Mixed traffic flow-oriented vehicle eco-driving control method and electronic device

Abstract

Disclosed are a mixed traffic flow-oriented vehicle eco-driving control method and an electronic device. The method comprises: on the basis of shock wave evolution theory, calculating a congestion wave speed and a dissipation wave speed at an intersection, to obtain a farthest queuing point position of an intersection downstream of a connected and autonomous vehicle and a formation moment thereof, and predicting a time and vehicle status when the connected and autonomous vehicle passes a stop line; predicting longitudinal acceleration of a manual vehicle according to a risk field model, to acquire a predicted trajectory of the manual vehicle; constructing and solving an optimal ecological reference trajectory planning model for the connected and autonomous vehicle, to obtain an acceleration curve of the connected and autonomous vehicle, and acquire an ecological reference trajectory of the connected and autonomous vehicle; on the basis of predicted trajectories of manual vehicles in front of and behind the connected and autonomous vehicle and the risk field model, setting a risk factor, constructing a tracking target on the basis of the risk factor and the ecological reference trajectory, and using a model predictive control solution, to obtain a control input for the connected and autonomous vehicle.

Description

Vehicle ecological driving control method and electronic equipment for mixed traffic flow

[0001] The present invention claims priority to a Chinese patent application filed with the Patent Office of China on June 26, 2023, with application number 202310757724.X and title “Vehicle ecological driving control method and electronic device for mixed traffic flow”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present invention relates to the fields of artificial potential field theory, traffic flow theory and vehicle motion planning and control, and in particular to a vehicle ecological driving control method and electronic equipment for mixed traffic flow. Background Art

[0003] In recent years, with the rapid increase in the number of vehicles, energy consumption and pollutant emissions have become increasingly prominent. Furthermore, road vehicles, such as cars, trucks, and buses, account for approximately three-quarters of transportation emissions. Eco-driving is a strategy that improves the energy efficiency and emission reduction efficiency of transportation systems by modifying vehicle driving behavior. Its fundamental approach is to provide vehicles with real-time driving recommendations to reduce stop-and-start, idling, and abrupt acceleration / deceleration. Numerous studies and practices have demonstrated that eco-driving can reduce fuel consumption and emissions by 3%-20%. With the recent advancement of autonomous driving and communication technologies, controlling connected and autonomous vehicles (CAVs) to achieve eco-driving is considered an effective and promising approach. Compared to highway environments, urban road traffic flow is frequently interrupted by traffic lights. Vehicle acceleration, deceleration, and idling at signalized intersections increase fuel consumption and emissions. Therefore, developing eco-driving strategies for urban road environments is of great significance.

[0004] Currently, existing research on ecological driving strategies for connected and autonomous vehicles (CAVs) ignores the impact of other vehicles on them, or focuses solely on connected and autonomous driving scenarios. However, it is foreseeable that human-driven vehicles (MVs) and connected and autonomous vehicles (CAVs) will coexist on the road for a long time, forming mixed traffic flows.

[0005] Therefore, it is imperative to fully consider the mutual impacts between human-driven vehicles (MVs) and connected autonomous vehicles (CAVs) implementing eco-driving strategies in mixed traffic flows to ensure that eco-driving strategies do not negatively impact the safety of mixed traffic flows. Furthermore, it is important to emphasize that eco-driving vehicles often decelerate when approaching intersections, and this deceleration behavior is difficult for drivers of vehicles behind the eco-driving vehicle to predict. Therefore, the safety impact of following vehicles on vehicles implementing eco-driving strategies cannot be ignored, yet this point has been overlooked in many studies and practices.

[0006] Summary of the Invention

[0007] The present invention aims to provide a vehicle ecological driving control method and electronic equipment for mixed traffic flow environments of manually driven vehicles (MVs) and connected autonomous vehicles (CAVs).

