A signal intersection energy-saving control method considering effect threshold

Abstract

The present invention is applicable to the technical fields of traffic control and intelligent vehicle control, and provides an energy-saving control method for signal intersections that takes effect thresholds into consideration. First, a two-way six-lane signal intersection scene is constructed, and the intersection is divided into functional areas. The controller controls the vehicle to complete lane change in the lane change area first, and then performs speed control. Then, in view of the situation where the control effect deteriorates due to high traffic flow, a threshold that takes traffic flow into consideration is obtained through simulation analysis, and two types of control methods are designed in the controller. The first type is applied to vehicle-road cooperative control under medium and low traffic flow. Based on logical judgment and calculation, four types of control models are divided according to current vehicle parameters and traffic information, with the aim of reducing the number of stops and travel time, and taking into account economy; the second type is applied to intersection speed control under high traffic flow, with the vehicle energy consumption economy as the guide, and considering the vehicle speed obtained by the first type of control method, an adjustable weight factor is set to maximize traffic efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic control and intelligent vehicle control, and in particular relates to an energy-saving control method for a signal intersection considering an effect threshold. Background Art

[0002] Smart transportation system is an emerging direction for the integration and development of intelligent connected vehicles and transportation in the future. At present, the number of cars in my country is increasing year by year, and most of the built intersections are signalized intersections. When passing through signalized intersections, human-driven vehicles usually misjudge the current signal duration, resulting in heavy accelerator and sudden brake. For vehicles: a short-term large torque mutation will cause damage to the powertrain, and will also increase fuel consumption and the number of stops; for passengers, sudden changes in speed will also reduce ride comfort; for the transportation system, it will increase the travel time of the entire intersection and reduce the travel efficiency.

[0003] Currently, there are many methods for controlling vehicle traffic at signalized intersections, but most of the algorithms have the following problems: first, the methods are relatively simple and cannot cope with rapidly changing traffic flow; second, they are relatively complex, and the traffic flow at urban intersections is large, and the complexity of the algorithm will greatly affect the stability of the controller; at the same time, there is almost no discussion on the lateral safety of vehicles in the methods that consider vehicle speed collaborative control in signalized intersection environments. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a signalized intersection energy-saving control method taking into account an effect threshold, aiming to solve the problems raised in the above-mentioned background technology.

[0005] The embodiment of the present invention is implemented as follows: a signal intersection energy-saving control method considering an effect threshold comprises the following steps:

[0006] Step 1: Build a two-way six-lane single-signal intersection scenario, use monitoring equipment to collect statistics on key information such as traffic flow on the road section, and configure the intersection based on the Webster timing method, such as signal timing;

[0007] Step 2: Complete the functional area division of the intelligent signalized intersection; complete the vehicle's lane change to the target lane before performing speed coordination control, and then perform speed control;

[0008] Step 3: Design the first type of control method, based on concise logical judgment and calculation, divide the control model into four types according to the current vehicle parameters and traffic information, and obtain the optimized speed of different categories;

[0009] Step 4: Use the first type of control method to simulate and calibrate the flow of the road section to obtain a threshold value at which the control effect deteriorates;

[0010] Step 5: When the traffic flow reaches the threshold, the controller switches to the second control method; the second control method: uses a dynamic programming algorithm to solve the control speed that takes economy into consideration, while considering the speed obtained by the first control method and applying an adjustable weight to obtain the final vehicle control optimization speed.

[0011] An embodiment of the present invention provides a signal intersection energy-saving control method considering an effect threshold, and its beneficial effects are as follows:

[0012] (1) The first type of control method uses simple logical judgment and calculation to divide the speed control model into four types. While achieving good control effect, it can also greatly reduce the calculation time and reduce the operating pressure on the controller to a greater extent.

[0013] (2) Design two types of control methods in the same controller, obtain a threshold through simulation calibration, and design different control methods based on the effect threshold to cope with different traffic flow conditions. In different stages, the priority is given to passability and economy, respectively, to improve the final control effect;

[0014] (3) A separate lane change zone is designated in the functional area division of the entire intersection. First, it can give vehicles more time and space to change lanes, making it easier for vehicles to return to their corresponding driving lanes according to the target lane. Second, it can reasonably optimize the lane allocation under different traffic flows to avoid vehicle collisions due to lane changes during speed control. At the same time, it can also indirectly reduce the amount of calculation required for vehicles in the speed control zone to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a signal intersection energy-saving control method considering an effect threshold provided by an embodiment of the present invention;

[0016] Figure 2 Assign a deployment map to intersection lanes;

[0017] Figure 3 Assign a map to lane IDs;

[0018] Figure 4 It is an eight-phase four-stage distribution diagram;

[0019] Figure 5 It is the functional area division and hardware architecture distribution diagram of the intelligent intersection;

[0020] Figure 6 It is the speed control flow chart;

[0021] Figure 7To accelerate the control model;

