Test method and device for truck formation control system

Abstract

The invention provides a test method and device for a truck formation control system, and the method comprises the steps: obtaining vehicle control information corresponding to a simulation traction truck and a simulation following truck in a tested truck formation control system, the vehicle control information is generated based on each tested controller device in the tested truck formation control system; and based on the vehicle control information and the truck formation control system simulation test scene, carrying out simulation test on the tested truck formation control system to obtain a test result corresponding to the tested truck formation control system. Real vehicle testing is replaced by a hardware-in-the-loop simulation environment, complex and changeable traffic scenes are accurately reproduced, it is ensured that the performance of the truck formation control system is verified under various conditions, the testing efficiency is improved, meanwhile, safety risks are effectively avoided, and the safety of testing personnel and equipment is protected.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving, and particularly to a test method and device for a truck platooning control system. Background Art

[0002] In the field of autonomous driving technology, especially the test technology of truck platooning control systems, it is crucial for ensuring the safety and reliability of autonomous driving. Truck platooning technology relies on effective communication and coordination between vehicles, aiming to achieve a tight convoy driving mode, thereby improving transportation efficiency and reducing energy consumption.

[0003] However, there are many limitations in conducting tests on actual vehicles. Real vehicle testing not only involves high risks but also means high cost investment. Many complex test scenarios are difficult to reproduce in the actual road environment, which undoubtedly increases the difficulty and uncertainty of testing.

[0004] Therefore, there is an urgent need for a test method and device for a truck platooning control system to solve the above problems. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a test method and device for a truck platooning control system.

[0006] The present invention provides a test method for a truck platooning control system, including: Obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the truck platooning control system to be tested, wherein the vehicle control information is generated based on each tested controller device in the truck platooning control system to be tested; Based on the vehicle control information and the truck platooning control system simulation test scenario, performing a simulation test on the truck platooning control system to be tested to obtain the test result corresponding to the truck platooning control system to be tested; Wherein, the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, a traffic scenario simulation environment, a first dynamic model, and a second dynamic model, the first dynamic model is the dynamic model corresponding to the simulated leading truck, and the second dynamic model is the dynamic model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0007] According to the test method for a truck platooning control system provided by the present invention, the traffic flow simulation environment data is obtained through the following steps: Construct the traffic flow simulation environment data according to the simulation vehicle status information, where the simulation vehicle status information is the status information of the simulated traffic vehicles; the simulated traffic vehicles are other traffic vehicles except the simulated towing truck and the simulated following truck.

[0008] According to a test method for a truck platooning control system provided by the present invention, the constructing the traffic flow simulation environment data according to the simulation vehicle status information includes: Construct a preset simulation road network according to the road shape information, the number of lanes, and the traffic signal information in the preset traffic map status information; Construct the simulation vehicle status information corresponding to each of the simulated traffic vehicles according to the position information, driving speed information, and acceleration information of each of the simulated traffic vehicles in the preset simulation road network; Based on the simulation vehicle status information, simulate the driving trajectories of each of the simulated traffic vehicles in the preset simulation road network; Determine the traffic flow data formed by the interactions between each of the simulated traffic vehicles according to the driving trajectories, and construct the traffic flow simulation environment data according to the traffic flow data, where the traffic flow data at least includes traffic volume data, vehicle density data, and vehicle queue length.

[0009] According to a test method for a truck platooning control system provided by the present invention, the first dynamic model and the second dynamic model are obtained through the following steps: Construct the first dynamic model according to the size information, tire information, powertrain information, suspension system information, steering system information, and braking system information of the simulated towing truck; Construct the second dynamic model according to the size information, tire information, powertrain information, suspension system information, load information, and braking system information of the simulated following truck.

[0010] According to a test method for a truck platooning control system provided by the present invention, the obtaining the vehicle control information corresponding to the simulated towing truck and the simulated following truck in the tested truck platooning control system includes: Based on the tested controller devices of the simulated towing truck and the simulated following truck in the tested truck platooning control system, obtain the corresponding throttle force data, braking force data, steering wheel angle data, gear state data, and clutch state data; Obtain the vehicle control information corresponding to each of the simulated towing truck and the simulated following truck according to the throttle force data, the braking force data, the steering wheel angle data, the gear state data, and the clutch state data.

[0011] According to a test method for a truck platooning control system provided by the present invention, based on the vehicle control information and the simulation test scenario of the truck platooning control system, a simulation test is performed on the to-be-tested truck platooning control system to obtain the test results corresponding to the to-be-tested truck platooning control system, including: According to the vehicle control information, the first dynamic model, and the second dynamic model, generate the vehicle initial pose information corresponding to each of the simulation leading truck and the simulation following truck in the simulation environment; According to the traffic flow simulation environment data, determine the interactions between the simulation leading truck and each of the simulation traffic vehicles, and update the vehicle initial pose information corresponding to each of the simulation leading truck and the simulation following truck in the simulation environment respectively according to the interactions between the simulation leading truck and each of the simulation traffic vehicles, to obtain the real-time truck pose information corresponding to the simulation leading truck and the real-time truck pose information corresponding to the simulation following truck; Based on the traffic scenario simulation environment, the real-time truck pose information corresponding to the simulation leading truck, and the real-time truck pose information corresponding to the simulation following truck, simulate the sensor data of the simulation following truck following the simulation leading truck through an automatic driving algorithm in the simulation environment to obtain sensor simulation perception information; According to the sensor simulation perception information, the real-time truck pose information corresponding to the simulation leading truck, and the real-time truck pose information corresponding to the simulation following truck, perform a simulation test on the to-be-tested truck platooning control system to obtain the test results corresponding to the to-be-tested truck platooning control system.

[0012] According to a test method for a truck platooning control system provided by the present invention, the method further includes: Obtain simulation environment parameter adjustment data, where the simulation environment parameter adjustment data includes road layout adjustment data, weather condition adjustment data, and vehicle type adjustment data; Based on the simulation environment parameter adjustment data, adjust the traffic scenario simulation environment to obtain an adjusted traffic scenario simulation environment.

[0013] According to a test method for a truck platooning control system provided by the present invention, the method further includes: According to the truck platooning command, the preset fault information, and the truck platooning light information, update the test conditions of the to-be-tested truck platooning control system, so as to perform a simulation test on the to-be-tested truck platooning control system based on the updated test conditions.

[0014] The present invention also provides a test device for a truck platooning control system, including: The simulation truck control information acquisition module is used to obtain the vehicle control information corresponding to the simulation tractor truck and the simulation following truck in the truck platoon control system to be tested, where the vehicle control information is generated based on each tested controller device in the truck platoon control system to be tested; The simulation algorithm test module is used to perform a simulation test on the truck platoon control system to be tested based on the vehicle control information and the truck platoon control system simulation test scenario, and obtain the test result corresponding to the truck platoon control system to be tested; Among them, the truck platoon control system simulation test scenario is constructed based on traffic flow simulation environment data, traffic scenario simulation environment, a first dynamics model, and a second dynamics model. The first dynamics model is the dynamics model corresponding to the simulation tractor truck, and the second dynamics model is the dynamics model corresponding to the simulation following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the test method for the truck platoon control system as described in any one of the above.

