CN107200020B - It is a kind of based on mixing theoretical pilotless automobile self-steering control system and method - Google Patents
It is a kind of based on mixing theoretical pilotless automobile self-steering control system and method Download PDFInfo
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- CN107200020B CN107200020B CN201710327853.XA CN201710327853A CN107200020B CN 107200020 B CN107200020 B CN 107200020B CN 201710327853 A CN201710327853 A CN 201710327853A CN 107200020 B CN107200020 B CN 107200020B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/182—Selecting between different operative modes, e.g. comfort and performance modes
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
- B60W50/0205—Diagnosing or detecting failures; Failure detection models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
Abstract
The invention discloses a kind of based on the pilotless automobile self-steering control system and method that mix theory, belong to pilotless automobile crosswise joint field, pilotless automobile self-steering control system includes environmental perception module, vehicle oneself state information module, path planning module, Decision Control module, manipulation execution module.The present invention also provides a kind of based on the pilotless automobile self-steering control method for mixing theory, mix control theory by introducing, the switching coordination problem between multi-mode control and control algolithm is converted by the control problem of steering system under complex working condition to solve, realize the control under pilotless automobile self-steering control system full working scope, suitable control algolithm is introduced in each operating condition, both it had been able to satisfy system Partial controll performance, it can achieve the purpose that global optimization again, realize optimal pilotless automobile course changing control performance, improve the control stability and intelligent level of automatic driving vehicle.
Description
Technical field
The present invention relates to a kind of pilotless automobile crosswise joint fields more particularly to a kind of based on mixing theoretical nobody
The self-steering control system that drives a car and method.
Background technique
, structure lightened, running intelligentization three big scientific and technological automotive engineering great change for core electrified with power is
Deeply it is unfolded in global range.State Council in 2015 formally issues " made in China 2025 ", will " energy conservation and new-energy automobile " column
One of Yu Shi great key breakthrough development field generally refers to that energy-saving automobile, new-energy automobile and intelligent network connection automotive engineering is illustrated
Developing direction and path, this is that intelligent network connection automobile is thus lifted to the level of national strategy for the first time.Ministry of Industry and Information further clarifies:
To the year two thousand twenty, intelligently auxiliary drives general technical and every key technology to master, tentatively establishes intelligent network connection automobile and independently grinds
Hair system and production matching system；By 2025, to master automatic Pilot general technical and every key technology, foundation were more perfect
Intelligent network connection automobile independent research system, production matching system and industrial colony.
Pilotless automobile, which refers to, carries the devices such as advanced onboard sensor, controller, actuator, and merges modern logical
It is shared to realize that vehicle is exchanged with X (people, vehicle, road, backstage etc.) intelligent information, has complex environment perception, intelligence for letter and network technology
The functions such as decision, Collaborative Control and execution are, it can be achieved that safety, comfortable, energy conservation, efficiently traveling, and final alternative people operates
Young mobile.
Towards unpiloted ultimate aim, it is desirable that intelligent automobile crosswise joint system has essence under the conditions of multi-state
Really, efficiently, reliable control ability, guarantee Vehicle turning stability, driving safety and ride comfort, it is traditional single to turn
It not can solve and coordinate demand for control and control function of the self-steering control system under different operating conditions to control algolithm.
From the point of view of the safety of pilotless automobile control, stability angle, different operating conditions should have different control mesh
Mark and emphasis, and whole synthesis performance is optimal, the control of pilotless automobile self-steering is inherently discrete
Event and continuous dynamic are simultaneously deposited and discrete event and the interactional feature of continuous dynamic behaviour, and typical switching control is shown
Hybrid system feature.Hybrid control system theory has the superperformance better than conventional method, can obtain than individually using company
Continuous dynamic or the better effect of discrete event dynamic system, solve the insurmountable challenge of traditional controller.
