists of two parts, namely the distribution of the traffic capacity of the automatic driving special phase, the interpenetration traffic of the vehicles in the automatic driving special phase, the equal proportion distribution of the traffic capacity of the corresponding automatic driving special lane according to the initial traffic flow, and the sharing of the traffic capacity of the common phase between the automatic driving vehicles and the manual driving vehicles; therefore, the traffic capacity of the driveway-only autonomous vehicle is calculated as follows:
Figure BDA0003840201060000041
wherein the content of the first and second substances,
Figure BDA0003840201060000042
indicating the traffic capacity, τ, of a driveway exclusively for steering k in the direction of entry i A Representing a saturated headway for the autonomous vehicle;
step 34: when the dynamic lane is set as the automatic driving special lane, beta l,k =1, the traffic capacity of which is equal to that of the driverless lane; when the dynamic lane is set as a normal lane, beta l,k =0, the traffic capacity of which is equal to that of the ordinary lane, and therefore the dynamic lane traffic capacity is calculated as follows:
Figure BDA0003840201060000043
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003840201060000044
representing the dynamic lane capacity of steering k in the entry direction i.
Preferably, the step 4 designs a phase structure of the intersection dedicated automatic driving phase, and constructs a signal timing model of the dedicated automatic driving phase, including the following steps:
step 41: designing a phase structure of the automatic driving special phase, and additionally arranging two automatic driving special phases on the basis of the traditional signal phase, wherein the manual driving traffic flows turning left and going straight in the east and west directions form a first group of common phases, the manual driving traffic flows turning left and going straight in the south and north directions form a second group of common phases, the automatic driving traffic flows turning left and going straight in the south and north directions form a 3 rd group of north-south automatic driving special phase, and the automatic driving traffic flows turning right and going left in the east and west directions form a 4 th group of east-west-automatic driving special phase;
step 42: the intersection signal cycle duration T is not more than the maximum cycle T max Not less than the minimum period T min
T min ≤T≤T max (14)
Step 43: the full red time AR is set after each group of phases are operated to empty vehicles in the intersection, the cycle full red time is equal to the sum of the full red time of the common phase and the full red time of the special phase for automatic driving, and therefore the phase cycle full red time T 0
Figure BDA0003840201060000045
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003840201060000046
is a binary variable, and is characterized in that,
Figure BDA0003840201060000047
it indicates that the intersection is set with the autopilot-specific phase p,
Figure BDA0003840201060000048
indicating that the intersection is not provided with the automatic driving special phase p;
step 44: the sum of the green light time of the east-entry left-turn traffic flow and the west-entry straight traffic flow in the first group of common phases is equal to the sum of the green light time of the west-entry left-turn traffic flow and the east-entry straight traffic flow, and is expressed as a formula (16); the sum of the green light time of the south-entry left-turn traffic flow and the north-entry straight traffic flow in the second group of common phases is equal to the sum of the left-turn flow time of the north-entry and the straight flow time of the south-entry to the green light, as shown in formula (17):
t EL +t WS =t WL +t ES (16)
t SL +t NS =t NL +t SS (17)
wherein, t EL 、t WS 、t WL 、t ES Respectively representing the green light time t of the traffic flow of the east import left-turn traffic flow, the west import straight traffic flow, the west import left-turn traffic flow and the east import straight traffic flow SL 、t NS 、t NL 、t SS Respectively representing the green light time of the south-entry left-turn traffic flow, the north-entry straight traffic flow, the north-entry left-turn traffic flow and the south-entry straight traffic flow;
step 45: the signal period duration is equal to the sum of the green light time of each group of phases and the full red time of the period, wherein the first group of common phase green light time is equal to the sum of the green light time of the east import left turn and the west import straight, and the second group of common phase green light time is equal to the sum of the green light time of the south import left turn and the north import straight, so the signal period duration is calculated as follows:
T=t EL +t WS +t SL +t NS +∑ p∈P t p +T 0 (18)
step 46: the green time of each traffic flow under the common phase is not less than the minimum green time g min
t l,k ≥g min (19)
Wherein, t l,k Indicating the green time of the traffic flow with the intersection inlet direction l turning to k;
step 47: when the intersection is provided with the special automatic driving phase, the green time is not less than the minimum green time:
Figure BDA0003840201060000051
preferably, the step 5 of allocating the optimal traffic volume of each entrance lane by using the constraint that the traffic volume of the lane does not exceed the traffic capacity of the lane comprises the following steps:
step 51: when the dynamic lane is set as the ordinary lane, beta l,k =0, the initial traffic volume of the ordinary lane is equal to the ratio of the sum of the initial traffic volumes of automatic driving and manual driving to the sum of the number of the ordinary lane and the dynamic lane, and the initial traffic volume of the dynamic lane is equal to the initial traffic volume of the ordinary lane; when the dynamic lane is set as the autonomous driving exclusive lane, beta l,k =1, the initial traffic volume of the ordinary lane is equal to the ratio of the manual driving initial traffic volume to the number of ordinary lanes, the optimal traffic volume of the dynamic lane is equal to the ratio of the automatic driving initial traffic volume to the number of dynamic lanes, and the optimal traffic volume of the lane is equal to the initial traffic volume of the lane multiplied by an amplification factor λ, so the optimal traffic volume of the entrance lane at the intersection is calculated as follows:
Figure BDA0003840201060000052
Figure BDA0003840201060000053
wherein the content of the first and second substances,
Figure BDA0003840201060000054
represents the optimal traffic volume of a common lane with the intersection inlet direction I turning to k,
Figure BDA0003840201060000055
represents the optimal traffic volume of the dynamic lane with the intersection inlet direction I turning to k,
