CN115083164A - Signal intersection double-layer optimization method for mixed traffic flow - Google Patents
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Abstract
The invention relates to a mixed traffic flow-oriented signalized intersection double-layer optimization method, which comprises the following steps of: step 1: the method comprises the steps of establishing a signalized intersection scene of a mixed traffic flow consisting of automatic driving vehicles and artificial driving vehicles, dividing the signalized intersection scene into a free driving area, an observation area, a control area and a junction area, formulating corresponding driving rules, dividing the states of the automatic driving vehicles into four states, wherein the four states are respectively uncontrolled, calculated, controlled and recalculated, and the double-layer optimization method facing the signalized intersection under the mixed traffic flow is provided for the mixed traffic flow.
Description
Technical Field
The invention belongs to the technical field of intelligent network connection automobile and road cooperation, and particularly relates to a hybrid traffic flow-oriented signalized intersection double-layer optimization method.
Background
China proposes a double-carbon target, is accelerating to form a green low-carbon transportation mode, promotes new energy, intelligent and digital transportation equipment, encourages to guide green travel, and enables the traffic to be more environment-friendly and lower-carbon. In the face of various challenges, the intelligent networked automobile integrates the advantages of intellectualization and networking, provides unprecedented opportunities for realizing energy conservation and emission reduction of traffic travel and improving traffic efficiency, can remarkably relieve energy and environmental crisis in China, and effectively relieves increasingly serious traffic jam and road safety problems to a certain extent.
The research of internet-connected automatic driving vehicles in the prior art is mainly based on the condition that the permeability of the automatic driving vehicles is assumed to be 100%, however, as the technology of intelligent automobiles is continuously and deeply developed, the mixed traffic flow of the internet-connected vehicles with different automation levels can be an inevitable trend in the future. Existing research on mixed traffic flow intersections has mainly focused on the estimation of traffic states and the optimization of traffic signals. Compared with a pure automatic driving environment, the interactive cooperation between the human-driven automobile and the automatic driving automobile in the mixed traffic flow scene is a research content with more practical significance. However, uncertainty in human driver behavior and driving behavior differences between different human drivers further increase the complexity of mixed traffic flows. The intelligent networked automobile must execute a differentiated control strategy on the basis of fully identifying the behavior of a human driver so as to ensure the safety and the high efficiency of the running of the automobile in a mixed running environment. The cooperative coordination between the automatic driving vehicle and the man-made driving vehicle in the intelligent traffic environment is the key point of research.
Therefore, the invention takes the mixed traffic flow (automatic driving vehicles and man-made driving vehicles) in the intelligent traffic environment as an object, divides the main road of the signalized intersection into a free driving area, an observation area, a control area and an intersection area, and divides the state of the automatic driving vehicles into four states, namely uncontrolled, calculated, controlled and recalculated, so as to achieve the optimal traffic flow passing efficiency in the driving area, and researches the multi-vehicle cooperative energy-saving driving strategy of the mixed traffic flow at the signal intersection of the urban road by using an optimal control method.
Disclosure of Invention
The invention aims to solve the technical problem of providing a hybrid traffic flow-oriented signalized intersection double-layer optimization method to solve the problems of traffic signal timing deadplate and difficulty in cooperative matching of automatically-driven vehicles and artificially-driven vehicles in the hybrid traffic flow in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows: the double-layer optimization method for the signalized intersection facing the mixed traffic flow comprises the following steps:
step 1: establishing a signalized intersection scene of a mixed traffic flow consisting of automatic driving vehicles and artificial driving vehicles, dividing the signalized intersection scene into a free driving area, an observation area, a control area and a junction area, formulating corresponding driving rules, dividing the states of the automatic driving vehicles into four states, wherein the four states are respectively uncontrolled, calculated, controlled and recalculated, and formulating rules based on event triggering according to the four states to avoid collision;
step 2: when a vehicle drives into an observation area in a scene of a signalized intersection, obtaining the traffic flow of each lane through a traffic flow detector, and optimizing the signal timing of a traffic signal lamp by combining the saturated flow and a following model to maximize the average traffic efficiency as an optimization target of the timing of the traffic signal lamp;
and step 3: when an automatically driven vehicle enters a control area, the central controller combines the optimized traffic signal lamp timing information, the vehicle kinematic model and the speed and position of the vehicle at the current moment to maximize the average traffic efficiency as the optimization target of the vehicle track optimization model, and the optimal speed track is solved through a pseudo-spectrum method;
and 4, step 4: and (3) the automatic driving vehicle drives according to the optimal speed track solved by the pseudo-spectrum method, and reaches a stop line under the green light phase under the condition of meeting the maximum acceleration and the minimum acceleration of the vehicle in the step 3, and the artificial driving vehicle smoothly passes through the intersection along with the automatic driving vehicle through the following model.
