Intelligent vehicle autonomous cruise control method

Abstract

The invention relates to the technical field of vehicle automatic driving, and provides an intelligent vehicle autonomous cruise control method, which comprises the following steps of: determining a control requirement and an expected speed of a controlled vehicle, establishing a workshop kinematics model, designing an acceleration controller based on an LQR theory, and calculating an expected acceleration; calculating an expected driving force according to the expected acceleration; determining a driving / braking mode according to the expected driving force, and calculating an expected engine torque; according to the deviation between the expected speed and the actual speed of the controlled vehicle, the engine torque compensation amount and the acceleration compensation amount based on the feedback IP are calculated; adding the engine torque compensation amount and the expected engine torque to obtain a first final control amount, and adding the acceleration compensation amount and the expected acceleration to obtain a second final control amount; and the first final control quantity and the second final control quantity are fed back to an actuator, the actuator adjusts control data of the controlled vehicle, and the speed control precision and the driving comfort of the controlled vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle automatic driving, and in particular to an autonomous cruise control method for an intelligent vehicle. Background Art

[0002] In recent years, driverless technology has developed rapidly. Assisted driving technologies such as cruise control, adaptive cruise control, and lane keeping have gradually entered people's daily traffic life, with huge development potential and broad application prospects. Longitudinal control technology is one of the key technologies in the field of driverless driving. By controlling the power system to change the motion state such as speed and acceleration, the controlled vehicle has the ability to cruise at a preset speed or keep a certain distance from the dynamic target in front, and maintain good comfort. The accuracy of speed tracking and the switching of driving / braking modes are important indicators to measure the quality of longitudinal control methods.

[0003] The method based on the traditional longitudinal dynamics model can achieve higher speed tracking accuracy, and it is more reasonable to determine the switching rules of the drive / brake mode according to the force conditions of the whole vehicle. However, the longitudinal dynamics model has many parameters and is difficult to measure, which affects the speed tracking accuracy. The vehicle driving force calculated by the conventional model is inaccurate, which makes the drive / brake mode switching unreasonable, thus affecting the driving comfort. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides an intelligent vehicle autonomous cruise control method to improve the speed control accuracy and driving comfort of the controlled vehicle.

[0005] The present invention provides an intelligent vehicle autonomous cruise control method, comprising the following steps: Determine the cruise control requirements of the controlled vehicle, determine the expected speed of the controlled vehicle, establish a vehicle kinematic model, design an acceleration controller based on the Linear Quadratic Regulator (LQR) theory, and calculate the expected acceleration; Calculating a desired driving force based on a desired acceleration and a longitudinal dynamics model of a controlled vehicle; determining a driving / braking mode according to the expected driving force, and calculating an expected engine torque of the controlled vehicle according to the expected driving force; Calculating the engine torque compensation and acceleration compensation of the controlled vehicle based on proportional-integral controller (PI) feedback according to the deviation between the desired speed and the actual speed of the controlled vehicle; Adding the engine torque compensation amount to the expected engine torque to obtain a first final control amount, and adding the acceleration compensation amount to the expected acceleration to obtain a second final control amount; The first final control amount and the second final control amount are fed back to the actuator according to the determined driving / braking mode, and the actuator adjusts the control data of the controlled vehicle.

[0006] According to an intelligent vehicle autonomous cruise control method provided by the present invention, establishing a vehicle kinematics model includes the following steps: Calculate the deviation between the target vehicle and the controlled vehicle: in, is the actual distance between the target vehicle and the controlled vehicle, is the expected distance between the target vehicle and the controlled vehicle, is the deviation between the actual distance and the expected distance between the controlled vehicle and the target vehicle, is the speed of the target vehicle, is the speed of the controlled vehicle, is the deviation between the speed of the controlled vehicle and the speed of the target vehicle; The state space representation of the workshop kinematic model is as follows: in, , , , , a is the acceleration of the controlled vehicle, x is the state quantity, A is the state quantity coefficient, u is the input quantity, B is the input coefficient.