Summary of the invention
In order to solve the above technical problems, the present invention provide it is a kind of based on mixing theoretical pilotless automobile self-steering control
System and method processed introduce hybrid control system theory and analyze steering control system hybrid characters, realize unmanned
Automobile steering control system mixes control, can be improved the control ability of pilotless automobile steering control system and meets certainly
It is dynamic to drive multi-state riding stability demand.
It is a kind of based on mixing theoretical pilotless automobile self-steering control system, including environmental perception module, vehicle
Oneself state information module, path planning module, Decision Control module, manipulation execution module.
The environmental perception module is believed using the external environment that vision, radar and positioning system etc. perceive vehicle driving
Breath；
The vehicle oneself state information module is believed using the operating status that inertia device and CAN bus obtain vehicle
It ceases and driving parameters is estimated；Vehicle oneself state information module is controlled using automobile man-machine interaction unit prompt driver
System whether there is failure.
The information that the path planning module is transmitted according to environmental perception module, it is absolute in map system based on vehicle
Position and the relative position of vehicle and periphery barrier, lane line carry out global and local path planning, according to energy consumption
The minimum or shortest evaluation criterion of path length finds a collisionless vehicle expected travel path.
The Decision Control module includes: upper control unit and the next control unit, mix controller belong to it is upper
Control unit, actuating motor belong to the next control unit；Mixing controller includes: steering controller, switching monitor, switching control
Device processed stablizes monitor；Upper control is used to export the front wheel angle and yaw velocity value at each moment, upper control it is defeated
Value is transmitted to the next control as control amount out, bottom control realized by control actuating motor vehicle each moment corner and
Yaw velocity；
Steering controller meets the controller of control target for separately designing under low speed, middling speed, high-speed working condition, to meet
Control requirement of the vehicle under different operating conditions；
Switch monitor and failure whether there is according to the variation of vehicle speed and self-steering control system, driving mixes
Controller module carries out effective pattern switching, while guaranteeing the stability of system in handoff procedure；
Switch controller selects to close in steering controller according to the pilotless automobile operating mode of switching monitor identification
Suitable control algolithm, switching of the Lai Shixian pilotless automobile between different control models and control strategy are coordinated.
Stablize monitor and is used to monitor in real time the unstable characteristic quantity under each control model and its corresponding control algolithm, identification
Its unstable trend, pressure limit its output amplitude, guarantee that the global bounded of system is stablized.
The manipulation execution module, for executing pilotless automobile self-steering.
In above scheme, the controller module that mixes includes four kinds of modes, respectively low-speed mode m1, middle fast mode
M2, high-speed mode m3, system failure mode m4；
In above scheme, low-speed mode m1, middling speed mode m 2, high-speed mode m3 belongs to state of a control, system failure mould
Formula m4 belongs to no-console condition.
It is a kind of based on mixing theoretical pilotless automobile self-steering control method, comprising the following steps:
Step 1), environmental perception module perceive the external environmental information of vehicle driving, and the information of acquisition is sent to road
Diameter planning module；
Step 2), the information that path planning module is obtained according to environmental perception module are exhausted in map system based on vehicle
Global and local path planning is carried out to the relative position of position and vehicle and periphery barrier, lane line；
Step 3), the dynamics state and operation shape of the vehicle that Decision Control module is obtained according to vehicle self information module
The result of path planning is converted executing instruction for track following by state, realizes lateral direction of car position by manipulation execution module
With the control of course angle, expected travel path is tracked.Wherein position deviation and course deviation are as mixing control in upper control
The input of steering controller in device, switching monitor whether there is failure according to vehicle speed and self-steering control system
It determines current operation mode, and guarantees the stability switched between each mode, switch controller identifies not in switching monitor
With selected under mode in steering controller most suitable control algolithm, front wheel angle and yaw velocity as mixing controller
Final output, bottom control realize vehicle by control actuating motor using front wheel angle and yaw velocity as control amount
Front wheel angle and yaw velocity.
