MIXED TRAFFIC FLOW-ORIENTED VEHICLE ECO-DRIVING CONTROL METHOD AND ELECTRONIC DEVICE

Abstract

A mixed traffic flow-oriented vehicle eco-driving control method and an electronic device are provided. The method includes: calculating a gathering wave speed and a dissipation wave speed at an intersection, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time, acquiring predicted trajectories of the manual-driving vehicle; constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, obtaining an acceleration curve of the connected and autonomous vehicle, and acquiring an ecological reference trajectory of the connected and autonomous vehicle; and setting a risk factor based on the predicted trajectories of the manual-driving vehicles, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle.

Description

TECHNICAL FIELD

The present disclosure relates to the fields of an artificial potential field theory, a traffic flow theory and vehicle motion planning and control, in particular to a mixed traffic flow-oriented vehicle eco-driving control method and an electronic device.

BACKGROUND

In recent years, with the rapid increase in the number of vehicles, the problems of energy consumption and pollutant emission are becoming increasingly prominent. In addition, road vehicles, such as cars, trucks and buses, account for about three-quarters of emissions in the transportation industry. Eco-driving is a strategy to improve the efficiency of energy conservation and emission reductions of a transportation system by changing the driving behavior of vehicles. The basic measure is to provide a real-time driving advice for vehicles to reduce stopping and starting, idling and sharp acceleration/deceleration behaviors. Numerous studies and practices show that eco-driving can reduce fuel consumption and emissions by 3% to 20%. In recent years, with the development of autonomous driving and communication technology, controlling Connected and Autonomous Vehicles (CAVs) to achieve eco-driving is considered as an effective and promising means. Compared with the highway environment, urban road traffic flow is often interrupted by traffic signal lights, and the behavior of vehicles at traffic signal light intersections such as acceleration, deceleration and idling will lead to increased fuel consumption and emissions thereof. Therefore, it is of great significance to develop an eco-driving strategy for the urban road environment.

At present, the existing technology ignores the influence of other social vehicles when studying the eco-driving strategy of Connected and Autonomous Vehicles (CAVs), or is only oriented toward the pure connected and autonomous scenarios. However, it can be predicted that Manual-driving vehicles (MVs) and Connected and Autonomous Vehicles (CAVs) will coexist on the road for a long time, forming a mixed traffic flow.

Therefore, under the background of mixed traffic flow, it is urgent to full consider to the influence of Manual-driving vehicles (MVs) and Connected and Autonomous Vehicles (CAVs) implementing the eco-driving strategies on each other to ensure that the eco-driving strategy will not bring negative safety impact to mixed traffic flow. In addition, it should be emphasized that eco-driving vehicles often slow down vehicles to approach intersections, and such deceleration behavior is unpredictable for drivers of vehicles behind eco-driving vehicles. Therefore, the safety influence of the following vehicles on vehicles implementing the eco-driving strategies cannot be ignored, which has been ignored in many studies and practices.

SUMMARY

The present disclosure aims to be oriented to a mixed traffic flow environment of Manual-driving Vehicles (MVs) and Connected and Autonomous Vehicles (CAVs), and provides a mixed traffic flow-oriented vehicle eco-driving control method and an electronic device.

According to a first aspect of the embodiment of the present disclosure, a mixed traffic flow-oriented vehicle eco-driving control method is provided, where the method includes:

• • acquiring signal phase and timing information, historical vehicle state information and real-time vehicle state information; calculating a gathering wave speed and a dissipation wave speed at an intersection based on a shock wave evolution theory, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time when a farthest queue is formed;
  • predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
  • predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle;
  • taking an instantaneous fuel consumption of the vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle; and
  • setting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicles before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle.

