An automatic driving special lane vehicle fleet control method and system

Abstract

The application relates to a kind of automatic driving special lane vehicle fleet control method and system.The method first shoots entrance ramp traffic flow in real time, when detecting crossing vehicle, establish the relationship between its position and time, obtain and calibrate the time-space window of automatic driving special lane being crossed by vehicle;Then calculate the longitudinal trajectory of automatic driving car driving in automatic driving special lane, the time-space part of the intersection of the time-space window of automatic driving special lane being crossed by vehicle as control range limit;Solve the trajectory of each automatic driving vehicle in control range limit, thereby obtaining trajectory control instruction;Finally, trajectory control instruction is conveyed to automatic driving vehicle to execute, then, a new round of calculation is carried out and state is updated.Compared with prior art, the application has the advantages of realizing effective control of vehicle fleet in automatic driving special lane when all vehicles on ramp are manually driven, improving efficiency, increasing universality and safety, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving vehicle motion control, and in particular to a method and system for controlling a vehicle fleet in an autonomous driving dedicated lane. Background Art

[0002] Dedicated lanes for autonomous driving on highways can separate autonomous vehicles from regular human-driven vehicles, reducing interference from manual drivers and improving safety and efficiency. Designating outer lanes on highways as dedicated lanes is feasible in practical applications, such as the dedicated lanes for autonomous trucks on the Donghai Bridge. However, it is inevitable that at highway entrances and exits, manual drivers need to cross the dedicated lanes to enter and exit mainline traffic, which inevitably interferes with the normal flow of autonomous vehicles.

[0003] Existing autonomous vehicle following technologies, such as Cooperative Adaptive Cruise Control (CACC), can enable front-and-rear following of autonomous vehicles in a platoon based on data exchange between multiple vehicles. However, this information is limited to the autonomous vehicles themselves. When a manually driven vehicle crosses a platoon, CACC cannot respond effectively and promptly. Only the first vehicle in the platoon detects the crossing of a human driver through its own sensors, and then takes emergency deceleration measures, ultimately stopping to yield, significantly reducing traffic efficiency. The root cause of this problem lies in its inability to detect human-driven vehicles merging or crossing beyond visual range, preventing early response.

[0004] Existing autonomous vehicle merging control methods, such as those in patents CN109598950A, CN110930697A, and CN115273501A, mostly address fully autonomous driving scenarios. However, the reality is that, due to cost and market development, autonomous driving will likely coexist with manual driving for a considerable period of time. Patents CN114999152A and CN116386385A consider the integration of manual driving, but the solutions disclosed in these patents require coordination between the intelligent vehicle, the guide vehicle, and the auxiliary vehicle. Control is impossible if there are no intelligent vehicles on the ramp. Furthermore, existing technologies only address merging into the main line and fail to consider the control of autonomous vehicles when manually driven vehicles cross dedicated autonomous vehicle lanes. If existing merging technologies are directly applied to crossing scenarios, the distance between autonomous vehicles will increase, reducing subsequent driving efficiency. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method and system for controlling a fleet in an autonomous driving lane.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] According to one aspect of the present invention, a method for controlling a fleet in an autonomous driving lane is provided, the method comprising the following steps:

[0008] S1. Real-time video capture of on-ramp traffic. When a crossing vehicle is detected, the position, velocity, and acceleration of the crossing vehicle are calculated, and the relationship between the crossing vehicle's position and time is established. This allows the spatial and temporal window in which the autonomous driving lane is occupied by the crossing vehicle to be determined and calibrated.

[0009] S2. Calculate the longitudinal trajectory of the autonomous driving vehicle in the dedicated autonomous driving lane, and use the spatiotemporal portion of the trajectory that intersects the spatiotemporal window of the dedicated autonomous driving lane occupied by the crossing vehicle as the control range boundary.

[0010] S3. Starting with the autonomous driving vehicle closest to the crossing point, calculate the trajectory of each autonomous driving vehicle within the control range in order from far to near, and solve the optimal acceleration value, velocity value, and position value of the corresponding autonomous driving vehicle at each time step, thereby obtaining the trajectory control command;

[0011] S4: Communicate the trajectory control instructions to all autonomous driving vehicles involved in trajectory adjustment for execution. After that, return to step S1 to perform a new round of calculations and update the status.

[0012] As a preferred technical solution, the specific steps of calculating the position, velocity and acceleration data of the crossing vehicle and establishing the position and time relationship of the crossing vehicle in S1 are:

[0013] S11. When a crossing vehicle enters the monitoring range, its instantaneous position, instantaneous speed, and instantaneous acceleration are monitored;

[0014] S12, calculating the time and distance for the crossing vehicle to accelerate to the maximum speed limit, and establishing a relationship between the position of the crossing vehicle and the time;

[0015] S13. Execute S11 and S12 once for the crossing vehicle at a set interval, and update the estimated time of arrival of the vehicle at the crossing point until its instantaneous position exceeds the crossing point. At the end of the monitoring period, the vehicle is removed from the monitoring range.

[0016] As a preferred technical solution, the specific formula for the relationship between the position and time of the crossing vehicle is:

[0017]

[0018]

[0019]

[0020]

[0021] Where t′ is the time it takes for the crossing vehicle to accelerate to the maximum speed limit, v M is the maximum speed limit, v mt is the instantaneous speed of the crossing vehicle, a mt is the instantaneous acceleration of the crossing vehicle, s′ is the distance between the crossing vehicle and the monitoring point when the crossing vehicle accelerates to the maximum speed limit, that is, the relative position of the crossing vehicle. is the estimated time when the crossing vehicle arrives at the crossing point, L R is the distance between the crossing point and the detection point, and t is the current time.

