Trajectory smoothing method for intelligent connected vehicles at signalized intersections based on platoon control

Abstract

The present invention relates to the technical field of vehicle trajectory control, and discloses a method for smoothing the trajectory of intelligent networked vehicles at a signalized intersection based on formation control, the method comprising dividing the upstream area of ​​the signalized intersection into a free driving area, a vehicle formation area, and a speed guidance area, and determining the length of the speed guidance area and the formation length of the intelligent networked vehicles; according to the driving state and motion relationship of the intelligent networked vehicles entering the vehicle formation area from the free driving area, an incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles; according to the signal light state when the intelligent networked vehicles enter the speed guidance area from the vehicle formation area, the intelligent networked vehicles are guided at speed to smoothly pass through the signalized intersection. The present invention realizes the refined coordinated control of intelligent vehicles in the upstream area of ​​the intersection under different signal light conditions, thereby greatly reducing the energy consumption and pollutant emissions of vehicles while ensuring the safety and efficiency of vehicle traffic.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle trajectory control, and in particular to a method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control. Background Art

[0002] The emergence and development of intelligent connected driving technology is one of the most revolutionary advances in the field of modern transportation. The purpose of this technology is to improve vehicle autonomy and driving efficiency through real-time communication and data exchange, which is crucial to reducing road accidents, fuel consumption and road congestion. Although there are studies on traffic strategies for unsignalized intersections, it is still important to manage traffic through traffic lights for intelligent connected vehicles in the process of cities gradually transforming to smart transportation. When vehicles pass through signalized intersections, frequent bad driving behaviors such as starting and stopping, idling and galloping will lead to increased energy consumption and pollutant emissions. In this regard, how to optimize the operation trajectory of intelligent connected vehicles entering the intersection, with the goal of reducing sudden acceleration / deceleration and idling, thereby improving the traffic operation conditions at the intersection, is a key issue that needs to be solved urgently. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for smoothing trajectories of intelligent connected vehicles at signalized intersections based on formation control.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control, comprising the following steps:

[0006] Divide the upstream area of ​​the signalized intersection into a free driving area, a vehicle formation area, and a speed guidance area, and determine the length of the speed guidance area and the formation length of the intelligent connected vehicles;

[0007] According to the driving state and motion relationship of the intelligent networked vehicles from the free driving area to the vehicle formation area, the incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles;

[0008] According to the signal light status when the intelligent connected vehicle enters the speed guidance area from the vehicle formation area, the speed of the intelligent connected vehicle is guided so that it can pass the signal intersection smoothly.

[0009] Optionally, determining the length of the speed guide area comprises the following steps:

[0010] According to the intelligent driving mode following model, the characteristics of intelligent connected vehicles are analyzed. When the intelligent connected vehicles are in congestion, the vehicle acceleration tends to the congestion flow driving state. The length of the speed guidance area is calculated as:

[0011]

[0012] Among them, l g is the length of the speed guide area, max is the maximum value function, v max is the maximum speed of the intelligent connected vehicle, v min is the minimum speed of the intelligent connected vehicle, a max is the maximum acceleration of the intelligent connected vehicle, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, t d is the driver’s reaction time, d max It is the maximum deceleration of the intelligent connected vehicle.

[0013] Optionally, according to the driving state and motion relationship of the intelligent networked vehicles entering the vehicle formation area from the free driving area, an incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles, including the following steps:

[0014] Determine the driving state and motion relationship of the intelligent networked vehicle based on its speed, acceleration and longitudinal position from the signalized intersection when it enters the vehicle formation area from the free driving area;

[0015] According to the driving state and motion relationship of the intelligent connected vehicle, a first-order linear differential equation of the longitudinal velocity and acceleration of the vehicle is established based on the longitudinal dynamics model of the vehicle;

[0016] According to the first-order linear differential equation of vehicle longitudinal velocity and acceleration, an incremental PID algorithm is constructed with the incremental calculation of the integral term to control the distance and speed between intelligent connected vehicles.

[0017] As an option, the control model of the incremental PID algorithm is:

[0018] ω(k)=K p Δerror(k)+K i Δerror(k)+K d [Δerror(k)-Δerror(k-1)]

[0019] Among them, ω(k) is the change of position error, K p is the proportional coefficient, Δerror(k) and Δerror(k-1) are the k-th and k-1-th controller errors respectively, K i is the integration coefficient, K d is the differential coefficient.

