Driving control method based on acc vehicle following model and related device

Abstract

The application provides a driving control method based on an ACC vehicle following model and related equipment, and comprises the following steps: obtaining vehicle following track data of a target vehicle under the control of an adaptive cruise control system; introducing acceleration inertia into an intelligent driver model and adjusting the expected speed of the intelligent driver model to obtain an improved intelligent driver model, and inputting the vehicle following track data into the improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model; inputting the speed difference between the target vehicle and a preceding vehicle, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the acceleration of the target vehicle and the distance between the target vehicle and the preceding vehicle into the adaptive cruise control vehicle following model for calculation to obtain the acceleration of the target vehicle; and controlling the adaptive cruise driving of the target vehicle through the acceleration, so that the driving efficiency and safety of the adaptive cruise control vehicle are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a driving control method based on an ACC vehicle following model and related equipment. BACKGROUND

[0002] Autonomous vehicles (AV) is one of the most promising technologies in the field of transportation, and vehicle automation has achieved commercial success. As an application of advanced driving assistance system (ADAS), adaptive cruise control (ACC) has been successfully applied to new vehicles. Whether ACC will affect the current traffic flow and how to affect the problem has attracted the interest of many researchers. At present, the research on the following behavior of ACC vehicles is mostly based on theory, and it is believed that ACC vehicles have the potential to maintain stability and reduce congestion. When simulating the performance of ACC vehicles, the road conditions are good, such as highways or viaducts, and the judgment of the target vehicle is in the main lane. However, in recent years, researchers have shown through real vehicle experiments that ACC vehicles may have instability and poor safety, for example, when the front vehicle suddenly enters a curve, the host vehicle may consider that the safety distance is too large, resulting in misjudgment and sudden acceleration into the curve, leading to accidents. Therefore, the influence of ACC vehicles on traffic flow is still controversial.

[0003] In the past few decades, vehicle longitudinal dynamics models have been extensively studied, and car-following models include optimal velocity model, stimulus-response model, psycho-physiological model, Gipps model, intelligent driver model (IDM), etc. These models can be used to estimate the driving state of each vehicle at a microscopic level. Although these models are mature, the longitudinal dynamics of these models are different from vehicles equipped with ACC systems, and the former is mainly a model of manually driven vehicles. The main problem of current ACC vehicle research is that the control model of ACC vehicles is confidential and will not be disclosed by car manufacturers, and at the same time, there are few complete and open data sets about ACC vehicles, which affects the reliability of the research on the influence of ACC vehicles on traffic flow.

[0004] Intelligent Driver Model (IDM) is a widely used model for longitudinal dynamics modeling of vehicles in adaptive cruise control. Compared with other ACC models, IDM models have advantages such as parameter availability and intuitiveness, thus leading to their widespread application and improvement. However, existing improvements rarely consider dynamically adjusting the vehicle's desired speed, focusing only on improving performance and neglecting the acceleration and inertia caused by the vehicle's own dynamic performance. This results in low stability and safety of the vehicle under adaptive cruise control. Summary of the Invention

[0005] This invention provides a driving control method and related equipment based on the ACC vehicle following model, the purpose of which is to improve the stability and safety of vehicles controlled by adaptive cruise control.

[0006] To achieve the above objectives, the present invention provides a driving control method based on an ACC (Adaptive Cruise Control) vehicle following model, comprising:

[0007] Step 1: Obtain the vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system;

[0008] Step 2: Introduce acceleration inertia into the intelligent driver model and adjust the desired speed of the intelligent driver model to obtain the improved intelligent driver model. Input the vehicle following trajectory data into the improved intelligent driver model for calibration to obtain the adaptive cruise control vehicle following model.

[0009] Step 3: Input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation to obtain the acceleration of the target vehicle.

[0010] Step 4: Control the target vehicle's adaptive cruise control using acceleration.

[0011] Furthermore, the improved intelligent driver model is as follows:

[0012]

[0013] Among them, a IIDM Let τ represent the acceleration of the target vehicle, and τ represent the acceleration inertia weight. a represents the rate of change of vehicle acceleration. max Indicates the maximum acceleration. K represents the desired speed without adaptive cruise control. p K represents the proportionality coefficient, v represents the speed of the target vehicle, and K represents the speed of the target vehicle. d denoted by the differential coefficient, s0 represents the distance between the two vehicles under stationary conditions, T represents the minimum steady-state time interval, and b represents the desired deceleration.