According to a second aspect of the embodiment of the present disclosure, a mixed traffic flow-oriented vehicle eco-driving control system is provided, which is used to implement the above-mentioned mixed traffic flow-oriented vehicle eco-driving control method, the system includes: a central controller and a plurality of local controllers deployed on the connected and autonomous vehicle;

• • where the central controller includes:
  • a first result acquiring module, configured to acquire signal phase and timing information, historical vehicle state information and real-time vehicle state information; calculate the gathering wave speed and the dissipation wave speed at the intersection based on the shock wave evolution theory, and obtain the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
  • a second result acquiring module, configured to predict the vehicle state and the time when the connected and autonomous vehicle passes through the stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;
  • a manual-driving vehicle trajectory predicting module, configured to construct a longitudinal acceleration prediction method of the manual-driving vehicle based on the risk field model, so as to acquire the predicted trajectory of the manual-driving vehicle;
  • a connected and autonomous vehicle trajectory planning module, configured to take the instantaneous fuel consumption of the vehicle and the penalty for the state of passing through the stop line as the objective function, set dynamic constraints and trajectory constraints of the vehicle, construct and solve the optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtain the acceleration curve of the connected and autonomous vehicle, so as to acquire the ecological reference trajectory of the connected and autonomous vehicle; and
  • a first communication module, configured to send the ecological reference trajectory of the connected and autonomous vehicle to the local controller;
  • the local controller includes:
  • a second communication module, configured to receive the ecological reference trajectory of the connected and autonomous vehicle;
  • a connected and autonomous vehicle tracking module, configured to set the risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicles before and after the connected and autonomous vehicle, construct the tracking target based on the risk factor and the ecological reference trajectory, use the model predictive control for solving, and obtain the control input of the connected and autonomous vehicle.

According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device including a memory and a processor, where the memory is coupled with the processor; the memory is configured to store program data, and the processor is configured to execute the program data to implement the above-mentioned mixed traffic flow-oriented vehicle eco-driving control method.

According to a fourth aspect of the embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, where the program, when executed by a processor, implements the above-mentioned mixed traffic flow-oriented vehicle eco-driving control method.

Compared with the prior art, the present disclosure has the following beneficial effects. The present disclosure provides a mixed traffic flow-oriented vehicle eco-driving control method. Under the background of mixed traffic flow of Manual-driving vehicles (MVs) and Connected and Autonomous Vehicles (CAVs), the risks brought by manual-driving vehicles in front of and behind connected and autonomous vehicles are considered, and on the premise of ensuring traffic efficiency, the driving risk is reduced at the same time, and the efficiency of energy conservation and emission reduction is improved. The mixed traffic flow-oriented vehicle eco-driving control system according to the present disclosure belongs to a hierarchical distributed architecture. The ecological reference trajectory of the connected and autonomous vehicle is acquired through the central controller. The ecological reference trajectory is solved through the local controller deployed on the connected and autonomous vehicle under the consideration of the risk factors brought by the manual-driving vehicles in front of and behind the connected and autonomous vehicle. Compared with the classical central control mode, the communication cost and the calculation cost of eco-driving are effectively reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to explain the technical solution in the embodiment of the present disclosure more clearly, the drawings needed in the description of the embodiment will be briefly introduced hereinafter. Apparently, the drawings described below are only some embodiments of the present disclosure. Other drawings can be obtained according to these drawings without creative labor for those skilled in the art.

FIG. 1 is a flowchart of a mixed traffic flow-oriented vehicle eco-driving control method according to an embodiment of the present disclosure;

FIG. 2 is a structural block diagram of the mixed traffic flow-oriented vehicle eco-driving control method according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of a queuing evolution and shock waves at an intersection according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a queuing evolution when a plurality of eco-driving vehicles coexist at the intersection according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a mixed traffic flow-oriented vehicle eco-driving control system according to an embodiment of the present disclosure; and

FIG. 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical schemes in the embodiments of the present disclosure will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present disclosure hereinafter. Apparently, the described embodiments are only some embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiment of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present disclosure.

It should be noted that features in the following embodiments and implementations can be combined with each other without conflict.

As shown in FIG. 1 and FIG. 2, the embodiment of the present disclosure provides a mixed traffic flow-oriented vehicle eco-driving control method, where the method includes the following steps.

Step S1, signal phase and timing information, historical vehicle state information and real-time vehicle state information are acquired; a gathering wave speed and a dissipation wave speed at an intersection are calculated based on a shock wave evolution theory, and a farthest queue point position at which a connected and autonomous vehicle is to be positioned at a downstream intersection and a time when a farthest queue is formed are obtained.

Specifically, the Step S1 specifically includes the following steps.

Step S101, Signal Phase and Timing (SPaT) information, historical vehicle state information and real-time vehicle state information are collected, where the vehicle state information includes a vehicle position, a vehicle speed and a vehicle acceleration.

Step S102, the saturation flow q_s, the saturation flow density ρ_s and the congestion flow density ρ_j at the intersection are calibrated according to the historical vehicle state information.

