Vehicle longitudinal control method, device and equipment and storage medium

Abstract

The invention provides a vehicle longitudinal control method and device, equipment and a storage medium, and the method comprises the steps: obtaining an actual measurement value of a state space at a current moment through a sensor of a target vehicle, and the state space comprises the motion state of the target vehicle and an obstacle in front of the target vehicle; calculating an estimated value of the state space at the current moment according to a predicted value of the state space at the current moment at the previous moment and a measured value of the state space at the current moment; and the expected longitudinal acceleration of the target vehicle to be determined serves as the control quantity, and the expected longitudinal acceleration of the target vehicle is determined and correspondingly adjusted according to the weight parameter of the estimated value, the weight parameter of the control quantity and the state quantity. The motion state estimation value is generated by fusing the historical prediction value and the real-time sensing data, so that the sensor noise can be effectively inhibited; the control quantity is dynamically optimized based on multi-dimensional weight parameters, and the anti-interference capability and robustness in a complex traffic flow scene can be enhanced through closed-loop collaborative iteration of state estimation and control optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle longitudinal control method, device, equipment and storage medium. BACKGROUND

[0002] Vehicle longitudinal control refers to precisely controlling the vehicle speed or the safety distance between the vehicle and the front obstacle by adjusting the acceleration, deceleration and braking behavior of the vehicle, and is one of the core functions in the automatic driving system. The main goals of vehicle longitudinal control include maintaining a safe following distance, responding to dynamic changes of the front vehicle, optimizing fuel economy and improving driving comfort. This technology is widely used in adaptive cruise control (ACC) and automatic emergency braking (AEB) scenarios. In the heavy truck field, due to large load changes and significant inertia, the stability and accuracy of longitudinal control are particularly important for driving safety and transportation efficiency.

[0003] Currently, the longitudinal control of vehicles mainly relies on model-based algorithms, such as the Intelligent Driver Model (IDM) and its improved solutions. Existing technologies usually adjust the desired acceleration by introducing acceleration inertia or optimizing safety distance models (such as Responsibility-Sensitive Safety, RSS), and combine multiple sensors (such as millimeter wave radar and camera) to obtain the motion state of the front vehicle. In addition, some methods calibrate model parameters by fusing trajectory data, or use empirical weights to balance the following distance and acceleration response. For example, the acceleration of the target vehicle can be calculated by an adaptive cruise control model, or the longitudinal planning result can be adjusted using an optimization algorithm.

[0004] However, the existing vehicle longitudinal control technology has the following defects: 1. Insufficient accuracy of state estimation: sensor noise (such as radar ranging error and camera delay) can cause deviations in the measurement of relative distance, speed and acceleration, thereby affecting the precision of control input; 2. Limited adaptability to dynamic scenarios: models relying on empirical parameter tuning are difficult to adapt to high dynamic scenarios such as sudden acceleration and deceleration of the front vehicle and sudden changes in complex traffic flow, which can easily cause control lag or oscillation. SUMMARY

[0005] The application provides a vehicle longitudinal control method, device, equipment and storage medium, to solve the technical problems of the existing vehicle longitudinal control technology: 1. Insufficient accuracy of state estimation: sensor noise (such as radar ranging error and camera delay) will cause deviation in the measurement of relative distance, speed and acceleration, thereby affecting the accuracy of control input; 2. Limited adaptability of dynamic scenarios: models relying on experience parameter tuning are difficult to adapt to high dynamic scenarios such as sudden acceleration and deceleration of the preceding vehicle and sudden changes in complex traffic flow, which can easily cause control lag or oscillation.

[0006] In a first aspect, the application provides a vehicle longitudinal control method, the method comprising:

[0007] acquiring, by a sensor of a target vehicle, a measured value of a state space at a current time, the state space comprising a motion state of the target vehicle and a motion state of a front obstacle of the target vehicle;

[0008] calculating an estimated value of the state space at the current time according to a predicted value of the state space at the current time at a previous time and the measured value of the state space at the current time;

[0009] determining a desired longitudinal acceleration of the target vehicle as a control variable according to

[0010] a weight parameter of the estimated value, a weight parameter of the control variable and the state variable;

[0011] adjusting a longitudinal acceleration of the target vehicle to the desired longitudinal acceleration.

[0012] In a possible design, the acquiring, by a sensor of a target vehicle, a measured value of a state space at a current time comprises:

[0013] acquiring, by the sensor, a measured value of an actual longitudinal distance between the front obstacle and the target vehicle at the current time, a measured value of a speed of the front obstacle, a measured value of a speed of the target vehicle and a measured value of an actual longitudinal acceleration of the target vehicle;

[0014] acquiring a desired longitudinal distance between the target vehicle and the front obstacle, the state space comprising a distance difference between the desired longitudinal distance and the actual longitudinal distance, a speed difference between the speed of the front obstacle and the speed of the target vehicle and the actual longitudinal acceleration of the target vehicle;

[0015] The measured distance difference between the expected longitudinal distance and the measured value of the actual longitudinal distance, the measured speed difference between the measured value of the front obstacle speed and the measured value of the target vehicle speed, and the measured value of the actual longitudinal acceleration of the target vehicle are taken as the measured value of the state space at the current time.