[0014] Furthermore, the vehicle following trajectory data is input into the improved intelligent driver model for calibration, resulting in an adaptive cruise control vehicle following model, including:

[0015] The vehicle following trajectory data is input into the improved intelligent driver model. The acceleration inertia weight, maximum acceleration, expected speed without adaptive cruise control, proportional coefficient, differential coefficient, distance between the two vehicles under stationary conditions, minimum steady-state time interval, and expected deceleration in the improved intelligent driver model are calibrated by a genetic algorithm to minimize the error between the kinematics of the real vehicle and the simulated vehicle, and thus obtain the adaptive cruise control vehicle following model.

[0016] Furthermore, the fitness function used to calibrate the improved intelligent driver model is:

[0017] NRMSE(s, v, a) = γ s NRMSE(s)+γ v NRMSE(v)+γ a NRMSE(a)

[0018] Where, γ s γ v γ a denoted as weighting factors, NRMSE(s) represents the distance between the target vehicle and the vehicle in front, NRMSE(v) represents the speed of the target vehicle, and NRMSE(a) represents the normalized root mean square error of the target vehicle's acceleration.

[0019] Furthermore, in the calibration process of the improved intelligent driver model, the coefficient of variation is used to measure the relative variability during the calibration process. The expression for the coefficient of variation is as follows:

[0020]

[0021] Wherein, Coefficient of variation represents the coefficient of variation, i represents the number of calibration experiments, n represents the total number of calibration experiments, and GoF... i This represents the fitness function value in the i-th calibration experiment. This represents the average fitness function value from n calibration experiments.

[0022] Furthermore, prior to step 3, the following steps are also included:

[0023] Stability analysis was performed on the adaptive cruise control vehicle following model.

[0024] The speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front are input into the adaptive cruise control vehicle following model after stability analysis for calculation.

[0025] The present invention also provides a driving control device based on an adaptive cruise control vehicle following model, comprising:

[0026] The acquisition module is used to acquire vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system.

[0027] The calibration module is used to introduce acceleration inertia into the intelligent driver model and adjust the desired speed of the intelligent driver model to obtain an improved intelligent driver model. The vehicle following trajectory data is then input into the improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model.

[0028] The calculation module is used to input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation, so as to obtain the acceleration of the target vehicle.

[0029] The control module is used to control the adaptive cruise control of the target vehicle through acceleration.

[0030] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a driving control method based on an ACC vehicle following model.

[0031] The present invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a driving control method based on the ACC vehicle following model.

[0032] The above-described solution of the present invention has the following beneficial effects:

[0033] This invention acquires vehicle following trajectory data of a target vehicle under the control of an adaptive cruise control system; inputs this data into an improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model; inputs the speed difference between the target vehicle and the preceding vehicle, the rate of change of the speed difference, the target vehicle's speed, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the preceding vehicle into the adaptive cruise control vehicle following model for calculation to obtain the target vehicle's acceleration; and controls the target vehicle's adaptive cruise driving using acceleration. Compared with existing technologies, this invention's adaptive cruise control vehicle following model better describes the driving behavior of the adaptive cruise control vehicle by further adjusting the expected speed of the intelligent driver model, while introducing acceleration inertia to reflect the dynamic performance of the adaptive cruise control vehicle. By adjusting the expected speed, introducing acceleration inertia to calculate the target vehicle's acceleration, and using acceleration to control the target vehicle to achieve adaptive cruise driving, the driving efficiency and safety of the adaptive cruise control vehicle are improved.

[0034] Other beneficial effects of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating an embodiment of the present invention;

[0036] Figure 2 This is a graph showing the speed changes over time of the target vehicle and the preceding vehicle used for calibration and testing in this embodiment of the invention.

[0037] Figure 3 The figure shows the parameter calibration results of the ACC vehicle following model of PATH and the improved intelligent driver model in this embodiment of the invention.

[0038] Figure 4 (a) shows the speed change curves of the target vehicle equipped with the ACC vehicle following model with PATH; (b) shows the speed change curves of the target vehicle equipped with the improved intelligent driver model.

[0039] Figure 5 The graph shows the change in distance between the target vehicle and the vehicle in front over time when using the ACC following model equipped with PATH and the improved intelligent driver model, respectively.