[0020] Optionally, according to the state of the signal light when the intelligent networked vehicle enters the speed guidance area from the vehicle formation area, the speed of the intelligent networked vehicle is guided to smoothly pass through the signal intersection, including the following steps:

[0021] According to the current position, current speed, current time and signal light status of the head vehicle in the target vehicle queue when it arrives at the starting point of the speed guidance area, it is judged whether the target vehicle queue can pass through the signal intersection at a uniform speed at the current speed; if so, the intelligent networked vehicle is controlled to maintain the original speed and pass through the signal intersection at a uniform speed; otherwise, the next step is performed;

[0022] Determine whether the target vehicle queue can accelerate through the signal intersection in the current traffic light cycle; if so, control the intelligent connected vehicle to accelerate through the signal intersection without exceeding the road speed limit; otherwise, control the speed of the intelligent connected vehicle to perform quadratic function smooth deceleration so that the vehicle can pass through the signal intersection without stopping.

[0023] Optionally, the judgment condition for judging whether the target vehicle queue can pass the signal intersection at a uniform speed at the current speed is:

[0024]

[0025] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0026] Optionally, the judgment condition for judging whether the target vehicle queue can accelerate through the signal intersection in the current signal light cycle is:

[0027]

[0028] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, t a is the time it takes for the intelligent connected vehicle to accelerate to the road speed limit, a (m,n)is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, v limit is the maximum speed limit of the intelligent connected vehicle, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0029] Optionally, controlling the intelligent networked vehicle to accelerate through a signalized intersection without exceeding the road speed limit is specifically as follows:

[0030] The target speed of the leading vehicle in the target vehicle queue is controlled to accelerate to the road speed limit, and the acceleration of the following vehicles in the target vehicle queue is controlled to be:

[0031]

[0032]

[0033] in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, and δ is the acceleration index.

[0034] As an option, the speed of the intelligent networked vehicle is controlled to perform quadratic function smooth deceleration so that the vehicle passes through the signalized intersection without stopping, and the following conditions are met:

[0035]

[0036] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, a (m,n) is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0037] Optionally, controlling the speed of the intelligent networked vehicle to perform quadratic function smooth deceleration is specifically as follows:

[0038] The speed of the first vehicle in the target vehicle queue is controlled as follows:

[0039] v advice =v (m,n) (t)+μ(v target -v (m,n) (t))×(t-t0) 2

[0040]

[0041] Among them, v advice is the speed of the first vehicle in the target vehicle queue, v (m,n) (t) is the speed of the first vehicle in the target vehicle queue at time t, μ is the speed coefficient, v target is the target speed of the first vehicle in the target vehicle queue, t0 is the initial time when the first vehicle in the target vehicle queue enters the speed guidance area, d head is the distance between the first vehicle in the target vehicle queue and the signalized intersection, C r The duration of the red light;

[0042] The vehicle acceleration of the rear vehicle in the target vehicle queue is controlled as follows:

[0043]

[0044] in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,Δv) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, and δ is the acceleration index.

[0045] The present invention has the following beneficial effects:

[0046] (1) The present invention aims at non-saturated road sections. In the formation control area, according to the characteristics of the following model, the reasonable length of the intersection guide section and the convoy is derived under different road speed limits to optimize the formation scenario. A vehicle active platooning method based on an incremental PID controller is proposed. The PID controller is optimized by parameter adjustment to achieve coordinated control of vehicle spacing and speed, improve the stability and response performance of the platoon system, and realize rapid vehicle formation under different traffic demand conditions, providing theoretical support for trajectory optimization control in the speed guidance area.

[0047] (2) The present invention establishes a trajectory smoothing strategy for intelligent connected vehicles based on platoon control, and guides the speed of vehicles with heterogeneous power on the guided section according to the traffic light conditions to optimize the traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a trajectory smoothing method for intelligent connected vehicles at a signalized intersection based on platoon control;

[0049] Figure 2 It is a schematic diagram of vehicle formation and speed guidance;

[0050] Figure 3 This is a schematic diagram of a vehicle without a guide passing at a uniform speed;

[0051] Figure 4 To accelerate and guide the vehicle through the schematic diagram;

[0052] Figure 5 A schematic diagram for guiding vehicles to pass through in order to reduce speed;

[0053] Figure 6 Build a schematic diagram for the simulation scenario;

[0054] Figure 7 This is the Carla simulation interface diagram;

[0055] Figure 8 This is a schematic diagram of the situation where speed guidance is not performed;

[0056] Fig. 9 This is a schematic diagram of the passing situation using the speed smoothing strategy;

[0057] Fig.10 This is a comparison chart of traffic efficiency and control effect.