[0022] As a preferred technical solution, the specific method of marking the time-space window in S1 is to draw a time-space diagram in the form of a rectangle. In the time-space diagram, the top edge of the rectangle represents the downstream safety distance when a crossing vehicle occupies the autonomous driving lane, and the bottom edge represents the upstream safety distance when a crossing vehicle occupies the autonomous driving lane. The left side represents the earliest safe time when a crossing vehicle occupies the autonomous driving lane, and the right side represents the latest safe time when a crossing vehicle occupies the autonomous driving lane. The specific formula is:

[0023] L1=L+ε

[0024] L2=L-ε

[0025]

[0026]

[0027] Where L1 is the downstream safety distance, L2 is the upstream safety distance, t1 is the earliest safety time, t2 is the latest safety time, L is the coordinate of the crossing point, and ε is the safety distance between the crossing vehicle and the preceding or following vehicle in the autonomous driving lane when the crossing vehicle passes through the crossing point. is the moment when the center point of the crossing vehicle passes through the crossing point, and τ is the safe time interval between the crossing vehicle and the preceding or following vehicle in the autonomous driving lane when the crossing vehicle passes through the crossing point.

[0028] As a preferred technical solution, the intersection of the space-time graph and the longitudinal trajectory of the autonomous driving vehicle in S2 is a collision, and the boundary of the intersection is the control range limit.

[0029] As a preferred technical solution, the calculation of the longitudinal trajectory of the autonomous driving vehicle in the autonomous driving lane in S2 is specifically to establish the vehicle position and velocity transfer equation between two adjacent unit time steps, and to establish the relationship between the autonomous driving vehicle position and time. The specific formula is:

[0030]

[0031] v i+1 =v i +a i ·Δt

[0032]

[0033] Where t0 is the time of the initial state when calculating the vehicle trajectory; t is the position coordinate s to be calculated t moment; s0 is the coordinate of the initial state when calculating the vehicle trajectory; s t is the displacement of the vehicle at time t; v0 is the initial velocity when calculating the vehicle trajectory; v t is the speed of the vehicle at time t; a0 is the acceleration of the initial state when calculating the vehicle trajectory; a t is the acceleration of the vehicle at time t; Δt is the unit time step, i is the sequence number of the time step, that is, the initial moment of the i-th time step, is the floor operator, and % is the remainder operator.

[0034] As a preferred technical solution, the trajectory of each autonomous vehicle in S3 is calculated by solving the optimal trajectory of the autonomous vehicle based on the constraints. The specific formula is:

[0035]

[0036] Where n is the number of the autonomous driving vehicle, a i，n is the acceleration of vehicle n at the initial moment of the i-th time step, t is the current moment, Δt is the unit time step, t0 is the moment of the initial state when calculating the vehicle trajectory, and i is the sequence number of the time step, that is, the initial moment of the i-th time step.

[0037] As a preferred technical solution, the constraints for calculating the trajectory of each autonomous driving vehicle in S3 include the first to seventh constraints, and their specific formulas are:

[0038]

[0039] Wherein, the first constraint describes the coordinate transfer equation of any vehicle n within the control range, the second constraint describes the speed transfer equation of n, the third and fourth constraints restrict the trajectory from intersecting any rectangle, and the fifth constraint restricts the speed to not exceed the maximum speed limit v max , and is not a negative number, that is, no reversing occurs. The sixth constraint limits the safe distance μ between the two vehicles in succession, that is, there is no intersection between the two autonomous driving vehicles. The seventh constraint limits the controllable acceleration a of vehicle n in step size i. i,n Can only be selected from a finite number of values, such as a + Indicates the acceleration value specified when the vehicle accelerates, a- deceleration value when the vehicle is decelerating, 0 indicates that the vehicle is not decelerating; wherein, s i,n is the coordinate of the vehicle n at the initial moment of the i-th time interval, s i,n is the speed of the vehicle n at the initial moment of the i-th time interval, s t1 is the coordinate of the autonomous vehicle at the t1 moment, s t2 is the coordinate of the autonomous vehicle at the t2 moment, l is the length of the autonomous vehicle, L is the coordinate of the crossing point, and ε is half the distance between the front and rear autonomous vehicles in the autonomous vehicle lane when the crossing vehicle passes through the crossing point, v max is the maximum speed limit, a + is the acceleration value specified when the vehicle is accelerating, a - is the deceleration value specified when the vehicle is decelerating, 0 indicates that the vehicle is not decelerating, and μ is the safety distance between vehicles.

[0040] As a preferred technical solution, when the autonomous vehicle trajectory and the crossing vehicle occupy the space-time window in S2, the intersection of the autonomous vehicle trajectory and the crossing vehicle includes the case where the vehicle is far from the downstream safety distance and the case where the vehicle is close to the upstream safety distance, and the specific formula is:

[0041]

[0042]

[0043] wherein, s t1 is the coordinate of the autonomous vehicle at the t1 moment, s 12 is the coordinate of the autonomous vehicle at the t2 moment, l is the length of the autonomous vehicle, L is the coordinate of the crossing point, and ε is half the distance between the front and rear autonomous vehicles in the autonomous vehicle lane when the crossing vehicle passes through the crossing point.

