Vehicle platoon cooperative control method, electronic device, vehicle, medium and product

Abstract

The application discloses a vehicle queue cooperative control method, an electronic device, a vehicle, a storage medium and a computer program product, relates to the technical field of vehicles, and comprises the following steps: in the case that it is detected that the vehicle type is a following vehicle, processing the driving parameters of a leading vehicle and the physical parameters of a controlled vehicle by a preset car-following dynamics model to obtain a first front traffic coefficient corresponding to the target controlled vehicle; processing the driving parameters of the leading vehicle and the physical parameters of the first following vehicle by the car-following dynamics model to obtain a second front traffic coefficient; converting and processing the second front traffic coefficient according to a preset weight parameter to obtain a first rear car-following coefficient corresponding to the target controlled vehicle; determining a first vehicle control parameter group based on the first front traffic coefficient and the first rear car-following coefficient, and controlling the target controlled vehicle according to the first vehicle control parameter group. The application can significantly improve the stability of the vehicle queue in the driving process.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle platooning cooperative control method, electronic equipment, vehicle, storage medium, and computer program product. Background Technology

[0002] With the continuous development of the automotive industry, vehicle platooning control technology, with its advantages of multi-vehicle cooperative driving, has become a core solution for solving road congestion and improving traffic efficiency, and has shown broad application prospects in both passenger cars and commercial vehicles.

[0003] In related technologies, technicians typically design fixed vehicle queuing control strategies based on human driving habits, thereby controlling the vehicles in the queue through fixed vehicle queuing control strategies to achieve multi-vehicle following functionality.

[0004] However, fixed vehicle platoon control strategies typically ignore the following behavior of vehicles during execution. Furthermore, the following behavior of vehicles is affected by multiple factors such as the movement performance of the vehicle in front, driving characteristics, and communication delays. Therefore, fixed vehicle control strategies are prone to problems such as increased vehicle spacing deviation and insufficient synchronization, which in turn disrupt platoon consistency. Summary of the Invention

[0005] The main objective of this application is to provide a vehicle platooning cooperative control method, electronic device, vehicle, storage medium, and computer program product, aiming to solve the technical problem in related technologies that fixed vehicle platooning control strategies cannot adjust their own control logic in a timely manner based on the movement performance of the preceding vehicle and the following vehicle's following behavior.

[0006] To achieve the above objectives, this application proposes a vehicle platoon cooperative control method, which is applied to target controlled vehicles within a target vehicle platoon. The method includes: Determine the vehicle type of the target controlled vehicle, wherein the vehicle type is a lead vehicle or a follower vehicle; When the vehicle type is detected to be the following vehicle, the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle queue are obtained, and the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle are processed by a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle. The physical parameters of the first following vehicle in the target vehicle queue are obtained, and the driving parameters of the lead vehicle and the physical parameters of the first following vehicle are processed by the car-following dynamics model to obtain the second forward traffic coefficient corresponding to the first following vehicle. The first following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The second forward traffic coefficient is converted according to preset weight parameters to obtain the first rear following coefficient corresponding to the target controlled vehicle. A first vehicle control parameter set is determined based on the first forward traffic coefficient and the first rear following coefficient, and the target controlled vehicle is controlled according to the first vehicle control parameter set.

[0007] In one embodiment, the step of processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle using a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle includes: The discrete-time state quantities of the target controlled vehicle are determined by combining the physical parameters of the controlled vehicle. Based on the driving parameters of the lead vehicle and the discrete-time state quantities, determine the speed consistency constraints and the spacing consistency constraints. Based on the speed consistency constraint and the spacing consistency constraint, the dynamic expected spacing of the target controlled vehicle is determined; The first forward traffic coefficient corresponding to the target controlled vehicle is determined by combining the dynamic expected spacing, the preset influence weight coefficient, and the preset maximum braking deceleration.

[0008] In one embodiment, the step of determining the discrete-time state quantities of the target controlled vehicle in conjunction with the physical parameters of the controlled vehicle includes: Construct the third-order nonlinear dynamic equations corresponding to the target controlled vehicle based on the physical parameters of the controlled vehicle; The real-time state change rate model of the target controlled vehicle is obtained by optimizing the third-order nonlinear dynamic equation based on the real-time state variables and real-time control variables of the controlled vehicle. The real-time change rate model of the state is discretized according to a preset discrete time step to determine the discrete-time state quantities of the target controlled vehicle.

[0009] In one embodiment, the step of determining the first vehicle control parameter set based on the first forward traffic coefficient and the first rear following coefficient includes: Determine the expected coefficient of the lead vehicle corresponding to the target controlled vehicle; The target optimization decision model is obtained by integrating the expected coefficient of the leading vehicle, the first forward traffic coefficient, and the first rear following coefficient. By combining speed consistency constraints and spacing consistency constraints, the target optimization decision model is iteratively optimized to determine the first set of vehicle control parameters for the target controlled vehicle.

[0010] In one embodiment, after the step of determining the vehicle type of the target controlled vehicle, the method further includes: If the vehicle type is detected to be the following vehicle, determine whether the target controlled vehicle is a merging vehicle; The third forward traffic coefficient corresponding to the target controlled vehicle is obtained by processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model. The physical parameters of the second following vehicle in the target vehicle convoy are obtained, and the driving parameters of the lead vehicle and the physical parameters of the second following vehicle are processed by the car-following dynamics model to obtain the fourth forward traffic coefficient corresponding to the second following vehicle. The second following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The fourth forward traffic coefficient is converted according to the preset weight parameters to obtain the second rear following coefficient corresponding to the target controlled vehicle. The second vehicle control parameter set of the target controlled vehicle is determined based on the third forward traffic coefficient and the second rear following coefficient, and the target controlled vehicle is controlled according to the second vehicle control parameter set.

[0011] In one embodiment, after the step of determining the vehicle type of the target controlled vehicle, the method further includes: If the vehicle type is detected to be the following vehicle, detect whether there is a first following vehicle behind the target controlled vehicle; If it is detected that there is no first following vehicle behind the target controlled vehicle, then the first forward traffic coefficient corresponding to the target controlled vehicle is determined; The first forward traffic coefficient is sent to the third following vehicle so that the third following vehicle can determine a third vehicle control parameter set based on the first forward traffic coefficient, wherein the third following vehicle is in front of and adjacent to the target controlled vehicle.

[0012] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle platoon cooperative control method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a vehicle that includes the electronic equipment described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the vehicle queuing cooperative control method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle queuing cooperative control method described above.

[0016] The vehicle platoon cooperative control method provided in this application is applied to target controlled vehicles within a target vehicle platoon. It determines the vehicle type of the target controlled vehicle, wherein the vehicle type is either a lead vehicle or a follower vehicle. When the vehicle type is detected as a follower vehicle, it acquires the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle platoon. It then processes the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle using a preset car-following dynamics model to obtain a first forward traffic coefficient corresponding to the target controlled vehicle. Next, it acquires the physical parameters of a first follower vehicle in the target vehicle platoon and processes these parameters using the same car-following dynamics model to obtain a second forward traffic coefficient corresponding to the first follower vehicle, wherein the first follower vehicle is located behind and adjacent to the target controlled vehicle. The second forward traffic coefficient is then converted according to preset weight parameters to obtain a first rear car-following coefficient corresponding to the target controlled vehicle. Finally, a first vehicle control parameter group is determined based on the first forward traffic coefficient and the first rear car-following coefficient, and the target controlled vehicle is controlled according to the first vehicle control parameter group.