[0016] In a possible design, the estimated value of the state space at the current time is calculated by the filter algorithm according to the predicted value of the state space at the current time at the last time, and the measured value of the state space at the current time, including:

[0017] The predicted value of the distance difference, the predicted value of the speed difference, and the predicted value of the actual longitudinal acceleration of the filter algorithm at the last time are taken as the predicted value of the state space, and the estimated value of the distance difference, the estimated value of the speed difference, and the estimated value of the actual longitudinal acceleration are calculated by the filter algorithm according to the predicted value of the state space and the measured value of the state space at the current time, and taken as the estimated value of the state space.

[0018] In a possible design, the expected longitudinal distance of the target vehicle and the front obstacle includes:

[0019] The minimum safety distance of the target vehicle and the front obstacle is obtained, and the arrival time of the target vehicle running to the longitudinal distance of the front obstacle being the minimum safety distance is obtained;

[0020] The product of the arrival time and the measured value of the target vehicle speed is calculated, and the expected longitudinal distance is determined according to the sum of the product and the minimum safety distance.

[0021] In a possible design, the minimum safety distance of the target vehicle and the front obstacle includes:

[0022] The road surface friction coefficient of the road where the target vehicle is currently located is obtained;

[0023] The minimum safety distance is determined according to the road surface friction coefficient and the measured value of the target vehicle speed, and the greater the road surface friction coefficient is, the smaller the minimum safety distance is, and the greater the measured value of the target vehicle speed is, the greater the minimum safety distance is.

[0024] In a possible design, the arrival time of the target vehicle running to the longitudinal distance of the front obstacle being the minimum safety distance includes:

[0025] The traffic flow density of the road where the target vehicle is currently located is obtained;

[0026] determining a reference time according to the traffic flow density, the greater the traffic flow density, the smaller the reference time;

[0027] determining a proportional coefficient of the reference time according to the measured value of the target vehicle speed, the greater the measured value of the target vehicle speed, the greater the proportional coefficient;

[0028] determining the arrival time according to the product of the proportional coefficient and the reference time.

[0029] In a possible design, the determining of the expected longitudinal acceleration of the target vehicle to be determined as a control variable according to the weight parameter of the estimated value, the weight parameter of the control variable and the state variable includes:

[0030] the estimated value of the state space is taken as a state variable of a linear quadratic regulator algorithm, the expected longitudinal acceleration of the target vehicle to be determined is taken as a control variable of the linear quadratic regulator algorithm, and the expected longitudinal acceleration of the target vehicle is determined by the linear quadratic regulator algorithm according to the weight parameter of the state variable, the weight parameter of the control variable and the state variable.

[0031] In a second aspect, the present application provides a vehicle longitudinal control device, the device includes:

[0032] an acquisition module, configured to acquire a measured value of a state space at a current moment by a sensor of a target vehicle, the state space including a motion state of the target vehicle and a motion state of an obstacle in front of the target vehicle;

[0033] a state estimation module, configured to calculate an estimated value of the state space at the current moment according to a predicted value of the state space at the current moment at a previous moment and the measured value of the state space at the current moment;

[0034] an expected longitudinal acceleration calculation module, configured to determine an expected longitudinal acceleration of the target vehicle to be determined as a control variable according to a weight parameter of the estimated value, a weight parameter of the control variable and a state variable;

[0035] a control module, configured to adjust a longitudinal acceleration of the target vehicle to the expected longitudinal acceleration.

[0036] In a third aspect, the present application provides an electronic device, including a processor and a memory connected with the processor in communication;

[0037] the memory stores computer execution instructions;

[0038] The processor executes computer execution instructions stored in the memory to implement the vehicle longitudinal control method according to the first aspect.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, wherein computer execution instructions are stored in the computer readable storage medium, and the computer execution instructions are executed by a processor to implement the vehicle longitudinal control method according to the first aspect.

[0040] The vehicle longitudinal control method, device, equipment and storage medium provided by the present application have the following technical effects:

[0041] In the present solution, since the motion state estimation value is generated by fusing the historical prediction value and real-time perception data, the sensor noise interference can be effectively suppressed and the cumulative deviation in the dynamic scene can be corrected, so that the accuracy of state estimation can be improved. At the same time, based on the dynamic optimization control quantity of multi-dimensional weight parameters, the system can adaptively adjust the control strength and response priority according to the real-time motion state change, so as to overcome the control lag or oscillation caused by the dependence of the traditional method on fixed experience parameters. Through the closed-loop cooperative iteration of state estimation and control optimization, the anti-interference ability and robustness in the complex traffic flow scene are further strengthened, and finally the precision, adaptability and stability of longitudinal control are simultaneously enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] Figure 1 Vehicle longitudinal control method flowchart provided by the present application Figure 1 ;

[0044] Figure 2 Vehicle longitudinal control method flowchart provided by the present application Figure 2 ;

[0045] Figure 3 Method flowchart for acquiring time of arrival provided by the present application

[0046] Figure 4 Structure diagram of vehicle longitudinal control device provided by the present application;

[0047] Figure 5 Structure diagram of electronic device provided by the present application.