[0008] According to a first aspect of an embodiment of the present invention, a vehicle ecological driving control method for mixed traffic flow is provided, the method comprising:

[0009] Obtain signal intersection timing information, historical vehicle status information, and real-time vehicle status information; calculate the intersection's gathering wave velocity and dissipation wave velocity based on shock wave evolution theory, and determine the location of the farthest queue point at the downstream intersection of connected autonomous vehicles and the time when the farthest queue formed;

[0010] Predict the time and vehicle status of connected autonomous vehicles passing the stop line based on the location of the furthest queue point at the downstream intersection and the moment the furthest queue forms.

[0011] Predict the longitudinal acceleration of the manually driven vehicle based on the risk field model to obtain the predicted trajectory of the manually driven vehicle;

[0012] Taking the vehicle's instantaneous fuel consumption and the penalty for crossing the stop line as the objective function, setting vehicle dynamics constraints and trajectory constraints, constructing and solving the optimal ecological reference trajectory planning model for the connected autonomous vehicle, and obtaining the acceleration curve of the connected autonomous vehicle, thereby obtaining the ecological reference trajectory of the connected autonomous vehicle;

[0013] A risk factor is set based on the risk field model and the predicted trajectories of manually driven vehicles before and after the connected autonomous vehicle. A tracking target is constructed based on the risk factor and the ecological reference trajectory. The model predictive control is used to solve the problem and obtain the control input of the connected autonomous vehicle.

[0014] According to a second aspect of an embodiment of the present invention, a vehicle ecological driving control system for mixed traffic flow is provided, which is used to implement the above-mentioned vehicle ecological driving control method for mixed traffic flow. The system includes: a central controller and a plurality of local controllers deployed on connected autonomous vehicles;

[0015] The central controller includes:

[0016] The first result acquisition module is used to collect signal intersection timing information, historical vehicle status information, and real-time vehicle status information. Based on shock wave evolution theory, it calculates the intersection's gathering wave velocity and dissipation wave velocity to determine the location of the farthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the farthest queue formed.

[0017] The second result acquisition module is used to predict the time when the connected autonomous vehicle passes the stop line and the vehicle status based on the position of the farthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the farthest queue is formed;

[0018] A manually driven vehicle trajectory prediction module is used to construct a longitudinal acceleration prediction method for manually driven vehicles based on the risk field model, thereby obtaining a predicted trajectory of the manually driven vehicle;

[0019] The connected autonomous vehicle trajectory planning module is used to set vehicle dynamics constraints and trajectory constraints based on the vehicle's instantaneous fuel consumption and the penalty for crossing the stop line as the objective function. This module then constructs and solves the optimal ecological reference trajectory planning model for the connected autonomous vehicle, obtaining the acceleration curve of the connected autonomous vehicle and thereby acquiring the ecological reference trajectory of the connected autonomous vehicle.

[0020] A first communication module is used to send the ecological reference trajectory of the networked autonomous driving vehicle to the local controller;

[0021] The local controller includes:

[0022] The second communication module is used to receive the ecological reference trajectory of the connected autonomous driving vehicle;

[0023] The connected autonomous vehicle tracking module is used to set risk factors based on the risk field model and the predicted trajectories of manually driven vehicles before and after the connected autonomous vehicle, construct tracking targets based on the risk factors and ecological reference trajectories, and use model predictive control to solve them to obtain the control input of the connected autonomous vehicle.

[0024] According to a third aspect of an embodiment of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the above-mentioned vehicle ecological driving control method for mixed traffic flow.

