Vehicle fleet control method and device, storage medium and controller, vehicle

Abstract

The application provides a vehicle fleet control method and device, a storage medium, a controller and a vehicle. The method comprises the following steps: receiving a head vehicle data packet in real time, determining a target data packet according to the head vehicle data packet; acquiring information sequences measured by sensors of each following vehicle, determining a target control law of each following vehicle according to the target data packet and the information sequences; and controlling the vehicle fleet to travel according to the corresponding target control law of each following vehicle by using a dynamics model of an automatic driving vehicle fleet. The method can compensate for delay by using a nonlinear system state estimation algorithm, so that the vehicle fleet control model can remain effective when there are uncertain factors such as delay, packet loss and out-of-order of wireless communication.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving control technology, and in particular to a fleet control method, device, storage medium, controller, and vehicle. Background Technology

[0002] With the development of autonomous driving technology, multi-vehicle platooning cooperative autonomous driving has been widely studied. Vehicle platooning can effectively utilize road capacity, and cooperative control technology can significantly reduce collision risks. Vehicles interact with each other through sensors and wireless communication networks, and feedback control enables dynamic connection and formation coordination within the platoon. In real-world environments, wireless communication between vehicles is subject to uncertainty, including communication delays, packet loss, and out-of-order delivery. Summary of the Invention

[0003] This invention aims to at least partially address one of the technical problems in related technologies. To this end, one objective of this invention is to propose an autonomous driving fleet control method that enables the fleet control model to remain effective even when wireless communication suffers from uncertainties such as delays, packet loss, and out-of-order delivery.

[0004] The second objective of this invention is to provide an autonomous driving fleet control device.

[0005] A third objective of this invention is to provide a computer-readable storage medium.

[0006] The fourth objective of this invention is to provide a controller.

[0007] The fifth objective of this invention is to provide a vehicle.

[0008] To achieve the above objectives, a first aspect of the present invention provides an autonomous driving fleet control method, the method comprising: receiving a data packet from a lead vehicle in real time, determining a target data packet based on the data packet from the lead vehicle; acquiring information sequences measured by sensors of each following vehicle, determining a target control law for each following vehicle based on the target data packet and the information sequences; and controlling the fleet to move according to the target control law corresponding to each following vehicle using a dynamic model of the autonomous driving fleet.

[0009] The autonomous driving fleet control method according to embodiments of the present invention uses a nonlinear system state estimation algorithm for delay compensation, enabling the fleet control model to remain effective even when there are uncertainties such as delay, packet loss, and out-of-order delivery in wireless communication.

[0010] In addition, the autonomous driving fleet control method proposed in the above embodiments of the present invention may also have the following additional technical features:

[0011] According to one embodiment of the present invention, the state information includes speed and acceleration, and determining the target data packet based on the lead vehicle data packet includes:

[0012] The data packet σ from the lead vehicle is received at time k. l (k), then select output And let

[0013] If a new data packet σ from the lead vehicle is received l (i), the new lead vehicle data packet σ l (i) It is reached during the time interval from k to k+1 and Then store σ l (i), and let Until time k+1;

[0014] If no new lead vehicle data packet is received, then set k = k + 1 and perform the selection output. And let

[0015] According to one embodiment of the present invention, the information sequence M is:

[0016]

[0017] in, Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment. Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment, (e j ,v j ,a j ) k Let represent the distance error, speed, and acceleration of vehicle j at time k.

[0018] According to one embodiment of the present invention, determining the target control law for each of the following vehicles based on the target data packet and the information sequence includes:

[0019] For each following vehicle, determine whether the data packet of the lead vehicle should be updated at the current moment;

[0020] If updated, the measurement information corresponding to the target data packet is found from the information sequence and used as the target measurement information, and the measurement information is updated. If not updated, the target measurement information from the previous moment is used.

[0021] The target control law is determined based on the target data packet and the target measurement information.

[0022] According to one embodiment of the present invention, the dynamic model of the autonomous driving fleet is as follows:

[0023]

[0024] in, T is the sampling period.

[0025] According to one embodiment of the present invention, the target control law is:

[0026]

[0027] Among them, c j (k) indicates that c p c v c a k v k a For the calibrated parameters, v represents the time delay at which the j-th vehicle receives data from the lead vehicle. l Indicates the speed information of the lead vehicle, a l This indicates the acceleration information of the lead vehicle.