[0022] Figure 8 It is the deceleration control model;

[0023] Fig. 9 It is a deceleration and parking control model;

[0024] Fig.10 This is the control effect diagram under the condition of increasing traffic flow;

[0025] Fig.11 This is a flow chart of the second type of control method. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0027] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0028] like Figure 1 As shown, a signal intersection energy-saving control method considering an effect threshold is provided in one embodiment of the present invention, comprising the following steps:

[0029] Step 1: Build a two-way six-lane single-signal intersection scenario, use monitoring equipment to collect statistics on key information such as traffic flow on the road section, and configure the intersection based on the Webster timing method, such as signal timing;

[0030] Step 2: Complete the functional area division of the intelligent signalized intersection; complete the vehicle's lane change to the target lane before performing speed coordination control, and then perform speed control;

[0031] Step 3: Design the first type of control method, based on concise logical judgment and calculation, divide the control model into four types according to the current vehicle parameters and traffic information, and obtain the optimized speed of different categories;

[0032] Step 4: Use the first type of control method to simulate and calibrate the flow of the road section to obtain a threshold value at which the control effect deteriorates;

[0033] Step 5: When the traffic flow reaches the threshold, the controller switches to the second control method; the second control method: uses a dynamic programming algorithm to solve the control speed that takes economy into consideration, while considering the speed obtained by the first control method and applying an adjustable weight to obtain the final vehicle control optimization speed.

[0034] As a preferred embodiment of the present invention, step 1 includes the following specific steps:

[0035] Step 1.1: Allocation and numbering of lanes at intersections:

[0036] First of all, with regard to the lane allocation at the intersection, if left-turning and straight-moving vehicles are released at the same time, the left-turning vehicles in this lane will conflict with the straight-moving vehicles in the opposite lane. When the traffic volume gradually increases, it will cause traffic jams, seriously affecting traffic efficiency and increasing energy consumption.

[0037] If a dedicated right-turn lane is set up, there will only be one lane for going straight. In order to make full use of traffic resources, a lane for going straight and right-turn is set up. Therefore, for the same lane, from left to right, it is divided into: dedicated left-turn lane, dedicated straight-through lane, and combined straight-through and right-turn lanes. The specific deployment is as follows: Figure 2 shown.

[0038] For subsequent control, the existing lanes can be Figure 3 The lanes are defined as shown in Table 1. The order of the numbers is not important, but the lane numbers themselves are important, so that the subsequent edge controller MEC can use this information.

[0039] Table 1 Lane definition table in road network

[0040]

[0041] Step 1.2, intersection signal timing:

[0042] Since a dedicated left-turn lane is set up, the eight-phase four-stage type is selected for signal allocation, and the traffic is released in the order of north-south straight + right turn, north-south left turn, east-west straight + right turn, and east-west left turn. Figure 4 shown.

[0043] The traffic flow in each direction of the intersection is counted by means including but not limited to monitoring equipment, electronic devices, etc., and then the optimal signal time allocation for each phase is obtained according to the Webster signal timing method.

[0044] As a preferred embodiment of the present invention, in step 2, the functional area division of the smart signalized intersection includes the following steps:

[0045] Step 2.1, Intelligent Transportation System Structure Hardware Division:

[0046] RSU is a communication equipment unit deployed on the roadside, usually installed on the roadside or on a traffic monitoring pole. Its main function in the smart intersection is data collection, that is, receiving vehicle information through the PC5 protocol and using Ethernet to receive MEC's ​​dispatch instructions to CAV (intelligent connected vehicles, after entering the intersection control area, the human-driven vehicle automatically switches to an intelligent connected vehicle). The data transmission function is to forward V2X messages, including sending vehicle information to MEC through Ethernet and sending dispatch instructions to CAV using the PC5 protocol.

[0047] The on-board unit (OBU) is an intelligent device installed on the vehicle that can collect the vehicle's position, speed, acceleration, driving intention, etc. through sensors and cameras and transmit them to the MEC;

[0048] The intersection edge controller MEC is an edge computing device deployed near the intersection. Its main function is to generate control instructions in a timely manner according to the current traffic conditions to achieve the purpose of controlling the CAV.

[0049] Step 2.2: Division of functional areas of signalized intersections:

[0050] According to the order of vehicles entering and exiting, the intersection area is divided into lane change area, speed regulation area, core traffic area and free driving area. Figure 5 The following two goals are mainly achieved in the lane change area:

[0051] 1) Return the current lane and target lane of the controlled vehicle. In order to avoid changing lanes in the speed regulation area and affecting the safe driving of other vehicles, determine whether the vehicle can reach the target lane while still driving in the current lane. If feasible, do not intervene in the vehicle's driving status; if not feasible, change the vehicle to the corresponding appropriate lane when the safe distance is sufficient.