[0048] The specific embodiments of the application have been shown by way of example in the above figures, and will be described in more detail hereafter. These figures and this written description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate the inventive concept to one of ordinary skill in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0049] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same numbers are used in different drawings to represent the same or similar elements. The following detailed description is not intended to limit the application, as claimed, in any way. Rather, the following description is intended to describe the embodiments of the application in sufficient detail to enable one of ordinary skill in the art to practice the application, including making and using the application.

[0050] First, the terms related to the present application are explained:

[0051] Linear quadratic regulator control algorithm: that is, LQR algorithm, an optimal control method based on state space model, by minimizing the quadratic cost function containing state deviation and control input to design feedback controller.

[0052] In view of the following defects of the existing vehicle longitudinal control technology: 1, the accuracy of state estimation is insufficient: sensor noise (such as radar ranging error and camera delay) will cause the measurement of relative distance, speed and acceleration to deviate, thereby affecting the accuracy of control input; 2, the adaptability of dynamic scene is limited: the model relying on experience parameter tuning is difficult to adapt to high dynamic scenes such as sudden acceleration and deceleration of the preceding vehicle and sudden change of complex traffic flow, which is easy to cause control lag or oscillation, the technical concept of the present application is:

[0053] Through the sensor of the target vehicle, the measured motion state data of the target vehicle and the obstacle in front of it at the current time are obtained, according to the predicted value of the motion state of the target vehicle and the obstacle in front of it at the current time at the last time, combined with the measured value of the motion state of the target vehicle and the obstacle in front of it at the current time, the estimated value of the motion state of the target vehicle and the obstacle in front of it at the current time is given. The expected longitudinal acceleration of the target vehicle to be determined is taken as the control quantity, and the expected longitudinal acceleration of the target vehicle is determined according to the weight parameters of the estimated value and the weight parameters of the control quantity and the estimated value, and the longitudinal acceleration of the target vehicle is adjusted to the expected longitudinal acceleration.

[0054] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail in the following specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present application will be described below with reference to the drawings.

[0055] Embodiment one

[0056] The embodiment of the present application provides a vehicle longitudinal control method, which can be applied to an adaptive cruise system of a vehicle, Figure 1 The vehicle longitudinal control method provided by the embodiment of the present application is shown in the flowchart Figure 1 As shown in the figure, the method comprises the following steps: Figure 1

[0057] S101, acquiring, by a sensor of a target vehicle, a measured value of a state space at a current moment, the state space comprising a motion state of the target vehicle and a motion state of a front obstacle of the target vehicle;

[0058] It should be noted that the front obstacle in the present application can be a front vehicle, a pedestrian, a non-motor vehicle or the like. This step can realize detection of the motion states of the target vehicle and the front obstacle through multi-source sensor fusion perception. In specific implementation, the target vehicle can collect original data through a front-view millimeter wave radar and a camera (1V1R) of a fusion perception system, for example, the millimeter wave radar can acquire a relative distance and a relative speed of the target vehicle and the front obstacle, and the camera can acquire lane line information and a target vehicle contour feature through image recognition. After time synchronization and space alignment of the multi-source data, the fusion perception system outputs a measured value of the state space at the current moment through a target tracking algorithm, for example, the measured value can comprise a host vehicle speed , a front obstacle speed , an actual following distance d of the target vehicle relative to the front obstacle, a front obstacle acceleration , and the like. Since the millimeter wave radar has an all-weather ranging advantage and the camera can provide target category recognition, data fusion of the two can improve the accuracy of motion state perception.

[0059] S102, calculating an estimated value of the state space at the current moment according to a predicted value of the state space at the current moment at a previous moment and the measured value of the state space at the current moment;

[0060] Specifically, historical sensor data in a time window can be used to suppress instantaneous noise of the sensor through weighted average. For example, a time window length (such as the last 5 control periods, about 500 ms) can be defined, and original data such as an actual following distance and a front-rear vehicle speed difference measured by each sensor in the window is stored, then, the data in the window is assigned a decreasing weight, for example, the weight of the data at the current moment is 0.5, the weight of the data at the previous moment is 0.3, and the weight of the data at an earlier moment is 0.2, so as to emphasize the credibility of recent data, and then the weighted actual following distance and front-rear vehicle speed difference data are averaged to output a smoothed state estimation value. This method has the advantages of simple calculation and high real-time performance.