[0016] The test method and device for the truck platoon control system provided by the present invention replace the real vehicle test with a hardware-in-the-loop simulation environment, accurately reproduce complex and changeable traffic scenarios, ensure that the performance of the truck platoon control system is verified under various conditions, improve the test efficiency, and effectively avoid safety risks, protecting the safety of test personnel and equipment. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic flow chart of the test method for the truck platoon control system provided by the present invention; Figure 2 It is a schematic structural diagram of the test device for the truck platoon control system provided by the present invention; Figure 3 It is a schematic overall structural diagram of the test device for the truck platoon control system provided by the present invention; Figure 4 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0019] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] The current testing technologies for truck platooning control systems mainly include on-road vehicle testing and Software-in-the-Loop (SIL) simulation.

[0021] On-road vehicle testing can provide a real-world-like application environment for truck platooning control systems. However, its high cost and potential safety risks cannot be ignored. Conducting on-road vehicle testing on public roads, especially when simulating extreme or dangerous scenarios, the safety risks are particularly prominent. In addition, on-road vehicle testing not only requires a large number of vehicles and human resources but also is difficult to accurately reproduce specific traffic scenarios that are crucial for the performance of truck platooning control systems. These scenarios are often special and complex and are essential for the comprehensive verification of truck platooning control systems. Therefore, on-road vehicle testing has obvious limitations in terms of the coverage of testing scenarios and is difficult to ensure that the performance of truck platooning control systems can be verified under all circumstances.

[0022] Software-in-the-Loop simulation, on the other hand, can simulate complex traffic scenarios, but its limitation is that it cannot directly test the hardware performance. This means that although the simulation can predict the behavior of the truck platooning control system to a certain extent, it cannot comprehensively verify the stability and reliability of the truck platooning control system, especially its performance at the hardware level. In addition, existing testing methods, whether on-road vehicle testing or Software-in-the-Loop simulation, face the problems of slow response to changes in testing requirements and low testing efficiency.

[0023] Aiming at the deficiencies of truck platooning control systems in terms of safety, cost, scenario reproducibility, hardware performance verification, testing efficiency, and testing coverage in on-road vehicle testing and Software-in-the-Loop simulation testing, the present invention proposes a Hardware-in-the-Loop (HIL) simulation testing method for truck platooning control systems, thereby improving the safety, economy, efficiency, and comprehensiveness of truck platooning control system testing and providing reliable testing support for the development of autonomous driving technology.

[0024] Figure 1 The flow chart of the testing method for the truck platooning control system provided by the present invention is as follows Figure 1As shown, the present invention provides a test method for a truck platooning control system, including: Step 101, obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the truck platooning control system to be tested, where the vehicle control information is generated based on each controller device to be tested in the truck platooning control system to be tested; Step 102, performing a simulation test on the truck platooning control system to be tested based on the vehicle control information and the truck platooning control system simulation test scenario, and obtaining the test result corresponding to the truck platooning control system to be tested; Wherein, the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, traffic scenario simulation environment, a first dynamics model and a second dynamics model, the first dynamics model is the dynamics model corresponding to the simulated leading truck, and the second dynamics model is the dynamics model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0025] The truck platooning control system enables a group of trucks (usually including one leading truck and multiple following trucks) to drive cooperatively through control algorithms and communication technologies, maintaining a safe distance and a consistent driving speed, thereby improving transportation efficiency and safety. For the truck platooning control system, it contains various controller devices, such as engine controllers, braking system controllers, and steering controllers, etc., and these controller devices work together to achieve platooning control.

[0026] In the present invention, in order to simulate platooning driving in a real scenario, simulation software is used to create virtual leading truck and following truck models, namely simulated leading trucks and simulated following trucks. These truck models can simulate the dynamic characteristics, response time, and communication protocols of real vehicles, so as to test and optimize the truck platooning control system without actually using physical vehicles.

[0027] Furthermore, during the simulation test process, hardware-in-the-loop testing is adopted. In this simulation test process, the actual physical controllers (such as engine controllers, braking controllers, etc.) in the truck platooning control system are connected to the simulation environment, rather than just testing in a pure software environment (i.e., software-in-the-loop). That is, in hardware-in-the-loop testing, the controller device to be tested (hardware) is directly connected to the simulation system (software), enabling the simulation system to simulate the vehicle operating environment, sensor inputs, and other vehicle behaviors, while the controller device generates control instructions based on these inputs. These control instructions are then fed back through the simulation system to simulate the responses of the simulated truck's corresponding actuators (such as engines, brakes, etc.), forming a closed-loop test system.

[0028] The software-in-the-loop test of the existing truck platoon control system mainly focuses on the software algorithm itself and usually does not involve the actual physical controller hardware, so it cannot fully simulate the interaction between the controller and the real hardware. The hardware-in-the-loop test adopted in the present invention includes the actual physical controller, which can more accurately reflect the performance of the controller in the actual working environment, including its reaction to hardware failures, real-time performance, and integration ability with external systems. It can comprehensively evaluate the functions, performance, and safety of the controller without relying on physical vehicles.

[0029] Furthermore, in the present invention, a traffic flow simulation environment can be constructed by using a relevant traffic flow simulation platform. Among them, the simulation vehicle state information is the core data for constructing a high-precision simulation environment, and the simulation vehicle state information details the real-time states of all simulation traffic vehicles (such as cars, buses, and motorcycles, etc.) except the simulation trucks.

[0030] Based on the above embodiments, the traffic flow simulation environment data is obtained through the following steps: According to the simulation vehicle state information, the traffic flow simulation environment data is constructed, where the simulation vehicle state information is the state information of the simulation traffic vehicles; the simulation traffic vehicles are other traffic vehicles except the simulation towing truck and the simulation following truck.

[0031] In the present invention, the simulation vehicle state information includes but is not limited to: Position information: The precise coordinates of each vehicle in the simulation road network, which reflects the spatial position of the vehicle.

[0032] Speed information: The instantaneous speed of each vehicle, which reflects the driving speed of the vehicle.

[0033] Acceleration information: The acceleration change of each vehicle, which reflects the acceleration or deceleration state of the vehicle.

[0034] Driving direction: The driving direction of each vehicle, which reflects the movement trend of the vehicle in the road network.

[0035] Lane occupancy: The lane information where each vehicle is currently located, which reflects the lateral position of the vehicle.

[0036] In the present invention, the simulation vehicle state information is updated and recorded in real time through the road network construction ability and vehicle dynamic simulation function of the traffic flow simulation platform.

[0037] In the traffic flow simulation environment, the simulated trucks also have the above-mentioned state information such as position, speed, and acceleration. The initial state information of the simulated trucks is generated based on the dynamic model. When the simulated trucks are running in the simulation test scenario of the truck platoon control system, combined with the interaction between the simulated traffic vehicles and the simulated trucks in the traffic flow simulation environment data (such as overtaking, lane changing, etc.), the state information of the simulated trucks will be updated in real time, and then the relevant tests of the truck platoon control system will be executed. In addition, for the simulated towing truck and the simulated following truck, the towing and following relationship information between them reflects the cooperation in platoon driving. The load information reflects the load condition of the truck, which may affect the dynamic behaviors such as the acceleration performance and braking distance of the vehicle. The dimension information such as the length and width of the truck is crucial for simulating the driving and lane changing behaviors of the vehicle in the road network.

