CN109131317A - Automatic vertical parking system and method based on multisection type planning and machine learning - Google Patents
Automatic vertical parking system and method based on multisection type planning and machine learning Download PDFInfo
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- CN109131317A CN109131317A CN201810812794.XA CN201810812794A CN109131317A CN 109131317 A CN109131317 A CN 109131317A CN 201810812794 A CN201810812794 A CN 201810812794A CN 109131317 A CN109131317 A CN 109131317A
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- 238000010801 machine learning Methods 0.000 title claims abstract description 26
- 230000004888 barrier function Effects 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 10
- 238000010586 diagram Methods 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
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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
- B60W30/06—Automatic manoeuvring for parking
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/54—Audio sensitive means, e.g. ultrasound
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Abstract
The present invention provides a kind of automatic vertical parking system based on multisection type planning with machine learning and methods of parking, by looking around camera collection image, ultrasonic sensor detects the validity information that obstacle information judges the position of opposite warehouse compartment, course information and warehouse compartment.When recognizing suitable size and without the warehouse compartment of vehicle occupancy, automatic parking process is initially entered.Automated parking system is adjusted using multistage R-S curve from parking stall appearance to suitable position if needed according to the planning for currently carrying out parking path from parking stall appearance and warehouse compartment information, is generated secondary spiral line further according to learning network and is parked track.Steering wheel, throttle and brake pedal, which are controlled, by electric control gear carries out storage of parking.The present invention utilizes secondary spiral line training set and learning network, improve the efficiency for process of parking and the adaptability to path offset, carry out multisection type planning in conjunction with R-S curve, realizes that parking in very low range plans high success rate, the scope of application is wider, and process of parking is relatively reliable.
Description
Technical field
The present invention relates to the automatic parking planning fields of intelligent automobile, more particularly, to one kind based on multisection type planning and machine
The automatic vertical parking system and method for device study.
Background technique
The unbalanced development of domestic automobile ownership and Transportation Infrastructure Construction in recent years, so that parking space is increasingly narrow
Small, technical requirements of parking are higher and higher.The limitation of parking space, it is desirable that vehicle should stop according to parking stall line gauge model, can make to park
Space resources obtains maximized rationally utilization, is also beneficial to the overall planning of parking lot and the appearance of the city.On the other hand, parking space
Limitation, be equally the great challenge to driver's technology, problem of parking expends the great time and efforts of people, produces when serious
The accidents such as raw scraping, collision.Thus, fan and expectation of the automatic parking technology by market.
Traditional automated parking system, method for planning track generally consider to use R-S curve, helix, spline curve
Deng one of carry out Global motion planning, and only consider the constraint of theoretic vehicle kinematics.Planning side based on R-S curve
Method, track generate clear thinking, calculate simply, to the adaptable of environment, the parking space needed is small.But R-S curve is deposited
In the discontinuous problem of curvature so that vehicle need to repeatedly stop after adjustment direction disk corner, it is serious to tire wear.Phase therewith
Instead, use the curves such as helix, spline curve as track of parking, the discontinuous problem of curvature is not present, but be needed pool
Vehicle space is larger, and high to initial pose requirement of parking, and is not suitable for directly applying in present environment of parking.Although in addition,
Speed is lower when parking, and tyre slip angle is small, and vehicle lateral sliding is few.But, it is understood that there may be roadside parking stall inclination turns to
There is very big tracking error often in the situations such as motor tracking delay, the simple track for controlling vehicle tracking planning.
Therefore, how to provide a kind of parking strategy to solve the above problems and system is that those skilled in the art are urgently to be resolved
The problem of.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be advised based on multisection type
Draw the automatic vertical parking system and method with machine learning.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of automatic vertical parking system based on multisection type planning and machine learning, the system include:
Sensing module: including looking around camera and ultrasonic radar, described looks around camera for acquiring image, identifies
Warehouse compartment line simultaneously judges opposite warehouse compartment posture information using binocular ranging method, and the ultrasonic radar is for detecting barrier letter
Breath, judges whether warehouse compartment is occupied；
Decision-making module: according to the fuse information of sensing module and instruction record, judge warehouse compartment validity and current institute
Place parks the stage, and provides posture information for planning parking path for planning module；
Planning module: the pose according to warehouse compartment information and from vehicle with respect to warehouse compartment plans the track R-S or sends out posture information
Machine study module is given, after obtaining planning path, motion-control module is sent to and is tracked；
Machine learning module: for industrial computer, determine that starting point pose of vertically parking is defeated by machine learning method
Enter the relationship with secondary spiral line parameter, and the helix planned track of parking is sent to planning module；
Motion-control module: include ECU and line control system, receive the track scatterplot that planning module transmits, pass through controller
Steering wheel, gear, throttle and brake pedal are controlled, and controls vehicle tracking planning path.