[0025] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the above-mentioned vehicle ecological driving control method for mixed traffic flow is implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a vehicle ecological driving control method for mixed traffic flow. In the context of mixed traffic flow of manually driven vehicles (MVs) and connected autonomous vehicles (CAVs), the present invention takes into account the risks posed by manually driven vehicles in front and behind the connected autonomous vehicle. While ensuring traffic efficiency, the present invention also reduces driving risks and improves energy conservation and emission reduction efficiency. Furthermore, the vehicle ecological driving control system for mixed traffic flow provided by the present invention has a layered distributed architecture. The ecological reference trajectory of the connected autonomous vehicle is obtained through a central controller. Local controllers deployed on the connected autonomous vehicle solve the ecological reference trajectory while taking into account the risk factors posed by manually driven vehicles in front and behind the connected autonomous vehicle. Compared with the classic centralized control method, the communication cost and computing power cost of ecological driving are effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] FIG1 is a flow chart of a vehicle ecological driving control method for mixed traffic flow provided by an embodiment of the present invention;

[0029] FIG2 is a structural block diagram of a vehicle ecological driving control method for mixed traffic flow provided by an embodiment of the present invention;

[0030] FIG3 is a schematic diagram of the evolution of the intersection queue and the shock wave according to an embodiment of the present invention;

[0031] FIG4 is a schematic diagram of queue evolution when multiple eco-driving vehicles coexist at an intersection according to an embodiment of the present invention;

[0032] FIG5 is a schematic diagram of a vehicle ecological driving control system for mixed traffic flow provided by an embodiment of the present invention;

[0033] FIG6 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] It should be noted that, unless there is any conflict, the features in the following embodiments and implementations may be combined with each other.

[0036] As shown in FIG1 and FIG2 , an embodiment of the present invention provides a vehicle ecological driving control method for mixed traffic flow, the method comprising:

[0037] Step S1: Obtain signal intersection timing information, historical vehicle status information, and real-time vehicle status information; based on shock wave evolution theory, calculate the intersection gathering wave velocity and dissipation wave velocity to obtain the location of the farthest queue point at the downstream intersection of the networked autonomous driving vehicle and the time when the farthest queue is formed.

[0038] Specifically, step S1 includes the following steps:

[0039] Step S101: collecting signal phase and timing information (SPaT), historical vehicle status information, and real-time vehicle status information, wherein the vehicle status information includes vehicle position, vehicle speed, and vehicle acceleration.

[0040] Step S102: calibrate the intersection saturation flow rate q based on historical vehicle status information s , saturation flow density ρ s and congestion flow density ρ j .

[0041] Step S103: Obtain the arrival flow rate q based on the real-time vehicle status information a and the arrival flow density ρ a .

[0042] Step S104: Calculate the intersection gathering wave speed w1, which is expressed as follows: w1 = (0-q a ) / (ρ j -ρ a ).

[0043] Calculate the intersection evanescent wave velocity w2, the expression is as follows: w2=(q s -0) / (ρ s -ρ j ).

[0044] Also included: intersection departure wave w3 is: w3=(q s -qa ) / (ρ s -ρ a ).

[0045] It should be noted that the schematic diagram of intersection queue evolution and shock waves is shown in Figure 3. The gathering wave w1 refers to the shock wave generated by the traffic state changing from arrival flow to congestion flow, that is, the straight line AB in Figure 3; the dissipating wave w2 refers to the shock wave generated by the traffic state changing from congestion flow to saturation flow, that is, the straight line BC in Figure 3; the departing wave w3 refers to the shock wave generated by the traffic state changing from arrival flow to saturation flow, that is, the straight line BD in Figure 3.

[0046] Step S105: predict the queue situation of the vehicles in front of the CAV based on the shock wave propagation speed at the intersection and the vehicle status information, and obtain the farthest queue point position l at the intersection that the CAV will face. mq and the time t at which the furthest queue is formed mq .

[0047] Specifically, the current queue length L0 is obtained according to the signal intersection timing information and the real-time vehicle status information.

[0048] The time when the first connected autonomous vehicle CAV enters the intersection is t0, and the signal will be at t g Turn green all the time, t r In order to unify the calculation of the farthest queue point position and formation time in different situations, it is necessary to calculate the green light start time t g Adjust, t′ g Indicates the adjusted green light start time, calculated by the following formula:

[0049] Where c is the signal cycle length.