Step 4), the rotation direction for the actuating motor that manipulation execution module is exported according to Decision Control module and output are turned round
Square, driving manipulation executing agency, executes pilotless automobile self-steering, so that pilotless automobile be made to track desired trajectory；
The above method further include: when self-steering control system generating system failure, vehicle self information mould will be passed through
Man-machine interaction unit prompts driver in block, and vehicle driving model is switched to people immediately and drives mode by driver.
The invention has the benefit that
1. being converted the operation of steering system under complex working condition and control problem to more present invention introduces Hybrid System Theory
Switching coordination problem between scheme control and control algolithm is solved, different using speed as self-steering control system
Complicated crosswise joint is resolved into the control of the synthesis under several single operating conditions, constructs unmanned vapour by the partitioning standards of mode
" mixing switching model system " of vehicle steering control system, realize building for pilotless automobile self-steering Hybrid Dynamic System
Mould and control.
2. the present invention introduces suitable control algolithm in self-steering control system difference operating condition, meet under different operating conditions
Demand for control and control function, and meet the demand for control during overall operation, improve pilotless automobile steering behaviour
And control stability.
3. establishing self-steering control system switching monitor and stablizing monitor, system is progressive in guarantee handoff procedure
Stable and global bounded is stablized, so that not only having met the stability of system part, but also achievees the purpose that global optimization and system are stable,
Improve the stability of pilotless automobile self-steering control.
Detailed description of the invention
Fig. 1 is pilotless automobile self-steering control system architecture schematic diagram；
Fig. 2 mixes controller structure diagram for the multi-mode switching control of pilotless automobile self-steering.
Specific embodiment
It is described in detail below with reference to attached drawing of the present invention.
As shown in Figure 1, a kind of based on the pilotless automobile self-steering control system for mixing theory, including environment sensing
Module, vehicle oneself state information module, path planning module, Decision Control module, manipulation execution module；
Environmental perception module includes video camera, radar system, inertial navigation/GPS positioning system, information processing system, video camera
It is mounted in vehicle front and rear windshield and is responsible for the information such as acquisition lane line, sign board, traffic lights；Radar system is for detecting barrier
Hinder object；Inertial navigation/GPS positioning system is for obtaining vehicle attitude parameter, speed and location information；Information processing system is for handling
Video camera, radar system, inertial navigation/GPS positioning system information obtained.
Vehicle oneself state information module includes inertia device, CAN bus, man-machine interaction unit；Inertia device refers specifically to
Be inertial navigation, vehicle parameter is measured using accelerometer and gyro sensor；Inertia device, CAN bus are for obtaining vehicle
Running state information and driving parameters are estimated；Due to directly measuring certain crucial shapes in vehicle traveling process
State, there are measurement costs it is high, precise measurement difficulty is big the problems such as.Therefore, it on the basis of the vehicle-posture information that inertial navigation receives, adopts
The complete lateral velocity of vehicle, yaw velocity and mass center lateral deviation are obtained by vehicle dynamic model with the method for estimation
The state parameters such as angle, the accurate state parameter obtained in vehicle travel process can make course changing control strategy matched with controlled device and
Reach good control performance；Man-machine interaction unit prompts driver in self-steering control system generating system failure.
Path planning module is opposite based on vehicle and periphery barrier, the relative position of lane line and in map system
Position carries out global path planning and local paths planning, according to minimum power consumption or the shortest evaluation criterion of path length,
Find a collisionless vehicle expected travel path.