Step S103, the arrival flow q_a and the arrival flow density ρ_a are acquired according to the real-time vehicle state information.

Step S104: the gathering wave speed w₁ at the intersection is calculated, in which the expression is as follows:

w ₁=(0−q_a)/(ρ_j-ρ_a).

The dissipation wave speed w₂ at the intersection is calculated, in which the expression is as follows:

$w_2=\left(q_s-0\right)/\left({\rho }_s-{\rho }_j\right).$

The departure wave w₃ at the intersection is further calculated as:

$w_3=\left(q_s-q_a\right)/\left({\rho }_s-{\rho }_a\right).$

It should be noted that a schematic diagram of a queuing evolution and shock waves at the intersection is shown in FIG. 3. The gathering wave w₁ refers to a shock wave generated when the traffic state changes from the arrival flow to the congestion flow, that is, a straight line AB in FIG. 3. The dissipation wave w₂ refers to a shock wave generated when the traffic state changes from the congestion flow to the saturation flow, that is, a straight line BC in FIG. 3. The departure wave w₃ refers to a shock wave generated when the traffic state changes from the arrival flow to the saturation flow, that is, a straight line BD in FIG. 3.

Step S105, the queuing situation of vehicles in front of the Connected and Autonomous Vehicle (CAV) is predicted according to the shock wave propagation speed at the intersection and the vehicle state information, to obtain the farthest queue point position l_mq at the intersection that the Connected and Autonomous Vehicle (CAV) will face and the time t_mq when the farthest queue is formed.

Specifically, a current queue length L₀ is acquired according to the signal phase and timing information and the real-time vehicle state information.

The time when the first Connected and Autonomous Vehicle (CAV) enters the intersection is denoted as to, and the signal will turn green at the time t_g and turn red at the time t_r. In order to unify the calculations of the farthest queue point position and the time when the farthest queue is formed under different conditions, it is necessary to adjust the green light start time t_g. t′_g indicates the adjusted green light start time, which is calculated by the following formula:

$t_g^{\prime }=\{\begin{array}{cc}{t}_g,& t_g<t_r\\ t_g-c,& t_g>t_r\end{array}$

• • where c indicates a signal period length.

The adjusted green light start time t′_g is used to calculate a distance L_mq between the farthest queue point and the stop line, in which the expression is as follows:

$L_{\mathrm{mq}}=\min \left(w_2·\frac{w_1\left(t_g^{\prime }-t_0\right)-L_0}{w_2-w_1},\frac{N_0}{{\rho }_j}\right),$

• • where w₁ is the gathering wave speed, w₂ is the dissipation wave speed, t′_g indicates an adjusted green light start time, to is a time when a first connected and autonomous vehicle enters the intersection, ρ_j is the congestion flow density, and N₀ is a total number of vehicles between the first connected and autonomous vehicle and the stop line.

The farthest queue point position l_mq of the connected and autonomous vehicle at the downstream intersection is calculated, in which the expression is as follows:

$l_{\mathrm{mq}}=l_s-L_{\mathrm{mq}}.$

The time t_mq when the farthest queue is formed is calculated, in which the expression is as follows:

$t_{\mathrm{mq}}=-L_{\mathrm{mq}}/w_2,$

• • where l_s indicates a position of the stop line.

Step S2, the time when the connected and autonomous vehicle passes through a stop line and a vehicle state are predicted based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed.

It should be noted that in order to ensure the traffic efficiency of the original intersection, it is necessary to predict the time {tilde over (t)}_f when the Connected and Autonomous Vehicle (CAV) passes through the stop line and the vehicle state {tilde over (x)}_f (terminal state), where the terminal state {tilde over (x)}_f includes the position and speed at which the Connected and Autonomous Vehicle (CAV) passes through the stop line. The speed at which the connected and autonomous vehicle passes through the stop line is the speed of the saturation flow. Because vehicles usually pass through intersections where queues are often formed, under saturated traffic conditions, in this example, it is considered that the controlled Connected and Autonomous Vehicle (CAV) should also pass through the intersections at this speed, which not only ensures the original traffic efficiency, but also reduces the speed at which the Connected and Autonomous Vehicle (CAVs) passes through the intersections, thereby further ensuring the safety.