[0050] Use the adjusted green light start time t′ g , calculate the distance L between the farthest queue point and the stop line mq , the expression is as follows:

[0051] Where w1 is the velocity of the gathering wave, w2 is the velocity of the dissipating wave, and t′ g represents the adjusted green light start time, t0 is the time when the first connected autonomous vehicle enters the intersection, and ρ j is the congestion flow density, and N0 is the total number of vehicles between the first connected autonomous vehicle and the stop line.

[0052] Calculate the farthest queue point position l at the downstream intersection of the connected autonomous vehicle mq , the expression is as follows: mq =l s -Lmq .

[0053] Calculate the time t when the furthest queue is formed mq , the expression is as follows: mq =-L mq / w2.

[0054] Where, l s Indicates the stop line position.

[0055] Step S2, based on the position of the farthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the farthest queue is formed, predict the time when the connected autonomous vehicle passes the stop line and the vehicle status.

[0056] It should be noted that in order to ensure the original traffic efficiency of the intersection, it is necessary to predict the time when the networked autonomous driving vehicle CAV passes the stop line. and vehicle status (Terminal state). Among them, the terminal state This includes the position and speed of the CAV as it passes through the stop line. The speed at which the CAV passes through the stop line corresponds to the saturated flow speed. Since vehicles often pass through intersections with saturated traffic conditions, where queues often form, this example assumes that the controlled CAV should also pass through at this speed. This not only maintains existing traffic efficiency but also reduces the CAV's speed through the intersection, further ensuring safety.

[0057] Get the time it takes for a connected autonomous vehicle to pass the stop line The expression is as follows:

[0058] Get the vehicle status of a connected autonomous vehicle passing a stop line The expression is as follows:

[0059] Where, v s It represents the speed at which a connected autonomous vehicle passes the stop line, i.e. the speed of the saturated flow.

[0060] Step S3: constructing a method for predicting the longitudinal acceleration of a manually driven vehicle based on the risk field model to obtain a predicted trajectory of the manually driven vehicle.

[0061] The expression is as follows: f (t)=max(a min ,min(a max ,g(t))).

[0062] Where a f(t) represents the longitudinal acceleration of the manually driven vehicle, g(t) represents the acceleration expected by the driver, and a min Indicates the minimum acceleration of the vehicle, a max Indicates the maximum acceleration of the vehicle, T p Indicates the driver's preview time, r d represents the driver's expected risk, η represents the impact factor of speed on risk, λ represents the impact factor of distance on risk, l represents the position of the vehicle, v represents the speed of the vehicle, L represents the length of the vehicle, t represents time, i represents the vehicle number, where the i-1th vehicle is in front of the i-th vehicle.

[0063] In step S4, the vehicle's instantaneous fuel consumption and the penalty for crossing the stop line are used as objective functions, the vehicle dynamics constraints and trajectory constraints are set, and an optimal ecological reference trajectory planning model for the connected autonomous vehicle is constructed and solved to obtain the acceleration curve of the connected autonomous vehicle, thereby obtaining the ecological reference trajectory of the connected autonomous vehicle.

[0064] Taking the vehicle's instantaneous fuel consumption and the penalty for passing the stop line as the objective function, the expression is as follows:

[0065] Where, Indicates the time it takes for a connected autonomous vehicle to pass the stop line Vehicle status; Indicates the vehicle status of a connected autonomous vehicle passing a stop line; represents the penalty for the connected autonomous vehicle passing the stop line; L(v(t), a(t), t) represents the instantaneous fuel consumption of the vehicle; v(t) represents the speed of the connected autonomous vehicle at time t, and a(t) represents the acceleration of the connected autonomous vehicle at time t; Q f and α1 both represent penalty weights.

[0066] In addition, the vehicle motion must satisfy the following dynamic formulas and dynamic constraints:

[0067] 0≤v(t)≤v max .

[0068] a min ≤a(t)≤a max .

[0069] Where, v max Indicates the road speed limit, a min Indicates the minimum acceleration of the vehicle, a max represents the maximum acceleration of the vehicle, v(t) represents the speed of the connected autonomous driving vehicle at time t, and a(t) represents the acceleration of the connected autonomous driving vehicle at time t.