The camera of looking around is CMOS camera, and screen pixels are having a size of 640:480.
It is described look around camera and ultrasonic sensor be equipped with it is multiple, including being arranged in vehicle body all around four
Camera and and vehicle body around 12 ultrasonic sensors, left and right sides looks around camera and is mounted below rearview mirror, vehicle body
4 ultrasonic radars are respectively arranged in front rear, and 2 ultrasonic radars are each side arranged.
It is a kind of to be parked method based on multisection type planning and the automatic vertical of machine learning, comprising the following steps:
1) when driver control after parking vehicles to park warehouse compartment periphery after, open automatic parking mode；
2) the slow straight-line travelling of moving control module for controlling vehicle；
3) by around vehicle body look around camera and ultrasonic radar carries out warehouse compartment angular coordinate and limited block position
Detection obtains warehouse compartment regional location and size, judges whether the warehouse compartment meets the requirement on parking stall, determine parking stall, and carry out
Step 4), otherwise, return step 2)；
4) according to the coordinate and current pose from vehicle with respect to warehouse compartment, parking path is planned；
5) by first segment park curve whether be able to achieve collisionless storage, judge whether to need multistage path planning, if so,
Step 6) is then carried out, if it is not, then carrying out step 7)；
6) multistage path planning adjusts to obtain using multistage R-S curve, specifically includes the following steps:
61) premised on vehicle rear axle right side is without impinging on warehouse compartment angle point, the first segment straight line rail of first segment R-S curve is determined
Mark；
62) vehicle beats to the right steering wheel to extreme position, and moves backward to right back, when to be located at warehouse compartment left for vehicle left back point
Stop when on side line or its extended line；
63) vehicle beats steering wheel to extreme position to the left, and advances to left front, until vehicle pose is adjusted to lead to
Secondary spiral line tracking is crossed to realize the angle for storage of once parking or be located in safe distance at a distance from front obstacle；
64) basis looks around camera and ultrasonic sensor and detects warehouse compartment coordinate again, redefines warehouse compartment in conjunction with boat position
Information and from parking stall appearance, eliminates the error of boat position, and return step 4)；
7) according to current vehicle location coordinate and course angle information, using based on the machine learning network of secondary spiral line
Storage track is sought in calculation；
8) motion-control module is according to storage track, control vehicle storage, end of parking, and exits park mode.
In the step 3), when detecting multiple warehouse compartments, the preferential selection warehouse compartment nearest apart from this vehicle, and judgement is
It is no to meet the requirements, if not meeting, selects and judge next warehouse compartment.
In the step 3), warehouse compartment meets the requirement on parking stall while meeting the following conditions:
Warehouse compartment type matching, warehouse compartment size be appropriate and warehouse compartment in barrier is not present.
The step 7) specifically includes the following steps:
71) according to the coordinate range of setting and course angular region, the secondary spiral line tracking ginseng under each initial state is obtained
Number；
72) initial coordinate values and course angle and corresponding secondary spiral line parameter, training learning network parameter are utilized；
73) using the coordinate information of current vehicle and course angle as input, the learning network after training is inputted, storage is obtained
Trace information, be sent to motion-control module.
When driver by vehicle parking to warehouse compartment position when, open automatic driving mode:
When the warehouse compartment fuse information that sensing module is sent has not been obtained in decision-making module, planning module sends straight line and plans road
Diameter controls vehicle low speed and moves ahead, sensing module continues to detect to motion-control module；
After detecting available warehouse compartment, judge whether warehouse compartment is available and obtains warehouse compartment type, and judgement is current by decision-making module
Which kind of vehicle is in and parks the stage, and planning module is according to this phase paths of warehouse compartment information planning, and transmitting path scatterplot extremely moves
Control module is tracked；
When automatic parking mode ends, decision-making module completes parking backed off after random automatic Pilot mould by control brake pedal
Formula.