[0040] Figure 6 For K d When K = 0.01 and τ = 0.1, different K p Under the given value, z3 is related to A curve graph;

[0041] Figure 7 For K pWhen K = 0.1 and τ = 0.1, different K d Under the given value, z3 is related to A curve graph;

[0042] Figure 8 For K p =0.1,K d When τ = 0.01, for different values ​​of τ, z3 with respect to... The curve graph. Detailed Implementation

[0043] To make the technical problems, solutions, and advantages of this invention clearer, a detailed description will be provided below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0044] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0045] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a locking connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0047] This invention addresses existing problems by providing a driving control method and related equipment based on the ACC vehicle following model.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a driving control method based on an ACC vehicle following model, including:

[0049] Step 1: Obtain the vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system;

[0050] Specifically, this embodiment of the invention obtains vehicle following trajectory data of a target vehicle undergoing a road test on a public highway from Ispra to Casale Monferrato in northern Italy. The vehicle following trajectory data includes: the speed of the target vehicle, the acceleration of the target vehicle, the speed and acceleration of the vehicle in front, and the position information of the target vehicle and the vehicle in front. The target vehicle is equipped with an adaptive cruise control system and is controlled by the adaptive cruise control system.

[0051] The vehicle following trajectory data was preprocessed to obtain noise-filtered vehicle following trajectory data. Cubic spline interpolation was then performed on the noise-filtered vehicle following trajectory data to achieve a 10Hz frequency. Therefore, the recorded data was divided into 9 parts, such as... Figure 2 The diagram shows the speed changes of the preceding and target vehicles over time. Based on the characteristics of speed changes over time, the curves are divided into different stages:

[0052] (1) Start-up and rapid deceleration stopping phases: P1, P9;

[0053] (2) Small-amplitude oscillation phase: P2, P3;

[0054] (3) Large-amplitude oscillation phase: P5, P6, P7, P8;

[0055] (4) The first half of P4 is a small oscillation, while the second half is characterized by rapid acceleration and deceleration and large oscillation.

[0056] Step 2: Introduce acceleration inertia into the intelligent driver model and adjust the desired speed of the intelligent driver model to obtain the improved intelligent driver model. Input the vehicle following trajectory data into the improved intelligent driver model for calibration to obtain the adaptive cruise control vehicle following model.

[0057] Intelligent Driver Models (IDMs) are used for modeling autonomous and partially autonomous vehicles and are widely used as a reference for Adaptive Cruise Control (ACC) vehicle following models. The acceleration of an IDM is a continuous function, encompassing different driving modes at all speeds in highway and urban scenarios. In addition to considering the bumper gap *s* and the actual speed *v*, the IDM also considers the speed difference Δv between the two vehicles. The IDM acceleration function is:

[0058]

[0059]

[0060] Where v represents the current vehicle speed, v0 represents the expected speed under free traffic conditions, s represents the distance between the bumpers of two vehicles, s0 represents the distance between vehicles when stationary, T represents the minimum steady-state time interval, and a max 'b' represents the maximum acceleration, and 'b' represents the desired deceleration.

[0061] Although intelligent driver models are widely used in the simulation of ACC vehicles, there are still some differences between traditional intelligent driver models and the actual operating conditions of commercial ACC vehicles. First, the expected speed of traditional intelligent driver models is a constant value, while commercial ACC vehicles can not only set the speed according to the driver's requirements, but also automatically adjust the expected speed through appropriate control of the engine and brakes without driver intervention. Second, traditional intelligent driver models do not consider the delayed response of ACC vehicles to acceleration. Therefore, this invention improves the traditional intelligent driving model by introducing acceleration inertia into the intelligent driver model and adjusting the expected speed of the intelligent driver model, making it more consistent with the operating conditions and performance of ACC vehicles.

[0062] In traditional intelligent driver models, the desired speed of a vehicle is a fixed constant. However, in real-world driving scenarios, it is affected by various factors such as vehicle density, road conditions, and weather. If the desired speed can be adjusted according to real-time traffic conditions, it can better adapt to different driving environments and improve vehicle driving efficiency and safety.

[0063] Specifically, in this embodiment of the invention, the desired speed is adjusted based on the difference between the current speed of the target vehicle and the speed of the vehicle in front, as expressed in the following expression:

[0064]

[0065] in, This represents the basic desired speed, i.e., the desired speed without adaptive cruise control; v represents the current speed of the target vehicle; v front K represents the speed of the vehicle ahead; t represents the travel time; K represents the speed of the vehicle ahead. p and K d These represent the proportional and derivative coefficients, respectively. These coefficients can be adjusted experimentally to achieve optimal adaptive cruise control performance.