The time {tilde over (t)}_f when the connected and autonomous vehicle passes through the stop line is acquired, in which the expression is as follows:

${\tilde{t}}_f=t_g^{\prime }+\left(\frac{L_{\mathrm{mq}}}{{\rho }_j}+1\right)·\frac{1}{q_s}.$

The vehicle state {tilde over (x)}_f that the connected and autonomous vehicle passes through the stop line is acquired, in which the expression is as follows:

${\tilde{x}}_f={\left[l_s,v_s\right]}^T,$ $v_s=\frac{q_s}{{\rho }_s}.$

Where v_s indicates the speed at which the connected and autonomous vehicle passes through the stop line, that is, the speed of saturation flow.

Step S3, a longitudinal acceleration prediction method of a manual-driving vehicle is constructed based on a risk field model, so as to acquire a predicted trajectory of the manual-driving vehicle.

The Expression is as Follows:

$a^f\left(t\right)=\max \left(a_{\min },\min \left(a_{\max },g\left(t\right)\right)\right);$ $g\left(t\right)=l_{i-1}\left(t\right)+v_{i-1}\left(t\right)\left[T_p-\frac{\eta }{\lambda }\left(\frac{1}{r_d}-1\right)\right]-l_i\left(t\right)-v_i\left(t\right)T_p-\frac{1}{\lambda }\left(\frac{1}{r_d}-1\right)-\frac{L_i+L_{i-1}}{2},$

• • where a^f(t) indicates the longitudinal acceleration of the manual-driving vehicle, g(t) indicates an acceleration expected by a driver, a_min indicates a minimum acceleration of the vehicle, a_max indicates a maximum acceleration of the vehicle, T_p indicates the preview time of the driver, r_d a indicates a risk expected by the driver, η indicates an influence factor of the speed on the risk, λ indicates an influence factor of the distance on the risk, l indicates a position of the vehicle, v indicates a speed of the vehicle, L indicates a length of the vehicle, t indicates a time, and i indicates a vehicle serial number, in which the (i−1)th vehicle is in front of the i-th vehicle.
  • S4, an instantaneous fuel consumption of the vehicle and a penalty for a state of passing through the stop line are taken as an objective function, dynamic constraints and trajectory constraints of the vehicle are set, an optimal ecological reference trajectory planning model of the connected and autonomous vehicle is constructed and solved, and an acceleration curve of the connected and autonomous vehicle is obtained, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle.

The instantaneous fuel consumption of the vehicle and the penalty for the state of passing through the stop line are taken as the objective function, in which the expression is as follows:

${\underset{a\left(t\right)}{\mathrm{minimize}}J=x\left({\tilde{t}}_f\right)-{\tilde{x}}_f)}_{Q_f}^2+{\alpha }_1{\int }_{t_0}^{{\tilde{t}}_f}L\left(v\left(t\right),a\left(t\right),t\right)\mathrm{dt};$

• • where x({tilde over (t)}_f) indicates the vehicle state of the connected and autonomous vehicle at the time {tilde over (t)}_f when the vehicle passes through the stop line; {tilde over (x)}_f indicates the vehicle state that the connected and autonomous vehicle passes through the stop line; ∥(x({tilde over (t)}_f)−{tilde over (x)}_f)∥_Qf² indicates the penalty for the state that the connected and autonomous vehicle passes through the stop line; L(v(t), a(t),t) indicates the instantaneous fuel consumption of the vehicle; v(t) indicates the speed of the connected and autonomous vehicle at time t, and a(t) indicates the acceleration of the connected and autonomous vehicle at time t; and each of Q_f and a₁ indicates a penalty weight.

In addition, the vehicle movement satisfies the following dynamic formulas and dynamic constraints:

$\stackrel{.}{x}\left(t\right)=\left[\begin{array}{c}v\left(t\right)\\ a\left(t\right)\end{array}\right]$ $0\le v\left(t\right)\le v_{\max }$ $a_{\min }\le a\left(t\right)\le a_{\max }$

• • where v_max indicates a road speed limit, a_min indicates the minimum acceleration of the vehicle, a_max indicates the maximum acceleration of the vehicle, indicates the speed of the connected and autonomous vehicle at the time t, and a indicates the acceleration of the connected and autonomous vehicle at the time t.