[0061] ​S103, taking the expected longitudinal acceleration of the target vehicle to be determined as a control quantity, determining the expected longitudinal acceleration of the target vehicle according to the weight parameter of the estimated value, the weight parameter of the control quantity and the state quantity;

[0062] In this step, the model predictive control (MPC) algorithm can be used to dynamically optimize the expected longitudinal acceleration of the target vehicle, so as to take into account the multi-objective constraints and real-time requirements. In specific implementation, the control sequence in the future limited time domain can be optimized based on the estimated value of the state space at the current time. For example, the following implementation can be used:

[0063] Prediction model construction: based on the vehicle longitudinal kinematic model (such as the integral relationship between acceleration, speed and displacement), a motion trajectory prediction model of the target vehicle and the front obstacle in the future 3-5 seconds is established;

[0064] Objective function design: define a multi-objective cost function including the following: distance deviation, speed tracking error, and acceleration smoothness, and assign dynamic weight parameters to each objective, for example, increase the distance deviation weight to shorten the following distance in congestion following, and increase the speed tracking weight to maintain the vehicle speed stable in high-speed cruising;

[0065] Constraint condition setting: add acceleration change rate limit and maximum braking / driving force constraint to ensure that the control quantity meets the capability range of the vehicle actuator;

[0066] Rolling optimization solution: in each control period, the quadratic programming (QP) solver is used to calculate the optimal acceleration sequence, and only the first control quantity (i.e. the expected longitudinal acceleration at the current time) is output to the target vehicle actuator.

[0067] Through the rolling optimization mechanism of MPC, the front obstacle acceleration change and sensor noise can be responded in real time, for example, when the radar detects that the front obstacle suddenly decelerates, the controller automatically increases the braking weight to avoid collision. The dynamic weight adjustment capability of MPC can significantly improve the control adaptability in complex traffic scenarios.

[0068] S104, adjusting the longitudinal acceleration of the target vehicle to the expected longitudinal acceleration.

[0069] In specific implementation, after the vehicle actuator of the target vehicle receives the expected longitudinal acceleration instruction, the engine torque output can be controlled through the electronic throttle valve to accelerate, or the deceleration control can be performed through the electric hydraulic braking system. For example, a PID controller can be used to dynamically adjust the throttle opening degree according to the deviation between the actual acceleration of the target vehicle and the expected longitudinal acceleration. When the target vehicle is a heavy vehicle, a first-order inertia link compensation can be added to the execution link due to the lag characteristic of the transmission system of the heavy vehicle, so that the phase difference between the actual acceleration response and the expected value is controlled within a preset range, for example, through the following transfer function model:

[0070]

[0071] wherein, is an actual acceleration response of the target vehicle, is an expected longitudinal acceleration, is a gain coefficient of a first-order system, is a time constant of the first-order system, is a complex variable in Laplace transform.

[0072] The technical effects of the embodiment are as follows:

[0073] In the scheme, since the motion state estimation value is generated by fusing the historical prediction value and the real-time perception data, the sensor noise interference can be effectively inhibited and the cumulative deviation in the dynamic scene can be corrected, so that the accuracy of state estimation can be improved; at the same time, based on the dynamic optimization control quantity of the multi-dimensional weight parameter, the system can adaptively adjust the control strength and the response priority according to the real-time motion state change, so as to overcome the control lag or oscillation caused by the dependence of the traditional method on fixed experience parameters; through the closed-loop collaborative iteration of state estimation and control optimization, the anti-interference ability and robustness in the complex traffic flow scene are further strengthened, and finally the precision, adaptability and stability of longitudinal control are simultaneously enhanced.

[0074] Embodiment two

[0075] Figure 2 A vehicle longitudinal control method flowchart provided by the embodiment of the application Figure 2 As Figure 3 shown, the method comprises:

[0076] S201, acquiring, by a sensor, a measured value of an actual longitudinal distance between a front obstacle and a target vehicle, a measured value of a speed of the front obstacle, a measured value of a speed of the target vehicle, and a measured value of an actual longitudinal acceleration of the target vehicle at a current time;

[0077] When specifically implemented, the millimeter wave radar may, for example, measure the relative distance and speed of the front obstacle by emitting a frequency-modulated continuous wave (FMCW), and the camera may extract lane line information and obstacle types by an image recognition algorithm; after the fusion perception system aligns the data in space and time, the measured values of the actual longitudinal distance of the target vehicle, the speed of the front obstacle, the speed of the target vehicle, and the actual longitudinal acceleration are output.

[0078] S202, acquiring an expected longitudinal distance between the target vehicle and the front obstacle, and a state space comprising a distance difference value between the expected longitudinal distance and the actual longitudinal distance, a speed difference value between the speed of the front obstacle and the speed of the target vehicle, and the actual longitudinal acceleration of the target vehicle;

[0079] Specifically, the state space is defined as ,

[0080] Wherein:

[0081]

[0082] is a distance difference value, is an expected longitudinal distance, is an actual longitudinal distance;

[0083]

[0084] is a speed difference value, is a speed of the front obstacle, is a speed of the target vehicle;

[0085] is an actual longitudinal acceleration of the target vehicle.