[0070] To avoid queuing, the farthest queuing point l obtained in step S1 should be used. mq and its formation time t mq Establish trajectory constraints so that the connected autonomous vehicle CAV can mq When the position is l(t mq ) Upstream of the furthest queue point, that is:

[0071] l(t mq )≤l mq .

[0072] In particular, when there is more than one CAV at the intersection, the queue at the intersection ahead may be eliminated for the second CAV due to the eco-driving behavior of the first CAV, as shown in Figure 4. Therefore, for the CAVs after the first one, the predicted trajectory of the manually driven vehicle MV obtained in step S3 will replace the queue information as the trajectory constraint for the second CAV, that is:

[0073] l(t)≤l p (t).

[0074] Where, l p (t) represents the predicted trajectory of MV.

[0075] After solving the optimal ecological reference trajectory planning model, the acceleration curve a(t) of the connected autonomous vehicle CAV can be obtained. Based on the acceleration curve, the ecological reference trajectory x(t) of the connected autonomous vehicle CAV can be calculated. ref .

[0076] In step S5, a risk factor is set based on the risk field model and the predicted trajectories of the manually driven vehicles before and after the connected autonomous vehicle. A tracking target is constructed based on the risk factor and the ecological reference trajectory. Model Predictive Control (MPC) is used to solve the target and obtain the control input of the connected autonomous vehicle.

[0077] Since eco-driving strategies typically involve slowing down vehicles as they approach intersections, the risks posed by vehicles preceding and following the CAV are also considered. To enhance the robustness of the control strategy, this example employs Model Predictive Control (MPC) to solve the problem in a rolling manner.

[0078] The risk posed by the vehicles in front and behind the connected autonomous vehicle (CAV) involves the trajectory prediction of the vehicles in front and behind the CAV. In this example, the trajectory of the manually driven vehicle behind the CAV can be obtained by inputting the planned trajectory of the CAV into the trajectory prediction method in step S3; and for the manually driven vehicle in front of all CAVs, its predicted trajectory is obtained based on the assumption that its acceleration will remain unchanged for a period of time in the future. According to the predicted trajectory of the manually driven vehicles in front and behind the connected autonomous vehicle, the risk factor of the leading vehicle is set. and rear vehicle risk factors Set the signal light risk factor based on signal timing information

[0079] Set tracking objectives and vehicle dynamics constraints to solve for the control inputs of the connected autonomous vehicle.

[0080] Among them, the expression of the tracking target is:

[0081] In the formula, x(k+i) represents the state of the connected autonomous driving vehicle at step k+i, k represents the current time step, i represents the number of predicted steps, and x(k+i) ref represents the ecological reference trajectory of the connected autonomous driving vehicle, Q ref , β2, β3 and β4 all represent penalty weights, and P represents the prediction time domain.

[0082] After solving the tracking target, the control sequence in the prediction time domain will be obtained: a k , a k+1 ,...,a k+P-1 However, this example only uses a k Control is applied to the CAV, and at the next time step, the environment information is retrieved, the vehicle status information and signal timing information are updated, and the problem is solved again. This process is repeated until the CAV passes the intersection.

[0083] As shown in FIG5 , an embodiment of the present invention further provides a vehicle ecological driving control system for mixed traffic flow, which is used to implement the above-mentioned vehicle ecological driving control method for mixed traffic flow. The system includes: a central controller and several local controllers deployed on networked autonomous driving vehicles.

[0084] The central controller includes:

[0085] The first result acquisition module is used to collect signal intersection timing information, historical vehicle status information and real-time vehicle status information; based on the shock wave evolution theory, it calculates the intersection assembly wave speed and dissipation wave speed to obtain the position of the farthest queue point at the downstream intersection of the networked autonomous driving vehicle and the time when the farthest queue is formed.