[0017] Thus, this application solves the technical problem in related technologies where fixed vehicle platoon control strategies cannot adjust their control logic in a timely manner based on the movement performance of the vehicle in front and the following behavior of the vehicle behind. Specifically, this application adopts a method in which electronic devices construct vehicle control parameter sets based on the traffic coefficients in front and the following coefficients behind the target controlled vehicle. This enables the electronic devices to establish a two-way dynamic coupling relationship between the controlled vehicle and the following and front following vehicles, thereby ensuring that the strategy adjustment process can fully consider the following ability of the vehicle behind and the traffic conditions in front, thus significantly improving the stability of the vehicle platoon during driving. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the vehicle queuing collaborative control method of this application.

[0021] Figure 2 This is a schematic diagram of the vehicle queuing following results in an embodiment of the vehicle queuing collaborative control method of this application.

[0022] Figure 3 This is a schematic diagram of the module structure of the vehicle queue collaborative control device according to an embodiment of this application.

[0023] Figure 4 This is a schematic diagram of the hardware operating environment involved in the vehicle queue collaborative control method in this application embodiment.

[0024] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0025] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0026] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0027] In this embodiment, for ease of description, the following description will focus on electronic devices configured in the vehicle, or terminals such as mobile terminals, data storage control terminals, and PCs that are associated with electronic devices.

[0028] Based on the aforementioned electronic equipment, the overall concept of the vehicle platooning cooperative control method of this application is proposed.

[0029] With the continuous development of the automotive industry, vehicle queuing control technology, with its advantages of multi-vehicle cooperative driving, has become a core solution for solving road congestion and improving traffic efficiency, showing broad application prospects in both passenger and commercial vehicle sectors. In related technologies, engineers typically design fixed vehicle queuing control strategies based on human driving habits, thereby controlling vehicles within the queue to achieve multi-vehicle following functionality. However, during execution, fixed vehicle queuing control strategies often ignore the following behavior of vehicles behind, and this following behavior is affected by multiple factors such as the movement and driving characteristics of the vehicle in front, and communication delays. Therefore, fixed vehicle control strategies are prone to problems such as increased vehicle spacing deviation and insufficient synchronization, thereby disrupting queue consistency.

[0030] To address the above phenomena, this application provides a vehicle platoon cooperative control method. The method is applied to target controlled vehicles within a target vehicle platoon. The method includes: determining the vehicle type of the target controlled vehicle, wherein the vehicle type is a lead vehicle or a follower vehicle; when the vehicle type is detected to be a follower vehicle, acquiring the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle platoon, and processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle using a preset car-following dynamics model to obtain a first forward traffic coefficient corresponding to the target controlled vehicle; and acquiring the target vehicle platoon... The physical parameters of the first following vehicle are obtained, and the driving parameters of the lead vehicle and the physical parameters of the first following vehicle are processed by the car-following dynamics model to obtain the second forward traffic coefficient corresponding to the first following vehicle. The first following vehicle is located behind and adjacent to the target controlled vehicle. The second forward traffic coefficient is transformed according to preset weight parameters to obtain the first rear car-following coefficient corresponding to the target controlled vehicle. Based on the first forward traffic coefficient and the first rear car-following coefficient, a first vehicle control parameter group is determined, and the target controlled vehicle is controlled according to the first vehicle control parameter group.

[0031] Thus, this application solves the technical problem in related technologies where fixed vehicle platoon control strategies cannot adjust their control logic in a timely manner based on the movement performance of the vehicle in front and the following behavior of the vehicle behind. Specifically, this application adopts a method in which electronic devices construct vehicle control parameter sets based on the traffic coefficients in front and the following coefficients behind the target controlled vehicle. This enables the electronic devices to establish a two-way dynamic coupling relationship between the controlled vehicle and the following and front following vehicles, thereby ensuring that the strategy adjustment process can fully consider the following ability of the vehicle behind and the traffic conditions in front, thus significantly improving the stability of the vehicle platoon during driving.

[0032] Based on the overall concept of the vehicle platoon cooperative control method of this application, the embodiments of this application provide a vehicle platoon cooperative control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the vehicle platoon cooperative control method of this application. In this embodiment, the vehicle platoon cooperative control method is applied to target controlled vehicles within a target vehicle platoon, and the method includes steps S10-S50: Step S10: Determine the vehicle type of the target controlled vehicle, wherein the vehicle type is either a lead vehicle or a follow vehicle; Step S20: When the vehicle type is detected as a following vehicle, the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle queue are obtained, and the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle are processed by a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle. Step S30: Obtain the physical parameters of the first following vehicle in the target vehicle queue, and process the driving parameters of the lead vehicle and the physical parameters of the first following vehicle through the car-following dynamics model to obtain the second forward traffic coefficient corresponding to the first following vehicle. The first following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. Step S40: Convert the second forward traffic coefficient according to the preset weight parameters to obtain the first rear following coefficient corresponding to the target controlled vehicle; It should be noted that the vehicle type is a classification used to distinguish the roles and functions of target controlled vehicles in the queue, including: lead vehicles and follower vehicles. The lead vehicle is typically at the front of the queue, and follower vehicles are located behind it. Furthermore, the forward traffic coefficient is a quantitative evaluation parameter calculated based on the forward traffic conditions and the vehicle's following demand. It can be understood that the forward traffic coefficient reflects the demand pressure exerted on the target controlled vehicle's vehicle control strategy by changes in the preceding vehicles or traffic flow. Additionally, the rear following coefficient is a quantitative constraint parameter calculated based on the following capability of adjacent vehicles. It can be understood that the rear following coefficient reflects the limitation on the ability of following vehicles to adapt to changes in the movement of the target controlled vehicle. Finally, the lead vehicle driving parameters are the real-time motion status information of the lead vehicle in the target vehicle queue, specifically including: the lead vehicle's longitudinal speed, longitudinal acceleration, and position information.

[0033] In this embodiment, when the target controlled vehicle is in motion within the target vehicle queue, the electronic equipment configured inside the target controlled vehicle first obtains the vehicle type identifier pre-assigned to the target controlled vehicle in the target vehicle queue through the inter-vehicle communication system. The electronic equipment determines the vehicle type of the target controlled vehicle based on the vehicle type identifier. Subsequently, if the electronic equipment detects that the vehicle type is a following vehicle, it obtains the status information of the vehicle ahead, the driving parameters of the lead vehicle, and the physical parameters of the controlled vehicle of its own vehicle through the perception system. It then calls a preset car-following dynamics model to process the physical parameters of the controlled vehicle of its own vehicle to determine the discrete-time state variables of the target controlled vehicle. The preset car-following dynamics model then integrates the driving parameters of the lead vehicle and the discrete-time state variables. The system constructs a speed consistency constraint with the speed of the lead vehicle as the tracking target and calculates a spacing consistency constraint with the desired following distance as the target. Based on the speed consistency constraint and the spacing consistency constraint, it calculates the first forward traffic coefficient of the target controlled vehicle. Then, the electronic device receives the first following vehicle physical parameters sent by the first following vehicle adjacent to the target vehicle in the target vehicle queue, and calls the preset car-following dynamics model to process the physical parameters of the first following vehicle and the driving parameters of the lead vehicle to obtain the second forward traffic coefficient. The electronic device then processes the second forward traffic coefficient according to the preset weight parameters through the preset conversion operation process to obtain the first rear car-following coefficient that characterizes the following vehicle's car-following ability.