Compared with prior art, the invention has the following advantages that
One, adaptable, reliable and stable: the present invention provides the rule of parking of a kind of combination R-S curve and secondary spiral line
The method of drawing reduces process original place adjustment direction number of parking, suitable for the narrow environment of parking in city incity, to the adaptability of environment
By force, it is short to calculate the time, and can be adjusted when there is tracking offset, process of parking is reliable and stable.
Two, automatic parking: driver only needs to open parking system switch in suitable position in the present invention, and system can be certainly
Dynamic identification warehouse compartment sideline simultaneously judges the information such as warehouse compartment type, relative position and validity, and hereafter planning acts, participate in without the mankind
Judgement；
Three, it improves efficiency: after parking system obtains warehouse compartment information in the present invention, can completely automatically carry out planning of parking
And motion control, will park process programming and automation, improve people's Working Life efficiency.
Four, planning is easy: the present invention generates spiral trajectory using the method for machine learning, improve track formation speed and
Success rate keeps planning process more easy.
Detailed description of the invention
Fig. 1 is the flow chart of automatic parking method process of the invention.
Fig. 2 is the path multisection type R-S of the present invention schematic diagram.
Fig. 3 is the structural schematic diagram for the combination combined positioning method that the present invention uses.
Fig. 4 is the structural schematic diagram of automated parking system of the invention.
Fig. 5 is machine learning schematic network structure of the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
Fig. 1 is the flow chart of automatic parking method process of the invention, and this method specific steps include:
Step 1: being controlled by driver to warehouse compartment periphery of parking, open automatic parking mode, parking system adapter tube vehicle
Control；
Step 2: module of parking controls the slow straight-line travelling of vehicle；
Step 3: by looking around camera and ultrasonic sensor progress warehouse compartment angular coordinate and limited block around vehicle body
The detection of position calculates warehouse compartment regional location and size, and detects barrier presence or absence within warehouse compartment region；If warehouse compartment region
Shape size meets the requirements, and barrier is not present, and determines parking stall, enters step 4；Otherwise, step 2 is returned to；
If selecting the warehouse compartment nearest apart from this vehicle preferably, detecting multiple warehouse compartments in step 3, judging whether to meet
Standard；If not meeting, reselection judges next warehouse compartment.
Whether judgment criteria whether judging warehouse compartment properly in step 3 includes depositing in warehouse compartment type, warehouse compartment size and warehouse compartment
In barrier.
As shown in Fig. 2, preferably, first planning the track (midpoint Fig. 21 to 4 sections of tracks of point) based on R-S curve.
Step 4: according to the coordinate and current pose from vehicle with respect to warehouse compartment, planning parking path；
Step 5: by first segment park curve whether be able to achieve collisionless storage, judge whether to need multistage path planning,
If so, entering step 6；If it is not, entering step 7；
Preferably, step 6 is as shown in Fig. 2, the specific steps adjusted are as follows: is adjusted using multistage R-S curve, adjustment side
Method (for parking to the right) is as follows:
Step 61: premised on the right side of vehicle rear axle without impinging on warehouse compartment angle point, determining first segment R-S curve first segment straight line
Track (midpoint Fig. 21 to point 2 sections)；
Step 62: vehicle beats to the right steering wheel to extreme position, moves backward afterwards to the right, until vehicle left back point is on the left of warehouse compartment
On line or its extended line (midpoint Fig. 22 to point 3 sections)；
Step 63: vehicle beats steering wheel to extreme position to the left, advances to left front, until vehicle pose is adjusted to be easy to logical
It crosses secondary spiral line tracking and realizes the angle (it is stored in secondary spiral line tracking cluster) for storage of once parking or to apart from front
At barrier safe distance (midpoint Fig. 23 to point 4 sections)；
Step 64: detecting warehouse compartment coordinate again with ultrasonic sensor using looking around, redefine library in conjunction with dead reckoning
Position information and from parking stall appearance, eliminates the error of dead reckoning, returns to step 4；
Again opposite warehouse compartment is detected in step 64 and sits calibration method, as shown in figure 3, needing to merge secondary positioning, boat position pushes away
The location informations such as calculation, inertial navigation, such as in outdoor, also it is contemplated that fusion GPS information judges from parking stall appearance.
