Unmanned vehicle formation driving vehicle following distance optimization calculation method and system and medium

Abstract

The invention discloses an optimized calculation method and system for the following distance of an unmanned vehicle team during running, and a medium. The method comprises the following steps: analyzing the quantitative change characteristics of air resistance coefficients and lift coefficients of vehicles in the unmanned vehicle team; calculating the minimum following distance of the following vehicle based on the quantitative change characteristics, the nonlinear dynamic model and the prediction iteration of the time-varying state data of the vehicle; according to the minimum car following distance, a car following distance optimization interval is determined, and influence parameters between the car following distance and the braking margin, the energy consumption, the lift coefficient and the tracking capacity of the front car are analyzed; in the car following distance optimization interval, dynamically calculating the optimal car following distance of the following car according to the influence parameters; according to the method, the air resistance coefficient and lift coefficient quantitative rule of the formation vehicles can be obtained, the vehicle speed, the road resistance, the transmission efficiency and the driving / braking element response characteristics are comprehensively considered, and the optimal following distance of each following vehicle is dynamically predicted in real time and iteratively calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle spacing control. Specifically, the present invention is applied to the field of joint control of unmanned vehicle subsystems, and particularly relates to an optimal calculation method, system and medium for the following distance of unmanned vehicle formation driving. Background Art

[0002] The technology of unmanned vehicle formation is regarded as a key part of the future intelligent transportation system, with significant application value and market potential; this technology effectively reduces the vehicle distance by precisely controlling the formation driving between vehicles, and utilizes the wind-breaking effect of the leading vehicle to reduce the wind resistance and fuel consumption of the following vehicle.

[0003] During formation driving, the following distance has a significant impact on energy consumption. Therefore, it is crucial to optimize the following control technology and reasonably set and adjust the following distance; current research mainly focuses on the overall stability and fuel economy of the formation, and calculates the control commands of the optimal drive elements through dynamic models, but usually only considers local optimal solutions, and there is relatively little research on the optimal following distance; common following distance strategies include fixed distance, fixed time interval and variable time interval strategies, but non-linear time-varying factors such as vehicle type differences, vehicle order, wind resistance coefficient, lift coefficient, drive efficiency, braking characteristics and road slope make the determination of the optimal following distance complicated; previously, no one has proposed a quantitative prediction method for the optimal following distance that comprehensively considers all non-linear time-varying factors and is iteratively calculated based on a dynamic model.

[0004] For example, Chinese Patent Publication No. CN108284836A discloses a vehicle longitudinal following control method, which proposes a vehicle formation control scheme based on non-linear model predictive control theory, aiming to control the speed and expected inter-vehicle distance of vehicles in the queue by optimizing the control quantity, so as to improve fuel economy; however, its key expected inter-vehicle distance is directly set, without quantitative optimization calculation, and factors such as the dynamic change of driving resistance and the change of the lift coefficient of the following vehicle caused by the leading vehicle are not considered.

[0005] For example, Chinese Patent Publication No. CN116749969A discloses a vehicle queue following distance control method based on driving conditions and road surface conditions, which can change the following strategy according to different road conditions to ensure following safety and queue stability; however, this method does not consider the non-linear dynamic characteristics of the vehicle to calculate the minimum following distance, nor does it deeply explore how to adjust the following distance according to the change of the wind resistance coefficient to optimize energy consumption.

[0006] For example, Chinese published patent CN110164124A discloses a longitudinal following control method for vehicles during the platoon driving of highway heavy trucks. This method takes into account the influence of uncertain factors such as road slope, driveline delay, wind speed, and the acceleration of the leading vehicle on vehicle speed and spacing control. However, this method also does not consider how to optimize the expected following distance, which is a key factor.

[0007] For example, in the literature <Optimizing Gap Tracking Subject to Dynamic Losses via Connected and Anticipative MPC in Truck Platooning>, this literature takes into account the aerodynamic drag coefficient varying with distance and establishes a dynamic model to optimize vehicle speed and spacing control. However, the target following distance is still directly given, and no specific calculation method is provided.

[0008] For example, Chinese published patent CN117850406A discloses an energy-saving scheduling method and system based on autonomous driving truck platooning. This method calculates the minimum instantaneous fuel consumption rate by constructing a fuel consumption module. However, this method only considers the speed of a single vehicle on the current sub-section and does not consider the aerodynamic drag influence caused by the change of following distance, the interaction between vehicles, and the overall effect of platoon driving. Therefore, the optimal solution obtained is local.

[0009] For example, Chinese published patent CN117270525A discloses a control method and device for unmanned container truck platooning based on energy consumption optimization. This solution calculates the optimal expected speed by establishing a dynamic model of unmanned container truck platooning, but does not consider the relationship between the optimal speed and the optimal spacing, nor does it explore how to determine the following distance according to energy consumption factors.

[0010] For example, Chinese published patent CN114818109A discloses a calculation method for the aerodynamic drag coefficient and fuel economy under vehicle platoon driving. This method obtains the aerodynamic drag coefficients of single vehicles and platoon vehicles through CFD calculation, and then calculates the fuel savings of the vehicle fleet. However, this method only calculates the fuel consumption at different spacings and does not solve the problem of how to predict and control the optimal spacing in real time and dynamically according to road conditions.