[0066] The proportional coefficient is used to enhance the adaptability of the desired speed to the vehicle in front. When the vehicle in front is traveling faster, the desired speed will be increased appropriately to avoid affecting driving efficiency; when the vehicle in front is traveling slower, the desired speed will be decreased to maintain a safe distance. The derivative coefficient is used to better adapt the desired speed to the acceleration of the vehicle in front. When the vehicle in front is accelerating faster, the desired speed will be increased appropriately to avoid being too conservative and affecting driving efficiency; when the vehicle in front is accelerating slower, the desired speed will be decreased to maintain a safe distance. Therefore, by adjusting the proportional coefficient and the derivative coefficient, the desired speed can be better adapted to the current traffic conditions, thereby improving vehicle driving efficiency and safety.

[0067] Specifically, in traditional intelligent driving models, vehicle acceleration is calculated based on the vehicle's speed, distance from the vehicle in front, and speed difference. However, in real-world driving scenarios, vehicle acceleration is also affected by other factors, such as vehicle inertia (system delay response), an effect determined by vehicle dynamics and unavoidable. Currently, most ACC models focus only on performance improvements. While these models can theoretically improve traffic flow efficiency and safety, they often fail to accurately reflect the longitudinal dynamics of the ACC vehicle. Therefore, it is necessary to consider vehicle inertia. When adaptive cruise control is added, vehicle acceleration can be expressed as:

[0068]

[0069] Where τ is the acceleration inertia weight, The rate of change of vehicle acceleration takes into account the vehicle's inertia, meaning the vehicle cannot instantly reach the desired acceleration. The impact of different τ values ​​on flow rate can be verified through experiments to determine the optimal τ value.

[0070] Therefore, based on the traditional intelligent driver model, this embodiment of the invention introduces acceleration inertia and dynamically adjusts the desired speed to obtain the improved intelligent driver model (IIDM):

[0071]

[0072] Among them, a IIDM Let τ represent the acceleration of the target vehicle, and τ represent the acceleration inertia weight. a represents the rate of change of vehicle acceleration. max Indicates the maximum acceleration. K represents the desired speed without adaptive cruise control. p K represents the proportionality coefficient, v represents the speed of the target vehicle, and K represents the speed of the target vehicle. d denoted by the differential coefficient, s0 represents the distance between the two vehicles under stationary conditions, T represents the minimum steady-state time interval, and b represents the desired deceleration.

[0073] Step 3: Input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation to obtain the acceleration of the target vehicle.

[0074] Step 4: Control the target vehicle's adaptive cruise control using acceleration.

[0075] Specifically, the vehicle following trajectory data is input into the improved intelligent driver model for calibration to obtain the adaptive cruise control vehicle following model, including:

[0076] The vehicle following trajectory data is input into the improved intelligent driver model. The acceleration inertia weight, maximum acceleration, expected speed without adaptive cruise control, proportional coefficient, differential coefficient, distance between the two vehicles under stationary conditions, minimum steady-state time interval, and expected deceleration in the improved intelligent driver model are calibrated by a genetic algorithm to minimize the error between the kinematics of the real vehicle and the simulated vehicle, and thus obtain the adaptive cruise control vehicle following model.

[0077] Specifically, model parameter calibration involves calculating optimal parameter values ​​using optimization algorithms to minimize the kinematic errors between real and simulated vehicles, thereby improving the model's realism, prediction accuracy, and applicability, and optimizing the performance of traffic flow simulation. Based on theoretical arguments and extensive empirical evidence, it is proven that the normalized root mean square error (NRMSE) of the distance, speed, and acceleration between two vehicles (s, v, a) is the optimal fitness function for the adaptive cruise control vehicle following model parameter calibration problem. The fitness function used to calibrate the improved intelligent driver model is:

[0078] GoF: f = NRMSE(Y) (6)

[0079] MoP: Y = {s, v, a} (7)

[0080]

[0081] Where, γ s γ v γ a Let represent the weighting factors, assuming each performance metric MoP is equally important and each is taken as 1 / 3; T represents the total measurement time in the vehicle trajectory; Δt represents the data acquisition time step; NRMSE(s) represents the distance between the target vehicle and the vehicle in front; NRMSE(v) represents the speed of the target vehicle; and NRMSE(a) represents the normalized root mean square error of the target vehicle's acceleration. Represents the observed performance metric. RMSE(Y) represents the performance metric of the simulation. i) represents the root mean square error of the performance metric, NRMSE(Y). i ) represents the normalized root mean square error of the performance metric.