Step 7: according to current vehicle location coordinate and course angle information (coordinate and course angle at the midpoint Fig. 2 4), utilizing
Machine learning network query function based on secondary spiral line seeks storage track, specific steps are as follows: learning network structure such as Fig. 5 institute
Show, machine learning network training method is as follows:
Step 71: according to expected coordinate range and course angular region, calculating suitable secondary spiral under each initial state
Line tracking parameter；
Step 72: utilizing initial state information (coordinate value and course angle) and corresponding secondary spiral line parameter, training
Learning network parameter；
Step 73: using the coordinate information of current vehicle and course angle as input, being carried out by the learning network after training
It calculates, the trace information that is put in storage (midpoint Fig. 24 to point 5 sections) is sent to control module.
Step 8: control module controls vehicle storage according to expected trajectory, and park mode is exited in end of parking.
Preferably, it parks in step 8, under automatic driving mode the specific steps of control are as follows:
When driver by vehicle parking to rational position when, automatic driving mode is opened in selection；
When the warehouse compartment fuse information that sensing module is sent has not been obtained in decision-making module, planning module sends straight line and plans road
Diameter, control vehicle low speed move ahead, and sensing module continues to detect；
After detecting available warehouse compartment, judge whether warehouse compartment available and warehouse compartment type by decision-making module, and judge to work as front truck
Which kind of park the stage in, planning module is according to this phase paths of warehouse compartment information planning, and transmitting path scatterplot is to controlling mould
Block is tracked；
When automatic parking mode ends, decision-making module completes parking backed off after random automatic Pilot mould by control brake pedal
Formula.
As shown in figure 4, Fig. 4 is a kind of structural schematic diagram of automated parking system provided by the invention, including sensing module,
Decision-making module, planning module, machine learning module and motion-control module:
Sensing module, including camera and ultrasonic radar are looked around, camera is looked around for acquiring image, identifies warehouse compartment line
And opposite warehouse compartment posture information is judged using binocular ranging method, ultrasonic radar judges warehouse compartment for detecting obstacle information
It is whether occupied.
Decision-making module, according to the fuse information of sensing module and instruction record, judge warehouse compartment validity and current institute
Place parks the stage, is supplied to planning module posture information for planning parking path；
Planning module, the pose according to warehouse compartment information and from vehicle with respect to warehouse compartment plan the track R-S or send out posture information
Machine study module is given, after obtaining planning path, control module is sent to and is tracked；
Machine learning module is industrial computer, determines starting point pose input of vertically parking by machine learning method
With the relationship of secondary spiral line parameter, and the helix planned track of parking is sent to planning module；
Motion-control module includes ECU and line control system, receives the track scatterplot that planning module transmits, passes through controller control
Steering wheel, gear, throttle and brake pedal processed control vehicle tracking planning path.
Preferably, machine learning module preferentially uses the learning method of neural network, and structure is as shown in figure 5, specific
Include:
Normalization unit is inputted, the vehicle posture information of input is normalized, convenient for calculating；
Neural network unit calculates output according to input according to machine learning method；
Track generation unit calculates spiral trajectory point coordinate according to the helix parameter of output, generates track scatterplot.
The present invention provides a kind of automatic vertical parking systems, look around camera acquisition figure by what vehicle body surrounding was arranged
Picture, ultrasonic sensor detect the validity information that obstacle information judges the position of opposite warehouse compartment, course information and warehouse compartment.When
Recognize suitable size and without vehicle occupy warehouse compartment when, into automatic parking process.Automated parking system is according to currently from vehicle
Pose and warehouse compartment information carry out the planning of parking path, are adjusted if needed using multistage R-S curve from parking stall appearance to suitable position
It sets, generates secondary spiral line parameter further according to learning network and calculate track of parking.Finally, by electric control gear control steering wheel,
Throttle and brake pedal carry out storage of parking, and can voluntarily judge warehouse compartment type and validity, realize the vertical pool in full-automatic ground
Vehicle.
The above is only the preferred embodiment of the present invention, it is noted that those skilled in the art are come
It says, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications also should be regarded as
Protection scope of the present invention.