[0086] The second result acquisition module is used to predict the time when the connected autonomous driving vehicle passes the stop line and the vehicle status based on the position of the farthest queue point at the downstream intersection of the connected autonomous driving vehicle and the time when the farthest queue is formed.

[0087] The manually driven vehicle trajectory prediction module is used to construct a longitudinal acceleration prediction method for the manually driven vehicle based on the risk field model, thereby obtaining the predicted trajectory of the manually driven vehicle.

[0088] The connected autonomous vehicle trajectory planning module is used to set vehicle dynamics constraints and trajectory constraints based on the vehicle's instantaneous fuel consumption and the state penalty for passing the stop line as objective functions, build and solve the optimal ecological reference trajectory planning model for the connected autonomous vehicle, and obtain the acceleration curve of the connected autonomous vehicle, thereby obtaining the ecological reference trajectory of the connected autonomous vehicle.

[0089] The first communication module is used to send the ecological reference trajectory of the networked autonomous driving vehicle to the local controller.

[0090] The local controller includes:

[0091] The second communication module is used to receive the ecological reference trajectory of the networked autonomous driving vehicle.

[0092] The connected autonomous vehicle tracking module is used to set risk factors based on the risk field model and the predicted trajectories of manually driven vehicles before and after the connected autonomous vehicle. The tracking target is constructed based on the risk factors and the ecological reference trajectory, and is solved using model predictive control to obtain the control input of the connected autonomous vehicle.

[0093] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0094] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The system embodiment described above is only illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0095] Accordingly, the present application also provides an electronic device comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the above-described vehicle ecological driving control method for mixed traffic flow. Figure 6 shows a hardware structure diagram of any data processing device in which the vehicle ecological driving control method for mixed traffic flow provided in an embodiment of the present invention is implemented. In addition to the processor, memory, and network interface shown in Figure 6, the data processing device in which the apparatus in the embodiments is implemented may also include other hardware, depending on the actual functionality of the device. This will not be further described.

[0096] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the above-mentioned vehicle ecological driving control method for mixed traffic flow. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities as described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0097] The above embodiments are intended only to illustrate the design concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. The scope of protection of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design concepts disclosed in the present invention are within the scope of protection of the present invention.

Claims

1. A vehicle ecological driving control method for mixed traffic flow, characterized in that: The method comprises: Obtain signal intersection timing information, historical vehicle status information, and real-time vehicle status information; based on the shock wave evolution theory, calculate the intersection gathering wave velocity and dissipation wave velocity, and obtain the location of the farthest queue point at the downstream intersection of the networked autonomous driving vehicle and the time when the farthest queue is formed; Based on the location of the furthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the furthest queue is formed, predict the time when the connected autonomous vehicle passes the stop line and the vehicle status; According to the risk field model, the longitudinal acceleration of the manually driven vehicle is predicted to obtain the predicted trajectory of the manually driven vehicle; Taking the vehicle's instantaneous fuel consumption and the state penalty for passing the stop line as the objective function, setting the vehicle dynamics constraints and trajectory constraints, constructing and solving the optimal ecological reference trajectory planning model for the networked autonomous driving vehicle, and obtaining the acceleration curve of the networked autonomous driving vehicle, thereby obtaining the ecological reference trajectory of the networked autonomous driving vehicle; The risk factor is set according to the risk field model and the predicted trajectories of the manually driven vehicles before and after the connected autonomous driving vehicle. The tracking target is constructed based on the risk factor and the ecological reference trajectory. The model predictive control is used to solve it and obtain the control input of the connected autonomous driving vehicle.

