A Cooperative Driving Control Method for Intelligent Connected Vehicle Queues at Signalized Intersections

Abstract

The present invention discloses a cooperative driving control method for an intelligent connected vehicle queue at a signal intersection, specifically as follows: Step 1: Calculate the steering angle of the i-th vehicle according to the difference between the actual position of the i-th vehicle at the current moment and the preset path; Step 2: Calculate the queue speed synthesis value of the i-th vehicle in the connected vehicle fleet; Step 3: Calculate the queue speed difference between the speed of the i-th vehicle in the connected vehicle fleet and the queue speed synthesis value corresponding to the i-th vehicle; Step 4: Calculate the acceleration of the i-th vehicle based on the queue speed difference; Step 6: Calculate the expected values of the spatial position and speed of the i-th vehicle at the next moment according to the acceleration and steering angle of the i-th vehicle, and go to Step 1. The method of the present invention can effectively improve the operation efficiency of the cooperative vehicle queue and the stability of the cooperative vehicle queue.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent connected vehicle control. Background Art

[0002] Signal intersections are potential traffic bottlenecks in urban traffic networks. Improving the vehicle passing efficiency at signal intersections is one of the effective ways to alleviate urban road traffic congestion. The vehicle speeds at signal intersections change frequently, and the unstable traffic waves caused by the frequent changes in vehicle speeds will greatly affect the overall operation efficiency of the road network, making signal intersections the main places where traffic congestion and accidents occur. Intelligent connected vehicles provide a new breakthrough for solving traffic congestion and traffic safety problems. On the one hand, intelligent connected vehicles can retrieve the dynamic driving information of other intelligent connected vehicles around them. On the other hand, intelligent connected vehicles can also obtain data such as signal timings of nearby intersections. Using this data, intelligent connected vehicles can reasonably plan their driving speeds and improve the passing efficiency of vehicles at intersections.

[0003] Currently, there are many studies on vehicle trajectory optimization and signal timing optimization at signal intersections in the intelligent connected environment. However, most of the research on cooperative driving control of intelligent connected vehicle platoons optimizes the longitudinal control strategy under the condition of platoon going straight, and the research on cooperative driving control strategies for intelligent connected vehicle platoons in specific traffic scenarios such as signal intersections is still relatively lacking. Summary of the Invention

[0004] Objective of the Invention: To solve the problems existing in the above-mentioned prior art, the present invention proposes a cooperative driving control method for intelligent connected vehicle platoons at signal intersections.

[0005] Technical Solution: The present invention provides a cooperative driving control method for intelligent connected vehicle platoons at signal intersections, and the method includes the following steps:

[0006] Step 1: Calculate the steering angle of the i-th vehicle according to the difference between the actual position of the i-th vehicle at the current moment and the preset path, where i = 2,..., n, and n represents the total number of vehicles in the connected vehicle platoon;

[0007] Step 2: Calculate the platoon speed synthesis value V of the i-th vehicle in the connected vehicle platoon i ;

[0008] Step 3: Calculate the speed v of the i-th vehicle in the connected vehicle platoon i and the platoon speed difference Δv between the platoon speed synthesis value V corresponding to the i-th vehicle i ; i ;

[0009] Step 4: Based on the platoon speed difference, calculate the acceleration of the i-th vehicle using the IDM algorithm;

[0010] Step 5: Calculate the expected values of the spatial position and speed of the i-th vehicle at the next moment based on the acceleration and steering angle of the i-th vehicle. After calculating the expected values of the spatial position and speed of all vehicles in the platoon at the next moment, enter the next moment and go to Step 1.

[0011] Furthermore, in Step 1, the PD control algorithm is used to calculate the steering angle of the i-th vehicle, and the proportional parameter K in the PD control algorithm p = 0.136, and K d = 0.864.

[0012] Furthermore, in Step 2, when i = 2, the platoon speed synthesis value V2 of the second vehicle in the connected platoon is V2 = v1; when 2 < i ≤ n, the platoon speed synthesis value V of the i-th vehicle in the connected platoon is calculated according to the following formula i :

[0013] V i = P × v i-1 + S × V i-1

[0014] where, v i-1 represents the speed of the (i - 1)-th vehicle, V i-1 represents the platoon speed synthesis value of the (i - 1)-th vehicle, and both P and S are attenuation coefficients, and v1 represents the speed of the first vehicle.