Decision Control module includes upper control unit and the next control unit；Mix controller and belongs to upper control list
Member, actuating motor belong to the next control unit；Mixing controller includes: steering controller, switching monitor, switch controller；
Upper control is used to export the front wheel angle and yaw velocity value at each moment, and the output valve of upper control is passed as control amount
It is controlled to bottom, the front wheel angle and yaw velocity at vehicle each moment are realized in bottom control by control actuating motor；
Decision Control module is according to environment sensing, the result of path planning and vehicle and each subsystem state information and executing agency
Feedback status information carries out steering control mode judgement and control strategy is formulated；
Steering controller meets the controller of control target for separately designing under low speed, middling speed, high-speed working condition, low
Suitable control algolithm is introduced under speed, middling speed, high-speed mode, to meet control requirement of the vehicle under different operating conditions；
Switch speed and human-computer interaction that monitor is obtained according to velocity sensor in vehicle oneself state information module
The self-steering control system that module provides whether there is failure, switches over to the operating mode for mixing controller, supervises simultaneously
The transition process between each control model of pilotless automobile is superintended and directed, is reduced in course changing control implementation procedure since pattern switching causes
Impact and oscillation, to reach the asymptotically stability of handoff procedure；Low speed switching point in proposed adoption 20km/h conduct, 60km/h make
For high speed switching point, car speed between low-speed mode, 20-60km/h be between 0-20km/h in fast mode, 60km/
The above are high-speed modes by h；
Switch controller selects to close in steering controller according to the pilotless automobile operating mode of switching monitor identification
Suitable control algolithm realizes that pilotless automobile switches over coordination between different control models and control strategy.
Stablize monitor and is used to monitor in real time the unstable characteristic quantity under each control model and its corresponding control algolithm, identification
Its unstable trend, pressure limit its output amplitude, guarantee that the global bounded of system is stablized.
Execution module is manipulated, for executing pilotless automobile self-steering.
Mixing controller module includes four kinds of modes, respectively low-speed mode m1, middling speed mode m 2, high-speed mode m3, is
Unite fault mode m4；Low-speed mode m1, middling speed mode m 2, high-speed mode m3 belongs to state of a control, and system failure mode m4 belongs to
In no-console condition.
It is a kind of based on mixing theoretical pilotless automobile self-steering control method, comprising the following steps:
Step 1), environmental perception module perceive the external environmental information of vehicle driving, and the information of acquisition is sent to road
Diameter planning module；
Step 2), the information that path planning module is obtained according to environmental perception module are exhausted in map system based on vehicle
Global and local path planning is carried out to the relative position of position and vehicle and periphery barrier, lane line, is disappeared according to energy
The minimum or shortest evaluation criterion of path length is consumed, a collisionless vehicle expected travel path is found；
Step 3), the dynamics state and operation shape of the vehicle that Decision Control module is obtained according to vehicle self information module
The result of path planning is converted executing instruction for track following by state, realizes lateral direction of car position by manipulation execution module
With the control of course angle, expected travel path is tracked.Wherein position deviation and course deviation are as mixing control in upper control
The input of steering controller in device, switching monitor whether there is failure according to vehicle speed and self-steering control system
It determines current operation mode, and guarantees the stability switched between each mode, switch controller identifies not in switching monitor
With under mode select steering controller in most suitable control algolithm, stablize monitor for monitor in real time each control model and its
Unstable characteristic quantity under corresponding control algolithm, identifies its unstable trend, and pressure limits its output amplitude, guarantees the complete of system
Office's bounded stability.As the final output for mixing controller, bottom is controlled front wheel angle for front wheel angle and yaw velocity
With yaw velocity as control amount, the front wheel angle and yaw velocity of vehicle are realized by control actuating motor；
The control algolithm used under each operating mode is as follows:
1) at low-speed mode m1, Vehicle Speed is lower, and safety is higher, and there is pid control algorithm control to calculate
Method is simple, using respectively, parameter adjustment is easier to, control effect is good the advantages that, therefore, low-speed mode can be used such as PID from
Suitable solution algorithm.
2) under middling speed mode m 2, in middling speed, vehicle travels on urban road more, and traffic complex is controlled to turning to
Required precision processed is higher, therefore, such as optimal control algorithm can be used under middle fast mode.
3) at high-speed mode m3, Vehicle Speed is higher, and Model Predictive Control Algorithm is solving automatic driving vehicle
There is Trajectory Tracking Control problem at high speeds unique advantage therefore can be used under high-speed mode for example based on mould
The predictive control algorithm of type.
4) at system failure mode m4, driving mode is switched to manual control by driver, does not need independently to be turned
To control.