Claims (8)
1. a kind of based on multisection type planning and the automatic vertical parking system of machine learning, which is characterized in that the system includes:
Sensing module: including looking around camera and ultrasonic radar, described looks around camera for acquiring image, identifies warehouse compartment
Line simultaneously judges opposite warehouse compartment posture information using binocular ranging method, and the ultrasonic radar is used to detect obstacle information,
Judge whether warehouse compartment is occupied；
Decision-making module: recording according to the fuse information of sensing module and instruction, judges the validity of warehouse compartment and is presently in
It parks the stage, and provides posture information for planning parking path for planning module；
Planning module: the pose according to warehouse compartment information and from vehicle with respect to warehouse compartment plans the track R-S or is sent to posture information
Machine learning module after obtaining planning path, is sent to motion-control module and is tracked；
Machine learning module: for industrial computer, determined by machine learning method vertically park the input of starting point pose with
The relationship of secondary spiral line parameter, and the helix planned track of parking is sent to planning module；
Motion-control module: include ECU and line control system, receive the track scatterplot that planning module transmits, controlled by controller
Steering wheel, gear, throttle and brake pedal, and control vehicle tracking planning path.
2. a kind of automatic vertical parking system based on multisection type planning and machine learning according to claim 1, special
Sign is that the camera of looking around is CMOS camera, and screen pixels are having a size of 640:480.
3. a kind of automatic vertical parking system based on multisection type planning and machine learning according to claim 1, special
Sign is, it is described look around camera and ultrasonic sensor be equipped with it is multiple, including being arranged in vehicle body all around four
Camera and and vehicle body around 12 ultrasonic sensors, left and right sides looks around camera and is mounted below rearview mirror, vehicle body
4 ultrasonic radars are respectively arranged in front rear, and 2 ultrasonic radars are each side arranged.
4. a kind of parked using as described in any one of claims 1-3 based on multisection type planning and the automatic vertical of machine learning
The method of parking of system, which comprises the following steps:
1) when driver control after parking vehicles to park warehouse compartment periphery after, open automatic parking mode；
2) the slow straight-line travelling of moving control module for controlling vehicle；
3) pass through the detection for looking around camera and ultrasonic radar progress warehouse compartment angular coordinate and limited block position around vehicle body,
Warehouse compartment regional location and size are obtained, judges whether the warehouse compartment meets the requirement on parking stall, determines parking stall, and carry out step
4), otherwise, return step 2)；
4) according to the coordinate and current pose from vehicle with respect to warehouse compartment, parking path is planned；
5) by first segment park curve whether be able to achieve collisionless storage, judge whether to need multistage path planning, if so, into
Row step 6), if it is not, then carrying out step 7)；
6) multistage path planning adjusts to obtain using multistage R-S curve, specifically includes the following steps:
61) premised on vehicle rear axle right side is without impinging on warehouse compartment angle point, the first segment straight path of first segment R-S curve is determined；
62) vehicle beats to the right steering wheel to extreme position, and moves backward to right back, when vehicle left back point is located at warehouse compartment left side line
Or stop when on its extended line；
63) vehicle beats steering wheel to extreme position to the left, and advances to left front, until be adjusted to can be by two for vehicle pose
Secondary spiral trajectory is realized the angle for storage of once parking or is located in safe distance at a distance from front obstacle；
64) basis looks around camera and ultrasonic sensor and detects warehouse compartment coordinate again, redefines warehouse compartment information in conjunction with boat position
With from parking stall appearance, the error of boat position, and return step 4 are eliminated)；
7) it according to current vehicle location coordinate and course angle information, is asked using the machine learning network query function based on secondary spiral line
It is taken into library track；
8) motion-control module is according to storage track, control vehicle storage, end of parking, and exits park mode.
The method 5. one kind according to claim 4 is parked, which is characterized in that multiple when detecting in the step 3)
When warehouse compartment, the preferential selection warehouse compartment nearest apart from this vehicle, and judge whether to meet the requirements, if not meeting, under selecting and judging
One warehouse compartment.
The method 6. one kind according to claim 4 is parked, which is characterized in that in the step 3), warehouse compartment meets parking
The requirement of position meets the following conditions simultaneously:
Warehouse compartment type matching, warehouse compartment size be appropriate and warehouse compartment in barrier is not present.
The method 7. one kind according to claim 4 is parked, which is characterized in that the step 7) specifically includes following step
It is rapid:
71) according to the coordinate range of setting and course angular region, the secondary spiral line tracking parameter under each initial state is obtained；
72) initial coordinate values and course angle and corresponding secondary spiral line parameter, training learning network parameter are utilized；
73) using the coordinate information of current vehicle and course angle as input, the learning network after training is inputted, the rail of storage is obtained
Mark information, is sent to motion-control module.