[0011] For example, Chinese Patent Publication No. CN116161031A discloses a fuel-saving automatic driving truck following control method and device. This method introduces a vehicle automatic following system that calculates and adjusts the following distance through a ranging device to achieve energy conservation. However, when discussing the minimum following distance, it does not consider the source of the braking deceleration value and the sampling error, nor does it consider the influence of the road gradient, and it does not clarify how to quantitatively convert the air resistance analysis into the following distance calculation. In practical applications, the optimization of the following distance needs to comprehensively consider various factors and cannot simply pursue minimization.

[0012] In addition, in the existing research on following distance calculation, traditional methods usually simulate the braking execution delay of the following vehicle by simply adding a preset time delay after the braking command is issued. However, this method is not applicable to new energy electric vehicles because the electric braking system of electric vehicles responds quickly and can apply braking quickly without waiting for the mechanical braking system to build up braking pressure, thus significantly reducing the braking delay under certain conditions. Therefore, when calculating the optimal following distance, the dynamic response capabilities of electric braking and mechanical braking should be considered separately, and quantitative calculations of non-linear characteristics should be carried out to ensure accuracy and effectiveness in practical applications. Summary of the Invention

[0013] The purpose of the present invention is to provide an optimization calculation method, system and medium for the following distance of unmanned vehicle formation driving, so as to solve all or one of the above problems existing in the prior art.

[0014] To solve the above technical problems, the specific technical solutions of the present invention are as follows: On the one hand, the present invention provides an optimization calculation method for the following distance of unmanned vehicle formation driving, including the following steps: Analyze the quantitative change characteristics of the air resistance coefficient and lift coefficient of the vehicles in the unmanned vehicle fleet; Establish a non-linear dynamic model of the following vehicle, and calculate the minimum following distance of the following vehicle based on the quantitative change characteristics, the non-linear dynamic model and the prediction iteration of the vehicle time-varying state data; Determine the following distance optimization interval according to the minimum following distance, and analyze the influencing parameters between the following distance, braking margin, energy consumption, lift coefficient and the tracking ability of the preceding vehicle; Based on the vehicle longitudinal dynamic model, the iterative relationship of the vehicle time-varying state data and the influencing parameters, dynamically calculate the optimal following distance of the following vehicle within the following distance optimization interval; Control the following strategy of the unmanned vehicle fleet according to the optimal following distance.

[0015] Further, establishing the non-linear dynamic model of the following vehicle, and calculating the minimum following distance of the following vehicle based on the prediction iteration of the quantitative change characteristics, the non-linear dynamic model, and the time-varying state data of the vehicle, includes: Determining the time iteration relationship during the following process, and determining the iteration relationship of the time-varying state data of the leading vehicle when the leading vehicle brakes with full force according to the time iteration relationship; Calculating the system discrete state space equation based on the non-linear dynamic model, and determining the iteration relationship of the time-varying state data of the following vehicle according to the quantitative change characteristics and the system discrete state space equation; Setting the dynamic following distance and the minimum distance condition, performing the iteration of the dynamic following distance based on the time iteration relationship, the iteration relationship of the time-varying state data of the leading vehicle, the system discrete state space equation, and the iteration relationship of the time-varying state data of the following vehicle, and determining whether the iterated dynamic following distance is used as the minimum following distance according to the minimum distance condition.

[0016] Further, calculating the system discrete state space equation based on the non-linear dynamic model includes: Constructing a non-linear MPC model predictive controller for the movement of the following vehicle, and converting the vehicle longitudinal dynamic model of the following vehicle according to the non-linear MPC model predictive controller to obtain a continuous state space equation based on the vehicle longitudinal dynamic model; Discretizing the continuous state space equation by using the forward Euler integration to obtain the system discrete state space equation.

[0017] Further, the time-varying state data of the leading vehicle includes: the time-varying driving speed and the time-varying driving distance when the leading vehicle brakes with full force; The time-varying state data of the following vehicle includes: the time-varying following distance, the time-varying air resistance coefficient, the time-varying lift coefficient, the time-varying road resistance coefficient, the time-varying road ramp manifold data, and the time-varying traction force or braking force of the following vehicle.

[0018] Further, the minimum distance condition includes: the first condition, the second condition, the third condition, and the fourth condition; The first condition is: D d 3 (K 3 ) d 2 (K 2 ); The second condition is: D = d 3 (K 3 ) d 2 (K 2 ); The third condition is: D d 3 (K 3 ) d 2 (K 2 ); The fourth condition is: ; Wherein, is the dynamic following distance, is the time-varying following distance of the following vehicle, is the time-varying driving distance when the leading vehicle brakes with full force, is the first threshold value regarding the following distance.