Step 4), the rotation direction for the actuating motor that manipulation execution module is exported according to Decision Control module and output are turned round
Square, driving manipulation executing agency, executes pilotless automobile self-steering, so that pilotless automobile be made to track desired trajectory；
In the above method further include: when self-steering control system generating system failure, vehicle self information will be passed through
Man-machine interaction unit prompts driver in module, and vehicle driving model is switched to people immediately and drives mode by driver.
Above to it is provided by the present invention it is a kind of based on mix theoretical pilotless automobile self-steering control system and
Method is described in detail, the foregoing is merely present pre-ferred embodiments, be merely to illustrate design philosophy of the invention and
Feature is not intended to restrict the invention, all any modification, equivalent replacement, improvement and so under technical thought of the invention,
It should be included within protection scope of the present invention.
Claims (8)
1. a kind of based on the pilotless automobile self-steering control system for mixing theory characterized by comprising environment sensing
Module, vehicle oneself state information module, path planning module, Decision Control module, manipulation execution module；
The environmental perception module utilizes vision, the external environmental information of radar and positioning system perception vehicle driving；
The vehicle oneself state information module obtains the running state information of vehicle simultaneously using inertia device and CAN bus
Driving parameters are estimated；
The information that the path planning module is transmitted according to environmental perception module is based on vehicle absolute position in map system
And the global and local path planning of relative position progress of vehicle and periphery barrier, lane line, according to minimum power consumption
Or the shortest evaluation criterion of path length, find a collisionless vehicle expected travel path；
The Decision Control module includes: upper control unit and the next control unit；Upper control unit is every for exporting
The output valve of the front wheel angle and yaw velocity value at a moment, upper control unit is transmitted to the next control list as control amount
Member, the next control unit realize the corner and yaw velocity at vehicle each moment；
The manipulation execution module, for executing pilotless automobile self-steering；
The upper control unit includes mixing controller；It is described mix controller include: steering controller, switching monitor,
Switch controller stablizes monitor；
The steering controller meets the controller of control target for separately designing under low speed, middling speed, high-speed working condition, to meet
Control requirement of the vehicle under different operating conditions；
The switching monitor whether there is failure according to the variation of vehicle speed and self-steering control system, and driving mixes
Controller module carries out effective pattern switching, while guaranteeing the stability of system in handoff procedure；
The switch controller selects to close in steering controller according to the pilotless automobile operating mode of switching monitor identification
Suitable control algolithm, switching of the Lai Shixian pilotless automobile between different control models and control strategy are coordinated；
The stable monitor is used to monitor in real time the unstable characteristic quantity under each control model and its corresponding control algolithm, identification
Its unstable trend, pressure limit its output amplitude, guarantee that the global bounded of system is stablized.
2. it is according to claim 1 a kind of based on the pilotless automobile self-steering control system for mixing theory, it is special
Sign is that the bottom control unit includes actuating motor；The bottom control unit realizes vehicle by control actuating motor
The corner and yaw velocity at each moment.
3. it is according to claim 1 a kind of based on the pilotless automobile self-steering control system for mixing theory, it is special
Sign is, the controller module that mixes includes four kinds of modes, respectively low-speed mode m1, middling speed mode m 2, high-speed mode m3,
System failure mode m4.
4. it is according to claim 3 a kind of based on the pilotless automobile self-steering control system for mixing theory, it is special
Sign is, the low-speed mode m1, middling speed mode m 2, high-speed mode m3 belongs to state of a control, the system failure mode m4
Belong to no-console condition.
5. it is according to claim 1 a kind of based on the pilotless automobile self-steering control system for mixing theory, it is special
Sign is that the vehicle oneself state information module can also be using automobile man-machine interaction unit prompt rider control system
It is no that there are failures.