[0058] Fig.11 A three-dimensional diagram for comparing power consumption;

[0059] Fig.12 A 3D chart comparing fuel consumption. DETAILED DESCRIPTION

[0060] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0061] The traffic scenarios that the existing solutions focus on are highways, urban expressways, and saturated urban signalized intersections. These sections have large traffic volumes, and vehicles are more likely to form stable formations based on arrival conditions. However, there is insufficient discussion on research strategies for vehicle queues at unsaturated urban intersections. In addition, most of the existing solutions discuss the CAV trajectory smoothing energy consumption evaluation models based on a single power source, which is not applicable to today's real road conditions. In this regard, the present invention provides a trajectory smoothing method for intelligent networked vehicles at signalized intersections. Based on the analysis of the characteristics of the intelligent driving mode following model of vehicles with different power types, the vehicle active formation method is designed based on the incremental proportional-integral-differential (PID) control method for the intelligent networked vehicles in the upstream formation area to form a vehicle formation. In the area near the signalized intersection, a speed balancing strategy is designed according to different signal light states to provide a speed guidance method for the power heterogeneous vehicle fleet entering the area, thereby obtaining a complete set of trajectory control strategies for intelligent networked vehicles from formation to speed smoothing through the signalized intersection.

[0062] The present invention first analyzes the characteristics of intelligent connected vehicles and the driving mechanism of platooning based on the Intelligent Driver Model (IDM) following model, designs a vehicle active platooning technology based on an incremental proportional-integral-derivative (PID) controller, and optimizes the spacing and speed control between vehicles in the upstream platooning area by accurately adjusting the parameters of the PID controller to improve the stability and response speed of the platooning system. Then, in the area close to the intersection, according to the different signal light states, a corresponding speed guidance method is provided for the power heterogeneous platoons entering the area, so that the platoons can pass through the intersection smoothly.

[0063] like Figure 1 As shown, an embodiment of the present invention provides a method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control, comprising the following steps S1 to S3:

[0064] S1. Divide the upstream area of ​​the signalized intersection into a free driving area, a vehicle formation area, and a speed guidance area, and determine the length of the speed guidance area and the formation length of the intelligent connected vehicles;

[0065] In an optional embodiment of the present invention, the upstream area of ​​the intersection is first divided into three main areas: a free driving area, a vehicle formation area, and a speed guidance area. Figure 2 as shown to achieve a comprehensive traffic management strategy.

[0066] Free driving zone: In this area, the vehicle autonomously chooses the direction of travel based on its destination and performs necessary lane changes. The purpose of this stage is to allow the vehicle to naturally enter the appropriate lane and prepare to enter the subsequent management area.

[0067] Vehicle platooning area: This area introduces an active vehicle platooning technology based on an incremental proportional-integral-derivative (PID) controller. By precisely adjusting the PID controller parameters, this method aims to optimize the spacing and speed control between vehicles to improve the stability and response speed of the platooning system. This technology can quickly achieve effective vehicle platooning according to different traffic demand conditions and further optimize traffic flow.

[0068] Speed ​​Guidance Zone: In the area close to the intersection, according to the different signal light states, corresponding speed guidance methods are provided for the heterogeneous power convoys entering the area. The goal is to enable the convoy to pass the intersection smoothly by reducing the aggressive acceleration, deceleration and parking behaviors within the convoy. This strategy not only improves the traffic efficiency of the intersection, but also significantly reduces the fuel and energy consumption of vehicles.

[0069] When forming a platoon, the length of the vehicle platoon area depends on the length of the vehicle platoon and the available area, as long as the platoon can be completed. Here we only explain in detail the optimal length of the speed guidance area. The determination of the shortest control range of the speed guidance area usually allows the vehicle to have enough time to adjust the speed at any speed, and the longest control length is that the vehicle can leave the intersection within one signal cycle. According to the IDM characteristics, under different road speed limits, the reasonable length of the intersection guide section and the platoon are derived to optimize the formation scenario. According to the IDM characteristics, when vehicles are congested, the driving state is close to the congested flow state. At this time, v / v0 in IDM tends to 0, and the vehicle acceleration tends to the congested flow driving state. Upper limit of boot area length control g It is expressed as:

[0070]

[0071] Among them, l g is the length of the speed guide area, max is the maximum value function, v max is the maximum speed of the intelligent connected vehicle, v min is the minimum speed of the intelligent connected vehicle, a max is the maximum acceleration of the intelligent connected vehicle, s *(v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, t d is the driver’s reaction time, d max It is the maximum deceleration of the intelligent connected vehicle.