[0028] To achieve the above objectives, a second aspect of the present invention provides an autonomous driving fleet control device, characterized in that the device comprises:

[0029] The first determining module is used to receive the data packet from the lead vehicle and determine the target data packet based on the data packet from the lead vehicle.

[0030] The second determining module is used to acquire the information sequence measured by the sensors of each following vehicle, and determine the target control law of each following vehicle based on the target data packet and the information sequence;

[0031] The control module is used to control the convoy's movement based on the target control law corresponding to each following vehicle, using the dynamic model of the autonomous driving convoy.

[0032] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the autonomous driving fleet control method as proposed in the first aspect of the present invention.

[0033] To achieve the above objectives, a fourth aspect of the present invention provides a controller, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the autonomous driving fleet control method as proposed in the first aspect of the present invention.

[0034] To achieve the above objectives, a fifth aspect of the present invention provides a vehicle including a controller as provided in the fourth aspect of the present invention. Attached Figure Description

[0035] Figure 1 A flowchart of an autonomous driving fleet control method according to one embodiment of the present invention is shown;

[0036] Figure 2 A schematic diagram of an autonomous vehicle fleet according to one embodiment of the present invention is shown;

[0037] Figure 3 A linear control system model for a connected autonomous driving fleet according to one embodiment of the present invention is shown;

[0038] Figure 4 A schematic diagram of an improved fleet linear control system model with a zero-order holder is shown in one embodiment of the present invention;

[0039] Figure 5 A schematic diagram of the operation of the ZOH data processor in one embodiment of the present invention is shown;

[0040] Figure 6 A schematic diagram of an autonomous vehicle fleet control device according to one embodiment of the present invention is shown;

[0041] Figure 7 A schematic diagram of a vehicle according to one embodiment of the present invention is shown. Detailed Implementation

[0042] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0043] The following is in conjunction with the instruction manual appendix. Figure 1-7 The specific implementation methods of the present invention will be described in detail below, including the fleet control method, device, storage medium, controller, and vehicle.

[0044] Figure 1 A flowchart of an autonomous driving fleet control method according to one embodiment of the present invention is shown.

[0045] like Figure 1 As shown, autonomous driving fleet control methods may include:

[0046] S101 receives data packets from the lead vehicle in real time and determines the target data packet based on the data packets from the lead vehicle.

[0047] S102, acquire the information sequence measured by the sensors of each following vehicle, and determine the target control law of each following vehicle based on the target data packet and the information sequence;

[0048] S103 uses the dynamics model of the autonomous driving fleet to control the fleet's movement according to the target control law corresponding to each following vehicle.

[0049] The autonomous driving fleet control method of this invention is an autonomous driving fleet control method based on the zero-order hold (ZOH) information processing mechanism. It proposes a delay compensation strategy and its control law, and performs delay compensation through a nonlinear system state estimation algorithm, so that the fleet control model can remain effective when there are uncertainties such as delay, packet loss, and out-of-order delivery in wireless communication.

[0050] like Figure 2 As shown, Vehicle0, Vehicle1, ..., Vehicler form a convoy within the same lane. At time t = 0, let point O be the point on the horizontal axis where the vertical projection of the midpoint of the rear bumper of the last vehicle in the convoy is located. Let point O be the starting point of the entire convoy. Let p be the distance between the point on the horizontal axis where the vertical projection of the midpoint of the rear bumper of the j-th vehicle is located at time t and the starting point O. j (t), vehicle speed v j (t) varies with time t (j = 1, 2, ..., r). Let the distance between the lead vehicle and point O be p0(t), and its speed be v0(t), with v0(t) representing the target speed of all following vehicles at time t. Considering factors such as weather, road conditions, traffic safety, and convoy speed, let β... j (j=1,2,…,r) is the target distance set between the midpoint of the rear bumper of the j-th vehicle and the midpoint of the rear bumper of the (j-1)-th vehicle (the preceding vehicle) (including the length of the j-th vehicle, β) j (For a certain value).

[0051] Command: d j (t)=p j-1 (t)-p j (t), d j (t) represents the actual distance between the midpoint of the rear bumper of the j-th vehicle and the midpoint of the rear bumper of the vehicle in front at time t (including the length of the j-th vehicle, j = 1, 2, ..., r).