In order to avoid queuing, it is necessary to establish trajectory constraints by using the farthest queue point l_mq and the time t_mq when the farthest queue is formed obtained based on the Step S1, so that the position of the Connected and Autonomous Vehicle (CAV) at the time t_mq is at the upstream of the farthest queue point l(t_mq), that is:

l(t_mq)≤l_mq

Especially, when there is more than one Connected and Autonomous Vehicle (CAV) at the intersection, for the second Connected and Autonomous Vehicle (CAV), the queuing phenomenon at the intersection ahead may be eliminated due to the eco-driving behavior of the first Connected and Autonomous Vehicle (CAV), as shown in FIG. 4. Therefore, for the Connected and Autonomous Vehicle (CAV) after the first vehicle, the predicted trajectory of the Manual-driving vehicle (MV) obtained in the Step S3 will replace the queuing information as the trajectory constraint of the second Connected and Autonomous Vehicle (CAV), namely:

l(t)≤l^p(t)

• • where l^p(t) indicates the predicted trajectory of the MV.

After solving the optimal ecological reference trajectory planning model, the acceleration curve a(t) of the Connected and Autonomous Vehicle (CAV) can be obtained, and the ecological reference trajectory x(t)_ref of the Connected and Autonomous Vehicle (CAV) can be calculated according to the acceleration curve.

Step S5, a risk factor is set according to the risk field model and the predicted trajectories of the manual-driving vehicles before and after the connected and autonomous vehicle, a tracking target is constructed based on the risk factor and the ecological reference trajectory, Model Predictive Control (MPC) is used for solving, and a control input of the connected and autonomous vehicle is obtained.

Because the eco-driving strategy is usually to slow down the vehicles to approach the intersection, the risks caused by vehicles before and after the Connected and Autonomous Vehicle (CAV) are considered at the same time. In order to enhance the robustness of the control strategy, this embodiment uses Model Predictive Control (MPC) to solve the problem by rolling.

The risks caused by vehicles before and after the Connected and Autonomous Vehicle (CAV) involve the trajectory prediction of vehicles before and after the CAV. In this embodiment, the trajectory of the manual-driving vehicle behind the CAV can be obtained by inputting the planned trajectory of the CAV into the trajectory prediction method in the Step S3. For all manual-driving vehicles in front of the CAV, the predicted trajectory is obtained based on the assumption that the acceleration will remain unchanged for a period of time in the future. According to the predicted trajectories of the manual-driving vehicles in front of and behind the connected and autonomous vehicles, the risk factor ε_i^px(k+i) of the preceding vehicle and the risk factor ε_i^f(x(k+i)) of the following vehicle are set. The risk factor of a traffic signal light is set according to the signal phase and timing information.

The tracking target and the dynamic constraints of the vehicle are set, and the tracking target and the dynamic constraints of the vehicle are solved to obtain the control input of the connected and autonomous vehicle.

The Expression of the Tracking Target is:

$\underset{a_k,a_{k+1},\dots ,a_{k+P-1}}{\min \mathrm{imize}}J=\sum _{i=1}^P{x\left(k-i\right)-{x\left(k+i\right)}_{\mathrm{ref}}}_{Q_{\mathrm{ref}}}^2+\sum _{i=1}^P\left({{\xi }_i^f\left(x\left(k+i\right)\right)}_{{\beta }_2}^2+{{\xi }_i^p\left(x\left(k+i\right)\right)}_{{\beta }_3}^2+{{\xi }_i^s\left(x\left(k+i\right)\right)}_{{\beta }_4}^2\right);$

• • where x(k+i) indicates a state of the connected and autonomous vehicle at step k+i, k indicates the current time step, i indicates a number of predicted steps, x(k+i)_ref indicates the ecological reference trajectory of the connected and autonomous vehicle, each of Q_ref, β₂, β₃ and β₄ indicates a penalty weight, and P indicates a prediction time domain.

After solving the tracking target, the control sequence a_k, a_k+1, . . . , a_k+P−1 in the prediction time domain will be obtained. However, in this example, the Connected and Autonomous Vehicle (CAV) is controlled by only using a_k, and in the next time step, the environmental information is re-obtained, the vehicle state information and signal phase and timing information are updated, and the problem is resolved. This is repeated until the Connected and Autonomous Vehicle (CAV) passes through the intersection.