[0007] According to an intelligent vehicle autonomous cruise control method provided by the present invention, an acceleration controller based on linear quadratic regulator theory calculates the expected acceleration including the following steps: Design an acceleration controller based on the linear quadratic regulator theory, the formula is as follows: in, J is the cost function, is the state quantity x The weight matrix, For control u The weight matrix, K is the optimal linear negative feedback matrix; Based on optimal control theory, the expression of expected acceleration is: in, is the expected acceleration.

[0008] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force includes the following steps: Under the condition of wheel rolling, determine the wheel speed of the controlled vehicle calculated from the real-time engine speed, the formula is as follows: The driving condition of the controlled vehicle is determined according to the desired speed of the controlled vehicle, the wheel speed calculated from the real-time engine speed, and the gear position of the transmission. The relationship is as follows: in, To calculate the wheel speed of the controlled vehicle from the real-time engine speed, is the wheel radius, is the real-time engine speed, is the transmission ratio, Main reducer transmission ratio.

[0009] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force also includes the following steps: The resistance of the controlled vehicle during driving is calculated as follows: in, is the rolling resistance of the controlled vehicle, is the air resistance of the controlled vehicle, is the slope resistance of the controlled vehicle, is the acceleration resistance of the controlled vehicle, is the vehicle gravity of the controlled vehicle, is the rolling resistance coefficient of the controlled vehicle, is the slope angle, is the air resistance coefficient, is the frontal area of ​​the controlled vehicle, is the speed of the controlled vehicle, is the rotation mass conversion factor, is the vehicle mass of the vehicle being charged.

[0010] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force also includes the following steps: The formula for calculating the braking force of the engine of the controlled vehicle under low gear idling condition is as follows: in, is the braking force of the controlled vehicle's engine under low gear idling conditions, is the output torque of the engine of the controlled vehicle, which is determined by the real-time speed and external characteristic curve of the engine of the controlled vehicle. The mechanical efficiency of the transmission system of the controlled vehicle; The formula for calculating the braking force of the engine of the controlled vehicle in the gear-glide condition is as follows: in, is the braking force of the engine of the controlled vehicle in the gear-geared coasting condition, The mechanical efficiency of the engine of the vehicle under investigation; According to the driving conditions, the expected driving force is calculated as follows: in, is the expected driving force of the controlled vehicle.

[0011] According to an intelligent vehicle autonomous cruise control method provided by the present invention, determining a driving / braking mode according to a desired driving force comprises the following steps: The expected driving force of the controlled vehicle is less than 0, and the braking mode is determined; The expected driving force of the controlled vehicle is greater than or equal to 0, which is determined as the driving mode.

[0012] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected engine torque of the controlled vehicle according to the expected driving force includes the following steps: in, is the desired engine torque of the controlled vehicle, is the expected driving force of the controlled vehicle, is the mechanical efficiency of the drive train of the controlled vehicle, is the wheel radius, is the transmission ratio, Main reducer transmission ratio.

[0013] According to an intelligent vehicle autonomous cruise control method provided by the present invention, the steps of calculating the engine torque compensation amount and the acceleration compensation amount based on proportional-integral controller feedback are as follows: In driving mode, the engine torque compensation is obtained: in, is the torque compensation amount in driving mode, is the proportional coefficient in driving mode, is the integral coefficient in driving mode; In braking mode, the acceleration compensation is obtained: in, is the acceleration compensation in braking mode, is the proportional coefficient in braking mode, is the integral coefficient in braking mode.

[0014] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the first final control amount and the second final control amount includes the following steps: Obtaining a first final control amount in the driving mode, the first final control amount being the sum of the torque compensation amount in the driving mode and the desired engine torque of the controlled vehicle; A second final control amount is obtained in the braking mode, and the second final control amount is the sum of the acceleration compensation amount and the expected acceleration in the braking mode.

[0015] The above one or more technical solutions in the embodiments of the present invention have at least one of the following technical effects: According to the intelligent vehicle autonomous cruise control method of the embodiment of the present invention, the driving / braking instructions are compensated respectively by adopting a method based on proportional-integral controller feedback, thereby reducing the influence of the inaccurate parameters of the traditional longitudinal dynamics model on the control effect, and can improve the speed tracking accuracy and driving comfort of the controlled vehicle.