Further, in the step 1, the signalized intersection is divided into a free driving area, an observation area, a control area and an intersection area, and the specific steps are as follows: in a signal intersection scene, an area within a stop line is an intersection area, an annular area between the stop line and 320 meters outside the stop line is a control area, an annular area between 860 meters and 320 meters outside the stop line is an observation area, and an area beyond 860 meters outside the stop line is a free-driving area.
Further, the driving rule formulated in step 1 is specifically: the vehicle can change lanes and accelerate or decelerate under the safety premise when in a free running area; the vehicle completes lane changing in the observation area; the lane change is prohibited when the vehicle is in the control area; vehicles strictly follow the traffic light rule in the intersection area and pass in order.
Further, the dividing of the vehicle state and the defined rule based on event triggering in step 1 specifically include: before the automatic driving vehicle reaches the control area, the automatic driving vehicle is in an uncontrolled state; when the automatic driving vehicle reaches the boundary of the observation area and the control area, the state is a calculation state; after the automatic driving vehicle enters the control area, judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not, if the automatic driving vehicle does not keep a sufficient safe distance and has the possibility of collision, changing to a recalculation state and judging whether a sufficient space exists for adjusting the speed or not, and if the sufficient space exists, decelerating until the automatic driving vehicle keeps the sufficient safe distance with the front vehicle; if there is not enough space, the vehicle is controlled and the following model is used to make the automatic driving vehicle follow the front vehicle to run, if there is enough safe distance, the vehicle is kept running in normal state.
Further, the specific judgment of whether the automatic driving vehicle keeps a safe distance with the front vehicle is as follows: judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not through a central controller, wherein the safe distance is 5 meters, if the distance with the front vehicle is less than 5 meters, the automatic driving vehicle does not keep enough safe distance, and if the distance with the front vehicle is more than or equal to 5 meters, the automatic driving vehicle keeps enough safe distance; the determination of whether the autonomous vehicle has sufficient space to adjust the speed is: whether the automatic driving vehicle has an adjustable distance with the front vehicle is judged through the central controller, the adjustable distance is 3 meters, if the distance with the front vehicle is less than 3 meters, enough space is not available for adjusting the speed, and if the distance with the front vehicle is more than or equal to 3 meters, enough space is available for adjusting the speed.
Further, in the step 2, the traffic flow detector is arranged in the observation area and close to the control area, and the traffic flow detector can measure the traffic flow of each lane in real time.
Further, in step 2, the OVM following model is adopted by the following model, and the motion state of the vehicle which is artificially driven is described by using the OVM following model, and a specific expression of the OVM following model is as follows:
wherein the content of the first and second substances,
representing vehicle acceleration, k representing driver sensitivity, v i Indicating the current speed, V des Indicating the driver's headway d i Desired speed of (V) des The specific expression of (a) is as follows:
V des (d i )＝V 1 +V 2 tanh[C 1 (d i -L veh )-C 2 ]
wherein L is veh Indicating the length of the vehicle body, V 1 、V 2 、C 1 、C 2 As parameters, the parameter values were 6.75m/s, 7.91m/s, 0.13, and 1.57, respectively.
Further, in the step 2, the maximum average traffic efficiency of the mixed traffic flow is represented by minimizing the average travel time delay, and the calculation method is as follows:
wherein ATTD represents the average travel time delay, n represents the total number of vehicles,
indicating the time at which the vehicle i enters the control zone,
indicating the moment at which vehicle i leaves the control zone, i.e. vehicle cost
Passing control zone while in free driving conditions, the vehicle costs L ctrl /v max Through a control zone, L ctrl Denotes the length of the control zone, v max Representing the maximum speed of the vehicle.