[0015] Furthermore, P = 0.75 and S = 0.25.

[0016] Furthermore, in Step 3, Δv i = v i - V i .

[0017] Furthermore, the IDM algorithm used in Step 4 is an improved IDM algorithm, and the acceleration a of the i-th vehicle is calculated based on the following formula:

[0018]

[0019] where, a max represents the maximum acceleration of the i-th vehicle, v0 represents the expected speed of the i-th vehicle, s represents the net headway, and the expression of s * (v0Δv i ) is as follows:

[0020]

[0021] where, T represents the safe headway time, and a comfortable represents the comfortable deceleration.

[0022] Beneficial effects: The vehicle platoon adopting the M-CACC control strategy proposed by the present invention can well adapt to the speed change of the leading vehicle, quickly adjust the speed and then keep consistent with the leading vehicle. The intelligent connected vehicle queue under M-CACC control has good coordination, and in principle, it can achieve safe driving at a lower headway, thereby improving the traffic efficiency at intersections. The method of the invention can effectively improve the operation efficiency of the cooperative vehicle queue and the stability of the cooperative vehicle queue. Brief description of the drawings

[0023] Figure 1 is the flowchart of the method of the present invention;

[0024] Figure 2 is the phase diagram of the signal intersection; where (a) is the phase diagram of the north-south straight, (b) is the phase diagram of the west left turn, (c) is the phase diagram of the east-west straight, and (d) is the phase diagram of the north-south left turn;

[0025] Figure 3 is the schematic diagram of the signal intersection;

[0026] Figure 4 is the driving trajectory diagram of the vehicle departure position under different PD parameters;

[0027] Figure 5 is the driving trajectory diagram of the vehicle left turn position under different PD parameters;

[0028] Figure 6 is the platoon speed change diagram;

[0029] Figure 7 is the comparison diagram of the headway of each vehicle and its ideal value under the cooperative control strategy of the present invention and the original IDM control method.

[0030] Figure 8 is the comparison diagram of the headspace of each vehicle and its ideal value under the cooperative control strategy of the present invention and the original IDM control method. Specific implementation manners

[0031] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0032] As Figure 1 shown, a cooperative control strategy for an intelligent connected vehicle queue at a signal intersection provided by the present invention includes the following steps:

[0033] S1. Retrieve the real-time dynamic basic safety driving information (BSMs) of the connected vehicle platoon; according to the difference between the vehicle position and the preset path, perform lateral control with a PD control algorithm to obtain the steering angle of the controlled vehicle;

[0034] S2. Calculate the combined speed value of each vehicle queue using the attenuation coefficient formula in the M-CACC cooperative control algorithm, and convert the combined speed value of the queue into a queue speed difference;

[0035] S3. Import the IDM algorithm module in M-CACC to obtain the acceleration of each vehicle in the vehicle fleet;

[0036] S4. Substitute the steering angles and accelerations of each vehicle calculated in the previous steps into the vehicle dynamics model, calculate the expected values of the spatial positions and speeds of each vehicle in the next unit time, and execute steps S1 to S4 again.

[0037] The schematic diagram of the traffic scenario in this embodiment is as shown in Figure 2 and Figure 3 as shown.

[0038] In this embodiment, for the consideration of optimizing the main body of the control strategy first, the vehicle mechanics model adopted in the simulation simplifies the traffic scenario to a two-dimensional scenario, adopts a particle model, and the acceleration direction of the vehicle is consistent with the steering wheel. Based on the spatial position data of the controlled vehicle, the steering angle of the controlled vehicle is calculated through the PD algorithm. The PID algorithm is simple, has good robustness and high reliability, and is widely used in industrial process control, especially suitable for deterministic control systems that can establish accurate mathematical models. The PD algorithm is the P and D parts in the PID algorithm. In the simulation environment, PD control can well realize intersection passing. P proportionally reflects the deviation signal of the control system, and D can reflect the change trend of the deviation signal. It should be noted that the time in the simulation is actually discrete, so the differential of D is similar to the difference with an infinitesimal interval. K p , K d are the proportional parameters set for P and D respectively, representing the importance of P and D in the control. Before the vehicle starts running, its position is set on the left side at a certain distance from the preset trajectory. After the vehicle starts driving, it will return to the preset trajectory through PD control and complete a left turn. Figure 4 and Figure 5 are the driving trajectories of the vehicle at the starting position and the left-turn position respectively under different PD control parameters; when the value of K d is larger and the value of K p is smaller, the vehicle can often drive with a relatively comfortable centripetal acceleration and return to the preset path in an elegant curve. On the contrary, when K p is larger and K d is smaller, the driving trajectory of the vehicle can never be stabilized. After the vehicle touches the preset trajectory, it can never fully fit the trajectory and continuously sways left and right, which is obviously unreasonable. On the other hand, when the vehicle makes a left turn, if the value of K d is too large and K pIf the value is too small, the vehicle cannot strictly follow the preset trajectory, resulting in a large error and generally deviating towards the outside of the trajectory. By observing the driving trajectories of the vehicle under different parameters and comprehensively considering safety and traffic efficiency, this embodiment obtains the optimal PD control parameters: K p = 0.136, K d = 0.864.