[0052] 2) For vehicles whose target lane is a straight lane but will appear in the straight and right-turn lane in the future, the number of vehicles in the straight lane and the straight and right-turn lane in the current speed regulation area is returned through the roadside equipment. When it is determined that there are more vehicles in the current right-turn lane, a lane change instruction is sent to this type of target vehicle in advance, and the middle straight lane is switched to first. Since the middle lane is a dedicated straight lane, there is no need to consider the impact of vehicles in different target lanes in the core traffic area, which is more conducive to improving the traffic efficiency of the intersection.

[0053] Step 2.3, intersection system architecture assumptions:

[0054] 1) The communication quality between the on-board unit OBU and the roadside unit RSU is good, meeting the real-time dispatching requirements;

[0055] 2) The speed regulation zone is large enough for the vehicle to continue driving for a certain distance after completing acceleration;

[0056] 3) All controlled vehicles strictly follow the dispatch instructions.

[0057] As a preferred embodiment of the present invention, step 3 comprises the following steps:

[0058] In the V2X environment, the vehicle-mounted unit OBU interacts with traffic equipment, and the vehicle-mounted unit OBU transmits its own speed, position, current lane, target lane and other information to the edge controller MEC via the roadside unit RSU; the edge controller MEC will analyze and judge key information such as the current signal timing, the signal phase, and the traffic flow collected by the roadside unit RSU, and divide the vehicle speed control types into four categories: 1) Maintaining a constant speed; 2) The vehicle accelerates; 3) The vehicle decelerates; 4) The vehicle stops at the intersection and waits for the green light; then the optimized speed is obtained through logical calculation and transmitted to the vehicle-mounted unit OBU via the roadside unit RSU. Specifically:

[0059] The extraction and utilization of intersection information mainly involves the following information being returned to the controller by the networked traffic equipment:

[0060] (1) The signal cycle time of the entire phase of the current intersection t c ;

[0061] (2) Return to the lane with a green light at each phase;

[0062] (3) Traverse the start time of the next green light in the target lane of the current vehicle t gs and end time t ge .

[0063] Since the vehicles face different traffic light states when entering the speed regulation area, and the different signal light states will lead to differences in the cycle time obtained, so the motion control model of the vehicle under different traffic light states will be discussed separately in the following. The whole process is as follows Figure 6 shown.

[0064] As a preferred embodiment of the present invention, the control model of uniform speed driving is as follows:

[0065] (1) Green light status:

[0066] After the vehicle enters the speed regulation area, if it continues to drive at the current speed and can successfully pass the stop line within the current green light end time, it means that there is enough time, that is:

[0067] ;

[0068] Then the command for the CAV to travel at a constant speed is returned, and its speed is adjusted as follows:

[0069] ;

[0070] in: t g The current remaining time of the green light, unit: s; d This is the distance from the vehicle's position in the speed regulation area to the center of the intersection, in meters. V 0 is the original speed before optimization, unit: m / s; V ad is the optimized target vehicle speed, unit: m / s;

[0071] (2) Red or yellow light status:

[0072] After the vehicle enters the speed regulation area, if the vehicle still travels at the current speed and can successfully pass the stop line during the next green light, the CAV will return to the instruction of uniform speed driving, and its speed adjustment is the same as the previous case.

[0073] As a preferred embodiment of the present invention, the control model of accelerating driving is as follows:

[0074] (1) Green light status:

[0075] After the vehicle enters the speed control area, if it continues to drive at the current speed, it cannot pass the stop line smoothly within the current green light time. However, when the vehicle accelerates to the maximum speed limit of the intersection area, it can pass smoothly, which means that the vehicle needs to speed up appropriately to pass the signal intersection, that is:

[0076] ;

[0077] ;

[0078] Then the CAV is returned with the instruction to accelerate, and the optimized target speed is as follows:

[0079] ;

[0080] Where: a is the preset vehicle acceleration, a positive value, unit: m / s 2 ; V max The maximum speed that a vehicle can reach at an intersection, unit: m / s;

[0081] (2) Red or yellow light status:

[0082] After a vehicle enters the speed regulation zone, if it cannot pass the stop line in time before the next green light ends at the current speed, but can do so after accelerating to the maximum speed allowed by the intersection, it means that the vehicle needs to accelerate appropriately to pass the intersection without stopping, that is:

[0083] ;

[0084] ;

[0085] Then the CAV (intelligent connected vehicle, after entering the intersection control area, the human-driven vehicle automatically switches to an intelligent connected vehicle) acceleration instruction is returned, and the optimized target speed is as follows:

[0086] ;

[0087] in: t ge The time interval from the current moment to the next green light end moment, unit: s; t gs The time interval from the current moment to the next green light start moment, unit: s;

[0088] The acceleration diagrams of the above two cases are as follows: Figure 7 shown.