[0072] After determining the length of the speed guidance area, it is necessary to determine the length of the intelligent connected vehicle platoon. Generally speaking, the number of vehicles in a platoon ranges from: Where V in is the speed of the leading vehicle when it reaches the starting point of the speed guidance area, G is the effective green light duration in the phase cycle, h d For safe headway.

[0073] S2. Based on the driving state and motion relationship of the intelligent networked vehicles entering the vehicle formation area from the free driving area, the incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles;

[0074] In an optional embodiment of the present invention, this embodiment uses an incremental PID algorithm to control the spacing and speed between intelligent networked vehicles according to the driving state and motion relationship of the intelligent networked vehicles entering the vehicle formation area from the free driving area, including the following steps:

[0075] Determine the driving state and motion relationship of the intelligent networked vehicle based on its speed, acceleration and longitudinal position from the signalized intersection when it enters the vehicle formation area from the free driving area;

[0076] According to the driving state and motion relationship of the intelligent connected vehicle, a first-order linear differential equation of the longitudinal velocity and acceleration of the vehicle is established based on the longitudinal dynamics model of the vehicle;

[0077] According to the first-order linear differential equation of vehicle longitudinal velocity and acceleration, an incremental PID algorithm is constructed with the incremental calculation of the integral term to control the distance and speed between intelligent connected vehicles.

[0078] Exemplarily, this embodiment performs active platooning operations on the intelligent connected vehicles that have arrived at the platooning area. First, the state and motion relationship of the vehicles are defined, which can be expressed as:

[0079] S i (t) = [x i (t),v i (t),a i (t)]

[0080] Among them, i is the number of the arriving vehicle, S i (t) is the state variable of the i-th vehicle at time t, x i(t), v i (t) and a i (t) are the longitudinal position of the vehicle at time t from the signal intersection, the vehicle's own speed, and acceleration. The relationship between the three is:

[0081]

[0082] This embodiment does not consider the lateral movement of the vehicle. Therefore, the first-order inertia equation of the longitudinal velocity and acceleration of the vehicle is established according to the longitudinal dynamic model of the vehicle, which is used to characterize that the change of the velocity of the object under the action of external force has a certain "lag" or inertia effect, which is expressed as:

[0083]

[0084] Where a(t) is the acceleration of the vehicle at time t, Τ is a time constant representing the time required to reach a new stable state; v(t) is the speed of the vehicle at time t, v f is the expected speed under the current input, and u(t) is the external input acting on the object at time t (such as the driver's acceleration control).

[0085] The first-order inertia equation can be further expressed as the following first-order linear differential equation:

[0086]

[0087] Among them, K is the gain coefficient, which characterizes the strength of the influence of external input on speed; u(t) is the acceleration control value of the intelligent connected vehicle at time t, T is the time constant, and v(t) is the speed of the intelligent connected vehicle at time t.

[0088] Based on the vehicle motion state, this embodiment designs an incremental PID algorithm to control the spacing and speed between vehicles to improve the stability and response speed of the formation system. PID control is a classic control method. Its name comes from the three control parameters in the controller: proportional (P), integral (I) and differential (D). The basic idea is to convert the error signal into a control signal by measuring the error between the system output and the expected value, thereby adjusting the system input and finding a balance between fast response, steady-state accuracy and stability. It is suitable for the control and regulation of various systems, such as pressure control, position control, etc. The design of the PID controller needs to be debugged and optimized according to the characteristics and requirements of the specific system to achieve the best control effect. To make the system output reach the expected value, the general form of the PID control algorithm is:

[0089] e(t)=r(t)-c(t)

[0090]

[0091] Among them, e(t) is the system error, r(t) is the given value, c(t) is the control quantity, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, and the output of the PID controller is μ(t).