6. a kind of based on the pilotless automobile self-steering control method for mixing theory, which comprises the following steps:
Step 1), environmental perception module perceives the external environmental information of vehicle driving, and the information of acquisition is sent to path rule
Draw module；
Step 2), the information that path planning module is obtained according to environmental perception module are based on vehicle absolute position in map system
It sets and the relative position of vehicle and periphery barrier, lane line carries out global and local path planning；
Step 3), the dynamics state and operating status of the vehicle that Decision Control module is obtained according to vehicle self information module,
Executing instruction for track following is converted by the result of path planning, lateral direction of car position and boat are realized by manipulation execution module
Control to angle tracks expected travel path；Wherein position deviation and course deviation in upper control as mixing in controller
Steering controller input, switching monitor is according to vehicle speed and self-steering control system with the presence or absence of failure determination
Current operation mode, and guarantee the stability switched between each mode, different moulds of the switch controller in switching monitor identification
Select in steering controller most suitable control algolithm, front wheel angle and yaw velocity as mixing the final of controller under formula
Output, bottom control regard front wheel angle and yaw velocity as control amount, realizes vehicle by controlling actuating motor
Front wheel angle and yaw velocity；
Step 4), the rotation direction and output torque of the actuating motor that manipulation execution module is exported according to Decision Control module, drives
Dynamic manipulation executing agency, executes pilotless automobile self-steering, so that pilotless automobile be made to track desired trajectory.
7. it is according to claim 6 a kind of based on the pilotless automobile self-steering control method for mixing theory, it is special
Sign is that the control algolithm used under each operating mode in the step 3) is as follows:
1) at low-speed mode m1, Vehicle Speed is lower, and safety is higher, adaptive using PID in this low speed mode
Control algolithm；
2) under middling speed mode m 2, vehicle travels on urban road more, and traffic complex is higher to course changing control required precision,
Optimal control algorithm is used under middle fast mode；
3) at high-speed mode m3, Vehicle Speed is higher, is calculated under this high-speed mode using the PREDICTIVE CONTROL based on model
Method；
4) at system failure mode m4, driving mode is switched to manual control by driver, does not need to carry out self-steering control
System.
8. it is according to claim 6 a kind of based on the pilotless automobile self-steering control method for mixing theory, it is special
Sign is, the method also includes: when self-steering control system generating system failure, vehicle self information module will be passed through
Middle man-machine interaction unit prompts driver, and vehicle driving model is switched to people immediately and drives mode by driver.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110254512A (en) * | 2019-06-24 | 2019-09-20 | 清华大学 | Distributed intelligence electric vehicle steering system functional safety architecture design method |
Families Citing this family (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10606273B2 (en) * | 2017-04-20 | 2020-03-31 | Baidu Usa Llc | System and method for trajectory re-planning of autonomous driving vehicles |
| CN107656519B (en) * | 2017-09-30 | 2020-03-10 | 北京新能源汽车股份有限公司 | Driving control method and device for electric vehicle |
| CN108001447A (en) * | 2017-11-23 | 2018-05-08 | 江苏大学 | A kind of intelligent vehicle path trace front wheel angle compensating control method |
| US11130497B2 (en) * | 2017-12-18 | 2021-09-28 | Plusai Limited | Method and system for ensemble vehicle control prediction in autonomous driving vehicles |
| CN108170126B (en) * | 2017-12-27 | 2020-09-25 | 湖北汽车工业学院 | Control system and automobile |