[0089] As a preferred embodiment of the present invention, the control model of deceleration driving is as follows:

[0090] (1) Green light status:

[0091] After the vehicle enters the speed adjustment zone, even if it accelerates to the maximum speed, it cannot pass the stop line smoothly when the green light ends. At the same time, when the vehicle decelerates to the minimum speed allowed, it can pass smoothly after the red light ends, which means that the vehicle needs to slow down appropriately to pass the signal intersection, that is:

[0092] ;

[0093] ;

[0094] Then the instruction for the CAV to slow down is returned, and the average of the start and end times of the next green light is taken:

[0095] ;

[0096] Calculate its optimized target speed:

[0097] ;

[0098] Among them: a dIt is the preset absolute value of vehicle deceleration, unit: m / s 2 ; V min The minimum speed that the vehicle can reach, unit: m / s; t gm It is the average value of the start and end time of the next green light, in seconds.

[0099] (2) Red or yellow light status:

[0100] After the vehicle enters the speed regulation area, if the traffic light is still red when the vehicle reaches the stop line at the current speed, but the vehicle can pass in time when the next green light comes on at the minimum speed allowed by the vehicle, it means that the vehicle needs to slow down appropriately, that is:

[0101] ;

[0102] ;

[0103] Then the command for the CAV to decelerate is returned, as shown below:

[0104] ;

[0105] The deceleration diagrams of the above two cases are as follows: Figure 8 shown.

[0106] As a preferred embodiment of the present invention, the control model of stopping after driving to an intersection is as follows:

[0107] (1) Green light status

[0108] After the vehicle enters the speed adjustment area, even when the vehicle accelerates to the maximum speed, it cannot pass the stop line smoothly when the green light ends. At the same time, when the vehicle decelerates to the minimum speed allowed, the vehicle still does not reach the stop line of the intersection before the next green light comes on, indicating that the vehicle needs to (decelerate) stop, that is:

[0109] ;

[0110] ;

[0111] Then the instruction for CAV to decelerate is returned, and the optimized target speed is as follows:

[0112] ;

[0113] (2) Red or yellow light status:

[0114] After the vehicle enters the speed regulation area, if the vehicle reaches the intersection stop line at the minimum speed allowed by the vehicle, and the next green light still does not light up in time, it indicates that the vehicle needs to (slow down) and stop, that is:

[0115] ;

[0116] Then the command for the CAV to decelerate and stop is returned, as shown below:

[0117] ;

[0118] The schematic diagram of deceleration and parking in two situations is as follows Fig. 9 shown.

[0119] As a preferred embodiment of the present invention, in step 4, the sensor equipment is used to collect statistics on the traffic flow of a day to obtain the traffic flow change pattern and verify the control result. The basic verification result can be obtained by simulating and calibrating the existing control strategy with incremental working conditions, such as Fig.10 shown.

[0120] The results show that when the traffic flow is near w, the control effect deteriorates, so the one-way traffic threshold of the intersection can be preliminarily selected as w.

[0121] like Fig.11 As shown, as a preferred embodiment of the present invention, in step 5, the specific implementation scheme of the second type of control method is as follows:

[0122] Step a: Vehicle dynamics model and state equation establishment;

[0123] For the second type of control method, the vehicle driving force balance equation is obtained from the vehicle speed and acceleration as follows:

[0124] ;

[0125] In the formula, F t It is the driving force required to balance the external resistance when the car is driving, unit: N; G is the vehicle gravity, unit: N; f is the rolling resistance coefficient; v is the vehicle speed, unit: m / s; i is the road slope, which is the ratio of slope height to base length; C D is the air resistance coefficient; A is the windward area, i.e. the projection area in the direction of the car's travel, unit: m 2 , δ is the rotation mass conversion factor; m is the vehicle mass, unit: kg; aVehicle acceleration (deceleration) Unit: m / s 2 .

[0126] In order to facilitate the solution, it is necessary to make certain assumptions and simplify the driving force equation:

[0127] For urban roads, the road surface construction has certain standards, so the road surface adhesion coefficient f There is little difference; for most cities in the country, their signalized intersections are located on flat terrain, so the road slope i It can be approximated to 0; when a vehicle is driving at an intersection, the speed is slow due to the speed limit on the road, and the wind resistance on the vehicle can be ignored at this time; the rotational mass conversion coefficient is related to the rotational inertia of the rotating parts such as the flywheel and wheels of the car and the transmission ratio of the transmission system, and is usually taken as 1.1~1.4. For cars driving in low gear at intersections, the middle value commonly used when selecting low gear is δ =1.2.

[0128] In summary, the driving force balance equation of vehicles traveling at intersections can be simplified as follows:

[0129] ;

[0130] In summary, the vehicle longitudinal dynamics model can be expressed as follows after discretization:

[0131] ;

[0132] The speed curve in the entire speed regulation area is solved in stages. The power and engine torque values ​​required by the vehicle in the current state can be calculated from the speed and acceleration of each stage. For the kth stage:

[0133] ;

[0134] ;

[0135] In the formula, P e (k) is the power generated by the vehicle in the kth stage, unit: W; F t (k) is the total driving force required by the vehicle in the kth stage, unit: N; v(k) is the speed in the kth stage, unit: m / s; η t is the mechanical efficiency of the transmission system; T tq (k) The engine torque required by the vehicle in the kth stage state, unit: Nm; ris the wheel radius, unit: m; i g is the transmission ratio; i 0 Main reducer transmission ratio.