[0052] Let: v(t)=[v1(t),v2(t),…v r (t)] T β=[β1,β2,…β r ] T If the distance error between all vehicles in the convoy is e(t) and the speed error is ε(t), then we have:

[0053]

[0054] Let the control objective of the vehicle's state space be: e(t)→0 and ε(t)→0, as t→∞. Since the speed error ε(t) will eventually converge as the distance error e(t) converges, we will only discuss the convergence of the distance error e(t).

[0055] Command: x j (t)=p j (t)-p j (0) represents the distance traveled by the j-th vehicle at time t. Let x(t) = [x1(t), x2(t), ... x r (t)] T Based on the engine model, the state equations of the fleet model can be expressed as (j = 1, 2, ..., r):

[0056]

[0057] In the formula: c j It is an external input quantity, and has

[0058]

[0059] In the formula:

[0060]

[0061]

[0062] In the formula: The engine time constant is related to vehicle speed; μ j The input parameter K represents the control of the throttle opening. dj d mj It is a constant. The control law for linear feedback is:

[0063]

[0064] c p ,c v ,c a ,k v ,k a These are all parameters that need to be calibrated. The linear control system model for a connected autonomous driving fleet is as follows: Figure 3 As shown.

[0065] exist Figure 2 In the connected vehicle communication architecture shown, the j-th vehicle follows the (j-1)-th vehicle in a car-following motion. The speed of Vehicle0 (the lead vehicle) can be used as the target speed for all following vehicles, i.e., the convoy speed v. l(t) = v0(t). All following vehicles within the convoy can obtain the speed information v of the lead vehicle via wireless communication. l (t), acceleration information a l (t), relying on vehicle body sensors, the position information of the vehicle in front can be obtained p j-1 (t) and the vehicle's location information p j (t). Under good communication conditions, this autonomous driving fleet control model can be used to complete vehicle platooning.

[0066] Due to the uncertainty of communication network channel quality, the accuracy and effectiveness of information during data packet transmission are crucial to the stability of the fleet. To address this issue, a zero-order hold (ZOH) data processing method is proposed. Figure 2 The ZOH information processing mechanism in the communication network architecture overcomes the problems of packet loss and out-of-order delivery in Vehicle0 (lead vehicle) wireless transmission. An improved fleet linear control system model with a zero-order hold is shown below. Figure 4 As shown.

[0067] At sampling point k, the lead vehicle encapsulates the state information into a data packet σ. l (k), and send the encapsulated status information σ to all vehicles in a multicast manner. l (k) T =[v l (k),a l [k], vehicle j needs to receive the data packet from the lead vehicle. To ensure the accuracy of the received information, a Smart Data Processor (SDP) is used to perform the following tasks:

[0068] 1) Receive the latest data packet σ from Vehicle0 (lead vehicle) l (k) T =[v l (k),a l (k)];

[0069] 2) Match the corresponding data packet from the data buffer of vehicle j.

[0070] The ZOH information processing mechanism is explained as follows: Let σ be the time at which the j-th vehicle receives the latest data packet from Vehicle0 (the lead vehicle) at time k. l (0), let The processor follows these principles:

[0071] ① At time k, choose to output σ l (k)=σ l (h j k), and let

[0072] ②If the data packet of the lead vehicle is σ l (i) It is reached during the time interval from k to k+1 and Then store σ l (i), and let

[0073] ③ Between time k and k+1, each time a data packet arrives, cycle ② once, until time k+1. If no new data packet arrives, set k = k+1 and return to step ①.

[0074] The working principle of the ZOH processor is as follows: Figure 5 As shown. By Figure 5 It can be known When data packets are out of order (e.g., when k=2, k=6), they will be discarded directly.

[0075] In the event of a data packet delay, the state information of the lead vehicle and the j-th vehicle may arrive at the vehicle controller at different times, resulting in an asynchronous data packet problem. To avoid this asynchrony, the buffer of the j-th vehicle stores the data from... The sequence of information measured by the vehicle's sensors up to time k:

[0076] The core working mechanism of the buffer is to determine whether the data processor has updated the data packet of the lead vehicle at the current moment, find the corresponding state information of the current vehicle from the information sequence, and update the sequence. If it has not been updated, the previous state information is used. Thus, the control law for following vehicle j is used... and This method of computation effectively avoids asynchronous issues.