[0034] For example, the target controlled vehicle When the target vehicle is in motion within the target vehicle convoy, the vehicle controller configured inside the target controlled vehicle first reads the target controlled vehicle... Pre-assigned vehicle type identifiers in the target vehicle queue, and determine the target controlled vehicle based on the vehicle type identifiers. The vehicle type is then determined, and the vehicle controller detects the target controlled vehicle. If the vehicle type is a following vehicle, then the vehicle controller will control the target vehicle. The vehicle-to-vehicle communication system established with other vehicles in the target vehicle convoy obtains the lead vehicle's driving parameters and the parameters of the vehicles ahead from the lead vehicle. Forward vehicle status information, target controlled vehicle The vehicle controller then uses its pre-configured car-following dynamics model to process the physical parameters of the controlled vehicle, the driving parameters of the lead vehicle, and the status information of the preceding vehicle. The pre-configured car-following dynamics model incorporates the mechanical efficiency of the transmission system within the physical parameters of the controlled vehicle. Vehicle quality Tire rolling radius Windward area Substituting the parameters into the preset third-order nonlinear model: ,in, For air drag coefficient, air density, For rolling resistance system, It is the acceleration due to gravity. The longitudinal dynamic time constant is Desired control torque; Thus, the target controlled vehicle is obtained through a third-order nonlinear model. vertical position Current speed of the controlled vehicle Braking / driving torque of controlled vehicle The vehicle controller then controls the target vehicle. The longitudinal position, current speed of the controlled vehicle, and acceleration of the controlled vehicle are defined as state variables. : ; At the same time, the preset car-following dynamics model will target the controlled vehicle. Controlled vehicle braking / driving torque Defined as control quantity Thus based on state variables and control quantity The third-order nonlinear model was rewritten to obtain: ; The pre-defined car-following dynamics model is then used to discretize the rewritten third-order nonlinear model using the Euler method to obtain the target controlled vehicle. Discrete-time states of the vehicle: ; The preset car-following dynamics model is based on the target controlled vehicle. The discrete-time states and the driving parameters of the lead vehicle are used as inputs to determine the speed consistency constraints and spacing consistency constraints: ; A pre-set car-following dynamics model is used to determine the target controlled vehicle based on speed consistency constraints and spacing consistency constraints. discrete time state Calculate the target controlled vehicle The dynamic expected spacing is ,in, , It is a constant; At the same time, the preset car-following dynamics model obtains information about the vehicles ahead. For the target controlled vehicle Influence weight coefficient and acquire the target controlled vehicle Maximum braking deceleration Simultaneously, a pre-set car-following dynamics model is used to obtain the target controlled vehicle. The car following in front Real-time driving speed And calculate the target controlled vehicle and the car following in front speed deviation between Pre-set car-following dynamics model and then based on speed deviation and maximum braking deceleration Constructing the g function: ; And the g function, influence weight coefficient Substitute the preset formula for calculating the impact coefficient of the preceding vehicle: , ; in, For the vehicle ahead For the target controlled vehicle Influence weighting coefficient For the target controlled vehicle The maximum braking deceleration, and >0, , It is a constant. For the target controlled vehicle The speed of the car, For the target controlled vehicle Maximum longitudinal acceleration, For the target controlled vehicle The vertical position, For the vehicle ahead The longitudinal position; To calculate the target controlled vehicle First front traffic coefficient Then, the vehicle controller receives information about the target vehicles in the target vehicle queue via a bidirectional queue communication topology. Behind, and with the target controlled vehicle The first following vehicle adjacent The first following vehicle's physical parameters are sent, and the first following vehicle's driving parameters, the lead vehicle's driving parameters, and the preceding vehicle's state information are processed by a preset car-following dynamics model to obtain the second forward traffic coefficient. The vehicle controller then applies the second forward traffic coefficient. Enter the preset formula for converting the rear following coefficient: , ; By using preset weight parameters The first rear following coefficient is calculated using the formula for converting rear following coefficients. .

[0035] Understandably, the second-highest traffic coefficient From the first following vehicle Through its own and the target controlled vehicle Speed ​​deviation between, first following vehicle The parameters, such as its maximum braking deceleration, are calculated. The specific calculation process and the first forward traffic coefficient are also discussed. The calculation process is the same, so it will not be repeated here.

[0036] In this way, by actively acquiring the first forward traffic coefficient and the first rear following coefficient when the detected target controlled vehicle is a following vehicle, the electronic device ensures that the subsequent strategy adjustment process can construct quantitative constraints that take into account the rear following capability, thereby enabling the fleet control strategy to simultaneously consider the impact of forward traffic and the impact of rear following capability.

[0037] In one feasible implementation, the step S20 above, "processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle using a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle," may specifically include steps S201 to S204: Step S201: Determine the discrete-time state quantities of the target controlled vehicle by combining the physical parameters of the controlled vehicle; Step S202: Determine the speed consistency constraint and the spacing consistency constraint based on the driving parameters of the lead vehicle and the discrete time state quantities; Step S203: Determine the dynamic desired spacing of the target controlled vehicle based on the speed consistency constraint and the spacing consistency constraint; Step S204: Determine the first forward traffic coefficient corresponding to the target controlled vehicle by combining the dynamic expected spacing, the preset influence weight coefficient and the preset maximum braking deceleration.

[0038] It should be noted that the discrete-time state variables are the values ​​of the state variables described by the vehicle dynamics model at the time points discretized according to the preset sampling period for the target controlled vehicle. Specifically, these include the controlled vehicle's position, speed, and actual driving / braking torque. Furthermore, the speed consistency constraint and spacing consistency constraint are mathematical restrictions used to ensure platoon coordination performance. The speed consistency constraint aims to make the speed of following vehicles as close as possible to the speed of the lead vehicle. Similarly, the spacing consistency constraint ensures that the actual vehicle spacing is maintained near the dynamically expected spacing calculated based on the minimum safe spacing strategy. Additionally, the dynamically expected spacing is the expected following distance value calculated based on the current vehicle state of the target controlled vehicle and the minimum safe spacing strategy. Furthermore, the preset influence weight coefficient is a preset adjustable parameter used to adjust the relative importance of different influencing factors when calculating the first forward traffic coefficient. Finally, the preset maximum braking deceleration is the maximum braking intensity limit allowed for the target controlled vehicle.