[0136] The accumulated energy consumption is further obtained:

[0137] ;

[0138] In the formula, L energy is the total energy consumed, w(k) is the energy consumed in the kth stage, unit: J; Δs is the distance step, set to a fixed step, unit: m;

[0139] In summary, the control optimization objective function and its corresponding constraints can be obtained:

[0140] ;

[0141] ;

[0142] In the formula, a min The critical maximum deceleration that will not cause physical discomfort to human beings is preset, unit: m / s 2 ; a max The maximum acceleration that the human body can withstand without causing physical discomfort is preset. Unit: m / s 2 ; a(k) is the acceleration of the kth stage, unit: m / s 2 ; v min The minimum speed limit on the road, unit: m / s; v max The maximum speed limit of the road, unit: m / s; T min The minimum torque output by the engine, unit: Nm; T max The maximum torque output by the engine, unit: Nm; Δt(k) The time spent in the kth stage, unit: s.

[0143] Step b: Construction and solution of dynamic programming algorithm;

[0144] 1) Stage division and discretization:

[0145] The problem of solving the economic reference speed is a continuous problem in the time domain. In order to perform dynamic programming, it is transformed and discretized. The distance d is discretized into N stages, and the speed solution process is divided into several interrelated stages, among which k In the functional area division of the intersection, the next functional area after the lane change area is the speed regulation area, and the distance of the speed regulation area is determined by the actual situation of the local intersection at the beginning, that is, the total distance to be planned is certain and will not be too long, so the corresponding solution time and solution process are relatively easy and can meet the real-time requirements.

[0146] 2) Selection of state variables and control variables:

[0147] State variables are mainly used to describe the objective situation of the system at each stage or moment. In dynamic programming, it is necessary to ensure that they have no aftereffects. In solving the economic reference speed, speed and driving force are selected as state variables, as shown below:

[0148] ;

[0149] In the formula, x(k) represents the state of the kth stage, v(k) represents the speed state of the kth stage, F t (k) Represents the driving force state of the kth stage.

[0150] The control variable is the effort required to transfer from the current state to the next stage state after the previous state is determined. The vehicle's driving force change rate is selected as the control variable for the dynamic programming of economic vehicle speed, as shown below:

[0151] ;

[0152] 3) Definition of state transfer equation:

[0153] The state transition equation of dynamic programming is as follows:

[0154] ;

[0155] The dynamic programming state transition cost function is as follows:

[0156] ;

[0157] The initial value of the cumulative cost in the initial stage of dynamic programming is as follows:

[0158] ;

[0159] Then the cumulative cost of the dynamic programming end point is:

[0160] ;

[0161] In the formula, J * [x(k)] represents the cumulative cost corresponding to a state in the kth stage, J * [ x (k +1)] represents the cumulative cost corresponding to a state in the k+1th stage, X(k) represents the set of all states in the kth stage, U(k) represents the set of all possible control variables when the state transition occurs from the kth stage to the k+1th stage, L[x(k),u(k),k] Represents the state transition cost from stage k to stage k+1.

[0162] 4) Construction of cost function:

[0163] The problem of solving the economic reference speed can be regarded as a multi-objective optimization problem, with the energy consumed by the vehicle as the main optimization target:

[0164] ;

[0165] Where: L energy For the energy consumption cost, L t The time cost is mainly to prevent the planned time from being too long in order to save the energy of the whole vehicle; w t is the time consumption cost weight function. At the same time, since the two cost units have different dimensions and orders of magnitude, it is convenient to adjust the weight coefficient and introduce the normalization constant of energy consumption and time. C energy and C t .

[0166] The energy consumption cost is as follows:

[0167] ;

[0168] The time cost is:

[0169] ;

[0170] In the formula, t ref The greater the deviation from the set reference time, the more detrimental it is to traffic efficiency.

[0171] 5) Dynamic programming algorithm solution:

[0172] Reverse optimization and forward solution. Solve the optimal control variables of the Nth stage, N-1th stage, k+1th stage, 2nd stage and 1st stage in sequenceu * (k) , thus we get u * [x(0)] and J * [x(0)] , and then traverse the optimal control sequence with the global minimum cumulative cost in reverse order according to the system initial state x(0):

[0173] ;

[0174] Get the optimal state sequence:

[0175] ;

[0176] The cumulative cost sequence of the optimal stage:

[0177] ;

[0178] The optimal state sequence includes the economic speed sequence of the entire process, and then the economic speed curve is obtained.