[0082] Specifically, in this embodiment of the invention, a genetic algorithm (GA) is used to calibrate the parameters of the adaptive cruise control vehicle following model (ACC) proposed in this invention and the following model developed based on experimental data from real vehicles equipped with ACC. The following model developed based on experimental data from real vehicles equipped with ACC is the ACC following model proposed by the PATH project of the Transportation Research Institute at the University of California, Berkeley. This research team has conducted extensive research on ACC vehicle traffic flow models. The ACC following model proposed by the PATH project is as follows:

[0083] a ACC (s, v, Δv) = k1 (s-Tv-s0) + k2Δv

[0084] Where v represents the current vehicle speed, s represents the distance between the bumpers of two consecutive vehicles, represents the speed difference between the target vehicle and the vehicle in front, s0 is the vehicle spacing under stationary conditions, T represents the minimum steady-state time interval, and k1 and k2 are the position error weight and speed error weight, respectively.

[0085] Because GA optimization is relatively fast and has been widely used in similar studies. In this embodiment of the invention, the genetic algorithm's gene crossover probability is set to 0.75, the gene mutation probability is set to 0.05, the fitness function is NRMSE(s,v,a), the number of parameter iterations is set to 100, and each model calibration experiment is performed 10 times to verify whether the algorithm can converge to the same minimum value in each calibration repetition. Table (1) lists the upper and lower limits of the parameters that need to be calibrated for the two car-following models:

[0086] Table 1

[0087]

[0088] The parameters were calibrated according to Table 1 above, and the calibration results are as follows: Figure 3 As shown.

[0089] Specifically, during the calibration of the improved intelligent driver model, the strong nonlinearity of the model response surface, the large-dimensional solution space, and the randomness of some optimization algorithms make the algorithm prone to getting trapped in local minima, resulting in poor reproducibility of the calibration results. To address this issue, this embodiment of the invention repeats the calibration experiment 10 times. Furthermore, during the calibration of the improved intelligent driver model (IIDM) and the ACC following model proposed by the PATH project, the coefficient of variation is used to measure the relative variability during the calibration process. The coefficient of variation is the ratio of the standard deviation to the mean, and its expression is as follows:

[0090]

[0091] Wherein, Coefficient of variation represents the coefficient of variation, i represents the number of calibration experiments, n represents the total number of calibration experiments, and GoF... i This represents the fitness function value in the i-th calibration experiment. This represents the average fitness function value from n calibration experiments.

[0092] Based on the expression for the coefficient of variation, the coefficient of variation of the ACC following model proposed by the PATH project is 0.39%, while the coefficient of variation of the improved intelligent driver model is 0.31%. The results show that the optimal GoF values ​​of both models are highly reproducible, and the coefficient of variation is always less than 20%.

[0093] like Figure 3 As shown, after 10 repeated calibration experiments, different parameter combinations may produce similar calibration effects. Therefore, after the calibration experiment, each model obtained 10 sets of parameters. This invention analyzes and classifies these parameters. The parameter classification results of the ACC following model proposed by the PATH project are shown in Table 2, and the parameter classification results of the improved intelligent driver model are shown in Table 3.

[0094] Table 2

[0095]

[0096] Table 3

[0097]

[0098] Based on Tables 2 and 3, the parameter set with the smallest combined error NRMSE(s,v,a) between the observed and simulated vehicle kinematics values ​​in each scenario is selected as the final parameter set for each model. Therefore, cross-validation is performed on the two models.

[0099] The cross-validation results are shown in Table 4:

[0100] Table 4

[0101]

[0102]

[0103] Table 4 shows that, under the third parameter group, the PATH car-following model has the best fit to all datasets except P5. Although it is not the optimal parameter group for P5, the NRMSE(s, v, a) of the three parameter groups on P5 is not significantly different. Therefore, after comprehensive consideration, the third parameter group was selected as the final parameters for the ACC car-following model proposed by the PATH project. Regarding the cross-validation results of IIDM, we found that under the third parameter group, the NRMSE(s, v, a) of IIDM on P9 is significantly lower than that of the other two parameter groups. Although the third parameter group does not have the best fit to other datasets, the fit of the three parameter groups is not significantly different. Therefore, after comprehensive consideration, the third parameter group was selected as the final parameters for IIDM. The results show that the IIDM model has a better fit than the ACC car-following model proposed by the PATH project in the start-up and stopping phases.

[0104] To further analyze vehicle safety under different following models, this embodiment of the invention assesses traffic conflicts based on time-to-collision (TTC). TTC, due to its simplicity, is widely used in rear-end collision risk assessment. TTC defines the remaining time before a collision; if the vehicle continues to maintain its speed and trajectory, TTC can be calculated using the following formula:

[0105]

[0106] Where, d le V represents the positional distance between two consecutive vehicles. ego and V lead These represent the speeds of the target vehicle and the vehicle in front, respectively. Generally, the TTC threshold is used to determine whether the state is safe. A threshold of 1.5 to 4 seconds is widely used in conflict analysis.