[0039] In this embodiment, after acquiring the driving parameters of the lead vehicle, the electronic device further combines the acquired physical parameters of the controlled vehicle to determine the discrete-time state variables of the target controlled vehicle. Then, the electronic device integrates the driving parameters of the lead vehicle and the discrete-time state variables to construct a speed consistency constraint with the speed of the lead vehicle as the tracking target, and calculates a spacing consistency constraint with the desired following distance as the target. Next, the electronic device calculates the dynamic desired distance of the target controlled vehicle based on the speed consistency constraint and the spacing consistency constraint. Finally, the electronic device detects the actual following distance of the target controlled vehicle and compares the dynamic desired distance with the actual following distance to obtain the deviation between the dynamic desired distance and the actual following distance. At the same time, the electronic device determines the speed deviation between the target controlled vehicle and the lead vehicle, and then weights and fuses the spacing deviation and speed deviation according to a preset influence weight coefficient to obtain the first forward traffic coefficient.

[0040] For example, after acquiring the driving parameters of the lead vehicle, the vehicle controller further acquires the target controlled vehicle. The controlled vehicle's physical parameters are determined, and the transmission system's mechanical efficiency within these parameters is calculated using a pre-configured car-following dynamics model. Vehicle quality Tire rolling radius Windward area Substituting the parameters into the preset third-order nonlinear model: ; A pre-defined car-following dynamics model is used to obtain the target controlled vehicle through a third-order nonlinear model. vertical position Current speed of the controlled vehicle Braking / driving torque of controlled vehicle The vehicle controller then controls the target vehicle. The longitudinal position, current speed of the controlled vehicle, and acceleration of the controlled vehicle are defined as state variables. : ; At the same time, the preset car-following dynamics model will target the controlled vehicle. Controlled vehicle braking / driving torque Defined as control quantity Thus based on state variables and control quantity The third-order nonlinear model was rewritten to obtain: ; The pre-defined car-following dynamics model is then used to discretize the rewritten third-order nonlinear model using the Euler method to obtain the target controlled vehicle. Discrete-time states: ; Subsequently, the preset car-following dynamics model is based on the target controlled vehicle. The discrete-time states and the driving parameters of the lead vehicle are used as inputs to determine the speed consistency constraints and spacing consistency constraints: ; Subsequently, the pre-set car-following dynamics model is based on speed consistency constraints and spacing consistency constraints, and the target controlled vehicle. discrete time state Calculate the target controlled vehicle The dynamic expected spacing is ,in, , It is a constant; Finally, the car-following dynamics model is preset to obtain the vehicle in front. Target controlled vehicle Influence weight coefficient and acquire the target controlled vehicle Maximum braking deceleration Simultaneously, a pre-set car-following dynamics model is used to obtain the target controlled vehicle. The car following in front Real-time driving speed And calculate the target controlled vehicle and the car following in front speed deviation between Pre-set car-following dynamics model and then based on speed deviation and maximum braking deceleration Constructing the g function: ; And the g function, influence weight coefficient Substitute the preset formula for calculating the impact coefficient of the preceding vehicle: , ; To calculate the target controlled vehicle First front traffic coefficient .

[0041] In this way, by actively acquiring the first forward traffic coefficient and the first rear following coefficient when the detected target controlled vehicle is a following vehicle, the electronic device ensures that the subsequent strategy adjustment process can construct quantitative constraints that take into account the rear following capability, thereby enabling the fleet control strategy to simultaneously consider the impact of forward traffic and the impact of rear following capability.

[0042] In one feasible implementation, step S201 above may specifically include steps S2011 to S2013: Step S2011: Obtain the controlled vehicle physical parameters of the target controlled vehicle, and construct the third-order nonlinear dynamic equation corresponding to the target controlled vehicle based on the controlled vehicle physical parameters; Step S2012: Based on the real-time state variables and real-time control variables of the controlled vehicle, optimize the third-order nonlinear dynamic equation to obtain the real-time state change rate model of the target controlled vehicle; Step S2013: Discretize the real-time change rate model of the state according to the preset discrete time step to determine the discrete time state quantity of the target controlled vehicle.

[0043] It should be noted that the third-order nonlinear dynamic equation is a differential equation that describes the longitudinal motion of the vehicle as the evolution of three state variables—position, velocity, and actual braking / driving torque—over time. Furthermore, the real-time state variables of the controlled vehicle... A predefined set of variables that can fully characterize the internal state of the vehicle dynamics system, specifically including the target controlled vehicle. vertical position Current speed of the controlled vehicle Controlled vehicle acceleration In addition, the real-time control volume of the controlled vehicle For predefined applications that can be directly applied to the target controlled vehicle The input variables can specifically include the target controlled vehicle. Controlled vehicle braking / driving torque Furthermore, the real-time change rate model of this state is determined by the third-order nonlinear dynamic equation, and the first derivative of the real-time state quantity of the controlled vehicle with respect to time is used to represent the instantaneous change rate of the state quantity at any given moment.

[0044] In this embodiment, if the electronic device detects that the target controlled vehicle is a following vehicle, in addition to directly acquiring the driving parameters of the lead vehicle, it also needs to acquire the physical parameters of the target controlled vehicle through the internal bus. The electronic device then processes the physical parameters of each controlled vehicle according to the principle of vehicle longitudinal dynamics to construct a third-order nonlinear dynamic equation describing the longitudinal motion of the target controlled vehicle. After that, the electronic device inputs the predefined real-time state variables and real-time control variables of the controlled vehicle as variables into the third-order nonlinear dynamic equation to rewrite the third-order nonlinear dynamic equation into a standard state-space form, thereby obtaining a real-time state change rate model that characterizes the functional relationship between the real-time state change rate and the current state variables and control variables. Finally, the electronic device optimizes the real-time state change rate model according to a preset discrete time step to obtain the update formula for the discrete-time state variables, thereby determining the discrete-time state variables of the target controlled vehicle through the update formula for the discrete-time state variables.

[0045] For example, if the vehicle controller detects that the target controlled vehicle i is a following vehicle, it first obtains the target controlled vehicle's driving parameters before obtaining the lead vehicle's driving parameters. The controlled vehicle's physical parameters are determined, and a preset car-following dynamics model is invoked to calculate the mechanical efficiency of the transmission system within the controlled vehicle's physical parameters. Vehicle quality Tire rolling radius Windward area Substituting the parameters into the preset third-order nonlinear model: ; Subsequently, the pre-defined car-following dynamics model is used to obtain the target controlled vehicle through a third-order nonlinear model. vertical position Current speed of the controlled vehicle Braking / driving torque of controlled vehicle The vehicle controller then controls the target vehicle. The longitudinal position, current speed of the controlled vehicle, and acceleration of the controlled vehicle are defined as state variables. : ; At the same time, the preset car-following dynamics model will target the controlled vehicle. Controlled vehicle braking / driving torque Defined as control quantity Thus based on state variables and control quantity The real-time rate of change of state model is obtained by rewriting the third-order nonlinear model: ; The pre-defined car-following dynamics model is then used to discretize the rewritten real-time rate of change model of the state using the Euler method to obtain the target controlled vehicle. Discrete-time states: .

[0046] In this way, by actively acquiring the first forward traffic coefficient and the first rear following coefficient when the detected target controlled vehicle is a following vehicle, the electronic device ensures that the subsequent strategy adjustment process can construct quantitative constraints that take into account the rear following capability, thereby enabling the fleet control strategy to simultaneously consider the impact of forward traffic and the impact of rear following capability.