[0016] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the intelligent vehicle autonomous cruise control method provided by the present invention.

[0019] Figure 2 It is a workshop kinematic model of the intelligent vehicle autonomous cruise control method provided by the present invention.

[0020] Figure 3 It is the longitudinal dynamics model of the intelligent vehicle autonomous cruise control method provided by the present invention.

[0021] FIG. 4( a ) shows the relationship between the speed and gear change of the traditional longitudinal dynamics model provided by the present invention when the target speed is low.

[0022] FIG. 4( b ) shows the relationship between the speed and the gear change of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is low.

[0023] FIG. 5( a ) shows the change of the driving / braking mode of the traditional longitudinal dynamics model provided by the present invention when the target speed is low.

[0024] FIG. 5( b ) shows the change of the driving / braking mode of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is low.

[0025] FIG. 6( a ) shows the relationship between the speed and gear change of the traditional longitudinal dynamics model provided by the present invention when the target speed is high.

[0026] FIG. 6( b ) shows the relationship between the speed and the gear change of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is high.

[0027] Figure 7 The invention provides an intelligent vehicle autonomous cruise control method for conducting a distance keeping test on the speeds of a controlled vehicle and a target vehicle and a speed deviation.

[0028] Figure 8 The present invention provides an intelligent vehicle autonomous cruise control method for conducting a distance keeping test to determine the distance and distance deviation between a controlled vehicle and a target vehicle. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0030] In the description of the embodiments of the present invention, it should be noted that the terms “first”, “second” and “third” are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0031] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0032] Figure 1 It is a flow chart of the intelligent vehicle autonomous cruise control method provided by the present invention. Figure 2 It is a workshop kinematic model of the intelligent vehicle autonomous cruise control method provided by the present invention. Figure 3 It is the longitudinal dynamics model of the intelligent vehicle autonomous cruise control method provided by the present invention.

[0033] The present invention provides an intelligent vehicle autonomous cruise control method, such as Figures 1 to 3 As shown, the intelligent vehicle autonomous cruise control method includes the following steps: S1: Determine the cruise control requirements of the controlled vehicle, determine the expected speed of the controlled vehicle, establish a vehicle kinematic model, design an acceleration controller based on the linear quadratic regulator theory, and calculate the expected acceleration; S2: Calculate the expected driving force according to the expected acceleration and the longitudinal dynamics model of the controlled vehicle; S3: determining a driving / braking mode according to the expected driving force, and calculating an expected engine torque of the controlled vehicle according to the expected driving force; S4: Calculating an engine torque compensation amount and an acceleration compensation amount of the controlled vehicle based on feedback from a proportional-integral controller according to a deviation between a desired speed and an actual speed of the controlled vehicle; S5: adding the engine torque compensation amount to the expected engine torque to obtain a first final control amount, and adding the acceleration compensation amount to the expected acceleration to obtain a second final control amount; S6: Feeding back the first final control amount and the second final control amount to the actuator according to the determined driving / braking mode, and the actuator adjusting the control data of the controlled vehicle.

[0034] In this embodiment, the driving / braking instructions are compensated respectively by using a method based on proportional-integral controller feedback, thereby reducing the influence of the inaccurate parameters of the traditional longitudinal dynamics model on the control effect, thereby improving the speed tracking accuracy and driving comfort of the controlled vehicle.

[0035] According to some embodiments of the present invention, in order to enable the controlled vehicle to have both cruise control and distance keeping capabilities, a vehicle-to-vehicle kinematic model is used to describe the speed and distance change relationship between the controlled vehicle and the target vehicle traveling in the same direction, and the LQR theory is used to solve the expected acceleration of the controlled vehicle.

[0036] According to an intelligent vehicle autonomous cruise control method provided by the present invention, Figure 2 As shown in the figure, establishing the workshop kinematic model includes the following steps: S11: Calculate the deviation between the target vehicle and the controlled vehicle: in, is the actual distance between the target vehicle and the controlled vehicle, is the expected distance between the target vehicle and the controlled vehicle, is the deviation between the actual distance and the expected distance between the controlled vehicle and the target vehicle, is the speed of the target vehicle, is the speed of the controlled vehicle, is the deviation between the speed of the controlled vehicle and the speed of the target vehicle; S12: State space of the workshop kinematic model The statement is as follows: in, , , , , a is the acceleration of the controlled vehicle, x is the state quantity, A is the state quantity coefficient, u is the input quantity, B is the input coefficient.