[0086] Optionally, the expected longitudinal distance of the target vehicle and the front obstacle is obtained, comprising:

[0087] obtaining a minimum safety distance of the target vehicle and the front obstacle, and an arrival time of the target vehicle traveling to a longitudinal distance of the front obstacle being the minimum safety distance;

[0088] calculating a product of the arrival time and a measured value of the speed of the target vehicle, and determining the expected longitudinal distance according to a sum of the product and the minimum safety distance.

[0089] Specifically, the expected longitudinal distance can be calculated according to the following formula:

[0090]

[0091] Wherein, is the expected longitudinal distance, is the minimum safety distance, is the measured value of the speed of the target vehicle, is the arrival time.

[0092] Specifically, the method dynamically calculates the expected longitudinal distance, combines the minimum safety distance with the arrival time, guarantees braking safety in extreme cases (through the physical model constraint of the minimum safety distance), and dynamically expands the expected longitudinal distance on the basis of the safety baseline by introducing the arrival time to reflect the current motion trend, thereby balancing the safety redundancy in high-speed scenarios and the following efficiency in low-speed scenarios.

[0093] Further, the minimum safety distance of the target vehicle and the front obstacle can include:

[0094] obtain a road surface friction coefficient of a road where the target vehicle is currently located;

[0095] determine a minimum safety distance according to the road surface friction coefficient and a measured value of a speed of the target vehicle, the greater the road surface friction coefficient, the smaller the minimum safety distance, and the greater the measured value of the speed of the target vehicle, the greater the minimum safety distance.

[0096] Specifically, since the calculation of the minimum safety distance needs to consider the dynamic relationship between the braking performance of the vehicle and the adhesion condition of the road surface, the braking potential of the current road can be reflected by obtaining the road surface friction coefficient in real time, for example, the friction coefficient can be directly measured by a vehicle-mounted road surface detection sensor (such as an optical sensor or a tire slip rate estimation module), or the friction coefficient data marked in a pre-stored high-precision map can be received by a vehicle-mounted system, and the technical effect lies in that the calculation of the minimum safety distance can dynamically adapt to different road surfaces (such as dry asphalt road and ice and snow road), thereby avoiding the estimation deviation of the braking distance caused by the fixed friction coefficient assumption, and improving the accuracy of the safety distance.

[0097] After the road surface friction coefficient is determined, the minimum safety distance can be calculated, for example, by the following formula:

[0098]

[0099] wherein, the minimum safety distance is d, the road surface friction coefficient is μ, the gravitational acceleration is g, the reaction time of the target vehicle is t, for example, 1.5 seconds. The technical effect is that the safety distance increases with the square of the speed (the higher the speed, the greater the safety distance requirement), and decreases with the increase of the friction coefficient (high adhesion road allows shorter safety distance), thereby optimizing the following efficiency under the premise of ensuring braking safety.

[0100] Figure 3 A method flowchart diagram for obtaining the arrival time provided by the embodiment of the present application is shown in FIG. 1, and the method includes the following steps: Figure 4

[0101] S301, obtain a traffic flow density of a road where a target vehicle is currently located;

[0102] Since the traffic flow density directly affects the degree of freedom and the decision time window of the vehicle driving, the technical idea of this step is to quantify the road congestion degree through the real-time traffic flow density, thereby providing a basis for the dynamic adjustment of the subsequent reference time. Specifically, for example, the vehicle density (such as the number of vehicles per unit length) in the front lane can be counted through a vehicle-mounted camera combined with an image recognition algorithm, or global traffic flow data broadcast by a roadside unit (RSU) can be received through V2X communication. ​

[0103] S302, determining a reference time according to the traffic flow density, the greater the traffic flow density, the smaller the reference time;

[0104] The idea of this step is to establish an inverse correlation between traffic flow density and reference time (the greater the density, the smaller the reference time) to reflect the urgency demand of the driver in the congestion scenario. For example, the reference time can be calculated by a pre-set inverse proportional function:

[0105]

[0106] wherein, is the reference time, is an empirical constant, is the traffic flow density. The technical effect of this step is to dynamically adjust the reference time according to the traffic flow density.

[0107] S303, determining a proportional coefficient of the reference time according to the measured value of the target vehicle speed, the greater the measured value of the target vehicle speed, the greater the proportional coefficient;

[0108] The technical idea of this step is to weight and correct the reference time by the vehicle speed to reflect the physical constraint that the higher the speed, the greater the safety margin required. For example, the proportional coefficient can be calculated by the following formula:

[0109]

[0110] wherein, is the proportional coefficient, is a reference speed (e.g. 30 km per hour), is the measured value of the target vehicle speed. The technical effect of this step is to make the proportional coefficient grow with the speed, thereby extending the arrival time to reserve more braking distance in high-speed scenarios to avoid the risk of following too fast.

[0111] S304, determining the arrival time according to the product of the proportional coefficient and the reference time.