[0044] According to another aspect of the present application, an autonomous vehicle lane vehicle fleet control system is provided, which works with an autonomous vehicle lane vehicle fleet control method as described above, and the system includes a video detector, a roadside controller, and a communicator;

[0045] The video detector uses a general road traffic monitoring camera to completely capture the ramp and acceleration lane area, real-time monitors and identifies vehicles on the ramp, uses the image processing technology configured therein to calculate the real-time position coordinates, speed, and acceleration data of the vehicle, and sends the data to the roadside controller;

[0046] The road side controller is placed in a control area range, used for receiving data of the video detector and the autonomous vehicle, establishing the relationship between the position and time of the crossing vehicle, calculating the longitudinal trajectory of the autonomous vehicle in the autonomous lane, calculating the trajectory control instruction, and delivering the trajectory control instruction to all the autonomous vehicles involved in the trajectory adjustment;

[0047] The communicator is installed in the video detector, the road side controller and the autonomous vehicle, the video detector sends the data of the ramp crossing vehicle to the road side controller through the communicator, the autonomous vehicle sends the vehicle trajectory data in the autonomous lane to the road side controller through the communicator, and the road side controller issues the trajectory control instruction to the autonomous vehicle through the communicator.

[0048] Compared with the prior art, the present application has the following beneficial effects:

[0049] 1、The present application can realize the mixed flow control without the cooperation among the autonomous vehicles on the ramp, the guide vehicles on the main line and the auxiliary vehicles (cooperative vehicles) on the main line, and can realize the effective control of the vehicle group in the autonomous lane when all the vehicles on the ramp are manually driven.

[0050] 2、The specific way of calibrating the space-time window in the present application is to draw a space-time graph in the form of a rectangle, in which the top edge of the rectangle represents the downstream safety distance when the crossing vehicle occupies the autonomous lane, the bottom edge represents the upstream safety distance when the crossing vehicle occupies the autonomous lane, the left edge represents the earliest safety time when the crossing vehicle occupies the autonomous lane, and the right edge represents the latest safety time when the crossing vehicle occupies the autonomous lane. By predicting the time of the ramp vehicle passing through the vehicle crossing point and establishing the space-time window, the acceleration of the autonomous vehicle on the main road is adjusted to avoid the space-time window. The present application is not only suitable for the ramp vehicles being manually driven, but also suitable for the ramp vehicles being autonomous, that is, the autonomous vehicle is tracked as a manually driven vehicle to predict its trajectory, which widens the application range of the method and makes the vehicle group control method universal.

[0051] 3. The present invention calculates the trajectory of each autonomous driving vehicle within the control range, starting with the autonomous driving vehicle closest to the crossing point, in order from far to near, and obtains trajectory control instructions. This increases the distance between autonomous driving vehicles when there is a crossing vehicle, and narrows it after the crossing vehicle leaves. This solves the problem of controlling autonomous driving vehicles in scenarios where manually driven vehicles cross autonomous driving lanes, and improves driving efficiency within autonomous driving lanes.

[0052] 4. The present invention calculates the trajectory of each autonomous driving vehicle by solving the optimal trajectory of the autonomous driving vehicle based on the constraints of the first to seventh constraints. The first constraint describes the coordinate transfer equation of any vehicle n within the control range, the second constraint describes the speed transfer equation of n, the third and fourth constraints restrict the trajectory from intersecting any rectangle, the fifth constraint restricts the speed to not exceed the maximum speed limit and not be negative, that is, reversing does not occur, the sixth constraint restricts the safe distance between the two consecutive vehicles, and the seventh constraint restricts the controllable acceleration of vehicle n within the step size to only a finite number of values. By using the constraints, the number of times the vehicle acceleration is adjusted is minimized, and the avoidance of crossing vehicles is smoothly achieved with a lower control cost. The goal is to minimize the sum of the absolute values ​​of the acceleration of all vehicles in the control range. While avoiding crossing vehicles, energy consumption is minimized to ensure transportation efficiency.

[0053] 5. When constructing the space-time window occupied by the crossing vehicle, the present invention introduces a time-space margin, that is, ε and τ are half the distance between the two autonomous driving vehicles in the autonomous driving lane when the ε crossing vehicle passes through the crossing point. is the time at which the center point of the crossing vehicle passes through the crossing point, and τ is the safe time interval between the two vehicles in the automated driving lane, from the time the rear of the first vehicle passes through the crossing point to the time the front of the second vehicle passes through the crossing point. By introducing these two parameters, the success rate of manually driven vehicles crossing the automated driving lane is improved, enhancing overall safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of a fleet control system in an autonomous driving dedicated lane according to the present invention;

[0055] Figure 2 This is a schematic diagram of the steps of a method for controlling a vehicle fleet in an autonomous driving dedicated lane according to the present invention;

[0056] Figure 3 This is a flow chart of the fleet control in the dedicated autonomous driving lane to deal with the crossing of manually driven vehicles in the embodiment;

[0057] Figure 4 This is a schematic diagram showing a rectangular representation of the space-time window of a crossing vehicle occupying an autonomous driving lane on a space-time graph in an embodiment;

[0058] Figure 5 Schematic diagram of the rules for determining the intersection of the spatiotemporal window between the trajectory of the autonomous driving vehicle and the time-space window of the dedicated lane occupied by the crossing vehicle in the embodiment;

[0059] Figure 6a A flowchart for solving the feasible acceleration operation in the trajectory of the autonomous driving vehicle in an embodiment;

[0060] Figure 6b This is a flow chart of solving the problem of infeasible acceleration operation in the trajectory of the autonomous driving vehicle in the embodiment;

[0061] Figure 7 Schematic diagram of the spatiotemporal window of the crossing vehicle and the avoidance trajectory of the first autonomous driving vehicle in the embodiment;

[0062] Figure 8 Schematic diagram of the trajectories of six autonomous driving vehicles avoiding five crossing vehicles in the embodiment. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0064] Example

[0065] Existing autonomous vehicle following technologies, such as Cooperative Adaptive Cruise Control (CACC), can enable platooning of autonomous vehicles following each other based on data exchange between multiple vehicles. However, this information is limited to the autonomous vehicles themselves. When a human-driven vehicle crosses a platoon, CACC cannot respond effectively and promptly. Only the first vehicle in the platoon detects the crossing of a human driver through its own sensors and then takes emergency deceleration measures, ultimately stopping to yield, significantly reducing traffic efficiency.