[0179] Step c: speed control curve output;

[0180] Assume that the control optimization speed output by the solution process is V2, and the control optimization speed output by the first type of control method is V1. V2 mainly considers economy, and V1 mainly considers passability. In order to cope with the frequent changes in traffic under high traffic volume, dynamic adjustment is required to a certain extent, so the final vehicle control optimization speed is:

[0181] ;

[0182] Where: V is the final vehicle control optimization speed, unit: m / s; λ It is an adjustable weight factor, which makes it easier to output a more economical speed when there are continuous oscillations in high traffic flow.

[0183] Step d: The second type of control method continues, and the sensor device continuously monitors the vehicle flow;

[0184] Step e: When the sensor continuously detects that the current traffic volume is less than the set threshold value for a certain period of time, it indicates that the current intersection has been cut out of the morning / evening peak period, and the intersection changes the control strategy, and the controller automatically switches to the first type of control method;

[0185] Step f: Repeat step a.

[0186] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A signal intersection energy-saving control method considering effect threshold, characterized in that: The following steps are involved: Step 1: Construct a two-way six-lane single-signal intersection scenario, use monitoring equipment to count the traffic flow of the constructed single-signal intersection scenario, and configure the intersection based on the Webster timing method; Step 2: Complete the functional area division of the intelligent signalized intersection; complete the lane change of the vehicle to the target lane before performing speed coordination control, and then perform speed control; Step 3: Design the first type of control method, based on logical judgment and calculation, divide the control model into four types according to the current vehicle parameters and traffic information, and obtain the optimized speed of different categories; Step 4: Use the first type of control method to simulate and calibrate the traffic flow of the constructed single-signal intersection scenario to obtain a threshold value at which the control effect deteriorates; Step 5: When the traffic flow reaches the threshold, the controller switches to the second control method; the second control method uses a dynamic programming algorithm to solve the control speed that takes into account economy, while considering the speed obtained by the first control method and applying an adjustable weight to obtain the final vehicle control optimization speed; The control model of uniform speed driving is as follows: (1) Green light status: After the vehicle enters the speed regulation area, if it continues to drive at the current speed and can successfully pass the stop line within the current green light end time, it means that there is enough time, that is: ； Then the command for the CAV to travel at a constant speed is returned, and its speed is adjusted as follows: ； in: t g The current remaining time of the green light, unit: s; d This is the distance from the vehicle's position in the speed regulation area to the center of the intersection, in meters. V 0 is the original speed before optimization, unit: m / s; V ad is the optimized target vehicle speed, unit: m / s; (2) Red or yellow light status: After the vehicle enters the speed adjustment area, if the vehicle still drives at the current speed and can successfully pass the stop line during the next green light, the CAV returns to the instruction of driving at a constant speed, and its speed adjustment is the same as the previous case; The control model for accelerating driving is as follows: (1) Green light status: After the vehicle enters the speed control area, if it continues to drive at the current speed and cannot pass the stop line smoothly within the current green light time, but the vehicle accelerates to the maximum speed limit of the intersection and can pass smoothly, it means that the vehicle needs to speed up appropriately to pass the signal intersection, that is: ； ； Then the CAV is returned with the instruction to accelerate, and the optimized target speed is as follows: ； Where: a is the preset vehicle acceleration, a positive value, unit: m / s 2 ; V max The maximum speed that a vehicle can reach at an intersection, unit: m / s; (2) Red or yellow light status: After a vehicle enters the speed regulation zone, if it cannot pass the stop line in time before the next green light ends at the current speed, but can do so after accelerating to the maximum speed allowed by the intersection, it means that the vehicle needs to accelerate appropriately to pass the intersection without stopping, that is: ； ； Then the CAV is returned with the instruction to accelerate, and the optimized target speed is as follows: ； in: t ge The time interval from the current moment to the next green light end moment, unit: s; t gs The time interval from the current moment to the next green light start moment, unit: s; The control model for deceleration driving is as follows: (1) Green light status: After the vehicle enters the speed adjustment area, even when the vehicle accelerates to the maximum speed, it cannot pass the stop line smoothly when the green light ends. At the same time, when the vehicle decelerates to the minimum speed allowed, it can pass smoothly after the red light ends, which means that the vehicle needs to slow down appropriately to pass the signal intersection, that is: ； ； Then the instruction for the CAV to slow down is returned, and the average of the start and end times of the next green light is taken: ； Calculate its optimized target speed: ； Among them: a d It is the preset absolute value of vehicle deceleration, unit: m / s 2 ; V min The minimum speed that the vehicle can reach, unit: m / s; t gm The average value of the start and end time of the next green light, unit: s; (2) Red or yellow light status: After the vehicle enters the speed regulation area, if the traffic light is still red when the vehicle reaches the stop line at the current speed, but the vehicle can pass in time after the next green light comes on at the minimum speed allowed by the vehicle, it means that the vehicle needs to slow down appropriately, that is: ； ； Then the command for the CAV to decelerate is returned, as shown below: ； The control model for stopping after driving to the intersection is as follows: (1) Green light status After the vehicle enters the speed control area, even if it accelerates to the maximum speed, it cannot pass the stop line smoothly when the green light ends. At the same time, when the vehicle decelerates to the minimum speed allowed, it still does not reach the stop line of the intersection before the next green light comes on, indicating that the vehicle needs to stop, that is: ； ； Then the instruction for CAV to decelerate is returned, and the optimized target speed is as follows: ； (2) Red or yellow light status: After the vehicle enters the speed regulation area, if the vehicle drives to the intersection stop line at the minimum speed allowed by the vehicle, and the next green light still does not light up in time, it means that the vehicle needs to stop, that is: ； Then the command for the CAV to decelerate and stop is returned, as shown below: ； In step 4, the traffic