[0038] In the present invention, based on the above-mentioned simulated vehicle state information, highly realistic traffic flow simulation environment data can be constructed. These traffic flow simulation environment data not only reflect the real-time state of the vehicles in the road network, but also include the interaction relationships between the vehicles (such as following distance, lane changing intention, etc.), as well as the geometric features of the road network itself (such as road width, number of lanes, traffic signals, etc.). Optionally, the simulation platform adopted in the present invention can be the SUMO (Simulation of Urban Mobility) traffic flow simulation platform.

[0039] Furthermore, dynamic modeling is a mathematical model that describes how a vehicle moves according to external inputs (such as driver operations, road conditions, etc.) and internal states (such as speed, acceleration, position, etc.). When performing the dynamic modeling of the simulated towing truck, that is, the first dynamic model, it first describes the motion state of the towing truck under given road conditions and driver inputs. At the same time, factors such as the mass, inertia, and friction between the tires and the ground of the towing truck are considered. For the dynamic modeling of the simulated following truck, that is, the second dynamic model, it is similar to the modeling of the first dynamic model. In addition, it is also necessary to consider how the following truck adjusts according to the road conditions and its own state while maintaining a certain distance and speed difference from the towing truck.

[0040] In the simulation test of the truck platoon control system, it is first necessary to obtain the vehicle control information corresponding to the simulated towing truck and the simulated following truck respectively. The vehicle control information usually includes the current speed, acceleration, steering angle, braking state, etc. of the vehicle, as well as the control instructions issued by the driver or the autonomous driving system, such as accelerating, decelerating, steering, etc. These information are the basis of the simulation test and are used to simulate the vehicle behaviors in the real driving environment.

[0041] In the present invention, in order to effectively simulate and test the truck platoon control system to be measured, it is necessary to construct a test scenario close to the actual situation, which is constructed based on multiple factors, including: Traffic flow simulation environment data: These data simulate the traffic flow, vehicle density, vehicle speed distribution, etc. in the real road, and are used to simulate the driving conditions of the truck platoon in the traffic flow.

[0042] Traffic scenario simulation environment: It is constructed based on the preset traffic map status information (such as road layout, intersections, signal lamp positions, etc.), and is used to simulate the driving paths and interaction behaviors of the truck platoon in different traffic scenarios.

[0043] The first dynamic model and the second dynamic model: They respectively represent the dynamic characteristics of the simulated towing truck and the simulated following truck, and are used to simulate the physical responses of the vehicle under the given control inputs, such as the dynamic changes during acceleration, deceleration, and steering.

[0044] Furthermore, after constructing the simulation test scenario, the vehicle control information of the simulated towing truck and the simulated following truck is input into the truck platoon control system to be measured. The truck platoon control system to be measured calculates the corresponding control commands according to this information and the preset control strategies (such as maintaining a vehicle distance, cooperative lane change, etc.), and applies them to the simulated vehicles. By simulating the driving process of the vehicle in the actual traffic environment, observe and record the vehicle's behavior performance, such as driving trajectory, speed change, vehicle distance maintenance, etc.

[0045] During the simulation test process, by recording and analyzing the vehicle's behavior data, the performance of the truck platoon control system to be measured can be evaluated. The test results include the system response speed, cooperative driving ability (the performance of the truck platoon in maintaining a vehicle distance, cooperative lane change, etc.), safety, and adaptability (the performance under different traffic flows, road conditions, and weather conditions).

[0046] The test method for the truck platoon control system provided by the present invention uses a hardware-in-the-loop simulation environment to replace the real vehicle test, accurately reproduces complex and changeable traffic scenarios, ensures that the performance of the truck platoon control system is verified under various conditions, improves the test efficiency, and effectively avoids safety risks, protecting the safety of test personnel and equipment.

[0047] Based on the above embodiments, constructing the traffic flow simulation environment data according to the simulated vehicle status information includes: Constructing a preset simulation road network according to the road shape information, number of lanes, and traffic signal information in the preset traffic map status information; Constructing the simulated vehicle status information corresponding to each simulated traffic vehicle according to the position information, driving speed information, and acceleration information of each simulated traffic vehicle in the preset simulation road network; Based on the simulated vehicle state information, simulate the driving trajectories of each of the simulated traffic vehicles in the preset simulated road network; According to the driving trajectories, determine the traffic flow data formed by the interactions between each of the simulated traffic vehicles, and construct the traffic flow simulation environment data based on the traffic flow data, where the traffic flow data at least includes traffic volume data, vehicle density data, and vehicle queue length In the present invention, a preset simulated road network can be constructed through traffic flow simulation software. During the construction process, it mainly relies on preset traffic map state information, which usually comes from actual traffic map data or a processed traffic network model.

[0048] Specifically, road shape information is the basis for constructing the road network. It describes the geometric shape of the road, including various road types such as straight, curved, and intersection roads. In traffic flow simulation software, this road shape information is transformed into a digital road network, where each road has a clear starting point and ending point, as well as intermediate nodes and sections.

[0049] Lane number information is used to define the number of lanes on each road, which directly affects the traffic capacity of the road and the driving mode of vehicles. In traffic flow simulation software, the lane number is accurately recorded in the attributes of each road to accurately simulate the driving of vehicles during the simulation.

[0050] Traffic signal information is one of the key factors for controlling traffic flow. In traffic flow simulation software, traffic signal information is used to set the position of traffic lights, color change rules, and signal cycles, etc., to simulate traffic signal control in the real world.

[0051] Furthermore, after constructing the preset simulated road network, construct the simulated vehicle state information. The simulated vehicle state information describes the initial state of the simulated traffic vehicle in the road network, including position, driving speed, and acceleration, etc., and the simulated vehicle state information is continuously updated and changed during the subsequent simulation process.

[0052] Specifically, the position information defines the initial position of each vehicle in the road network. In traffic flow simulation software, this is usually achieved by specifying one or more starting points in the road network, and each vehicle starts driving from this starting point.

[0053] The driving speed information and acceleration information describe the initial speed and acceleration of the vehicle, which can be set according to a preset driving plan or randomly generated driving parameters.

[0054] Further, after constructing the simulated vehicle state information, the driving trajectory of the vehicle is simulated, which is achieved by continuously updating the vehicle state information in the simulation environment. During the simulation process, the traffic flow simulation software calculates the next state of each vehicle based on the current vehicle state information (such as position, speed, acceleration, etc.) and information such as the road shape, number of lanes, and traffic signals in the road network. This process is repeated until the simulation ends.

[0055] By continuously updating the vehicle state information, the traffic flow simulation software can generate the driving trajectories of each vehicle. These trajectories describe the driving paths and speed changes of the vehicles in the road network and other dynamic behaviors. During the simulation process, the traffic flow simulation software calculates information such as the relative position and speed difference between each vehicle and other vehicles to simulate the interactions between vehicles. These interactions include following, lane changing, and overtaking behaviors. By analyzing the interactions between vehicles and the driving trajectories of vehicles in the road network, the traffic flow simulation software can generate a series of traffic flow data (i.e., traffic flow simulation environment data), including traffic volume data, vehicle density data, and vehicle queue length, etc. The traffic flow simulation environment data describes the dynamic characteristics of the traffic flow during the simulation process and provides an important basis for subsequent traffic flow analysis and optimization.

[0056] Based on the above embodiments, the first dynamic model and the second dynamic model are obtained through the following steps: Construct the first dynamic model according to the size information, tire information, powertrain information, suspension system information, steering system information, and braking system information of the simulated tractor truck; Construct the second dynamic model according to the size information, tire information, powertrain information, suspension system information, load information, and braking system information of the simulated following truck.

[0057] In the present invention, first, a first dynamic model is constructed, that is, the tractor truck is modeled. Among them, when modeling the size of the tractor, the overall size (such as the length, width, and height of the vehicle body) and mass of the tractor truck are the basis for constructing the dynamic model, which are used to determine the physical occupancy and weight distribution of the vehicle in the simulation environment. The center of mass position is another key parameter, which affects the stability and controllability of the vehicle. In the dynamic model, the center of mass position is used to calculate the moment of inertia of the vehicle and its motion response when subjected to external forces.

[0058] The tire is the only interface between the vehicle and the road surface. Therefore, tire modeling is crucial for simulating the controllability, stability, and braking performance of the vehicle. Specifically, parameters such as the friction force, grip force, and slip angle of the tire describe the interaction between the tire and the road surface, and are used in the dynamic model to calculate the tire forces of the vehicle during turning, accelerating, and braking.

[0059] The powertrain includes an engine and a transmission system, which jointly determine the acceleration performance, driving efficiency, and energy consumption of the vehicle. Specifically, the engine's external characteristic curve describes the power and torque output of the engine at different speeds and loads, and is used to calculate the traction force and acceleration of the vehicle. The settings of components such as the clutch, transmission, and final drive affect the transmission efficiency and shift smoothness of the vehicle.

[0060] The suspension system is used to support the vehicle body and absorb vibrations caused by road unevenness. In the modeling of the suspension system, parameters such as the stiffness and damping of the suspension system are used to calculate the vibration response of the vehicle during driving. The choice of suspension type (such as independent suspension and non-independent suspension) also affects the comfort and stability of the vehicle.

[0061] The steering system is used to control the driving direction of the vehicle. In the modeling of the steering system, the type and transmission ratio of the steering shaft, as well as the non-linear nature of the steering system, are used to calculate the front wheel angle and turning radius of the vehicle during steering.

[0062] The braking system is used to control the driving speed of the vehicle and achieve stopping in case of emergency. The modeling of the braking system includes simulating various braking systems (such as pneumatic braking and hydraulic braking) and setting braking control strategies, and these parameters are used to calculate the deceleration and braking distance of the vehicle during braking.

[0063] At the same time, a second dynamic model is constructed, that is, the following truck modeling. Among them, size information, tire information, powertrain information, and suspension system information, these parameters are similar to those of the towing truck modeling, and are used to determine the physical occupancy, weight distribution, interaction between the tire and the road surface, power output, and vibration response of the following truck. At the same time, the steering system modeling is deleted to adapt to the specific dynamic characteristics of the following truck.

[0064] During the modeling process of the following truck modeling, load information also needs to be considered. The following truck usually carries goods, so load modeling is an important part of the following truck dynamic model. The mass and distribution of the load affect the stability and controllability of the vehicle.

[0065] In the present invention, road environment simulation is a key link in truck dynamic modeling, which directly affects the dynamic characteristics and driving performance of the vehicle. By simulating different road environments, the dynamic response of the vehicle during actual driving can be more realistically reflected, thus providing strong support for the control strategy and optimization of truck platooning.

[0066] Specifically, road type simulation includes different types of roads such as straight roads, curves, mountain roads, and uphill roads. These road types have different geometric characteristics and driving conditions, which have a significant impact on the dynamic characteristics of vehicles. By setting the three-dimensional road surface and road shape, the driving states of vehicles (tractor trucks and following trucks) under different road conditions can be simulated more accurately. The road shape includes parameters such as the undulation, slope, and curvature of the road surface, and these parameters will all affect the driving stability and controllability of vehicles.

[0067] Furthermore, the road surface adhesion coefficient is an important parameter reflecting the friction between the road surface and the tires. Different road surface materials and climate conditions will cause changes in the adhesion coefficient. By simulating different road surface adhesion coefficients, the braking performance, handling stability, and traction performance of vehicles under different road surface conditions can be analyzed.

[0068] In the dynamic model, various parameters of the road environment (such as road type, three-dimensional road surface shape, and road surface adhesion coefficient, etc.) are used as input parameters. By adjusting these parameters, different road environments can be simulated, and the dynamic responses of vehicles under these environments can be observed.

[0069] In the present invention, the above-mentioned relevant parameters can simulate the driving states of vehicles under different road conditions, including dynamic parameters such as the acceleration, speed, and displacement of vehicles. These relevant parameters can be imported or set through the user terminal, or directly use the provided Trucksim parameter file (cpar file) in cooperation with the vehicle manufacturer to overwrite all the parameters of the tractor head of the heavy truck.

[0070] Based on the above embodiments, obtaining the vehicle control information corresponding to the simulation tractor truck and the simulation following truck in the formation control system of the truck to be measured includes: Based on the respective measured controller devices of the simulation tractor truck and the simulation following truck in the formation control system of the truck to be measured, obtaining the corresponding throttle force data, brake force data, steering wheel angle data, gear state data, and clutch state data; According to the throttle force data, the brake force data, the steering wheel angle data, the gear state data, and the clutch state data, obtaining the vehicle control information corresponding to the simulation tractor truck and the simulation following truck respectively.

[0071] In the simulation test of the truck platoon control system, the simulated lead truck and the simulated following truck are used as the test objects, and their driving states and control strategies are reflected by a series of control data. These control data include throttle force, braking force, steering wheel angle, gear state, and clutch state, etc., which together constitute the basis of vehicle control information. In the present invention, the throttle force data is generated by devices such as the engine controller or the powertrain controller. These controller devices control the fuel injection amount of the engine or the torque output of the motor by adjusting the position of the throttle actuator. The braking force data is generated by the brake system controller, and the brake system controller adjusts the pressure or current of the brake actuator to achieve the required deceleration. The steering wheel angle data is provided by the steering controller or the electric power steering controller. These controllers control the driving direction of the vehicle by adjusting the angle of the steering actuator. The gear state data is generated by the transmission controller, and the transmission controller is used to control the shifting logic of the transmission. The clutch state data is provided by the clutch controller or the transmission integrated controller.

[0072] Specifically, the throttle force data reflects the acceleration demand of the simulated lead truck. During platoon driving, the lead truck may need to adjust the throttle force according to road conditions, vehicle distance, and the state of the following truck to maintain the overall stability and driving efficiency of the convoy.

[0073] The braking force data reflects the braking demand of the simulated lead truck when decelerating or stopping. In an emergency, the lead truck needs to respond quickly and apply appropriate braking force to ensure the safety of the convoy.

[0074] The steering wheel angle data records the steering situation of the simulated lead truck during driving. When driving on a curve or in complex road conditions, the lead truck needs to adjust the steering wheel angle to stay in the lane or follow a predetermined route.

[0075] The gear state data reflects the state of the transmission system of the simulated lead truck. When accelerating, decelerating, or driving at a constant speed, the lead truck needs to select an appropriate gear according to the current vehicle speed and power demand to improve driving efficiency and fuel economy.

[0076] The clutch state data is used to describe the clutch state of the simulated lead truck during gear shifting. The correct operation of the clutch is crucial for maintaining the smoothness of power transmission and avoiding vehicle jerks.

[0077] Similar to the lead truck, the throttle force data of the following truck also reflects its acceleration demand. However, the difference is that the following truck may need to adjust the throttle force according to the speed and position of the lead truck to maintain synchronous driving with the lead truck.

[0078] The braking force data of the following truck is equally important, especially in emergency braking situations. The following truck needs to quickly respond to the braking signal of the towing truck and apply appropriate braking force to ensure the safety and stability of the convoy.

[0079] During platooning, the steering wheel angle data of the following truck may be more affected by the driving trajectory of the towing truck. The following truck needs to maintain the same direction control as the towing truck to ensure the accuracy of the overall driving direction of the convoy.

[0080] The gear state data and clutch state data of the following truck are similar to those of the towing truck and are used to describe the state of its transmission system and clutch. The accuracy of these data is crucial for maintaining the driving efficiency and stability of the following truck.

[0081] In the simulation test, by collecting and analyzing the control data of the simulated towing truck and the simulated following truck, the corresponding vehicle control information can be obtained for each. The vehicle control information can be used to evaluate the performance, optimize the control strategy, and driving safety of the truck platoon control system. For example, by analyzing the throttle force and braking force data, the acceleration and braking performance of the control system can be evaluated; by analyzing the steering wheel angle data, the direction control accuracy of the control system can be evaluated; by analyzing the gear state and clutch state data, the efficiency and stability of the transmission system can be evaluated.

[0082] Based on the above embodiments, the simulated test of the measured truck platoon control system is carried out based on the vehicle control information and the simulation test scenario of the truck platoon control system, and the test results corresponding to the measured truck platoon control system are obtained, including: According to the vehicle control information, the first dynamic model, and the second dynamic model, the vehicle initial pose information corresponding to the simulated towing truck and the simulated following truck in the simulation environment is generated; According to the traffic flow simulation environment data, the interactions between the simulated towing truck and each of the simulated traffic vehicles are determined, and based on the interactions between the simulated towing truck and each of the simulated traffic vehicles, the vehicle initial pose information corresponding to the simulated towing truck and the simulated following truck in the simulation environment is updated respectively to obtain the real-time truck pose information corresponding to the simulated towing truck and the real-time truck pose information corresponding to the simulated following truck; Based on the traffic scene simulation environment, the real-time truck pose information corresponding to the simulated towing truck, and the real-time truck pose information corresponding to the simulated following truck, the sensor data of the simulated following truck following the simulated towing truck through the automatic driving algorithm in the simulation environment is simulated to obtain the sensor simulation perception information; According to the sensor simulation perception information, the real-time truck posture information corresponding to the simulated traction truck and the real-time truck posture information corresponding to the simulated following truck, the truck formation control system under test is simulated tested to obtain the test results corresponding to the truck formation control system under test.

[0083] In the present invention, vehicle control information, such as throttle, brake and steering wheel angle, is the basic instruction for the simulated vehicle driving. The first dynamic model and the second dynamic model respectively describe the physical behaviors of the simulated traction truck and the simulated following truck, such as acceleration, deceleration, turning radius, etc. These dynamic models are constructed based on the mechanical characteristics and kinematic principles of the vehicle.

[0084] Furthermore, by combining the vehicle control information and the dynamic model, the initial position (such as coordinates) and posture (such as orientation) of the simulated vehicle in the simulation environment can be calculated, thereby generating respective initial posture information for the simulated traction truck and the simulated following truck in the simulation environment. The initial posture information is the starting state when the vehicle starts simulation.

[0085] The traffic flow simulation environment data includes information about simulated roads, traffic signals and other simulated traffic vehicles (such as cars, trucks, etc.), as well as the interaction rules between them. The present invention determines the interaction between the simulated tractor truck and each simulated traffic vehicle, such as the following distance, relative speed, and steering avoidance, based on the traffic flow simulation environment data.

[0086] Furthermore, based on the interaction between vehicles and the dynamic models of the simulated tractor truck and the simulated following truck, the initial posture information of the simulated tractor truck and the simulated following truck is updated in real time, including the position change, speed change and direction change of the vehicle. After the update, the real-time posture information of the simulated tractor truck and the simulated following truck in the simulation environment is obtained, which reflects the status of the vehicle at the current simulation moment.

[0087] In the present invention, the sensors (such as cameras, lidars, millimeter-wave radars, etc.) on the simulated follow-up truck can be simulated through the relevant autonomous driving research platform based on the traffic scene simulation environment (including roads, traffic signals and other vehicles, etc.) and real-time posture information. Sensor simulation simulates the perception process of real sensors in the simulation environment and generates sensor data. Specifically, the acquired sensor simulation data is processed to obtain the perception results of the simulated follow-up truck on the simulation environment, that is, the sensor simulation perception information, which is the basis for the autonomous driving algorithm to make decisions and plans.

[0088] In the present invention, a complete simulation test environment is built by combining a traffic scene simulation environment, the real-time pose information of a simulated towing truck and the real-time pose information of a simulated following truck, as well as sensor simulation perception information. In the simulation test environment, the truck platoon control system under test is run. At this time, the truck platoon control system under test receives the sensor simulation perception information from the simulated following truck and the real-time pose information of the simulated towing truck, and makes decisions and plans.

[0089] The truck platoon control system under test adjusts the driving state of the simulated following truck through control instructions to achieve the function of following the simulated towing truck. At the same time, it monitors and records in real time the driving path, obstacle avoidance behavior, etc. of the simulated following truck during the simulation test, as well as the performance indicators related to the truck platoon control system under test (such as following accuracy, response time, etc.). After the simulation test, the test results corresponding to the truck platoon control system under test are obtained, which are used to evaluate the performance and reliability of the system.

[0090] Based on the above embodiments, the method further includes: Obtaining simulation environment parameter adjustment data, where the simulation environment parameter adjustment data includes road layout adjustment data, weather condition adjustment data, and vehicle type adjustment data; Based on the simulation environment parameter adjustment data, adjusting the traffic scene simulation environment to obtain an adjusted traffic scene simulation environment.

[0091] In the present invention, through the map editor or API interface of the autonomous driving research platform, road layout data can be imported or customized to simulate different urban traffic scenarios or test the performance of the autonomous driving system under specific road conditions. At the same time, the weather conditions in the simulation environment can be adjusted according to needs, including sunny, cloudy, rainy, foggy, and snowy days, etc. These weather data not only affect the visual effect (such as lighting, visibility), but also have a direct impact on the performance of vehicle sensors (such as the detection distance and accuracy of radar, camera, lidar). By adjusting these parameters, the robustness and adaptability of the autonomous driving system under different weather conditions can be evaluated.

[0092] In the autonomous driving research platform, the selection and configuration of vehicle types are crucial for simulating real traffic flow. The vehicle type adjustment data covers various types of vehicles from family cars, commercial trucks, public transportation vehicles to motorcycles, bicycles, etc. The present invention can adjust the types, quantities, speed distributions, and driving routes of vehicles as needed to create a traffic flow scenario that meets specific test requirements. In addition, the autonomous driving research platform also supports customizing vehicle models, including appearance, physical properties (such as weight, braking performance), and driving behavior patterns, making the simulation more realistic.

[0093] In the present invention, using the graphical user interface or programming interface (such as Python API) of the autonomous driving research platform, the obtained road layout, weather conditions, and vehicle type adjustment data are input into the system. The autonomous driving research platform will dynamically generate or modify the current simulation environment according to these data. For example, a new road layout will be loaded, the weather system will adjust the visual effects and physical parameters according to the set parameters, and the vehicles will appear at the designated positions according to the adjusted type and configuration and drive along the preset route. Optionally, the autonomous driving research platform in the present invention can be the CARLA (Car Learning to Act) platform.

[0094] Based on the above embodiments, the method further includes: Updating the test conditions of the measured truck platoon control system according to the truck platoon instruction, preset fault information, and truck platoon lighting information, so as to perform a simulation test on the measured truck platoon control system after the test conditions are updated.

[0095] In the present invention, the test conditions can also be updated according to key elements such as truck platoon instructions, preset fault information, and truck platoon lighting information. Specifically, truck platoon instructions include basic operations such as formation, dissolution, maintaining formation, acceleration, deceleration, and steering of the platoon. Before the test, it is necessary to clarify the specific content, sending method, and receiving response standard of these instructions to ensure the accuracy of the test. Truck platoon instructions can be sent to the measured truck platoon control system through a wireless communication system.

[0096] The preset fault information is used to simulate various abnormal situations that may occur during truck platoon driving, such as communication failures, sensor failures, and vehicle failures. These fault information are preset in the test system and can be selected and triggered according to test requirements. The accuracy and diversity of the preset fault information can evaluate the coping ability of the measured truck platoon control system in case of failures.

[0097] Truck platoon lighting information includes turn signals, brake lights, and clearance lights, etc., which are used to indicate the driving state and intention of the trucks. During the test, it is necessary to update and verify the correctness of the lighting information in real time according to the driving conditions and instructions of the truck platoon. The accuracy and consistency of the lighting information can test the safety and reliability of truck platoon driving.

[0098] In the present invention, when a new truck platoon instruction is received, the input parameters of the measured truck platoon control system are updated to simulate a real platoon driving scenario. The updated instruction will be transmitted to the control system, and the control system will adjust the driving state and platoon formation of the trucks according to the instruction.

[0099] During the simulation test process, preset fault information is triggered as needed to simulate abnormal situations during the platooning of trucks. The simulation test process requires real-time monitoring of the response of the control system and evaluation of its processing and recovery capabilities under fault conditions. At the same time, the update of the lighting information needs to be consistent with the driving state and instructions of the truck platoon, so as to verify whether the control system can correctly interpret and make corresponding responses after receiving the lighting information.

[0100] The test device for a truck platoon control system provided by the present invention will be described below. The test device for a truck platoon control system described below can be mutually corresponded and referred to the test method for a truck platoon control system described above.

[0101] Figure 2 The structural schematic diagram of the test device for a truck platoon control system provided by the present invention is as Figure 2 shown. The present invention provides a test device for a truck platoon control system, including a simulation truck control information acquisition module 201 and a simulation algorithm test module 202. Among them, the simulation truck control information acquisition module 201 is used to obtain the vehicle control information corresponding to the simulation leading truck and the simulation following truck in the to-be-tested truck platoon control system, where the vehicle control information is generated based on each to-be-tested controller device in the to-be-tested truck platoon control system; the simulation algorithm test module 202 is used to perform a simulation test on the to-be-tested truck platoon control system based on the vehicle control information and the truck platoon control system simulation test scenario, and obtain the test result corresponding to the to-be-tested truck platoon control system; among them, the truck platoon control system simulation test scenario is constructed based on traffic flow simulation environment data, traffic scenario simulation environment, a first dynamic model, and a second dynamic model, the first dynamic model is the dynamic model corresponding to the simulation leading truck, and the second dynamic model is the dynamic model corresponding to the simulation following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0102] In the present invention, a traffic flow simulation environment can be constructed by using a traffic flow simulation platform. Among them, simulation vehicle state information is the core data for constructing a high-precision simulation environment. The simulation vehicle state information details the real-time states of all simulation traffic vehicles (such as cars, buses, and motorcycles, etc.) except for the simulation trucks. These state information includes but is not limited to: position information, speed information, acceleration information, driving direction, and lane occupancy, etc.

[0103] In the present invention, the simulated vehicle state information is updated and recorded in real time through the road network construction ability and vehicle dynamic simulation function of the traffic flow simulation platform. In the traffic flow simulation environment, the simulated truck also has the above-mentioned state information such as position, speed, and acceleration. The initial state information of the simulated truck is generated based on the dynamic model. When the simulated truck runs in the simulation test scenario of the truck platoon control system, combined with the interaction between the simulated traffic vehicles and the simulated truck in the traffic flow simulation environment data (such as overtaking, lane changing, etc.), the state information of the simulated truck will be updated in real time, and then the relevant tests of the truck platoon control system will be executed. In addition, for the simulated towing truck and the simulated following truck, the towing and following relationship information between them reflects the coordination during platoon driving. The load information reflects the load condition of the truck, which may affect the dynamic behaviors such as the acceleration performance and braking distance of the vehicle. The dimension information such as the length and width of the truck is crucial for simulating the driving and lane-changing behaviors of the vehicle in the road network.

[0104] In the present invention, based on the above-mentioned simulated vehicle state information, highly realistic traffic flow simulation environment data can be constructed. These traffic flow simulation environment data not only reflect the real-time state of vehicles in the road network, but also contain the interaction relationships between vehicles (such as following distance, lane-changing intention, etc.), as well as the geometric characteristics of the road network itself (such as road width, number of lanes, traffic signals, etc.).

[0105] Furthermore, by finely modeling the powertrain, suspension system, steering system, braking system, and load, the accurate simulation of the heavy truck dynamic characteristics is achieved. Dynamic modeling is a mathematical model that describes how a vehicle moves according to external inputs (such as driver operations, road conditions, etc.) and internal states (such as speed, acceleration, position, etc.). When performing the dynamic modeling of the simulated towing truck, that is, the first dynamic model, first describe the motion state of the towing truck under given road conditions and driver inputs. At the same time, consider factors such as the mass, inertia, and friction between the tires and the ground of the towing truck. For the dynamic modeling of the simulated following truck, that is, the second dynamic model, it is similar to the modeling of the first dynamic model. In addition, it is also necessary to consider how the following truck adjusts according to the road conditions and its own state while maintaining a certain distance and speed difference from the towing truck.

[0106] Furthermore, in the simulation test of the truck platoon control system, the simulation truck control information acquisition module 201 obtains the vehicle control information corresponding to the simulation tractor truck and the simulation following truck respectively (obtained from the truck platoon control system under test, and then through data interaction, transmits the obtained vehicle control information to the simulation algorithm test module 202). The vehicle control information usually includes the current speed, acceleration, steering angle, braking state, etc. of the vehicle, as well as control commands issued by the driver or the autonomous driving system, such as acceleration, deceleration, steering, etc. These information are the basis of the simulation test and are used to simulate the vehicle behavior in the real driving environment.

[0107] In the present invention, in order to effectively simulate and test the truck platoon control system under test, a test scenario close to the actual situation is constructed by the simulation algorithm test module 202. This scenario is constructed based on multiple factors, including: Traffic flow simulation environment data: These data simulate the traffic flow, vehicle density, vehicle speed distribution, etc. in the real road, and are used to simulate the driving situation of the truck platoon in the traffic flow.

[0108] Traffic scenario simulation environment: Constructed based on the preset traffic map status information (such as road layout, intersections, signal light positions, etc.), and is used to simulate the driving path and interaction behavior of the truck platoon in different traffic scenarios.

[0109] The first dynamic model and the second dynamic model: respectively represent the dynamic characteristics of the simulation tractor truck and the simulation following truck, and are used to simulate the physical response of the vehicle under a given control input, such as the dynamic changes during acceleration, deceleration, and steering.

[0110] Furthermore, after constructing the simulation test scenario, the vehicle control information of the simulation tractor truck and the simulation following truck is input into the truck platoon control system under test. The truck platoon control system under test calculates the corresponding control commands according to this information and the preset control strategies (such as maintaining a vehicle distance, cooperative lane change, etc.), and applies them to the simulation vehicles. By simulating the driving process of the vehicles in the actual traffic environment, observe and record the vehicle behavior performance, such as driving trajectory, speed change, vehicle distance maintenance, etc.

[0111] During the simulation test process, the simulation algorithm test module 202 can evaluate the performance of the truck platoon control system under test by recording and analyzing the vehicle behavior data. The test results include the system response speed, cooperative driving ability (the performance of the truck platoon in maintaining a vehicle distance, cooperative lane change, etc.), safety and adaptability (the performance under different traffic flows, road conditions, weather conditions).

[0112] The present invention replaces real vehicle testing with a simulation environment, effectively avoiding safety risks and protecting the safety of test personnel and equipment. At the same time, the simulation process can accurately reproduce complex and variable traffic scenarios, including rare and important scenarios, improving the adaptability and comprehensiveness of testing. Moreover, the ability to quickly set and modify test scenarios according to test requirements enables the simulation platform to quickly respond to changes in test requirements, improving test efficiency. By combining hardware-in-the-loop simulation, it is possible to simulate the vehicle's powertrain, sensors, and actuators to achieve in-depth verification at the hardware level.

[0113] Figure 3 The overall structural schematic diagram of the test device for the truck platooning control system provided by the present invention can be referred to Figure 3 As shown, the test device for the truck platooning control system further includes an autonomous driving scenario editing and injection module. The autonomous driving scenario editing and injection module is used to create, edit, and manage scenarios for testing and validating the heavy truck autonomous driving system, and can generate diverse and representative test scenarios based on real data to meet the needs of different test objectives, and achieve parameterization and generalization of test scenarios.

[0114] Specifically, the autonomous driving scenario editing and injection module, through a comprehensive analysis of the complex traffic environment, identifies potential hazards and risk points to ensure that the test scenarios can cover various complex environments and potential dangerous situations. At the same time, based on real data, the autonomous driving scenario editing and injection module can generate a large number of test scenario instances with randomized risk factors to achieve parameter generalization of test scenarios to simulate real road conditions.

[0115] In the present invention, the user can, according to test requirements, add, delete, or modify various attributes in the scenario through the autonomous driving scenario editing and injection module, such as road layout, weather conditions, visibility, etc., to meet specific test objectives. In addition, according to test requirements, the simulated chassis data of the simulated truck can be sent to the truck platooning control system under test, so as to test truck platoons of different chassis types.

[0116] In the present invention, a scenario database can also be established through the autonomous driving scenario editing and injection module, and the test scenarios are classified and stored in the form of XML files for easy management and retrieval. For some specific test objectives, appropriate scenario instances can be automatically identified and selected, and these instances are organized into an ordered test scenario sequence.

[0117] In the present invention, the relevant information is input into the CARLA three-dimensional display and algorithm testing module (i.e., the simulation algorithm testing module) and the SUMO microscopic traffic flow simulation module (referred to as the microscopic traffic flow simulation module) through the autonomous driving scene editing and injection module, wherein the information input into the simulation algorithm testing module may be map status information, including weather information and vehicle models, etc.; the information input into the microscopic traffic flow simulation module may be vehicle status information, including adding and deleting vehicles, as well as vehicle speed and vehicle lane change information, etc. In the present invention, the microscopic traffic flow simulation module can be constructed based on the SUMO simulation software, and can autonomously control the simulation process, including initializing the simulation environment, configuring vehicle parameters, starting and stopping the simulation, etc., making the execution of large-scale simulation experiments more efficient and convenient. The simulation algorithm testing module can be constructed through CARLA, and through the subsystems such as the physical system, light and shadow effects, particle system, and collision detection that CARLA comes with, it can truly simulate the physical phenomena and visual effects in the real environment, making the simulation environment more realistic, and providing a close-to-real test platform for the autonomous driving algorithm.

[0118] During the data interaction process, the autonomous driving scenario editing and injection module interacts with the microscopic traffic flow simulation module and the simulation algorithm testing module. For the microscopic traffic flow simulation module, the module needs to convert the generated test scenario instances into road network files and vehicle configuration files that can be recognized by the traffic flow simulation software, so as to perform microscopic traffic flow simulation in the traffic flow simulation software.

[0119] For the simulation algorithm test module, the module needs to convert the test scenario instance into a three-dimensional model file that can be recognized by the autonomous driving research platform, and perform physical and perception simulations in the autonomous driving research platform to test the effectiveness of the autonomous driving algorithm. At the same time, the simulation algorithm test module transmits the simulated perception information to the relevant controller device module under test in the truck formation control system under test through the simulation functions of real sensors such as cameras, lidars and millimeter-wave radars based on the vehicle posture information transmitted by the micro-traffic flow simulation module. The micro-traffic flow simulation module will also synchronously transmit the vehicle position information of the simulated truck, such as vehicle heading angle, vehicle distance and vehicle speed, to the device module under test to evaluate the performance of the truck formation control system under test. It should be noted that the simulation truck control information acquisition module ( Figure 3 The control information acquisition module of the simulated truck can be set according to actual needs. For example, the control information acquisition module of the simulated truck can be set on the side of the heavy truck dynamics model building module or the control system of the truck platoon under test.

[0120] The test device for the truck platooning control system provided by the present invention replaces the real vehicle test through a hardware-in-the-loop simulation environment, accurately reproduces complex and variable traffic scenarios, ensures that the performance of the truck platooning control system is verified under various conditions, improves the test efficiency, and effectively avoids safety risks, protecting the safety of test personnel and equipment.

[0121] The system provided by the embodiments of the present invention is used to execute the above method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0122] Figure 4 It is a schematic structural diagram of the electronic device provided by the present invention. As Figure 4 shown, the electronic device may include: a processor (Processor) 401, a communication interface (Communications Interface) 402, a memory (Memory) 403, and a communication bus 404. Among them, the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404. The processor 401 can call the logical instructions in the memory 403 to execute the test method for the truck platooning control system. The method includes: obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the truck platooning control system to be tested, where the vehicle control information is generated based on each tested controller device in the truck platooning control system to be tested; based on the vehicle control information and the truck platooning control system simulation test scenario, performing a simulation test on the truck platooning control system to be tested to obtain the test result corresponding to the truck platooning control system to be tested; where the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, a traffic scenario simulation environment, a first dynamics model, and a second dynamics model. The first dynamics model is the dynamics model corresponding to the simulated leading truck, and the second dynamics model is the dynamics model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0123] In addition, when the logical instructions in the above-mentioned memory 403 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0124] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the test method for the truck platooning control system provided by the above-mentioned various methods. The method includes: obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the truck platooning control system to be tested, where the vehicle control information is generated based on each controller device to be tested in the truck platooning control system to be tested; based on the vehicle control information and the truck platooning control system simulation test scenario, performing a simulation test on the truck platooning control system to be tested to obtain the test result corresponding to the truck platooning control system to be tested; where the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, traffic scenario simulation environment, a first dynamic model, and a second dynamic model. The first dynamic model is the dynamic model corresponding to the simulated leading truck, and the second dynamic model is the dynamic model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0125] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is used to execute the test method for the truck platooning control system provided in the above embodiments. The method includes: obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the truck platooning control system to be tested, where the vehicle control information is generated based on each tested controller device in the truck platooning control system to be tested; based on the vehicle control information and the truck platooning control system simulation test scenario, performing a simulation test on the truck platooning control system to be tested to obtain the test result corresponding to the truck platooning control system to be tested; where the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, traffic scenario simulation environment, a first dynamic model, and a second dynamic model, the first dynamic model is the dynamic model corresponding to the simulated leading truck, and the second dynamic model is the dynamic model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

[0126] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0127] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A test method for a truck platooning control system, characterized in that, Including: Obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the tested truck platooning control system, where the vehicle control information is generated based on each tested controller device in the tested truck platooning control system; Based on the vehicle control information and the truck platooning control system simulation test scenario, performing a simulation test on the tested truck platooning control system to obtain the test result corresponding to the tested truck platooning control system; Wherein, the truck platooning control system simulation test scenario is constructed based on traffic flow simulation environment data, a traffic scenario simulation environment, a first dynamics model, and a second dynamics model. The first dynamics model is the dynamics model corresponding to the simulated leading truck, and the second dynamics model is the dynamics model corresponding to the simulated following truck; the traffic scenario simulation environment is constructed based on preset traffic map state information.

2. The test method for a truck platooning control system according to claim 1, wherein The traffic flow simulation environment data is obtained through the following steps: According to the simulated vehicle state information, constructing the traffic flow simulation environment data, where the simulated vehicle state information is the state information of simulated traffic vehicles; the simulated traffic vehicles are other traffic vehicles except the simulated leading truck and the simulated following truck.

3. The test method for a truck platooning control system according to claim 2, characterized in that, The constructing the traffic flow simulation environment data according to the simulated vehicle state information includes: According to the road shape information, the number of lanes, and the traffic signal information in the preset traffic map state information, constructing a preset simulation road network; According to the position information, driving speed information, and acceleration information of each of the simulated traffic vehicles in the preset simulation road network, constructing the simulated vehicle state information corresponding to each of the simulated traffic vehicles; Based on the simulated vehicle state information, simulating the driving trajectories of each of the simulated traffic vehicles in the preset simulation road network; According to the driving trajectories, determining the traffic flow data formed by the interactions between each of the simulated traffic vehicles, and constructing the traffic flow simulation environment data according to the traffic flow data, where the traffic flow data at least includes traffic volume data, vehicle density data, and vehicle queue length.

4. The test method for a truck platooning control system according to claim 3, wherein, The first dynamics model and the second dynamics model are obtained through the following steps: According to the size information, tire information, powertrain information, suspension system information, steering system information, and braking system information of the simulated leading truck, constructing the first dynamics model; According to the size information, tire information, powertrain information, suspension system information, load information, and braking system information of the simulated following truck, constructing the second dynamics model.

5. The test method for a truck platooning control system according to claim 3, characterized in that, The obtaining the vehicle control information corresponding to the simulated leading truck and the simulated following truck in the tested truck platooning control system includes: Based on the tested controller devices of the simulated leading truck and the simulated following truck in the tested truck platooning control system, obtaining the corresponding throttle force data, braking force data, steering wheel angle data, gear state data, and clutch state data; Based on the throttle force data, the brake force data, the steering wheel angle data, the gear state data, and the clutch state data, obtain the vehicle control information corresponding to the simulated towing truck and the simulated following truck respectively.

6. The test method for a truck platooning control system according to claim 3, wherein Based on the vehicle control information and the simulation test scenario of the truck platoon control system, conduct a simulation test on the tested truck platoon control system to obtain the test results corresponding to the tested truck platoon control system, including: Generate the vehicle initial pose information corresponding to the simulated towing truck and the simulated following truck respectively in the simulation environment according to the vehicle control information, the first dynamic model, and the second dynamic model; Determine the interactions between the simulated towing truck and each of the simulated traffic vehicles according to the traffic flow simulation environment data, and update the vehicle initial pose information corresponding to the simulated towing truck and the simulated following truck respectively in the simulation environment according to the interactions between the simulated towing truck and each of the simulated traffic vehicles, to obtain the truck real-time pose information corresponding to the simulated towing truck and the truck real-time pose information corresponding to the simulated following truck; Based on the traffic scenario simulation environment, the truck real-time pose information corresponding to the simulated towing truck, and the truck real-time pose information corresponding to the simulated following truck, simulate the sensor data of the simulated following truck following the simulated towing truck through an autonomous driving algorithm in the simulation environment to obtain sensor simulation perception information; Conduct a simulation test on the tested truck platoon control system according to the sensor simulation perception information, the truck real-time pose information corresponding to the simulated towing truck, and the truck real-time pose information corresponding to the simulated following truck, to obtain the test results corresponding to the tested truck platoon control system.

7. The test method for a truck platooning control system according to claim 2, characterized in that, The method further includes: Obtain simulation environment parameter adjustment data, where the simulation environment parameter adjustment data includes road layout adjustment data, weather condition adjustment data, and vehicle type adjustment data; Based on the simulation environment parameter adjustment data, adjust the traffic scenario simulation environment to obtain an adjusted traffic scenario simulation environment.

8. The test method for a truck platooning control system according to claim 7, wherein The method further includes: Update the test conditions of the tested truck platoon control system according to the truck platoon command, the preset fault information, and the truck platoon lighting information, so as to conduct a simulation test on the tested truck platoon control system based on the tested truck platoon control system with updated test conditions.

9. A test device for a truck platooning control system, characterized in that, Including: A simulation truck control information acquisition module, configured to obtain the vehicle control information corresponding to the simulated towing truck and the simulated following truck in the tested truck platoon control system, where the vehicle control information is generated based on each tested controller device in the tested truck platoon control system; A simulation algorithm test module, configured to conduct a simulation test on the tested truck platoon control system based on the vehicle control information and the simulation test scenario of the truck platoon control system, to obtain the test results corresponding to the tested truck platoon control system; Among them, the simulation test scenario of the truck platoon control system is constructed based on traffic flow simulation environment data, a traffic scenario simulation environment, a first dynamic model, and a second dynamic model. The first dynamic model is the dynamic model corresponding to the simulated towing truck, and the second dynamic model is the dynamic model corresponding to the simulated following truck. The traffic scenario simulation environment is constructed based on preset traffic map state information.

10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the test method for the truck platoon control system according to any one of claims 1 to 8.

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