CN108045435B - Pavement self-adaptive intelligent vehicle transverse hybrid control method - Google Patents
Pavement self-adaptive intelligent vehicle transverse hybrid control method Download PDFInfo
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Abstract
The invention discloses a pavement self-adaptive intelligent vehicle transverse hybrid control method, and belongs to the field of intelligent driving of automobiles. Aiming at the defect that the position deviation control commonly used by the intelligent vehicle cannot meet the large curvature path tracking, the invention introduces another transverse control strategy for tracking the expected state quantity of the vehicle, and designs a fuzzy controller to ensure that the intelligent vehicle is switched between the two control strategies in real time by taking the curvature of the target road monitored in real time and the curvature change rate thereof as switching conditions so as to realize high-precision path tracking suitable for different road surfaces. The invention can effectively reduce the steady-state error of intelligent vehicle tracking and make the steering control more accurate and stable.
Description
Technical Field
The invention relates to a pavement self-adaptive intelligent vehicle transverse hybrid control method, and belongs to the field of intelligent driving of automobiles.
Background
At present, a position deviation control method (such as a pure tracking control method) is widely adopted in the intelligent automobile transverse control to realize the path tracking performance of the vehicle, namely when a target path is a linear lane or a lane with small curvature, the transverse deviation and the azimuth deviation difference between a pre-aiming point and the closest point of the target lane relative to the vehicle are small, and the accurate lane tracking performance can be basically realized by controlling the position and the azimuth deviation between the vehicle and the pre-aiming point to be minimum. However, the target road condition of the actual vehicle is relatively complex, various conditions of mutual fusion of a straight lane, a curve with small curvature, a curve with large curvature and the like exist, and the common position deviation control method obviously cannot meet the lane following performance of the curve with large curvature. Therefore, an expert scholars provides a vehicle transverse control method for tracking the expected yaw velocity aiming at the problem of low control precision of a position deviation control method, and simulation experiments show that the control method can realize accurate tracking of each curvature, particularly large-curvature paths. However, this control method is poor in robustness, and there is a case where the yaw rate fluctuation is large in the process where the vehicle has not completely stably tracked the desired path, which directly affects the ride comfort of the vehicle. Therefore, how to control the vehicle to stably track the target path under various curvatures is an urgent problem to be solved.
Disclosure of Invention
In order to overcome the technical defects, the invention provides a pavement self-adaptive intelligent vehicle transverse hybrid control method, which comprises the following steps:
step 1) from the intelligent vehicleThe position information and the posture information of the vehicle measured by the sensor equipment are input into a lane recognition system, the target lane information and the input of the pre-aiming distance are integrated, the pre-aiming target point of the current vehicle and the closest point of the target lane relative to the vehicle are determined, and the transverse distance error Y between the vehicle and the pre-aiming target point is detectedeAnd the azimuth error phieAnd the curvature ρ of the target lane with respect to the closest point of the vehicle and its curvature change rate
And compares it with a reference curvature threshold p0Making a difference between the curvature error signal e and the curvature error change rate signal
Inputting a fuzzy controller;
step 2), the fuzzy controller outputs an intelligent vehicle transverse control mode according to the input error signal and the change rate thereof, namely, gamma is 1 (a position deviation control method) and gamma is 0 (a tracking expected transverse angular velocity control method);
step 3) when gamma is 1;
at the moment, the intelligent vehicle transverse hybrid controller is switched to a position deviation control method, and the PID controller outputs a vehicle aiming point relative transverse distance error Y according to the lane recognition systemeAnd the azimuth error phieAnd calculating and outputting a proper front wheel steering angle signal delta, and controlling the intelligent vehicle to steer by the steering executing mechanism according to the steering angle signal to finish the path tracking process.
Step 4) when gamma is equal to 0;
at the moment, the intelligent vehicle transverse hybrid controller is switched to a control method for tracking the expected yaw angular velocity, and the upper-layer expected yaw angular velocity generator outputs the relative transverse distance error Y of the vehicle aiming point according to the lane recognition systemeAnd the azimuth error phieOutputting the vehicle body sensor signal to generate the expected yaw angular velocity omegadAnd as a reference input to the underlying desired yaw rate tracking controller. The lower layer expected yaw rate tracking controller outputs proper front wheel rotation angle information through analysis and calculationAnd the steering executing mechanism controls the intelligent vehicle to steer according to the corner signal to finish the path tracking process.
Further, the position and posture information of the vehicle comprises information of coordinates, displacement, speed, acceleration, yaw rate, front wheel turning angle and the like of the automobile relative to a geodetic coordinate system XY in the running process, and the information is acquired in real time by a GPS (global positioning system), a Hall speed sensor, a gyroscope, a turning angle sensor and other equipment loaded on the intelligent vehicle.
Further, the road curvature is the road curvature and the curvature change rate thereof obtained by recognizing lane markings of the target road based on a lane recognition system, performing curve fitting on the recognized markings and calculating.
Further, the fuzzy controller of the second step is designed as follows:
selecting a road curvature error signal e and a curvature error change rate signal
The output signal is the transverse control mode as the input signal of the fuzzy controller. Inputting error signal e and curvature error change rate signal
The fuzzy sets are divided into 5 fuzzy sets: NB (negative large), NS (negative small), ZO (zero), PS (positive small), PB (positive large). The output γ is divided into two fuzzy sets: 0 (intelligent vehicle lateral control is switched to a tracking desired yaw rate control method), and 1 (intelligent vehicle lateral control is switched to a position deviation control method). The argument field of the road curvature error signal e of the controller is [ -0.04, 0.04 [ -0.04 [ ]]Rate of change of curvature error signal
The discourse domain is set to [ -0.04, 0.04]The universe of γ is set to {0, 1 }. For input quantity curvature error signal e and curvature error change rate signal
All using membership functions of the Gaussian type for the output quantity gammaTriangular membership functions are used. The fuzzy rule control table is shown as table one.
Further, the implementation of the PID controller can be expressed by the following equation:
wherein k isp、kdAnd kiAs a transverse distance error YeProportional, differential and integral coefficients of k'p、k′dAnd k'iAs an azimuthal error phieProportional, derivative and integral coefficients. By adjusting the transverse distance error YeAnd the azimuth error phieOutputs the front wheel steering angle signal delta at the current time.
Further, the desired yaw rate generator is based on the pre-aiming distance, the lateral distance error YeAnd the azimuth error phieInputting a signal, constructing a virtual driving path between the vehicle and the aiming point, and calculating the expected yaw angular speed omega of the vehicle approaching the target road along the virtual path at the current moment based on the virtual pathdThen the lower expected yaw angular velocity tracking controller controls the vehicle turning angle delta in real time to achieve the aim of accurately tracking the expected yaw angular velocity omegadThe purpose of (1).
The invention has the beneficial effects that: according to the invention, through real-time monitoring of the curvature and the change rate of the target road, the vehicle tracking surface can be freely switched between a position deviation control method and an expected tracking yaw rate control method when facing different curvature paths, so that the vehicle tracking performance with higher precision and better robustness can be obtained.
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FIG. 1 is a control scheme diagram of an embodiment of a road adaptive intelligent vehicle lateral hybrid control method according to the invention.
Detailed Description
The invention is further described with reference to the following drawings and specific embodiments. Vehicle measured by various sensor devices on intelligent vehicle during drivingThe current position and the attitude information thereof are input into a lane recognition system and a transverse hybrid controller, then the lane recognition system calculates according to the target lane information, the pre-aiming distance information and the vehicle position and attitude information to determine the pre-aiming target point of the current vehicle and the closest point of the target lane relative to the vehicle, and detects the transverse distance error Y between the vehicle and the pre-aiming target pointeAnd the azimuth error phieAnd the curvature ρ of the target lane with respect to the closest point of the vehicle and its curvature change rate
And compares it with a reference curvature threshold p0Making a difference between the curvature error signal e and the curvature error change rate signal
Inputting into a fuzzy controller while simultaneously correcting the transverse distance error YeAnd the azimuth error phieInputting the data into a transverse mixing controller;
TABLE 1 fuzzy rule control table of fuzzy controller
The fuzzy controller outputs an intelligent vehicle transverse control mode according to the input error signal and the change rate thereof, namely, the gamma is 1 (a position deviation control method) and the gamma is 0 (a tracking expected yaw rate control method);
if γ is 1, it indicates that the curvature of the lane segment tracked by the vehicle is small at this time, and the lateral hybrid controller tracks the desired path by using a position deviation control method. According to the error Y of the relative transverse distance of the vehicle aiming point output by the lane recognition systemeAnd the azimuth error phieThe PID controller is according to the following equation:
wherein k isp、kdAnd kiAs a lateral distance errorYeProportional, differential and integral coefficients of k'p、k′dAnd k'iAs an azimuthal error phieProportional, derivative and integral coefficients.
Adjusting the corresponding transverse distance error YeAnd the azimuth error phieThe proportional, integral and differential coefficients are calculated to output a proper front wheel steering angle signal delta, and the steering executing mechanism controls the intelligent vehicle to steer according to the steering angle signal to complete the path tracking process.
If gamma is 0, it indicates that the curvature of the lane section tracked by the vehicle is large at the moment, the transverse hybrid controller is switched to a control method for tracking the expected yaw rate, and the upper-layer expected yaw rate generator outputs the error Y of the relative transverse distance of the vehicle aiming point according to the lane recognition systemeAnd the azimuth error phieConstructing a virtual driving path between the vehicle and the pre-aiming point, and calculating the expected yaw angular speed omega of the vehicle approaching the target road along the virtual path at the current moment based on the virtual pathdAnd as a reference input to the underlying desired yaw rate tracking controller. The lower expected yaw rate tracking controller outputs a proper front wheel steering angle signal delta through analysis and calculation, and the steering executing mechanism controls the intelligent vehicle to steer according to the steering angle signal to complete the path tracking process.
In conclusion, the invention provides a road self-adaptive intelligent vehicle transverse hybrid control method, and belongs to the field of intelligent driving of automobiles. Aiming at the defect that the position deviation control commonly used by the intelligent vehicle cannot meet the large curvature path tracking, the invention introduces another transverse control strategy for tracking the expected state quantity of the vehicle, and designs a fuzzy controller to ensure that the intelligent vehicle is switched between the two control strategies in real time by taking the curvature of the target road monitored in real time and the curvature change rate thereof as switching conditions so as to realize high-precision path tracking suitable for different road surfaces. The invention can effectively reduce the steady-state error of intelligent vehicle tracking and make the steering control more accurate and stable.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. A road surface self-adaptive intelligent vehicle transverse hybrid control method is characterized by comprising the following steps:
step 1) inputting vehicle position information and posture information measured by sensor equipment on an intelligent vehicle into a lane recognition system, integrating the target lane information and the input of a pre-aiming distance, determining a pre-aiming target point of the current vehicle and the closest point of a target lane relative to the vehicle, and detecting a transverse distance error Y between the vehicle and the pre-aiming target pointeAnd the azimuth error phieAnd road curvature ρ of the target lane with respect to the closest point of the vehicle and its rate of change of curvature
And compares it with a reference curvature threshold p0Subtracting the curvature error signal e from the curvature error change rate signal
Inputting a fuzzy controller;
and 2) outputting an intelligent vehicle transverse control mode by the fuzzy controller according to the input error signal and the change rate thereof, and dividing output gamma into two fuzzy sets: changing the intelligent vehicle lateral control into the tracking expected yaw rate control method when the value of gamma is 0, and changing the intelligent vehicle lateral control into the position deviation control method when the value of gamma is 1;
the fuzzy controller of the step 2) is designed as follows:
selecting a road curvature error signal e and a curvature error change rate signal
The output signal is used as input signal of the fuzzy controller, the input error signal e and curvature error change rate signal are used as transverse control mode
The fuzzy sets are divided into 5 fuzzy sets: NB negative is large, NS negative is small, ZO is zero, PS is small, and PB is large; for input quantity curvature error signal e and curvature error change rate signal
Adopting Gaussian membership functions, and adopting triangular membership functions for the output quantity gamma;
step 3) when gamma is 1;
at the moment, the intelligent vehicle transverse hybrid controller is switched to a position deviation control method, and the PID controller outputs a vehicle aiming point relative transverse distance error Y according to the lane recognition systemeAnd the azimuth error phieCalculating and outputting a proper front wheel steering angle signal delta, and controlling the intelligent vehicle to steer by the steering executing mechanism according to the steering angle signal to finish the path tracking process;
step 4) when gamma is equal to 0;
at the moment, the intelligent vehicle transverse hybrid controller is switched to a control method for tracking the expected yaw angular velocity, and the upper-layer expected yaw angular velocity generator outputs the relative transverse distance error Y of the vehicle aiming point according to the lane recognition systemeAnd the azimuth error phieOutputting the vehicle body sensor signal to generate the expected yaw angular velocity omegadAnd as a reference input to a lower desired yaw-rate tracking controller that passes throughAnalyzing and calculating, outputting a proper front wheel steering angle signal delta, and controlling the intelligent vehicle to steer by the steering executing mechanism according to the steering angle signal to finish the path tracking process;
the upper layer expected yaw velocity generator generates the expected yaw velocity according to the pre-aiming distance and the transverse distance error YeAnd the azimuth error phieInputting a signal, constructing a virtual driving path between the vehicle and the aiming point, and calculating the expected yaw angular speed omega of the vehicle approaching the target road along the virtual path at the current moment based on the virtual pathdThen the lower expected yaw angular velocity tracking controller controls the vehicle turning angle delta in real time to achieve the aim of accurately tracking the expected yaw angular velocity omegadThe purpose of (1).