Further, the specific calculation formula for optimizing the traffic light signal timing in step 2 is as follows:
wherein E is j Indicating the green light duration of the j phase of the signalized intersection; c represents the cycle duration of the signalized intersection; l is j The method comprises the steps of representing the traffic of a lane corresponding to the j-th phase of the signalized intersection, and representing the total number of the phases of the signalized intersection by n; meanwhile, in consideration of the actual conditions of the vehicle starting time and the reaction time of a driver, the lower limit of the lane green light time is set to be 10 seconds on the basis, and the duration time of each phase is an integral multiple of 5.
Further, the vehicle kinematic model in step 3 is as follows:
t 1 ≤t≤t f ,u min ≤u(t)≤u max
wherein x is(t) represents the distance of the autonomous vehicle from the stop line at the intersection at time t; v (t) represents the speed of the autonomous vehicle at time t; u (t) represents the acceleration of the autonomous vehicle at time t; s represents the distance between an automatically-driven vehicle and a stop line of an intersection at the initial moment; v. of 1 Representing the speed of the autonomous vehicle at the initial time; v (t) f ) Representing a speed of the autonomous vehicle crossing a stop line; u. of min Represents a minimum acceleration; u. of max Indicating the maximum acceleration.
Compared with the prior art, the invention has the beneficial effects that:
(1) the method comprises the step of optimizing signal timing parameters and vehicle tracks of intersection signal lamps.
(2) And dividing the signalized intersection scene into a free driving area, an observation area, a control area and an intersection area, and formulating corresponding driving rules.
(3) The state of an autonomous vehicle is divided into four states, which are respectively: uncontrolled, computational, controlled, and recalculated, and a rule based on event triggering is designed to avoid collisions.
(4) The traffic efficiency of the whole signalized intersection is optimized by only controlling the automatic driving vehicles, and the problem of traffic jam in a mixed traffic environment is effectively relieved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the scene construction of the present invention.
Fig. 3 is a schematic phase diagram of a signal lamp according to the present invention.
Detailed Description
In order that those skilled in the art will better understand the disclosure, the invention will be further described with reference to the accompanying drawings and specific embodiments.
The invention provides a mixed traffic flow-oriented signalized intersection double-layer optimization method, which comprises the following steps of:
step 1: the method comprises the steps of establishing a signalized intersection scene of a mixed traffic flow consisting of automatic driving vehicles and artificial driving vehicles, dividing the signalized intersection scene into a free driving area, an observation area, a control area and a junction area as shown in figure 2, and specifically dividing the signalized intersection scene into the following steps: in a signalized intersection scene, an area within a stop line is an intersection area, an annular area between the stop line and 320 meters outside the stop line is a control area, an annular area between 860 meters and 320 meters outside the stop line is an observation area, and an area beyond 860 meters outside the stop line is a free-running area, wherein the central controller can receive speed and position information of each vehicle in the control area in real time and can send information and instructions to the automatic driving vehicle.
Making a corresponding driving rule for a signalized intersection scene, wherein the driving rule is that a vehicle can change lanes and accelerate or decelerate under the premise of safety when in a free driving area; the vehicle completes lane changing in the observation area; the lane change is prohibited when the vehicle is in the control area; vehicles strictly follow the traffic light rule in the intersection area and pass in order.
The states of the automatic driving vehicle are divided into four states, wherein the four states are respectively uncontrolled, calculated, controlled and recalculated, and the automatic driving vehicle under control runs according to an instruction sent by a central controller, so that the possibility of collision with a front vehicle is high, and a rule based on event triggering is formulated according to the four states to avoid collision.
The division of the vehicle state and the established rules based on event triggers are specifically as follows: before the automatic driving vehicle reaches the control area, the automatic driving vehicle is in an uncontrolled state; when the automatic driving vehicle reaches the boundary of the observation area and the control area, the state is a calculation state; after the automatic driving vehicle enters the control area, judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not, if the automatic driving vehicle does not keep a sufficient safe distance and has the possibility of collision, changing to a recalculation state and judging whether a sufficient space exists for adjusting the speed or not, and if the sufficient space exists, decelerating until the automatic driving vehicle keeps the sufficient safe distance with the front vehicle; if there is not enough space, the vehicle is controlled and the following model is used to drive the automatic driving vehicle to follow the front vehicle, and if there is enough safety distance, the vehicle is kept in normal state.