[0066] This embodiment constructs a convoy control system within a dedicated autonomous driving lane and employs a convoy control method to control the trajectory of a convoy within a dedicated autonomous driving lane at the entrance ramp of a highway with an outer dedicated autonomous driving lane. This allows on-ramp traffic to cross the dedicated autonomous driving lane in an orderly manner and enter the inner lane of the highway, avoiding deceleration, stopping, and congestion caused by crossing difficulties. The crossing vehicle can be either a manually driven vehicle or an autonomous vehicle.

[0067] In this embodiment, the fleet control system constructed in the autonomous driving dedicated lane includes a video detector, a roadside controller and a communicator.

[0068] In this embodiment, the video detector utilizes a standard road traffic surveillance camera capable of capturing a complete image of the ramp and acceleration lane area, detecting and identifying vehicles on the ramp in real time, and providing real-time vehicle location coordinates, velocity, and acceleration. A roadside controller, located within the control area, is capable of communicating with the autonomous vehicle and the video detector and executing the aforementioned fleet control algorithm within the autonomous driving lane on the highway. The video detector, roadside controller, and autonomous vehicle are all equipped with a communicator, which transmits on-ramp and autonomous driving lane vehicle trajectory data to the roadside controller. The roadside controller can also issue acceleration adjustment commands to the autonomous vehicle via the communicator.

[0069] In this embodiment, the method for controlling the fleet in the dedicated autonomous driving lane is as follows: Figure 2 As shown, it mainly includes the following steps:

[0070] S1. Real-time video capture of on-ramp traffic. When a crossing vehicle is detected, the position, velocity, and acceleration of the crossing vehicle are calculated, and the relationship between the crossing vehicle's position and time is established. This allows the spatial and temporal window in which the autonomous driving lane is occupied by the crossing vehicle to be determined and calibrated.

[0071] S2. Calculate the longitudinal trajectory of the autonomous driving vehicle in the dedicated autonomous driving lane, and use the spatiotemporal portion of the trajectory that intersects the spatiotemporal window of the dedicated autonomous driving lane occupied by the crossing vehicle as the control range boundary.

[0072] S3. Starting with the autonomous driving vehicle closest to the crossing point, calculate the trajectory of each autonomous driving vehicle within the control range in order from far to near, and solve the optimal acceleration value, velocity value, and position value of the corresponding autonomous driving vehicle at each time step, thereby obtaining the trajectory control command;

[0073] S4: Communicate the trajectory control instructions to all autonomous vehicles involved in trajectory adjustment. After a sampling time interval, return to step S1 to perform a new round of calculations and update the status.

[0074] In this embodiment, autonomous vehicles form a platoon within the autonomous driving lane in a coordinated adaptive cruise control mode. Each autonomous vehicle's longitudinal position coordinates, instantaneous speed, and instantaneous acceleration data along the highway are broadcast to the outside world via a communicator. This data can be directly received by the roadside controller. Manually driven vehicles entering the highway from an on-ramp must cross the autonomous driving lane and enter the inner lane. The crossing point for the manually driven vehicle is located on the centerline of the autonomous driving lane. After accelerating in the acceleration lane, the center point of the vehicle crosses the autonomous driving lane at this crossing point and enters the inner lane. The control range limit is set at a position L meters from the crossing point of the manually driven vehicle in the autonomous driving lane. When the center point of the autonomous vehicle enters the control range limit, the autonomous vehicle's trajectory data is received by the roadside controller, and the autonomous vehicle can also receive trajectory control commands issued by the roadside controller.

[0075] In this embodiment, the trajectory control command is solved by the roadside controller using the proposed convoy control method for handling manually driven vehicles crossing the autonomous driving lane of the highway, and then transmitted to the autonomous driving vehicle through the communicator with specific information, including the acceleration value, speed value, and position value of the corresponding vehicle at the specific moment.

[0076] In this embodiment, the highway merging area scene is set as follows Figure 1 As shown, the outermost lane of the road is the dedicated lane for autonomous driving. The crossing vehicles on the ramp need to cross the dedicated lane for autonomous driving and then merge into the inner lane of the main line. The control range is L = 200m. The distance from the crossing point L on the ramp is R = = 100m. A roadside controller is set up along the main line within the control range. Each autonomous vehicle is equipped with a communicator, and the video detector and each vehicle can exchange data with the roadside controller.

[0077] This embodiment adopts a method for controlling a fleet in an autonomous driving lane. The specific process of the method is as follows: Figure 3 The specific process is as follows:

[0078] S1. Real-time video capture of on-ramp traffic. When a crossing vehicle is detected, the position, velocity, and acceleration of the crossing vehicle are calculated, and the relationship between the crossing vehicle's position and time is established. This allows the spatial and temporal window in which the autonomous driving lane is occupied by the crossing vehicle to be determined and calibrated.

[0079] The specific process of S1 is as follows:

[0080] S11: Manually drive the vehicle across point L using the distance set on the ramp RThe video detector is a general road traffic monitoring camera. The camera has the functions of tracking vehicles in the field of view, measuring the distance between the center point of the vehicle and the camera setting position, and measuring the instantaneous speed and instantaneous acceleration of the vehicle.

