CN109733383A - A kind of adaptive automatic parking method and system - Google Patents
A kind of adaptive automatic parking method and system Download PDFInfo
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- CN109733383A CN109733383A CN201811527904.4A CN201811527904A CN109733383A CN 109733383 A CN109733383 A CN 109733383A CN 201811527904 A CN201811527904 A CN 201811527904A CN 109733383 A CN109733383 A CN 109733383A
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Abstract
The present invention relates to a kind of intelligent driving fields, and in particular to a kind of adaptive automatic parking method and system；It cannot achieve adaptive, comprehensive user individual service in the prior art.The present invention is capable of providing automatic parking storage mode and/or automatically from the mode of garage recall vehicles.Comprising: vehicle automated driving system detect whether have park or recall the corresponding map in region；User sets the vehicle in the map used, stop bit is set；The map by preset global map and finally used matches, for generating automatic drive route planning；It parks or recalls by automatic Pilot completion.The prepackage parking system provides one kind and builds figure function online for the ground library for not building figure and parking lot；Thus adaptive, comprehensive service can be realized.
Description
Technical field
The present invention relates to a kind of automatic Pilot technical fields, and in particular to a kind of method that automatic parking or vehicle are recalled or
System.
Background technique
With the development of every technology of artificial intelligence, automatic Pilot technology is also constantly grown up, and user to driving automatically
Sailing automatic stopping, automatic recall vehicles that technology is especially in parking lot indoors has further need.Parking number
Many and user will not be confined in some Parking, therefore automatic parking function needs while meeting to drive automatically
Sail the scene for build to parking lot figure in system in prepackage.
For such automated driving system, how adaptively, comprehensive offer user individual service is one
Crucial and yet unresolved issue.In fact, parking garage is adaptively moored under a variety of demands, various modes and more scenes
The many factors such as vehicle and corresponding map matching technology mix together, to the design of entire parking system, logicality design with
And Technology Selection is proposed very big challenge.
Existing parking garage automated parking system can not rely on these signals and be moored automatically because GPS signal is weaker
Vehicle is recalled；Definition mode can only be generally handled, such as automatic parking can only be carried out in the case where preloaded system has map,
Or storage service of parking can only be carried out, vehicle service of recalling automatically etc. can not be carried out.
Summary of the invention
In view of this, parking method present applicant proposes a kind of adaptive parking garage, this method can mention simultaneously
It, can be adaptive by dynamic semantics characteristic matching for automatic parking storage mode and automatically from the mode of garage recall vehicles
Reply park the variation in environment, improve the accuracy and speed of map match.In addition to this, do not have to prepackage parking system
The ground library and parking lot for building figure provide one kind and build figure function online.On the basis of above-mentioned key element, the present invention design from
Adapt to park method application layer park mode, with user in terms of also designed, and be finally fused into one
Cover the adaptive parking system in parking garage.
The first aspect of the invention is to provide a kind of global path planning side recalled for valet parking or automatically
Method, which is characterized in that the described method comprises the following steps:
Activate setting procedure；User setting simultaneously activates valet parking or automatic call back function, sets the vehicle, stop bit
It sets；It is risen described in wherein, stop bit is set to fixed point；
Map match step；The map match step includes: that current automatic Pilot is calculated by map-matching algorithm
Whether the map of currently parking or recall environment has been stored in system；And judge whether that matching is currently parked or called together
The map in winding border；
Figure step is built in self study；Figure is built as the map using self study；It includes parking path that figure is built in the self study
Habit mode and/or recall path learning mode；
Judge that the self study builds whether figure succeeds: when the self study builds driving terminating point of the figure comprising user setting simultaneously
And the self study, when building figure information and carrying out global path planning enough, figure is built in completion, into global path planning step；Judgement
If building figure failure, system can save the part for not completing to learn to build figure, and terminate self study and build figure function, terminate automatic
Global path planning；
Global path planning step；The starting point and ending point of automatic Pilot based on user setting is based on the self-study
Habit builds figure and generates global path planning.
The second aspect of the invention is to provide a kind of valet parking method, it is characterised in that: the method includes following
Step:
Step S101: user setting simultaneously activates valet parking function, on preset map, sets the vehicle and rises, only
Position；It is risen described in wherein, stop bit is set to fixed point；
Step S102: map match；The map match includes following sub-step:
S1021: vehicle according to the initial posture information of the vehicle determined from preset three-dimensional map target area with
And the target semantic feature in target area；
S1022: the semantic feature for looking around image zooming-out of the vehicle and the semantic feature progress of the map are utilized
Match；
S1023: by the map-matching algorithm of step S1021 and step S1022, the vehicle provides current automatic Pilot
Whether the map of currently park environment has been stored in system；If the map of environment obtained by matching of currently parking,
System is directly entered step S105；If being not matched to corresponding map, system enters step S103；
Step S103: self study builds figure as preset map；It includes parking path mode of learning that figure is built in the self study；
Step S104: judge whether self-built figure succeeds；The driving of the map that self study is established included user setting is whole
Stop and when the cartographic information established carries out global path planning enough, figure, system auto-returned step 101 are built in completion；
If building figure failure, system can save the part for not completing to learn to build figure, and terminate self study and build figure function；
Step S105: global path planning；The starting point and ending point of automatic Pilot based on user setting, based on described
Default map generates global path planning using the method for Dynamic Programming；
Step S106: confirm function use of parking；
Step S107: autonomous parking drives, and system enters driving condition of parking.