Preferably, the specific judgment of whether the automatic driving vehicle keeps the safe distance with the front vehicle in the invention is as follows: judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not through a central controller, wherein the safe distance is 5 meters, if the distance with the front vehicle is less than 5 meters, the automatic driving vehicle does not keep enough safe distance, and if the distance with the front vehicle is more than or equal to 5 meters, the automatic driving vehicle keeps enough safe distance; the determination of whether the autonomous vehicle has sufficient space to adjust the speed is: whether the automatic driving vehicle has an adjustable distance with the front vehicle is judged through the central controller, the adjustable distance is 3 meters, if the distance with the front vehicle is less than 3 meters, enough space is not available for adjusting the speed, and if the distance with the front vehicle is more than or equal to 3 meters, enough space is available for adjusting the speed.
Step 2: when a vehicle enters an observation area in a signalized intersection scene, the traffic flow of each lane is obtained through a traffic flow detector, the saturated flow and a following model are combined, the maximum average traffic efficiency is taken as an optimization target of traffic signal lamp timing, the signal timing of the traffic signal lamp is optimized, wherein the signal phase is shown in fig. 3, the vehicle on the left lane only turns left, the vehicle on the right lane can go straight or turn left, the signal phase is not changed, and only the duration time of green lamps of each phase is optimized.
The traffic flow detector is arranged in the observation area and close to the control area, and can measure the traffic flow of each lane in real time.
Preferably, the OVM following model is used for describing the motion state of the artificially driven vehicle, and the specific expression of the OVM following model is as follows:
wherein the content of the first and second substances,
represents the vehicle acceleration, k represents the driver's sensitivity, and the value in this embodiment is 0.85s -1 ，v i Indicating the current speed, V des Indicating the driver's headway d i Desired speed of (d), V des The specific expression of (a) is as follows:
V des (d i )＝V 1 +V 2 tanh[C 1 (d i -L veh )-C 2 ]
wherein L is veh The length of the vehicle body is expressed, and the value is 5m and V in the embodiment 1 、V 2 、C 1 、C 2 As parameters, the parameter values were 6.75m/s, 7.91m/s, 0.13, 1.57, respectively.
The invention represents the maximum average traffic efficiency of the mixed traffic flow by minimizing the average travel time delay, and the calculation method comprises the following steps:
where ATTD represents the average travel time delay, n represents the total number of vehicles,
indicating the time at which the vehicle i enters the control zone,
indicating the moment at which vehicle i leaves the control zone, i.e. vehicle cost
Passing through the control zone, while in free-driving conditions, the vehicle costs L ctrl /v max Through a control zone, L ctrl Represents the length of the control zone, 320 meters, v max The maximum speed of the vehicle was 17 m/s.
The specific calculation formula for optimizing the timing of the traffic light signal is as follows:
wherein E is j Indicating the green light duration of the j phase of the signalized intersection; c represents the cycle duration of the signalized intersection, and the value is 60s in the embodiment; l is a radical of an alcohol j The method comprises the steps of representing the traffic flow of a lane corresponding to the j-th phase of the signalized intersection, wherein n represents the total number of the phases of the signalized intersection and is 4; meanwhile, in consideration of the actual conditions of the vehicle starting time and the reaction time of a driver, the lower limit of the lane green light time is set to be 10 seconds on the basis, and the duration time of each phase is an integral multiple of 5.
And step 3: when an automatically driven vehicle enters a control area, the central controller combines the optimized traffic signal lamp timing information, the vehicle kinematic model and the speed and position of the vehicle at the current moment to maximize the average traffic efficiency as the optimization target of the vehicle track optimization model, and the optimal speed track is solved through a pseudo-spectrum method; the vehicle kinematic model is as follows:
t 1 ≤t≤t f ,u min ≤u(t)≤u max
wherein x (t) represents the distance of the autonomous vehicle from the intersection stop line at time t; v (t) represents the speed of the autonomous vehicle at time t; u (t) represents the acceleration of the autonomous vehicle at time t; s represents the distance between an automatically-driven vehicle and a stop line of an intersection at the initial moment; v. of 1 Representing the speed of the autonomous vehicle at the initial time; v (t) f ) Representing a speed of the autonomous vehicle crossing a stop line; u. of min Represents the minimum acceleration, which is-2.1 m/s in the present embodiment 2 ；u max Represents the maximum acceleration, which is 2.0m/s in the present embodiment 2 。
And 4, step 4: and (3) driving the automatic driving vehicle according to the optimal speed track solved by the pseudo-spectrum method, selecting a proper target time window under the condition of meeting the maximum acceleration and the minimum acceleration of the vehicle in the step (3), reaching a stop line under a green light phase, and smoothly passing through the intersection along with the automatic driving vehicle by the artificially driven vehicle through the following model.