[0092] Based on the traditional PID control algorithm, this embodiment further calculates the increment of the integral term instead of directly calculating the sum of the integral terms, thereby avoiding the situation where a large change occurs in the integral link. The traditional PID algorithm requires frequently changing control quantities in the velocity-acceleration first-order inertial system, while the incremental PID control can effectively avoid the integral saturation problem. Once K is determined, p , K i , K d , the control increment can be calculated by the formula by simply using the deviation of the three previous and subsequent measurement values. The obtained control quantity ω(k) corresponds to the change in the recent position error, not the actual position error, so there is no error accumulation. This is conducive to obtaining better control effects through weighted processing, and when the system fails, the incremental PID will not seriously affect the system operation.

[0093] The control model of the incremental PID algorithm is constructed as follows:

[0094] ω(k)=K p Δerror(k)+K i error(k)+K d [Δerror(k)-Δerror(k-1)]

[0095] Among them, ω(k) is the change of position error, K p is the proportional coefficient, Δerror(k) and Δerror(k-1) are the k-th and k-1-th controller errors respectively, K i is the integration coefficient, K d is the differential coefficient.

[0096] S3. According to the signal light status when the intelligent networked vehicle enters the speed guidance area from the vehicle formation area, the speed of the intelligent networked vehicle is guided so that it can pass the signal intersection smoothly.

[0097] In an optional embodiment of the present invention, after obtaining the vehicle formation, this embodiment constructs the trajectory smoothing target of the present invention for the non-saturated traffic signal intersection scenario, mainly considering the following aspects: (1) making the networked vehicles pass through the intersection without stopping as much as possible after learning the intersection signal; (2) reducing the speed fluctuation to make the speed change smoother. The formation vehicles are guided to pass through the urban road traffic signal intersection in a safe, comfortable and low-energy manner.

[0098] With regard to the specific vehicle speed guidance method, this embodiment will guide the vehicle to accelerate, decelerate or pass at a constant speed without guidance according to the remaining time of the current light color of the signal intersection. The specific judgment conditions are as follows:

[0099] Uniform speed passage without guidance: Under uniform speed guidance, that is, when the head vehicle in the target vehicle queue reaches the starting point of the speed guidance area, if the current phase is green and there is sufficient remaining time, or the current phase is red but the remaining time of the red light is small, it will not affect the passage of vehicles, and there are no vehicles downstream or there are vehicles but they will not affect the driving of the target vehicle queue, the vehicle can maintain the original speed and pass through the signal intersection at a uniform speed. The judgment conditions for judging whether the target vehicle queue can pass through the signal intersection at a uniform speed at the current speed are:

[0100]

[0101] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0102] like Figure 3 As shown in the figure, when the convoy arriving at the speed guidance area reaches the intersection stop line, there is sufficient green light time to pass the signal intersection, and the vehicle does not need to perform any operation at this time. The condition for judgment at this time is that when the rear vehicle of the convoy reaches the intersection stop line, the signal light is still green. This judgment is aimed at ensuring the continuity and safety of the convoy's driving, effectively managing vehicle flow, and reducing unnecessary acceleration and deceleration operations to achieve higher fuel economy.

[0103] Acceleration guidance: In the case of acceleration guidance, when the first vehicle in the target vehicle queue arrives at the starting point of the speed guidance area, if the current phase is green but the remaining time is short, the vehicle will stop at the intersection while maintaining the original speed, but can accelerate through without exceeding the road speed limit. In order to improve the traffic efficiency of the intersection, it is recommended to accelerate the target speed of the first vehicle to the road speed limit v in this signal cycle. max The conditions for judging whether the target vehicle queue can accelerate through the signal intersection in the current signal light cycle are:

[0104]

[0105] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, t a is the time it takes for the intelligent connected vehicle to accelerate to the road speed limit, a (m,n) is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, v limit is the maximum speed limit of the intelligent connected vehicle, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0106] like Figure 4 As shown in the figure, when the convoy arriving at the speed guidance area reaches the stop line of the intersection, the green light time is insufficient or the signal light has turned red. However, the vehicles can still accelerate through the signalized intersection within the speed guidance section. At this time, it should be noted that the vehicle should not exceed the road speed limit after acceleration, and when the rear vehicle of the convoy reaches the stop line of the intersection, the signal light is still green. At this time, for the leading vehicle, its target speed in the guidance area is set to v (m,n) =v max , the following vehicle acceleration a (m,n) The guidance of (t) is as follows:

[0107]

[0108] Combined with the vehicle acceleration performance constraints, the optimal acceleration of the following vehicle is:

[0109]

[0110] in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, and δ is the acceleration index.