[0081] S12: When a vehicle enters the detection range of the video detector, the vehicle is given a unique number m = 1, 2, ..., M in the order in which it enters. At time t, the instantaneous position S of the vehicle m is detected once. mt , instantaneous speed v mt , instantaneous acceleration a mt , where the instantaneous position S mt It refers to the distance from the center point of vehicle m to the location of the video detector.

[0082] S13: Predict the time when vehicle m reaches the crossing point of the manually driven vehicle

[0083] S131: Calculate the acceleration of vehicle m to the maximum speed limit v M The time t′=(v M -v mt ) / a mt , and accelerate to v M distance

[0084] S132: If s′<L R ,but If s′≥L R ,but

[0085] Where t′ is the time it takes for the crossing vehicle to accelerate to the maximum speed limit, v M is the maximum speed limit, v mt is the instantaneous speed of the crossing vehicle, a mt is the instantaneous acceleration of the crossing vehicle, s′ is the distance between the crossing vehicle and the monitoring point when the crossing vehicle accelerates to the maximum speed limit, that is, the relative position of the crossing vehicle. is the estimated time when the crossing vehicle arrives at the crossing point, L R is the distance between the crossing point and the detection point, and t is the current time.

[0086] The estimated arrival time of each vehicle crossing the entrance ramp at the crossing point obtained through the above calculation is shown in Table 1.

[0087] S14: Every Δt, execute S12 and S13 for each vehicle m within the range of the video detector, and update the estimated time when vehicle m arrives at the crossing point. Until S mt ≥L RThe vehicle m is terminated when the vehicle m is moved out of the video detector tracking range. If the vehicle m is closer to the crossing point, the predicted The more accurate it is.

[0088] Table 1: The estimated arrival time of each vehicle crossing the entrance ramp at this time

[0089]

[0090] In this embodiment, the following control parameters are set according to the actual scenario:

[0091] L=200m, Δt=1s, l=18m, v o =12m / s,v max =22m / s, ε=40m, τ=3s, a + =2m / s 2 , a - =-3m / s 2 .

[0092] S2. Calculate the longitudinal trajectory of the autonomous driving vehicle in the dedicated autonomous driving lane, and use the spatiotemporal portion of the trajectory that intersects the spatiotemporal window of the dedicated autonomous driving lane occupied by the crossing vehicle as the control range boundary.

[0093] In this embodiment, the specific process of S2 is as follows:

[0094] S21: Establish the vehicle position and velocity transfer equation between two adjacent time steps Δt:

[0095]

[0096] v i+1 =v i +a i ·Δt

[0097] S22: Export the position coordinates s of the autonomous driving vehicle at time t t The formula is as follows:

[0098]

[0099] Where t0 is the time of the initial state when calculating the vehicle trajectory; t is the position coordinate s to be calculated t moment; s0 is the coordinate of the initial state when calculating the vehicle trajectory; s t is the displacement of the vehicle at time t; v0 is the initial velocity when calculating the vehicle trajectory; v t is the speed of the vehicle at time t; a0 is the acceleration of the initial state when calculating the vehicle trajectory; a tis the acceleration of the vehicle at time t; Δt is the unit time step, i is the serial number of the time step, i.e. the initial time of the i-th time step, is the floor operator, and % is the remainder operator. The coordinate 0 is the coordinate of the control range boundary, and the coordinate of the point through which the manually driven vehicle passes is L. This formula can calculate the vehicle coordinate from any initial time t0 to any time t (t0 < t) in the state of t0.

[0100] S23: Establish a space-time graph. The space-time window in which the ramp-through vehicle occupies the exclusive lane obtained in S1 is drawn in the form of a rectangle in the space-time graph of this step, as shown in Figure 7 . Among them, the top edge of the rectangle represents the downstream safety distance when the through vehicle is occupied, the bottom edge represents the upstream safety distance when the through vehicle is occupied, the left edge represents the earliest safety time when the through vehicle is occupied, and the right edge represents the latest safety time when the through vehicle is occupied. Each time the prediction of the ramp-through vehicle trajectory is updated in S1, the space-time window rectangle in this step is updated in real time.

[0101] In this embodiment, the time when the center point of the through vehicle passes through the through point is The expression of the space-time window rectangle on the space-time graph is shown in Figure 4 . The expressions of the four edges are as follows:

[0102] S231 is the downstream safety distance: L1 = L + ε;

[0103] S232 is the upstream safety distance: L2 = L - ε;

[0104] S233 is the earliest safety time:

[0105] S234 is the latest safety time:

[0106] In the formula, ε is the safety distance between the through vehicle and the front or rear vehicle in the automatic driving exclusive lane when the through vehicle passes through the through point, is the time when the center point of the through vehicle passes through the through point, and τ is the safety time interval between the through vehicle and the front or rear vehicle in the automatic driving exclusive lane when the through vehicle passes through the through point.

[0107] Table 2: The time when each automatic driving vehicle n enters the detection range

[0108]

[0109] S24: For any vehicle, use the formula t to calculate the coordinates s t1 of the vehicle from any initial state, including position s0, speed v0, and acceleration a0, to t1, to t2, and so on.t2 , respectively judge the relationship with the above rectangular boundary. If the following relationship is satisfied, the autonomous driving vehicle trajectory does not intersect with the crossing vehicle, including the case where the vehicle is far away from the downstream safety distance and the case where the vehicle is close to the upstream safety distance. The judgment rules are as follows: Figure 5 As shown, specifically:

[0110] S241 is the case where the vehicle is farther than the downstream safety distance. The specific formula is:

[0111] S242 is the case where the vehicle is close to the upstream safety distance. The specific formula is:

[0112] S243 is the situation where the trajectory of the autonomous driving vehicle intersects with the crossing vehicle. t1 and s t2 The following relations are satisfied:

[0113] and

[0114] S25: If there are multiple vehicles crossing the on-ramp detection range, that is, if step S1 provides multiple spatiotemporal windows (rectangles) of vehicles occupying the lane, the rectangles are uniquely numbered m = 1, 2, ..., M in the order of the crossing vehicles. That is, rectangles closer to the left are considered first, and then the intersection of any vehicle trajectory within the lane with the rectangles is determined in sequence. The rectangle numbers of the rectangles that intersect with the vehicle trajectory are recorded.