As shown in FIG. 5, the embodiment of the present disclosure further provides a mixed traffic flow-oriented vehicle eco-driving control system, which is used to implement the above-mentioned mixed traffic flow-oriented vehicle eco-driving control method, where the system includes: a central controller and a plurality of local controllers deployed on the connected and autonomous vehicle;

The central controller includes a first result acquiring module, a second result acquiring module, a manual-driving vehicle trajectory predicting module, a connected and autonomous vehicle trajectory planning module and a first communication module.

The first result acquiring module is configured to acquire signal phase and timing information, historical vehicle state information and real-time vehicle state information; calculate the gathering wave speed and the dissipation wave speed at the intersection based on a shock wave evolution theory, and obtain the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed.

The second result acquiring module is configured to predict the vehicle state and the time when the connected and autonomous vehicle passes through the stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed.

The manual-driving vehicle trajectory predicting module is configured to construct a longitudinal acceleration prediction method of the manual-driving vehicle based on the risk field model, so as to acquire the predicted trajectory of the manual-driving vehicle.

The connected and autonomous vehicle trajectory planning module is configured to take the instantaneous fuel consumption of the vehicle and the penalty for the state of passing through the stop line as the objective function, set dynamic constraints and trajectory constraints of the vehicle, construct and solve the optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtain the acceleration curve of the connected and autonomous vehicle, so as to acquire the ecological reference trajectory of the connected and autonomous vehicle.

The first communication module is configured to send the ecological reference trajectory of the connected and autonomous vehicle to the local controller.

The local controller includes a second communication module and a connected and autonomous vehicle tracking module.

The second communication module is configured to receive the ecological reference trajectory of the connected and autonomous vehicle.

The connected and autonomous vehicle tracking module is configured to set a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicles before and after the connected and autonomous vehicle, construct the tracking target based on the risk factor and the ecological reference trajectory, use the model predictive control for solving, and obtain the control input of the connected and autonomous vehicle.

With regard to the system in the above-mentioned embodiments, the specific way in which each module performs operations has been described in detail in the embodiment of the method, which will not be described in detail here.

Since the system embodiment basically corresponds to the method embodiment, it is only necessary to refer to the partial description of the method embodiment for the relevant details. The system embodiments described above are only schematic, in which the units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, the components may be located in one place or distributed to a plurality of network units. Some or all of the modules can be selected according to the actual needs to achieve the purpose of the solution of the present disclosure. Those skilled ordinarily in the art can understand and implement the purpose without creative labor.

Correspondingly, the present disclosure further provides an electronic device, including one or more processors; a memory, where the memory is configured to store one or more programs. When executed by the one or more processors, the one or more programs causes the one or more processors to implement the mixed traffic flow-oriented vehicle eco-driving control method as described above. As shown in FIG. 6, FIG. 6 is a hardware structure diagram of any device with data processing capability that implements the mixed traffic flow-oriented vehicle eco-driving control method according to the embodiment of the present disclosure. In addition to the processor, the memory and the network interface shown in FIG. 6, any device with data processing capability where the apparatus in the embodiment is located usually includes other hardware according to the actual functions of the device with data processing capability, which will not be described in detail here.

Correspondingly, the present disclosure further provides a computer-readable storage medium on which computer instructions are stored, where the instructions, when executed by a processor, implements the mixed traffic flow-oriented vehicle eco-driving control method as described above. The computer-readable storage medium can be an internal storage unit of any device with data processing capability as described in any of the previous embodiments, such as a hard disk or a memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, Smart Media Card (SMC), SD card, Flash Card, etc. provided on the device. Further, the computer-readable storage medium can further include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program, other programs and data required by any device with data processing capability, and can also be used to temporarily store data that has been output or is to be output.

The above-mentioned embodiments are only used to illustrate the design ideas and characteristics of the present disclosure, and purpose thereof is to enable those skilled in the art to understand the content of the present disclosure and implement the content accordingly. The scope of protection in the present disclosure is not limited to the above-mentioned embodiments. Therefore, all equivalent changes or modifications made according to the principles and design ideas disclosed in the present disclosure fall within the scope of protection of the present disclosure.

Claims

1. A mixed traffic flow-oriented vehicle eco-driving control method, wherein the mixed traffic flow-oriented vehicle eco-driving control method comprises: acquiring signal phase and timing information, historical vehicle state information and real-time vehicle state information;calculating a gathering wave speed and a dissipation wave speed at an intersection based on a shock wave evolution theory, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time when a farthest queue is formed;predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle;taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle; andsetting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle.

2. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 1, wherein the calculating a gathering wave speed w1 and a dissipation wave speed w2 at an intersection comprises: acquiring a saturation flow qs, a saturation flow density ρs and a congestion flow density ρj at the intersection according to the historical vehicle state information;acquiring an arrival flow qa and an arrival flow density ρa according to the real-time vehicle state information;calculating the gathering wave speed w1 at the intersection, which is expressed as:

3. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 1, wherein the obtaining the farthest queue point position at the downstream intersection of the connected and autonomous vehicle and the time when the farthest queue is formed comprises: acquiring a current queue length L0 according to the signal phase and timing information and the real-time vehicle state information;calculating a distance Lmq between the farthest queue point and the stop line, in which an expression is as follows:

4. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 3, wherein the predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed comprises: acquiring the time {tilde over (t)}f when the connected and autonomous vehicle passes through the stop line, in which the expression is as follows:

5. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 1, wherein the predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle, comprises: an expression of the longitudinal acceleration of the manual-driving vehicle is as follows:

6. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 1, wherein the taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle, comprises: taking the instantaneous fuel consumption of the connected and autonomous vehicle and the penalty for the state of passing through the stop line as the objective function, which is expressed as:

7. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 1, wherein the setting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle, comprises: setting a risk factor εif(x(k+i)) of a following vehicle according to a predicted trajectory of the manual-driving vehicle behind the connected and autonomous vehicle;setting a risk factor εip(x(k+i) of a preceding vehicle according to a predicted trajectory of the manual-driving vehicle in front of the connected and autonomous vehicle;setting a risk factor εis(x(k+i)) of a traffic signal light;setting the tracking target and the dynamic constraints of the manual-driving vehicle for solving, to obtain the control input of the connected and autonomous vehicle;where an expression of the tracking target is:

8. A mixed traffic flow-oriented vehicle eco-driving control system, which is used to implement a mixed traffic flow-oriented vehicle eco-driving control method, said mixed traffic flow-oriented vehicle eco-driving control system comprising:a central controller and a plurality of local controllers deployed on the connected and autonomous vehicle;wherein the central controller comprises: a first result acquiring module, configured to acquire signal phase and timing information, historical vehicle state information and real-time vehicle state information;calculate the gathering wave speed and the dissipation wave speed at the intersection based on the shock wave evolution theory, and obtain the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;a second result acquiring module, configured to predict the vehicle state and the time when the connected and autonomous vehicle passes through the stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;a manual-driving vehicle trajectory predicting module, configured to construct a longitudinal acceleration prediction method of the manual-driving vehicle based on the risk field model, so as to acquire predicted trajectory of the manual-driving vehicle;a connected and autonomous vehicle trajectory planning module, configured to take the instantaneous fuel consumption of the connected and autonomous vehicle and the penalty for the state of passing through the stop line as the objective function, set dynamic constraints and trajectory constraints of the connected and autonomous vehicle, construct and solve the optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtain the acceleration curve of the connected and autonomous vehicle, so as to acquire the ecological reference trajectory of the connected and autonomous vehicle; anda first communication module, configured to send the ecological reference trajectory of the connected and autonomous vehicle to a local controller of the plurality of local controllers; wherein the local controller comprises: a second communication module, configured to receive the ecological reference trajectory of the connected and autonomous vehicle;a connected and autonomous vehicle tracking module, configured to set the risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, construct the tracking target based on the risk factor and the ecological reference trajectory, use the model predictive control for solving, and obtain the control input of the connected and autonomous vehicle.

9. An electronic device comprising: a memory and a processor,wherein the memory is coupled with the processor,wherein the memory is configured to store program data, andwherein the processor is configured to execute the program data to implement a mixed traffic flow-oriented vehicle eco-driving control method,wherein the mixed traffic flow-oriented vehicle eco-driving control method comprisesacquiring signal phase and timing information, historical vehicle state information and real-time vehicle state information;calculating a gathering wave speed and a dissipation wave speed at an intersection based on a shock wave evolution theory, and obtaining a farthest queue point position of a connected and autonomous vehicle at a downstream intersection and a time when a farthest queue is formed;predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed;predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle;taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle; andsetting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle.