[0047] Step S50: Determine the first vehicle control parameter group based on the first forward traffic coefficient and the first rear following coefficient, and control the target controlled vehicle according to the first vehicle control parameter group; It should be noted that the first vehicle control parameter is a set of parameters used to control the longitudinal actuator of the target controlled vehicle, calculated based on the first forward traffic coefficient and the first rear following coefficient, including but not limited to: target acceleration sequence, target driving torque, target braking pressure or desired engine output torque, etc.

[0048] In this embodiment, after calculating the first forward traffic coefficient and the first rear following coefficient of the target controlled vehicle, the electronic device further uses the first forward traffic coefficient and the first rear following coefficient as inputs and introduces them into a preset optimization decision model. The optimization decision model then fuses the first forward traffic coefficient and the first rear following coefficient to calculate the first vehicle control parameter set. The electronic device parses the first vehicle control parameter set and generates specific control commands based on it. These commands are then sent to the actuators within the vehicle via the vehicle's internal communication network, allowing each actuator to control the longitudinal movement of the target controlled vehicle according to its received commands.

[0049] For example, the vehicle controller calculates the first rear following coefficient. and the first front traffic coefficient Then, further adjust the first rear following coefficient. First front traffic coefficient Substitute into the pre-defined optimization decision model: ; This ensures that the optimization direction of the optimal decision-making model is to set the first rear following coefficient. First front traffic coefficient Based on the optimal overall impact result, the vehicle controller then uses a distributed model predictive control algorithm to iteratively solve the optimization decision model according to the aforementioned speed consistency constraints and spacing consistency constraints to obtain the first vehicle control parameter set. The vehicle controller analyzes the first vehicle control parameter set and generates specific control commands based on it. These commands are then sent to the actuators within the vehicle via the vehicle's internal communication network, allowing each actuator to control the longitudinal movement of the target vehicle according to its received commands.

[0050] In this way, by constructing an optimization problem with the first forward traffic coefficient and the first rear following coefficient as the core influencing factors, the electronic device ensures that the generated vehicle control parameter set can fully consider the global coordination solution after the controlled vehicle's own actions affect the target vehicle queue, thereby ensuring that the vehicle queue control process is smooth and stable.

[0051] In addition, please refer to this embodiment and another embodiment. Figure 2 , Figure 2 This is a schematic diagram illustrating the queuing and vehicle following results according to an embodiment of the vehicle queuing cooperative control method of this application, as shown below. Figure 2 As shown, when there are multiple following vehicles in the target vehicle platoon, the electronic equipment can generate corresponding vehicle control parameter sets to control each following vehicle to precisely respond to all driving actions of the lead vehicle, such as acceleration, deceleration, constant speed, and braking. This achieves synchronization of the actions of all vehicles in the platoon and avoids situations such as loss of following stability or rear-end collisions. In this way, the electronic equipment ensures that the theoretical parameters output by the vehicle platoon control strategy can be converted into reliable control commands, thereby improving the stability of the vehicle platoon during operation.

[0052] In one feasible implementation, the step S50 above, "determining the first vehicle control parameter group based on the first forward traffic coefficient and the first rear following coefficient," may specifically include steps S401 to S403: Step S501: Determine the expected coefficient of the lead vehicle corresponding to the target controlled vehicle; Step S502: Integrate the expected coefficient of the leading vehicle, the first forward traffic coefficient, and the first rear following coefficient to obtain the target optimization decision model; Step S503: Combining the speed consistency constraint and the spacing consistency constraint, iteratively optimize the target optimization decision model to determine the first set of vehicle control parameters for the target controlled vehicle.

[0053] It should be noted that the lead vehicle expectation coefficient is a parameter used to quantify the intrinsic driving force required by the target controlled vehicle to track the speed of the lead vehicle. Furthermore, the target optimization decision model is a unified vector result obtained by fusing the lead vehicle expectation coefficient, the first forward traffic coefficient, and the first rear following coefficient through a specific mathematical method.

[0054] In this embodiment, after calculating the first forward traffic coefficient and the first rear following coefficient of the target controlled vehicle, the electronic device first determines the speed deviation between the target controlled vehicle and the lead vehicle, and calculates the lead vehicle expectation coefficient, which characterizes the strength of the target controlled vehicle's tracking intention, based on the speed deviation. Then, the electronic device integrates the lead vehicle expectation coefficient, the first forward traffic coefficient, and the first rear following coefficient according to preset rules to obtain a single target optimization decision model. Finally, the electronic device iteratively updates the target optimization decision model by combining the speed consistency constraint and spacing consistency constraint mentioned above, thereby determining a first vehicle control parameter set that can balance the influence of the lead vehicle expectation coefficient, the first forward traffic coefficient, and the first rear following coefficient.

[0055] For example, the vehicle controller calculates the first rear following coefficient. and the first front traffic coefficient Subsequently, the aforementioned target controlled vehicles were further deployed. discrete time state To identify the target controlled vehicle Expected acceleration and expected speed At the same time, the vehicle controller detects the target controlled vehicle. Current speed and acceleration index The vehicle controller then bases its response on the desired acceleration. Expected speed Current speed and acceleration index The expected coefficient of the leading vehicle was calculated. : ; Subsequently, the integrated controller's expectation coefficient for the navigating vehicle was determined. First rear following coefficient and the first front traffic coefficient By integrating these components, we obtain the objective optimization decision model: ; The vehicle controller then iteratively solves the target optimization decision model based on the aforementioned speed consistency constraints and spacing consistency constraints. When it is determined that all constraints are satisfied and the target optimization decision model function is minimized, the target optimization decision model function is substituted into a pre-defined distributed model predictive control optimization problem. ; Thus, the first set of vehicle control parameters is obtained.

[0056] In this way, by constructing an optimization problem with the first forward traffic coefficient and the first rear following coefficient as the core influencing factors, the electronic device ensures that the generated vehicle control parameter set can fully consider the global coordination solution after the controlled vehicle's own actions affect the target vehicle queue, thereby ensuring that the vehicle queue control process is smooth and stable.

[0057] In this embodiment, when the target controlled vehicle is in motion within the target vehicle queue, the electronic equipment configured within the target controlled vehicle first obtains the vehicle type identifier pre-assigned to the target controlled vehicle in the target vehicle queue through the vehicle-to-vehicle communication system. The electronic equipment determines the vehicle type of the target controlled vehicle based on the vehicle type identifier. Then, if the electronic equipment detects that the vehicle type is a following vehicle, it obtains the forward vehicle status information, the lead vehicle's driving parameters, and the controlled vehicle's physical parameters through the perception system. It then calls a preset car-following dynamics model to process the controlled vehicle's physical parameters to determine the discrete-time state variables of the target controlled vehicle. The preset car-following dynamics model further integrates the lead vehicle's driving parameters and discrete-time state variables to construct a speed consistency constraint with the lead vehicle's speed as the tracking target, and calculates a spacing consistency constraint with the desired following distance as the target. Based on the speed consistency constraint and the spacing consistency constraint, it calculates the first forward traffic coefficient of the target controlled vehicle. Afterward, the electronic equipment receives the target vehicle's... The electronic device receives the physical parameters of the first following vehicle adjacent to the target vehicle in the queue of vehicles, and processes the physical parameters of the first following vehicle and the driving parameters of the lead vehicle using a preset car-following dynamics model to obtain a second forward traffic coefficient. The electronic device then processes the second forward traffic coefficient through a preset transformation operation according to preset weight parameters to obtain a first rear car-following coefficient that characterizes the following vehicle's car-following capability. Finally, the electronic device uses the first forward traffic coefficient and the first rear car-following coefficient as inputs and introduces them into a preset optimization decision model. The optimization decision model then fuses the first forward traffic coefficient and the first rear car-following coefficient to calculate a first vehicle control parameter set. The electronic device parses the first vehicle control parameter set and generates specific control commands based on it. These commands are then sent to the actuators within the vehicle via the vehicle's internal communication network, allowing each actuator to control the longitudinal movement of the target controlled vehicle according to its received commands.