Preferably, the method also includes steps:
Step S108: the fault detection during being parked by real-time positioning failure module；
Step S109: the detection of obstacles during being parked by real-time detection of obstacles module；
Step S110: autonomous driving function is interrupted；According to the real-time positioning failure module and step S109 of step S108
Real-time detection of obstacles module failure and detection of obstacles, encounter failure when vehicle front running region during parking
Or when barrier, system can carry out stablizing braking automatically, guarantee that vehicle is in the state of a safety；
Step S111: valet parking is completed；Vehicle automatic running to target location completes task, after the completion of valet parking,
System can notify driver's function to complete by mobile phone terminal, and be attached to vehicle position information.
Preferably, the step S103 includes following sub-step:
Step S1031, the current posture information of target vehicle is obtained；
Step S1032, according to current posture information and automatic Pilot digital navigation map, target vehicle subsequent time is predicted
Estimation posture information；
Step S1033, it to estimate posture information as foundation, is obtained in preset range in automatic Pilot digital navigation map
Target map data；
Step S1034, combining target map datum and estimation posture information, generate for guiding user's automatic Pilot
Target driving strategy.
Preferably, it after the step 103 path planning success, prompts the user whether to carry out automatic parking；Under park mode,
User enters step 107 autonomous parkings by selection and drives or terminate automatic parking function.
The third aspect of the present invention is to provide a kind of vehicle and recalls method automatically, it is characterised in that: the method includes with
Lower step:
Step S101. user setting simultaneously activates automatic call back function, on preset map, sets the vehicle and rises, only
Position；It is risen described in wherein, stop bit is set to fixed point；
Step S102. map match；The map match includes following sub-step:
S1021. vehicle according to the initial posture information of the vehicle determined from preset three-dimensional map target area with
And the target semantic feature in target area；
S1022. the semantic feature for looking around image zooming-out of the vehicle and the progress of the semantic feature of the map are utilized
Match；
S1023. pass through the map-matching algorithm of step S1021 and step S1022, the vehicle provides current automatic Pilot
Whether the map of currently recalling environment has been stored in system；If the map obtained by matching for currently recalling environment,
System is directly entered step S105；If being not matched to corresponding map, system enters step S103；
Step S103 self study builds figure as preset map；It includes recalling path learning mode that figure is built in the self study；
Step S104 judges whether self-built figure succeeds；Self study establish map included user setting driving terminate
Point and when the cartographic information established carries out global path planning enough, completes to build figure, system auto-returned step 101；Such as
Fruit builds figure failure, then system can save the part for not completing to learn to build figure, and terminate self study and build figure function；
Step S105. global path planning；The starting point and ending point of automatic Pilot based on user setting, based on described
Preset map generates global path planning using the method for Dynamic Programming；
Step S106: confirmation call back function uses；
Step S107: independently recalling driving, and system, which enters, recalls driving condition.
Preferably, the step S103 includes following sub-step:
Step S1031, the current posture information of target vehicle is obtained；
Step S1032, according to current posture information and automatic Pilot digital navigation map, target vehicle subsequent time is predicted
Estimation posture information；
Step S1033, it to estimate posture information as foundation, is obtained in preset range in automatic Pilot digital navigation map
Target map data；
Step S1034, combining target map datum and estimation posture information, generate for guiding user's automatic Pilot
Target driving strategy.
Preferably, it after the step 105 path planning success, prompts the user whether to be recalled automatically；Under park mode,
User independently recalls drivings, or termination automatic parking function by may be selected to enter step 107.
The fourth aspect of the present invention is to provide a kind of vehicle automated parking system, which is characterized in that the system comprises:
User's setting module: activation park mode, and on preset map, set the vehicle, stop bit is set；Wherein
Described, stop bit be set to fixed point；
Mapping module: using existing map or the map built is learnt by oneself as preset map；
Map-matching module: the semantic feature for looking around image zooming-out of the vehicle and the language of the start-stop point map are utilized
Adopted feature is matched；
Global path planning module: the starting point and ending point of the automatic Pilot based on user setting, based on described default
Map generates global path planning using the method for Dynamic Programming；
Automatic Pilot module: according to the global path planning, the vehicle automatic parking.