2. The method as claimed in claim 1, wherein the vehicle position information and its attitude information include coordinates, displacement, velocity, acceleration, yaw rate, and front wheel rotation angle information of the vehicle relative to the earth coordinate system XY during driving, which are collected in real time by a GPS positioning system, a hall velocity sensor, a gyroscope, and a rotation angle sensor device mounted on the intelligent vehicle.
3. The method as claimed in claim 1, wherein the road curvature is calculated by recognizing lane markings of the target road based on a lane recognition system, curve fitting the recognized markings, and calculating the road curvature and its curvature change rate.
4. The method of claim 1, wherein the PID controller is implemented in a manner expressed by the following equation:
wherein k isp、kdAnd kiAs a transverse distance error YeProportional, differential and integral coefficients of k'p、k′dAnd k'iAs an azimuthal error phieBy adjusting the transverse distance error YeAnd the azimuth error phieOutputs the front wheel steering angle signal delta at the current time.
5. The method of claim 1, wherein the reference curvature threshold p is a p0The value was set to 0.04.
6. The method of claim 4 wherein the fuzzy controller sets the domain of the road curvature error signal e to [ -0.04, 0.04 [ -0.04 [ ]]The curvature error rate of change signal
The discourse domain is set to [ -0.04, 0.04]The universe of γ is set to {0, 1 }.
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| CN110673593B (en) * | 2018-07-03 | 2022-11-15 | 杭州海康威视数字技术股份有限公司 | Vehicle control method and device |
| CN110968082B (en) * | 2018-09-28 | 2023-08-08 | 广州汽车集团股份有限公司 | Automatic driving vehicle path tracking method and device |
| CN109886215B (en) * | 2019-02-26 | 2021-10-19 | 常熟理工学院 | Low-speed park unmanned vehicle cruise and emergency braking system based on machine vision |
| CN110160806A (en) * | 2019-06-17 | 2019-08-23 | 北京艾尔动力科技有限公司 | Automated driving system and test method for automobile durable test |
| CN111158397B (en) * | 2020-01-14 | 2023-08-22 | 一飞智控(天津)科技有限公司 | Unmanned aerial vehicle cluster flight path following control system and method and unmanned aerial vehicle |
| CN111158377B (en) * | 2020-01-15 | 2021-04-27 | 浙江吉利汽车研究院有限公司 | Transverse control method and system for vehicle and vehicle |
| CN111796521B (en) * | 2020-07-08 | 2022-06-10 | 中国第一汽车股份有限公司 | Foresight distance determining method, device, equipment and storage medium |
| CN114253241B (en) * | 2021-12-21 | 2023-12-22 | 昆山星际舟智能科技有限公司 | Path tracking method for industrial intelligent trolley |
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| CN104626914B (en) * | 2014-12-31 | 2016-08-31 | 广西科技大学 | The fuzzy control method of Vehicle Nonlinear active suspension system |
| CN107097785B (en) * | 2017-05-25 | 2019-08-27 | 江苏大学 | A kind of intelligent vehicle crosswise joint method that preview distance is adaptive |
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| CN107253453B (en) * | 2017-07-05 | 2019-08-27 | 厦门大学 | A kind of distributed electric automobile lateral stability adaptive control system and method |
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| KR20120088923A (en) * | 2011-02-01 | 2012-08-09 | 주식회사 만도 | Apparatus and method of controlling vehicle |
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