[0077] definition The time delay for the j-th vehicle to receive data from the lead vehicle is given by Figure 5 As can be seen from the controller shown, It can represent both the number of lost packets and the number of delays. Let the upper bounds of the delay and the number of consecutive lost packets be τmax and Nd, respectively, to define boundary conditions for subsequent simulation experiments and industrial applications. Under the ZOH information processing mechanism, the linear system model of the fleet is optimized to solve the network disorder and packet loss problems. The control law (4) is discretized considering the time delay and can be written as:

[0078]

[0079]

[0080] Among them, c j (k) indicates that c p c v ca k v k a For the calibrated parameters, v represents the time delay at which the j-th vehicle receives data from the lead vehicle. l Indicates the speed information of the lead vehicle, a l This indicates the acceleration information of the lead vehicle.

[0081] Accordingly, the state equation of the optimized model can be discretized as follows:

[0082]

[0083] in,

[0084] In the formula: T1 is the sampling period.

[0085] definition:

[0086] X j (k)=[X j (k),v j (k),a j (k)] T , j = 1, 2, ..., r.

[0087] make:

[0088] X(k) = [X1(k), ..., X r (k)] T

[0089] C(k) = [C1(k), ..., C r (k)] T

[0090] The discrete system consisting of the autonomous driving fleet model can then be represented as:

[0091] X(k+1) = AX(k) + BC(k)

[0092] in,

[0093] The current connected vehicle platooning autonomous driving technology in this embodiment of the invention considers an overly idealized wireless communication environment, that is, it does not take into account the compensation measures for the aforementioned uncertainties, and therefore has certain limitations.

[0094] Figure 6 A schematic diagram of an autonomous vehicle fleet control device according to one embodiment of the present invention is shown. Figure 6 As shown, the autonomous driving fleet control device 100 may include a first determining module 10, a second determining module 20, and a control module 30.

[0095] The first determining module 10 is used to receive the data packet of the lead vehicle and determine the target data packet based on the data packet of the lead vehicle; the second determining module 20 is used to acquire the information sequence measured by the sensors of each following vehicle and determine the target control law of each following vehicle based on the target data packet and the information sequence; the control module 30 is used to control the driving of the convoy using the dynamic model of the autonomous driving convoy and the target control law corresponding to each following vehicle.

[0096] It should be noted that other specific embodiments of the autonomous driving fleet control device of the present invention can be found in the specific embodiments of the autonomous driving fleet control method of the above embodiments of the present invention.

[0097] The autonomous driving fleet control device of this invention uses a nonlinear system state estimation algorithm for delay compensation, enabling the fleet control model to remain effective when there are uncertainties such as delay, packet loss, and out-of-order delivery in wireless communication, thereby achieving dynamic connection and formation coordination of the fleet.

[0098] This invention provides a computer-readable storage medium.

[0099] In this embodiment, a computer program is stored on a computer-readable storage medium, which, when executed by a processor, implements the autonomous driving fleet control method described above.

[0100] This invention provides a controller.

[0101] In this embodiment, the controller includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the autonomous driving fleet control method described above.

[0102] The computer-readable storage medium and controller of this invention enable the fleet control model to remain effective when there are uncertainties such as delay, packet loss, and out-of-order delivery in wireless communication, thereby achieving dynamic connection and formation coordination of the fleet.

[0103] This invention provides a vehicle.

[0104] In this embodiment, such as Figure 7 As shown, vehicle 1000 includes a controller as described above.

[0105] The vehicles in this embodiment of the invention can achieve dynamic platoon connection and formation coordination when there are uncertainties such as delay, packet loss, and out-of-order delivery in wireless communication.