flow of a day is counted by using sensor equipment to obtain the traffic flow change pattern and verify the control result; the existing control strategy is simulated and calibrated under the incremental working condition to obtain the verification result; In step 5, the specific implementation scheme of the second type of control method is as follows: Step a: Vehicle dynamics model and state equation establishment; For the second type of control method, the vehicle driving force balance equation is obtained from the vehicle speed and acceleration as follows: ； In the formula, F t It is the driving force required to balance the external resistance when the car is driving, unit: N; G is the vehicle gravity, unit: N; f is the rolling resistance coefficient; v is the vehicle speed, unit: m / s; i is the road slope, which is the ratio of slope height to base length; C D is the air resistance coefficient; A is the windward area, i.e. the projection area in the direction of the car's travel, unit: m 2 , δ is the rotation mass conversion factor; m is the vehicle mass, unit: kg; a is the vehicle acceleration or deceleration, unit: m / s 2 ; In order to facilitate the solution, certain assumptions are made and the driving force equation is simplified: The rolling resistance coefficient of the signalized intersection road f Approximately the same; the road slope at the signal intersection i Approximately 0; the wind resistance of the vehicle when driving at the intersection is ignored; the rotation mass conversion coefficient is δ =1.2; In summary, the driving force balance equation of vehicles traveling at intersections can be simplified as follows: ； In summary, the vehicle longitudinal dynamics model is discretized as follows: ； The speed curve in the entire speed regulation area is solved in stages, and the power and engine torque values ​​required by the vehicle in the current state are obtained from the speed and acceleration of each stage. For the kth stage: ； ； In the formula, P e (k) is the power generated by the vehicle in the kth stage, unit: W; F t (k) is the total driving force required by the vehicle in the kth stage, unit: N; v(k) is the speed in the kth stage, unit: m / s; η t is the mechanical efficiency of the transmission system; T tq (k) The engine torque required by the vehicle in the kth stage state, unit: Nm; r is the wheel radius, unit: m; i g is the transmission ratio; i 0 is the main reducer transmission ratio; The accumulated energy consumption is further obtained: ； In the formula, L energy is the total energy consumed, w(k) is the energy consumed in the kth stage, unit: J; Δs is the distance step, set to a fixed step, unit: m; In summary, the control optimization objective function and its corresponding constraints are: ； ； In the formula, a min To pre-set the maximum deceleration that will not cause physical discomfort to people, unit: m / s 2 ; a max To pre-set the maximum acceleration that will not cause physical discomfort to people, unit: m / s 2 ; a(k) is the acceleration of the kth stage, unit: m / s 2 ; v min The minimum speed limit on the road, unit: m / s; v max The maximum speed limit of the road, unit: m / s; T min The minimum torque output by the engine, unit: Nm; T max The maximum torque output by the engine, unit: Nm; Δt(k) The time spent in the kth stage, unit: s; Step b: Construction and solution of dynamic programming algorithm; 1) Stage division and discretization: The solution of the economic reference speed is a continuous problem in the time domain. In order to perform dynamic programming, it is transformed and discretized. The distance d is discretized into N stages, and the speed solution process is divided into several interrelated stages, among which k represents the kth stage; 2) Selection of state variables and control variables: In solving the economic reference speed, speed and driving force are selected as state variables, as shown below: ； In the formula, x(k) represents the state of the kth stage, v(k) represents the speed state of the kth stage, F t (k) represents the driving force state of the kth stage; The vehicle's driving force change rate is selected as the control variable for the economical vehicle speed dynamic programming, as shown below: ； 3) Definition of state transfer equation: The state transition equation of dynamic programming is as follows: ； The dynamic programming state transition cost function is as follows: ； The initial value of the cumulative cost in the initial stage of dynamic programming is as follows: ； Then the cumulative cost of the dynamic programming end point is: ； In the formula, J * [x(k)] represents the cumulative cost corresponding to a state in the kth stage, J * [ x (k +1)] represents the cumulative cost corresponding to a state in the k+1th stage, X(k) represents the set of all states in the kth stage, U(k) represents the set of all possible control variables when the state transition occurs from the kth stage to the k+1th stage, L[x(k),u(k),k] represents the state transition cost from stage k to stage k+1; 4) Construction of cost function: The energy consumed by the vehicle is taken as the optimization target: ； Where: L energy For the energy consumption cost, L t The time cost is used to prevent the planned time from being too long in order to save the energy of the whole vehicle; w t is the time consumption cost weight function; at the same time, due to the different dimensions and magnitudes of the two cost units, in order to facilitate the adjustment of the weight coefficient, the normalization constants of energy consumption and time are introduced C energy and C t ; The energy consumption cost is as follows: ； The time cost is: ； In the formula, t ref The more deviation from the set reference time, the more detrimental it is to traffic efficiency; 5) Dynamic programming algorithm solution: Reverse optimization and forward solution; solve the optimal control variables of the Nth stage, N-1th stage, k+1th stage, 2nd stage and 1st stage in turn u * (k) , thus we get u * [x(0)] and J * [x(0)] , and then traverse the optimal control sequence with the global minimum cumulative cost in reverse order according to the system initial state x(0): ； Get the optimal state sequence: ； The cumulative cost sequence of the optimal stage: ； The optimal state sequence includes the economic speed sequence of the whole process, and then the economic speed curve is obtained; Step c: speed control curve output; Assume that the control optimization speed output by the solution process is V2, and the control optimization speed output by the first type of control method is V1; V2 considers economy, and V1 considers passability. In order to facilitate dynamic adjustment to cope with the frequent changes in traffic flow under high traffic flow, an adjustable weight factor is introduced, so the final vehicle control optimization speed is: ； Where: V is the final vehicle control optimization speed, unit: m / s; λ is an adjustable weight factor; Step d: The second type of control method continues, and the sensor device continuously monitors the vehicle flow; Step e: When the sensor continuously detects that the current traffic volume is less than the set threshold value for a certain period of time, it indicates that the current intersection has been cut out of the morning / evening peak period, and the intersection changes the control strategy, and the controller automatically switches to the first type of control method; Step f: Repeat step a.