[0111] Deceleration guidance: In the case of deceleration guidance, when the head vehicle in the target vehicle queue arrives at the starting point of the speed guidance area, if the current phase is green but the remaining time is very short, or the red light has just turned on and there is sufficient remaining time, the vehicle cannot accelerate through under the road speed limit. In order to improve the traffic efficiency of the intersection and reduce the waiting time, it is recommended to decelerate the vehicle speed with a quadratic function in this signal cycle so that the vehicle can pass the current intersection without stopping. At this time, the arrival of the intelligent connected vehicle meets the following conditions:

[0112]

[0113] Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, a (m,n) is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

[0114] like Figure 5 As shown in the figure, when the convoy arrives at the stop line of the signalized intersection, there is plenty of time left on the red light. Since the vehicles are restricted by the road speed limit and cannot accelerate through, the convoy can only stop and wait for the remaining time on the red light. In this case, the intelligent connected vehicles can obtain the intersection signal phase information in advance, thereby slowing down in advance and achieving the goal of passing the intersection without stopping.

[0115] At this time, the speed of the leading vehicle needs to be adjusted according to the distance to the stop line. Then, the difference between the current time, vehicle speed and target speed is calculated, and the speed change is set using a quadratic curve to smoothly adjust the speed, as follows:

[0116] v advice =v (m,n) (t)+μ(v target -v (m,n) (t))×(t-t0) 2

[0117] At this time, when the vehicle can no longer pass through this signal cycle, v target Set as the average speed that can pass when the green light turns on in the next cycle:

[0118]

[0119] Among them, v advice is the speed of the first vehicle in the target vehicle queue, v (m,n) (t) is the speed of the first vehicle in the target vehicle queue at time t, μ is the speed coefficient, v target is the target speed of the first vehicle in the target vehicle queue, t0 is the initial time when the first vehicle in the target vehicle queue enters the speed guidance area, d head is the distance between the first vehicle in the target vehicle queue and the signalized intersection, C r The duration of the red light.

[0120] Similarly, for the following vehicle, its vehicle acceleration a is (m,n) The guidance of (t) is as follows:

[0121]

[0122] At this time, the actual applied acceleration of the rear vehicle is:

[0123]

[0124] in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, and δ is the acceleration index.

[0125] The present invention adopts an incremental proportional-integral-differential control algorithm to achieve rapid vehicle formation, and adopts an intelligent networked vehicle trajectory smoothing strategy to finely coordinate the control of intelligent vehicles under different traffic light conditions in the upstream area of ​​the intersection, thereby greatly reducing vehicle energy consumption and pollutant emissions while ensuring vehicle safety and efficiency.

[0126] In order to explain the method of the present invention, an implementation case is given below to verify and analyze the model and algorithm. SUMO and Python are used for joint development to verify the intersection vehicle trajectory control scheme based on vehicle formation of the present invention. Among them, SUMO (Simulation of Urban MObility) is an open source, highly portable, multimodal traffic simulation tool suitable for large-scale transportation networks, which allows accurate modeling of traffic light control in intersections and complex traffic flows in road networks. First, the formation strategy is simulated, and the relevant experimental parameters used in the simulation are shown in Table 1:

[0127] Table 1 Simulation experiment parameters

[0128]

[0129] Building in SUMO Figure 6 The intersection scene shown. When simulating the intersection arrival scene, the west entrance road is used as the effect display. The length of the west entrance road is 450m, the lane width is 3.75m, the simulation time is 1200s, the simulation step is 1s, the lateral position of the vehicle is the center of the lane, and the road speed limit is 16.7m / s.

[0130] Based on the SUMO simulation platform, a simulation experiment was conducted on the speed guidance behavior of vehicles in the upstream section of the intersection after entering the guidance section. First, the effectiveness of the trajectory smoothing strategy of the connected autonomous driving vehicle based on platoon control was verified through a case study. On this basis, the effect of vehicles passing through the intersection smoothly without control and with the proposed trajectory smoothing strategy under different traffic demand levels was compared, and relevant data (waiting time, energy consumption) were analyzed. At the same time, Carla was used for simulation rendering to obtain the visualization effect of the control strategy, such as Figure 7 shown.

[0131] When applied to specific road scenarios, in the case of a non-saturated road intersection, when vehicles enter evenly from the upstream of the road, the case shows two situations when the traffic demand is 500veh / h / ln: one is that no control is applied at all in the speed guidance area, and the other is that the vehicle trajectory smoothing strategy used in the study is applied.