Figure BDA0003840201060000056
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure BDA0003840201060000057
an initial amount of traffic of an autonomous vehicle indicating that an intersection approach direction/turns to k,
Figure BDA0003840201060000058
representing the number of ordinary lanes turning k in the intersection approach direction/,
Figure BDA0003840201060000059
representing the number of dynamic lanes with k steering in the inlet direction l of the intersection;
step 52: the best traffic volume of the common lane cannot exceed the traffic capacity of the common lane:
Figure BDA00038402010600000510
step 53: when the dynamic lane is set as a normal lane, beta l,k =0, the optimal traffic volume of which cannot exceed the capacity of a normal lane; when the dynamic lane is set as the automatic driving special lane, beta l,k =1, the optimal traffic volume cannot exceed the capacity of the driveway exclusive for automatic driving, and therefore the optimal traffic volume for dynamic lane satisfies:
Figure BDA00038402010600000511
preferably, the step 6 comprises the following steps:
step 61: introducing an amplification coefficient variable lambda, wherein lambda is equal to the ratio of the optimal traffic volume of each turning vehicle type in each entrance direction of the intersection after distribution optimization to the initial traffic flow of each turning vehicle type in each entrance direction of the intersection:
Figure BDA00038402010600000512
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00038402010600000513
an optimal traffic volume representing an intersection approach direction/steering k vehicle type z,
Figure BDA00038402010600000514
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure BDA00038402010600000515
indicating that the turning direction I of an intersection is the initial traffic volume of the vehicle type z;
step 62: lambda represents the relative size of the traffic capacity of the intersection, the maximum traffic capacity of the intersection is a control target, and an intersection signal timing scheme is obtained through optimization:
maxλ (26)。
the invention has the beneficial effects that:
the method is characterized in that an automatic driving special lane is arranged for an intersection where automatic driving vehicles and manual driving vehicles run in a mixed mode, an automatic driving special phase is provided, the automatic driving vehicles pass through in a penetrating mode during running of the automatic driving special phase, an automatic driving special phase traffic capacity prediction model is constructed, a phase structure of the automatic driving special phase is designed, the optimal traffic volume of each entrance lane is distributed, the intersection traffic capacity is the maximum control target, a signal timing scheme of the intersection is obtained in an optimized mode, and the intersection running efficiency is improved. In the prior art, the mixed crossing is less separately controlled, and a unified control mode is mostly adopted.
Drawings
FIG. 1 is a general flow diagram of the present invention.
Fig. 2 is a schematic view of an intersection of the present invention.
Fig. 3 is a phase structure diagram of the intersection automated driving exclusive phase of the present invention.
En route numbering illustrates: 201 is a manually driven vehicle, 202 is an automatically driven vehicle, 203 is a general signal light, 204 is an automatically driving dedicated phase signal light, 205 is a dynamic lane, 301 is an east-west-automatically driving dedicated phase, and 302 is a north-south-automatically driving dedicated phase.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and embodiments, in which the present invention is actually considered to be an intersection traffic control method of an automatic driving exclusive phase, and the present invention is not limited to this single example; any other changes, modifications, substitutions, combinations, and simplifications which are made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Example 1:
an intersection traffic control method considering an automatic driving special phase comprises the following steps:
step 1: collecting the number of lanes at the entrance and the exit of each direction of an intersection and the traffic volume of automatic driving and manual driving vehicles with different steering directions at each entrance;
step 2: considering the cross-over type passing of the automatic driving vehicle, establishing a prediction model of the phase passing capacity special for automatic driving;
and step 3: the intersection entrance lane is divided into a common lane and a dynamic lane, and an intersection lane layout and traffic capacity model is established;
and 4, step 4: designing a phase structure considering the special automatic driving phase, and constructing an intersection signal timing model;
and 5: distributing the optimal traffic volume of each entrance lane by taking the traffic volume of the lane not exceeding the traffic capacity as a constraint;
and 6: and optimizing and obtaining an intersection signal timing scheme considering the automatic driving special phase by taking the intersection traffic capacity maximization as an objective.
Example 2:
on the basis of the embodiment 1, the step 1 of collecting the number of the lanes at the entrance and the exit of each direction of the intersection and the traffic volume of the automatic driving vehicle and the manual driving vehicle with different steering directions at each entrance specifically comprises the following steps:
step 11: acquiring the number of the lanes of the inlet and the outlet in each direction of the intersection, using a parameter L to represent the inlet direction, using a parameter L ' to represent the outlet direction, using a set L to represent an inlet direction set, using a set L ' to represent an outlet direction set, and using L = L ' = { E, S, W, N }, wherein E, S, W, N respectively represent the east, south, west, north directions, and using a parameter E l RepresentNumber of lanes in the entry direction l;
step 12: the method comprises the steps of obtaining traffic volumes of automatic driving and manual driving vehicles with different steering directions at each entrance direction of an intersection, representing vehicle steering by using a parameter K, representing a vehicle steering set by using a set K, wherein K = { S, L }, K belongs to K, S and L respectively represent vehicle straight running and left turning, representing a vehicle type by using a parameter Z, representing a vehicle type set by using a set Z, Z = { A, H }, Z belongs to Z, wherein A and H respectively represent an automatic driving vehicle and a manual driving vehicle, and parameters A and H respectively represent an automatic driving vehicle and a manual driving vehicle
Figure BDA0003840201060000061
Indicating an initial amount of traffic of type z turning k vehicles in the intersection approach direction i.