[0037] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected acceleration includes the following steps: S13: Acceleration controller based on linear quadratic regulator theory, the formula is as follows: in, J is the cost function, is the state quantity x The weight matrix of For control u The weight matrix of K is the optimal linear negative feedback matrix; S14: Assume that there is a constant matrix P To make the system stable, we can get: S15: Constant Matrix P The calculation of is as follows: S16: Based on optimal control theory, the expression of expected acceleration is: in, is the expected acceleration, K is the optimal linear negative feedback matrix.

[0038] According to some preferred embodiments of the present invention, the weight matrix =diag(100,10), weight matrix =[1].

[0039] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force includes the following steps: S21: Under the condition of wheel rolling, determine the wheel speed of the controlled vehicle calculated from the real-time engine speed, the formula is as follows: S22: Determine the driving condition of the controlled vehicle according to the desired speed of the controlled vehicle, the wheel speed calculated from the real-time engine speed, and the gear position of the transmission, and the relationship is as follows: in, is the wheel speed of the controlled vehicle calculated from the real-time engine speed, is the wheel radius, is the real-time engine speed, is the transmission ratio, Main reducer transmission ratio.

[0040] According to some embodiments of the present invention, the controlled vehicle overcomes various resistances and accelerates, and the output torque of the engine is positive, providing positive driving force for the whole vehicle. This working condition is a normal working condition during driving; the controlled vehicle decelerates, the engine output torque is positive, providing positive driving force for the whole vehicle, so that the controlled vehicle decelerates slowly or even travels at a constant speed. This working condition is a low-gear idling condition. The controlled vehicle decelerates, the engine output torque is negative, providing braking force for the whole vehicle, making the controlled vehicle decelerate significantly. This working condition is a gear-shifting working condition.

[0041] Figure 3 It is the longitudinal dynamics model of the intelligent vehicle autonomous cruise control method provided by the present invention.

[0042] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force also includes the following steps: S23: Calculate the resistance of the controlled vehicle during driving, such as Figure 3 As shown, the formula is as follows: in, is the rolling resistance of the controlled vehicle, is the air resistance of the controlled vehicle, is the slope resistance of the controlled vehicle, is the acceleration resistance of the controlled vehicle, is the vehicle gravity of the controlled vehicle, is the rolling resistance coefficient of the controlled vehicle, is the slope angle, is the air resistance coefficient, is the frontal area of ​​the controlled vehicle, is the speed of the controlled vehicle, is the rotation mass conversion factor, is the vehicle mass of the vehicle being charged.

[0043] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected driving force also includes the following steps: S24: Calculate the braking force of the engine of the controlled vehicle under the low gear idling condition, the formula is as follows: in, is the braking force of the controlled vehicle's engine under low gear idling conditions, is the output torque of the engine of the controlled vehicle, which is determined by the real-time speed and external characteristic curve of the engine of the controlled vehicle. The mechanical efficiency of the drive train of the controlled vehicle.

[0044] According to some embodiments of the present invention, the driving force of the engine under low-gear idle conditions is calculated, the output torque is obtained from the real-time engine speed and the external characteristic curve of the engine, and then the driving force for the entire vehicle is formed through the transmission system.

[0045] S25: Calculate the braking force of the engine of the controlled vehicle in the gear-engaged coasting condition, the formula is as follows: in, is the braking force of the engine of the controlled vehicle in the gear-geared coasting condition, The mechanical efficiency of the engine of the vehicle being controlled.

[0046] According to some embodiments of the present invention, when the engine performs external work, it needs to overcome the inertia of the piston crankshaft and its affiliated mechanisms, the pumping resistance and the friction of the mechanical structure, etc., to achieve its own operation; in the coasting condition with gear, the engine stops fuel injection and does not perform external work, but because the transmission system between the wheel and the engine is not disengaged, the wheel enables the engine to overcome mechanical friction through the transmission system, driving the piston crankshaft and its affiliated mechanisms to keep running, which is basically the same as the purpose of the engine's lost power. Therefore, the engine's lost power under normal working conditions is regarded as the braking power under the coasting condition with gear, and then the braking force under the coasting condition with gear is obtained.

[0047] S26: Calculate the expected driving force according to the driving condition, such as Figure 3 As shown, the formula is as follows: in, is the expected driving force of the controlled vehicle.

[0048] According to an intelligent vehicle autonomous cruise control method provided by the present invention, determining a driving / braking mode according to a desired driving force comprises the following steps: S31: If the expected driving force of the controlled vehicle is less than 0, it is determined to be a braking mode; if the expected driving force of the controlled vehicle is greater than or equal to 0, it is determined to be a driving mode.

[0049] In this embodiment, the expected driving force is used as the judgment basis for the driving / braking mode switching, which solves the problem of reduced driving comfort caused by frequent switching of driving / braking modes during the driving of the controlled vehicle, thereby improving driving comfort.

[0050] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the expected engine torque of the controlled vehicle according to the expected driving force includes the following steps: S32: Calculate the expected engine torque of the controlled vehicle, the formula is as follows: in, is the desired engine torque of the controlled vehicle.

[0051] According to an intelligent vehicle autonomous cruise control method provided by the present invention, the steps of calculating the engine torque compensation amount and the acceleration compensation amount based on proportional-integral controller feedback are as follows: In driving mode, the engine torque compensation is obtained: in, is the torque compensation amount in driving mode, is the proportional coefficient in driving mode, is the integral coefficient in driving mode; In braking mode, the acceleration compensation is obtained: in, is the acceleration compensation in braking mode, is the proportional coefficient in braking mode, is the integral coefficient in braking mode.

[0052] In this embodiment, the traditional longitudinal dynamics model has parameters including transmission ratio, mechanical efficiency, vehicle mass, etc. It is difficult to accurately measure the parameters, which directly affect the longitudinal control effect. The feedback PI method is used to compensate and optimize the control instructions, reduce the influence of the traditional longitudinal dynamics model parameters on the control effect, improve the longitudinal control accuracy of the controlled vehicle, and improve the adaptability of the controller.

[0053] According to some preferred embodiments of the present invention, The value of is 20. The value of is 0.1, The value of is 10. The value of is 0.1.

[0054] According to some embodiments of the present invention, the proportional-integral controller has the advantages of simple structure, strong robustness, and independence from models, and can serve as an effective supplement to the traditional longitudinal dynamics model-based control method.

[0055] According to an intelligent vehicle autonomous cruise control method provided by the present invention, calculating the first final control amount and the second final control amount includes the following steps: S61: obtaining a first final control amount in the driving mode, where the first final control amount is the sum of the torque compensation amount in the driving mode and the expected engine torque of the controlled vehicle; S62: Obtain a second final control amount in the braking mode, where the second final control amount is the sum of the acceleration compensation amount and the expected acceleration in the braking mode.

[0056] The technical solution of the present invention is further explained below in conjunction with a specific embodiment. It should be noted that this specific embodiment is only for those skilled in the art to better understand the technical solution of the present invention, and should not be regarded as an unreasonable limitation on the protection scope of the present invention.

[0057] Embodiment 1 Firstly, a workshop kinematic model considering speed and distance is established, and an acceleration controller based on LQR is designed; then, the expected driving force is calculated by using an intelligent vehicle autonomous cruise control method suitable for low-gear idling conditions and coasting conditions; then, the driving / braking mode switching rules are designed according to the expected driving force, and the expected engine torque of the engine is calculated according to the expected driving force; finally, the feedback PI method is used to compensate the driving / braking commands respectively to reduce the influence of the inaccurate parameters of the traditional longitudinal dynamics model on the control effect. Experiments are carried out based on a real vehicle platform, and the control parameter settings are shown in Table 1.

[0058] Table 1 Control parameters

[0059] (1) Cruise control test Cruise control means that there is no target vehicle in front of the controlled vehicle. By setting the target speed, it is assumed that there is a target vehicle traveling at a constant speed in front to establish a kinematic model.

[0060] 1) When the target speed is low FIG4(a) is a diagram showing the relationship between the speed and gear change of the conventional longitudinal dynamics model provided by the present invention when the target speed is low. FIG4(b) is a diagram showing the relationship between the speed and gear change of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is low. FIG5(a) is a diagram showing the change of the driving / braking mode of the conventional longitudinal dynamics model provided by the present invention when the target speed is low. FIG5(b) is a diagram showing the change of the driving / braking mode of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is low.

[0061] The target speed is set to 10 km / h, and the transmission gear is ensured to be 2nd gear. Then the target speeds are set to 8 km / h, 5 km / h, and 3 km / h in sequence. The test results are shown in Figures 4(a), 4(b), 5(a), and 5(b).

[0062] When the target speed is 10km / h and 8km / h, the tracking deviation is small. When the target speed decreases, the braking mode is adopted. When the actual speed is relatively small, it is quickly switched to the driving mode. The main reason is that the improved longitudinal dynamics model of the intelligent vehicle autonomous cruise control method adopted by the method of the present invention takes into account the influence of the engine on the force of the whole vehicle under low-gear idling conditions.

[0063] When the target speed is 5km / h, the controlled vehicle is judged to enter the low-gear idle condition. The expected driving force calculated by the longitudinal dynamics model improved by the intelligent vehicle autonomous cruise control method is less than zero, and the braking mode is always maintained. The control amount is continuously adjusted through PI feedback compensation to keep the actual speed near 5km / h; when the target speed changes to 3km / h, the braking amount is increased to switch the gear of the controlled vehicle from 2nd gear to 1st gear, and the actual speed is maintained near 3.3km / h, with a small speed tracking deviation. Under the low-gear idle condition, the controlled vehicle always remains in the braking mode.

[0064] As shown in FIG4(a), when the target speed changes, the control algorithm of the traditional longitudinal dynamics model causes the speed of the controlled vehicle to fluctuate greatly, and cannot achieve good speed maintenance accuracy, with a large deviation; as shown in FIG4(b), when the target speed changes, the present invention can quickly approach the target speed, with small speed fluctuations and high speed tracking accuracy.

[0065] As shown in Figure 5 (a), during the entire deceleration process, the driving / braking mode of the control algorithm using the traditional longitudinal dynamics model experienced 12 mode switches, while as shown in Figure 5 (b), the control algorithm of the model improved by the intelligent vehicle autonomous cruise control method only experienced 2 mode switches, indicating that the longitudinal dynamics model improved by the intelligent vehicle autonomous cruise control method can effectively reduce unreasonable mode switches and improve driving comfort.

[0066] 2) When the target speed is high Figure 6 (a) shows the relationship between the speed and gear change of the traditional longitudinal dynamics model provided by the present invention when the target speed is high. Figure 6 (b) shows the relationship between the speed and gear change of the intelligent vehicle autonomous cruise control method provided by the present invention when the target speed is high.

[0067] Initially, the controlled vehicle is stationary. The target speed is gradually increased to 45 km / h, and then gradually decreased to zero. Each change is maintained for a period of time. The test results are shown in Figure 6 (a) and Figure 6 (b).

[0068] During the acceleration phase and the speed maintenance phase, the present invention adopts a feedback PI compensation strategy based on the traditional longitudinal dynamics model to eliminate the influence of inaccurate parameters of the traditional longitudinal dynamics model on speed tracking, thereby accelerating quickly during the acceleration phase, eliminating steady-state deviations during the speed maintenance phase, and maintaining a relatively low speed deviation.

[0069] When the target speed increases, as shown in Figure 6 (a), the control algorithm using the traditional longitudinal dynamics model takes longer to reach the specified target speed, and there is a certain steady-state error with some target speeds. For example, when the target speed is 10 km / h, there is a stable deviation between the dotted line and the solid line. As shown in Figure 6 (b), the control algorithm of the longitudinal dynamics model improved by the intelligent vehicle autonomous cruise control method takes less time to reach the specified target speed, has high speed tracking accuracy, and does not have a steady-state error.

[0070] When the target speed decreases, the control algorithm of the traditional longitudinal dynamics model can make the controlled vehicle slow down quickly and approach the target speed, but there is still a steady-state error when it is reduced to 25km / h and 10km / h; the control algorithm of the longitudinal dynamics model improved by the intelligent vehicle autonomous cruise control method can make the controlled vehicle slow down faster, and there is no steady-state error when the target speed is 25km / h and 10km / h, and the speed tracking accuracy is high, which reflects the advantage of the control algorithm of the longitudinal dynamics model improved by the intelligent vehicle autonomous cruise control method.

[0071] (3) Distance maintenance test Figure 7 The invention provides an intelligent vehicle autonomous cruise control method for conducting a distance keeping test on the speeds of a controlled vehicle and a target vehicle and a speed deviation. Figure 8 The present invention provides an intelligent vehicle autonomous cruise control method for conducting a distance keeping test to determine the distance and distance deviation between a controlled vehicle and a target vehicle.

[0072] Two transport vehicles were used for the test. Both test platforms were equipped with DSRC (Dedicated Short Range Communications) equipment, which can realize real-time transmission of driving status. The target vehicle was manually driven from a standstill, and then accelerated, decelerated, and kept at a constant speed until it stopped, with a maximum speed of 50 km / h. To ensure the safety of the test, the time-varying expected distance method was used for the test, as shown in the following formula. The test results are as follows: Figure 7 and Figure 8 shown.

[0073] in: As the expected distance base value, the present invention takes the value as 20m.

[0074] like Figure 7 and Figure 8As shown in the figure, the target vehicle circled in the box accelerates and decelerates, and the speed of the controlled vehicle is quickly adjusted following the target vehicle to maintain a stable vehicle-to-vehicle distance and a small speed deviation, and the acceleration and deceleration trend responds quickly; the target vehicle circled in the circle box travels at a constant speed, the speed curves of the target vehicle and the controlled vehicle basically coincide, the speed deviation is small, and the expected distance and actual distance curves coincide with each other to a high degree, and the distance deviation is small.

[0075] Compared with the prior art, the intelligent vehicle autonomous cruise control method of the embodiment of the present invention has the following technical effects: The improved longitudinal dynamics model of the intelligent vehicle autonomous cruise control method considering low-gear idling conditions and coasting conditions with gear engaged adopts the feedback PI method to compensate the driving / braking commands respectively, reducing the influence of the inaccurate parameters of the traditional longitudinal dynamics model on the control effect, and can improve the speed tracking accuracy and driving comfort.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent vehicle autonomous cruise control method, characterized in that: The steps include: Determine the cruise control requirements of the controlled vehicle, determine the desired speed of the controlled vehicle, establish a workshop kinematic model, design an acceleration controller based on linear quadratic regulator theory, and calculate the desired acceleration; Calculating a desired driving force based on a desired acceleration and a longitudinal dynamics model of a controlled vehicle; determining a driving / braking mode according to the expected driving force, and calculating an expected engine torque of the controlled vehicle according to the expected driving force; Calculating an engine torque compensation amount and an acceleration compensation amount of the controlled vehicle based on feedback from a proportional-integral controller according to a deviation between a desired speed and an actual speed of the controlled vehicle; Adding the engine torque compensation amount to the expected engine torque to obtain a first final control amount, and adding the acceleration compensation amount to the expected acceleration to obtain a second final control amount; The first final control amount and the second final control amount are fed back to the actuator according to the determined driving / braking mode, and the actuator adjusts the control data of the controlled vehicle.

2. The intelligent vehicle autonomous cruise control method according to claim 1, characterized in that: Establishing the workshop kinematic model includes the following steps: Calculate the deviation between the target vehicle and the controlled vehicle: in, is the actual distance between the target vehicle and the controlled vehicle, is the expected distance between the target vehicle and the controlled vehicle, is the deviation between the actual distance and the expected distance between the controlled vehicle and the target vehicle, is the speed of the target vehicle, is the speed of the controlled vehicle, is the deviation between the speed of the controlled vehicle and the speed of the target vehicle; The state space of the workshop kinematic model The statement is as follows: in, , , , , a is the acceleration of the controlled vehicle, x is the state quantity, A is the state quantity coefficient, u To control the amount, B is the control quantity coefficient.

3. The intelligent vehicle autonomous cruise control method according to claim 2, characterized in that: Based on the linear quadratic regulator theory, the acceleration controller calculates the desired acceleration including the following steps: Design an acceleration controller based on the linear quadratic regulator theory, the formula is as follows: in, J is the cost function, is the state quantity x The weight matrix of For control u The weight matrix of K is the optimal linear negative feedback matrix; Based on optimal control theory, the expression of expected acceleration is: in, is the expected acceleration.

4. The intelligent vehicle autonomous cruise control method according to claim 3, characterized in that: Calculating the expected driving force involves the following steps: Under the condition of wheel rolling, the wheel speed of the controlled vehicle is determined by calculating the real-time engine speed. The formula is as follows: The driving condition of the vehicle is determined according to the desired speed of the controlled vehicle, the wheel speed calculated from the real-time engine speed, and the gear position of the transmission. The relationship is as follows: in, is the wheel speed of the controlled vehicle calculated from the real-time engine speed, is the wheel radius, is the real-time engine speed, is the transmission ratio, Main reducer transmission ratio.

5. The intelligent vehicle autonomous cruise control method according to claim 4, characterized in that: Calculating the expected driving force also includes the following steps: The resistance of the controlled vehicle during driving is calculated as follows: in, is the rolling resistance of the controlled vehicle, is the air resistance of the controlled vehicle, is the slope resistance of the controlled vehicle, is the acceleration resistance of the controlled vehicle, is the vehicle gravity of the controlled vehicle, is the rolling resistance coefficient of the controlled vehicle, is the slope angle, is the air resistance coefficient, is the frontal area of ​​the controlled vehicle, is the speed of the controlled vehicle, is the rotation mass conversion factor, is the vehicle mass of the vehicle being charged.

6. The intelligent vehicle autonomous cruise control method according to claim 5, characterized in that: Calculating the expected driving force also includes the following steps: The formula for calculating the braking force of the engine of the controlled vehicle under low gear idling condition is as follows: in, is the braking force of the controlled vehicle's engine under low gear idling conditions, is the output torque of the engine of the controlled vehicle, which is determined by the real-time speed and external characteristic curve of the engine of the controlled vehicle. The mechanical efficiency of the transmission system of the controlled vehicle; The formula for calculating the braking force of the engine of the controlled vehicle in the gear-glide condition is as follows: in, is the braking force of the engine of the controlled vehicle in the gear-geared coasting condition, The mechanical efficiency of the engine of the vehicle under investigation; According to the driving conditions, the expected driving force is calculated as follows: in, is the expected driving force of the controlled vehicle.

7. The intelligent vehicle autonomous cruise control method according to claim 6, characterized in that: Determining the driving / braking mode according to the desired driving force includes the following steps: The expected driving force of the controlled vehicle is less than 0, and the braking mode is determined; The expected driving force of the controlled vehicle is greater than or equal to 0, which is determined as the driving mode.

8. The intelligent vehicle autonomous cruise control method according to claim 7, characterized in that: Calculating the desired engine torque of the controlled vehicle according to the desired driving force includes the following steps: in, is the desired engine torque of the controlled vehicle.

9. The intelligent vehicle autonomous cruise control method according to claim 8, characterized in that: The steps to calculate the engine torque compensation and acceleration compensation based on proportional-integral controller feedback are as follows: In driving mode, the engine torque compensation is obtained: in, is the torque compensation amount in driving mode, is the proportional coefficient in driving mode, is the integral coefficient in driving mode; In braking mode, the acceleration compensation is obtained: in, is the acceleration compensation in braking mode, is the proportional coefficient in braking mode, is the integral coefficient in braking mode.

10. The intelligent vehicle autonomous cruise control method according to claim 9, characterized in that: Calculating the first final control amount and the second final control amount comprises the following steps: Obtaining a first final control amount in the driving mode, the first final control amount being the sum of the torque compensation amount in the driving mode and the desired engine torque of the controlled vehicle; A second final control amount is obtained in the braking mode, and the second final control amount is the sum of the acceleration compensation amount and the expected acceleration in the braking mode.

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