[0132] Figure 8 It shows that when the speed trajectory smoothing strategy is not adopted, the vehicle fails to utilize its network function and only stops and drives under the guidance of the signal light, which does not achieve the expected connectivity and autonomous decision-making effect. On the contrary, after applying the trajectory smoothing strategy considering platoon control, such as Fig. 9 As shown in the figure, the vehicle adjusts its speed in advance according to the arrival situation and passes through the signalized intersection without stopping. This strategy significantly improves the intelligence and orderliness of vehicle driving and increases overall operating efficiency.

[0133] In the fixed signalized intersection scenario, two vehicle control schemes were compared: one was to use only the incremental PID algorithm for active platooning and drive according to the signal; the other was to use the trajectory smoothing strategy to adjust the speed after platooning to smoothly pass the intersection. The experiment was divided into four groups of traffic volume (200, 300, 400, 500veh / h / ln) and three simulations were conducted to evaluate the total delay, waiting time and average vehicle speed. In addition, 16 groups of experiments were conducted with different traffic volumes (100, 150, 200, 250veh / h / ln) for fuel and electric vehicles to evaluate the energy consumption effect with fuel and electric energy consumption.

[0134] In terms of traffic efficiency, Fig.10 As shown in the figure, the traffic efficiency is significantly improved after adopting the connected vehicle trajectory smoothing strategy considering platoon control compared with platoon control alone, especially the reduction of waiting time at intersections. Speed ​​guidance allows vehicles to pass through intersections with almost no need to stop, while reducing delays, especially when traffic is less saturated. When traffic flow is low and the fleet is dispersed, the strategy is more proactive in vehicle control. As traffic flow increases, the number of following vehicles increases, and the effect decreases slightly. This strategy has a positive effect on improving traffic efficiency and system operation at unsaturated intersections.

[0135] In terms of energy consumption control, Fig.11 and Fig.12 As shown in the figure, after applying the formation control trajectory smoothing strategy, the energy consumption of both fuel and electric vehicles has improved, especially thanks to the kinetic energy recovery during deceleration, the energy consumption of electric vehicles has been significantly reduced. When the proportion of electric vehicles increases, the reduction in electric energy consumption is more obvious. When the number of fuel vehicles increases, more electric vehicles follow and decelerate, so that the reduction in electric energy consumption increases with the increase in the number of fuel vehicles. Since fuel vehicles have no kinetic energy recovery, the main reason for their energy saving is to adopt smooth driving, reduce emergency braking and stopping, and reduce fuel consumption.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0140] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control, characterized in that: The following steps are involved: Divide the upstream area of ​​the signalized intersection into a free driving area, a vehicle formation area, and a speed guidance area, and determine the length of the speed guidance area and the formation length of the intelligent connected vehicles; Determining the length of the velocity guide zone includes the following steps: According to the intelligent driving mode following model, the characteristics of intelligent connected vehicles are analyzed. When the intelligent connected vehicles are in congestion, the vehicle acceleration tends to the congestion flow driving state. The length of the speed guidance area is calculated as: Among them, l g is the length of the speed guide area, max is the maximum value function, v max is the maximum speed of the intelligent connected vehicle, v min is the minimum speed of the intelligent connected vehicle, a max is the maximum acceleration of the intelligent connected vehicle, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, t d is the driver’s reaction time, d max is the maximum deceleration of the intelligent connected vehicle; According to the driving state and motion relationship of the intelligent networked vehicles from the free driving area to the vehicle formation area, the incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles; According to the signal light status when the intelligent networked vehicle enters the speed guidance area from the vehicle formation area, the speed of the intelligent networked vehicle is guided to smoothly pass through the signal intersection, including the following steps: According to the current position, current speed, current time and signal light status of the head vehicle in the target vehicle queue when it arrives at the starting point of the speed guidance area, it is judged whether the target vehicle queue can pass the signal intersection at a uniform speed at the current speed; if so, the intelligent networked vehicle is controlled to maintain the original speed and pass the signal intersection at a uniform speed; otherwise, the next step is performed; the judgment condition for judging whether the target vehicle queue can pass the signal intersection at a uniform speed at the current speed is: Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) The time it takes for the last vehicle in the target vehicle queue to pass through the signalized intersection; Determine whether the target vehicle queue can accelerate through the signal intersection in the current traffic light cycle; if so, control the intelligent connected vehicle to accelerate through the signal intersection without exceeding the road speed limit; otherwise, control the speed of the intelligent connected vehicle to perform quadratic function smooth deceleration so that the vehicle can pass through the signal intersection without stopping.

2. According to the method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control in claim 1, it is characterized in that: According to the driving state and motion relationship of the intelligent networked vehicles entering the vehicle formation area from the free driving area, the incremental PID algorithm is used to control the spacing and speed between the intelligent networked vehicles, including the following steps: Determine the driving state and motion relationship of the intelligent networked vehicle based on its speed, acceleration and longitudinal position from the signalized intersection when it enters the vehicle formation area from the free driving area; According to the driving state and motion relationship of the intelligent connected vehicle, a first-order linear differential equation of the longitudinal velocity and acceleration of the vehicle is established based on the longitudinal dynamics model of the vehicle; According to the first-order linear differential equation of vehicle longitudinal velocity and acceleration, an incremental PID algorithm is constructed with the incremental calculation of the integral term to control the distance and speed between intelligent connected vehicles.

3. The method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control according to claim 2, characterized in that: The control model of the incremental PID algorithm is: ω(k)=K p Δerror(k)+K i Δerror(k)+K d [Δerror(k)-Δerror(k-1)] Among them, ω(k) is the change of position error, K p is the proportional coefficient, Δerror(k) and Δerror(k-1) are the k-th and k-1-th controller errors respectively, K i is the integration coefficient, K d is the differential coefficient.

4. The method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control according to claim 1, characterized in that: The conditions for judging whether the target vehicle queue can accelerate through the signal intersection in the current signal light cycle are: Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, t a is the time it takes for the intelligent connected vehicle to accelerate to the road speed limit, a (m,n) is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, v limit is the maximum speed limit of the intelligent connected vehicle, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

5. The method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control according to claim 1, characterized in that: Controlling intelligent connected vehicles to accelerate through signalized intersections without exceeding the road speed limit is as follows: The target speed of the leading vehicle in the target vehicle queue is controlled to accelerate to the road speed limit, and the acceleration of the following vehicles in the target vehicle queue is controlled to be: in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, and δ is the acceleration index.

6. The method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control according to claim 1, characterized in that: Control the speed of the intelligent networked vehicle to perform quadratic function smooth deceleration so that the vehicle can pass through the signalized intersection without stopping, and meet the following conditions: Among them, l g is the length of the velocity guide area, v (m,n) is the speed of the first vehicle in the target vehicle queue to the starting point of the speed guidance area, m is the fleet number, n is the vehicle number, t g is the green light time of the current signal cycle, t r is the red light time of the current signal cycle, a (m,n) is the acceleration of the intelligent connected vehicle in the target vehicle queue, d safe is the safe distance between intelligent connected vehicles, l c is the length of the intelligent networked vehicle, D is the number of intelligent networked vehicles in the target vehicle queue, t (m,n) It is the time when the last vehicle in the target vehicle queue passes through the signalized intersection.

7. The method for smoothing the trajectory of intelligent connected vehicles at a signalized intersection based on formation control according to claim 1, characterized in that: Controlling the speed of the intelligent connected vehicle to perform quadratic function smooth deceleration is specifically as follows: The speed of the first vehicle in the target vehicle queue is controlled as follows: v advice =v (m,n) (t)+μ(v target -v (m,n) (t))×(t-t0) 2 Among them, v advice is the speed of the first vehicle in the target vehicle queue, v (m,n) (t) is the speed of the first vehicle in the target vehicle queue at time t, μ is the speed coefficient, v target is the target speed of the first vehicle in the target vehicle queue, t0 is the initial time when the first vehicle in the target vehicle queue enters the speed guidance area, d head is the distance between the first vehicle in the target vehicle queue and the signalized intersection, C r The duration of the red light; The vehicle acceleration of the rear vehicle in the target vehicle queue is controlled as follows: in, is the acceleration of the intelligent connected vehicle in the target vehicle queue at time t, a max is the maximum acceleration of the intelligent connected vehicle, V (m,n) (t) is the speed of the intelligent connected vehicle in the target vehicle queue at time t, s * (v,v0) is the expected following distance, s is the distance between the current intelligent connected vehicle and the previous intelligent connected vehicle, v is the current speed of the intelligent connected vehicle, v0 is the expected speed of the intelligent connected vehicle, δ is the acceleration index, d max It is the maximum deceleration of the intelligent connected vehicle.

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