The method 8. one kind according to claim 4 is parked, which is characterized in that in the method,
When driver by vehicle parking to warehouse compartment position when, open automatic driving mode:
When the warehouse compartment fuse information that sensing module is sent has not been obtained in decision-making module, planning module send straight line planning path to
Motion-control module, control vehicle low speed move ahead, and sensing module continues to detect；
After detecting available warehouse compartment, judge whether warehouse compartment is available and obtains warehouse compartment type, judges current vehicle by decision-making module
Which kind of park the stage in, planning module is according to this phase paths of warehouse compartment information planning, and transmitting path scatterplot is to motion control
Module is tracked；
When automatic parking mode ends, decision-making module completes parking backed off after random automatic driving mode by control brake pedal.
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| CN109398349A (en) * | 2018-09-11 | 2019-03-01 | 同济大学 | A kind of automatic parking method and system based on geometric programming and intensified learning |
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| CN110562244A (en) * | 2019-08-07 | 2019-12-13 | 武汉乐庭软件技术有限公司 | automatic parking trajectory planning method based on target parking space warehouse-out |
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| WO2020168769A1 (en) * | 2019-02-19 | 2020-08-27 | 惠州市德赛西威智能交通技术研究院有限公司 | Vertical parking method |
| CN111619553A (en) * | 2020-05-26 | 2020-09-04 | 三一专用汽车有限责任公司 | Engineering vehicle and parking control method of engineering vehicle |
| CN111731270A (en) * | 2020-06-04 | 2020-10-02 | 重庆长安汽车股份有限公司 | Parking method and system for 1D forward running of automatic parking system and vehicle |
| CN111746525A (en) * | 2020-07-07 | 2020-10-09 | 东风柳州汽车有限公司 | Parking path planning method, device, equipment and storage medium |
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| CN114030463A (en) * | 2021-11-23 | 2022-02-11 | 上海汽车集团股份有限公司 | Path planning method and device of automatic parking system |
| DE102020127216A1 (en) | 2020-10-15 | 2022-04-21 | Valeo Schalter Und Sensoren Gmbh | Method for determining a trajectory for a vehicle, computer program product, parking assistance system and vehicle |
| CN114506383A (en) * | 2022-02-23 | 2022-05-17 | 奇瑞汽车股份有限公司 | Steering wheel aligning control method, device, terminal, storage medium and product |
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| CN114789723A (en) * | 2022-06-10 | 2022-07-26 | 小米汽车科技有限公司 | Vehicle running control method and device, vehicle, storage medium and chip |
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| CN116513168A (en) * | 2023-07-03 | 2023-08-01 | 广汽埃安新能源汽车股份有限公司 | Path planning method and device, electronic equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130094997A (en) * | 2012-02-17 | 2013-08-27 | 현대모비스 주식회사 | Apparatus and method detectinc obstacle and alerting collision |
| KR20140118157A (en) * | 2013-03-28 | 2014-10-08 | 현대모비스 주식회사 | System and Method for alarming collision of vehicle with support vector machine |
| CN105109482A (en) * | 2015-08-24 | 2015-12-02 | 奇瑞汽车股份有限公司 | Parking garage entry method and device |
-
2018
- 2018-07-23 CN CN201810812794.XA patent/CN109131317A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130094997A (en) * | 2012-02-17 | 2013-08-27 | 현대모비스 주식회사 | Apparatus and method detectinc obstacle and alerting collision |
| KR20140118157A (en) * | 2013-03-28 | 2014-10-08 | 현대모비스 주식회사 | System and Method for alarming collision of vehicle with support vector machine |
| CN105109482A (en) * | 2015-08-24 | 2015-12-02 | 奇瑞汽车股份有限公司 | Parking garage entry method and device |
Non-Patent Citations (2)
| Title |
|---|
| 熊璐，严森炜，余卓平，张培志: "基于库位跟踪的自动泊车决策规划系统", 《汽车技术》 * |
| 郭奕璀，蒋涛，张葛祥: "基于动态窗口和绕墙走的自动垂直泊车轨迹规划", 《交通信息与安全》 * |
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