[0112] The technical idea of this step is to comprehensively reflect the coupling effect of traffic flow density and speed on arrival time through the product of the proportional coefficient and the reference time. For example, the arrival time can be directly calculated by multiplying the two, or a smoothing filter (such as a first-order lag filter) can be introduced to avoid the jump of arrival time caused by parameter mutation. The technical effect is to dynamically balance the competitive relationship between traffic flow density and speed, for example, to extend the arrival time in low-density high-speed scenarios, and to reduce the arrival time in high-density low-speed scenarios, so as to make the arrival time adapt to the road conditions and meet the safety requirements of the speed, providing reasonable time constraints for subsequent longitudinal control.

[0113] S203, the measured distance difference value between the expected longitudinal distance and the measured value of the actual longitudinal distance, the measured speed difference value between the measured value of the speed of the front obstacle and the measured value of the speed of the target vehicle, and the measured value of the actual longitudinal acceleration of the target vehicle are taken as the measured values of the state space at the current time.

[0114] S204, the predicted values of the distance difference value, the speed difference value and the actual longitudinal acceleration of the filter algorithm at the previous time are taken as the predicted values of the state space, and the estimated values of the distance difference value, the speed difference value and the actual longitudinal acceleration are calculated by the filter algorithm according to the predicted values of the state space and the measured values of the state space at the current time, and are taken as the estimated values of the state space.

[0115] This step can realize the optimal estimation of the state quantity by the Kalman filter algorithm. Since there are process noise and measurement noise in the longitudinal motion of the vehicle, the state space transition equation can be:

[0116]

[0117] wherein is the state space of the longitudinal control system;

[0118]

[0119] is the state transition matrix;

[0120]

[0121] is the control input matrix

[0122]

[0123] The parameters of the state transition matrix and the control matrix can be defined with reference to the parameters of the transfer function model in Embodiment 1, which will not be repeated here.

[0124] is the process noise input matrix, which is used to map the noise to the state space;

[0125]

[0126] is the process noise;

[0127] is the acceleration of the front obstacle;

[0128] State prediction stage:

[0129] Calculate the predicted state quantity and the covariance matrix

[0130]

[0131]

[0132] wherein, is the state transition matrix obtained by discretization, representing the dynamics of state variables over time; is the control input matrix, corresponding to the aforementioned , used to map the control variable, i.e., the desired acceleration to be solved, to the state space; is the process noise covariance matrix, representing the uncertainty of the system model (such as the disturbance caused by sudden changes in the acceleration of the preceding vehicle).

[0133]

[0134] wherein, , , respectively represent the noise variance of the distance difference, the speed difference, and the actual longitudinal acceleration of the target vehicle.

[0135] Next, the Kalman gain is calculated using the following formula , and the state estimation value and the covariance matrix are updated:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141] wherein:

[0142] represents the measured value of the state space at the current time; is the observation matrix; is the observation noise covariance matrix; is the innovation, i.e., the residual between the measured value and the predicted value, used to quantify the prediction bias; the Kalman gain is used to dynamically adjust the weight of the predicted value and the measured value, and when the noise is large, the gain is reduced to trust the model prediction, and vice versa.

[0143] ​Through the recursive calculation of the Kalman filter, the sensor noise (such as the random jump of the millimeter wave radar) and the model error (such as the sudden change of the acceleration of the preceding vehicle) can be effectively suppressed, so as to output smooth and accurate state estimation values, and provide reliable input for subsequent LQR control.

[0144] It should be noted that, in addition to the Kalman filter algorithm, the filter algorithm in the embodiment can also include extended Kalman filter, adaptive Kalman filter, particle filter and other types of filter algorithms.

[0145] S205, taking the estimation value of the state space as the state quantity of the linear quadratic regulator algorithm, taking the expected longitudinal acceleration of the target vehicle to be determined as the control quantity of the linear quadratic regulator algorithm, and determining the expected longitudinal acceleration of the target vehicle according to the weight parameter of the state quantity, the weight parameter of the control quantity and the state quantity through the linear quadratic regulator algorithm;

[0146] Specifically, the optimal control quantity is solved through the LQR algorithm in this step to balance the tracking performance and energy consumption. Since the cost function needs to be optimized in multiple targets, when specifically implemented, the cost function can be designed as follows:

[0147]

[0148] wherein is the state quantity, is the control quantity, is the state quantity weight parameter matrix, is the control quantity weight parameter:

[0149]

[0150]

[0151] is the weight parameter of the distance difference value;

[0152] is the weight parameter of the speed difference value;

[0153] is the weight parameter of the actual longitudinal acceleration of the target vehicle;

[0154] is the weight parameter of the control quantity.

[0155] When the speed difference weight parameter increases, the sensitivity of the controller to the speed deviation of the target vehicle and the front obstacle can be enhanced, so that the vehicle actively eliminates the speed difference through acceleration or braking, which is suitable for scenarios that require rapid convergence of speed (such as cutting into a following vehicle); on the contrary, when the speed difference weight parameter decreases, the response of the controller to the speed deviation tends to be flat, which is suitable for low-speed congestion scenarios to avoid the jerk caused by frequent acceleration and deceleration, and to improve ride comfort.