[0115] S26: For autonomous vehicles in the dedicated lane that enter the control range, unique vehicle numbers n = 1, 2, ..., N are assigned in order of the vehicle that first enters the range, i.e., starting with the vehicle closest to the crossing point. Based on the determination method in S24, all rectangles described in S25 are determined to be intersecting. The vehicle numbers of the intersecting vehicles and the corresponding intersecting rectangles are recorded.

[0116] In this embodiment, Figure 7 As shown, the solid line represents the new control trajectory, the rectangle represents the space-time diagram of the crossing vehicles, and the dashed line represents the original trajectory of the first autonomous vehicle (n=1). This will obviously intersect with the space-time window of the first human-driven crossing vehicle (m=1). At this point, we must proceed to step S3 to calculate the new control trajectory of autonomous vehicle n=1.

[0117] S3. Starting with the autonomous driving vehicle closest to the crossing point, calculate the trajectory of each autonomous driving vehicle within the control range in order from far to near, and solve the optimal acceleration value, velocity value, and position value of the corresponding autonomous driving vehicle at each time step, thereby obtaining the trajectory control command;

[0118] In this embodiment, the specific process of S3 is as follows:

[0119] S31: To minimize the number of vehicle acceleration adjustments and smoothly avoid crossing vehicles at a lower control cost, the goal is to minimize the sum of the absolute acceleration values ​​of all vehicles within the control range. The optimal trajectory satisfies the following equation:

[0120]

[0121]

[0122] Among them, the first constraint describes the coordinate transfer equation of any vehicle n within the control range, the second constraint describes the speed transfer equation of n, the third and fourth constraints restrict the trajectory from intersecting any rectangle, and the fifth constraint restricts the speed to not exceed the maximum speed limit v max , and is not negative (i.e., no reversing occurs). The sixth constraint limits the safe distance μ between the two vehicles (i.e., no intersection occurs between the two autonomous driving vehicles). The seventh constraint limits the controllable acceleration a of vehicle n in step size i. i,n Can only be selected from a finite number of values, such as a + Indicates the acceleration value specified when the vehicle accelerates, a - The deceleration value specified when the vehicle decelerates. 0 means the vehicle does not accelerate or decelerate.

[0123] S32: To satisfy the above inequality constraints, a concise and feasible solution algorithm is provided.

[0124] S321: For vehicles in the dedicated lane within the control range, calculate in sequence according to n=1, 2, ..., N.

[0125] S322: Use S24 method to verify whether intersection occurs. If n intersects with the corresponding rectangle m, then immediately accelerate at the current moment, starting from the initial moment t0, the vehicle to a i，n Assign a + , recalculate v i，n and s i，n , and calculate s t1,m，n and s t2,m，n , until satisfied Except for n=1, the position relationship between n and n-1 needs to be considered. Existence k，n +μ>s k，n-1 , then the safety distance between the front and rear vehicles is insufficient, and the Give a - Slow down vehicle n. If v i，n >v max , or if all step length calculations are completed but not satisfied If the acceleration operation is not feasible, it is necessary to go to S323 for deceleration operation. Otherwise, go directly to S325. Figure 6a shown.

[0126] S323: If an intersection occurs but acceleration is not possible, deceleration control is immediately adopted at the current moment, starting from the initial moment t0, the vehicle to a i，n Assign a - , recalculate v i，n and s i，n , and calculate s t1,m,n and s t2,m，n , until satisfied Except for n=1, the position relationship between n and n-1 needs to be considered. Existence k，n +μ>s k，n-1 , then the safety distance between the front and rear vehicles is insufficient, and Give a - Slow down vehicle n. If v i+1，n ≤0, then a i，n You can no longer assign a - , should be assigned a value of 0. The flow chart of step S323 is as follows Figure 6b shown.

[0127] S324: If the vehicle implemented deceleration control in the previous round of decision-making and the intersection in S322 does not occur in the current round of decision-making, acceleration control is preferentially executed in the current round of decision-making to compensate for the speed loss caused by deceleration control. The process is the same as S322. If not, the process proceeds to S325.

[0128] S325: If there is no intersection in this round of decision-making, press to a i，n Assign a value of 0.

[0129] S326: After a time step of Δt, the position coordinates s of all vehicles n = 1, 2, ..., N are updated with the latest observed state values 0，n , speed v 0，n , acceleration a 0，n If the vehicles and windows within the control range change, n = 1, 2, ..., N is updated with the latest vehicles and corresponding state parameters. S1 is re-executed to obtain the updated m = 1, 2, ..., M and its parameters. Return to S322 to execute a new round of decision making.

[0130] In this embodiment, Figure 7 As shown, n = 1 intersects with a rectangle m = 1, but does not intersect with other rectangles m. Then, the vehicle is immediately accelerated at the current moment. Starting from the initial moment t0, the vehicle is accelerated according to to a i，n Assign a + , recalculate v i，n and s i，n , and calculate s t1,m,n and s t2,m，n , until satisfied Condition. v does not appear in the current calculation object. i，n >v max , then the acceleration operation is feasible and directly transfer to S325.