2. The vehicle ecological driving control method for mixed traffic flow according to claim 1 is characterized in that: Calculation of intersection gathering wave velocity and dissipation wave velocity includes: Obtain intersection saturation flow q based on historical vehicle status information s , saturated flow density ρ s and congestion flow density ρ j ; Get arrival flow q based on real-time vehicle status information a and the arrival flow density ρ a ; Calculate the intersection gathering wave speed w1, the expression is as follows: w1=(0-q a ) / (ρ j -ρ a ); Calculate the evanescent wave velocity w2 at the intersection, the expression is as follows: w2=(q s -0) / (ρ s -r j )。 3. The vehicle ecological driving control method for mixed traffic flow according to claim 1 or 2, characterized in that: Calculating the location of the furthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the furthest queue is formed includes: Obtain the current queue length L0 based on the signal intersection timing information and real-time vehicle status information; Calculate the distance L between the farthest queue point and the stop line mq , the expression is as follows: Where w1 is the gathering wave velocity, w2 is the evanescent wave velocity, and t′ g represents the adjusted green light start time, t0 is the time when the first connected autonomous driving car enters the intersection, ρ j is the congestion flow density, N0 is the total number of vehicles between the first connected autonomous vehicle and the stop line; Calculate the farthest queue point position l of the downstream intersection of the connected autonomous vehicle mq , the expression is as follows: l mq ＝l s -L mq ； In the formula, l s Indicates the stop line position; Calculate the time t when the furthest queue is formed mq , the expression is as follows: t mq =-L mq / w2; Among them, the adjusted green light start time includes: Where, t g Indicates the time when the traffic light turns green, t r It indicates the moment when the traffic light turns red, and c is the length of the signal cycle.

4. The vehicle ecological driving control method for mixed traffic flow according to claim 3 is characterized in that: Based on the location of the furthest queue point at the downstream intersection of the connected autonomous vehicle and the time when the furthest queue is formed, the time and vehicle status of the connected autonomous vehicle passing the stop line are predicted, including: Get the time it takes for a connected autonomous vehicle to pass the stop line The expression is as follows: Get the vehicle status of a connected autonomous vehicle passing a stop line The expression is as follows: In the formula, v s It represents the speed at which the connected autonomous vehicle passes the stop line, i.e. the speed of the saturated flow.

5. The vehicle ecological driving control method for mixed traffic flow according to claim 1 is characterized in that: According to the risk field model, the longitudinal acceleration of the manually driven vehicle is predicted to obtain the predicted trajectory of the manually driven vehicle including: The expression for the longitudinal acceleration of a manually driven vehicle is as follows: a f (t)=max(a min ,min(a max ,g(t))); In the formula, a f (t) represents the longitudinal acceleration of the manually driven vehicle, g(t) represents the acceleration expected by the driver, and a min represents the minimum acceleration of the vehicle, a max represents the maximum acceleration of the vehicle, T p represents the driver's preview time, r d represents the driver's expected risk, η represents the impact factor of speed on risk, λ represents the impact factor of distance on risk, l represents the position of the vehicle, v represents the speed of the vehicle, L represents the length of the vehicle, t represents time, i represents the vehicle number, and the i-1th vehicle is in front of the i-th vehicle.

6. The vehicle ecological driving control method for mixed traffic flow according to claim 1 is characterized in that: Taking the vehicle's instantaneous fuel consumption and the state penalty for passing the stop line as the objective function, setting the vehicle dynamics constraints and trajectory constraints, constructing and solving the optimal ecological reference trajectory planning model for the networked autonomous driving vehicle, and obtaining the acceleration curve of the networked autonomous driving vehicle, the ecological reference trajectory of the networked autonomous driving vehicle is obtained, including: Taking the vehicle's instantaneous fuel consumption and the penalty for passing the stop line as the objective function, the expression is as follows: In the formula, Indicates the time it takes for a connected autonomous vehicle to pass the stop line The vehicle status; Indicates the vehicle status of a connected autonomous vehicle passing a stop line; represents the penalty for the state of the connected autonomous driving vehicle passing the stop line; L(v(t), a(t), t) represents the instantaneous fuel consumption of the vehicle; v(t) represents the speed of the connected autonomous driving vehicle at time t, and a(t) represents the acceleration of the connected autonomous driving vehicle at time t; Q f and α1 both represent penalty weights; The vehicle dynamics constraints are set based on the road speed limit and the minimum and maximum acceleration of the vehicle; Setting trajectory constraints includes: The trajectory constraint for the first connected autonomous vehicle that passes the stop line is: l(t mq )≤l mq ； Where l(t mq ) indicates that the networked autonomous driving vehicle is at t mq The position at the time; The trajectory constraint corresponding to the jth connected autonomous driving vehicle passing the stop line is: l(t)≤l p (t); Where j is a positive integer greater than or equal to 2, l(t) represents the position of the connected autonomous driving vehicle, and l p (t) represents the trajectory of the manually driven vehicle in front of the connected autonomous vehicle.