[0156] When the distance difference weight parameter increases, the attention of the controller to the deviation of the actual distance and the expected distance can be enhanced, and the vehicle is more actively adjusted to reduce the distance error, which is suitable for short-distance following or front vehicle emergency deceleration scenarios, and safety is prioritized; when the actual longitudinal distance is large, the distance difference weight parameter is reduced to reduce the sensitivity to distance deviation, and unnecessary aggressive control (such as excessive braking at a long distance) is inhibited, thereby optimizing fuel economy.

[0157] The greater the weight parameter of the control quantity indicates that the limitation on acceleration change is more strict, and the control strategy that pays more attention to driving comfort and fuel economy is corresponded.

[0158] The feedback gain matrix is obtained by solving the Riccati equation The Riccati equation is:

[0159]

[0160]

[0161] wherein, is a state transition matrix, is a control input matrix, is a coefficient matrix of the cost function.

[0162] The expected acceleration is finally calculated:

[0163] It should be noted that the LQR algorithm is based on the high-precision motion state estimation value output by the filter algorithm, and by adjusting the state quantity weight (such as the distance difference priority) and the energy cost of the control quantity (such as the acceleration smoothness constraint), an adaptive expected longitudinal acceleration is dynamically generated, so that it can quickly respond to sudden acceleration or deceleration of the front vehicle or sudden changes in traffic flow, and avoid control lag or oscillation caused by traditional experience parameter optimization; further, the closed-loop coordination mechanism of the filter and the LQR continuously iterates the state estimation and control decision. Ultimately, the robustness of longitudinal control is improved (anti-interference ability is enhanced) and the multi-objective performance balance (tracking accuracy, fuel economy, and driving comfort are simultaneously optimized) is balanced in complex dynamic scenarios.

[0164] S206, adjust the longitudinal acceleration of the target vehicle to the expected longitudinal acceleration.

[0165] The specific implementation process of this step can refer to Example 1, which will not be repeated here.

[0166] Figure 4 The structural schematic diagram of the vehicle longitudinal control device provided in the present application is shown in FIG. 1. Figure 5 As shown in the figure, the vehicle longitudinal control device 40 provided in the present embodiment comprises:

[0167] The acquisition module 401 is configured to acquire, by a sensor of a target vehicle, a measured value of a state space at a current time, the state space comprising a motion state of the target vehicle and a motion state of an obstacle in front of the target vehicle.

[0168] The state estimation module 402 is configured to calculate an estimated value of the state space at the current time according to a predicted value of the state space at the current time at a previous time and the measured value of the state space at the current time.

[0169] The expected longitudinal acceleration calculation module 403 is configured to determine an expected longitudinal acceleration of the target vehicle as a control quantity according to a weight parameter of the estimated value, a weight parameter of the control quantity and a state quantity.

[0170] The control module 404 is configured to adjust a longitudinal acceleration of the target vehicle to the expected longitudinal acceleration.

[0171] The vehicle longitudinal control device provided in the present embodiment can execute the method provided in the above method embodiment, and has similar implementation principles and technical effects, which will not be repeated here.

[0172] Figure 5 The structural schematic diagram of the electronic device provided in the present application is shown in FIG. 1. ​ As shown in the figure, the electronic device 50 provided in the present embodiment comprises at least one processor 501 and a memory 502. Optionally, the device 50 further comprises a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected through a bus 504.

[0173] In the specific implementation process, the at least one processor 501 executes the computer execution instructions stored in the memory 502, so that the at least one processor 501 executes the above method.

[0174] The specific implementation process of the processor 501 can refer to the above method embodiment, which has similar implementation principles and technical effects, and will not be repeated here.

[0175] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0176] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0177] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0178] The present application also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above method.

[0179] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when a processor executes the computer execution instructions, the above method is implemented.

[0180] The above readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0181] An example readable storage medium is coupled to the processor such that the processor can read information from the readable storage medium and can write information to the readable storage medium. Of course, the readable storage medium can also be a part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0182] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0183] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0184] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0185] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0186] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes various media capable of storing program codes, such as ROM, RAM, magnetic disk, or optical disk.

[0187] Finally, it should be noted that other embodiments of the present application will readily occur to those skilled in the art upon consideration of the specification and practice of the present application disclosed herein. The present application is intended to include all such variations as fall within the general scope of the application, and includes the generic principles disclosed and the best mode known to the inventors to be currently practiced as well as variations thereof, without departing from the scope of the present application as defined by the claims. The specification and examples give the best application of the present application as known to at least one of the inventors at the time of the filing of this application. It is to be understood that since numerous modifications and changes will readily occur to those skilled in the art, the application is not to be limited to the exact construction and operation as illustrated and described. Accordingly, all such variations are intended to be included within the scope of the present application as defined in the claims. The application is to be limited only by the claims.

Claims

1. A vehicle longitudinal control method, characterized by, The method comprises: acquiring, by a sensor of a target vehicle, a measured value of a state space at a current time, the state space comprising a motion state of the target vehicle and a motion state of a front obstacle of the target vehicle; calculating an estimated value of the state space at the current time according to a predicted value of the state space at the current time at a previous time and the measured value of the state space at the current time; determining a desired longitudinal acceleration of the target vehicle as a control quantity, and determining the desired longitudinal acceleration of the target vehicle according to a weight parameter of the estimated value, a weight parameter of the control quantity and the state quantity; adjusting a longitudinal acceleration of the target vehicle to the desired longitudinal acceleration.