[0107] In this embodiment of the invention, an arbitrary leading vehicle is set, and the target vehicle adopts two following models respectively. The speed change curves are as follows: Figure 4 As shown in (a) and (b) of the figure, it can be seen that both models can adapt well to the speed changes of the leading vehicle. To evaluate safety performance, this embodiment of the invention calculates the portion of TTC between 0 and 4 seconds during the entire driving process, representing the collision risk during driving. This is represented in the figure using a heatmap; the darker the color, the smaller the TTC, and therefore the more dangerous it is. Figure 4It can be clearly seen that when the target vehicle is equipped with the ACC following model proposed by the PATH project, the risk of collision is generally higher than when the target vehicle is equipped with the improved intelligent driver model. At certain times, the TTC of the target vehicle equipped with the ACC following model proposed by the PATH project is close to 0, indicating that rear-end collisions will occur at these times. Figure 5 The curves showing the distance between the two vehicles over time are displayed. When the distance is negative, a rear-end collision occurs. It can be observed that the vehicle equipped with the improved intelligent driver model is able to maintain a smaller distance than the vehicle equipped with the ACC following model proposed by the PATH project. Furthermore, the target vehicle equipped with the ACC following model proposed by the PATH project experienced three rear-end collisions.

[0108] Specifically, before step 3, the following is also included:

[0109] Stability analysis was performed on the adaptive cruise control vehicle following model.

[0110] The speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front are input into the adaptive cruise control vehicle following model after stability analysis for calculation.

[0111] To investigate the impact of adaptive cruise control vehicle following model on traffic system stability, this embodiment of the invention uses linear stability theory to analyze the stability of the adaptive cruise control vehicle following model, simplifying equation (5) to obtain:

[0112]

[0113] Among them, v n (t) represents the speed of vehicle n at time t.

[0114] Assuming vehicles travel with the same headway s and the same speed v in the initial traffic flow, the position of vehicle n in the uniform traffic flow is as follows:

[0115]

[0116] Define y n (t) represents a small perturbation, and its specific expression is:

[0117] y n (t)=e ikn+zt (13)

[0118] When a small disturbance y is added to a uniform traffic flow n At time (t), the actual position of vehicle n in the traffic flow is:

[0119]

[0120] The actual headway, actual speed, and actual acceleration are shown in the following formulas:

[0121]

[0122]

[0123] a n (t)=y″ n (t) (17)

[0124] Substituting equation (17) into equation (11), we get:

[0125] y″ n (t)=f n (s(t), v(t), Δv(t), Δv′(t), a′(t)) (18)

[0126] The Taylor expansion of equation (18) is as follows:

[0127]

[0128] in, It is the partial derivative of the adaptive cruise control vehicle following model in equilibrium, i.e.

[0129] If linearized vehicle dynamics are subject to rational driving constraints (RDC): These conditions indicate that the driver increases acceleration as the headway *s*, relative velocity *Δv*, or relative acceleration *Δv′* increases, but decreases acceleration as velocity *v* increases. It is important to note that *a′* is determined by the vehicle's dynamics, not by the driver's control. It is the inertial weight of acceleration, and is always positive.

[0130] Because y n (t)=e ikn+zt The following formula can be derived:

[0131] y′ n (t)=ze ikn+zt (20)

[0132] y″ n (t)=z 2 e ikn+zt (twenty one)

[0133] y (3) n (t)=z 3 e ikn+zt(twenty two)

[0134] Let e ikn+zt =e M Substituting equations (20)-(22) into equation (19), we obtain equation (23):

[0135]

[0136] Based on long-wavelength theory, the parameter z in formula (23) is calculated as z = z1(ik) + z2(ik). 2 +z3(ik) 3 +…and Expanding on this, to simplify the calculation, we only retain terms with an order less than or equal to 3. The simplified equation containing (ik) first-order, second-order, and third-order terms is as follows:

[0137]

[0138] When the coefficients of the first-order, second-order, and third-order terms of (ik) in equation (24) are zero, the results of z1, z2, and z3 can be derived through calculation, and their expressions are as follows:

[0139]

[0140]

[0141]

[0142] According to long-wave theory, if the value of variable z2 is positive, it indicates that the initial steady-state traffic flow has deviated from the steady state due to a small disturbance, and after a period of time, the vehicles in the traffic flow will return to a stable state. If the value of z2 is negative, it means that when the initial steady-state traffic flow deviates from the steady state due to a small disturbance, the vehicle speed fluctuations become larger over time, and the traffic flow becomes increasingly unstable, resulting in traffic congestion. Therefore, the stability condition for the adaptive cruise control vehicle following model is z2 > 0.

