Unmanned truck following modeling method considering information uncertainty

Abstract

The invention relates to the technical field of unmanned transportation, in particular to an unmanned truck following modeling method considering information uncertainty, and the method comprises the steps: obtaining the motion state of an unmanned truck queue; the unmanned truck queue comprises a guiding vehicle and a following vehicle; determining following modeling parameters of the unmanned truck queue; establishing a truck queue following dynamics model, wherein the following dynamics model is used for acquiring the acceleration of each vehicle in the unmanned truck queue according to the motion state and the following modeling parameters; frequency domain transformation processing is conducted on the truck queue following dynamics model, a space-time phase diagram of information uncertainty and acceleration sensitivity coefficients is obtained, and the space-time phase diagram comprises a stable area and an unstable area; controlling an unmanned truck queue to run based on following modeling parameters of a stable region in the space-time phase diagram; according to the invention, the stability of unmanned truck queue following can be ensured.

Description

Technical Field

[0001] This invention relates to the field of unmanned transportation technology, and more specifically to a method for modeling unmanned truck following that takes into account information uncertainty. Background Technology

[0002] Heavy-duty road transport is a fundamental guarantee for national engineering construction, accounting for 72.4% of total freight transport. However, road heavy-duty transport accidents are frequent. According to incomplete statistics, there were over 80,000 heavy-duty accidents in China in 2023. Among them, human factors are the primary cause of transport accidents, seriously affecting driver safety and freight transport efficiency. Influenced by Connected and Autonomous Vehicles (CAVs), the heavy-duty truck industry is experiencing a surge in electrification and automation. New energy unmanned heavy-duty trucks are entering a period of rapid growth, and the application of new intelligent highway truck unmanned transport systems will become an inevitable trend in highway transport development. These new intelligent highway truck unmanned transport systems, designed around freight transport needs, utilize vehicle-to-vehicle communication technology for platooning, which will greatly improve vehicle traffic efficiency and reduce energy consumption. Therefore, focusing on the accuracy and reliability of vehicle motion status information is particularly important for the future application of these new intelligent highway truck unmanned transport systems.

[0003] The driving status of an autonomous truck platoon depends on feedback from the motion status information of other vehicles in the platoon. Communication delays, interruptions, or errors can significantly impact the control decisions of the autonomous trucks, potentially leading to platoon instability or even rear-end collisions. Currently, there is no method to analyze how to build an autonomous truck following model under conditions of information uncertainty and to provide a range of model parameters to ensure platoon stability. Therefore, it is necessary to propose an autonomous truck following modeling method that considers information uncertainty, which has significant engineering application value for the development of unmanned heavy-duty road transportation. Summary of the Invention

[0004] In view of the above problems, the present invention provides a method for modeling autonomous truck following that takes into account information uncertainty, which solves the technical problem that it is difficult to accurately simulate autonomous truck following in the prior art.

[0005] This invention provides a method for modeling autonomous truck following that considers information uncertainty, comprising the following steps: Step S1: Obtain the motion state of the unmanned truck convoy; the unmanned truck convoy includes a guide vehicle and following vehicles; the motion state includes the speed, spacing, and maximum deceleration of each vehicle in the unmanned truck convoy; Step S2: Determine the following modeling parameters for the unmanned truck platoon, including truck emergency braking reaction time, information uncertainty, acceleration sensitivity coefficient, expected safety margin, road gradient, and truck rolling resistance; Step S3: Establish a truck platoon following dynamics model, which is used to obtain the acceleration of each vehicle in the unmanned truck platoon based on the motion state and following modeling parameters; Step S4: Perform frequency domain transformation on the truck platoon following dynamics model to obtain a spatiotemporal phase diagram of information uncertainty and acceleration sensitivity coefficient, wherein the spatiotemporal phase diagram includes stable regions and unstable regions; Step S5: Control the unmanned truck convoy to drive based on the car-following modeling parameters of the stable region in the spatiotemporal phase diagram.