| CN109969192B (en) * | 2017-12-28 | 2021-06-11 | 郑州宇通客车股份有限公司 | Vehicle and automatic driving control system |
| CN107943060B (en) * | 2017-12-29 | 2021-12-07 | 上海司南卫星导航技术股份有限公司 | Autopilot, method for guiding a vehicle along a tracking line, and computer-readable medium |
| KR102496654B1 (en) * | 2018-02-21 | 2023-02-07 | 현대자동차주식회사 | Apparatus and method for controlling driving mode change of vehicle, vehicle system |
| WO2019203022A1 (en) * | 2018-04-20 | 2019-10-24 | ソニー株式会社 | Moving body, information processing device, information processing method, and program |
| DE102018206712B4 (en) * | 2018-05-02 | 2022-02-03 | Audi Ag | Operating method for an autonomously operable device and autonomously operable device |
| CN109283923A (en) * | 2018-07-02 | 2019-01-29 | 清博（昆山）智能科技有限公司 | A kind of modeling of tractor self-steering system |
| CN109062205A (en) * | 2018-07-26 | 2018-12-21 | 武汉水草能源科技研发中心(有限合伙) | Artificial intelligence automobile Unmanned Systems |
| CN108931981A (en) * | 2018-08-14 | 2018-12-04 | 汽-大众汽车有限公司 | A kind of paths planning method of automatic driving vehicle |
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| CN109733474A (en) * | 2019-01-21 | 2019-05-10 | 江苏大学 | A kind of intelligent vehicle steering control system and method based on piecewise affine hierarchical control |
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| JP7158367B2 (en) * | 2019-12-09 | 2022-10-21 | 本田技研工業株式会社 | vehicle control system |
| CN111026117B (en) * | 2019-12-17 | 2023-07-18 | 江苏大学 | Obstacle avoidance optimal control system and control method for intelligent rice transplanter |
| CN111208838B (en) * | 2020-04-20 | 2020-11-03 | 北京三快在线科技有限公司 | Control method and device of unmanned equipment |
| CN111791887B (en) * | 2020-07-03 | 2021-06-04 | 北京理工大学 | Vehicle energy-saving driving method based on layered vehicle speed planning |
| CN111966103B (en) * | 2020-08-18 | 2021-04-20 | 国以贤智能科技(上海)股份有限公司 | Method, device, equipment and medium for dynamically correcting zero deflection angle of unmanned forklift |
| CN112606848B (en) * | 2020-11-24 | 2022-06-10 | 浙大宁波理工学院 | Vehicle-road cooperative unmanned control method |
| CN112622935B (en) * | 2020-12-30 | 2022-04-19 | 一汽解放汽车有限公司 | Automatic vehicle driving method and device, vehicle and storage medium |
| CN113341994B (en) * | 2021-06-23 | 2022-10-28 | 江苏大学 | Intelligent automobile path tracking control method based on optimal control of piecewise affine system |
| CN114435394A (en) * | 2021-12-22 | 2022-05-06 | 安徽建德基文化传媒有限公司 | Control system of unmanned automobile |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101618733A (en) * | 2009-08-06 | 2010-01-06 | 上海交通大学 | Front wheel and rear wheel drive steering control system of automobile |
| CN103921788A (en) * | 2014-04-02 | 2014-07-16 | 奇瑞汽车股份有限公司 | Automobile traveling control system and automobile traveling control method |
| CN106527428A (en) * | 2016-10-19 | 2017-03-22 | 东风汽车公司 | Expressway-based embedded integrated automatic driving controller |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6260544B2 (en) * | 2015-01-19 | 2018-01-17 | トヨタ自動車株式会社 | Automatic driving device |
| US10000124B2 (en) * | 2015-11-04 | 2018-06-19 | Zoox, Inc. | Independent steering, power, torque control and transfer in vehicles |
-
2017
- 2017-05-11 CN CN201710327853.XA patent/CN107200020B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101618733A (en) * | 2009-08-06 | 2010-01-06 | 上海交通大学 | Front wheel and rear wheel drive steering control system of automobile |
| CN103921788A (en) * | 2014-04-02 | 2014-07-16 | 奇瑞汽车股份有限公司 | Automobile traveling control system and automobile traveling control method |
| CN106527428A (en) * | 2016-10-19 | 2017-03-22 | 东风汽车公司 | Expressway-based embedded integrated automatic driving controller |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110254512A (en) * | 2019-06-24 | 2019-09-20 | 清华大学 | Distributed intelligence electric vehicle steering system functional safety architecture design method |
| CN110254512B (en) * | 2019-06-24 | 2020-10-20 | 清华大学 | Design method for functional safety architecture of steering system of distributed intelligent electric vehicle |
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