The fifth aspect of the present invention is to provide a kind of automatic recalling system of vehicle, which is characterized in that the system comprises:
User's setting module: mode is recalled in activation, and on preset map, sets the vehicle, stop bit is set；Wherein
Described, stop bit be set to fixed point；
Mapping module: using existing map or the map built is learnt by oneself as preset map；
Map-matching module: the semantic feature for looking around image zooming-out of the vehicle and the language of the start-stop point map are utilized
Adopted feature is matched；
Global path planning module: the starting point and ending point of the automatic Pilot based on user setting, based on described default
Map generates global path planning using the method for Dynamic Programming；
Automatic Pilot module: according to the global path planning, the vehicle is recalled automatically.
Inventive point of the invention is the following, but is not limited to the following:
(1) automatic parking storage mode can be provided simultaneously and automatically from the mode of garage recall vehicles；For garage,
Especially underground garage, because the signals such as GPS are weaker, conventional positioning method cannot achieve to automatic parking and recall accurate
Control, the self-built figure that the present invention uses still have higher automatic parking and recall essence independent of signals such as external GPSs
Degree.This is one of inventive point of the invention.
(2) of the invention to park or vehicle recalling system is also equipped with and builds figure function online, to adapt to not pre-install map
Situation；Using figure function is built online, do not need to acquire data in each garage using map data collecting vehicle thus.It is considered that
The vehicle of each normally travel is all the collecting vehicle of ground library map.Because the path that may be travelled in ground library every time is different,
In this way by the traveling of limited times several times, the image data more completed is adopted, for building figure.
(3) semantic feature that autonomous parking start-stop point is saved when figure is built in self study is used as in the application initial semantic special
Sign is stored, and weight is highest in map match.In order to be carried out certainly to start-stop point ambient image semantic feature
It adapts to update, also proposes that a kind of dynamic semantics feature, weight are inferior to initial semantic feature in map match in the application.?
In the prior art without finding in automatic parking, recalling link that these need High Precision Automatic driving strategy and use map
Match, the map match of dynamic semantics feature does not more occur.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, not
Constitute limitation of the invention.In the accompanying drawings:
Fig. 1 is a kind of adaptive automatic valet parking method flow diagram in the embodiment of the present application；
Fig. 2 is a kind of adaptive automatic recall vehicles method flow diagram in the embodiment of the present application；
Fig. 3 is the park mode being related in the embodiment of the present application and the explanatory diagram for recalling mode.
The flow chart of driving strategy is generated in the embodiment of the present application of the position Fig. 4 based on map.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, right below with reference to embodiment and attached drawing
The present invention is described in further details.Here, exemplary embodiment and its explanation of the invention is used to explain the present invention, but simultaneously
It is not as a limitation of the invention.
Present application example provides a kind of adaptive method of parking.Above-mentioned park mode builds figure function online, can be with
In application of parking applied to scenes other other than parking garage.
The specific implementation of the embodiment of the present application is introduced with reference to the accompanying drawing.
Firstly, parking garage the park method adaptive to the one kind provided in the embodiment of the present application is introduced.
Fig. 1 show the valet parking system flow chart of present application example；Fig. 2 is generation objective recall vehicles in present application example
System flow chart, valet parking and is applied to automatic Pilot field for objective recall vehicles, and specific method includes:
Step 101: user setting simultaneously activates valet parking function.
User setting car travel mode is needed after valet parking activation, park mode refers to vehicle from the automatic row in designated position
Sail storage of parking.As shown in Figure 3 from point B, i.e. friendship vehicle place is travelled to point A, i.e. parking stall or garage.The mode of recalling refers to vehicle from vehicle
Library automatic running needs to pick up place to driver.From point A, i.e. parking stall or garage travels to the i.e. driver of point B and picks up place.
What needs to be explained here is that still point B is fixed point to either point A, fixed point here refers in some embodiments
It activates before valet parking function just built-in in parking system, and cannot change, this is to reduce because arbitrarily selecting
Select the biggish calculation amount of beginning or end bring.On application, these fixed points can refer in some embodiments
Starting point and the terminal of parking of receiving guests of parking of receiving guests formulated inside parking factory.
Step 102: map match
In the embodiment of the present invention, preset three-dimensional map can be high-precision map.Wherein, high-precision map can also be by
Referred to as High Resolution Ground Map (High Definition Map, HD Map) is a kind of special service in the ground of Vehicular automatic driving
Figure.The various traffic elements in traffic scene are store in HD Map, for example, road network data, lane network data, lane
Line number accordingly and the data such as traffic sign data, to assist vehicle to realize automatic Pilot.In addition, high-precision map can also include
Prior information, for example, the curvature of road, course, the gradient and horizontal slope angle, so that electronic equipment passes through these prior informations pair
Vehicle carries out automatic Pilot control, and then improves safety and the comfort of vehicle.