[0058] Thus, this application solves the technical problem in related technologies where fixed vehicle platoon control strategies cannot adjust their control logic in a timely manner based on the movement performance of the vehicle in front and the following behavior of the vehicle behind. Specifically, this application adopts a method in which electronic devices construct vehicle control parameter sets based on the traffic coefficients in front and the following coefficients behind the target controlled vehicle. This enables the electronic devices to establish a two-way dynamic coupling relationship between the controlled vehicle and the following and front following vehicles, thereby ensuring that the strategy adjustment process can fully consider the following ability of the vehicle behind and the traffic conditions in front, thus significantly improving the stability of the vehicle platoon during driving.

[0059] Based on the first embodiment of this application, a second embodiment of this application is proposed herein. In this second embodiment, content that is the same as or similar to the above embodiments can be referred to the above description and will not be repeated hereafter. Based on this, after step S10, the vehicle platoon cooperative control method of this application may further include steps A10 to A50: Step A10: If the vehicle type is detected as the following vehicle, determine whether the target controlled vehicle is a merging vehicle; Step A20: Process the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle using a preset car-following dynamics model to obtain the third forward traffic coefficient corresponding to the target controlled vehicle; Step A30: Obtain the physical parameters of the second following vehicle in the target vehicle convoy, and process the driving parameters of the lead vehicle and the physical parameters of the second following vehicle through the car-following dynamics model to obtain the fourth forward traffic coefficient corresponding to the second following vehicle, wherein the second following vehicle is located behind the target controlled vehicle and adjacent to the target controlled vehicle; Step A40: Convert the fourth forward traffic coefficient according to the preset weight parameters to obtain the second rear following coefficient corresponding to the target controlled vehicle; Step A50: Determine the second vehicle control parameter group for the target controlled vehicle based on the third forward traffic coefficient and the second rear following coefficient, and control the target controlled vehicle according to the second vehicle control parameter group.

[0060] It should be noted that the merging vehicle is another vehicle in the adjacent lane of the target vehicle queue, intending to cut into and merge with the target weighing queue.

[0061] In this embodiment, after determining the vehicle type of the target controlled vehicle, the electronic device can further detect adjacent lanes of the target vehicle queue. If the electronic device detects a merging vehicle in an adjacent lane, it further determines whether the target controlled vehicle is a merging vehicle with merging intent. If the electronic device detects that the target controlled vehicle is a merging vehicle, it acquires the forward vehicle status information, the lead vehicle's driving parameters, and the vehicle's own status information through the perception system. It then calls a preset car-following dynamics model to process and calculate a third forward traffic coefficient based on these information. Simultaneously, the electronic device receives a fourth forward traffic coefficient from the adjacent second following vehicle via the inter-vehicle communication system and processes it accordingly. The preset weight parameters are used to transform the fourth and second forward traffic coefficients to obtain the second rear following coefficient corresponding to the target controlled vehicle. Finally, the electronic device takes the third forward traffic coefficient and the second rear following coefficient as inputs and introduces them into the preset optimization decision model. The optimization decision model then fuses the third forward traffic coefficient and the second rear following coefficient to calculate the second vehicle control parameter set. The electronic device then analyzes the second vehicle control parameter set and generates a second optimal control strategy based on it. The electronic device sends the specific instructions contained in the second optimal control strategy to each actuator in the vehicle through the vehicle's internal communication network, so that each actuator can control the longitudinal movement of the target controlled vehicle according to the specific instructions it receives.

[0062] For example, the vehicle controller detects the target controlled vehicle. After identifying the vehicle type, the system further scans adjacent lanes using a radar sensor array. If a merging vehicle is detected in an adjacent lane... The vehicle controller further determines the target controlled vehicle. Is this the vehicle that was transferred in? If the vehicle controller detects And then through the target controlled vehicle The workshop communication system established between the target vehicle and other vehicles in the convoy obtains information from the lead vehicle and the vehicle ahead. The system uses vehicle status information and the physical parameters of the controlled vehicles to calculate the traffic coefficient ahead based on a preset formula. , ; The third forward traffic coefficient was calculated. Meanwhile, the target controlled vehicle The second following vehicle is obtained through the workshop communication system. The calculated fourth forward traffic coefficient And it is calculated using a preset formula for the rear following coefficient: , ; By using preset weight parameters The formula for converting rear-following coefficients to the fourth-front traffic coefficient. Perform the conversion to obtain the second rear-following coefficient. ; Then, the vehicle controller will set the second rear following coefficient. Third forward traffic coefficient Substituting into the above optimization decision model, we can ensure that the optimization direction of the optimization decision model is to set the second rear following coefficient. Third forward traffic coefficient Influence coefficient of the lead vehicle Based on the optimal overall impact result, the vehicle controller then uses a distributed model predictive control algorithm to iteratively solve the optimization decision model according to the aforementioned speed consistency constraints and spacing consistency constraints to obtain the second vehicle control parameter set. The controller then generates specific instructions according to the second vehicle control parameter set. The vehicle controller sends each specific instruction to each actuator in the vehicle through the vehicle's internal communication network, so that each actuator can control the longitudinal movement of the target controlled vehicle according to the specific instructions it receives.

[0063] In this way, when the electronic device detects a merging vehicle in the target vehicle queue, it can actively control the merging vehicle to generate constraints based on its corresponding forward traffic coefficient and backward car-following coefficient. This allows the final control strategy to simultaneously consider the forward traffic demand and backward car-following impact of the merging vehicle, ensuring that the vehicle can smoothly merge into the target vehicle queue.

[0064] Based on the first and / or second embodiments of this application, a third embodiment of this application is proposed herein. In this third embodiment, content that is the same as or similar to the above embodiments can be referred to the above description and will not be repeated hereafter. Furthermore, after step S10, the vehicle platooning cooperative control method of this application may further include steps B10 to B30: Step B10: If the vehicle type is detected to be the following vehicle, detect whether there is a first following vehicle behind the target controlled vehicle; Step B20: If it is detected that there is no first following vehicle behind the target controlled vehicle, then determine the first forward traffic coefficient corresponding to the target controlled vehicle; Step B30: The first forward traffic coefficient is sent to the third following vehicle so that the third following vehicle can determine a third vehicle control parameter group based on the first forward traffic coefficient, wherein the third following vehicle is in front of and adjacent to the target controlled vehicle.

[0065] In this embodiment, after determining the vehicle type of the target controlled vehicle, if the electronic device detects that the vehicle type is a following vehicle, it further determines whether there is an adjacent first following vehicle behind the target controlled vehicle. At this time, if the electronic device detects that there is no first following vehicle behind the target controlled vehicle, it obtains the status information of the vehicle in front, the driving parameters of the navigating vehicle, and the status information of the vehicle itself through the perception system, and calculates the first forward traffic coefficient based on the status information of the vehicle in front, the driving parameters of the navigating vehicle, and the status information of the vehicle itself. The first forward traffic coefficient is then sent to the third following vehicle in front of the target controlled vehicle so that the third following vehicle can calculate the control parameter set based on the first forward traffic coefficient.

[0066] For example, the vehicle controller is located in the target controlled vehicle. After identifying the vehicle type, if the vehicle type is detected to be one of the aforementioned following vehicles, then the target controlled vehicle is further detected. Is there a first following vehicle adjacent to it behind? At this time, if the vehicle controller detects the target controlled vehicle... There is no first following vehicle behind. Then through the target controlled vehicle The workshop communication system established between the target vehicle and other vehicles in the convoy obtains information from the lead vehicle and the vehicle ahead. Vehicle status information, target controlled vehicle The controlled vehicle's physical parameters are then used to calculate the forward traffic coefficient based on a preset formula: , ; The first forward traffic coefficient was calculated. ; Finally, the vehicle controller does not set the first forward traffic coefficient. Instead of using it for its own backward constraint calculation, the first forward traffic coefficient is transmitted through the workshop communication system. Send to the adjacent vehicle in front. For vehicles ahead Based on this first forward traffic coefficient The control parameter set is calculated.

[0067] In this way, when the electronic device detects that the controlled vehicle is the last vehicle in the target vehicle queue, it controls the target controlled vehicle to perform only the single operation of passing the traffic coefficient forward. This defines the endpoint of the bidirectional alternating communication topology, so that the information flow of the entire distributed system has a clear transmission path and reduces the overall energy consumption of the system.

[0068] This application also provides a vehicle platooning coordination control device, please refer to... Figure 3 The vehicle queuing coordination control device is applied to target controlled vehicles within a target vehicle queuing, and the vehicle queuing coordination control device includes: The type identification module 10 is used to determine the vehicle type of the target controlled vehicle, wherein the vehicle type is a lead vehicle or a follow vehicle; The first coefficient calculation module 20 is used to obtain the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle queue when the vehicle type is detected to be the following vehicle, and to process the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle. The second coefficient calculation module 30 is used to obtain the physical parameters of the first following vehicle in the target vehicle queue, and to process the driving parameters of the lead vehicle and the physical parameters of the first following vehicle through the following dynamics model to obtain the second forward traffic coefficient corresponding to the first following vehicle, wherein the first following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The coefficient conversion module 40 is used to obtain a second forward traffic coefficient and convert the second forward traffic coefficient according to a preset weight parameter to obtain a first rear following coefficient corresponding to the target controlled vehicle. The second forward traffic coefficient is calculated by a first following vehicle, which is located behind the target controlled vehicle and adjacent to the target controlled vehicle. The queue control module 50 is used to determine a first vehicle control parameter group based on the first forward traffic coefficient and the first rear following coefficient, and to control the target controlled vehicle according to the first vehicle control parameter group.

[0069] In one feasible implementation, the first coefficient calculation module 20 is further configured to: The discrete-time state quantities of the target controlled vehicle are determined by combining the physical parameters of the controlled vehicle. Based on the driving parameters of the lead vehicle and the discrete-time state quantities, determine the speed consistency constraints and the spacing consistency constraints. Based on the speed consistency constraint and the spacing consistency constraint, the dynamic expected spacing of the target controlled vehicle is determined; The first forward traffic coefficient corresponding to the target controlled vehicle is determined by combining the dynamic expected spacing, the preset influence weight coefficient, and the preset maximum braking deceleration.

[0070] In one feasible implementation, the first coefficient calculation module 20 is further configured to: Obtain the controlled vehicle physical parameters of the target controlled vehicle, and construct the third-order nonlinear dynamic equations corresponding to the target controlled vehicle based on the controlled vehicle physical parameters; The real-time state change rate model of the target controlled vehicle is obtained by optimizing the third-order nonlinear dynamic equation based on the real-time state variables and real-time control variables of the controlled vehicle. The real-time change rate model of the state is discretized according to a preset discrete time step to determine the discrete-time state quantities of the target controlled vehicle.

[0071] In one feasible implementation, the queue control module 50 is further configured to: Determine the expected coefficient of the lead vehicle corresponding to the target controlled vehicle; The target optimization decision model is obtained by integrating the expected coefficient of the leading vehicle, the first forward traffic coefficient, and the first rear following coefficient. By combining speed consistency constraints and spacing consistency constraints, the target optimization decision model is iteratively optimized to determine the first set of vehicle control parameters for the target controlled vehicle.

[0072] In one feasible implementation, the first coefficient calculation module 20 is further configured to: If the vehicle type is detected to be the following vehicle, determine whether the target controlled vehicle is a merging vehicle; The third forward traffic coefficient corresponding to the target controlled vehicle is obtained by processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model. The physical parameters of the second following vehicle in the target vehicle convoy are obtained, and the driving parameters of the lead vehicle and the physical parameters of the second following vehicle are processed by the car-following dynamics model to obtain the fourth forward traffic coefficient corresponding to the second following vehicle. The second following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The fourth forward traffic coefficient is converted according to the preset weight parameters to obtain the second rear following coefficient corresponding to the target controlled vehicle. The second vehicle control parameter set of the target controlled vehicle is determined based on the third forward traffic coefficient and the second rear following coefficient, and the target controlled vehicle is controlled according to the second vehicle control parameter set.

[0073] In one feasible implementation, the coefficient calculation module 20 is further used for: If the vehicle type is detected to be the following vehicle, detect whether there is a first following vehicle behind the target controlled vehicle; If it is detected that there is no first following vehicle behind the target controlled vehicle, then the first forward traffic coefficient corresponding to the target controlled vehicle is determined; The first forward traffic coefficient is sent to the third following vehicle so that the third following vehicle can determine a third vehicle control parameter set based on the first forward traffic coefficient, wherein the third following vehicle is in front of and adjacent to the target controlled vehicle.

[0074] The vehicle platooning coordination control device provided in this application, employing the vehicle platooning coordination control method in the above embodiments, can solve the technical problem in related technologies where fixed vehicle platooning control strategies cannot adjust their control logic in a timely manner based on the movement performance of the preceding vehicle and the following vehicle's following behavior. Compared with the prior art, the beneficial effects of the vehicle platooning coordination control device provided in this application are the same as those of the vehicle platooning coordination control method provided in the above embodiments, and other technical features in the vehicle platooning coordination control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0075] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the vehicle queuing cooperative control method in Embodiment 1 above.

[0076] The following is for reference. Figure 4 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic device in these embodiments may include, but is not limited to, an electronic device configured in a vehicle, or a mobile terminal, data storage control terminal, PC, or other terminal associated with the electronic device. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 4As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0078] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0079] The electronic device provided in this application, employing the vehicle queuing cooperative control method in the above embodiments, can solve the technical problem in related technologies where fixed vehicle queuing control strategies cannot adjust their control logic in a timely manner based on the movement performance of the preceding vehicle and the following vehicle's driving behavior. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the vehicle queuing cooperative control method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0080] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0082] This application provides a vehicle having electronic equipment configured thereon, the electronic equipment being used to execute the vehicle queuing cooperative control method in the above embodiments.

[0083] The vehicle provided in this application solves the technical problem in related technologies where fixed vehicle queuing control strategies cannot adjust their control logic in a timely manner based on the movement of the vehicle in front and the following behavior of the vehicle behind. Compared with the prior art, the beneficial effects of the vehicle provided in this application are the same as those of the vehicle queuing cooperative control method provided in the above embodiments, and will not be repeated here.

[0084] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle queuing cooperative control method in the above embodiments.

[0085] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0086] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0087] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: determine the vehicle type of the target controlled vehicle, wherein the vehicle type is a lead vehicle or a follower vehicle; if the vehicle type is detected to be a follower vehicle, acquire the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle queue, and process the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model to obtain a first forward traffic coefficient corresponding to the target controlled vehicle; acquire the physical parameters of the first follower vehicle in the target vehicle queue, and process the driving parameters of the lead vehicle and the physical parameters of the first follower vehicle through the car-following dynamics model to obtain a second forward traffic coefficient corresponding to the first follower vehicle, wherein the first follower vehicle is behind and adjacent to the target controlled vehicle; convert the second forward traffic coefficient according to preset weight parameters to obtain a first rear car-following coefficient corresponding to the target controlled vehicle; determine a first vehicle control parameter group based on the first forward traffic coefficient and the first rear car-following coefficient, and control the target controlled vehicle according to the first vehicle control parameter group.

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0090] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0091] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle platooning cooperative control method. This solves the technical problem in related technologies where fixed vehicle platooning control strategies cannot adjust their control logic in a timely manner based on the movement of the preceding vehicle and the following vehicle's behavior. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the vehicle platooning cooperative control method provided in the above embodiments, and will not be elaborated upon here.

[0092] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle queuing cooperative control method described above.

[0093] The computer program product provided in this application can solve the technical problem in related technologies where fixed vehicle queuing control strategies cannot adjust their control logic in a timely manner based on the movement performance of the vehicle in front and the following behavior of the vehicle behind. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle queuing cooperative control method provided in the above embodiments, and will not be repeated here.

[0094] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A vehicle platoon cooperative control method, characterized in that, The vehicle platoon cooperative control method is applied to target controlled vehicles within a target vehicle platoon, and the method includes: Determine the vehicle type of the target controlled vehicle, wherein the vehicle type is a lead vehicle or a follower vehicle; When the vehicle type is detected to be the following vehicle, the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle in the target vehicle queue are obtained, and the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle are processed by a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle. The physical parameters of the first following vehicle in the target vehicle queue are obtained, and the driving parameters of the lead vehicle and the physical parameters of the first following vehicle are processed by the car-following dynamics model to obtain the second forward traffic coefficient corresponding to the first following vehicle. The first following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The second forward traffic coefficient is converted according to preset weight parameters to obtain the first rear following coefficient corresponding to the target controlled vehicle. A first vehicle control parameter set is determined based on the first forward traffic coefficient and the first rear following coefficient, and the target controlled vehicle is controlled according to the first vehicle control parameter set.

2. The vehicle platooning cooperative control method as described in claim 1, characterized in that, The step of processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model to obtain the first forward traffic coefficient corresponding to the target controlled vehicle includes: The discrete-time state quantities of the target controlled vehicle are determined by combining the physical parameters of the controlled vehicle. Based on the driving parameters of the lead vehicle and the discrete-time state quantities, determine the speed consistency constraints and the spacing consistency constraints. Based on the speed consistency constraint and the spacing consistency constraint, the dynamic expected spacing of the target controlled vehicle is determined; The first forward traffic coefficient corresponding to the target controlled vehicle is determined by combining the dynamic expected spacing, the preset influence weight coefficient, and the preset maximum braking deceleration.

3. The vehicle platooning cooperative control method as described in claim 2, characterized in that, The step of determining the discrete-time state quantities of the target controlled vehicle by combining the physical parameters of the controlled vehicle includes: Construct the third-order nonlinear dynamic equations corresponding to the target controlled vehicle based on the physical parameters of the controlled vehicle; The real-time state change rate model of the target controlled vehicle is obtained by optimizing the third-order nonlinear dynamic equation based on the real-time state variables and real-time control variables of the controlled vehicle. The real-time change rate model of the state is discretized according to a preset discrete time step to determine the discrete-time state quantities of the target controlled vehicle.

4. The vehicle platooning cooperative control method as described in any one of claims 1 to 3, characterized in that, The step of determining the first vehicle control parameter group based on the first forward traffic coefficient and the first rear following coefficient includes: Determine the expected coefficient of the lead vehicle corresponding to the target controlled vehicle; The target optimization decision model is obtained by integrating the expected coefficient of the leading vehicle, the first forward traffic coefficient, and the first rear following coefficient. By combining speed consistency constraints and spacing consistency constraints, the target optimization decision model is iteratively optimized to determine the first set of vehicle control parameters for the target controlled vehicle.

5. The vehicle platooning cooperative control method as described in claim 1, characterized in that, After the step of determining the vehicle type of the target controlled vehicle, the method further includes: If the vehicle type is detected to be the following vehicle, determine whether the target controlled vehicle is a merging vehicle; The third forward traffic coefficient corresponding to the target controlled vehicle is obtained by processing the driving parameters of the lead vehicle and the physical parameters of the controlled vehicle through a preset car-following dynamics model. The physical parameters of the second following vehicle in the target vehicle convoy are obtained, and the driving parameters of the lead vehicle and the physical parameters of the second following vehicle are processed by the car-following dynamics model to obtain the fourth forward traffic coefficient corresponding to the second following vehicle. The second following vehicle is located behind the target controlled vehicle and is adjacent to the target controlled vehicle. The fourth forward traffic coefficient is converted according to the preset weight parameters to obtain the second rear following coefficient corresponding to the target controlled vehicle. The second vehicle control parameter set of the target controlled vehicle is determined based on the third forward traffic coefficient and the second rear following coefficient, and the target controlled vehicle is controlled according to the second vehicle control parameter set.

6. The vehicle platooning cooperative control method as described in claim 1, characterized in that, After the step of determining the vehicle type of the target controlled vehicle, the method further includes: If the vehicle type is detected to be the following vehicle, detect whether there is a first following vehicle behind the target controlled vehicle; If it is detected that there is no first following vehicle behind the target controlled vehicle, then the first forward traffic coefficient corresponding to the target controlled vehicle is determined; The first forward traffic coefficient is sent to the third following vehicle so that the third following vehicle can determine a third vehicle control parameter set based on the first forward traffic coefficient, wherein the third following vehicle is in front of and adjacent to the target controlled vehicle.

7. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle platooning cooperative control method as described in any one of claims 1 to 6.

8. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the vehicle queuing cooperative control method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the vehicle queuing cooperative control method as described in any one of claims 1 to 6.

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