[0019] Furthermore, the confirmation of whether the dynamic following distance obtained by iterating according to the minimum distance condition is used as the minimum following distance includes: In response to the dynamic following distance satisfying the first condition, it is determined that the following vehicle collides with the leading vehicle, the dynamic following distance is increased, and iterative calculations are performed on the time-varying following distance of the following vehicle and the time-varying driving distance when the leading vehicle brakes with full force according to the increased dynamic following distance, and the minimum distance condition is judged; In response to the dynamic following distance satisfying the second condition, it is determined that a critical collision point appears between the following vehicle and the leading vehicle; In response to the dynamic following distance satisfying the third condition, it is determined that the following vehicle does not collide with the leading vehicle, the dynamic following distance is decreased, and iterative calculations are performed on the time-varying following distance of the following vehicle and the time-varying driving distance when the leading vehicle brakes with full force according to the decreased dynamic following distance, and the minimum distance condition is judged; In response to the dynamic following distance satisfying the fourth condition, it is determined that the dynamic following distance is the minimum following distance.

[0020] Furthermore, the dynamic calculation of the optimal following distance of the following vehicle within the following distance optimization interval based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influence parameters includes: Determining a final optimization function regarding the following distance based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influence parameters; Dynamically optimizing the following distance within the following distance optimization interval based on the final optimization function to obtain the optimal following distance.

[0021] Furthermore, the influencing parameters include: a braking margin influencing parameter, an energy consumption influencing parameter, a lift coefficient influencing parameter, and a tracking ability influencing parameter; The braking margin influencing parameter is: the minimum following distance; The energy consumption influencing parameter is: an optimal efficiency value following distance determined according to iterative energy consumption data obtained by performing iterative calculations on energy consumption data based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the dynamic following distance; The lift coefficient influencing parameter is: a lift coefficient that varies with time according to the dynamic following distance; The tracking ability influencing parameter is: a confidence level of the leading vehicle that varies with time according to the dynamic following distance; The final optimization function is: , where is the braking margin influencing parameter, is the energy consumption influencing parameter, is the lift coefficient influencing parameter, is the tracking ability influencing parameter, is the dynamic following distance.

[0022] On the other hand, the present invention further provides an optimization calculation system for the following distance of platooning of driverless vehicles, including: A change characteristic analysis module for analyzing the quantitative change characteristics of the air resistance coefficient and the lift coefficient of vehicles in a driverless vehicle fleet; A minimum following distance analysis module for establishing a non-linear dynamics model of a following vehicle, and calculating the minimum following distance of the following vehicle based on the quantitative change characteristics, the non-linear dynamics model, and the prediction iteration of vehicle time-varying state data; An optimal following strategy control module for determining an optimization interval for the following distance according to the minimum following distance, analyzing the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient, and the tracking ability of the leading vehicle; dynamically calculating the optimal following distance of the following vehicle within the optimization interval for the following distance based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters; and controlling the following strategy of the driverless vehicle fleet according to the optimal following distance.

[0023] On the other hand, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the optimization calculation method for the following distance of platooning of driverless vehicles are implemented.

[0024] The beneficial effects of the technical solution of the present invention are: 1. The optimal following distance optimization calculation method for the formation driving of driverless vehicles according to the present invention can obtain the quantitative laws of the air resistance coefficient and lift coefficient of the formation vehicles, comprehensively consider the vehicle speed, road resistance, transmission efficiency, response characteristics of drive / brake components, and road slope angle, establish a nonlinear dynamics model, and dynamically predict and iteratively calculate the optimal following distance of each following vehicle in real time. Finally, on the premise of ensuring safety, it can effectively improve the driving efficiency and fuel economy of the driverless vehicle formation, with a perfect calculation framework, strong adaptability adjustment ability, accurate nonlinear dynamics model, comprehensive consideration of various factors in the calculation of the optimal distance, and strong expansibility, making up for the defects of the prior art and having high application value.

[0025] 2. The optimal following distance optimization calculation system for the formation driving of driverless vehicles according to the present invention can, through the mutual cooperation of system modules, further implement the optimal following distance optimization calculation method for the formation driving of driverless vehicles according to the present invention.

[0026] 3. The computer-readable storage medium according to the present invention can guide the system modules to cooperate, thereby implementing the optimal following distance optimization calculation method for the formation driving of driverless vehicles according to the present invention, and the computer-readable storage medium according to the present invention can also effectively improve the operability of the optimal following distance optimization calculation method for the formation driving of driverless vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific 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.

[0028] Figure 1 It is a schematic flowchart of the optimal following distance optimization calculation method for the formation driving of driverless vehicles in Embodiment 1 of the present invention; Figure 2 It is a schematic diagram of the coordinate system in the optimal following distance optimization calculation method for the formation driving of driverless vehicles in Embodiment 1 of the present invention; Figure 3 It is a schematic flowchart of the calculation in the optimal following distance optimization calculation method for the formation driving of driverless vehicles in Embodiment 1 of the present invention; Figure 4 It is a schematic curve diagram of the air resistance coefficient and lift coefficient varying with the vehicle distance in the optimal following distance optimization calculation method for the formation driving of driverless vehicles in Embodiment 1 of the present invention; Figure 5 It is a schematic diagram of the spatial relationship of the minimum following distance condition in the optimal following distance optimization calculation method for the formation driving of driverless vehicles in Embodiment 1 of the present invention; Figure 6 It is a schematic diagram of the time relationship corresponding to the spatial relationship of the minimum following distance condition in the method for optimizing the following distance in the platoon driving of the unmanned vehicle according to Embodiment 1 of the present invention; Figure 7 It is a schematic diagram of the architecture of the system for optimizing the calculation of the following distance in the platoon driving of the unmanned vehicle according to Embodiment 2 of the present invention. Specific embodiments

[0029] The following elaborates on the preferred embodiments of the present invention in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making the scope of protection of the present invention more clearly defined.