10. The electronic device of claim 9, further comprising a non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the mixed traffic flow-oriented vehicle eco-driving control method.

11. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 2, wherein the calculating the farthest queue point position at the downstream intersection of the connected and autonomous vehicle and the time when the farthest queue is formed comprises: acquiring a current queue length L0 according to the signal phase and timing information and the real-time vehicle state information;calculating a distance Lmq between the farthest queue point and the stop line, in which the expression is as follows:

12. The mixed traffic flow-oriented vehicle eco-driving control method according to claim 11, wherein the predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed comprises: acquiring the time {tilde over (t)}f when the connected and autonomous vehicle passes through the stop line, in which an expression is as follows:

13. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 8, wherein the calculating a gathering wave speed and a dissipation wave speed at an intersection comprises: acquiring a saturation flow qs, a saturation flow density ρs and a congestion flow density ρj at the intersection according to the historical vehicle state information;acquiring an arrival flow qa and an arrival flow density ρa according to the real-time vehicle state information;calculating the gathering wave speed w1 at the intersection, which is expressed as:

14. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 8, wherein the calculating the farthest queue point position at the downstream intersection of the connected and autonomous vehicle and the time when the farthest queue is formed comprises: acquiring a current queue length L0 according to the signal phase and timing information and the real-time vehicle state information;calculating a distance Lmq between the farthest queue point and the stop line, in which an expression is as follows:

15. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 14, wherein the predicting a vehicle state and a time when the connected and autonomous vehicle passes through a stop line based on the farthest queue point position of the connected and autonomous vehicle at the downstream intersection and the time when the farthest queue is formed comprises: acquiring the time {tilde over (t)}f when the connected and autonomous vehicle passes through the stop line, in which the expression is as follows:

16. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 8, wherein the predicting a longitudinal acceleration of a manual-driving vehicle based on a risk field model, so as to acquire predicted trajectories of the manual-driving vehicle, comprises: an expression of the longitudinal acceleration of the manual-driving vehicle is as follows:

17. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 8, wherein the taking an instantaneous fuel consumption of the connected and autonomous vehicle and a penalty for a state of passing through the stop line as an objective function, setting dynamic constraints and trajectory constraints of the connected and autonomous vehicle, constructing and solving an optimal ecological reference trajectory planning model of the connected and autonomous vehicle, and obtaining an acceleration curve of the connected and autonomous vehicle, so as to acquire an ecological reference trajectory of the connected and autonomous vehicle, comprises: taking the instantaneous fuel consumption of the vehicle and the penalty for the state of passing through the stop line as the objective function, which is expressed as:

18. The mixed traffic flow-oriented vehicle eco-driving control system according to claim 8, wherein the setting a risk factor according to the risk field model and the predicted trajectories of the manual-driving vehicle before and after the connected and autonomous vehicle, constructing a tracking target based on the risk factor and the ecological reference trajectory, using a model predictive control for solving, and obtaining a control input of the connected and autonomous vehicle, comprises: setting a risk factor εif(x(k+i)) of a following vehicle according to a predicted trajectory of the manual-driving vehicle behind the connected and autonomous vehicle;setting a risk factor εipx(k+i) of a preceding vehicle according to a predicted trajectory of the manual-driving vehicle in front of the connected and autonomous vehicle;setting a risk factor εis(x(k+i)) of a traffic signal light;setting the tracking target and the dynamic constraints of the manual-driving vehicle for solving, to obtain the control input of the connected and autonomous vehicle;where an expression of the tracking target is:

19. The electronic device according to claim 9, wherein the calculating a gathering wave speed w1 and a dissipation wave speed w2 at an intersection comprises: acquiring a saturation flow qs, a saturation flow density ρs and a congestion flow density ρj at the intersection according to the historical vehicle state information;acquiring an arrival flow qa and an arrival flow density ρa according to the real-time vehicle state information;calculating the gathering wave speed w1 at the intersection, which is expressed as:

20. The electronic device according to claim 10, wherein the calculating a gathering wave speed and a dissipation wave speed at an intersection comprises: acquiring a saturation flow qs, a saturation flow density ρs and a congestion flow density ρj at the intersection according to the historical vehicle state information;acquiring an arrival flow qa and an arrival flow density ρa according to the real-time vehicle state information;calculating the gathering wave speed w1 at the intersection, which is expressed as: