A left turn method without protection for intelligent network connected vehicle fleet

Abstract

The application relates to a left turn method for an intelligent networked vehicle fleet, and the method comprises the following steps: step 1: when the vehicle fleet enters an intersection to start left turn, a conflict vehicle in an opposite straight-flow vehicle flow is determined; step 2: according to historical operation data of the conflict vehicle, driving behavior of the conflict vehicle is recognized, and a driving behavior model of the conflict vehicle is obtained; step 3: according to real-time states of the vehicle fleet and the driving behavior model of the conflict vehicle, a model predictive control problem is constructed, and a left turn trajectory of the vehicle fleet is solved; and step 4: each vehicle in the vehicle fleet travels according to the left turn trajectory of the vehicle fleet. Compared with the prior art, the application has the advantages of improving the safety and efficiency of left turn of the vehicle fleet.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent networked vehicles, in particular to a left turn method for intelligent networked vehicle platoon. BACKGROUND

[0002] With the development of intelligent and networked vehicles, intelligent networked vehicle platoon has become a new technology. Intelligent networked vehicle platoon can effectively shorten the distance between vehicles through cooperation between vehicles, thereby helping to reduce vehicle energy consumption and improve traffic efficiency.

[0003] However, at present, the research on intelligent networked vehicle platoon rarely considers the direct conflict with surrounding vehicles. Especially in the scene of unprotected left turn at the intersection, there is a conflict and game phenomenon between the intelligent networked vehicle platoon and the opposite straight flow, which seriously affects the safety and efficiency of the intelligent networked vehicle platoon. SUMMARY

[0004] The purpose of the present application is to improve the safety of the platoon left turn, to make the trajectory planning of the platoon more active under the condition of ensuring safety, and to improve the left turn efficiency, and to provide a left turn method for intelligent networked vehicle platoon.

[0005] The purpose of the present application can be realized by the following technical solutions:

[0006] A left turn method for intelligent networked vehicle platoon, the method comprising the following steps:

[0007] Step 1: when the platoon enters the intersection to start left turn, determine the conflict vehicle in the opposite straight flow;

[0008] Step 2: according to the historical operation data of the conflict vehicle, identify the driving behavior of the conflict vehicle, and obtain the driving behavior model of the conflict vehicle;

[0009] Step 3: according to the real-time state of the platoon and the driving behavior model of the conflict vehicle, construct a model predictive control problem, and solve to obtain the left turn trajectory of the platoon;

[0010] Step 4: each vehicle in the platoon travels according to the left turn trajectory of the platoon.

[0011] Further, step 1 is specifically:

[0012] Step 1.1: sort the opposite straight flow from near to far according to the distance from the intersection;

[0013] Step 1.2: select the vehicle in the opposite straight flow that meets the following conditions as the conflict vehicle:

[0014] The position does not exceed the conflict area;

[0015] The distance between the vehicle and the conflict zone is greater than the distance between the vehicle platoon and the conflict zone.

[0016] The time for the vehicle to reach the conflict zone is greater than the time for the vehicle platoon to reach the conflict zone.

[0017] Further, step 2 is specifically:

[0018] Step 2.1: Give the conflict vehicle driving behavior model formula;

[0019] Step 2.2: Obtain the historical operation data of the conflict vehicle before entering the intersection;

[0020] Step 2.3: Based on the data of each time point in the historical operation data, the least squares method is used to fit the conflict vehicle driving behavior model by adjusting the to-be-identified parameters, to obtain the optimal parameters, and to obtain the conflict vehicle driving behavior model under the optimal parameters.

[0021] Further, the conflict vehicle driving behavior model formula is:

[0022]

[0023] wherein, is a to-be-identified parameter, is the distance between the conflict vehicle and the preceding vehicle; is the speed of the preceding vehicle; is the speed of the conflict vehicle; is the acceleration of the conflict vehicle; , is the remaining parameter, and the preceding vehicle is the first vehicle in the vehicle platoon projected to the front of the conflict vehicle.

[0024] Further, step 3 is specifically:

[0025] Step 3.1: Define the Frenet coordinate system;

[0026] Step 3.2: Define the state quantity of the vehicle platoon;

[0027] Step 3.3: Define the control quantity of the vehicle platoon, and define the state update equation of the vehicle platoon based on the linearized bicycle model;

[0028] Step 3.4: Define the state update equation of the conflict vehicle;

[0029] Step 3.5: Calculate the acceleration of the conflict vehicle in the prediction time domain in the state update equation based on the conflict vehicle driving behavior model;

[0030] Step 3.6: Set the objective function of the model predictive control problem based on the state update equation;

[0031] Step 3.7: Set the control quantity constraint of the state quantity;

[0032] Step 3.8: Set the obstacle avoidance constraint;

[0033] Step 3.9: Solve the model predictive control problem composed of the objective function, the control variable constraint and the obstacle avoidance constraint.

[0034] Further, the state update equation of the vehicle platoon is:

[0035]

[0036] wherein subscript i represents the i-th prediction horizon; represents the linearization step; represents the wheelbase of the vehicle, and superscript k represents the k-th vehicle, , , , respectively represent the x-axis position, the y-axis position, the speed and the heading angle.

[0037] Further, the state update equation of the conflict vehicle is:

[0038]

[0039] wherein superscript represents the conflict vehicle, and i represents the i-th prediction horizon, represents the acceleration of the conflict vehicle in the i-th prediction horizon;

[0040] The acceleration of the conflict vehicle in the i-th prediction horizon is:

[0041]

[0042] wherein is the speed of the first vehicle.

[0043] Further, the objective function is:

[0044]

[0045] wherein is the expected speed of the vehicle platoon, is the expected vehicle distance of the vehicle platoon, , , , , , , is a weighting factor, represents the front wheel steering angle. CV represents the conflict vehicle, represents the speed of the i-th prediction horizon of the first vehicle, and subscript i represents the i-th prediction horizon; superscript k represents the k-th vehicle, , , These represent the x-axis position, y-axis position, and velocity, respectively. Let represent the acceleration of the k-th vehicle in the i-th prediction time domain.

[0046] Furthermore, the control constraints are: the speed of vehicles in the platoon is less than the road speed limit, and the acceleration and front wheel angle in the control variables meet the vehicle performance requirements.

[0047] Furthermore, the obstacle avoidance constraint is as follows:

[0048]

[0049] in, Location of the conflict zone; For the train commander; For additional safety distance.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] This invention considers the future reactions of conflicting vehicles in its modeling, that is, the reactions of conflicting vehicles to "platoon behavior." The acceleration of the conflicting vehicles is related to the distance between the conflicting vehicle and the vehicle in front (i.e., the first vehicle in the projected platoon) and the speed of the vehicle in front. Compared with the conventional "predict first, then plan" trajectory prediction method, this invention improves the safety of platoon left turns. While ensuring safety, it enables more proactive trajectory planning for the platoon, improving left-turn efficiency. Attached Figure Description

[0052] Figure 1 For determining the conflicting vehicles of the present invention;

[0053] Figure 2 This is a diagram illustrating the driving behavior of the conflicting vehicles according to the present invention.

[0054] Figure 3 This is a diagram defining the Frenet coordinate system of the present invention;

[0055] Figure 4 This is a projection diagram of the vehicle fleet according to the present invention. Detailed Implementation

[0056] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0057] In the unprotected left turn scene at the intersection, there is a conflict and game phenomenon between the intelligent connected vehicle fleet and the opposite straight flow, which affects the safety and efficiency of the intelligent connected vehicle fleet. To solve this problem, the invention proposes an unprotected left turn method for intelligent connected vehicle fleet. The method first identifies the driving behavior of the conflict vehicle based on the historical operation data of the conflict vehicle, and obtains the driving behavior model of the conflict vehicle. And apply this model to the left turn trajectory planning of the vehicle fleet, based on the optimal control method, plan the left turn trajectory of each vehicle in the vehicle fleet. Compared with other methods, the invention expands the control object from a single vehicle to a vehicle fleet, and improves the safety and efficiency of the vehicle fleet left turn. At the same time, the algorithm has the computing efficiency of the engineering application level, which can significantly reduce the operation load.

[0058] The invention comprises the following steps:

[0059] Step 1: When the vehicle fleet enters the intersection to start left turn, determine the conflict vehicle in the opposite straight flow;

[0060] Step 2: According to the historical operation data of the conflict vehicle, identify the driving behavior of the conflict vehicle, and obtain the driving behavior model of the conflict vehicle;

[0061] Step 3: According to the real-time state of the vehicle fleet and the driving behavior model of the conflict vehicle, construct a model predictive control problem, and solve the left turn trajectory of the vehicle fleet;

[0062] Step 4: Each vehicle in the vehicle fleet drives according to the left turn trajectory of the vehicle fleet.

[0063] In step 1, as shown in Figure 1 , the determination method of the conflict vehicle is as follows:

[0064] Step 1.1: Sort the opposite straight flow from near to far according to the distance from the intersection.

[0065] Step 1.2: Select the vehicle that meets the following conditions at the same time as the candidate vehicle.

[0066] 1. The position of the vehicle does not exceed the conflict area.

[0067] 2. Compared with the distance of the vehicle fleet from the conflict area, the vehicle is farther away from the conflict area.

[0068] 3. Assuming uniform motion, compared with the vehicle fleet, the vehicle takes longer to reach the conflict area.

[0069] Step 1.3: Select the vehicle with the smallest serial number as the conflict vehicle.

[0070] In step 2, the driving behavior identification method of the conflict vehicle is as follows:

[0071] Step 2.1: Give the driving behavior model, where the parameter is the parameter to be identified.

[0072]

[0073] where is the distance between the conflicting vehicle and the leading vehicle, see Figure 2 ; is the leading vehicle speed; is the conflicting vehicle speed; is the conflicting vehicle acceleration; , are the rest parameters.

[0074] Step 2.2: Obtain the trajectory data of the conflicting vehicle before entering the intersection, as follows:

[0075]

[0076] The subscript represents the time; represents the current time. The trajectory data records the self-vehicle state, leading vehicle state, and self-vehicle acceleration response of the conflicting vehicle in the time period [0, T].

[0077] Step 2.3: Based on the data at each time point in , use the least squares method to adjust the parameters to fit the formula in step 2.1. Thus, the optimal parameters , , are obtained.

[0078] In step 3, the vehicle platoon left-turn trajectory method based on model predictive control contains the following steps, without loss of generality, the following takes a three-vehicle platoon as an example.

[0079] Step 3.1: Define the Frenet coordinate system, as shown in Figure 3 .

[0080] Step 3.2: Define the state quantities of the platoon, as follows.

[0081]

[0082] where the superscripts 1, 2, and 3 represent the serial numbers of the vehicles in the platoon. represents the x-axis position, y-axis position, speed, and heading angle of the first vehicle, respectively. represents the y-axis position and speed of the conflicting vehicle.

[0083] Step 3.3: Define the control quantities of the platoon, as follows.

[0084]

[0085] where superscripts 1, 2, 3 denote the index of the vehicle in the platoon. denote the acceleration and front wheel steering angle of the first vehicle, respectively.

[0086] Step 3.3: Define the state update equation of the platoon based on the linearized bicycle model, take the kth vehicle as an example, as follows

[0087]

[0088] where subscript i denotes the ith prediction horizon; denotes the linearization step size; denotes the wheelbase of the vehicle.

[0089] Step 3.4: Define the state update equation of the conflicting vehicle.

[0090]

[0091] where denotes the acceleration of the conflicting vehicle at the ith step, which can be determined by Step 3.5.

[0092] Step 3.5: The acceleration of the conflicting vehicle within the prediction horizon can be determined by the following formula:

[0093]

[0094] To determine , we need to get and . However, since the platoon and the conflicting vehicle are not on the same lane, we project the first vehicle in the platoon to the front of the conflicting vehicle using projection, as shown in Figure 4 After projection, considering the geometric relationship, we can get . is the speed of the first vehicle, i.e. .

[0095] Step 3.6: Considering the safety and efficiency of the platoon turning left, set the objective function of the model predictive control problem as follows:

[0096]

[0097] where is the desired speed of the platoon, is the desired distance of the platoon. The first term of guarantees driving efficiency, the second term improves safety by maintaining a safe distance between the platoon and the conflicting vehicle, the third term ensures that the platoon does not deviate significantly from the road centerline, and the fourth and fifth terms guarantee the stability of the distance within the platoon. The first and second terms of , , , , , , The preset adjustment can be adjusted according to the needs of the user.

[0098] Step 3.7: The model predictive control problem needs to consider the control quantity constraint of the state quantity, that is, the speed of the vehicle in the vehicle fleet needs to be less than the road speed limit; the acceleration and front wheel angle in the control quantity need to meet the performance requirements of the vehicle.

[0099] Step 3.8: The model predictive control problem needs to consider the obstacle avoidance constraint, as follows:

[0100]

[0101] Wherein is the position of the conflict area; is the vehicle length; is the additional safety distance.

[0102] Step 3.9: The problem is converted into a mixed integer quadratic programming problem for solving.

[0103] The present application proposes a left turn method for intelligent connected vehicle fleet. The method first identifies the driving behavior of the conflict vehicle based on the historical operation data of the conflict vehicle, obtains the driving behavior model of the conflict vehicle, and applies the model to the left turn trajectory planning of the vehicle fleet. Based on the optimal control method, the left turn trajectory of each vehicle in the vehicle fleet is planned.

[0104] The present application has the following beneficial effects:

[0105] 1. The left turn method for intelligent connected vehicle fleet is proposed, which fills the gap in the research.

[0106] 2. The future reaction of the conflict vehicle is considered in the modeling, which improves the safety of the left turn of the vehicle fleet. In the case of ensuring safety, the trajectory planning of the vehicle fleet can be more active, and the left turn efficiency is improved.

[0107] 3. The algorithm has engineering application level calculation efficiency, and reduces the operation load.

[0108] The above detailed the preferred embodiments of the present application. It should be understood that those skilled in the art can make many modifications and changes without creative labor according to the concept of the present application. Therefore, any technical solution obtained by logical analysis, reasoning or limited experiment on the basis of the prior art by those skilled in the art according to the concept of the present application shall be within the protection scope determined by the claims.

Claims

1. A method for unprotected left turn for intelligent vehicle platoon, characterized in that, The method comprises the following steps: Step 1: when the vehicle platoon starts to turn left at the intersection, determine the conflict vehicle in the opposite straight-flow vehicle flow; Step 2: according to the historical operation data of the conflict vehicle, identify the driving behavior of the conflict vehicle, and obtain a driving behavior model of the conflict vehicle; Step 3: according to the real-time state of the vehicle platoon and the driving behavior model of the conflict vehicle, a model predictive control problem is constructed, and a left-turn trajectory of the vehicle platoon is solved; Step 4: each vehicle in the vehicle platoon travels according to the left-turn trajectory of the vehicle platoon; The formula of the driving behavior model of the conflict vehicle is: wherein, is the parameter to be identified, is the distance of the conflicting vehicle from the leading vehicle; is the speed of the leading vehicle; is the speed of the conflicting vehicle; is the acceleration of the conflicting vehicle; , are the remaining parameters, the leading vehicle being the vehicle in front of the conflicting vehicle in the platoon. Step 3 is specifically: Step 3.1: define the Frenet coordinate system; Step 3.2: define the state quantity of the vehicle platoon; Step 3.3: define the control quantity of the vehicle platoon, and define the state update equation of the vehicle platoon based on the linearized bicycle model; Step 3.4: define the state update equation of the conflict vehicle; Step 3.5: calculate the acceleration of the conflict vehicle in the prediction time domain in the state update equation based on the driving behavior model of the conflict vehicle; Step 3.6: set the objective function of the model predictive control problem based on the state update equation; Step 3.7: set the control quantity constraint of the state quantity; Step 3.8: set the obstacle avoidance constraint; Step 3.9: solve the model predictive control problem composed of the objective function, the control quantity constraint and the obstacle avoidance constraint; The objective function is: wherein, is a desired speed of the platoon, is a desired inter-vehicle distance of the platoon, is a weighting factor, denotes a front wheel steering angle; CV denotes a conflicting vehicle, denotes a speed of the 1st vehicle in the i-th prediction horizon, the subscript i denotes the i-th prediction horizon; the superscript k denotes the k-th vehicle, denote an x-axis position, a y-axis position, a speed, respectively; denotes an acceleration of the k-th vehicle in the i-th prediction horizon;​​​​​​​​ The obstacle avoidance constraint is: wherein is the position of the conflict zone; is the vehicle length; is the additional safety distance. 2.The method of claim 1, wherein, Step 1 is specifically: Step 1.1: sort the opposite straight-flow vehicle flow from near to far according to the distance from the intersection; Step 1.2: select the vehicle in the opposite straight-flow vehicle flow that meets the following conditions as the conflict vehicle: The position does not exceed the conflict area; The distance between the vehicle and the conflict area is greater than the distance between the vehicle platoon and the conflict area; Under uniform motion, the time for the vehicle to reach the conflict area is greater than the time for the vehicle platoon to reach the conflict area. 3.The method of claim 2, wherein, Step 2 is specifically: Step 2.1: give the formula of the driving behavior model of the conflict vehicle; Step 2.2: obtain the historical operation data of the conflict vehicle before entering the intersection; Step 2.3: based on the data at each time point in the historical operation data, the least squares method is used to fit the driving behavior model of the conflict vehicle by adjusting the to-be-identified parameters, to obtain the optimal parameters, and to obtain the driving behavior model of the conflict vehicle under the optimal parameters. 4.The method of claim 1, wherein, The state update equation of the vehicle platoon is: wherein subscript i denotes the i-th prediction time horizon; denotes the linearization step size; denotes the wheel base of the vehicle, superscript k denotes the k-th vehicle, , , , denote the x-axis position, y-axis position, velocity, heading angle, respectively.

5. The method of claim 4, wherein, The state update equation of the conflict vehicle is: wherein the superscript represents a conflicting vehicle, i represents the i-th prediction horizon, represents the acceleration of the conflicting vehicle over the prediction horizon i; The acceleration of the conflict vehicle in the prediction time domain i is: wherein, is the speed of the first vehicle, the optimal parameter , , .

6. The method of claim 1, wherein, The control quantity constraint is that the speed of the vehicle in the vehicle platoon in the control quantity is less than the road speed limit, and the acceleration and front wheel steering angle in the control quantity meet the performance requirements of the vehicle.

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