[0143] The partial derivative of the adaptive cruise control vehicle following model near the equilibrium state is:

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Note that z2 only includes the scaling factor K. p Unable to determine the differential coefficient K d The influence of acceleration *a* on model stability was investigated, but it was found that when *z2* < 0 (i.e., the model is unstable), *z3* < 0. Therefore, the influence of parameters on model instability can be understood through *z3*. The proof that *z3* < 0 when *z2* < 0 (i.e., the model is unstable) is as follows:

[0150] Substituting equation (25) into equation (26) yields

[0151]

[0152] When z2 < 0, we can use RDC to know

[0153]

[0154]

[0155] therefore,

[0156]

[0157] When RDC is satisfied,

[0158]

[0159] Therefore, z3 < 0.

[0160] Note that for those in equilibrium and There is a non-linear relationship between them:

[0161]

[0162] Therefore, when z3 < 0, the adaptive cruise control vehicle following model is unstable. Substituting equations (25), (26), (28)-(32), and (38) into equation (27) yields z3 with respect to... The relational expression.

[0163] Based on parameters s0 = 3m, T = 0.6s, a max =2m / s 2 b = 1.5 m / s 2 Figure (6) shows when K d When K = 0.01 and τ = 0.1, different K p Under the given value, z3 is related to The curve of Kp, Figure (7) shows the curve when Kp ＝ When τ = 0.1, different K values dUnder the given value, z3 is related to The curve graph, Figure (8) shows when K p =0.1,K d When τ = 0.01, for different values ​​of τ, z3 with respect to... The curve graph.

[0164] In conclusion, with K p and K d As the acceleration inertia weight α increases, the range of z3<0 gradually decreases, indicating that considering the dynamic adjustment of the desired speed v* can effectively reduce the instability of traffic flow. In addition, it can also be found that as the acceleration inertia weight α increases, the range of z3<0 gradually increases, indicating that the slower the vehicle's response speed to acceleration changes, the more detrimental it is to the stability of traffic flow. This is consistent with the actual situation. Therefore, this paper considers the influence of acceleration inertia on following behavior.

[0165] This invention acquires vehicle following trajectory data of a target vehicle under the control of an adaptive cruise control system; inputs the vehicle following trajectory data into an improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model; inputs the speed difference between the target vehicle and the preceding vehicle, the rate of change of the speed difference, the target vehicle's speed, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the preceding vehicle into the adaptive cruise control vehicle following model for calculation to obtain the target vehicle's acceleration; and controls the target vehicle's adaptive cruise driving through acceleration. Compared with the prior art, the adaptive cruise control vehicle following model of this invention adjusts the expected speed of the existing intelligent driver model to better describe the driving behavior of the adaptive cruise control vehicle, and introduces acceleration inertia to reflect the dynamic performance of the adaptive cruise control vehicle, thereby improving the driving efficiency and safety of the adaptive cruise control vehicle.

[0166] This invention also provides a driving control device based on an adaptive cruise control vehicle following model, comprising:

[0167] The acquisition module is used to acquire vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system;

[0168] The calibration module is used to introduce acceleration inertia into the intelligent driver model and adjust the expected speed of the intelligent driver model to improve the intelligent driving model, thereby obtaining an improved intelligent driver model. The vehicle following trajectory data is then input into the improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model.

[0169] The calculation module is used to input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation, so as to obtain the acceleration of the target vehicle.

[0170] The control module is used to control the adaptive cruise control of the target vehicle through acceleration.

[0171] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of the embodiments of the present invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0173] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a driving control method based on an ACC vehicle following model.

[0174] If the integrated unit is implemented as 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 embodiments of the present invention can implement all or part of the processes in the methods described above by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a building device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAm), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0175] This invention also provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a driving control method based on an ACC vehicle following model.

[0176] It should be noted that the terminal device can be a mobile phone, tablet computer, laptop computer, Ultra-mobile Personal Computer (UmPC), netbook, Personal Digital Assistant (PDA), etc. For example, the terminal device can be a ST in a WLAN, a cellular phone, cordless phone, Session Initiation Protocol (SiP) phone, Wireless Local Loop (WLL) station, PDA, handheld device with wireless communication capabilities, computing device or other processing device connected to a wireless modem, computer, laptop computer, handheld communication device, handheld computing device, satellite wireless device, etc. This embodiment of the invention does not impose any restrictions on the specific type of terminal device.