[0131] In S325, if there is a crossover in this round of decision, a i，n Assign 0. At this point, the calculation for n=1 is completed. Then perform S2 and S3 for n=2, 3, 4, 5, and 6. When executing step S322 for n=2, the acceleration operation is not feasible, so the process goes to S323 and executes S323. After S323 is executed, S324 is executed sequentially. Since the deceleration control was implemented in the current decision-making round (time t), the acceleration control is preferentially performed on n=2 in the decision-making process of the next decision-making round (time t+Δt) to compensate for the speed loss caused by the deceleration control. The process is the same as S322.

[0132] Through the above calculation, a deceleration control trajectory sequence of n=2 is obtained, and after the deceleration is completed, acceleration control is implemented to reduce the interval with n=1.

[0133] In this embodiment, the S2 and S3 operations for n=3, 4, 5, 6 are similar to the above process and can be deduced in this way. In this embodiment, the trajectory of the automatic driving vehicle n=1, 2, 3, 4, 5, 6 avoiding the crossing vehicle m=1, 2, 3, 4, 5 is as follows: Figure 8 As shown, the rectangle is the space-time window and the solid line is the trajectory of the autonomous driving vehicle.

[0134] S4: Communicate the trajectory control instructions to all autonomous vehicles involved in trajectory adjustment. After a sampling time interval, return to step S1 to perform a new round of calculations and update the status.

[0135] In this embodiment, the specific process of S4 is:

[0136] S41: The roadside controller obtains the acceleration instruction (acceleration, deceleration, no acceleration or deceleration) of each autonomous driving vehicle within the control range through the operation of step S3, and transmits the instruction to all autonomous driving vehicles involved in trajectory adjustment through the communicator.

[0137] S42: Taking the starting time of the acceleration adjustment of the autonomous vehicle as the starting point, repeating the steps of S1, S2 and S3 in a sampling time interval Δt to calculate the optimal acceleration, speed and position parameters of each time step. The calculated acceleration instruction (acceleration, deceleration, no acceleration or deceleration) is transmitted to all autonomous vehicles involved in trajectory adjustment through the communicator. The time interval between the starting time of each acceleration adjustment of each autonomous vehicle and the last adjustment time is a sampling time interval.

[0138] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for controlling a fleet of vehicles in an autonomous driving lane, characterized in that: The method comprises the following steps: S1. Real-time video capture of on-ramp traffic. When a crossing vehicle is detected, the position, velocity, and acceleration of the crossing vehicle are calculated, and the relationship between the crossing vehicle's position and time is established. This allows the spatial and temporal window in which the autonomous driving lane is occupied by the crossing vehicle to be determined and calibrated. S2. Calculate the longitudinal trajectory of the autonomous driving vehicle in the dedicated autonomous driving lane, and use the spatiotemporal portion of the trajectory that intersects the spatiotemporal window of the dedicated autonomous driving lane occupied by the crossing vehicle as the control range boundary. S3. Starting with the autonomous vehicle closest to the crossing point, in order from far to near, solve the optimal trajectory of each autonomous vehicle within the control range limit based on the constraints, and solve the optimal acceleration value, velocity value, and position value of the corresponding autonomous vehicle at each time step, thereby obtaining the trajectory control command; S4: Communicate the trajectory control instructions to all autonomous vehicles involved in trajectory adjustment for execution, then return to step S1 to perform a new round of calculations and update the status; The specific method of marking the time-space window in S1 is to draw a time-space diagram in the form of a rectangle. In the time-space diagram, the top edge of the rectangle represents the downstream safety distance when a crossing vehicle occupies the autonomous driving lane, and the bottom edge represents the upstream safety distance when a crossing vehicle occupies the autonomous driving lane. The left side represents the earliest safe time when a crossing vehicle occupies the autonomous driving lane, and the right side represents the latest safe time when a crossing vehicle occupies the autonomous driving lane. The specific formula is: Where, is the downstream safety distance, is the upstream safety distance, For the earliest safe moment, For the latest safe moment, are the coordinates of the crossing point, When a crossing vehicle passes through the crossing point, it is half the distance between the two autonomous driving vehicles in the autonomous driving lane. is the moment when the center point of the crossing vehicle passes through the crossing point, The safe time interval between a crossing vehicle and the preceding or following vehicle in the automated driving lane when the crossing vehicle passes through the crossing point; The intersection of the space-time graph and the longitudinal trajectory of the autonomous vehicle in S2 is a collision, and the boundary of the intersection is the control range limit; The calculation of the longitudinal trajectory of the autonomous driving vehicle in the autonomous driving lane in S2 is specifically to establish the vehicle position and speed transfer equation between two adjacent unit time steps, and to establish the relationship between the autonomous driving vehicle position and time. The specific formula is: Where, For the The coordinates of the vehicle at the initial moment of the step; For the The sequence number of the next step in the time step, i.e. ; For the The coordinates of the vehicle at the initial moment of the step; For the The speed of the vehicle at the initial moment of the step length; For the The vehicle's acceleration at the initial moment of the step; For the The speed of the vehicle at the initial moment of the step length; For the The speed of the vehicle at the initial moment of the step length; For the The vehicle's acceleration at the initial moment of the step; is the moment of the initial state when calculating the vehicle trajectory; Calculate the location coordinates as needed moment; is the coordinate of the initial state when calculating the vehicle trajectory; For vehicles displacement of moments; The velocity of the initial state when calculating the vehicle trajectory; vehicle the speed of the moment; is the acceleration of the initial state when calculating the vehicle trajectory; For vehicles acceleration of the moment; is the unit time step, is the sequence number of the time step, i.e. The initial moment of the time step, For The floor operation of is the remainder operator.