In this embodiment, step 11 collects the number information of the lanes of the entrance and the exit in each direction of the intersection, and the specific numerical values in this embodiment are shown in table 1;
table 1 is a summary table of the number of lanes in each direction of the intersection
Figure BDA0003840201060000062
Figure BDA0003840201060000071
In the embodiment, step 12 collects initial traffic volumes of automatically-driven vehicles and manually-driven vehicles in each entrance direction of the intersection, and specific numerical values in the embodiment are shown in table 2;
table 2 is an initial traffic volume summary table (unit: vehicle/hour) for automatically and manually driven vehicles at the intersection
Direction Steering Type of vehicle Traffic volume Direction Steering Type of vehicle Traffic volume
East Go straight Autonomous vehicle 890 East Straight going Manually driven vehicle 231
East Left turn Autonomous vehicle 136 East Left turn Manually driven vehicle 479
East Right turn Autonomous vehicle 208 East Right turn Manually driven vehicle 160
South China Go straight Autonomous vehicle 498 South China Straight going Manually driven vehicle 537
South China Left turn Autonomous vehicle 464 South China Left turn Manually driven vehicle 520
South China Right turn Autonomous vehicle 148 South China Right turn Manually driven vehicle 178
Western medicine Go straight Autonomous vehicle 948 West of China Straight going Manually driven vehicle 391
Western medicine Left turn Autonomous vehicle 231 Western medicine Left turn Manually driven vehicle 372
Western medicine Right turn Autonomous driving vehicle 312 Western medicine Right turn Manually driven vehicle 281
North China Straight going Autonomous vehicle 361 North China Straight going Manually driven vehicle 598
North China Left turn Autonomous vehicle 431 North China Left turn Manually driven vehicle 415
North China Right turn Autonomous driving vehicle 209 North China Right turn Manually driven vehicle 312
Example 3:
on the basis of the embodiment 2, the step 2 of establishing a prediction model of the phase passing capacity special for automatic driving by considering the cross-through passing of the automatic driving vehicle specifically comprises the following steps:
step 21: determining the safe time interval of the automatic driving vehicle, considering the cross-over type traffic of the automatic driving vehicle, and in order to prevent the collision between the vehicles, a safe time interval delta h must be kept between two vehicles n and n' which drive into the intersection in succession n Wherein, the vehicle n comes from the traffic flow i, n belongs to i, the vehicle n 'comes from the traffic flow j, n' belongs to j, according to the conflict relationship of the two vehicle tracks, the safety time distance between vehicles specifically includes the following three situations:
(1) vehicles n and n' from the same set of traffic flows, Δ h n =h 0 ,j=i;
(2) Vehicles n and n' are from two sets of traffic flows, Δ h, without conflict n =h 1 ,j∈A i
(3) Vehicles n and n' are from two sets of traffic flows with conflict, Δ h n =h 2 ,j∈B i
Therefore, the specific calculation of the vehicle safety time interval is shown as the formula (1):
Figure BDA0003840201060000072
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Set of traffic flows, h, representing conflicts with traffic flow i 0 、h 1 、h 2 Indicating safety between two vehicles driving into an intersection in succession from the same traffic flow, two sets of traffic flows without conflict, and two sets of traffic flows with conflictDistance;
step 22: determining the service time of the automatic driving vehicle, and taking the time gap between two vehicles n and n' which successively enter the intersection as the service time S based on the first-come first-serve and queuing theory n On the premise of ensuring the safety of vehicle passing, the service time of the vehicle is reduced to improve the passing efficiency of the intersection to the maximum extent, so that the service time of the automatic driving vehicle is equal to the safety time interval S n =Δh n The specific relationship is as follows:
Figure BDA0003840201060000081
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Set of traffic flows, s, representing conflicts with traffic flow i 0 、s 1 、s 2 Respectively representing the service time between vehicles which are driven into the intersection successively from the same traffic flow, two groups of traffic flows without conflict and two groups of traffic flows with conflict;
step 23: calculating the service probability of the automatic driving vehicles, wherein the arrival of the vehicles is independent, and the probability that the m-th vehicle entering the intersection comes from the traffic flow i
Figure BDA0003840201060000082
Equal to the average arrival rate mu of vehicles on the traffic flow i i Dividing by the sum of the average arrival rates of vehicles on each group of traffic flows at the intersection, and calculating as follows:
Figure BDA0003840201060000083
wherein the set I represents an intersection traffic flow set, I, j belongs to I, P i Representing the probability that the vehicle is from the traffic flow i;
the probability of collision states of two vehicles driving into an intersection successively comprises three conditions: (1) probability that two vehicles come from the same traffic flow, i.e. following probability P 0 As calculated in equation (4); (2) probability that two vehicles come from two groups of non-conflict traffic flows, i.e. probability of being in the same row P 1 As calculated in equation (5); (3) probability of two vehicles coming from two groups of traffic flows with conflict, i.e. conflict probability P 2 Calculated by equation (6), as follows:
Figure BDA0003840201060000084
Figure BDA0003840201060000085
Figure BDA0003840201060000086
and step 24: calculating average service time of automatic driving vehicles, and running down-inserting type traffic flow I in phase p special for automatic driving p Is equal to the sum of the service times multiplied by their probabilities, calculated as:
Figure BDA0003840201060000087
step 25: predicting the traffic capacity of the special automatic driving phase, and two vehicles from two groups of conflict-free traffic flows can simultaneously drive into the intersection with the safe time interval s 1 =0, so its service time s 1 =0, average service time of autonomous vehicle, throughput C of autonomous phase p p The calculation is as follows:
Figure BDA0003840201060000088
wherein the parameter p represents the autopilot-specific phase, set
Figure BDA0003840201060000089
A set of traffic flows representing a conflict with the autopilot-specific phase p traffic flow i.
Example 4:
on the basis of embodiment 3, the intersection approach in step 3 is divided into a common lane and a dynamic lane, and an intersection lane layout and traffic capacity model is established, which specifically comprises the following steps:
step 31: the intersection entrance lane is divided into a common lane and a dynamic lane; when the dynamic lane is set as a common lane, the automatic driving vehicle and the manual driving vehicle are driven in the common lane in a mixed mode; when the dynamic lane is set as an automatic driving special lane, the automatic driving vehicle runs on the automatic driving special lane, and the manual driving vehicle runs on the common lane; when the green time of the automatic driving special phase at the intersection is more than 0, the corresponding dynamic lane is set as an automatic driving special lane; when the special phase for automatic driving at the intersection is equal to 0, the corresponding dynamic lane is set as a common lane; therefore, the lane layout constraint is as follows:
t p ≥M×(β l,k -1) (34)
t p ≤M×β l,k (35)
wherein, a set P intersection automatic driving special phase set is used, P = { EWA, SNA }, P belongs to P, EWA, SNA respectively represent east-west automatic driving special phase, north-south automatic driving special phase; m represents a large positive integer, t p Green time, beta, indicating autopilot-specific phase p l,k Is a binary variable, beta l,k =1 denotes that the dynamic lane with steering k in the entry direction i is set as the autopilot lane, β l,k =0 indicates that the dynamic lane turned to k in the entrance direction i is set as the ordinary lane;
step 32: the traffic capacity of the common lane is equal to the unit time divided by the saturated headway time of the manually driven vehicle multiplied by the green signal ratio, and the green signal ratio is equal to the green light time divided by the signal cycle duration:
Figure BDA0003840201060000091
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003840201060000092
indicating the capacity of the ordinary lane to pass with a turn k in the direction of entry i, τ H Representing the saturated headway of the manually driven vehicle;
step 33: the traffic capacity of the automatic driving special lane consists of two parts, namely the distribution of the traffic capacity of the automatic driving special phase, the interpenetration traffic of the vehicles in the automatic driving special phase, the equal proportion distribution of the traffic capacity of the corresponding automatic driving special lane according to the initial traffic flow, and the sharing of the traffic capacity of the common phase between the automatic driving vehicles and the manual driving vehicles; thus, the throughput of the autopilot-specific lane is calculated as follows:
Figure BDA0003840201060000093
wherein the content of the first and second substances,
Figure BDA0003840201060000094
traffic capacity of driveways specified for automatic steering with k steering in the direction of entry, l, τ A Representing a saturated headway of the autonomous vehicle;
step 34: when the dynamic lane is set as the automatic driving special lane, beta l,k =1, its capacity is equal to that of the autopilot lane; when the dynamic lane is set as a normal lane, beta l,k =0, the traffic capacity is equal to that of the ordinary lane, and therefore the dynamic lane traffic capacity is calculated as follows:
Figure BDA0003840201060000095
wherein the content of the first and second substances,
Figure BDA0003840201060000096
representing the dynamic lane capacity of steering k in the entry direction i.
Example 5:
on the basis of embodiment 4, the step 4 of designing a phase structure considering the dedicated phase for automatic driving and constructing an intersection signal timing model specifically includes the following steps:
step 41: designing a phase structure of the automatic driving special phase, and additionally arranging two automatic driving special phases on the basis of the traditional signal phase, wherein the manual driving traffic flows turning left and going straight in the east and west directions form a first group of common phases, the manual driving traffic flows turning left and going straight in the south and north directions form a second group of common phases, the automatic driving traffic flows turning left and going straight in the south and north directions form a 3 rd group of north-south automatic driving special phase, and the automatic driving traffic flows turning right and going left in the east and west directions form a 4 th group of east-west-automatic driving special phase;
step 42: the intersection signal cycle duration T is not more than the maximum cycle T max Not less than the minimum period T min
T min ≤T≤T max (39)
Step 43: after each group of phases operates, the full red time AR is set to empty vehicles in the intersection, the cycle full red time is equal to the sum of the full red time of the common phase and the full red time of the special automatic driving phase, and therefore the phase cycle full red time T 0
Figure BDA0003840201060000097
Wherein the content of the first and second substances,
Figure BDA0003840201060000098
is a binary variable, and is characterized in that,
Figure BDA0003840201060000099
indicating that the intersection is set with the autopilot-specific phase p,
Figure BDA00038402010600000910
indicating that the intersection is not provided with the automatic driving special phase p;
and step 44: the sum of the green light time of the east-west-import straight traffic flow and the east-west-import left-turn traffic flow in the first group of common phases is equal to the sum of the green light time of the west-import left-turn traffic flow and the east-west-import straight traffic flow, as shown in formula (16); the sum of the green light time of the south-entry left-turn traffic flow and the north-entry straight traffic flow in the second group of common phases is equal to the sum of the green light time of the north-entry left-turn traffic flow and the south-entry straight traffic flow, as shown in formula (17):
t EL +t WS =t WL +t ES (41)
t SL +t NS =t NL +t SS (42)
wherein, t EL 、t WS 、t WL 、t ES Respectively represents the green light time t of the traffic flow of east import left-turn, west import straight-going, west import left-turn and east import straight-going SL 、t NS 、t NL 、t SS Respectively representing the green light time of the south-entry left-turn traffic flow, the north-entry straight traffic flow, the north-entry left-turn traffic flow and the south-entry straight traffic flow;
step 45: the signal cycle duration is equal to the sum of the green time of each phase and the full red time of the cycle, wherein the first group of common phase green time is equal to the sum of the green time of the east entry left turn and the west entry straight, and the second group of common phase green time is equal to the sum of the green time of the south entry left turn and the north entry straight, so the signal cycle duration is calculated as follows:
T=t EL +t WS +t SL +t NS +∑ p∈P t p +T 0 (43)
step 46: the green time of each traffic flow under the common phase is not less than the minimum green time g min
t l,k ≥g min (44)
Wherein, t l,k Indicating the green time of the traffic flow with the turning of the inlet direction l of the intersection as k;
step 47: when the intersection is provided with the special automatic driving phase, the green time is not less than the minimum green time:
Figure BDA0003840201060000101
example 6:
on the basis of the embodiment 5, the step 5 of allocating the optimal traffic volume of each entrance lane by taking the traffic volume of the lane not exceeding the traffic capacity as a constraint specifically comprises the following steps:
step 51: when the dynamic lane is set as the ordinary lane, beta l,k =0, the initial traffic volume of the ordinary lane is equal to the ratio of the sum of the initial traffic volumes of automatic driving and manual driving to the sum of the number of the ordinary lane and the dynamic lane, and the initial traffic volume of the dynamic lane is equal to the initial traffic volume of the ordinary lane; when the dynamic lane is set as the autonomous driving exclusive lane, beta l,k =1, the initial traffic volume of the ordinary lane is equal to the ratio of the manual driving initial traffic volume to the number of ordinary lanes, the optimal traffic volume of the dynamic lane is equal to the ratio of the automatic driving initial traffic volume to the number of dynamic lanes, and the optimal traffic volume of the lane is equal to the initial traffic volume of the lane multiplied by an amplification factor λ, so the optimal traffic volume of the entrance lane at the intersection is calculated as follows:
Figure BDA0003840201060000102
Figure BDA0003840201060000103
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003840201060000104
represents the optimal traffic volume of a common lane with the intersection inlet direction I turning to k,
Figure BDA0003840201060000105
represents the optimal traffic volume of the dynamic lane with the intersection inlet direction I turning to k,
Figure BDA0003840201060000106
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure BDA0003840201060000107
an initial traffic volume of an autonomous vehicle representing an intersection approach direction/steering k,
Figure BDA0003840201060000108
representing the number of ordinary lanes turning k in the intersection approach direction/,
Figure BDA0003840201060000109
representing the number of dynamic lanes with the turning direction l of the intersection inlet direction being k;
step 52: the best traffic volume of the ordinary lane cannot exceed the traffic capacity of the ordinary lane:
Figure BDA00038402010600001010
step 53: when the dynamic lane is set as a normal lane, beta l,k =0, the optimal traffic volume of which cannot exceed the capacity of a normal lane; when the dynamic lane is set as the automatic driving special lane, beta l,k =1, the optimal traffic volume cannot exceed the capacity of the driveway exclusive for automatic driving, and therefore the optimal traffic volume for dynamic lane satisfies:
Figure BDA00038402010600001011
example 7:
on the basis of the embodiment 6, the intersection traffic capacity maximization in the step 6 is taken as a target, and the intersection signal timing scheme considering the automatic driving special phase is obtained through optimization, and the method specifically comprises the following steps:
step 61: introducing an amplification coefficient variable lambda, wherein lambda is equal to the ratio of the optimal traffic volume of each turning vehicle type in each inlet direction of the intersection after distribution optimization to the initial traffic flow of each turning vehicle type in each inlet direction of the intersection:
Figure BDA0003840201060000111
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003840201060000112
indicating an optimum amount of traffic for the intersection approach direction/steering k vehicle type z,
Figure BDA0003840201060000113
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure BDA0003840201060000114
indicating that the intersection inlet direction l turns to an initial traffic volume with k vehicle types being z;
step 62: and lambda represents the relative size of the traffic capacity of the intersection, the maximum traffic capacity of the intersection is taken as a control target, and an intersection signal timing scheme is obtained through optimization:
maxλ (26)。
according to the steps 1 to 6, the amplification factor λ =1.011 can be obtained through optimization in the present embodiment, and the green time of each phase at the intersection is obtained, where the green time of the phase at the intersection is shown in table 3;
table 3 shows the intersection signal timing scheme summary table (unit: second)
Figure BDA0003840201060000115
According to the steps 1-6, the attribute function of the dynamic lane in each entrance direction of the intersection and the optimal traffic volume of each entrance lane can be obtained in an optimized manner, in the embodiment, the attribute of the dynamic lane is shown in a table 4, and the optimal traffic volume of the entrance lane is specifically taken as shown in a table 5;
table 4 is a summary of dynamic lane attribute functions at the intersection
Steering Direction Properties Steering Direction Properties
Left turn East Automatic driving special lane Go straight Dong (east) Common driveway
Left turn South China Common driveway Go straight South China Common driveway
Left turn Western medicine Automatic driving special lane Straight going West of China Common driveway
Left turn North China Common driveway Straight going North China Common driveway
TABLE 5 optimal traffic volume summary table for entrance lane at intersection (unit: vehicle/lane/hour)
Direction Steering to Type of lane Traffic volume Direction Steering to Type of lane Traffic volume
East Straight going Dynamic lane 899.362 East Straight going Common driveway 233.43
East Left turn Dynamic lane 137.431 Dong (east) Left turn Common driveway 484.038
East Right turn Dynamic lane 210.186 East Right turn Common driveway 161.681
South China Straight going Dynamic lane 522.943 South China Straight going Common driveway 522.943
South China Left turn Dynamic lane 497.175 South China Left turn Common driveway 497.175
South China Right turn Dynamic lane 149.555 South China Right turn Common driveway 179.871
Western medicine Go straight Dynamic lane 957.972 West of China Go straight Common driveway 395.113
Western medicine Left turn Dynamic lane 233.43 West of China Left turn Common driveway 375.913
West of China Right turn Dynamic lane 315.279 Western medicine Right turn Common driveway 283.953
North China Straight going Dynamic lane 484.544 North China Straight going Common driveway 484.544
North China Left turn Dynamic lane 427.449 North China Left turn Common driveway 427.449
North China Right turn Dynamic lane 211.197 North China Right turn Common driveway 315.279

Claims (7)

1. An intersection traffic control method considering an automatic driving dedicated phase is characterized by comprising the following steps:
step 1: collecting the number of the lanes at the entrance and the exit of each direction of the intersection and the traffic volume of the automatic driving and manual driving vehicles with different steering directions at each entrance;
step 2: considering the cross-over type passing of the automatic driving vehicle, establishing a prediction model of the phase passing capacity special for automatic driving;
and 3, step 3: dividing an intersection entrance lane into a common lane and a dynamic lane, and establishing an intersection lane layout model;
and 4, step 4: designing a phase structure considering the special automatic driving phase, and constructing an intersection signal timing model;
and 5: distributing the optimal traffic volume of each entrance lane by taking the traffic volume of the lane not exceeding the traffic capacity as a constraint;
step 6: and optimizing to obtain an intersection signal timing scheme considering the special automatic driving phase by taking the maximization of the traffic capacity of the intersection as an objective.
2. The intersection traffic control method considering the autopilot-specific phase according to claim 1, characterized in that step 1 comprises the steps of:
step 11: acquiring the number of the lanes of the inlet and the outlet in each direction of the intersection, using a parameter L to represent the inlet direction, using a parameter L ' to represent the outlet direction, using a set L to represent an inlet direction set, using a set L ' to represent an outlet direction set, and using L = L ' = { E, S, W, N }, wherein E, S, W, N respectively represent the east, south, west, north directions, and using a parameter E l Number of lanes representing the entry direction l;
step 12: obtaining the traffic volume of automatic driving and manual driving vehicles with different steering directions at each entrance of the intersection, representing the steering of the vehicles by using a parameter K and representing the vehicles by using a set KAnd the steering set, K = { S, L }, K belongs to K, wherein S and L respectively represent that the vehicle runs straight and turns left, the vehicle type is represented by a parameter Z, the set Z represents the vehicle type set, Z = { A, H }, Z belongs to Z, A and H respectively represent an automatic driving vehicle and a manual driving vehicle, and the parameters A and H respectively represent that the vehicle runs automatically and manually, and the K belongs to K
Figure FDA0003840201050000012
Indicating an initial amount of traffic of type z turning k vehicles in the intersection approach direction i.
3. The intersection traffic control method considering the autopilot-specific phase according to claim 1, characterized in that said step 2 comprises the steps of:
step 21: determining the safe time interval of the automatic driving vehicles, based on the fact that the vehicles arrive and obey Poisson distribution, considering the alternating type passing of the automatic driving vehicles, and in order to prevent the vehicles from colliding, a safe time interval delta h must be kept between two vehicles n and n' which successively drive into an intersection n Wherein, the vehicle n comes from the traffic flow i, n belongs to i, the vehicle n 'comes from the traffic flow j, n' belongs to j, according to the conflict relationship of the two vehicle tracks, the safety time distance between vehicles specifically includes the following three situations:
(1) vehicles n and n' from the same group of traffic flow,. DELTA.h n =h 0 ,j=i;
(2) Vehicles n and n' are from two sets of traffic flows, Δ h, without conflict n =h 1 ,j∈A i
(3) Vehicles n and n' are from two sets of traffic flows with conflict, Δ h n =h 2 ,j∈B i
Therefore, the specific calculation of the vehicle safety time interval is shown as the formula (1):
Figure FDA0003840201050000011
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Presentation and traffic flow inventoryIn a conflicted traffic flow set, h 0 、h 1 、h 2 Respectively representing the safe time distance between two vehicles which are driven into the intersection successively from the same traffic flow, two groups of traffic flows without conflict and two groups of traffic flows with conflict;
step 22: determining the service time of the automatic driving vehicle, and taking the time gap between two vehicles n and n' which successively drive into the intersection as the service time S based on the first-come-first-serve and queuing theory n On the premise of ensuring the safety of vehicle passing, the service time of the vehicle is reduced to improve the passing efficiency of the intersection to the maximum extent, so that the service time of the automatic driving vehicle is equal to the safety time interval S n =Δh n The specific relationship is as follows:
Figure FDA0003840201050000021
wherein, set A i Set B of traffic flows representing no conflict with traffic flow i i Set of traffic flows, s, representing conflicts with traffic flow i 0 、s 1 、s 2 Respectively representing the service time between vehicles which are driven into the intersection successively from the same traffic flow, two groups of traffic flows without conflict and two groups of traffic flows with conflict;
step 23: calculating the service probability of the automatic driving vehicles, wherein the arrival of the vehicles is independent, and the probability that the m-th vehicle entering the intersection comes from the traffic flow i
Figure FDA0003840201050000022
Equal to the average arrival rate mu of vehicles on the traffic flow i i Dividing by the sum of the average arrival rates of vehicles on each group of traffic flows at the intersection, and calculating as follows:
Figure FDA0003840201050000023
wherein the set I represents an intersection traffic flow set, I, j belongs to I, P i Probability that the vehicle is from traffic flow i;
the probability of collision states of two vehicles driving into an intersection successively comprises three conditions: (1) probability that two vehicles are from the same traffic flow, i.e. following probability P 0 As calculated in equation (4); (2) probability that two vehicles come from two groups of non-conflicted traffic flows, i.e. probability of being in the same row P 1 As calculated in equation (5); (3) probability of two vehicles coming from two groups of traffic flows with conflict, i.e. conflict probability P 2 Calculated as equation (6), specifically as follows:
Figure FDA0003840201050000024
Figure FDA0003840201050000025
Figure FDA0003840201050000026
step 24: calculating average service time of automatic driving vehicle, and running down-inserting type traffic flow I by automatic driving special phase p p Is equal to the sum of the service times multiplied by their probabilities, calculated as:
Figure FDA0003840201050000027
step 25: predicting the traffic capacity of the automatic driving special phase, two vehicles from two groups of non-conflict traffic flows can drive into the intersection at the same time, and the safe time interval s of the vehicles 1 =0, service time s thereof 1 =0, traffic capacity C of autopilot-specific phase p p The calculation is as follows:
Figure FDA0003840201050000028
wherein the parameter p represents the autopilot-specific phase, set
Figure FDA0003840201050000029
A set of traffic flows representing a conflict with the autopilot-specific phase p traffic flow i.