Map carries out mainly for generation of automatic Pilot strategy and to the starting of user setting, terminating point in present example
Confirmation.
1) electronic equipment determines target area and mesh according to the initial posture information of vehicle from preset three-dimensional map
Mark the target semantic feature in region；Wherein, initial posture information includes at least longitude, latitude and the height above sea level of vehicle position
Highly and the course angle of vehicle, pitch angle and roll angle.Target area is using vehicle position as the center of circle and with default length
Degree is the region of radius.Car-mounted terminal has multiple cameras, multiple target figures that synchronization takes in present example
Picture is simultaneously spliced into a panoramic view.Target semantic feature may is that the traffic elements such as lane line, warehouse compartment line, lane arrow.Terminal
Semantic feature is extracted in panoramic view first, the method for extracting semantic feature can be the depth based on Encoder-Decoder model
Spend learning method or image partition method.
2) it is matched using the semantic feature for looking around image zooming-out with the semantic feature of start-stop point map.In the application certainly
The semantic feature of main extracted start-stop point map of parking can be extracted and save in systems.In matching, ring is directly utilized
The semantic feature that visible image is extracted is matched with the start-stop point semantic feature of preservation, without in the semanteme special type of global map
It is matched, so can also greatly improve matching speed while improving matching accuracy.
It include three kinds of warehouse compartment line, warehouse compartment point and lane arrow image, semantic features in the application local map.If used
Warehouse compartment line and warehouse compartment point are matched, and it is identical as local map to match multiple regions from global map, are matched at this time
Accuracy rate is lower.And for the arrow of lane, the lane arrow in different location form, size and with periphery
Warehouse compartment line, the positional relationship of warehouse compartment point are also different, therefore, the matching of local map and global map are carried out using lane arrow,
The probability of successful match can be improved.
For autonomous parking system, over time, the semantic feature in environment of parking can be because of environment light, depreciation
Etc. factors there are some small variations, such as the abrasion of lane arrow etc..It is accumulation that these, which change some, if can not be certainly
The to map of adaptation is updated, it is possible to will increase the probability that it fails to match.To autonomous parking start-stop point certainly in the application
The semantic feature that study saves when building figure is stored as initial semantic feature, and weight is highest in map match.
In order to carry out adaptive updates to start-stop point ambient image semantic feature, also propose that a kind of dynamic semantics are special in the application
Sign, weight are inferior to initial semantic feature in map match.Dynamic semantics feature may be multiple groups, and quantity depends primarily on
The gap between corresponding semantic feature and the semantic feature of preservation is extracted during autonomous parking.With the semantic feature of starting point
For:
Step1 matches the big Mr. Yu's threshold of confidence level with the initial semantic feature in map when starting point ambient image semantic feature
When value, it is believed that successful match, and do not need to be updated dynamic semantics feature.
Step2 matches the small Mr. Yu's threshold of confidence level with the initial semantic feature in map when starting point ambient image semantic feature
When value, then it is compared using starting point ambient image semantic feature with dynamic semantics feature, if the big Mr. Yu's threshold value of confidence level
Then successful match.
Step3 is calculated between starting point ambient image semantic feature and the initial semantic feature of map and dynamic semantics feature
Distance, Euclidean distance or cosine distance can be used.Assuming that
D=min (d0, d1 ..., dn)
Wherein, d0 is the distance between starting point ambient image semantic feature and the initial semantic feature of map, d1 ..., dn
For the distance between starting point ambient image semantic feature and map n group dynamic semantics feature.When the big Mr. Yu's threshold value of d, then will
Initial point ambient image semantic feature is added to state semantic feature, and the quantity of dynamic semantics feature becomes n+1.
In map match, the semantic feature of the start-stop point ambient image of all storages is needed to be traversed for, it in this way can be significantly
Improve the usage experience of successful match rate and system.Behavioral characteristics are used herein and are used for map match, and given threshold
Judged, is based on being carried out on the basis of real-time self-built figure.It is above-mentioned to be characterized in innovative point of the invention using dynamic semantics
One of.
3) by the map-matching algorithm of step 1) and step 2), system can provide in current automated driving system whether
The map for environment of currently parking is stored；If the map of environment obtained by matching of currently parking, system enter step
Rapid 103；If being not matched to corresponding map, system enters step 104.
Step 103: global path planning:
The starting point and ending point of automatic Pilot based on user setting uses Dynamic Programming based on the map learnt
Method generate global path planning.Specific paths planning method refers to Fig. 4, and detailed process is as follows:
Step 401, the current posture information for obtaining target vehicle.