[0030] In the description of the present invention, it should be noted that the embodiments described in the present invention are part of the embodiments of the present invention, rather than all embodiments; based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0031] The terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0032] In the description of the present invention, it should be noted that the following vehicle discussed in the present invention is a vehicle located in a non-leading position in the vehicle fleet. The present invention studies the optimization problem of the expected following distance between the following vehicle and its preceding vehicle, that is, the optimization value problem of Δd; behind the following vehicle of the present invention, there may be other subsequent vehicles following this vehicle; the leading vehicle is not necessarily the first vehicle in the vehicle fleet, that is, the leading vehicle is not necessarily the leading vehicle.

[0033] In the description of the present invention, it should be noted that before the method of the present invention starts, a set of calculation coordinate systems O-xyz is established, as Figure 2; The x-axis is horizontal and forward, representing the displacement distance in front of the following vehicle. The z-axis is vertical and upward, representing the vertical height. The y-axis is determined according to the right-hand rule. O-xyz is fixed to the vehicle head. The xy plane always remains horizontal, and x always faces the vehicle head. The actual vehicle speed v is the linear speed value of the vehicle, which is a scalar. The angle between v and the x-axis is the slope angle α, and α changes continuously with the road terrain.

[0034] Embodiment 1. This embodiment provides an optimized calculation method for the following distance in the formation driving of unmanned vehicles. As Figures 1 - 6 shown, it includes the following steps: S100. Measure the quantitative change characteristics of the air resistance coefficient and lift coefficient of each vehicle in the unmanned vehicle fleet, including: In a preferred implementation, through wind tunnel tests / CAE simulations, fit the wind resistance law function regarding the air resistance coefficient C D and lift coefficient C L as follows: ; In the above formula, i is the serial number of the following vehicle in the fleet, N is the number of vehicles in the fleet, Δd is the following distance, v is the current vehicle speed of the following vehicle, represents the corresponding functional relationship; It should be noted that during the following process, factors such as the vehicle shape, following speed, following distance, the number of vehicles in the fleet, and the vehicle serial number can all affect the wind resistance coefficient C D and lift coefficient C L show different laws; as Figure 4 the example of (where C D∞ is the wind resistance coefficient when the following distance is infinite, that is, when the vehicle is driving alone). Analyzing this example, it can be found that when the following distance is relatively small, the wind resistance is smaller. After the distance increases to a certain extent, the change in the resistance coefficient becomes smaller and smaller; for the 5th vehicle, its wind resistance coefficient increases as the following distance decreases, which is counterintuitive; furthermore, the lift coefficient increases as the distance decreases, which is also not conducive to the stable driving of the vehicle; in summary, the wind resistance coefficient C D and lift coefficient C L in the following process have complex, non-linear and even counterintuitive laws. Therefore, in order to calculate the optimal following distance, targeted calculations need to be carried out for different following conditions of different vehicle models.

[0035] S200. According to the vehicle speed, road driving resistance, drive / brake element response characteristics, (especially) road slope angle changes, air resistance changes caused by the formation, and lift changes, establish a non-linear dynamic model of the following vehicle. Based on this non-linear dynamic model and the prediction iteration of various parameters, calculate the minimum following distance of the following vehicle, including: S201. Determine the time iteration relationship as follows: (1) Draw a spatial relationship diagram of the minimum following distance condition, as Figure 5 shown; for the convenience of description, the leading vehicle and the following vehicle are drawn in two rows, and for the convenience of understanding, the driving road surface is drawn in a flat state; in the figure, the solid-line vehicle represents the x-direction relative position between the leading vehicle and the following vehicle at the moment before the leading vehicle starts braking; the dashed-line vehicle represents the relative position between the leading vehicle and the following vehicle when braking to a stop; Δd is the following distance, and in the subsequent iterative calculation process, the variable D represents the set initial following distance; d 2 is the distance traveled by the leading vehicle from the start of braking to a complete stop; d 3 is the distance traveled by the following vehicle from the start of braking to a complete stop; since the leading vehicle may be manually driven and its control law is uncertain, the leading vehicle and the following vehicle do not necessarily stop at the same time, so there are the following three following distance relationships: (1.1) , the two vehicles collide; (1.2) , the two vehicles just do not collide, or it is called the critical collision point; in this extreme condition, usually the leading vehicle brakes to a stop with the maximum braking ability, and the following vehicle also brakes to a stop with the maximum braking ability, and the following vehicle just does not collide with the leading vehicle, is the minimum following distance, and this condition focuses on following vehicle safety; (1.3) , the two vehicles will not collide; in addition, Δd is also related to the slope. When the slope fluctuates, Δd is the integral of the infinitesimal element of the driving distance along the slope.