4. The intersection traffic control method considering the autopilot-specific phase according to claim 1, characterized in that said step 3 comprises the steps of:
step 31: the intersection entrance lane is divided into a common lane and a dynamic lane; when the dynamic lane is set as a common lane, the automatic driving vehicle and the manual driving vehicle are driven in the common lane in a mixed mode; when the dynamic lane is set as an automatic driving special lane, the automatic driving vehicle runs on the automatic driving special lane, and the manual driving vehicle runs on the common lane; when the green time of the automatic driving special phase at the intersection is more than 0, the corresponding dynamic lane is set as an automatic driving special lane; when the special phase for automatic driving at the intersection is equal to 0, the corresponding dynamic lane is set as a common lane; therefore, the lane layout constraint is as follows:
t p ≥M×(β l,k -1) (9)
t p ≤M×β l,k (10)
wherein, a set P intersection automatic driving special phase set is used, P = { EWA, SNA }, P ∈ P, EWA, SNA respectively represent east-west automatic driving special phase, south-north automatic driving special phase; m represents a large positive integer, t p Green time, beta, indicating autopilot-specific phase p l,k Is a binary variable, beta l,k =1 denotes that the dynamic lane turned to k in the entry direction l is set as the driveway for automatic driving, β l,k =0 indicates that the dynamic lane turned to k in the entry direction i is set as the ordinary lane;
step 32: the traffic capacity of the common lane is equal to the unit time divided by the saturated headway time of the manually driven vehicle multiplied by the green signal ratio, and the green signal ratio is equal to the green light time divided by the signal cycle duration:
Figure FDA0003840201050000031
wherein the content of the first and second substances,
Figure FDA0003840201050000032
indicating the capacity of the ordinary lane to pass with a turn k in the direction of entry i, τ H Representing the saturated headway of the manually driven vehicle;
step 33: the traffic capacity of the automatic driving special lane consists of two parts: the method comprises the following steps of firstly, distributing the traffic capacity of the special automatic driving phase, wherein the traffic capacity of the special automatic driving phase is distributed in a penetrating mode, the traffic capacity of the corresponding special automatic driving lane is distributed in equal proportion according to the initial traffic flow, and secondly, the traffic capacity of the common phase shared by the automatic driving vehicle and the manual driving vehicle is calculated as follows:
Figure FDA0003840201050000033
wherein the content of the first and second substances,
Figure FDA0003840201050000034
indicating the traffic capacity, τ, of a driveway exclusively for steering k in the direction of entry i A Indicating a saturated headway for the autonomous vehicle,
Figure FDA0003840201050000035
representing the initial traffic volume of the automatic driving vehicle with the steering k in the inlet direction l of the intersection;
step 34: when the dynamic lane is set as the autonomous driving exclusive lane, beta l,k =1, the traffic capacity of which is equal to that of the driverless lane; when the dynamic lane is set as a normal lane, beta l,k =0, the traffic capacity of which is equal to that of the ordinary lane, and therefore the dynamic lane traffic capacity is calculated as follows:
Figure FDA0003840201050000036
wherein the content of the first and second substances,
Figure FDA0003840201050000037
representing the dynamic lane capacity of the turn k in the entry direction i.
5. The intersection traffic control method considering the autopilot-specific phase according to claim 1, characterized in that said step 4 comprises the steps of:
step 41: designing a phase structure considering the special automatic driving phases, and additionally arranging two special automatic driving phases on the basis of a traditional signal phase, wherein the manual driving traffic flows turning left and going straight in the east and west directions form a first group of common phases, the manual driving traffic flows turning left and going straight in the south and north directions form a second group of common phases, the automatic driving traffic flows turning left and going straight in the south and north directions form a 3 rd group of north-south-automatic driving special phases, and the automatic driving traffic flows turning right and going left in the east and west directions form a 4 th group of east-west-automatic driving special phases;
step 42: the intersection signal cycle duration T is not more than the maximum cycle T max Not less than the minimum period T min
T min ≤T≤T max (14)
Step 43: and setting full red time AR after each group of phases operates so as to empty vehicles in the intersection. The period full red time is equal to the sum of the full red time of the ordinary phase and the full red time of the autopilot-specific phase, so that the phase period full red time T 0
Figure FDA0003840201050000038
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003840201050000039
is a binary variable, and is characterized in that,
Figure FDA00038402010500000310
indicating that the intersection is set with the autopilot-specific phase p,
Figure FDA00038402010500000311
indicating that the intersection is not provided with the automatic driving special phase p;
step 44: the sum of the green light time of the east-entry left-turn traffic flow and the west-entry straight traffic flow in the first group of common phases is equal to the sum of the green light time of the west-entry left-turn traffic flow and the east-entry straight traffic flow, and is expressed as a formula (16); the sum of the green light time of the south-entry left-turn traffic flow and the north-entry straight traffic flow in the second group of common phases is equal to the sum of the left-turn flow time of the north-entry and the straight flow time of the south-entry to the green light, as shown in formula (17):
t EL +t WS =t WL +t ES (16)
t SL +t NS =t NL +t SS (17)
wherein, t EL 、t WS 、t WL 、t ES Respectively represents the green light time t of the traffic flow of east import left-turn, west import straight-going, west import left-turn and east import straight-going SL 、t NS 、t NL 、t SS Respectively representing the green light time of the traffic flow of the left turn of the south entrance, the straight travel of the north entrance, the left turn of the north entrance and the straight travel of the south entrance;
step 45: the signal period duration is equal to the sum of the green light time of each group of phases and the full red time of the period, wherein the first group of common phase green light time is equal to the sum of the green light time of the east import left turn and the green light time of the west import straight line, the second group of common phase green light time is equal to the sum of the green light time of the south import left turn and the green light time of the north import straight line, and the third and the fourth groups of phase green light time are equal to the phase green light time special for automatic driving, so the signal period duration is calculated as follows:
T=t EL +t WS +t SL +t NS +∑ p∈P t p +T 0 (18)
step 46: the green time of each traffic flow under the common phase is not less than the minimum green time g min
t l,k ≥g min (19)
Wherein, t l,k Indicating the green time of the traffic flow with the intersection inlet direction l turning to k;
step 47: when the intersection is provided with the special automatic driving phase, the green time is not less than the minimum green time:
Figure FDA0003840201050000041
6. the intersection traffic control method considering the autopilot-specific phase according to claim 1, characterized in that said step 5 comprises the steps of:
step 51: when the dynamic lane is set as a normal lane, beta l,k =0, the initial traffic volume of the ordinary lane is equal to the ratio of the sum of the initial traffic volumes of automatic driving and manual driving to the sum of the ordinary and dynamic lanes, and the initial traffic volume of the dynamic lane is equal to the initial traffic volume of the ordinary lane; when the dynamic lane is set as the automatic driving special lane, beta l,k =1, the initial traffic volume of the ordinary lane is equal to the ratio of the manual driving initial traffic volume to the number of ordinary lanes, the optimal traffic volume of the dynamic lane is equal to the ratio of the automatic driving initial traffic volume to the number of dynamic lanes, and the optimal traffic volume of the lane is equal to the initial traffic volume of the lane multiplied by an amplification factor λ, so the optimal traffic volume of the entrance lane at the intersection is calculated as follows:
Figure FDA0003840201050000042
Figure FDA0003840201050000043
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003840201050000044
represents the optimal traffic volume of a common lane with the intersection inlet direction I turning to k,
Figure FDA0003840201050000045
represents the optimal traffic volume of the dynamic lane with the intersection inlet direction I turning as k,
Figure FDA0003840201050000046
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure FDA0003840201050000047
an initial traffic volume of an autonomous vehicle representing an intersection approach direction/steering k,
Figure FDA0003840201050000048
indicating the number of ordinary lanes with the intersection entry direction 1 turning k,
Figure FDA0003840201050000049
representing the number of dynamic lanes with the turning direction l of the intersection inlet direction being k;
step 52: the best traffic volume of the ordinary lane cannot exceed the traffic capacity of the ordinary lane:
Figure FDA00038402010500000410
step 53: when the dynamic lane is set as a normal lane, beta l,k =0, the optimal traffic volume of which cannot exceed the capacity of a normal lane; when the dynamic lane is set as the autonomous driving exclusive lane, beta l,k =1, its optimal traffic volume cannot exceed automaticThe traffic capacity of the special lane is driven, so that the optimal traffic volume of the dynamic lane meets the following requirements:
Figure FDA00038402010500000411
7. the intersection traffic control method considering an autopilot-specific phase according to claim 1, characterized in that said step 6 comprises the steps of:
step 61: introducing an amplification coefficient variable lambda, wherein lambda is equal to the ratio of the optimal traffic volume of different turning vehicle types in each entrance direction of the optimized intersection to the initial traffic volume:
Figure FDA00038402010500000412
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00038402010500000413
indicating an optimum amount of traffic for the intersection approach direction/steering k vehicle type z,
Figure FDA00038402010500000414
indicating the initial traffic volume of a human-driven vehicle turning k in the intersection approach direction/,
Figure FDA00038402010500000415
indicating that the turning direction I of an intersection is the initial traffic volume of the vehicle type z;
step 62: determining a control target, wherein lambda represents the relative size of the traffic capacity of the intersection, and the maximum traffic capacity of the intersection is taken as the control target, and optimizing to obtain an intersection signal timing scheme:
maxλ (26)。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115953903A (en) * 2023-03-14 2023-04-11 武汉理工大学 Intersection straight vehicle continuous passing method based on Internet of things

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108399762A (en) * 2018-05-08 2018-08-14 北京航空航天大学 A kind of automatic Pilot and pilot steering vehicle mix the intersection control method under the conditions of row
CN111785043A (en) * 2020-07-09 2020-10-16 同济大学 Intersection control method for intelligent internet connection
CN112071074A (en) * 2020-11-12 2020-12-11 长沙理工大学 Method for setting special phase for automatic driving vehicle at intersection
CN112216122A (en) * 2020-12-10 2021-01-12 长沙理工大学 Intersection lane laying and signal timing method in automatic driving process
CN113327448A (en) * 2021-08-02 2021-08-31 长沙理工大学 Vehicle track optimization method under special phase for automatic driving
CN113593226A (en) * 2021-07-22 2021-11-02 同济大学 Control method for automatic driving special road intersection in mixed traffic flow environment

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108399762A (en) * 2018-05-08 2018-08-14 北京航空航天大学 A kind of automatic Pilot and pilot steering vehicle mix the intersection control method under the conditions of row
CN111785043A (en) * 2020-07-09 2020-10-16 同济大学 Intersection control method for intelligent internet connection
CN112071074A (en) * 2020-11-12 2020-12-11 长沙理工大学 Method for setting special phase for automatic driving vehicle at intersection
CN112216122A (en) * 2020-12-10 2021-01-12 长沙理工大学 Intersection lane laying and signal timing method in automatic driving process
CN113593226A (en) * 2021-07-22 2021-11-02 同济大学 Control method for automatic driving special road intersection in mixed traffic flow environment
CN113327448A (en) * 2021-08-02 2021-08-31 长沙理工大学 Vehicle track optimization method under special phase for automatic driving

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
WANG, M.I.-C等: "Roadrunner: Autonomous Intersection Management with Dynamic Lane Assignment", 2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), pages 1 - 7 *
WEI WU等: "Autonomous Intersection Management for Connected and Automated Vehicles:A Lane-Based Method", IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, pages 15901 - 15106 *
WEI WU等: "Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment", JOURNAL OF SENSORS, pages 1 - 7 *
刘洋: "自动驾驶环境下交叉口"穿插式"通行模式及控制方法研究", 中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑), pages 035 - 223 *
吴伟;刘洋;马万经;: "自动驾驶环境下面向交叉口自由转向车道的交通控制模型", 中国公路学报, no. 12, pages 25 - 35 *
蒋婷;李志晗;: "无人车对交通流的影响分析", 科技创新与应用, no. 16, pages 285 *
陈晓荣;张涵双;: "无人驾驶公交专用道系统", 交通与港航, no. 02, pages 74 - 79 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115953903A (en) * 2023-03-14 2023-04-11 武汉理工大学 Intersection straight vehicle continuous passing method based on Internet of things

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