[0106] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0107] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An autonomous driving fleet control method, characterized in that, The method includes: Receive data packets from the lead vehicle in real time, and determine the target data packet based on the data packets from the lead vehicle; Acquire the information sequence measured by the sensors of each following vehicle, and determine the target control law for each following vehicle based on the target data packet and the information sequence; Using the dynamic model of the autonomous driving fleet, the fleet is controlled to drive according to the target control law corresponding to each following vehicle; The information sequence includes velocity and acceleration, and the target data packet is determined based on the lead vehicle data packet, including: If the data packet from the lead vehicle is received at time k, then output is selected. and let ; If a new data packet from the lead vehicle is received The new lead vehicle data packet It is reached within the time interval from k to k+1 and i> Then store and let =i, until time k+1; If no new lead vehicle data packet is received, then set k = k + 1 and perform the selection output. and let ; in, This represents the data packet received from the lead vehicle at time k; This indicates the time identifier of the data packet of the lead vehicle corresponding to the j-th following vehicle at time k; i indicates the time identifier of the newly received data packet of the lead vehicle; j indicates the number of the following vehicle; The information sequence M is: in, Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment. Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment. This represents the distance error, speed, and acceleration of the j-th vehicle at time k; The dynamic model of the autonomous driving fleet is as follows: in, , In the formula The sampling period; in, Indicates the position of the j-th following vehicle at time k; This represents the speed of the j-th following vehicle at time k; Let represent the acceleration of the j-th following vehicle at time k; Represents the state transition matrix; This represents the control input of the j-th following vehicle at time k; Represents the control input matrix; The target control law is: in, This represents the control input of the j-th following vehicle at time k; This indicates that the j-th following vehicle experiences a time delay. Position tracking error during the process; This indicates that the j-th following vehicle experiences a time delay. The position tracking error at that time, i.e., the first derivative of the position tracking error; This indicates that the j-th following vehicle experiences a time delay. The position tracking error at that time is the second derivative of the position tracking error; This indicates the time delay at which the j-th vehicle receives data from the lead vehicle; These are the parameters for calibration. This indicates the time delay at which the j-th vehicle receives data from the lead vehicle; Indicates the speed information of the lead vehicle; This indicates the acceleration information of the lead vehicle.

2. The autonomous driving fleet control method according to claim 1, characterized in that, Based on the target data packet and the information sequence, the target control law for each following vehicle is determined, including: For each following vehicle, determine whether the data packet of the lead vehicle should be updated at the current moment; If updated, the measurement information corresponding to the target data packet is found from the information sequence and used as the target measurement information, and the measurement information is updated. If not updated, the target measurement information from the previous moment is used. The target control law is determined based on the target data packet and the target measurement information.

3. An autonomous driving fleet control device, characterized in that, The device includes: The first determining module is used to receive the data packet from the lead vehicle and determine the target data packet based on the data packet from the lead vehicle. The second determining module is used to acquire the information sequence measured by the sensors of each following vehicle, and determine the target control law of each following vehicle based on the target data packet and the information sequence; The control module is used to control the convoy's movement based on the target control law corresponding to each following vehicle, using the dynamic model of the autonomous driving convoy. The information sequence includes velocity and acceleration, and the target data packet is determined based on the lead vehicle data packet, including: If the data packet from the lead vehicle is received at time k, then output is selected. and let ; If a new data packet from the lead vehicle is received The new lead vehicle data packet It is reached within the time interval from k to k+1 and i> Then store and let =i, until time k+1; If no new lead vehicle data packet is received, then set k = k + 1 and perform the selection output. and let ; in, This represents the data packet received from the lead vehicle at time k; This indicates the time identifier of the data packet of the lead vehicle corresponding to the j-th following vehicle at time k; i indicates the time identifier of the newly received data packet of the lead vehicle; j indicates the number of the following vehicle; The information sequence M is: in, Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment. Indicates that the j-th car is in The distance error, velocity, and acceleration corresponding to each moment. This represents the distance error, speed, and acceleration of the j-th vehicle at time k; The dynamic model of the autonomous driving fleet is as follows: in, , In the formula The sampling period; in, Indicates the position of the j-th following vehicle at time k; This represents the speed of the j-th following vehicle at time k; Let represent the acceleration of the j-th following vehicle at time k; Represents the state transition matrix; This represents the control input of the j-th following vehicle at time k; Represents the control input matrix; The target control law is: in, This represents the control input of the j-th following vehicle at time k; This indicates that the j-th following vehicle experiences a time delay. Position tracking error during the process; This indicates that the j-th following vehicle experiences a time delay. The position tracking error at that time, i.e., the first derivative of the position tracking error; This indicates that the j-th following vehicle experiences a time delay. The position tracking error at that time is the second derivative of the position tracking error; This indicates the time delay at which the j-th vehicle receives data from the lead vehicle; These are the parameters for calibration. This indicates the time delay at which the j-th vehicle receives data from the lead vehicle; Indicates the speed information of the lead vehicle; This indicates the acceleration information of the lead vehicle.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the autonomous driving fleet control method as described in claim 1 or 2.

5. A controller comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the autonomous driving fleet control method as described in claim 1 or 2.

6. A vehicle, characterized in that, Includes the controller as described in claim 5.

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