An intelligent container truck platoon driving simulation platform system

Abstract

The application relates to a smart container truck platoon driving simulation platform, which comprises three modules of traffic simulation, platoon management and truck control connected in sequence, and the traffic simulation module is interactively connected with the platoon management module and the truck control module. The traffic simulation module is used for human-driven vehicle simulation, and the module is composed of seven units of road network, path decision, traffic control, traffic detection, human-driven vehicle control, information visualization and traffic generation; the platoon management module is used for intelligent container truck platoon behavior simulation, and the module is composed of two units of fleet decision maker and platoon management strategy; and the truck control module is used for intelligent container truck simulation, and the module is composed of three units of single vehicle decision maker, controller and vehicle dynamics. Compared with the prior art, the application supports verification of various platoon driving functions, can evaluate the influence of intelligent container truck platoon on the traffic system under the background of new mixed traffic flow, and can evaluate the efficiency of the platoon management strategy.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation simulation technology, and in particular to an intelligent container truck platooning driving simulation platform system. Background Technology

[0002] Intelligent container truck platooning utilizes technologies such as vehicle-to-everything (V2X) and automated vehicle control to enable a group of container trucks to travel closely in a platoon. Intelligent container truck platooning technology offers cost reduction and efficiency improvement in the freight transportation sector, primarily through: 1) energy conservation and emission reduction; 2) alleviating traffic congestion and increasing road capacity; and 3) reducing labor costs. Therefore, the demand for the commercialization of intelligent container truck platooning is growing rapidly.

[0003] To achieve the commercialization of intelligent truck platooning, this technology needs to possess the following capabilities: 1) reliable safety; 2) substantial economic benefits; and 3) a positive impact on the transportation system. To verify these capabilities, a comprehensive evaluation and verification of the intelligent truck platooning technology is required, including verification of platooning driving functions, evaluation of platooning management strategies, and assessment of the impact of truck platooning on the transportation system.

[0004] Simulation plays a crucial role in the evaluation and verification of intelligent truck platooning driving technology. Currently, intelligent truck platooning driving simulation platforms on the market can be divided into two categories: vehicle simulation platforms and traffic simulation platforms. Vehicle simulation platforms can support the verification of various platooning driving functions, but they cannot generate interactive background traffic flows, thus failing to assess the impact of truck platooning on the traffic system. Traffic simulation platforms can assess the impact of truck platooning on the traffic system, but they lack a realistic truck platooning driving module, thus failing to support the verification of platooning driving functions. Furthermore, neither of these platforms can evaluate the effectiveness of platooning management strategies. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an intelligent container truck platooning driving simulation platform system that can support the verification of various platooning driving functions, evaluate the impact of intelligent truck platooning on the traffic system under the background of new mixed traffic flow, and evaluate the effectiveness of platooning management strategies.

[0006] The objective of this invention can be achieved through the following technical solution: an intelligent container truck platooning driving simulation platform system, comprising a traffic simulation module, a platooning management module, and a truck control module connected in sequence, wherein the traffic simulation module and the truck control module are interactively connected, and the traffic simulation module and the platooning management module are interactively connected;

[0007] The traffic simulation module is used for human-driven vehicle simulation and includes a road network unit, a path decision unit, a traffic control unit, a traffic detection unit, a human-driven vehicle control unit, an information visualization unit, and a traffic generation unit. The road network unit is used to generate a road network.

[0008] The path decision unit is used to determine the vehicle's driving path;

[0009] The traffic control unit is used to formulate traffic control plans;

[0010] The traffic detection unit is used to detect traffic conditions;

[0011] The human-driven vehicle control unit is used to control the behavior of human drivers.

[0012] The information visualization unit is used to display simulation results;

[0013] The traffic generation unit is used to randomly generate mixed traffic flows;

[0014] The platooning management module is used for intelligent truck platooning behavior simulation. It includes a platooning decision-maker and a platooning management strategy unit. The platooning decision-maker is used to determine the intelligent truck platooning mode. The road network unit, route decision unit, traffic control unit, and traffic detection unit are respectively connected to the platooning decision-maker.

[0015] The formation management strategy unit is used to formulate intelligent truck formation management strategies, and the formation management strategy unit is connected to the traffic generation unit;

[0016] The truck control module, used for intelligent truck simulation, includes a single-vehicle decision-maker, a controller, and a vehicle dynamics unit connected in sequence. The vehicle dynamics unit is interactively connected to the single-vehicle decision-maker. The single-vehicle decision-maker is used to determine the intelligent truck mode. The traffic detection unit and the fleet decision-maker are respectively connected to the single-vehicle decision-maker.

[0017] The controller is used to generate lateral and longitudinal control commands;

[0018] The vehicle dynamics unit is used to generate the vehicle's realistic response to control commands, and the vehicle dynamics unit is connected to the information visualization unit.

[0019] Furthermore, the traffic generation unit includes a human-driven vehicle generation subunit and an intelligent truck generation subunit. The traffic generation unit is connected to the truck control module. The human-driven vehicle generation subunit generates human-driven vehicle flow according to traffic demand and vehicle type composition information.

[0020] The intelligent truck generation subunit is used to generate intelligent truck flow, including three generation modes: single truck, fleet, and single truck-fleet hybrid. The single truck generation mode refers to the random generation of intelligent trucks in the form of single trucks.

[0021] The fleet generation mode refers to the random generation of smart trucks in the form of a fleet.

[0022] The single-vehicle-fleet hybrid generation mode refers to a portion of smart trucks being randomly generated as single vehicles, and another portion of smart trucks being randomly generated as a fleet.

[0023] Furthermore, the intelligent truck convoy modes determined by the convoy decision-maker include five modes: convoy maintenance, convoy merging, convoy splitting, convoy lane changing, and convoy returning to the lane.

[0024] Furthermore, the formation management strategy includes two strategies: temporary formation and global coordination. The temporary formation strategy specifically involves temporarily forming a fleet of two smart trucks that are following each other.

[0025] The global coordination strategy specifically refers to pre-grouping intelligent trucks according to their starting point, destination, and departure time.

[0026] Furthermore, the intelligent truck mode determined by the single-vehicle decision-maker includes five modes: ACC (Adaptive Cruise Control), CACC (Cooperative Adaptive Cruise Control), Lane Return, Automatic Lane Change, and Manual Lane Change.

[0027] Furthermore, the controller includes a longitudinal controller and a lateral controller. The longitudinal controller is used to generate longitudinal control commands for the intelligent truck and includes an upper controller and a lower controller. The upper controller includes a first control subunit and a second control subunit. The first control subunit is used to generate control commands for controlling the first truck in the intelligent truck fleet to cruise freely and follow the vehicle in driving mode.

[0028] The second control subunit is used to generate control commands to control the following distance between vehicles in the intelligent truck convoy;

[0029] The underlying controller generates control commands that meet the requirements of the vehicle dynamics model based on the control commands generated by the first control subunit and the second control subunit.

[0030] The lateral controller is used to generate lateral control commands for the smart truck.

[0031] Furthermore, the first control subunit adopts IIDM (Improved Intelligent Driver Model), and the second control subunit adopts MPC (Model Predictive Control) longitudinal control system, including corresponding system dynamics, cost function and control constraints.

[0032] Furthermore, the system dynamics of the MPC longitudinal control system specifically include:

[0033] State vector ξ lon =[d l -v i h i -s0,v l -v i -a i h i and control vector u lon =[a c ], where d l The distance between the front of the current truck in the convoy and the lead truck in the convoy, s0 is the safety distance, v l a is the speed of the lead car in the convoy. c For acceleration control commands, v i a i and h i Let be the speed, acceleration, and distance between the i-th truck, respectively;

[0034] The system dynamic equation formula for the MPC longitudinal control system is:

[0035]

[0036]

[0037]

[0038]

[0039] in, Let be the state vector at step t+1. Let A be the control vector at step t. t B t and C t Let these be the state coefficient matrix, control coefficient matrix, and constant matrix at step t, respectively. For the desired acceleration step response time, d t To control the step size, a l The acceleration of the lead car in the convoy;

[0040] The cost function of the MPC vertical control system is specifically as follows:

[0041]

[0042] in, and The weights of the state vector, and R represents the weights of the final state vector. lon The weights are for controlling the vector.

[0043] The control constraints of the MPC longitudinal control system are as follows:

[0044] a min ≤a c ≤a max

[0045] Among them, a min and a max These are the minimum acceleration and the maximum acceleration, respectively.

[0046] Furthermore, the lateral controller adopts an MPC lateral control system, including corresponding system dynamics, cost functions, and control constraints.

[0047] Furthermore, the system dynamics of the MPC lateral control system specifically include:

[0048] State vector and control vector Among them, l i , and δ fi These represent the lateral position, heading angle, and front wheel deflection angle of the i-th truck, respectively. This is the command for controlling the front wheel deflection angle;

[0049] The system dynamic equation formula of the MPC lateral control system is:

[0050]

[0051]

[0052]

[0053]

[0054] in, This is the state vector at step s+1. Let A be the control vector at step s. s B s and C sThese are the state coefficient matrix, control coefficient matrix, and constant matrix at step s, respectively. w Wheelbase To determine the desired step response time of the front wheel deflection angle, d s To control the step size, k s Let be the trajectory curvature at step s;

[0055] The cost function formula for the MPC lateral control system is:

[0056]

[0057] in, and The weights of the state vector, and R represents the weights of the final state vector. lat To control the weights of the vector, and These are the reference state vectors for the s-th step and the final step, respectively;

[0058] The control constraints of the MPC lateral control system are as follows:

[0059]

[0060] in, and These are the minimum and maximum front wheel deflection angles, respectively.

[0061] Compared with existing technologies, this invention designs three sequentially connected modules: traffic simulation, platoon management, and truck control. The traffic simulation module is interactively connected with the truck control module. The traffic simulation module is used to simulate human driving of vehicles, the platoon management module is used to simulate intelligent truck platooning behavior, and the truck control module is used to simulate intelligent trucks. This achieves a modular design structure that is compatible with various truck platooning driving decision-makers, controllers, and vehicle dynamics models, and supports the verification of various truck platooning driving functions.

[0062] In this invention, a traffic generation unit is set up within the traffic simulation module to generate large-scale random mixed traffic flows. This traffic generation unit includes a human-driven vehicle generation subunit and an intelligent truck generation subunit. The human-driven vehicle generation subunit generates human-driven vehicle flows based on traffic demand, vehicle type composition, etc.; the intelligent truck generation subunit generates intelligent truck flows, including three generation modes: single vehicle, platoon, and a hybrid single-vehicle-platoon. This allows for the evaluation of the impact of intelligent truck platooning on the traffic system under the new mixed traffic flow background.

[0063] In this invention, a formation management strategy unit is set up within the formation management module to realize two strategies: temporary formation and global coordination. The formation management strategy unit outputs management strategies to the traffic generation unit, which then randomly generates mixed traffic flows. This provides reliable data support for subsequent evaluation of the effectiveness of the formation management strategy. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0065] Figure 2 This is a schematic diagram illustrating the working process of the fleet decision-maker in this invention;

[0066] Figure 3 This is a schematic diagram illustrating the working process of the single-vehicle decision-maker in this invention;

[0067] Figure 4 This is a schematic diagram illustrating the working process of the vehicle dynamics unit in this invention. Detailed Implementation

[0068] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0069] Example

[0070] like Figure 1 As shown, an intelligent container truck platooning driving simulation platform system includes a traffic simulation module, a platooning management module, and a truck control module:

[0071] The traffic simulation module is used for human-driven vehicle simulation, such as... Figure 1 As shown, this module comprises seven units: road network, path decision, traffic control, traffic detection, human-driven vehicle control, information visualization, and traffic generation. The road network unit generates the road network, the path decision unit determines vehicle travel routes, the traffic control unit formulates traffic control schemes, the traffic detection unit detects traffic conditions, the human-driven vehicle control unit controls human-driven vehicle behavior, the information visualization unit displays simulation results, and the traffic generation unit randomly generates mixed traffic flows.

[0072] The formation management module is used for simulating the formation behavior of intelligent trucks, such as... Figure 1 As shown, this module comprises two units: a fleet decision-maker and a fleet management strategy unit. The fleet decision-maker determines the intelligent truck fleet mode, while the fleet management strategy unit formulates the intelligent truck fleet management strategy.

[0073] Truck control modules are used for intelligent truck simulation, such as Figure 1As shown, this module comprises three units: a vehicle decision-maker, a controller, and vehicle dynamics. The vehicle decision-maker determines the intelligent truck mode, the controller unit generates lateral and longitudinal control commands, and the vehicle dynamics unit generates the vehicle's actual response to the control commands.

[0074] Specifically, the traffic generation unit includes a human-driven vehicle generation subunit and an intelligent truck generation subunit. The human-driven vehicle generation subunit generates human-driven vehicle traffic flow based on traffic demand and vehicle type composition. The intelligent truck generation subunit generates intelligent truck traffic flow, including three generation modes: single-vehicle, convoy, and a hybrid single-vehicle-convoy mode. The single-vehicle generation mode refers to the random generation of intelligent trucks as individual vehicles; the convoy generation mode refers to the random generation of intelligent trucks as convoys; and the hybrid single-vehicle-convoy mode refers to the random generation of some intelligent trucks as individual vehicles and others as convoys.

[0075] like Figure 2 As shown, the intelligent truck fleet modes determined by the fleet decision-making unit include five modes: fleet maintenance, fleet merging, fleet splitting, fleet lane changing, and fleet returning to the lane.

[0076] The platooning management strategy includes two types: temporary platooning and global coordination. The temporary platooning strategy refers to the two smart trucks temporarily forming a platoon when they happen to be following another smart truck. The global coordination strategy refers to the smart trucks being platooned in advance based on factors such as their origin, destination, and departure time.

[0077] like Figure 3 As shown, the intelligent truck modes determined by the single-vehicle decision-making unit include five modes: ACC, CACC, return lane, automatic lane change, and manual lane change.

[0078] The controller unit includes a longitudinal controller and a lateral controller. The longitudinal controller is used to generate longitudinal control commands for the intelligent truck. It includes an upper controller and a lower controller. The upper controller includes a first control subunit and a second control subunit. The first control subunit is used to generate control commands for the first truck in the intelligent truck fleet to cruise freely and follow the truck in the following driving mode. The second control subunit is used to generate control commands for the following distance of the following trucks in the intelligent truck fleet.

[0079] The underlying controller generates control commands that meet the requirements of the vehicle dynamics model based on the control commands generated by the first control subunit and the second control subunit.

[0080] The lateral controller is used to generate lateral control commands for intelligent trucks.

[0081] Specifically, the first control subunit adopts the IIDM model, and the second control subunit adopts the MPC longitudinal control system, including the corresponding system dynamics, cost function, and control constraints:

[0082] In the system dynamics of MPC longitudinal control, the state vector ξ is included. lon =[d l -v i h i -s0,v l -v i -a i h i and control vector u lon =[a c ], where d l It is the distance between the front of the current truck in the convoy and the lead truck in the convoy, s0 is the safety distance, v l It is the speed of the lead car in the convoy, a c It is an acceleration control command, v i a i and h i These are the speed, acceleration, and headway of the i-th truck, respectively.

[0083] The system dynamic equation formula for MPC longitudinal control is:

[0084]

[0085]

[0086]

[0087]

[0088] in, It is the state vector at step t+1. A is the control vector at step t. t B t and C t These are the state coefficient matrix, control coefficient matrix, and constant matrix at step t, respectively. It is the expected acceleration step response time, d t It controls the step size, a l It is the acceleration of the lead car in the convoy.

[0089] The cost function formula for MPC vertical control is:

[0090]

[0091] in, and These are the weights of the state vector. and The weights of the final state vector, R lon These are the weights of the control vector.

[0092] The control constraint for MPC longitudinal control is a min ≤a c ≤a max , where a min and a max These are the minimum acceleration and the maximum acceleration, respectively.

[0093] The lateral controller employs an MPC lateral control system, including corresponding system dynamics, cost functions, and control constraints. The system dynamics of MPC lateral control include state vectors. and control vector Among them, l i , and These are the lateral position, heading angle, and front wheel deflection angle of the i-th truck, respectively. It is the front wheel deflection control command.

[0094] The system dynamic equation formula for MPC lateral control is:

[0095]

[0096]

[0097]

[0098]

[0099] in It is the state vector at step s+1. A is the control vector at step s. s B s and C s These are the state coefficient matrix, control coefficient matrix, and constant matrix at step s, respectively. w It's the wheelbase. It is the expected step response time of the front wheel deflection angle, d s It controls the step size, k s It is the trajectory curvature at step s.

[0100] The formula for the lateral control cost function of MPC lateral control is:

[0101]

[0102] in, and These are the weights of the state vector. and The weights of the final state vector, R lat These are the weights of the control vector. and These are the reference state vectors for the s-th step and the final step, respectively.

[0103] The control constraints for MPC lateral control are:

[0104]

[0105] in, and These are the minimum and maximum front wheel deflection angles, respectively.

[0106] like Figure 4 As shown, the vehicle dynamics unit is used to simulate the vehicle's real response to upper-level control commands, thereby controlling the vehicle's acceleration, deceleration, and steering behavior.

[0107] In summary, this technical solution proposes an intelligent container truck platooning driving simulation platform. Compared with existing simulation platforms, this technical solution adopts a modular design structure, is compatible with various truck platooning driving decision-makers, controllers, and dynamic models, and supports the verification of various truck platooning driving functions.

[0108] It can generate large-scale random mixed traffic flows, supporting the assessment of the impact of platooning driving on the traffic system;

[0109] A formation management module was designed to support the evaluation of the effectiveness of formation management strategies;

[0110] In addition, this technical solution has a fast simulation speed. After testing, it can run about 300 intelligent trucks and unlimited human-driven vehicles per second.

Claims

1. A smart container truck platooning driving simulation platform system, characterized in that, It includes a traffic simulation module, a platoon management module, and a truck control module connected in sequence. The traffic simulation module and the truck control module are interactively connected, and the traffic simulation module and the platoon management module are interactively connected. The traffic simulation module is used for human-driven vehicle simulation and includes a road network unit, a path decision unit, a traffic control unit, a traffic detection unit, a human-driven vehicle control unit, an information visualization unit, and a traffic generation unit. The road network unit is used to generate a road network. The path decision unit is used to determine the vehicle's driving path; The traffic control unit is used to formulate traffic control plans; The traffic detection unit is used to detect traffic conditions; The human-driven vehicle control unit is used to control the behavior of human drivers. The information visualization unit is used to display simulation results; The traffic generation unit is used to randomly generate mixed traffic flows; The platooning management module is used for intelligent truck platooning behavior simulation. It includes a platooning decision-maker and a platooning management strategy unit. The platooning decision-maker is used to determine the intelligent truck platooning mode. The road network unit, route decision unit, traffic control unit, and traffic detection unit are respectively connected to the platooning decision-maker. The formation management strategy unit is used to formulate intelligent truck formation management strategies, and the formation management strategy unit is connected to the traffic generation unit; The truck control module, used for intelligent truck simulation, includes a single-vehicle decision-maker, a controller, and a vehicle dynamics unit connected in sequence. The vehicle dynamics unit is interactively connected to the single-vehicle decision-maker. The single-vehicle decision-maker is used to determine the intelligent truck mode. The traffic detection unit and the fleet decision-maker are respectively connected to the single-vehicle decision-maker. The controller is used to generate lateral and longitudinal control commands; The vehicle dynamics unit is used to generate the vehicle's realistic response to control commands, and the vehicle dynamics unit is connected to the information visualization unit.

2. The intelligent container truck platooning driving simulation platform system according to claim 1, characterized in that, The traffic generation unit includes a human-driven vehicle generation subunit and an intelligent truck generation subunit. The traffic generation unit is connected to the truck control module. The human-driven vehicle generation subunit generates human-driven vehicle flow according to traffic demand and vehicle type composition information. The intelligent truck generation subunit is used to generate intelligent truck flow, including three generation modes: single truck, fleet, and single truck-fleet hybrid. The single truck generation mode refers to the random generation of intelligent trucks in the form of single trucks. The fleet generation mode refers to the random generation of smart trucks in the form of a fleet. The single-vehicle-fleet hybrid generation mode refers to a portion of smart trucks being randomly generated as single vehicles, and another portion of smart trucks being randomly generated as a fleet.

3. The intelligent container truck platooning driving simulation platform system according to claim 1, characterized in that, The intelligent truck convoy modes determined by the convoy decision-maker include five modes: convoy maintenance, convoy merging, convoy splitting, convoy lane changing, and convoy returning to the lane.

4. The intelligent container truck platooning driving simulation platform system according to claim 1, characterized in that, The formation management strategy includes two strategies: temporary formation and global coordination. The temporary formation strategy specifically involves temporarily forming a fleet of two smart trucks that are following each other. The global coordination strategy specifically refers to pre-grouping intelligent trucks according to their starting point, destination, and departure time.

5. The intelligent container truck platooning driving simulation platform system according to claim 1, characterized in that, The intelligent truck mode determined by the single-vehicle decision-maker includes five modes: ACC, CACC, return lane, automatic lane change, and manual lane change.

6. The intelligent container truck platooning driving simulation platform system according to claim 1, characterized in that, The controller includes a longitudinal controller and a lateral controller. The longitudinal controller is used to generate longitudinal control commands for the intelligent truck. It includes an upper controller and a lower controller. The upper controller includes a first control subunit and a second control subunit. The first control subunit is used to generate control commands to control the first truck in the intelligent truck fleet to cruise freely and follow the truck in driving mode. The second control subunit is used to generate control commands to control the following distance between vehicles in the intelligent truck convoy; The underlying controller generates control commands that meet the requirements of the vehicle dynamics model based on the control commands generated by the first control subunit and the second control subunit. The lateral controller is used to generate lateral control commands for the smart truck.

7. The intelligent container truck platooning driving simulation platform system according to claim 6, characterized in that, The first control subunit adopts the IIDM model, and the second control subunit adopts the MPC longitudinal control system, including the corresponding system dynamics, cost function and control constraints.

8. The intelligent container truck platooning driving simulation platform system according to claim 7, characterized in that, The system dynamics of the MPC longitudinal control system specifically include: State vector and control vector ,in, This is the distance between the front of the current truck in the convoy and the front of the lead truck. For a safe distance, For the speed of the lead car in the convoy, For acceleration control commands, , and The first i Speed, acceleration, and distance between the front and rear of the trucks; The system dynamic equation formula for the MPC longitudinal control system is: in, Let be the state vector at step t+1. Let be the control vector at step t. , and Let these be the state coefficient matrix, control coefficient matrix, and constant matrix at step t, respectively. For the desired acceleration step response time, To control the step size, The acceleration of the lead car in the convoy; The cost function of the MPC vertical control system is specifically as follows: in, and The weights of the state vector, and The weights of the final state vector. The weights are for controlling the vector. The control constraints of the MPC longitudinal control system are as follows: in, and These are the minimum acceleration and the maximum acceleration, respectively.

9. The intelligent container truck platooning driving simulation platform system according to claim 6, characterized in that, The lateral controller adopts an MPC lateral control system, which includes corresponding system dynamics, cost functions, and control constraints.

10. The intelligent container truck platooning driving simulation platform system according to claim 9, characterized in that, The system dynamics of the MPC lateral control system specifically include: State vector and control vector ,in, , and The first i The lateral position, heading angle, and front wheel deflection angle of the truck. This is the command for controlling the front wheel deflection angle; The system dynamic equation formula of the MPC lateral control system is: in, This is the state vector at step s+1. This is the control vector at step s. , and These are the state coefficient matrix, control coefficient matrix, and constant matrix at step s, respectively. Wheelbase To determine the desired step response time of the front wheel deflection angle, To control the step size, Let be the trajectory curvature at step s; The cost function formula for the MPC lateral control system is: in, , and The weights of the state vector, , and The weights of the final state vector. To control the weights of the vector, and These are the reference state vectors for the s-th step and the final step, respectively; The control constraints of the MPC lateral control system are as follows: in, and These are the minimum and maximum front wheel deflection angles, respectively.

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