In the embodiment of the present invention, the current posture information of target vehicle be target vehicle current time location information with
Pose information, such as the current posture information of target vehicle can be to move forward at an angle；Wherein it is possible to utilize inertia
Measuring unit (Inertial Measurement Unit, IMU) obtains the IMU data at target vehicle current time, can also benefit
The IMG data that target vehicle current time is obtained with image (Image, IMG) sensor also can use other sensors and obtain
Other data at target vehicle current time calculate the present bit of target vehicle using IMU data, IMG data got etc.
Appearance information, this process can integrate the sensing data of multiple sensors acquisition to calculate the current pose letter of target vehicle
Breath, to obtain the current posture information of relatively reliable target vehicle.
Step 402, according to current posture information and automatic Pilot digital navigation map, predict target vehicle subsequent time
Estimate posture information.
In the embodiment of the present invention, automatic Pilot digital navigation map is a kind of with high-precision map.Determining the position
After confidence breath and road type, following steps can also be performed:
Determine sideways inclined angle of the location information in the road, wherein sideways inclined angle is the location information
In the section in the road and the angle between horizontal line；
When road type is straight road, the estimation posture information that prediction obtains target vehicle subsequent time is Xiang Qianhang
It sails and may include:
When road type is straight road, the estimation posture information that prediction obtains target vehicle subsequent time is with above-mentioned
Sideways inclined angle moves forward.
For example, when the location information is when the section in the road and the angle between horizontal line are certain angle, such as
When road type of the fruit location information in the road is straight road, prediction obtains the estimation position of target vehicle subsequent time
Appearance information is to be moved forward with certain angle.
By implementing this mode, the estimation posture information predicted can also include that the traveling angle of target vehicle is believed
Breath further improves the accuracy of estimation posture information.
Step 403, to estimate posture information as foundation, obtained in preset range in automatic Pilot digital navigation map
Target map data.
It may include the location information and target vehicle of target vehicle in the embodiment of the present invention, in estimation posture information
Posture information.
As an alternative embodiment, to estimate posture information as foundation, in automatic Pilot digital navigation map
Obtain preset range in target map data may include:
The location information of target vehicle is determined in automatic Pilot digital navigation map；
According to the location information, choose target vehicle posture information it is indicated towards direction preset range target
Diagram data.
For example, when target vehicle location information instruction target vehicle be located at road A (road A for north-south road,
The side of road A is connected to the south, and the other side of road A is connected to north) in, and the posture information of target vehicle indicates target
Vehicle towards road A the south when, choose the target map data in road A on the south the location information in preset range.Wherein,
Preset range can be pre-set range, if preset range can be 10m, or 80m, or Qi Tafan
It encloses, in the embodiment of the present invention without limitation.When preset range is 10m, 10m on the south the location information can be chosen in road A
Interior target map data.
By implementing this optional embodiment, it is not necessary to choose whole maps letter in automatic Pilot digital navigation map
Breath is analyzed, and is only needed therefrom to choose the target map data in more effective preset range according to estimation posture information, be improved
The efficiency of analytical map data, to improve the real-time for generating target driving strategy.
Step 404, combining target map datum and estimation posture information, generate the mesh for guiding user's automatic Pilot
Mark driving strategy.
Step 104: figure is built in self study:
Figure part is built in self study, is also classified into two kinds according to park mode set by user and the mode of recalling.
1) parking path mode of learning
In some embodiments, it " parks " and refers to that vehicle automatic running to future hands over vehicle point.Driver drive vehicle or
Vehicular automatic driving will hand over the place of vehicle point to the future.The future hands over vehicle point to can be parking position fixed built in the garage.
Driver's self-driving vehicle drives into parking garage, slightly stops in the desired following friendship vehicle point.Then, vehicle-mounted end (can lead to
Cross vehicle-carrying display screen) " self study valet parking " mode is first selected, parking path study " beginning " icon can be clicked later, immediately
Display screen will appear prompt " in parking path study progress ".
Driver's self-driving vehicle is travelled with low speed (10km/h or less) to target parking stall.Completion is parked after behavior, and pool is clicked
Vehicle path learning " termination " icon, vehicle-mounted end can show the systematic learning schedule percentage under the path immediately.
It is not up in the case that 100% path learning is completed (within product function 3 times) in single, driver can click
" store and do not complete parking path " saves the path.
For the parking lot of part parking path study is completed, when driver will be again introduced into the parking lot environment future,
Vehicle-mounted end can show " history parking path continues to learn " icon in time.The case where driver confirms " continuing path learning "
Under, the behavior of parking of identical parking path before self-driving vehicle is completed.After vehicle comes to a complete stop, vehicle-mounted end will appear corresponding completion hundred
Divide ratio, and the subsequent operation prompt of equivalent first parking path study.
If parking path study reaches 100%, after the completion of indicating study, system can prompt " parking path in vehicle-mounted end
Practise and completing, valet parking function can be used ", driver clicks " parking path storage ", completes the successful storage of this paths.
2) path learning mode is recalled
In some embodiments, it " recalls " and refers to vehicle from parking position automatic running to preassigned place.Driver
The arrival of vehicle is waited in the preassigned place.
Driver starts vehicle on parking stall, first selects " self study is recalled for visitor " mode, can click recall path later
Learn " beginning " icon, display screen will appear " recalling in path learning progress " prompt immediately.
Driver's self-driving vehicle is travelled to future expectation with low speed (10km/h or less) and is picked up a little, is slightly stopped, and road is recalled in click
Diameter learns " termination " icon, and vehicle-mounted end can show the systematic learning schedule percentage under the path.
It is not up in the case that 100% path learning is completed (within product function 3 times) in single, driver can click
" storage does not complete and recalls path " saves the path.
For the similar parking lot that part is completed and recalls path learning, driver's future is again from parking stall starting
Before, vehicle-mounted end can show " history recalls path and continues to learn " icon in time.In the feelings of driver's confirmation " continuing path learning "
Under condition, the identical driving behavior for recalling path before self-driving vehicle is completed.After reaching original expectation streetcar point, vehicle-mounted end will appear
Corresponding Percent Complete, and the equivalent subsequent operation prompt for recalling path learning for the first time.
It such as recalls path learning and reaches 100%, after the completion of indicating study, system can prompt " to recall path in vehicle-mounted end
Practise and completing, generation, objective call back function can be used ", driver clicks " recalling path storage ", completes the successful storage of this paths.
After figure success is built in self study, 105 are entered step.
Above-mentioned parking and recall in model has used self-built figure.It will be appreciated by those skilled in the art that because this is self-built
Whether figure is completed not being determining, therefore there are self-built figures can't be used for the case where parking or recalling appearance.Work as appearance
When above situation, selection terminates automatic parking or to recall, and driver is prompted to notice the termination of the automatic mode.Some
In embodiment, some parking lots are preinstalled with and have established complete map, then can be switched to above-mentioned mode, in some realities
It applies in example, the complete map of the foundation can be by requesting wireless downloading to be completed into system in real time.
Step 105: judge whether self-built figure succeeds:
The map that self study the is established driving terminating point of the included user setting and cartographic information established is enough
When carrying out global path planning, figure, system auto-returned step 101 are built in completion.If building figure failure, system, which can save, not to be had
It completes study and builds the part of figure, and terminate self study and build figure function.
Step 106: confirmation call back function uses:
After the success of step 103 path planning, prompt the user whether to carry out automatic parking.Under park mode, user's selection
" Yes " then enters step 107 automatic parking states；User selects " No " then to terminate automatic parking function.It recalls under mode, user
Selection " Yes " then enters step 107 and recalls state automatically；User selects " No " then to terminate automatic call back function.
Above-mentioned steps 105,106 include the usage mode for self-built figure.This is an innovative point of the invention, existing
For parking factory generally there are the problem of lacking map data of parking, especially underground garage in technology, GPS positioning is inaccurate, this
Technical obstacle is brought for automatic parking.And by the way of self-built figure, it does not need to use collecting vehicle thus, it is only necessary to
Automatic parking function can be completed after the traveling in garage several times in drive routine vehicle, this is an innovation of the invention
Point.
Step 107: autonomous driving:
When system carries out autonomous parking or recalls, the Global motion planning path that invocation step 103 generates, and starting step simultaneously
The real-time detection of obstacles module of 108 real-time positioning failure modules and step 109.Pass through the real-time of step 108 and step 109
Monitoring can make adaptive response to some burst situations in environment of parking and the transformation of interim environment, guarantee autonomous driving
Safety.Real-time positioning failure, which is mainly in, parks or recalls the interim biggish variation of environment appearance, causes currently to obtain in image
Semantic feature can not be matched with pre-stored map.
Step 108: real-time positioning failure module；
Step 109: real-time detection of obstacles module；
Step 110: autonomous driving function is interrupted；
Valet parking vehicle is parked and is recalled equipped with relatively high level detection of obstacles function and real-time navigation capability
In the process when vehicle front running region encounters barrier, system can carry out stablizing braking automatically, guarantee that vehicle is in one
In the state of safety.And if special traffic condition is not left yet or encountered to front obstacle in specific time, system can lead to
Crossing mobile phone terminal reminds driver's valet parking function or call back function to interrupt.System prompt needs driver to return to vehicle upper connecting tube, together
When vehicle can be automatically into double sudden strain of a muscle states.
Step 111: valet parking recalls completion.
Under normal circumstances, vehicle automatic running to target location completes task.If it is park mode, valet parking is completed
Afterwards, system can notify driver's function to complete by mobile phone terminal, and be attached to vehicle position information.
The above embodiments are to park to serve as theme and be illustrated, but it is understood that the process is also adapted to recall automatically
Mode.This can be borrowed to those skilled in the art.Wherein if it is the mode of recalling, vehicle, which reaches, picks up the car
Can be automatically into double sudden strain of a muscle states after point, while driver will receive mobile phone terminal vehicle please take over prompting in place.It is understood that
Be, the mode of recalling be it is similar with park mode, the similar mode that uses and park carries out.What needs to be explained here is that
For the mode of recalling, still point B is also fixed point to either point A, and fixed point here refers in some embodiments to swash
It is just built-in in recalling system before generation objective call back function living, and cannot change, this is to reduce because choosing at random
The biggish calculation amount of beginning or end bring.On application, these fixed points can refer in some embodiments to stop
Formulate inside depot receive guests to recall starting point and receive guests recalls terminal.
The above, above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although referring to before
Embodiment is stated the application is described in detail, those skilled in the art should understand that: it still can be to preceding
Documented technical solution in each embodiment is stated to modify or equivalent replacement of some of the technical features；And this
It modifies or replaces, the spirit and model of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution
It encloses.
It should be appreciated that in this application, " at least one (item) " refers to one or more, and " multiple " refer to two or two
More than a."and/or" indicates may exist three kinds of relationships, for example, " A and/or B " for describing the incidence relation of affiliated partner
It can indicate: only exist A, only exist B and exist simultaneously tri- kinds of situations of A and B, wherein A, B can be odd number or plural number.Word
Symbol "/" typicallys represent the relationship that forward-backward correlation object is a kind of "or"." at least one of following (a) " or its similar expression, refers to
Any combination in these, any combination including individual event (a) or complex item (a).At least one of for example, in a, b or c
(a) can indicate: a, b, c, " a and b ", " a and c ", " b and c ", or " a and b and c ", and wherein a, b, c can be individually, can also
To be multiple.
Claims (10)
1. a kind of global path planning method recalled for valet parking or automatically, which is characterized in that the method includes with
Lower step:
Activate setting procedure；User setting simultaneously activates valet parking or automatic call back function, and setting vehicle rises, stop bit is set；
Map match step；The map match step includes: that current automated driving system is calculated by map-matching algorithm
In whether stored and currently park or recall the map of environment；And judge whether that ring is currently parked or recalled in matching
The map in border；
Figure step is built in self study；Figure is built as the map using self study；It includes parking path study mould that figure is built in the self study
Formula and/or recall path learning mode；
Judge that the self study builds whether figure succeeds: when the driving terminating point that figure includes user setting and institute are built in the self study
When stating self study and building figure information and carry out global path planning enough, figure is built in completion, into global path planning step；If judgement
Figure failure is built, then system can save the part for not completing to learn to build figure, and terminate self study and build figure function, terminate global path
Planning；
Global path planning step；The starting point and ending point of automatic Pilot based on user setting is built based on the self study
Figure generates global path planning.
2. a kind of valet parking method, which is characterized in that the described method comprises the following steps:
Step S101: user setting simultaneously activates valet parking function, on preset map, sets the vehicle, stop bit is set；
It is risen described in wherein, stop bit is set to fixed point；
Step S102: map match；The map match includes following sub-step:
S1021: vehicle determines target area and mesh according to the initial posture information of the vehicle from preset three-dimensional map
Mark the target semantic feature in region；
S1022: the semantic feature for looking around image zooming-out of the vehicle and the target semantic feature progress of the map are utilized
Match；
S1023: by the map-matching algorithm of step S1021 and step S1022, the vehicle provides current automated driving system
In whether stored the map of environment of currently parking；If the map of environment obtained by matching of currently parking, system
It is directly entered step S105；If being not matched to corresponding map, system enters step S103；
Step S103: self study builds figure as preset map；It includes parking path mode of learning that figure is built in the self study；
Step S104: judge whether self-built figure succeeds；The driving terminating point for the map included user setting that self study is established
And when the cartographic information established carries out global path planning enough, figure, system auto-returned step 101 are built in completion；If
Figure failure is built, then system can save the part for not completing to learn to build figure, and terminate self study and build figure function；
Step S105: global path planning；The starting point and ending point of automatic Pilot based on user setting, based on described default
Map generates global path planning using the method for Dynamic Programming；
Step S106: confirm function use of parking；
Step S107: autonomous parking drives, and system enters driving condition of parking.
3. method of parking according to any one of claim 2-3, it is characterised in that: the step S103 includes following son
Step:
Step S1031: the current posture information of target vehicle is obtained；
Step S1032: according to current posture information and automatic Pilot digital navigation map, estimating for target vehicle subsequent time is predicted
Count posture information；
Step S1033: to estimate posture information as foundation, the mesh in preset range is obtained in automatic Pilot digital navigation map
Mark map datum；
Step S1034: it in conjunction with the target map data and estimation posture information, generates for guiding user's automatic Pilot
Target driving strategy.