[0036] (2) Draw a time-axis relationship diagram corresponding to the spatial relationship diagram of the minimum following distance condition, as Figure 6 shown; for the convenience of understanding, the time axes of the leading vehicle and the following vehicle are drawn separately; in the figure, t 0 is the moment when the leading vehicle starts braking, t 2 is the moment when the leading vehicle brakes to a stop, t 1 is the moment when the following vehicle has determined that the leading vehicle has started braking and itself starts braking, t 3 is the moment when the following vehicle brakes to a stop; the time interval between t 0 and t 1 may be affected by perception delay and logic calculation time consumption, and is determined by prior calibration; of course, with the support of an advanced perception system, the time interval between t 0 and t 1 may also be very small or even negligible, that is, t 0 ≈t 1 ; since the sequence of braking of the leading vehicle and the following vehicle is not constant, so t2 and t 3 will change according to the actual situation, and there is the following time conversion relationship: ; Among them, for the convenience of subsequent iterative recursive prediction, K is used to represent the k-th moment, representing the corresponding time step; based on this, in the case of the minimum following distance, the control strategy / calculation rule of the leading vehicle and the following vehicle is: when k = 0, the leading vehicle brakes with the maximum intensity until it stops; the following vehicle maintains its motion state at the moment of k = 0 within the interval of k ∈ [0, K 1 , and the following vehicle brakes with the maximum intensity until it stops within the interval of k ∈ [K 1 , K 3 ; It should be noted that since the leading vehicle may be manually driven and its control strategy is uncertain, it is assumed that a uniform deceleration motion is a most reasonable prediction.

[0037] S202. Determine the time-varying driving speed v p (k) and the iterative calculation relationship of the time-varying driving distance d 2 (k) are as follows: (1) During the emergency braking of the leading vehicle and braking to a stop with an ideal constant maximum deceleration, the calculation relationship of the speed of the leading vehicle changing with time starting from the t 0 moment is: ; In this formula, v p is the current vehicle speed of the leading vehicle; (2) When k = K 2 , the speed of the leading vehicle decreases to 0, and at this time, the conversion is as follows: ; In this formula, a pmax is the maximum braking deceleration of the leading vehicle, which can be a fixed value or a time-varying value; (3) Therefore, the calculation relationship of the forward driving distance of the leading vehicle changing with time is: .

[0038] S203. Determine the iterative calculation relationship of the time-varying driving speed v(k) and the time-varying driving distance d 3 (k) are as follows: S2031. Construct the system discrete state space equation for the following vehicle, which is as follows: (1) Set the following vehicle to maintain its motion state at time k = 0 within the interval k ∈ [0, K 1 , and brake with maximum intensity until it stops within the interval k ∈ [K 1 , K 3 ; (2) In the prior art, the maximum braking deceleration is often used to represent the maximum braking intensity of the following vehicle, which does not conform to the non-linear characteristics of the actual vehicle. Therefore, in this step, a non-linear MPC model predictive controller for the motion of the following vehicle is constructed to iteratively update the state of the following vehicle, convert the vehicle longitudinal dynamics model of the following vehicle, and obtain the continuous state space equation based on the vehicle longitudinal dynamics model: ; In the above continuous state space equation, m is the vehicle mass, g is the acceleration due to gravity, A is the vehicle frontal area, ρ is the air density, δ is the conversion coefficient of the vehicle's rotating mass, and the above are all known parameters; is the first derivative of the vehicle driving speed v, and v is the state variable; f is the rolling resistance coefficient, C D is the air resistance coefficient, C L is the lift coefficient, α is the slope angle, and the above are all dynamic parameters, not constant values; F t is the vehicle traction / braking force, which is formed by the driving motor and the mechanical braking element as the actuating elements acting on the ground through the tires and is calculated according to the response characteristics of the vehicle's own components; (3) Use the forward Euler integration or other mature discretization methods (such as the Runge-Kutta method and the backward Euler integration, etc.) to discretize the above continuous state space equation to obtain the system discrete state space equation as follows: ; In the above system discrete state space equation, all time-varying variables are represented by the discrete values at time k. Based on the vehicle state and the vehicle state time-varying parameter values at time k in the above system discrete state space equation, the state of the following vehicle at time k + 1, such as v(k + 1) and d(k + 1), can be obtained.

[0039] S2032. Iterate on the vehicle state time-varying parameters that affect the time-varying state of the following vehicle based on the system discrete state space equation of the following vehicle, specifically as follows: (1) Iterate on the time-varying , C D and C L , and their iterative relationship with time is: ; (2) Iterate on the time-varying f(k), where f(k) is the road resistance coefficient in front of the vehicle, i.e., the driving resistance; this f(k) is related to the speed of the following vehicle. Given that factors such as the tire tread type and total mass of the vehicle are determined, there is the following iterative relationship: ; (3) Iterate on the time-varying α, where α is the road ramp manifold data in front of the vehicle, characterized in the form of a slope function and is a time-varying function of x. α is obtained through iterative look-up calculation based on x(k); (4) Iterate on the traction / braking force F t (k) as follows: (4.1) In the interval k ∈ [0, K 1 ], keep the current state unchanged. The iterative relationships for the system braking pressure and the output torque of the drive element varying with time are: ; In this relationship, P current is the braking pressure of the current mechanical braking system of the following vehicle, T current is the output torque of the current drive element of the following vehicle, T current being positive means it is a driving torque, T current being negative means it is a braking torque. If the vehicle has multiple drive elements, the corresponding state of multiple drive elements can be substituted here; (4.2) In the interval k ∈ [K 1 , K 3 ], all actuators output according to maximum braking. The iterative relationships for the system braking pressure and the output torque of the drive element varying with time are: ; In this relationship, in addition to being limited by the slope, the system braking pressure and output torque are also subject to their upper bounds, and the traction force is determined by these two values and is related to the road adhesion coefficient; therefore, the mechanical braking pressure is also converted into the braking force at the tire, and finally the iterative relationship of is as follows: ; In the above formula, r is the radius of the vehicle; is the mapping relationship between the mechanical braking pressure and the braking force generated by the mechanical braking on the wheel, calibrated in the static test according to the specific vehicle model; is the efficiency characteristic of the drive system, usually a map; is the road adhesion coefficient.