[0006] Preferably, step S1 specifically includes: In the unmanned truck convoy, each vehicle obtains its own position and speed information based on onboard sensors, thereby obtaining speed, vehicle-to-vehicle spacing and maximum deceleration.

[0007] Preferably, in step S2, the information uncertainty is... The random variable is determined to be uniformly distributed, and the expected safety margin SM is set to 0.9.

[0008] Preferably, in step S3, the expression for the car-following dynamics model is:

[0009]

[0010] in, Indicates the first A truck in acceleration at any moment Indicates the rolling resistance of the truck. It is the acceleration due to gravity. For road slope, The kinematic model that considers information uncertainty is the first... A truck in acceleration at any moment Indicates the acceleration sensitivity coefficient. Indicates the first A truck in The speed of time Indicates the truck's emergency braking reaction time. express The square of, Indicates the first A truck in The maximum deceleration at time t, Indicates the first A truck in Time and the The distance between trucks; Indicates the first A truck in The square of the velocity at time t, Indicates the first A truck in The maximum deceleration at time t, Indicates the expected safety margin. This represents the information uncertainty parameter.

[0011] Preferably, step S4 specifically includes: Step S4-1: Perform frequency domain transformation on the truck platoon following dynamics model to obtain vehicle stability conditions; Step S4-2: Calculate the relationship between information uncertainty and acceleration sensitivity coefficient based on vehicle stability conditions; Step S4-3: Obtain the spatiotemporal phase diagram of information uncertainty and acceleration sensitivity coefficient from the relationship between information uncertainty and acceleration sensitivity coefficient, and set the region in the spatiotemporal phase diagram that satisfies the relationship between information uncertainty and acceleration sensitivity coefficient as the stable region.

[0012] Preferably, in step S4-1, the expression for the vehicle stability condition is:

[0013]

[0014]

[0015]

[0016]

[0017] in, Describe the transfer function, The imaginary unit, For complex frequency domain variables, Represents the magnitude of a complex number. Indicates sensitivity to the speed of the vehicle in front. Indicates sensitivity to vehicle spacing. Indicates sensitivity to the speed of the vehicle. This represents the partial derivative of the truck's rolling resistance with respect to the speed of the vehicle in front at the equilibrium point. The value at that location, To achieve the desired speed for the queue to follow the carousel, This indicates the expected distance between vehicles in the queue. This represents the partial derivative of the truck's rolling resistance with respect to the distance between vehicles at the equilibrium point. The value at that location, This represents the partial derivative of the truck's rolling resistance with respect to its speed at the equilibrium point. The value at that location.

[0018] Preferably, in step S4-2, the relationship between the information uncertainty and the acceleration sensitivity coefficient is expressed as follows:

[0019]

[0020]

[0021]

[0022] in, This represents the square of the truck's emergency braking reaction time. This indicates the truck's maximum acceleration. There are three auxiliary variables.

[0023] Preferably, step S5 specifically includes: Uncertainty based on currently pre-set or assessed information The range of values ​​for the acceleration sensitivity coefficient is obtained in the stable region of the spatiotemporal phase diagram. The optimal acceleration sensitivity coefficient is obtained within the range of values, and the optimal acceleration sensitivity coefficient is used to control the unmanned truck platoon to drive.

[0024] Compared with the prior art, the present invention has at least the following beneficial effects: (1) This invention introduces information uncertainty parameters during the modeling of unmanned truck platoons, comprehensively considering the impact of multiple uncertainties, such as onboard sensor errors and communication delays, on the safety of platoon operation under actual road conditions. Based on obtaining the motion state of each vehicle and quantifying information uncertainties, this method explicitly introduces these parameters into the dynamic model and subsequent parameter analysis, leaving adjustable space for safety margins in practical engineering applications, thereby improving the adaptability and robustness of unmanned truck platoons to complex working conditions.

[0025] (2) This invention innovatively derives vehicle stability conditions by performing frequency domain transformation on the queuing dynamics model, and further establishes the relationship between information uncertainty and acceleration sensitivity coefficient to obtain a spatiotemporal phase diagram of the stable region. This spatiotemporal phase diagram intuitively reflects the influence of each parameter on the stability of the vehicle queuing, providing engineers with a clear and quantitative basis for model parameter tuning, improving the theoretical interpretability and engineering feasibility of the model for queuing stability, and thus effectively avoiding collisions or chain reactions between vehicles caused by information uncertainty.

[0026] (3) This invention proposes to optimize and dynamically adjust the acceleration sensitivity coefficient of the driverless truck following model by using a stable region as a constraint. In practical applications, based on the information uncertainty obtained from real-time detection or evaluation, the control parameters can be kept within the optimal range for queue stability, thereby ensuring that the entire driverless truck fleet maintains efficient, safe, and stable operation under various uncertain information conditions. This parameter adaptive capability significantly improves the safety and intelligence level of the driverless truck fleet. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0028] Figure 1 The flowchart shows the unmanned truck following modeling method that takes into account information uncertainty provided by the present invention.

[0029] Figure 2 A schematic diagram of the spatiotemporal phase diagram for information uncertainty and acceleration sensitivity coefficient provided by the present invention. Detailed Implementation

[0030] To better understand the above-described objectives, features, and advantages of the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other. Furthermore, the present invention can be implemented in other ways different from those described herein; therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0031] This invention provides a method for modeling autonomous truck following that considers information uncertainty. A dynamic truck platooning following model is established for a traffic scenario involving autonomous trucks following a guided vehicle, based on information uncertainty, road gradient, and rolling resistance. Then, the transfer function of the acceleration model is derived by transforming it from the time domain to the frequency domain. Based on stability requirements, the relationship between the acceleration sensitivity coefficient ensuring platoon stability and information uncertainty is determined. This provides clear and quantifiable parameter selection criteria for the control of autonomous truck platoons, enabling engineers to select an acceleration sensitivity coefficient that ensures platoon stability and safety based on the reliability (i.e., information uncertainty) of actual autonomous truck following. Using this acceleration sensitivity coefficient to control autonomous truck following effectively avoids the risk of platoon instability caused by information disturbances.

[0032] like Figure 1 As shown, a method for modeling autonomous truck following that considers information uncertainty is disclosed. The specific implementation steps are as follows: Step S1: Obtain the motion state of the unmanned truck convoy; the unmanned truck convoy includes a guide vehicle and following vehicles; the motion state includes the speed, spacing, and maximum deceleration of each vehicle in the unmanned truck convoy; The specific embodiments of the present invention are applied to traffic scenarios of unmanned truck platoons, which include lead vehicles and follower vehicles.

[0033] In some embodiments, an unmanned truck equipped with a precision control system may be set up as the lead vehicle of the convoy, which can travel according to a pre-set speed curve and trajectory.

[0034] In this step, the present invention can obtain the motion status of each vehicle in the unmanned truck convoy through onboard sensors, and the motion status includes speed, vehicle spacing and maximum deceleration.

[0035] In some embodiments, each vehicle in the unmanned truck platoon can be equipped with onboard sensors such as GPS positioning systems and inertial measurement units, enabling it to acquire its own position and speed information in real time, thereby obtaining speed, vehicle-to-vehicle gaps, and maximum deceleration. Each vehicle can transmit this status information to other vehicles, remote communication devices, etc., through a communication system.

[0036] Step S2: Determine the following modeling parameters for the unmanned truck platoon, including truck emergency braking reaction time, information uncertainty, acceleration sensitivity coefficient, expected safety margin, road gradient, and truck rolling resistance; In this step, the present invention selects various parameter values ​​in the car-following modeling parameters based on the traffic scenario and road conditions of the set unmanned truck platoon, including the truck emergency braking reaction time. Information uncertainty Acceleration sensitivity coefficient Expected safety margin Road slope and truck rolling resistance .

[0037] Among them, truck emergency braking reaction time This refers to the time interval from when the driver or autonomous driving system senses an emergency to when the braking system begins to function. In some embodiments, considering the rapid response characteristics of autonomous driving systems, the emergency braking reaction time... This can be obtained through actual emergency braking experiments.

[0038] Information uncertainty This describes the uncertainty caused by various real-world uncertainties when collecting and transmitting the motion status of each vehicle. In some embodiments, information uncertainty can be... Set as a random variable that follows a preset probability distribution, for example, it can be... Satisfy uniform distribution This indicates that the received information may have a deviation range of plus or minus 30%.

[0039] Acceleration sensitivity coefficient This reflects the sensitivity of the following vehicle to changes in the motion state of the vehicle in front. A higher value indicates a faster vehicle response.

[0040] The expected safety margin SM is a dimensionless parameter used to ensure that the vehicle maintains sufficient safety margin during following. In some embodiments, the expected safety margin SM can be set to 0.9.

[0041] In addition, the truck's physical parameters, including road gradient, need to be set. and truck rolling resistance Road slope The rolling resistance of the trucks can be set based on the actual road gradient in which the current unmanned truck convoy is operating. It can be obtained through rolling resistance measurement experiments.

[0042] Step S3: Establish a truck platoon following dynamics model, which is used to obtain the acceleration of each vehicle in the unmanned truck platoon based on the motion state and following modeling parameters; In this invention, the lead truck in a truck convoy moves along a pre-specified trajectory, while for the other vehicles following behind, this invention establishes a truck convoy following dynamics model to describe the motion state of the following vehicles.

[0043] The basic kinematic model expression for the other trucks in the queue is:

[0044] in, The first kinematic model obtained from the basic kinematic model A truck in acceleration at any moment Indicates the acceleration sensitivity coefficient. Indicates the first A truck in The speed of time Indicates the truck's emergency braking reaction time. express The square of, Indicates the first A truck in The maximum deceleration at time t, Indicates the first A truck in Time and the The distance between trucks; Indicates the first A truck in The square of the velocity at time t, Indicates the first A truck in The maximum deceleration at time t, This indicates the expected safety margin.

[0045] In the basic kinematic model above, acceleration equals the acceleration sensitivity coefficient α multiplied by a control term, which consists of the difference between the current vehicle clearance utilization rate and the expected safety margin. Specifically, the vehicle clearance utilization rate comprises two parts: first, the safe distance required for the vehicle to maintain its current speed, including the distance traveled within reaction time. and the distance required to brake at maximum deceleration Secondly, the distance required for the vehicle in front to brake at maximum deceleration. The difference between these two distances divided by the actual vehicle clearance This yields the vehicle clearance utilization rate. When the vehicle clearance utilization rate is less than... When the vehicle gap is large enough, it indicates that the current vehicle gap is large enough for the vehicle to accelerate; when the vehicle gap utilization rate is greater than 10%, it indicates that the vehicle gap is large enough for the vehicle to accelerate. When the distance between vehicles is too small, it indicates that the vehicles need to slow down to maintain a safe distance.

[0046] In real-world driving environments, the acquisition and transmission of motion states by each vehicle often involves a degree of uncertainty due to factors such as acquisition errors and interference from wireless signal propagation. To describe the impact of this information uncertainty on platoon following behavior, an information uncertainty parameter is introduced into the basic kinematic model. A kinematic model considering information uncertainty was established, with the expression:

[0047] in, The kinematic model that considers information uncertainty is the first... A truck in acceleration at any moment This represents the information uncertainty parameter.

[0048] when A value >0 indicates that uncertainty causes the vehicle to overreact to changes in the motion of the vehicle in front; when When the value is less than 0, it indicates that uncertainty leads to insufficient response of the vehicle to changes in the motion state of the vehicle in front.

[0049] In some embodiments, information uncertainty The uncertainty is estimated by real-time monitoring of the communication link quality, including indicators such as signal strength, packet reception rate, and transmission delay. The system establishes a mapping relationship between communication quality and uncertainty; when communication quality deteriorates, the absolute value of the uncertainty increases, indicating a decrease in the reliability of the received information. In some embodiments, the information uncertainty... It can also be estimated based on the various measurement errors of the on-board sensors.

[0050] Besides information uncertainty, dynamic factors such as road gradient and truck rolling resistance also significantly affect the actual motion of a truck. During actual driving, when a truck is traveling on an incline, the component of gravity generates additional resistance or assistance; simultaneously, the rolling resistance between the tires and the road surface continuously consumes power. To establish a more accurate and practical car-following model, the effects of road gradient and rolling resistance are further introduced into the kinematic model that considers information uncertainty.

[0051] The final expression for the truck platoon following dynamics model determined by this invention is:

[0052] in, Indicates the first A truck in acceleration at any moment The kinematic model that considers information uncertainty is the first... A truck in acceleration at any moment Indicates the rolling resistance of the truck. For gravitational acceleration, it can be set to m / s 2 , This refers to the road slope.

[0053] Through the above steps, this invention establishes a truck platoon following dynamics model that comprehensively considers information uncertainty, road gradient, and rolling resistance, which can accurately reflect the following behavior of unmanned trucks under actual road conditions.

[0054] Step S4: Perform frequency domain transformation on the truck platoon following dynamics model to obtain a spatiotemporal phase diagram of information uncertainty and acceleration sensitivity coefficient, wherein the spatiotemporal phase diagram includes stable regions and unstable regions; In this step, the present invention uses a vehicle queuing following dynamics model. The computational expression is processed by frequency domain transformation, and frequency domain analysis can more intuitively reveal the stability characteristics of the system. The acceleration equation is transformed by frequency, from the time domain to the frequency domain, to obtain the system's transfer function. .

[0055] According to the stability criterion in control theory, the requirement for each vehicle in a convoy to remain stable is that the amplitude of its transfer function does not exceed 1, i.e. For all non-negative frequencies Both are true. Therefore, the expression for the vehicle stability condition determined by this invention is:

[0056]

[0057]

[0058]

[0059]

[0060] in, Describe the transfer function, The imaginary unit, For complex frequency domain variables, Represents the magnitude of a complex number. Indicates sensitivity to the speed of the vehicle in front. Indicates sensitivity to vehicle spacing. Indicates sensitivity to the speed of the vehicle. This represents the partial derivative of the truck's rolling resistance with respect to the speed of the vehicle in front at the equilibrium point. The value at that location, To achieve the desired speed for the queue to follow the carousel, This indicates the expected distance between vehicles in the queue. This represents the partial derivative of the truck's rolling resistance with respect to the distance between vehicles at the equilibrium point. The value at that location, This represents the partial derivative of the truck's rolling resistance with respect to its speed at the equilibrium point. The value at that location.

[0061] Based on the inequalities in the vehicle stability conditions, the relationship between information uncertainty and acceleration sensitivity coefficient can be calculated, and the expression is as follows:

[0062]

[0063]

[0064]

[0065] in, This represents the square of the truck's emergency braking reaction time. This indicates the truck's maximum acceleration. There are three auxiliary variables.

[0066] The relationship between the above information uncertainty and the acceleration sensitivity coefficient indicates that, in order to ensure queue stability, the acceleration sensitivity coefficient... It should be greater than a critical value, which is the information uncertainty. Expected safety margin The maximum acceleration of the truck Road slope The function.

[0067] Based on the above relationship between information uncertainty and acceleration sensitivity coefficient, this invention establishes a spatiotemporal phase diagram of information uncertainty and acceleration sensitivity coefficient, as follows: Figure 2 As shown.

[0068] The horizontal axis of the spacetime phase diagram represents the acceleration sensitivity coefficient. The vertical axis represents the information uncertainty. In the phase diagram, the curves represent stability boundaries. Parameter combinations located above or to the right of the curve correspond to the stable region. Within this region, selecting parameters ensures the convoy remains stable during car following; even if the leading vehicle's motion changes, the disturbance will gradually attenuate as it propagates backward, preventing increasingly violent oscillations in following vehicles. Parameter combinations located below or to the left of the curve correspond to the unstable region. Selecting parameters within this region can lead to convoy instability; small disturbances from the leading vehicle will be gradually amplified as they propagate backward, potentially causing dangerous acceleration fluctuations in following vehicles.

[0069] Step S5: Control the unmanned truck convoy to drive based on the car-following modeling parameters of the stable region in the spatiotemporal phase diagram.

[0070] In this step, the car-following modeling parameters for the stable region in the spatiotemporal phase diagram are obtained, which may specifically include: based on the currently preset or evaluated information uncertainty. The range of values ​​for the acceleration sensitivity coefficient is obtained in the stable region of the spatiotemporal phase diagram.

[0071] The optimal acceleration sensitivity coefficient is obtained based on its range, and the optimal acceleration sensitivity coefficient is used to control the unmanned truck platoon to move.

[0072] To illustrate the effectiveness of the method proposed in this invention, the following detailed description of the above technical solution of this invention is provided through a specific embodiment.

[0073] Example 1 This embodiment is applied to an unmanned truck platoon transportation scenario on a highway. The unmanned truck platoon consists of one lead vehicle and three follower vehicles, with the lead vehicle traveling at a constant speed according to a preset schedule. The motion status of each vehicle is acquired in real time through an onboard GPS positioning system and an inertial measurement unit.

[0074] The car-following modeling parameters were determined based on actual road conditions and vehicle characteristics. The truck's emergency braking reaction time was measured through emergency braking experiments. The information uncertainty is set as a uniformly distributed random variable, ranging from -0.3 to 0.3, representing a deviation of ±30% in the received information. Expected safety margin. Set as The current highway gradient is... The rolling resistance coefficient of the truck was obtained through rolling resistance measurement experiments. Gravitational acceleration m / s 2 .

[0075] Based on the above parameters, a truck platoon following dynamics model is established. First, a basic kinematic model is built to calculate the difference between vehicle clearance utilization and the expected safety margin. Then, an information uncertainty parameter is introduced to establish a kinematic model considering information uncertainty. Finally, the effects of road gradient and rolling resistance are incorporated to obtain a complete following dynamics model. This model can calculate the acceleration of each following vehicle in real time based on the current motion state and following modeling parameters.

[0076] The car-following dynamics model is subjected to frequency domain transformation to obtain the system's transfer function. Based on the stability criterion that the transfer function amplitude does not exceed 1, the vehicle stability condition is derived. By calculating the partial derivatives at the equilibrium point, sensitivity parameters to the speed of the preceding vehicle, the distance between vehicles, and the vehicle's own speed are obtained. Finally, [the following is a continuation of the previous sentence, but the context is unclear]. , , , The relationship between information uncertainty and acceleration sensitivity coefficient is obtained, and a spacetime phase diagram is plotted. In this phase diagram, the stability boundary curve divides the parameter space into stable and unstable regions.

[0077] Uncertainty based on the information obtained from the current assessment The value is 0.2. The corresponding range of acceleration sensitivity coefficient values ​​is found within the stable region of the spacetime phase diagram. The results show the acceleration sensitivity coefficient... A value greater than 6 is required to ensure queue stability. Taking into account both response speed and comfort requirements, the optimal acceleration sensitivity coefficient is selected. The value is 8. This parameter is configured into the control system of each following vehicle to achieve stable following of the unmanned truck convoy, effectively avoiding convoy instability and oscillation.

[0078] While the specific embodiments of the present invention depict actions or steps in a particular order, this should be understood as requiring such actions or steps to be performed in the shown specific order or sequential order, or requiring all illustrated actions or steps to be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations. The above descriptions are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention.

[0079] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for modeling car following of an unmanned truck considering information uncertainty, characterized in that, The method comprises the following steps: Step S1, acquiring a motion state of an unmanned truck queue; the unmanned truck queue comprises a leading truck and a following truck; the motion state comprises a speed, a vehicle gap and a maximum deceleration of each vehicle in the unmanned truck queue; Step S2, determining a car-following modeling parameter of the unmanned truck queue, comprising a truck emergency braking reaction time, an information uncertainty, an acceleration sensitivity coefficient, an expected safety margin, a road slope and a truck rolling resistance; Step S3, establishing a truck queue car-following dynamics model, the car-following dynamics model being used to acquire an acceleration of each vehicle in the unmanned truck queue according to the motion state and the car-following modeling parameter; Step S4, performing a frequency domain transformation processing on the truck queue car-following dynamics model to acquire a time-space phase diagram of the information uncertainty and the acceleration sensitivity coefficient, the time-space phase diagram comprising a stable region and an unstable region; Step S5, controlling the unmanned truck queue to travel based on the car-following modeling parameter in the stable region of the time-space phase diagram.

2. The autonomous truck car following modeling method considering information uncertainty according to claim 1, wherein, Step S1 specifically comprises: Each vehicle in the unmanned truck queue acquires its own position and speed information based on a vehicle-mounted sensor to further obtain the speed, the vehicle gap and the maximum deceleration.

3. The autonomous truck car following modeling method considering information uncertainty according to claim 2, characterized in that, In step S2, the information uncertainty The expected safety margin SM is set to 0.9, determined to satisfy a random variable of uniform distribution.

4. The autonomous truck car following modeling method considering information uncertainty according to claim 3, characterized in that, In step S3, an expression of the car-following dynamics model is: in, Indicates the first A truck in acceleration at any moment Indicates the rolling resistance of the truck. It is the acceleration due to gravity. For road slope, The kinematic model that considers information uncertainty is the first... A truck in acceleration at any moment Indicates the acceleration sensitivity coefficient. Indicates the first A truck in The speed of time, Indicates the truck's emergency braking reaction time. express The square of, Indicates the first A truck in The maximum deceleration at time t, Indicates the first A truck in Time and the The distance between trucks; Indicates the first A truck in The square of the velocity at time t, Indicates the first A truck in The maximum deceleration at time t, Indicates the expected safety margin. This represents the information uncertainty parameter.

5. The autonomous truck car following modeling method considering information uncertainty according to claim 4, characterized in that, Step S4 specifically comprises: Step S4-1, performing a frequency domain transformation processing on the truck queue car-following dynamics model to acquire a vehicle stability condition; Step S4-2, calculating according to the vehicle stability condition to obtain a relationship between the information uncertainty and the acceleration sensitivity coefficient; Step S4-3, acquiring a time-space phase diagram of the information uncertainty and the acceleration sensitivity coefficient from the relationship between the information uncertainty and the acceleration sensitivity coefficient, and setting a region in the time-space phase diagram satisfying the relationship between the information uncertainty and the acceleration sensitivity coefficient as a stable region.

6. The autonomous truck car following modeling method considering information uncertainty according to claim 5, characterized in that, In step S4-1, an expression of the vehicle stability condition is: in, Describe the transfer function, The imaginary unit, For complex frequency domain variables, Represents the magnitude of a complex number. Indicates sensitivity to the speed of the vehicle in front. Indicates sensitivity to vehicle spacing. Indicates sensitivity to the speed of the vehicle. This represents the partial derivative of the truck's rolling resistance with respect to the speed of the vehicle in front at the equilibrium point. The value at that location, To achieve the desired speed for the queue to follow the carousel, This indicates the expected distance between vehicles in the queue. This represents the partial derivative of the truck's rolling resistance with respect to the distance between vehicles at the equilibrium point. The value at that location, This represents the partial derivative of the truck's rolling resistance with respect to its own speed at the equilibrium point. The value at that location.

7. The autonomous truck car following modeling method considering information uncertainty according to claim 6, characterized in that, In step S4-2, an expression of the relationship between the information uncertainty and the acceleration sensitivity coefficient is: wherein, represents the square of the truck emergency braking reaction time, represents the maximum acceleration of the truck, are three auxiliary variables.

8. The autonomous truck car following modeling method considering information uncertainty according to claim 7, characterized in that, Step S5 specifically comprises: According to the current preset or evaluated information uncertainty The acceleration sensitive coefficient is obtained in the stable region of the space-time phase diagram. Acquiring an optimal acceleration sensitivity coefficient within a value range of the acceleration sensitivity coefficient, and controlling the unmanned truck queue to travel by using the optimal acceleration sensitivity coefficient.

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