2. The method of claim 1, wherein, The acquiring, by a sensor of a target vehicle, a measured value of a state space at a current time comprises: acquiring, by the sensor, a measured value of an actual longitudinal distance between the front obstacle and the target vehicle, a measured value of a speed of the front obstacle, a measured value of a speed of the target vehicle and a measured value of an actual longitudinal acceleration of the target vehicle at the current time; acquiring a desired longitudinal distance between the target vehicle and the front obstacle, the state space comprising a distance difference between the desired longitudinal distance and the actual longitudinal distance, a speed difference between the speed of the front obstacle and the speed of the target vehicle and the actual longitudinal acceleration of the target vehicle; taking the measured distance difference between the measured value of the desired longitudinal distance and the actual longitudinal distance, the measured speed difference between the measured value of the speed of the front obstacle and the measured value of the speed of the target vehicle and the measured value of the actual longitudinal acceleration of the target vehicle as the measured value of the state space at the current time.

3. The method of claim 2, wherein, The calculating an estimated value of the state space at the current time according to a predicted value of the state space at the current time at a previous time and the measured value of the state space at the current time by a filter algorithm comprises: taking the predicted value of the distance difference, the predicted value of the speed difference and the predicted value of the actual longitudinal acceleration of the filter algorithm at the previous time as the predicted value of the state space, and calculating the estimated value of the distance difference, the estimated value of the speed difference and the estimated value of the actual longitudinal acceleration by the filter algorithm according to the predicted value of the state space and the measured value of the state space at the current time, and taking the estimated value of the distance difference, the estimated value of the speed difference and the estimated value of the actual longitudinal acceleration as the estimated value of the state space.

4. The method of claim 2, wherein, The acquiring a desired longitudinal distance between the target vehicle and the front obstacle comprises: acquiring a minimum safety distance between the target vehicle and the front obstacle, and an arrival time of the target vehicle running to a longitudinal distance from the front obstacle being the minimum safety distance; calculating a product of the arrival time and the measured value of the speed of the target vehicle, and determining the desired longitudinal distance according to a sum of the product and the minimum safety distance.

5. The method of claim 4, wherein, The acquiring a minimum safety distance between the target vehicle and the front obstacle comprises: acquiring a road surface friction coefficient of a road where the target vehicle is currently located. The minimum safety distance is determined according to the road surface friction coefficient and the measured value of the target vehicle speed, and the greater the road surface friction coefficient is, the smaller the minimum safety distance is, and the greater the measured value of the target vehicle speed is, the greater the minimum safety distance is.

6. The method of claim 4, wherein, The obtaining of the arrival time of the target vehicle driving to the longitudinal distance of the minimum safety distance from the front obstacle comprises: Obtaining the traffic flow density of the road currently where the target vehicle is located; The reference time is determined according to the traffic flow density, and the greater the traffic flow density is, the smaller the reference time is; According to the measured value of the target vehicle speed, the proportional coefficient of the reference time is determined, and the greater the measured value of the target vehicle speed is, the greater the proportional coefficient is; The arrival time is determined according to the product of the proportional coefficient and the reference time.

7. The method of claim 1, wherein, The desired longitudinal acceleration of the target vehicle to be determined is taken as a control amount, and the desired longitudinal acceleration of the target vehicle is determined according to the weight parameter of the estimated value, the weight parameter of the control amount and the state amount. The estimated value of the state space is taken as a linear quadratic regulator algorithm state amount, the desired longitudinal acceleration of the target vehicle to be determined is taken as a control amount of the linear quadratic regulator algorithm, and the desired longitudinal acceleration of the target vehicle is determined by the linear quadratic regulator algorithm according to the weight parameter of the state amount, the weight parameter of the control amount and the state amount.

8. A vehicle longitudinal control device characterized by comprising: The device comprises: The acquisition module is configured to acquire, by a sensor of a target vehicle, a measured value of a state space at a current time, the state space comprising a motion state of the target vehicle and a motion state of a front obstacle of the target vehicle; The state estimation module is configured to calculate an estimated value of the state space at the current time according to a predicted value of the state space at the current time at a previous time and the measured value of the state space at the current time; The desired longitudinal acceleration calculation module is configured to take the desired longitudinal acceleration of the target vehicle to be determined as a control amount, and determine the desired longitudinal acceleration of the target vehicle according to a weight parameter of the estimated value, a weight parameter of the control amount and the state amount. The control module is configured to adjust the longitudinal acceleration of the target vehicle to the desired longitudinal acceleration.

9. An electronic device, comprising: It comprises: A processor and a memory connected in communication with the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the vehicle longitudinal control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the vehicle longitudinal control method according to any one of claims 1 to 7.

Images