7. The vehicle ecological driving control method for mixed traffic flow according to claim 1 is characterized in that: The risk factor is set according to the risk field model and the predicted trajectory of the manually driven vehicles before and after the networked autonomous driving vehicle. The tracking target is constructed based on the risk factor and the ecological reference trajectory. The model predictive control is used to solve the problem. The control input of the networked autonomous driving vehicle includes: According to the predicted trajectory of the manually driven vehicle behind the connected autonomous vehicle, the risk factor of the following vehicle is set Set the risk factor of the vehicle ahead based on the predicted trajectory of the human-driven vehicle ahead of the connected autonomous vehicle Setting Traffic Light Risk Factor Set tracking objectives and vehicle dynamics constraints to solve for control inputs for connected autonomous vehicles; Among them, the expression of the tracking target is: In the formula, x(k+i) represents the state of the connected autonomous driving vehicle at step k+i, k represents the current time step, i represents the number of predicted steps, and x(k+i) ref represents the ecological reference trajectory of the connected autonomous driving vehicle, Q ref , β2, β3 and β4 all represent penalty weights, and P represents the prediction time domain.

8. A vehicle ecological driving control system for mixed traffic flow, characterized in that: A vehicle ecological driving control method for mixed traffic flow as described in any one of claims 1 to 7, wherein the system comprises: a central controller and a plurality of local controllers deployed on a networked autonomous driving vehicle; The central controller includes: The first result acquisition module is used to collect signal intersection timing information, historical vehicle status information and real-time vehicle status information; based on the shock wave evolution theory, the intersection gathering wave velocity and dissipation wave velocity are calculated to obtain the position of the farthest queue point at the downstream intersection of the networked autonomous driving vehicle and the time when the farthest queue is formed; The second result acquisition module is used to predict the time when the networked autonomous driving vehicle passes the stop line and the vehicle status according to the position of the farthest queue point at the downstream intersection of the networked autonomous driving vehicle and the time when the farthest queue is formed; A manually driven vehicle trajectory prediction module is used to construct a longitudinal acceleration prediction method of a manually driven vehicle based on a risk field model, so as to obtain a predicted trajectory of the manually driven vehicle; The connected autonomous vehicle trajectory planning module is used to set the vehicle's instantaneous fuel consumption and the state penalty for passing the stop line as the objective function, set the vehicle dynamics constraints and trajectory constraints, build and solve the optimal ecological reference trajectory planning model for the connected autonomous vehicle, and obtain the acceleration curve of the connected autonomous vehicle, so as to obtain the ecological reference trajectory of the connected autonomous vehicle; A first communication module, used to send the ecological reference trajectory of the networked autonomous driving vehicle to the local controller; The local controller includes: A second communication module is used to receive the ecological reference trajectory of the networked autonomous driving vehicle; The networked autonomous driving vehicle tracking module is used to set the risk factor according to the risk field model and the predicted trajectories of the manually driven vehicles before and after the networked autonomous driving vehicle, and to construct the tracking target based on the risk factor and the ecological reference trajectory, and to solve it using model predictive control to obtain the control input of the networked autonomous driving vehicle.

9. An electronic device, comprising a memory and a processor, characterized in that: The memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the vehicle ecological driving control method for mixed traffic flow as described in any one of claims 1-7 above.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle ecological driving control method for mixed traffic flow as described in any one of claims 1 to 7 is implemented.

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