Claims (10)
1. A hybrid traffic flow-oriented signalized intersection double-layer optimization method is characterized by comprising the following steps:
step 1: establishing a signalized intersection scene of a mixed traffic flow consisting of automatic driving vehicles and artificial driving vehicles, dividing the signalized intersection scene into a free driving area, an observation area, a control area and a junction area, formulating corresponding driving rules, dividing the states of the automatic driving vehicles into four states, wherein the four states are respectively uncontrolled, calculated, controlled and recalculated, and formulating rules based on event triggering according to the four states to avoid collision;
step 2: when a vehicle drives into an observation area in a scene of a signalized intersection, obtaining the traffic flow of each lane through a traffic flow detector, and optimizing the signal timing of a traffic signal lamp by combining the saturated flow and a following model to maximize the average traffic efficiency as an optimization target of the timing of the traffic signal lamp;
and step 3: when an automatically driven vehicle enters a control area, the central controller combines the optimized traffic signal lamp timing information, the vehicle kinematic model and the speed and position of the vehicle at the current moment to maximize the average traffic efficiency as the optimization target of the vehicle track optimization model, and the optimal speed track is solved through a pseudo-spectrum method;
and 4, step 4: and (3) the automatic driving vehicle drives according to the optimal speed track solved by the pseudo-spectrum method, and reaches a stop line under the green light phase under the condition of meeting the maximum acceleration and the minimum acceleration of the vehicle in the step 3, and the artificial driving vehicle smoothly passes through the intersection along with the automatic driving vehicle through the following model.
2. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 1, wherein the signalized intersection is divided into a free-running area, an observation area, a control area and an intersection area in step 1, and the specific steps are as follows: in a signalized intersection scene, an area inside a stop line is a junction area, an annular area between the stop line and 320 meters outside the stop line is a control area, an annular area between 860 meters and 320 meters outside the stop line is an observation area, and an area beyond 860 meters outside the stop line is a free-running area.
3. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 1, wherein the driving rule formulated in the step 1 is specifically: the vehicle can change lanes and accelerate or decelerate under the safety premise when in a free running area; the vehicle completes lane changing in the observation area; the lane change is prohibited when the vehicle is in the control area; vehicles strictly follow the traffic light rule in the intersection area and pass in order.
4. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 1, wherein the dividing of the vehicle state and the formulated event-trigger-based rule in the step 1 are specifically: before the automatic driving vehicle reaches the control area, the automatic driving vehicle is in an uncontrolled state; when the automatic driving vehicle reaches the boundary of the observation area and the control area, the state is a calculation state; after the automatic driving vehicle enters the control area, judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not, if the automatic driving vehicle does not keep a sufficient safe distance and has the possibility of collision, changing to a recalculation state and judging whether a sufficient space exists for adjusting the speed or not, and if the sufficient space exists, decelerating until the automatic driving vehicle keeps the sufficient safe distance with the front vehicle; if there is not enough space, the vehicle is controlled and the following model is used to drive the automatic driving vehicle to follow the front vehicle, and if there is enough safety distance, the vehicle is kept in normal state.
5. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 4, wherein the specific judgment on whether the automatic driving vehicle and the front vehicle keep a safe distance is as follows: judging whether the automatic driving vehicle keeps a safe distance with a front vehicle or not through a central controller, wherein the safe distance is 5 meters, if the distance with the front vehicle is less than 5 meters, the automatic driving vehicle does not keep enough safe distance, and if the distance with the front vehicle is more than or equal to 5 meters, the automatic driving vehicle keeps enough safe distance; the determination of whether the autonomous vehicle has sufficient space to adjust the speed is: whether the automatic driving vehicle has an adjustable distance with the front vehicle is judged through the central controller, the adjustable distance is 3 meters, if the distance with the front vehicle is less than 3 meters, enough space is not available for adjusting the speed, and if the distance with the front vehicle is more than or equal to 3 meters, enough space is available for adjusting the speed.
6. The method for double-layer optimization of a signalized intersection facing a mixed traffic flow according to claim 1, wherein in the step 2, the traffic flow detector is arranged in the observation area and close to the control area, and the traffic flow detector can measure the traffic flow of each lane in real time.
7. The method for double-layer optimization of a signalized intersection facing to a mixed traffic flow according to claim 1, wherein in the step 2, the OVM following model is adopted, and is used for describing the motion state of the human-driven vehicle, and a specific expression of the OVM following model is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing vehicle acceleration, k representing driver sensitivity, v i Indicating the current speed, V des Indicating driver at head of vehicleDistance d i Desired speed of (d), V des The specific expression of (a) is as follows:
V des (d i )＝V 1 +V 2 tanh[C 1 (d i -L veh )-C 2 ]
wherein L is veh Indicating the length of the vehicle body, V 1 、V 2 、C 1 、C 2 As parameters, the parameter values were 6.75m/s, 7.91m/s, 0.13, and 1.57, respectively.
8. The method and the system for double-layer optimization of signal intersections facing mixed traffic flow according to claim 1, wherein the maximum average traffic efficiency of the mixed traffic flow is represented by minimizing the average travel time delay in the step 2, and the calculation method is as follows:
where ATTD represents the average travel time delay, n represents the total number of vehicles,
indicating the time at which the vehicle i enters the control zone,
indicating the moment at which vehicle i leaves the control zone, i.e. vehicle cost
Passing through the control zone, while in free-driving conditions, the vehicle costs L ctrl /v max Through a control zone, L ctrl Denotes the length of the control zone, v max Representing the maximum speed of the vehicle.
9. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 1, wherein a specific calculation formula for optimizing traffic light signal timing in the step 2 is as follows:
wherein E is j Indicating the green light duration of the j phase of the signalized intersection; c represents the cycle duration of the signalized intersection; l is a radical of an alcohol j The method comprises the steps of representing the traffic of a lane corresponding to the j-th phase of the signalized intersection, and representing the total number of the phases of the signalized intersection by n; meanwhile, in consideration of the actual conditions of the vehicle starting time and the reaction time of a driver, the lower limit of the lane green light time is set to be 10 seconds on the basis, and the duration time of each phase is an integral multiple of 5.
10. The mixed traffic flow-oriented signalized intersection double-layer optimization method according to claim 1, wherein the vehicle kinematics model in the step 3 is as follows:
t 1 ≤t≤t f ,u min ≤u(t)≤u max
wherein x (t) represents the distance of the autonomous vehicle from the intersection stop line at time t; v (t) represents the speed of the autonomous vehicle at time t; u (t) represents the acceleration of the autonomous vehicle at time t; s represents the distance between an automatically-driven vehicle and a stop line of an intersection at the initial moment; v. of 1 Representing the speed of the autonomous vehicle at the initial time; v (t) f ) Indicating autonomous driving of a vehicleSpeed of crossing the stop line; u. of min Represents a minimum acceleration; u. of max Indicating the maximum acceleration.
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| CN116092310A (en) * | 2023-01-28 | 2023-05-09 | 西南交通大学 | Intersection collaborative ecological driving control method and system for mixed traffic environment |
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| CN110718074A (en) * | 2019-11-06 | 2020-01-21 | 清华大学 | Cooperative control method for signal lamp and vehicle of hybrid traffic intersection |
| CN110942645A (en) * | 2019-11-06 | 2020-03-31 | 清华大学 | Vehicle control method for hybrid traffic intersection |
| CN114495527A (en) * | 2022-02-23 | 2022-05-13 | 长安大学 | Method and system for collaborative optimization of vehicle paths at internet intersection in mixed traffic environment |
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| CN116092310A (en) * | 2023-01-28 | 2023-05-09 | 西南交通大学 | Intersection collaborative ecological driving control method and system for mixed traffic environment |
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| 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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