[0039] In this embodiment, the BSMs are composed of the following data: vehicle speed, acceleration, coordinates, steering angle, and the deviation from the preset path in the previous unit time.

[0040] The acceleration control in this embodiment adopts the M-CACC model improved on the basis of the IDM model and the cooperative adaptive cruise model (CACC). In the M-CACC model, it is necessary to first convert the vehicle speed data of each vehicle in the BSMs into the combined queue speed value of each vehicle using the attenuation coefficient formula. The attenuation coefficients are P and S. Based on the attenuation coefficients, recursive operations can be performed to calculate the combined queue speed value of each vehicle in the vehicle platoon. The calculation method of the combined queue speed value is as follows:

[0041] P = 0.75, S = 0.25

[0042] V2 = v1

[0043] V3 = P × v2 + S × V2 ......

[0045] V i = P × v i-1 + S × V i-1 ......

[0047] V n-1 = P × v n-2 + S × V n-2

[0048] V n = P × v n-1 + S × V n-1

[0049] Among them, v i-1 represents the speed of the (i - 1)-th vehicle, and V i-1 represents the combined queue speed value of the (i - 1)-th vehicle; there are n vehicles in the intelligent connected vehicle platoon, and they all follow the lead vehicle. The speed of each vehicle is v i . Use V i to represent the calculated combined queue speed value (in this embodiment, the 1st vehicle is the lead vehicle, and its combined queue speed value V1 does not need to be calculated. It does not belong to the queue and is not controlled by M-CACC, but is only used to test the performance of the queue), i = 2, 3,..., n.

[0050] When there are three vehicles in the vehicle platoon, the combined platoon speed value becomes V3 = 0.75×v2 + 0.25×v1. When there are four vehicles in the vehicle platoon, the combined platoon speed value is V4 = 0.75×v3 + 0.25×V3, that is:

[0051] V4 = 0.75×v3 + 0.25×0.75×v2 + 0.25×0.25×v1

[0052] Comparing the situation with four vehicles and the situation with two vehicles, it can be found that the combined platoon speed value is equivalent to replacing the leading vehicle speed in the IDM model with a linear combination of the speed values of all leading vehicles in the platoon. And P = 0.75, S = 0.25 are the coefficients of the linear combination, that is, the attenuation coefficients, which indicate the weights of the speed values at different platoon positions. The weight is larger the closer it is to the controlled vehicle. In the case of four vehicles driving in coordination, v3 determines the magnitude of V4 to the greatest extent.

[0053] Therefore, different values of the attenuation coefficient have a great impact on the performance of M-CACC. When the values of P and S are similar, the driving integrity of the entire platoon will be very good, but there will be greater risks at the same time. For example, if a vehicle suddenly decelerates due to a vehicle inserting from the side, then under the attenuation coefficient with similar values of P and S, M-CACC cannot respond well and may not be able to generate sufficient deceleration to avoid collisions. Therefore, the attenuation ratio must also find a balance between safety and integrity, and improve the efficiency and integrity of platoon driving on the premise of ensuring safety. After multiple debugging and comparison in the simulation environment, it is found that M-CACC has better performance when P = 0.75 and S = 0.25, which is significantly better than the IDM model.

[0054] Calculate the platoon speed difference Δv corresponding to the i-th vehicle i = v i - V i , and then substitute it into the IDM algorithm module to calculate the acceleration of this vehicle. The IDM algorithm module in the M-CACC algorithm is the IDM model adopted as the control group in the present invention when Δv is not the platoon speed difference but the speed difference between the leading vehicle and the following vehicle.

[0055]

[0056]

[0057] The parameter tuning of the M-CACC model is divided into two parts: the parameter tuning of the IDM algorithm module and the attenuation coefficient tuning. The first part is to tune the IDM algorithm module, and most of the parameters adopt the common parameter values in the IDM model. In this formula, s0 is the minimum headway, and the headway is the distance between two adjacent vehicles (the value in this embodiment is 10 m). T represents the safe headway time. The headway time represents the time difference between the front ends of two consecutive vehicles passing through the same point. Generally, it can be calculated by dividing the headway between the front and rear vehicles by the speed of the rear vehicle. However, in the IDM algorithm module, the safe headway time T is not calculated but a preset parameter. T and another parameter s0 are used to calculate the desired headway of the controlled vehicle. The larger T is, the larger the headway and headway time of the vehicle queue will be (the value of T in this embodiment is 1 s), Δv i is the speed difference between the front and rear vehicles in the traditional IDM, while in the M-CACC of the present invention, Δv i is the queue speed difference; a max is the maximum acceleration of the vehicle itself (the value is 3 m / s 2 ), a comfortable represents the comfortable deceleration (the value is 2 m / s 2 ); v0 is the desired speed of the vehicle itself (the value is 12 m / s), δ is the empirical coefficient (generally set to 4), and s is the headway; among them, the safe headway time T and the minimum headway s0 are the most critical for the performance of the cooperative control strategy. In the formula for calculating the desired headway, the left half of the formula, that is, s0 + v i T, plays a major decisive role. M-CACC considers the information of multiple leading vehicles based on the queue speed difference. In the right half of the formula where the queue speed difference is located , when the queue combined speed value increases, the queue speed difference Δv i will decrease, and the desired headway will also decrease accordingly, making the acceleration increase, which conforms to people's reaction when seeing the leading vehicle accelerating while driving. Similarly, if the queue combined speed value decreases, the desired headway will increase accordingly, and the acceleration will decrease.

[0058] As Figure 6 shown, after the leading vehicle accelerates rapidly, except for the large speed fluctuation of the leading vehicle when it just starts, which makes the following vehicle queue unable to stabilize quickly, the speeds of the following vehicle queue are quite consistent. It can be seen that by using the method of the present invention, the following vehicle queue can quickly adjust its own speed to be consistent with the leading vehicle.

[0059] In terms of the basic response mechanism, there is no essential difference between M-CACC and IDM, but the M-CACC model will consider more the driving speeds of vehicles other than the leading vehicle. For example, in the queue controlled by the IDM model, if the leading vehicle and this vehicle maintain the same speed and the vehicle in front of the leading vehicle starts to accelerate, because Δv i= 0, the acceleration of the vehicle will only be affected by s0 + v i T. However, if the M-CACC model is used to conduct cooperative control on the intelligent connected vehicle platoon, then when the speed of the leading vehicle is the same as that of this vehicle and the vehicle in front of the leading vehicle starts to accelerate, because Δv i < 0, the desired headway of this vehicle will be smaller than that of IDM, which will further increase the acceleration of this vehicle. This additional acceleration makes the vehicle platoon under M-CACC control drive more consistently and stably, reducing traffic fluctuations. Of course, due to the effect of the attenuation coefficient, the acceleration increase caused by the M-CACC model in this case is extremely limited, and it can only make certain preparations in advance for the acceleration of the vehicle in front of the leading vehicle on the premise of ensuring safety performance.

[0060] The second part is the parameter tuning of the attenuation coefficient. In the process of optimizing the parameter of the attenuation coefficient formula of a cooperative driving control strategy for intelligent connected vehicle platoons at signalized intersections proposed in the present invention, the difference between the headway of each vehicle and the ideal headway (such as Figure 7 ), and two groups of data of the difference between the headway of each vehicle and the desired headway (such as Figure 8 ) are used as the main basis for evaluating the pros and cons of the control strategy. At the same time, the performance of the M-CACC model in controlling the vehicle platoon under different attenuation coefficients is compared with the performance of the IDM model in controlling the vehicle platoon. The calculation formula of the desired headway is the same as above. s0 is taken as 10m, T is taken as 1s, and the desired headway per unit time is obtained. The ideal headway is a constant, that is, the safety headway T = 1s. In the experiment of testing the performance of the M-CACC control model, the speed of the leading vehicle is set as a function with repeated fluctuations and continuously changing speed, so that the reaction of the vehicle platoon to different signalized intersection environments, speed fluctuations, and emergencies can be well observed. According to the parameter tuning experience, different values of the attenuation coefficient (i.e., P and S) have a greater impact on the performance of M-CACC. When the values of P and S are close, the driving integrity of the entire platoon will be very good, but there will be greater risks at the same time. If an unexpected event occurs, such as a significant deceleration of the leading vehicle caused by a vehicle inserting from the side, then under the attenuation coefficient with similar values of P and S, M-CACC cannot respond well and may not be able to generate enough deceleration to avoid collisions. Therefore, the attenuation coefficient must also find a balance between safety and integrity, and improve the efficiency and integrity of platoon driving on the premise of ensuring safety. After multiple debugging and comparison in the simulation environment, it is found that M-CACC has better performance when P = 0.75 and S = 0.25, which is significantly better than the IDM model.

[0061] From Figure 7 and Figure 8It can be observed that, compared with the vehicle queue of Adaptive Cruise Control (ACC), in the vehicle queue adopting the M-CACC cooperative control strategy, the difference between the headway and the safe headway is larger, and the safety is better. In addition, the spacing of the intelligent connected vehicle queue always remains within a small range, and compared with the vehicle queue controlled by ACC, the fluctuation range of the difference between the inter-vehicle spacing and the ideal spacing in the vehicle fleet adopting M-CACC control is smaller and more stable.

[0062] In addition, it should be noted that, in the various specific technical features described in the above specific embodiments, they can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not separately describe various possible combination methods.

Claims

1. A cooperative driving control method for an intelligent connected vehicle queue at a signalized intersection, characterized in that Specifically, it includes the following steps: Step 1: Calculate the steering angle of the i-th vehicle according to the difference between the actual position of the i-th vehicle at the current moment and the preset path, where i = 2, …, n, and n represents the total number of vehicles in the connected vehicle fleet; Step 2: Calculate the combined platoon speed value V of the i-th vehicle in the connected vehicle platoon i ; Step 3: Calculate the speed v of the i-th vehicle in the connected vehicle fleet i and the queue speed composite value V corresponding to the i-th vehicle i of the queue speed difference Δv i ; Step 4: Based on the queue speed difference, use the IDM algorithm to calculate the acceleration of the i-th vehicle; Step 5: According to the acceleration and steering angle of the i-th vehicle, calculate the expected values of the spatial position and speed of the i-th vehicle at the next moment. After calculating the expected values of the spatial positions and speeds of all vehicles in the fleet at the next moment, enter the next moment and go to Step 1; In step 2, when i = 2, the queue speed synthesis value V2 of the second vehicle in the connected vehicle fleet is V2 = v1; when 2 < i ≤ n, the queue speed synthesis value V of the i-th vehicle in the connected vehicle fleet is calculated according to the following formula i :[[]]END]] V i = P × v i-1 + S × V i-1 Among them, v i-1 represents the speed of the (i - 1)-th vehicle, and V i-1 represents the combined value of the platoon speed of the (i - 1)-th vehicle. Both P and S are attenuation coefficients, and v1 represents the speed of the first vehicle.

2. The collaborative driving control method for an intelligent connected vehicle queue at a signalized intersection according to claim 1, wherein In the step 1, the steering angle of the i-th vehicle is calculated by using a PD control algorithm, and the proportional parameter K in the PD control algorithm p = 0.136, K d = 0.

864.

3. The collaborative driving control method for an intelligent connected vehicle queue at a signalized intersection according to claim 1, characterized in that, P = 0.75, S = 0.

25.

4. The collaborative driving control method for an intelligent connected vehicle queue at a signalized intersection according to claim 1, characterized in that, Δv in step 3 i = v i - V i .

5. The collaborative driving control method for an intelligent connected vehicle queue at a signalized intersection according to claim 1, wherein The IDM algorithm used in Step 4 is an improved IDM algorithm, and the acceleration a of the i-th vehicle is calculated based on the following formula: where a max represents the maximum acceleration of the i-th vehicle, v0 represents the desired speed of the i-th vehicle, and s represents the headway, where s * (v0Δv i ) is expressed as follows: Among them, T represents the safe headway time, and a confortable represents the comfortable deceleration.

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