[0040] S2033. Perform cyclic iteration based on S2031 - S2032 to recursively obtain the sequences of v(k) and d 3 (k) until v(k) is 0, indicating that the following vehicle has braked to a stop. Exit the iteration to obtain the K corresponding to a certain D value 3 and .

[0041] S205. Set a certain following distance D. Based on the above d 3 (k), d 2 (k) and the iterative relationship between D, perform cyclic iteration on D, and then calculate the minimum following distance d min , specifically as follows: Set the following distance D. Based on d 3 (k), d 2 (k) and the iterative relationship between D, calculate the distances d 2 (K 2 ) and d 3 (K 3 ) that the leading vehicle and the following vehicle have traveled when both brake to a stop. They have the following iterative relationship: (1) When D d 3 (K 3 ), it is determined that the following vehicle and the leading vehicle will collide, indicating that the following distance D is too small. The following distance D needs to be increased, and then based on d d 2 (k), d 2 (k) and the iterative relationship between D, calculate d 3 (k), d 2 (k) and D, and compare them; 2 (K 2 ) and d 3 (K 3 ) and compare them; (2) When D = d 3 (K 3 ), it is determined that the following vehicle and the leading vehicle just do not collide, which is the critical collision point situation; d 2 (K 2 ); (3) When D d 3 (K 3 ), it is determined that the following vehicle and the leading vehicle will not collide, indicating that there is still a surplus following distance between the following vehicle and the leading vehicle. The following distance D can be reduced. Then, based on d d 2 (k), d 2 (k) and the iterative relationship between D, calculate d 3 (k), d 2(k) Calculate d based on the iterative relationship between (k) and D 2 (K 2 ) and d 3 (K 3 ) and compare them; (4) In actual applications, generally D = d will not be adopted or occur 3 (K 3 ) d 2 (K 2 ) The situation, so set ε, ε is a relatively small positive number (i.e., the first threshold), when , the obtained D value is the minimum following distance d min .

[0042] S300. Determine the following distance optimization interval. Based on all the above iterative relationships and the vehicle longitudinal dynamics model, and comprehensively considering the braking margin, energy consumption, lift coefficient, and the ability to track the leading vehicle, finally, within the following distance optimization interval, dynamically calculate the optimal following distance of any following vehicle. This optimal following distance is selected without colliding with the leading vehicle, including: S301. Determine the following distance optimization interval, specifically as follows: When d current < d min , it will cause the following distance to be too close and there is a greater risk of collision; when the following distance is too large, the following vehicle may not be able to stably sense / monitor the position information of the leading vehicle, resulting in the formation being unstable. Therefore, the distance critical point that may cause the above situation is used as the maximum following distance, denoted as d max , then the optimal following distance should be within the following distance optimization interval d min and d max , that is ; where d current is the actual following distance between the host vehicle and the rear of the leading vehicle at the current moment, which can be wirelessly transmitted by the leading vehicle through the form of intelligent networking, or detected by components such as the host vehicle's sensors; when the situation of d current < d min occurs, automatically alarm the vehicle formation motion controller to prompt that the following distance is too close at this time; S302. Determine the influence parameters between d and the braking margin, energy consumption, lift coefficient, and the stable tracking ability of the leading vehicle, specifically as follows: (1) Braking margin: d min Although it can ensure braking safety, it is too extreme. Most fleet managers hope to leave a certain margin during braking. Therefore, set min the influence parameter between d and the braking margin according to d ;​ (2) Energy consumption: Under the condition of stable following, the energy consumption is predicted based on the equilibrium state of the aforementioned vehicle longitudinal dynamics model; when the inter-vehicle distance is D, the front and rear vehicles are in stable equilibrium, and at this time, the calculation formula of F t (D) is determined, and based on the map of η and this calculation formula, the corresponding efficiency value η(D) (i.e., energy consumption data) is obtained by reverse look-up, and the influence parameter between d and the energy consumption is determined; where, F t The calculation formula of (D) is as follows: ; In addition, since the drag coefficient is not linear, it is necessary to solve it by means of traversal iteration. Traverse in the interval D∈[d min , d max to obtain the minimum value of η(D), and the corresponding inter-vehicle distance at this time is denoted as D best (i.e., the inter-vehicle distance corresponding to the optimal efficiency value); (3) Lift coefficient C L : When the lift coefficient is too high, it will affect the adhesion effect and the braking force, and at this time, the vehicle passengers or drivers will have a feeling of instability like being light, which is not conducive to driving. Therefore, C L itself is also used as an optimization condition for the inter-vehicle distance; (4) The ability to stably track the vehicle in front: When the distance from the vehicle in front is too far, it may cause the on-vehicle sensor to lose the detection of the vehicle in front. Therefore, the confidence level B of the vehicle in front is used as an optimization condition for the inter-vehicle distance; S303. Based on the influence parameters d min , D best , C L and B, determine the final selection function for optimizing the inter-vehicle distance , specifically as follows: ; In the above final selection function, the norm can be the 1-norm or the 2-norm; according to this final selection function , optimize d in the interval [d min , d max to finally obtain the optimal inter-vehicle distance d for each following vehicle d best ; According to this optimal inter-vehicle distance d best control the following speed and other driving control parameters between the corresponding following vehicle and the leading vehicle.