2. The signal intersection energy-saving control method considering effect threshold according to claim 1 is characterized in that: The step 1 comprises the following specific steps: Step 1.1: Allocation and numbering of lanes at intersections: For the same lane, from left to right, they are: left turn only, straight driving only, and straight driving and right turn only, and the lanes are numbered; Step 1.2, intersection signal timing: Since a dedicated left-turn lane is set up, the eight-phase four-stage type is selected for signal allocation, and traffic is released in the order of north-south straight-through plus right turn, north-south left turn, east-west straight-through plus right turn, and east-west left turn; The traffic volume in each direction of the intersection is counted through monitoring equipment and electronic equipment, and then the optimal signal time allocation for each phase is obtained according to the Webster signal timing method.

3. The signal intersection energy-saving control method considering effect threshold according to claim 2 is characterized in that: In step 2, the functional area division of the smart signalized intersection includes the following steps: Step 2.1, Intelligent Transportation System Structure Hardware Division: Communication equipment unit RSU, used for data collection and sending dispatch instructions; The on-board unit (OBU) is used to collect the physical information and driving intention of the vehicle and transmit it to the MEC; The intersection edge controller MEC is used to generate control instructions in a timely manner according to the current traffic conditions; Step 2.2: Division of functional areas of signalized intersections: The intersection area is divided into lane change area, speed regulation area, core traffic area and free driving area according to the order of vehicle entry and exit; the following two goals are achieved in the lane change area: 1) Return the current lane and target lane of the controlled vehicle. In order to avoid changing lanes in the speed control area and affecting the safe driving of other vehicles, determine whether the controlled vehicle can reach the target lane while still driving in the current lane. If feasible, do not intervene in the vehicle's driving state; if not feasible, change the vehicle to the corresponding appropriate lane when the safe distance is sufficient; 2) For vehicles whose target lane is a through lane but will appear in the through and right turn lane in the future, the number of vehicles in the through lane and the through and right turn lane in the current speed regulation area is returned through the roadside equipment. When it is determined that there are more vehicles in the right turn lane, a lane change command is sent to the target vehicle in advance, giving priority to switching to the middle through lane; Step 2.3, intersection system architecture assumptions: 1) The communication quality between OBU and RSU is good, meeting the real-time scheduling requirements; 2) The speed regulation zone is large enough for the vehicle to continue driving for a certain distance after completing the acceleration; 3) All controlled vehicles strictly follow the dispatch instructions.

4. The signal intersection energy-saving control method considering effect threshold according to claim 2 is characterized in that: The step 3 comprises the following steps: Step 3.1: Extraction and utilization of intersection information: In this step, the networked traffic device returns the following information to the controller: (1) The signal cycle time of the entire phase of the current intersection t c ; (2) Lanes with green lights at each phase; (3) Traverse the start time of the next green light in the target lane of the current vehicle t gs and end time t ge。

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