4. method of parking according to any one of claim 2-3, it is characterised in that: step 103 path planning at
After function, prompt the user whether to carry out automatic parking；Under park mode, user enters step the driving of 107 autonomous parkings by selection
Or terminate automatic parking function.
5. a kind of vehicle recalls method automatically, it is characterised in that: the described method comprises the following steps:
Step S101: user setting simultaneously activates automatic call back function, on preset map, sets the vehicle, stop bit is set；
It is risen described in wherein, stop bit is set to fixed point；
Step S102: map match；The map match includes following sub-step:
S1021: vehicle determines target area and target area according to the initial posture information of vehicle from preset three-dimensional map
Target semantic feature in domain；
S1022: the semantic feature for looking around image zooming-out of the vehicle and the target semantic feature progress of the map are utilized
Match；
S1023: by the map-matching algorithm of step S1021 and step S1022, the vehicle provides current automated driving system
In whether stored and currently recall the map of environment；If the map obtained by matching for currently recalling environment, system
It is directly entered step S105；If being not matched to corresponding map, system enters step S103；
Step S103: self study builds figure as preset map；It includes recalling path learning mode that figure is built in the self study；
Step S104: judge whether self-built figure succeeds；The driving terminating point for the map included user setting that self study is established
And when the cartographic information established carries out global path planning enough, figure, system auto-returned step 101 are built in completion；If
Figure failure is built, then system can save the part for not completing to learn to build figure, and terminate self study and build figure function；
Step S105: global path planning；The starting point and ending point of automatic Pilot based on user setting, based on described default
Map using Dynamic Programming method generate global path planning；
Step S106: confirmation call back function uses；
Step S107: independently recalling driving, and system, which enters, recalls driving condition.
6. method according to claim 5 of parking, it is characterised in that: the method also includes steps:
Step S108: the fault detection during being parked by real-time positioning failure module；
Step S109: the detection of obstacles during being parked by real-time detection of obstacles module；
Step S110: autonomous driving function is interrupted；According to the real-time positioning failure module of step S108 and the reality of step S109
When detection of obstacles module failure and detection of obstacles, encounter failure or barrier when vehicle front running region during recalling
When hindering object, system can carry out stablizing braking automatically, guarantee that vehicle is in the state of a safety；
Step S111: completion is recalled for visitor；For vehicle automatic running to target location completion task, vehicle can be certainly after reaching pick-up point
It is dynamic to enter double sudden strain of a muscle states, while driver will receive mobile phone terminal vehicle please take over prompting in place.
7. method of parking according to any one of claim 5-6, it is characterised in that: the step S103 includes following son
Step:
Step S1031, the current posture information of target vehicle is obtained；
Step S1032, according to current posture information and automatic Pilot digital navigation map, estimating for target vehicle subsequent time is predicted
Count posture information；
Step S1033, to estimate posture information as foundation, the mesh in preset range is obtained in automatic Pilot digital navigation map
Mark map datum；
Step S1034, combining target map datum and estimation posture information, generate the target for guiding user's automatic Pilot
Driving strategy.
8. according to method of parking described in any one of 6-7 is required, it is characterised in that: after the step 105 path planning success,
It prompts the user whether to be recalled automatically；Under park mode, user independently recalls driving by may be selected to enter step 107, or
Terminate automatic parking function.
9. a kind of vehicle automated parking system, which is characterized in that the system comprises:
User's setting module: activation park mode, and on preset map, set the vehicle, stop bit is set；It is wherein described
It rises, stop bit is set to fixed point；
Mapping module: using existing map or the map built is learnt by oneself as preset map；
Map-matching module: special using the semantic feature for looking around image zooming-out of the vehicle and the semanteme of the start-stop point map
Sign is matched；
Global path planning module: the starting point and ending point of the automatic Pilot based on user setting is based on the default map
Global path planning is generated using the method for Dynamic Programming；
Automatic Pilot module: according to the global path planning, the vehicle automatic parking.
10. a kind of automatic recalling system of vehicle, which is characterized in that the system comprises:
User's setting module: mode is recalled in activation, and on preset map, sets the vehicle, stop bit is set；It is wherein described
It rises, stop bit is set to fixed point；
Mapping module: using existing map or the map built is learnt by oneself as preset map；
Map-matching module: special using the semantic feature for looking around image zooming-out of the vehicle and the semanteme of the start-stop point map
Sign is matched；
Global path planning module: the starting point and ending point of the automatic Pilot based on user setting is based on the default map
Global path planning is generated using the method for Dynamic Programming；
Automatic Pilot module: according to the global path planning, the vehicle is recalled automatically.
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