[0043] It should be noted that the above examples are only for explaining the present invention and should not limit the protection scope of the present invention accordingly.

[0044] Embodiment 2. Based on the same inventive concept as the method for optimizing the following distance calculation of an unmanned vehicle formation driving described in Embodiment 1, this embodiment provides a system for optimizing the following distance calculation of an unmanned vehicle formation driving, as Figure 7 shown, including: A variation characteristic analysis module, configured to: analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of vehicles in an unmanned vehicle fleet; A minimum following distance analysis module, configured to: establish a nonlinear dynamic model of a following vehicle, and calculate the minimum following distance of the following vehicle based on the prediction iteration of the quantitative variation characteristics, the nonlinear dynamic model, and the vehicle time-varying state data; An optimal following strategy control module, configured to: determine an optimized following distance interval according to the minimum following distance, and analyze the influencing parameters among the following distance, braking margin, energy consumption, lift coefficient, and the tracking ability of the leading vehicle; based on the vehicle longitudinal dynamic model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters, dynamically calculate the optimal following distance of the following vehicle within the optimized following distance interval; control the following strategy of the unmanned vehicle fleet according to the optimal following distance.

[0045] Embodiment 3. This embodiment provides a computer-readable storage medium, including: The storage medium is used to store computer software instructions for implementing the method for optimizing the following distance calculation of an unmanned vehicle formation driving described in Embodiment 1 above, which includes a program set for executing the method for optimizing the following distance calculation of an unmanned vehicle formation driving; specifically, this executable program can be built into the system for optimizing the following distance calculation of an unmanned vehicle formation driving described in Embodiment 2. In this way, the system for optimizing the following distance calculation of an unmanned vehicle formation driving can implement the method for optimizing the following distance calculation of an unmanned vehicle formation driving described in Embodiment 1 by executing the built-in executable program.

[0046] In addition, the computer-readable storage medium of this embodiment can adopt any combination of one or more readable storage media, where the readable storage media include electrical, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above.

[0047] Different from the prior art, by using an optimized calculation method, system and medium for the following distance of an unmanned vehicle formation driving in the present application, the quantitative laws of the air resistance coefficient and lift coefficient of the formation vehicles can be obtained. Considering the vehicle speed, road resistance, transmission efficiency, response characteristics of the drive / brake components and road slope angle comprehensively, a nonlinear dynamics model is established to predict and iteratively calculate the optimal following distance of each following vehicle in real time and dynamically. Finally, on the premise of ensuring safety, the driving efficiency and fuel economy of the unmanned vehicle formation can be effectively improved. The calculation framework is perfect, the adaptability adjustment ability is strong, the nonlinear dynamics model is accurate, the calculation of the optimal distance comprehensively considers various factors and the expansibility is strong, making up for the defects of the prior art and having high application value.

[0048] It should be understood that in various embodiments of this article, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this article.

[0049] It should also be understood that in the embodiments of this article, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the front and rear associated objects.

[0050] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this article.

[0051] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0052] In several embodiments provided in this document, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections between each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

[0053] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the objectives of the solution of the embodiments in this document.

[0054] In addition, in each embodiment of this document, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0055] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution in this document, or the part that contributes to the prior art, or all or 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 to enable 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 each embodiment of this document. The aforementioned storage medium includes: various media 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 that can store program codes.

[0056] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A method for optimizing the following vehicle spacing of unmanned vehicle platooning, characterized in that: The following steps are involved: Analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of vehicles in the unmanned vehicle fleet; Establishing a nonlinear dynamic model of the following vehicle, and calculating a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model and the prediction iteration of the vehicle time-varying state data; Determine an optimized following distance interval based on the minimum following distance, and analyze the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient, and tracking capability of the leading vehicle; dynamically calculate the optimal following distance of the following vehicle within the optimized following distance interval based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data, and the influencing parameters; and control the following strategy of the unmanned vehicle fleet based on the optimal following distance.

2. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 1 is characterized in that: The step of establishing a nonlinear dynamic model of the following vehicle and calculating a minimum following distance of the following vehicle based on the quantitative variation characteristics, the nonlinear dynamic model and the prediction iteration of the vehicle time-varying state data comprises: Determine a time iteration relationship in the process of following a vehicle, and determine an iteration relationship of the time-varying state data of the pilot vehicle when the pilot vehicle fully brakes according to the time iteration relationship; Calculating a discrete state space equation of the system based on the nonlinear dynamics model, and determining an iterative relationship of time-varying state data of the following vehicle according to the quantitative variation characteristics and the discrete state space equation of the system; A dynamic following distance and a minimum distance condition are set, and the dynamic following distance is iterated based on the time iteration relationship, the iteration relationship of the time-varying state data of the lead vehicle, the system discrete state space equation, and the iteration relationship of the time-varying state data of the following vehicle, and it is confirmed according to the minimum distance condition whether the iterated dynamic following distance is used as the minimum following distance.

3. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 2 is characterized in that: The discrete state space equation of the computing system based on the nonlinear dynamic model comprises: Constructing a nonlinear MPC model predictive controller for the motion of the following vehicle, converting the vehicle longitudinal dynamics model of the following vehicle according to the nonlinear MPC model predictive controller to obtain a continuous state space equation based on the vehicle longitudinal dynamics model; The continuous state space equation is discretized by forward Euler integration to obtain the discrete state space equation of the system.

4. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 2 is characterized in that: The time-varying state data of the pilot vehicle include: the time-varying driving speed and the time-varying driving distance of the pilot vehicle when fully braking; The time-varying state data of the following vehicle include: a time-varying following distance of the following vehicle, a time-varying air resistance coefficient, a time-varying lift coefficient, a time-varying road resistance coefficient, a time-varying road ramp manifold data, and a time-varying traction or braking force.

5. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 4 is characterized in that: The minimum distance condition includes: a first condition, a second condition, a third condition and a fourth condition; The first condition is: D d3(K3) d2(K2); The second condition is: D = d3 (K3) d2(K2); The third condition is: D d3(K3) d2(K2); The fourth condition is: ; in, is the dynamic following distance, is the time-varying following distance of the following vehicle, is the time-varying travel distance of the pilot vehicle when fully braking, is the first threshold value for the following vehicle distance.

6. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 5 is characterized by: The step of confirming whether the iterative dynamic following distance is used as the minimum following distance according to the minimum distance condition includes: In response to the dynamic following distance satisfying the first condition, determining that the following vehicle collides with the pilot vehicle, increasing the dynamic following distance, iteratively calculating the time-varying following distance of the following vehicle and the time-varying driving distance of the pilot vehicle when fully braking according to the increased dynamic following distance, and determining the minimum distance condition; In response to the dynamic following distance satisfying the second condition, determining that a critical collision point occurs between the following vehicle and the pilot vehicle; In response to the dynamic following distance satisfying the third condition, it is determined that the following vehicle and the pilot vehicle do not collide, the dynamic following distance is reduced, and the time-varying following distance of the following vehicle and the time-varying driving distance of the pilot vehicle when fully braking are iteratively calculated according to the reduced dynamic following distance, and the minimum distance condition is determined; In response to the dynamic following distance satisfying the fourth condition, it is determined that the dynamic following distance is the minimum following distance.

7. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 2 is characterized by: The method of dynamically calculating the optimal following distance of the following vehicle within the following distance optimization interval based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameter includes: Determining a final optimal function for the following vehicle distance based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data, and the influencing parameters; Based on the final optimal function, the following distance is dynamically optimized within the following distance optimization interval to obtain the optimal following distance.

8. The method for optimizing the following vehicle spacing of unmanned vehicle platooning according to claim 7 is characterized in that: The influencing parameters include: braking margin influencing parameters, energy consumption influencing parameters, lift coefficient influencing parameters and tracking capability influencing parameters; The braking margin influencing parameter is: the minimum following distance; The energy consumption influencing parameter is: performing energy consumption data iteration based on the vehicle longitudinal dynamics model, the iterative relationship between the vehicle time-varying state data and the dynamic following distance, and determining the optimal efficiency value following distance according to the iterated energy consumption data; The lift coefficient influencing parameter is: a lift coefficient that varies according to the dynamic following distance; The tracking capability influencing parameter is: a leading vehicle confidence level that varies with the dynamic following distance; The final optimization function is: ,in is the braking margin influencing parameter, is the energy consumption influencing parameter, is the lift coefficient influencing parameter, is the tracking capability influencing parameter, is the dynamic following distance.

9. A system for optimizing the distance between vehicles in an unmanned vehicle platoon, characterized in that: include: The variation characteristic analysis module is used to: analyze the quantitative variation characteristics of the air resistance coefficient and lift coefficient of the vehicles in the unmanned vehicle fleet; A minimum following distance analysis module is used to: establish a nonlinear dynamic model of the following vehicle, and calculate the minimum following distance of the following vehicle based on the quantitative change characteristics, the nonlinear dynamic model and the prediction iteration of the vehicle time-varying state data; The optimal following strategy control module is used to: determine the optimal following distance interval according to the minimum following distance, analyze the influencing parameters between the following distance and the braking margin, energy consumption, lift coefficient and the tracking ability of the leading vehicle; dynamically calculate the optimal following distance of the following vehicle within the optimal following distance interval based on the vehicle longitudinal dynamics model, the iterative relationship of the vehicle time-varying state data and the influencing parameters; and control the following strategy of the unmanned vehicle fleet according to the optimal following distance.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for optimizing the following vehicle distance calculation of the unmanned vehicle formation driving described in any one of claims 1 to 8 are implemented.

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