[0177] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASiCs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0178] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, smart media card (SmC), secure digital card (SD), flash card, etc. Furthermore, the memory may include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0179] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0180] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A driving control method based on ACC vehicle following model, characterized in that, include: Step 1: Obtain the vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system; Step 2: Introduce acceleration inertia into the intelligent driver model and adjust the desired speed of the intelligent driver model to obtain an improved intelligent driver model. Then, input the vehicle following trajectory data into the improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model. The improved intelligent driver model is as follows: ； in, This represents the acceleration of the target vehicle. Indicates acceleration inertia weight. Indicates the rate of change of vehicle acceleration. Indicates the maximum acceleration. This indicates the desired speed without adaptive cruise control. This represents the proportionality coefficient. Indicates the speed of the target vehicle. Denotes the differential coefficient. This indicates the distance between two vehicles when they are stationary. This indicates the speed difference between the two vehicles. Indicates the minimum steady-state time interval. Indicates the expected deceleration. This indicates the distance between the bumpers of two vehicles, one in front and one behind. Step 3: Input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the acceleration of the target vehicle, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation to obtain the acceleration of the target vehicle. Step 4: Control the target vehicle's adaptive cruise control using the acceleration.

2. The driving control method based on the ACC vehicle following model according to claim 1, characterized in that, The step of inputting the vehicle following trajectory data into the improved intelligent driver model for calibration to obtain the adaptive cruise control vehicle following model includes: The vehicle following trajectory data is input into the improved intelligent driver model. The acceleration inertia weight, maximum acceleration, expected speed without adaptive cruise control, proportional coefficient, differential coefficient, distance between the two vehicles under stationary conditions, minimum steady-state time interval, and expected deceleration in the improved intelligent driver model are calibrated by a genetic algorithm to minimize the error between the kinematics of the real vehicle and the simulated vehicle, thereby obtaining the adaptive cruise control vehicle following model.

3. The driving control method based on the ACC vehicle following model according to claim 2, characterized in that, The fitness function used to calibrate the improved intelligent driver model is: ； in, 、 、 They represent the weighting factors, Indicates the distance between the target vehicle and the vehicle in front. Indicates the speed of the target vehicle. This represents the normalized root mean square error of the target vehicle's acceleration.

4. The driving control method based on the ACC vehicle following model according to claim 2, characterized in that, During the calibration process of the improved intelligent driver model, the coefficient of variation is used to measure the relative variability during the calibration process. The expression for the coefficient of variation is as follows: ； in, Represents the coefficient of variation. Indicates the number of calibration experiments. Indicates the total number of calibration experiments. Indicates the first Fitness function values ​​in the second calibration experiment This represents the average fitness function value from n calibration experiments.

5. The driving control method based on the ACC vehicle following model according to claim 2, characterized in that, Before step 3, the following is also included: Stability analysis was performed on the adaptive cruise control vehicle following model. The speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the target vehicle's acceleration, and the distance between the target vehicle and the vehicle in front are input into the adaptive cruise control vehicle following model after stability analysis for calculation.

6. A driving control device based on an adaptive cruise control vehicle following model, characterized in that, include: The acquisition module is used to acquire vehicle following trajectory data of the target vehicle under the control of the adaptive cruise control system; The calibration module is used to introduce acceleration inertia into the intelligent driver model and adjust the desired speed of the intelligent driver model to obtain an improved intelligent driver model. The vehicle following trajectory data is then input into the improved intelligent driver model for calibration to obtain an adaptive cruise control vehicle following model. The improved intelligent driver model is as follows: ； in, This represents the acceleration of the target vehicle. Indicates acceleration inertia weight. Indicates the rate of change of vehicle acceleration. Indicates the maximum acceleration. This indicates the desired speed without adaptive cruise control. Represents the proportionality coefficient. Indicates the speed of the target vehicle. Represents the differential coefficient. This indicates the distance between two vehicles when they are stationary. This indicates the speed difference between the two vehicles. Indicates the minimum steady-state time interval. Indicates the expected deceleration. This indicates the distance between the bumpers of two vehicles, one in front and one behind. The calculation module is used to input the speed difference between the target vehicle and the vehicle in front, the rate of change of the speed difference, the speed of the target vehicle, the rate of change of the acceleration of the target vehicle, and the distance between the target vehicle and the vehicle in front into the adaptive cruise control vehicle following model for calculation, so as to obtain the acceleration of the target vehicle. A control module is used to control the adaptive cruise control of the target vehicle through the acceleration.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the driving control method based on the ACC vehicle following model as described in any one of claims 1 to 5.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the driving control method based on the ACC vehicle following model as described in any one of claims 1 to 5.

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