2. The method for controlling a fleet in an autonomous driving lane according to claim 1, characterized in that: The specific steps of calculating the position, velocity and acceleration data of the crossing vehicle and establishing the position and time relationship of the crossing vehicle in S1 are: S11. When a crossing vehicle enters the monitoring range, its instantaneous position, instantaneous speed, and instantaneous acceleration are monitored; S12, calculating the time and distance for the crossing vehicle to accelerate to the maximum speed limit, and establishing a relationship between the position of the crossing vehicle and the time; S13. Execute S11 and S12 once for the crossing vehicle at a set interval, and update the estimated time of arrival of the vehicle at the crossing point until its instantaneous position exceeds the crossing point. At the end of the monitoring period, the vehicle is removed from the monitoring range.

3. The method for controlling a fleet in an autonomous driving lane according to claim 2, characterized in that: The specific formula for the relationship between the position and time of the crossing vehicle is: Where, The time it takes for the crossing vehicle to accelerate to the maximum speed limit. is the maximum speed limit, is the instantaneous speed of the crossing vehicle, is the instantaneous acceleration of the crossing vehicle, The distance between the crossing vehicle and the monitoring point when the crossing vehicle accelerates to the maximum speed limit is the relative position of the crossing vehicle. is the estimated time when the crossing vehicle arrives at the crossing point, is the distance between the crossing point and the detection point, For the current moment.

4. The method for controlling a fleet in an autonomous driving lane according to claim 1, wherein: In S3, the optimal trajectory of the autonomous vehicle is solved by the constraints. In order to adjust the vehicle acceleration as few times as possible and smoothly avoid crossing vehicles with a lower control cost, the goal is to minimize the sum of the absolute values ​​of the acceleration of all vehicles within the control range. The optimal trajectory satisfies the following formula: The constraints for calculating the trajectory of each autonomous vehicle in S3 include the first to seventh constraints, and their specific formulas are: Where, is the total number of vehicles; For the The coordinates of vehicle n at the initial moment of the time step; For the The speed of vehicle n at the initial moment of a time step; The number of the space-time window of the marked autonomous driving lane occupied by the crossing vehicle, that is, the rectangle number; is the total number of space-time windows occupied by crossing vehicles in the calibrated autonomous driving lane, that is, the total number of rectangles; For the corresponding m The rectangles are numbered n autonomous vehicles coordinates of the moment; For the corresponding m The rectangles are numbered n autonomous vehicles coordinates of the moment; From the initial moment To the current moment A specific moment within the range; for The time number is n +1 is the coordinate of the vehicle; for The time number is n The coordinates of the vehicle; For the number The vehicle number of the next vehicle after the vehicle; Number the autonomous vehicle. For the The acceleration of vehicle n at the initial moment of a time step, For the current moment, is the unit time step, is the moment of the initial state when calculating the vehicle trajectory, is the sequence number of the time step, i.e. The first constraint describes the initial moment of the time step; the first constraint describes the control range of any vehicle The coordinate transfer equation, the second constraint describes The speed transfer equation, the third and fourth constraints restrict the trajectory from intersecting any rectangle, and the fifth constraint restricts the speed to not exceed the maximum speed limit , and is not a negative number, that is, no reversing occurs. The sixth constraint limits the safe distance between the two vehicles in succession. , that is, there is no intersection between the two autonomous vehicles. The seventh constraint restricts the vehicles In step length Controllable acceleration Can only be selected from a finite number of values, such as Indicates the acceleration value specified when the vehicle accelerates. The deceleration value specified when the vehicle decelerates. 0 means the vehicle does not accelerate or decelerate. For the At the initial moment of the time step, the vehicle n The coordinates of For the The velocity of vehicle n at the initial moment of a time step, For autonomous vehicles The coordinates of the moment, For autonomous vehicles The coordinates of the moment, is the length of the autonomous vehicle, are the coordinates of the crossing point, When a crossing vehicle passes through the crossing point, it is half the distance between the two autonomous driving vehicles in the autonomous driving lane. is the maximum speed limit, Indicates the acceleration value specified when the vehicle accelerates. The deceleration value specified when the vehicle decelerates. 0 means the vehicle does not accelerate or decelerate. The safe distance between vehicles.

5. A fleet control system in an autonomous driving lane, characterized in that: The system is operated by a method for controlling a fleet in an autonomous driving lane as described in any one of claims 1 to 4, and includes a video detector, a roadside controller, and a communicator; The video detector uses a common road traffic monitoring camera to fully capture the ramp and acceleration lane area, monitor and identify vehicles on the ramp in real time, and use the image processing technology configured in it to calculate the real-time position coordinates, speed and acceleration data of the vehicle, and send the data to the roadside controller; The roadside controller is placed within the control area and is used to receive data from the video detector and the autonomous driving vehicle, establish a relationship between the position and time of the crossing vehicle, calculate the longitudinal trajectory of the autonomous driving vehicle in the autonomous driving lane, calculate the trajectory control command, and transmit it to all autonomous driving vehicles involved in the trajectory adjustment; The communicator is installed in the video detector, the roadside controller and the autonomous driving vehicle. The video detector sends data of vehicles crossing the ramp to the roadside controller through the communicator. The autonomous driving vehicle sends vehicle trajectory data in the autonomous driving lane to the roadside controller through the communicator. The roadside controller issues trajectory control instructions to the autonomous driving vehicle through the communicator.

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